Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting economic dispatch constraints. In the present electric power market, where renewable energy power plants have been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed method implemented on IEEE 14 bus test system.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting economic dispatch constraints. In the present electric power market, where renewable energy power plants have been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed method implemented on IEEE 14 bus test system.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Power system operation considering detailed modelling of the natural gas supp...IJECEIAES
The energy transition from fossil-fuel generators to renewable energies represents a paramount challenge. This is mainly due to the uncertainty and unpredictability associated with renewable resources. A greater flexibility is requested for power system operation to fulfill demand requirements considering security and economic restrictions. In particular, the use of gas-fired generators has increased to enhance system flexibility in response to the integration of renewable energy sources. This paper provides a comprehensive formulation for modeling a natural gas supply network to provide gas for thermal generators, considering the use of wind power sources for the operation of the electrical system over a 24-hour period. The results indicate the requirements of gas with different wind power level of integration. The model is evaluated on a network of 20 NG nodes and on a 24-bus IEEE RTS system with various operative settings during a 24-hour period.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
Coordinated planning in improving power quality considering the use of nonlin...IJECEIAES
Power quality has an important role in the distribution of electrical energy. The use of non-linear load can generate harmonic spread which can reduce the power quality in the radial distribution system. This research is in form of coordinated planning by combining distributed generation placement, capacitor placement and network reconfiguration to simultaneously minimize active power losses, total harmonic distortion (THD), and voltage deviation as an objective function using the particle swarm optimization method. This optimization technique will be tested on two types of networks in the form 33-bus and 69-bus IEEE Standard Test System to show effectiveness of the proposed method. The use of MATLAB programming shows the result of simulation of increasing power quality achieved for all scenario of proposed method.
Impact of Dispersed Generation on Optimization of Power ExportsIJERA Editor
Dispersed generation (DG) is defined as any source of electrical energy of limited size that is connected directly to the distribution system of a power network. It is also called decentralized generation, embedded generation or distributed generation. Dispersed generation is any modular generation located at or near the load center. It can be applied in the form of rechargeable, such as, mini-hydro, solar, wind and photovoltaic system or in the form of fuel-based systems, such as, fuel cells and micro-turbines. This paper presents the impact of dispersed generation on the optimization of power exports. Computer simulation was carried out using the hourly loads of the selected distribution feeders on Kaduna distribution system as input parameters for the computation of the line loss reduction ratio index (LLRI). The result showed that the line loss reduced from 163.56MW to 144.61 MW when DG was introduced which is an indication of a reduction in line losses with the installation of DG at the various feeders of the distribution system. In all the feeders where DG is integrated, the average magnitude of the line loss reduction index is 0.8754 MW which is less than 1 indicating a reduction in the electrical line losses with the introduction of DG. The line loss reduction index confirmed that by integrating DG into the distribution system, the distribution losses are reduced and optimization of power exports is achieved The results of this research paper will form a basis to establish that proper location of distributed generation units have significant impact on their effective capacity.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
life cycle cost analysis of a solar energy based hybrid power systemINFOGAIN PUBLICATION
The importance of life-cycle cost analysis of an integrated solar power system is explained in this paper. To analyze the energy power and cash flow computations, there exist many commercial types of energy audit softwares like Emat, Optimizer, Homer, Energy gauge, Treat and so on. Among the aforementioned audit softwares, homer software is selected since it consists of several built-in options to perform audit studies. Homer software basically utilizes the concept of finding the total net present cost to represent the life-cycle cost of the total system. This software is vividly used for obtaining the optimized energy audit solutions to integrate several equipments embedding into a single workable system.
Distribution network reconfiguration for loss reduction using PSO method IJECEIAES
In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particle swarm optimization method (PSO), to reduce active losses and voltage deviations by taking into account certain technical constraints. The validity of this method is tested on a 33-IEEE test network and the results obtained are compared with the results of basic load flow.
Coyote multi-objective optimization algorithm for optimal location and sizing...IJECEIAES
Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.
Power system operation considering detailed modelling of the natural gas supp...IJECEIAES
The energy transition from fossil-fuel generators to renewable energies represents a paramount challenge. This is mainly due to the uncertainty and unpredictability associated with renewable resources. A greater flexibility is requested for power system operation to fulfill demand requirements considering security and economic restrictions. In particular, the use of gas-fired generators has increased to enhance system flexibility in response to the integration of renewable energy sources. This paper provides a comprehensive formulation for modeling a natural gas supply network to provide gas for thermal generators, considering the use of wind power sources for the operation of the electrical system over a 24-hour period. The results indicate the requirements of gas with different wind power level of integration. The model is evaluated on a network of 20 NG nodes and on a 24-bus IEEE RTS system with various operative settings during a 24-hour period.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
Coordinated planning in improving power quality considering the use of nonlin...IJECEIAES
Power quality has an important role in the distribution of electrical energy. The use of non-linear load can generate harmonic spread which can reduce the power quality in the radial distribution system. This research is in form of coordinated planning by combining distributed generation placement, capacitor placement and network reconfiguration to simultaneously minimize active power losses, total harmonic distortion (THD), and voltage deviation as an objective function using the particle swarm optimization method. This optimization technique will be tested on two types of networks in the form 33-bus and 69-bus IEEE Standard Test System to show effectiveness of the proposed method. The use of MATLAB programming shows the result of simulation of increasing power quality achieved for all scenario of proposed method.
Impact of Dispersed Generation on Optimization of Power ExportsIJERA Editor
Dispersed generation (DG) is defined as any source of electrical energy of limited size that is connected directly to the distribution system of a power network. It is also called decentralized generation, embedded generation or distributed generation. Dispersed generation is any modular generation located at or near the load center. It can be applied in the form of rechargeable, such as, mini-hydro, solar, wind and photovoltaic system or in the form of fuel-based systems, such as, fuel cells and micro-turbines. This paper presents the impact of dispersed generation on the optimization of power exports. Computer simulation was carried out using the hourly loads of the selected distribution feeders on Kaduna distribution system as input parameters for the computation of the line loss reduction ratio index (LLRI). The result showed that the line loss reduced from 163.56MW to 144.61 MW when DG was introduced which is an indication of a reduction in line losses with the installation of DG at the various feeders of the distribution system. In all the feeders where DG is integrated, the average magnitude of the line loss reduction index is 0.8754 MW which is less than 1 indicating a reduction in the electrical line losses with the introduction of DG. The line loss reduction index confirmed that by integrating DG into the distribution system, the distribution losses are reduced and optimization of power exports is achieved The results of this research paper will form a basis to establish that proper location of distributed generation units have significant impact on their effective capacity.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
life cycle cost analysis of a solar energy based hybrid power systemINFOGAIN PUBLICATION
The importance of life-cycle cost analysis of an integrated solar power system is explained in this paper. To analyze the energy power and cash flow computations, there exist many commercial types of energy audit softwares like Emat, Optimizer, Homer, Energy gauge, Treat and so on. Among the aforementioned audit softwares, homer software is selected since it consists of several built-in options to perform audit studies. Homer software basically utilizes the concept of finding the total net present cost to represent the life-cycle cost of the total system. This software is vividly used for obtaining the optimized energy audit solutions to integrate several equipments embedding into a single workable system.
Distribution network reconfiguration for loss reduction using PSO method IJECEIAES
In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particle swarm optimization method (PSO), to reduce active losses and voltage deviations by taking into account certain technical constraints. The validity of this method is tested on a 33-IEEE test network and the results obtained are compared with the results of basic load flow.
Coyote multi-objective optimization algorithm for optimal location and sizing...IJECEIAES
Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.
Optimal Siting of Distributed Generators in a Distribution Network using Arti...IJECEIAES
Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Multi-objective optimal placement of distributed generations for dynamic loadsIJECEIAES
Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Optimal Integration of the Renewable Energy to the Grid by Considering Small ...IJECEIAES
In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGAII). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.
Fuzzy optimization strategy of the maximum power point tracking for a variab...IJECEIAES
Wind power systems are gaining more and more interests; in order to diminish dependence on fossil fuels. In this paper, we present a variable speed-wind energy global system based on a synchronous generator with permanent magnetic (PMSG). The major goal of this study is to track the maximum power that is present in the turbine. An examination of control methods to extract the MPPT point, from a wind energy conversion system (WECS) under variable speed situations is presented. An intelligent controller based on the fuzzy logic control (FLC) is proposed for regulating permanent magnetic synchronous generator (PMSG) output power, in order to improve tracking performance. The principle of this maximum power point tracking (MPPT) algorithm consists in looking for an optimal operating relation of the maximum power, then tracking this last. We simulated our system with MATLAB-Simulink software. The found results will be debated to elucidate performance of the global system.
Optimal planning of RDGs in electrical distribution networks using hybrid SAP...IJECEIAES
The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA and PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA and PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Critical Review of Different Methods for Siting and Sizing Distributed-genera...TELKOMNIKA JOURNAL
Due to several benefits attached to distributed generators such as reduction in line losses,
improved voltage profile, reliable system etc., the study on how to optimally site and size distributed
generators has been on the increase for more than two decades. This has propelled several
researchers to explore various scientific and engineering powerful simulation tools, valid and reliable
scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size
distributed generator(s) for optimal benefits. This study gives a critical review of different methods
used in siting and sizing distributed generators alongside their results, test systems and gaps in
literature.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants
resulted. Leandro dos Santos Coelho a,*, Chu-Sheng Lee b, using chaotic and Gaussian particle swarm
optimization Solving economic load dispatch problem [2]. Hossein Shahinzadeh used Particle Swarm
Optimization algorithm to solve the economic load dispatch of units in power systems with valve-point
effects consideration [3]. PSO is used for the solution of Dynamic Economic Load Dispatch (DELD)
problem with valve point loading effects and ramp rate limits in the paper writen by G.Sreenivasan [4].
Important issues in electric power industries are effectively economically optimum operations of
electric power generation systems. The main utility target is to achieve the minimum operating cost and
system stability. As well as to find the minimum operating cost using economic dispatch method. There are
several kinds of methods to calculate the operating costs. Lagrange is the most commonly used to calculate
the minimum cost of electrical energy generation [5]. In addition to this method there are several others that
have been developed by researchers.
Quantum theory has been used widely in the field of electrical energy systems. In several
publications, quantum EAs are utilized to calculate economic dispatch. For example in the references [6], [7]
and [8]. Quantum-Inspired Evolutionary Algorithm for Real and Reactive Power Dispatch, in this paper,
QEA determines the settings of control variables, such as generator outputs, generator voltages, transformer
taps and shunt VAR compensation devices for optimal P-Q dispatch considering the bid-offered cost [6].
Chaotic Quantum Evolutionary Algorithm is used to solve Environmental Economic Dispatch of
Smart Microgrid Containing Distributed Generation System Problems. Quantum evolutionary algorithm is
used to confirm the accuracy and validity of the mathematical model used for determining environment and
economic dispatch of Smart Micro Grid, which is considered as generation cost and emission cost [9].
In this work, quantum evolutionary algorithm is used for the placement of DGs in the network and
for determining the capacity required to raise the voltage on the buses, so that the voltage at each bus is in
safe condition and increases the system load ability. Finally, we perform the calculation for the economic
dispatch to get the cheapest cost of generation. Quantum evolutionary algorithm is also used in similar way in
reference [10],[11].
2. RESEARCH METHOD
2.1. Economic Dispatch Theory
The mathematical formulation of the total cost function is formulated as follows:
min 𝐶 = ∑ 𝐶𝑖(𝑃𝑖)𝑛
𝑖=1 + ∑ 𝐶𝑔(𝑃𝑔)ℎ
𝑔=1 (1)
where 𝐶𝑖 is the total fuel cost for the i th generator (in $/h).
Generally, the fuel cost of thermal generating unit is represented in polynomial function,
𝐶𝑖(𝑃𝑖) = 𝑎𝑖 + 𝑏𝑖 𝑃𝑖 + 𝑐𝑖 𝑃𝑖
2
(2)
where 𝑎𝑖, 𝑏𝑖 and 𝑐𝑖 are cost coefficients of generator 𝑖.
The DG generation cost.
𝐶𝑔(𝑃𝑔) = 𝑑 𝑔 𝐺𝑔 (3)
where 𝑑 𝑔 is cost coefficients of DG
Conventionally, there is no or negligible DG capacity existing in the power system. In economic
dispatch, the system demand and delivery loss are served by utility generators only. With the outspread of
DG's in distribution networks, the non DG system evolves into a hybrid generation environment. The DG
generation capacity has to be taken into account in the new environment. To achieve this, a modification of
the conventional economic dispatch. In addition, N generators are connected to the system . The power
balance constraint becomes:
∑ 𝑃𝑖𝑁 + ∑ 𝑃𝑔ℎ = 𝑃𝐷 + 𝑃𝑙𝑜𝑠𝑠 (4)
where ∑ 𝑃𝑖𝑖 is the total power generated by utility generators and ∑ 𝑃𝑔𝑔 is the total power from DG installed
in buses, while 𝑃𝐷 is total load and 𝑃𝑙𝑜𝑠𝑠 is total losses of the system.
Consequently, the generation cost consists of two parts: the power generated from existing and power from
DG in load bus.
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𝐶 𝑇𝑜𝑡 = ∑ 𝐶𝑖 + ∑ 𝐶𝑔ℎ𝑁 (5)
𝐶 𝑇 is obtained by economically dispatching load and delivery loss among these online generators and DGs.
Some constraints for each generator must be also satisfied. Generation power of each generator should be
laid between maximum and minimum limits. The constraint for each generator is
𝑃𝑖
𝑚𝑖𝑛
≤ 𝑃𝑖 ≤ 𝑃𝑖
𝑚𝑎𝑥
(6)
where 𝑃𝑖
𝑚𝑖𝑛
and 𝑃𝑖
𝑚𝑎𝑥
are the output of the minimum and maximum operation of the generating unit i (in
MW), respectively.
Voltage at load buses
𝑉𝑖
𝑚𝑖𝑛
< 𝑉𝑖 < 𝑉𝑖
𝑚𝑎𝑥
(7)
where 𝑉𝑖
𝑚𝑖𝑛
and 𝑉𝑖
𝑚𝑎𝑥
are voltage minimum and maximum each buses.
Line Power Flow
Newthon Rapson (NR) is a very common method used to calculate the power flow on the system.
The NR power flow is used to calculate the losses in the system and the voltage on each bus. The theory of
power flow used in this paper refers to the reference [5].
Injected Power
The complex power at bus 'i ' is:
𝑆𝑖
∗
= 𝑉𝑖
∗
∑ 𝑌𝑖𝑘 𝑉𝑘
𝑛
𝑘=1 (8)
The variables updated after k th iteration are given as:
∆𝛿𝑖
(𝑘+1)
= ∆𝛿𝑖
(𝑘)
+ ∆𝛿𝑖 (9)
|𝑉𝑖|(𝑘+1)
= |𝑉𝑖|(𝑘)
+ ∆|𝑉𝑖| (10)
Power flow from ith bus to j th bus through the line connected between these buses is given by:
𝑆𝑖𝑗 = 𝑉𝑖 𝐼𝑖𝑗
∗
= 𝑉𝑖 (
𝑉 𝑖−𝑉 𝑗
𝑍 𝑖𝑗
+ 𝑉𝑖 𝑌𝑖𝑗0) (11)
The power flow from the j th bus to ith bus is:
𝑆𝑗𝑖 = 𝑉𝑗 𝐼𝑗𝑖
∗
= 𝑉𝑗 (
𝑉 𝑗−𝑉 𝑗𝑖
𝑍 𝑗𝑖
+ 𝑉𝑗 𝑌𝑗𝑖0) (12)
Line Losses
𝑷𝒍 = ∑ ∑ (𝑺𝒊𝒋 + 𝑺𝒋𝒊)𝒃𝒖𝒔 𝒏𝒐
𝒋=𝟏
𝒃𝒖𝒔 𝒏𝒐
𝒊=𝟏 (13)
= ∑ ∑ {(𝑷𝒊𝒋 + 𝒋𝑸𝒊𝒋) + (𝑷𝒋𝒊 + 𝒋𝑸𝒋𝒊)}𝒃𝒖𝒔 𝒏𝒐
𝒋=𝟏
𝒃𝒖𝒔 𝒏𝒐
𝒊=𝟏 (14)
2.2. Quantum Evolutionary Algorithm (QEA)
The base theories of Quantum Evolutionary Algorithm method are the concepts of qubits and the
superposition of states of quantum mechanics. Qubit is the smallest unit of information stored in a two-state
quantum computer. A qubit may be in the state ‘1’ or in the state ‘0’ or in any superposition of the two. The
state of a qubit can be represented as,
|𝜓〉 = |𝛼|2
+ |𝛽|2
(15)
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As also ilustrated in figure 1, α and β are complex numbers specifying the probability amplitudes of the state
‘0’ and ‘1’ respectively. |𝛼|2
gives the probability that the qubit will be found in ‘0’ state and |𝛽|2
gives the
probability that the qubit will be found in the ‘1’ state. Normalization of the state to unity guarantees,
|𝛼|2
+ |𝛽|2
= 1 (16)
2 𝑚
states can be represented at the same time by a system of m qubits. QGA is based on the concept of
qubits. One qubit is defined with a pair of complex numbers (α,β) as,
[
𝛼
𝛽] (17)
which is characterized by (8) and (9). And an m-qubits representation is defined as
[
𝛼1
𝛽1
|
𝛼2
𝛽2
|
…
… |
𝛼 𝑚
𝛽 𝑚
] (18)
where |𝛼𝑖|2
+ |𝛽𝑖|2
= 1, i = 1,2,3...m
The advantage of this representation is that it can represent any superposition of states. For instance, in a
three qubits system with three pairs of amplitudes such as,
[
1
√2
1
√2
|
√3
2
1
2
|
1.0
0
] (19)
The state of the system can be represented as,
√3
2√2
|000〉 + 0 |001〉 +
1
2√2
|010〉 + 0|011〉 +
√3
2√2
|100〉 + 0 |101〉 +
1
2√2
|110〉 + 0|111〉
The above result means that the probabilities to represent the state |000〉, |010〉, |100〉 and |110〉 are
3
8
,
1
8
,
3
8
and
1
8
respectively. The three qubits system of (11) has four states information at the same time.
Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical
approaches, since it can represent superposition of states. One qubit string such as (11) is enough to represent
four states. Convergence can be also obtained with the qubit representation. As |𝛼|2
or |𝛽|2
approaches to 1
or 0, the qubit string converges to a single state and the property of diversity disappears gradually. That is,
simultaneously, the qubit representation has both the characteristic of exploration and the characteristic of
exploitation.
Rotation Gate [10]
Q-gate (rotation gate) is defined as a variation operator of QEA, by which operation any updated
qubit should at all time satisfy the normalization condition, |𝛼′|2
+ |𝛽′|2
= 1 , where 𝛼′
and 𝛽′
are the
values of the updated qubit.
The following rotation gate is used as a qubit:
[
𝛼𝑗(𝑡 + 1)
𝛽𝑗(𝑡 + 1)
] = 𝑅𝑖(𝑡) × [
𝛼𝑗(𝑡)
𝛽𝑗(𝑡)
] (𝑗 = 1,2, … , 𝑛) (20)
𝑅𝑖(𝑡) = [
cos ∆𝜃𝑖 − sin ∆𝜃𝑖
sin ∆𝜃𝑖 cos ∆𝜃𝑖
] (𝑖 = 1, 2, … , 𝑛) (21)
∆𝜃𝑖is a rotation angle (Fig.1) of each member of the population. The rotation angle ∆𝜃𝑖is related to the
normalized difference ∆𝑓𝑖 between achievement of each of member of population and the global best
optimum.
∆𝜃𝑖 = ∆𝑓𝑖 × 𝑠𝑖𝑔𝑛 (𝛼 𝑏 − 𝛼𝑗) × 𝑠𝑖𝑔𝑛[𝛽𝑗 × sin ∆𝑓𝑖 − 𝛼𝑗 × (1 − cos〖∆𝑓𝑖)〗] (22)
where :
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∆𝑓𝑖 = 𝜋 × (1 −
𝑓(𝑏)
𝑓(𝑝 𝑖(𝑡)
) (𝑖 = 1,2, … , 𝑛) (23)
𝑠𝑖𝑔𝑛 (𝛼 𝑏 − 𝛼𝑗) = {
+1 𝑖𝑓 𝛼 𝑏 ≥ 𝛼𝑗
−1 𝑖𝑓 𝛼 𝑏 < 𝛼𝑗
(24)
𝑠𝑖𝑔𝑛 [𝛽𝑗 × sin ∆𝑓𝑖 − 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)] = {
+1 𝑖𝑓 𝛽𝑗 × sin ∆𝑓𝑖 ≥ 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)
−1 𝑖𝑓 𝛽𝑗 × sin ∆𝑓𝑖 < 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)
(25)
Figure 1.Basic Quantum-bit (qubit)
2.3. Problem Formulation
The objective function of this proposed method is :
min 𝐶 = ∑ 𝐶𝑖(𝑃𝑖)𝑛
𝑖=1 + ∑ 𝐶𝑔(𝑃𝑔)ℎ
𝑔=1 (26)
where 𝐶𝑖 is the total fuel cost for the i th thermal generator ($/h), 𝐶𝑔 is the total cost for generated power
of DG ($/h).
Bus voltage constrain:
|𝑉𝑖 𝑚𝑖𝑛| ≤ |𝑉𝑖| ≤ |𝑉𝑖 𝑚𝑎𝑥| (27)
Capacity DG constrain :
𝑃𝑔 𝑚𝑖𝑛 ≤ 𝑃𝑔 ≤ 𝑃𝑔 𝑚𝑎𝑥(28)
2.4. The Proposed Method
The proposed method can be described in the following steps and the flowchart of the algorithm is
in figure 2.
1. Start with t = 0
2. Initialize a population of n members (qubit string) :
𝑄 (0) = {𝑞1(0), 𝑞2(0), … , 𝑞 𝑛(0)}
3. Each qubit string is represented:
𝑞1(0) = [|
𝛼1(0)
𝛽1(0)
| |
𝛼2(0)
𝛽2(0)
| . . . |
𝛼 𝑚(0)
𝛽 𝑚(0)
|]
4. For all qubit strings initialize the amplitude amplification at :
𝑞1(0) = |
1
√2
1
√2
…
1
√2
1
√2
…
|
5. Randomly, make a set binary solution P(0) by observing Q(0) state :
𝑃(0) = {𝑝1(0), 𝑝2(0), … , 𝑝 𝑛(0) }
6. Each DG is represented by 8 bit binary number. The MSB (Most Significant Bit) signifies whether
the DG is present. If this bit is 1, DG is present at the bus and this DG’s output power is determined
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by the rest 7 bits and also based on the 𝑃𝑚𝑖𝑛 and 𝑃𝑚𝑎𝑥 specified in the input parameter matrix. If
the MSB is 0, DG is not present and the bus is considered as load bus.
7. Evaluate each 𝑝𝑖(0) (𝑖 = 1,2, … , 𝑛), prepare bus data matrix
8. Using the bus data, run load flow and obtain power generated by each generator.
9. Calculate the objective function and store the fitness value. For this economic dispatch, we use (26)
as the objective function.
10. Perform steps (5) – (7) for each population member.
11. Find population member with the highest fitness value and store this as the best string of the
iteration.
12. Next iteration ( t = t +1 )
13. Generate the next population from the current population by rotating each member such that the new
generation is closer to the best string. See fig 1. Rotation is done by rotator matrix that is calculated
as per equations (20) - (21).
14. Perform steps (4) - (10) as many times as needed.
15. The most optimum solution is the best string of the last iteration.
START
FOR EACH MEMBER: COMPOSE BUSDATA, RUN
POWERFLOW, OBTAIN POWER OF ALL GENERATORS,
USING THE SPECIFIED COST FUNCTIONS CALCULATE
THE FITNESS VALUE
GENERATE INITIAL
POPULATION OF N
MEMBERS (QUBIT STRINGS)
FIND MEMBER WITH THE
HIGHEST FITNESS VALUE
NUMBER OF ITERATION
REACHED?
PRINT OUT THE BEST SYSTEM
CONFIGURATION COMPRISED BY
THE POPULATION MEMBER WITH
THE HIGHEST FITNESS VALUE
STOP
BASED ON THE BEST POPULATION
MEMBER, GENERATE THE NEXT
POPULATION BY ROTATING EACH
MEMBER OF CURRENT POPULATION
QUBITS
READ IN SYSTEM PARAMETERS MATRICES: BUSDATA,
LINEDATA, POSSIBLE BUSES FOR DGs, POSSIBLE DGs
POWERS (pmin-pmax), COST FUNCTION OF EACH DG
READ IN ALGORITHM PARAMETERS:
NUMBER OF POPULATIONS, NUMBER OF
ITERATIONS, NUMBER OF BITS
Figure 2.The Flowchart
3. RESULTS AND ANALYSIS
This work exploits IEEE 30-bus systems (system with 100MVA base, 135 KV base and frequency
as 60Hz) to evaluate the performance of the proposed algorithm. In this system, the buses 1, 2, 5, 8, 11, and
13 are generator buses and others are load buses as shown in Figure 3. The generators parameters are in table
1. Power Generation Limits And Cost Generated Coefficients
Table 1. Generator data
Bus 𝑃𝑖
𝑚𝑖𝑛 𝑃𝑖
𝑚𝑎𝑥
𝑄𝑖
𝑚𝑖𝑛 𝑄𝑖
𝑚𝑎𝑥
𝑎 𝑏 𝑐
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1 50.00 200.0 - - 0 2.00 49.5 10-4
2 20.00 80.00 -40.00 50.00 0 1.75 175.0 10-4
5 15.00 50.00 -40.00 40.00 0 1.00 625.0 10-4
8 10.00 35.00 -10.00 60.00 0 3.25 83.0 10-4
11 10.00 30.00 -6.00 24.00 0 3.00 250.0 10-4
13 12.00 40.00 -6.00 24.00 0 3.00 250.0 10-4
In this simulation, system is operated in critical condition, in which the voltage of some of the buses
approaches the minimum allowable value. This is achieved by increasing the initial load by a small step
every time and then performing power flow calculation to get the voltage. As soon as the voltage falls out of
allowable range of 0.9 ≤ 𝑉𝑖 ≤ 1.1 𝑝𝑢 (as per IEEE standard for voltages on the buses), the load can be
considered as the maximum load of the system. At this maximum load, DGs start participating.
DGs are meant to enhance the capability of the power system (system load ability) and to improve
voltage profile. So as to bring voltages on the buses back up in the normal range of 0.9 ≤ 𝑉𝑖 ≤ 1.1 𝑝𝑢
again. Apart from voltage profile improvement, renewable generation placement and sizing are important
part of the economic dispatch strategy to minimize the overall generation cost. In this work, QEA is used to
devise the placement and the size of the generators in the system. The solution is near optimum and still
meets standard voltage profile requirement. DG posted on this simulation has a rating of 5 - 10 MW and the
cost equation for DG was y=4.50P. Simulations were performed before and after the renewable generators
installed.
Figure 5. Voltage profile of the buses
Table 2. Simulation result
Bus Load
(MW)
Bus
Voltage
(pu)
Generation
In initial
condition
(MW)
Bus Voltage
ED_QEA
with DG
(pu)
Generation
ED_QEA
with DG
(MW)
Bus Voltage
ED_NR
with DG
(pu)
Generation
ED_NR
with DG
(MW)
1 0.000 1.050 200 1.060 198.3 1.060 199.90
2 34.440 1.033 80 1.043 80.0 1.043 80.00
3 3.809 1.011 0 1.029 0.0 1.022 0.00
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
V_initial
V_QEA
V_NR
Figure 3. Single Line Diagram of The IEEE 30 Bus
Power System
Figure 4 . The Convergence of Economic Dispatch
0 10 20 30 40 50 60 70 80 90 100
1261.5
1262
1262.5
1263
1263.5
1264
1264.5
1265
> > Behaviour of Quantum Genetic Algoritm < <
Iteration
GeneratingCost(Rp/Hour)
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4 12.060 1.003 0 1.021 0.0 1.013 0.00
5 149.500 0.990 50 1.000 50.0 1.000 34.79
6 0.000 1.000 0 1.015 0.0 1.011 0.00
7 36.180 0.986 0 1.000 5.0 1.000 10.00
8 47.610 1.010 35 1.010 35.0 1.010 35.00
9 0.000 0.981 0 1.029 0.0 1.034 0.00
10 9.204 0.957 0 1.010 0.0 1.012 0.00
11 0.000 1.012 30 1.082 30.0 1.082 30.00
12 17.770 0.978 0 1.025 0.0 1.049 0.00
13 0.000 1.000 40 1.051 40.0 1.071 40.00
14 9.839 0.956 0 1.008 0.0 1.027 0.00
15 13.010 0.948 0 1.005 0.0 1.021 0.00
16 5.554 0.958 0 1.007 0.0 1.019 0.00
17 14.280 0.951 0 1.000 5.0 1.000 5.00
18 5.078 0.933 0 0.999 0.0 1.005 0.00
19 15.080 0.928 0 1.000 5.0 1.000 10.00
20 3.491 0.934 0 1.001 0.0 1.002 0.00
21 27.770 0.939 0 1.000 5.3 1.000 5.00
22 0.000 0.940 0 1.001 0.0 1.002 0.00
23 5.078 0.932 0 0.998 0.0 1.007 0.00
24 13.810 0.922 0 1.000 5.0 1.000 10.00
25 0.000 0.932 0 0.998 0.0 1.004 0.00
26 5.554 0.906 0 1.000 5.3 1.000 5.00
27 0.000 0.950 0 0.996 0.0 1.010 0.00
28 0.000 0.997 0 1.013 0.0 1.008 0.00
29 3.809 0.918 0 0.967 0.0 0.980 0.00
30 16.800 0.899 0 0.949 0.0 0.963 0.00
Tot. load
(MW)
449.900
Tot. Gen
(MW)
435.000 463.942 464.693
Losses (MW) 14.042 14.793
Cost ($/Hour) 1593.63 1558.90
By using Newton-Raphson Power Flow, from Table 2, we can see that when the total load of the
system is reaching maximum at 449.9 MW, the voltages at bus 30 are below standard (0.899 pu). New
generators need to be added in order to sufficiently satisfy power demand and to shoulder line losses and also
to increase the voltages of the buses under standard (bus30). Small capacity distributed generators (DGs) can
supplement the existing thermal generators in improving voltage profiles of the buses. QEA method is used
in combination with power flow for calculating generation cost of the thermal generators and for the
placement and sizing of DGs. The typical iteration convergence is in figure 4.
We can see from tabel 2 that placing DGs of sizes 5, 5, 5, 5.3, 5 , 5.3 MW on buses 7, 17, 19, 21,
24 and 26 respectively raises the voltages at some of them, so that the voltage on each bus remains at the
level permitted as seen in figure 5. The cheapest generating cost as obtained from the calculation is 1593.63$/
h with losses of 14.042 MW.
In order to confirm that QEA is effective enough in this optimisation, lambda iteration of the
Economic Dispatch was performed. In the iteration, DGs are placed on the same buses as the buses obtained
from the calculation done using QEA (the buses 7, 17, 19, 21, 24 and 26) and with the DGs capacities of 5-10
MW and at maximum load of 499.9 MW. The resulting generation cost is 1558.90 $/h with losses of 14.793
MW. The overall system stability is better, where the voltages of all buses are at levels permitted.
4. CONCLUSION
This is a study of Economic Dispatch using Quantum Evolutionary Algorithm (QEA) in Electrical
Power System involving Distributed Generators. QEA can be used for the placements, sizing and the
calculation of thermal power generation and distributed generation to get the cheapest cost of generation.
ACKNOWLEDGEMENTS
The authors thank the Power System Simulation Laboratory, Department of Electrical Engineering,
Institut Teknologi Sepuluh Nopember for all facilities and supports for this research.
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