This document presents an immunized-evolutionary algorithm technique for loss control in transmission systems with multiple load increments. The technique uses an immune evolutionary programming (IEP) approach to optimize the size and location of photovoltaic (PV) systems injected into the transmission network. IEP combines classical evolutionary programming with an immune algorithm to reduce computational burden and improve optimization performance. The algorithm is tested on IEEE 12-bus and 14-bus systems. Results show that IEP is able to determine the optimal PV configuration to control losses in the transmission system as load increases, demonstrating its effectiveness and potential for practical implementation.
Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
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 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.
Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
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 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.
Islanded microgrid congestion control by load prioritization and shedding usi...IJECEIAES
The continued growth in load demand and the gradual change of generation sources to smaller distributed plants utilizing renewable energy sources (RESs), which supply power intermittently, is likely to strain existing power systems and cause congestion. Congestion management still remains a challenging issue in open access transmission and distribution systems. Conventionally, this is achieved by load shedding and generator rescheduling. In this study, the control of the system congestion on an islanded micro grid (MG) supplied by RESs is analyzed using artificial bee colony (ABC) algorithm. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time during islanding mode operation. This is to ensure as minimal load as possible is shed during a contingency that leads to loss of mains and ensure a congestion free microgrid operation. This is tested and verified on a modified IEEE 30-bus distribution systems on MATLAB platform. The results are compared with other algorithms to prove the applicability of this approach.
A New Methodology for Active Power Transmission Loss Allocation in Deregulate...IJECEIAES
This paper presents a new method for transmission loss allocation in a deregulated power system. As the power loss is a nonlinear quantity, so to allocate the loss in a common transmission corrider is a difficult task. It allocates transmission losses to loads based on the actual power flow in the lossy lines due to the concerned load. Each lossy line is subdivided into as many sub-lines as corresponding to the numbers of load attached to it. The tracing of power flow through each sub-line is worked out by using proportional sharing method. The power loss in each lossy line is equal with the total loss due to all the sub-lines under it. Then by using Pro-rata for each lossy line, the individual loss for each sub-line is formulated. As the application of Pro-rata is limited to an individual line of the system, so the error in calculation is minimized. The total loss allocated to a particular load is the sum of losses occurred in each lossy lines through which the power is flowing to the concerned load. As this method is based on the actual flow of power in the transmission line corresponding to the concerned load, hence, the loss allocation made by the method gives proper and justifiable allocations to the different loads which are attached to the system. The proposed method is applied to a six-bus system and finds the mismatch in the commonly used methods. Then, it is applied to higher bus systems in which more accurate results are obtained compared to the other methods.
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.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
Optimal placement of distributed power flow controller for loss reduction usi...eSAT Journals
Abstract
The aim of this paper is to reduce power loss and improve the voltage profiles in an electrical system in optimal manner. The flexible AC transmission system (FACTS) device such as Distributed power flow controller (DPFC) can strongly improve the different parameters in a power system. DPFC can be used to reduce line losses and increase voltage profiles. The optimized allocation of FACTS devices is an important issue, so the Voltage stability index (L-index) has been used in order to place UPFC in power system. The advantage of the L-index is to accelerate the optimization process. After placing the DPFC, Firefly optimization method is used for finding the rating of DPFC. The results obtained using Firefly optimization method is compared with Genetic Algorithm. To show the validity of the proposed techniques and for comparison purposes, simulation carried out on an IEEE- 14 Bus and IEEE- 30 Bus test system for different loading conditions.
Keywords: Distributed power flow controllers (DPFC), Optimized Placement, Voltage stability index (L-index), Firefly optimization method, Genetic algorithm.
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.
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.
Network Reconfiguration of Distribution System for Loss Reduction Using GWO A...IJECEIAES
This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
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.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Optimal Siting And Sizing Of Distributed Generation For Radial Distribution S...inventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Genetic Algorithm based Optimal Placement of Distributed Generation Reducing ...IDES Editor
This paper proposes a genetic algorithm
optimization technique for optimal placement of distributed
generation in a radial distribution system to minimize the total
power loss and to improve the voltage sag performance. Load
flow algorithm and three phase short circuit analysis are
combined appropriately with GA, till access to acceptable
results of this operation. The suggested method is programmed
under MATLAB software. The implementation of the algorithm
is illustrated on a 34-node radial distribution system. Placement
of two DGs with fixed capacity has been considered for example.
Only the three phase symmetrical faults are considered for sag
analysis though other fault types are more common.
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
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 Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Islanded microgrid congestion control by load prioritization and shedding usi...IJECEIAES
The continued growth in load demand and the gradual change of generation sources to smaller distributed plants utilizing renewable energy sources (RESs), which supply power intermittently, is likely to strain existing power systems and cause congestion. Congestion management still remains a challenging issue in open access transmission and distribution systems. Conventionally, this is achieved by load shedding and generator rescheduling. In this study, the control of the system congestion on an islanded micro grid (MG) supplied by RESs is analyzed using artificial bee colony (ABC) algorithm. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time during islanding mode operation. This is to ensure as minimal load as possible is shed during a contingency that leads to loss of mains and ensure a congestion free microgrid operation. This is tested and verified on a modified IEEE 30-bus distribution systems on MATLAB platform. The results are compared with other algorithms to prove the applicability of this approach.
A New Methodology for Active Power Transmission Loss Allocation in Deregulate...IJECEIAES
This paper presents a new method for transmission loss allocation in a deregulated power system. As the power loss is a nonlinear quantity, so to allocate the loss in a common transmission corrider is a difficult task. It allocates transmission losses to loads based on the actual power flow in the lossy lines due to the concerned load. Each lossy line is subdivided into as many sub-lines as corresponding to the numbers of load attached to it. The tracing of power flow through each sub-line is worked out by using proportional sharing method. The power loss in each lossy line is equal with the total loss due to all the sub-lines under it. Then by using Pro-rata for each lossy line, the individual loss for each sub-line is formulated. As the application of Pro-rata is limited to an individual line of the system, so the error in calculation is minimized. The total loss allocated to a particular load is the sum of losses occurred in each lossy lines through which the power is flowing to the concerned load. As this method is based on the actual flow of power in the transmission line corresponding to the concerned load, hence, the loss allocation made by the method gives proper and justifiable allocations to the different loads which are attached to the system. The proposed method is applied to a six-bus system and finds the mismatch in the commonly used methods. Then, it is applied to higher bus systems in which more accurate results are obtained compared to the other methods.
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.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
Optimal placement of distributed power flow controller for loss reduction usi...eSAT Journals
Abstract
The aim of this paper is to reduce power loss and improve the voltage profiles in an electrical system in optimal manner. The flexible AC transmission system (FACTS) device such as Distributed power flow controller (DPFC) can strongly improve the different parameters in a power system. DPFC can be used to reduce line losses and increase voltage profiles. The optimized allocation of FACTS devices is an important issue, so the Voltage stability index (L-index) has been used in order to place UPFC in power system. The advantage of the L-index is to accelerate the optimization process. After placing the DPFC, Firefly optimization method is used for finding the rating of DPFC. The results obtained using Firefly optimization method is compared with Genetic Algorithm. To show the validity of the proposed techniques and for comparison purposes, simulation carried out on an IEEE- 14 Bus and IEEE- 30 Bus test system for different loading conditions.
Keywords: Distributed power flow controllers (DPFC), Optimized Placement, Voltage stability index (L-index), Firefly optimization method, Genetic algorithm.
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.
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.
Network Reconfiguration of Distribution System for Loss Reduction Using GWO A...IJECEIAES
This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
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.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Optimal Siting And Sizing Of Distributed Generation For Radial Distribution S...inventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Genetic Algorithm based Optimal Placement of Distributed Generation Reducing ...IDES Editor
This paper proposes a genetic algorithm
optimization technique for optimal placement of distributed
generation in a radial distribution system to minimize the total
power loss and to improve the voltage sag performance. Load
flow algorithm and three phase short circuit analysis are
combined appropriately with GA, till access to acceptable
results of this operation. The suggested method is programmed
under MATLAB software. The implementation of the algorithm
is illustrated on a 34-node radial distribution system. Placement
of two DGs with fixed capacity has been considered for example.
Only the three phase symmetrical faults are considered for sag
analysis though other fault types are more common.
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
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 Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
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.
Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.
Due to the ever-increasing energy demand, power system operators have attempted to cope with these demands while keeping the power system remain operable. Economic constraints have forced the power system operator to abandon their effort in expanding the power system. The increased load demand can cause the power system to suffer from voltage instability and voltage collapse, especially during contingency condition. Hence, a strategy is required to maintain the steady state operation of a power system. Various research has been conducted to tackle this problem. Therefore, this paper presents the implementation of Chaos Embedded Symbiotic Organisms Search technique to solve optimal FACTS device allocation problem in power transmission system. Various practical constraints are also considered in the optimisation process to emulate the real-life constraints in power system. The optimisation process is conducted on a 26-bus IEEE RTS has validated that the results obtained has not violated the power system stability. The results provided by the proposed optimisation technique has successfully improved the voltage profile and voltage security in the system. Comparative studies are also conducted involving Particle Swarm Optimization and Evolutionary Programming technique resulting good results agreement and superiority of the proposed technique. Results obtained from this study would be beneficial to the power system operators regarding optimisation in power system operation for the implementation in real power transmission network.
A hybrid artificial neural network-genetic algorithm for load shedding IJECEIAES
This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.
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.
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.
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.
Optimizing Size of Variable Renewable Energy Sources by Incorporating Energy ...Kashif Mehmood
The electricity sector contributes to most of the global warming emissions generated from
fossil fuel resources which are becoming rare and expensive due to geological extinction and climate
change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that
are economically sound, easy to access and improve public health. The carbon-free salient feature is the
driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES.
However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings
uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy
storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation
network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs,
primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily,
weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results
substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also
reduces annual costs and emissions up to 4.36 % and 45.17 %
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
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.
In our homes or offices, security has been a vital issue. Control of home security system remotely always offers huge advantages like the arming or disarming of the alarms, video monitoring, and energy management control apart from safeguarding the home free up intruders. Considering the oldest simple methods of security that is the mechanical lock system that has a key as the authentication element, then an upgrade to a universal type, and now unique codes for the lock. The recent advancement in the communication system has brought the tremendous application of communication gadgets into our various areas of life. This work is a real-time smart doorbell notification system for home Security as opposes of the traditional security methods, it is composed of the doorbell interfaced with GSM Module, a GSM module would be triggered to send an SMS to the house owner by pressing the doorbell, the owner will respond to the guest by pressing a button to open the door, otherwise, a message would be displayed to the guest for appropriate action. Then, the keypad is provided for an authorized person for the provision of password for door unlocking, if multiple wrong password attempts were made to unlock, a message of burglary attempt would be sent to the house owner for prompt action. The main benefit of this system is the uniqueness of the incorporation of the password and messaging systems which denies access to any unauthorized personality and owner's awareness method.
Augmented reality, the new age technology, has widespread applications in every field imaginable. This technology has proven to be an inflection point in numerous verticals, improving lives and improving performance. In this paper, we explore the various possible applications of Augmented Reality (AR) in the field of Medicine. The objective of using AR in medicine or generally in any field is the fact that, AR helps in motivating the user, making sessions interactive and assist in faster learning. In this paper, we discuss about the applicability of AR in the field of medical diagnosis. Augmented reality technology reinforces remote collaboration, allowing doctors to diagnose patients from a different locality. Additionally, we believe that a much more pronounced effect can be achieved by bringing together the cutting edge technology of AR and the lifesaving field of Medical sciences. AR is a mechanism that could be applied in the learning process too. Similarly, virtual reality could be used in the field where more of practical experience is needed such as driving, sports, neonatal care training.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
In the wake of the sudden replacement of wood and kerosene by gas cookers for several purposes in Nigeria, gas leakage has caused several damages in our homes, Laboratories among others. installation of a gas leakage detection device was globally inspired to eliminate accidents related to gas leakage. We present an alternative approach to developing a device that can automatically detect and control gas leakages and also monitor temperature. The system detects the leakage of the LPG (Liquefied Petroleum Gas) using a gas sensor, then triggred the control system response which employs ventilator system, Mobile phone alert and alarm when the LPG concentration in the air exceeds a certain level. The performance of two gas sensors (MQ5 and MQ6) were tested for a guided decision. Also, when the temperature of the environment poses a danger, LED (indicator), buzzer and LCD (16x2) display was used to indicate temperature and gas leakage status in degree Celsius and PPM respectively. Attension was given to the response time of the control system, which was ascertained that this system significantly increases the chances and efficiency of eliminating gas leakage related accident.
Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.
In this paper Gentoo Penguin Algorithm (GPA) is proposed to solve optimal reactive power problem. Gentoo Penguins preliminary population possesses heat radiation and magnetizes each other by absorption coefficient. Gentoo Penguins will move towards further penguins which possesses low cost (elevated heat concentration) of absorption. Cost is defined by the heat concentration, distance. Gentoo Penguins penguin attraction value is calculated by the amount of heat prevailed between two Gentoo penguins. Gentoo Penguins heat radiation is measured as linear. Less heat is received in longer distance, in little distance, huge heat is received. Gentoo Penguin Algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
08 20272 academic insight on applicationIAESIJEECS
This research has thrown up many questions in need of further investigation.There was an expressive quantitative-qualitative research, which a common investigation form was used in.The dialogue item was also applied to discover if the contributors asserted the media-based attitude supplements their learning of academic English writing classes or not.Data recounted academic” insights toward using Skype as a sustaining implement for lessons releasing based on chosen variables: their occupation, year of education, and knowledge with Skype discovered that there were no important statistical differences in the use of Skype units because of medical academics major knowledge. There are statistically important differences in using Skype units. The findings also, disclosed that there are statistically significant differences in using Skype units due to the practice with Skype variable, in favors of academics with no Skype use practice. Skype instrument as an instructive media is a positive medium to be employed to supply academic medical writing data and assist education. Academics who do not have enough time to contribute in classes believe comfortable using the Skype-based attitude in scientific writing. They who took part in the course claimed that their approval of this media is due to learning academic innovative medical writing.
Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision-Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.
Notary is an official authorized to make an authentic deed regarding all deeds, agreements and stipulations required by a general rule. Activities carried out at the notary office such as recording client data and file data still use traditional systems that tend to be manual. The problem that occurs is the inefficiency in data processing and providing information to clients. Clients have difficulty getting information related to the progress of documents that are being taken care of at the notary's office. The client must take the time to arrive to the notary's office repeatedly to check the progress of the work of the document file. The purpose of this study is to facilitate clients in obtaining information about the progress of the work in progress, and make it easier for employees to process incoming documents by implementing an administrative system. This system was developed with the waterfall system development method and uses the Multi-Channel Access Technology integrated in the website to simplify the process of delivering information and requesting information from clients and to clients with Telegram and SMS Gateway. Clients will come to the office only when there is a notification from the system via Telegram or SMS notifying that the client must come directly to the notary's office, thus leading to an efficient time and avoiding excessive transportation costs. The overall functional system can function properly based on the results of alpha testing. The results of beta testing conducted by distributing the system feasibility test questionnaire to end users, get a percentage of 96% of users agree the system is feasible to be implemented.
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians-both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication-based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore, to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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EP was used by many researchers to optimize the performance of a system [21–23].
EP has its advantage in a way that it can compute the optimal solution for a power system in a
very short time, but possesses an ability to produce nearly optimal settlement solution [21].
Therefore, cloning technique is adapted to create better individuals for mutation in EP. This
immune EP (IEP) would provide broader space for tournament selection. In this paper, IEP is
used to optimise the size and the location of PV to be injected into the transmission system with
low loss as the objective function. Results obtained from the study, implemented on the chosen
test systems demonstrated the effectiveness of the proposed technique.
2. Research Method
2.1. Compensation Scheme
One of the aims of this study is to see the feasibility of RE as a mean to compensate
loss in transmission system. Loss control issue is very crucial in power system as uncontrollable
loss would subject a system to fail. As loss and instability of a network would increase with the
increased reactive loaddemand, an injected PV may be a saver by providing more real power
supply. Nevertheless, a backup energy supply cannot be simply added to a power system
network. Figure 1 shows how the performance of a transmission system may deteriorate when
PV is incrementally injected withoutadhering to any constraint while load is fixed.
Figure 1. A network performance with injected non-optimized PV
Installation of PV to an existing network with improper sizing may lead to possible
higher loss and putting the network at high risk of collapse when no proper planning exists. As
such, the use of IEP is suggested as the optimisation tool in planning such compensating
scheme. IEP will be integrated into the pre-optimised load flow of transmission system to
calculate the optimum PV in terms of size and location to improve the performance of the
network. The overall idea of this work is depicted in Figure 2.
Figure 2. Overview of determining PV size and location using IEP
PV
Optimization Tool
AIS
∑EP
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Figure 3. Flowchart of the overall work
2.2. Conceptual Idea
The main objective of this work is to minimise the total loss when reactive load
increases by injecting PV at a load bus. IEP is employed to determine the optimum size and
location of PV such that highest total loss reduction percentage can be achieved while fulfilling
load demand and other network constraints. The idea can be conceptually presented by
Figure 3. The detailed formulation of the loss control problem is presented in the following
sections.
2.3. Objective Function
The objective function to be minimized is the system losses given by Kron’s loss
formula:
𝑃𝑙𝑜𝑠𝑠 = ∑ ∑ 𝑃𝑖 𝐵𝑖𝑗 𝑃𝑗 + ∑ 𝐵0𝑖 𝑃𝑖 + 𝐵00
𝑛 𝑔
𝑖 =1
𝑛 𝑔
𝑗=1
𝑛 𝑔
𝑖=1
(1)
Where 𝐵𝑖𝑗 , 𝐵0𝑖 and 𝐵00 are loss coefficients.
This objective function is subjected to the following constraint:
1. Power balance equality constraint
𝑃𝑑𝑒𝑚𝑎𝑛𝑑 + 𝑃𝑙𝑜𝑠𝑠 = ∑ 𝑃𝑖
𝑛
𝑖=1
(2)
Where 𝑃𝑑𝑒𝑚𝑎𝑛𝑑 is the total system load demand and 𝑃𝑙𝑜𝑠𝑠 is the total system loss. 𝑃𝑖 is
the total power at the 𝑖 𝑡ℎgenerator.
2. Inequality constraint
The inequality constraint for the power is given by Equation (3).
𝑃𝑖𝑚𝑖𝑛 ≤ 𝑃𝑖 ≤ 𝑃𝑖𝑚𝑎𝑥 , 𝑖 = 1, 2, … , 𝑛 (3)
Where 𝑃𝑖𝑚𝑖𝑛 and 𝑃𝑖𝑚𝑎𝑥 are the minimum and the maximum real power outputs of 𝑖 𝑡ℎ
generator, respectively.
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Another inequality constraint to be satisfied is the minimum voltage of the system,
𝑉𝑚𝑖𝑛. Following IEEE standard, ideal voltage would be in the range stated by Equation (4)
0.95 ≤ 𝑉𝑚𝑖𝑛 ≤ 1.05 p.u (4)
2.4. Proposed Immunized-Evolutionary Programming
Figure 4 shows the flowchart of IEP technique for PV injection to load bus. The IEP is
proposed to improve the global optimum search of the PV sizing and location by presenting
more candidates for the selection tournament.
The processes in Figure 4 are briefly explained;
Figure 4. Flowchart of the IEP for optimal PV size and location
a. Initialization Process and Fitness Calculation:
Initialization process is a process to generate all the control variables, which optimize
the fitness value. The number for individuals that forms the population depends on the nature of
the optimization process. In most literatures, 20 individuals are the acceptable number to
perform complete optimization process. Unlike genetic algorithm (GA), the number of individuals
that forms the population reaches 500. Random pairs are generated to be in the initial
population pool.
In this phase, all the generated random numbers or normally termed as control
variables must satisfy all the constraints equations involving inequality constraints and equality
constraints, including Equation (5).
𝐿𝑜𝑠𝑠 𝑡𝑜𝑡𝑎𝑙 ≤ 𝐿𝑜𝑠𝑠 𝑡𝑜𝑡𝑎𝑙 (𝑏𝑎𝑠𝑒) (5)
𝐿𝑜𝑠𝑠 𝑡𝑜𝑡𝑎𝑙(𝑏𝑎𝑠𝑒) is generated from pre-optimized load flow. It must be made sure that PV
will not be located at the swing bus or generator bus. A reliable initial population matrix should
have considered all the constraints, while the corresponding fitness values are computed
accordingly. The general parent matrix for the individuals during initial population is generally
given by Equation (6):
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𝑥 𝑛𝑘 = [
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
⋱
𝑥 𝑛1 𝑥 𝑛2 …
𝑥1,𝑘−1 𝑥1𝑘
𝑥2,𝑘−1
⋮
𝑥2𝑘
⋮
𝑥 𝑛,𝑘−1 𝑥 𝑛𝑘
] (6)
Matrix size:20x k.
where; 𝑛 is the population size.
𝑘 is the number of control variables.
The population size is 20in accordance to the suggestion given in [21]. For the first
iteration or evolution, the parent matrix is the same as those of the initial population
matrix.Calculation of fitness values takes all the values of the control variables. Nevertheless,
calculation of fitness for the second evolution or iteration onwards will have to consider the
individuals whom survived during the tournament and selection process. The parent population
is then represented by Equation (7), where 𝑓𝑛 is the fitness of the 𝑛 𝑡ℎ individual;
𝐹𝑖𝑡1 = [
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
…
𝑥 𝑛1 𝑥 𝑛2 …
𝑥1,𝑘 𝑓1
𝑥2𝑘
⋮
𝑓2
⋮
𝑥 𝑛𝑘 𝑓𝑛
] (7)
b. Cloning Process
Each individual in the parents’ matrix is then cloned via the cloning phase. This forms a
cloned matrix which has multiplied the individuals. The size of the cloned matrix depends on
how many multiplication is desired. The multiplication factor is uniform for each individual. The
general cloned matrix 𝑥 𝑚𝑛𝑘is given in (8).
𝑥 𝑚𝑛𝑘 =
[
[
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
…
𝑥 𝑛1 𝑥 𝑛2 …
𝑥1,𝑘 𝑓1
𝑥2𝑘
⋮
𝑓2
⋮
𝑥 𝑛𝑘 𝑓𝑛
] 1
[
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
…
𝑥 𝑛1 𝑥 𝑛2 …
𝑥1,𝑘 𝑓1
𝑥2𝑘
⋮
𝑓2
⋮
𝑥 𝑛𝑘 𝑓𝑛
] 2
⋮
[
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
…
𝑥 𝑛1 𝑥 𝑛2 …
𝑥1,𝑘 𝑓1
𝑥2𝑘
⋮
𝑓2
⋮
𝑥 𝑛𝑘 𝑓𝑛
] 𝑚
]
(8)
Matrix size : mn x k.= 200 x k
where ; 𝑛 is the population number = 20
𝑘 is the number of variables
m is the cloning number = 10
c. Mutation Process and New Fitness Calculation:
Mutation is a process to breed offspring. In this work, Gaussian mutation technique as
shown in Equation (9) is used for the mutation process. There are several other mutation
operators which can be adopted such as Cauchy, levy and chaotic. However, in this study
Gaussian technique is adopted due to its simplicity reported in previous works [21], [24–26].
𝑥 𝑖+𝑚,𝑗 = 𝑥 𝑖,𝑗 + 𝑁 (0, 𝛽( 𝑥𝑗𝑚𝑎𝑥 − 𝑥𝑗𝑚𝑖𝑛 ) (
𝑓𝑖
𝑓𝑚𝑎𝑥
)) (9)
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Where:
𝑥 𝑖+𝑚,𝑗 𝑖s mutated parent (offspring)
𝑥 𝑖,𝑗is parent
β is search step
𝑥 𝑗𝑚𝑎𝑥is maximum value of parent
𝑥 𝑗𝑚𝑖𝑛 is minimum value of parent
𝑓𝑖 is fitness of 𝑖 𝑡ℎrandom number
𝑓𝑚𝑎𝑥 is maximum fitness
Recalculation of fitness or termed as fitness 2 is conducted using the offspring values.
The size of this matrix is the same as the fitness 1.
d. Combination
The parent matrix and the offspring matrix are combined in cascoded form. If the parent
matrix and the offspring matrixare as represented by (10) and (11) respectively, then the
combined matrix, C, has the form as in Equation (12)
𝐴1 = [
𝑥11 𝑥12 …
𝑥21
⋮
𝑥22
⋮
…
𝑥 𝑚𝑛1 𝑥 𝑚𝑛2 …
𝑥1,𝑘 𝑓1
𝑥2𝑘
⋮
𝑓2
⋮
𝑥 𝑚𝑛𝑘 𝑓𝑚𝑛
] (10)
𝐴2 = [
𝑋11 𝑋12 …
𝑋21
⋮
𝑋22
⋮
…
𝑋 𝑚𝑛1 𝑋 𝑚𝑛2 …
𝑋1 ,𝑘 𝐹1
𝑋2𝑘
⋮
𝐹2
⋮
𝑋 𝑚𝑛𝑘 𝐹𝑚𝑛
] (11)
𝐶 = [
𝐴1
𝐴2
] (12)
e. Selection:
The combined matrix Cis to go through a selection process. The best candidates from
matrix C will be chosen for the next iteration. They will be ranked based on the loss produced
should they were selected. This approach is adopted due to its simplicity. Other selection
techniques such as piecewise comparison, elitism or rouloutte wheel can also be employed if
appropriate. Fitness compliance, mutation and selection process will be repeated until the
fitness value is stagnant.
f. Convergence test:
The convergence test will signal the evolution process to stop as the optimal solution is
now achieved. The criterion would be the difference between the maximum fitness and the
minimum fitness, while the fitness must be less than the initial value. It is mathematically
represented as in (11).
𝐿𝑜𝑠𝑠 𝑡𝑜𝑡𝑎𝑙(𝑚𝑎𝑥 ) − 𝐿𝑜𝑠𝑠 𝑡𝑜𝑡𝑎𝑙 ( 𝑚𝑖𝑛) ≤ 0.00001 (13)
3. Results and Discussion
The optimized PV is planned to be installed at one of the load bus of a 12-bus system.
The 12-bus system is a transmission system formed by connecting two IEEE 6-bus system by
two lines. The two systems are arranged such that they are the mirror-image of each other. The
system is shown in Figure 5.
In this study, four cases will be simulated:
a. Case I: Reactive load is varied at one load bus
b. Case II: Reactive load is varied at two load buses
c. Case III: Reactive load is varied at three load buses
d. Case IV: Contingency case
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Figure 5. Single line diagram of 12-Bus System Model
The application of the IEP technique to power system has been tested on the weakest
bus of a 12-bus transmission system. The weakest bus is identified from the load flow program;
reactive load was added on individual bus until the load flow close to the divergence point. The
bus that has the minimum tolerance to the incremental load will be selected as the weakest bus.
From the result of the pre-optimized load flow shown in Figure 6, it is concluded that bus 5 is the
weakest, followed by bus 7 and then bus 10. This is because the voltage at bus 5, Vm(5), is the
lowest at the 35 MVar point. The voltage is less than 0.6 p.u., which is when the system is
already collapse.
Figure 7 to figure 8 present the results for case I; where load variation is subjected to
only one bus, i.e. bus 5. Figure 7 shows how the loss of the 12-bus transmission network can be
reduced by installing PV at the optimize location within the optimal size. The pre-set data are
the loss values extracted from pre-optimized load flow. In this case, the optimal PV is located at
bus 7, with its corresponding sizing of 34 MW. Figure 8 presents the comparative results on the
percentage of loss reduction optimized using EP and IEP for case-I.
Figure 6. Base values of voltage from pre-optimized load flow of a 12-bus system
From the Figure 8, IEP performs better than EP at all loading conditions subjected to
the system. Percentage of loss reduction is higher at higher reactive loading using both
optimization techniques. At the minimum point, the loss reduction by EP technique is 48.53%
while by IEP technique is 48.62%. On the other hand, at the maximum point (Qd =35 MVAR),
the loss reduction by EP technique is 56.13%, while by IEP technique is 56.19%. This indicates
implementation of IEP is still worthy, especially when it is possibly translated to monetary.
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Figure 7. Network performance with increasing reactive load and PV injected
Figure 8. Performance comparison between EP and IEP
Since the 12-bus transmission system is not a standard network, the IEEE 14-bus
system is then used to check the feasibility of the proposed IEP technique. Figure 9 confirms
that this technique is able to determine the optimal sized of PV which reduces the loss suffered
by the IEEE 14-bus network when its reactive load is increased by installing optimum-sized PV
at the optimal location. IEP optimization technique is able to compensate the total network loss
by at least 48.62%. Comparing Figure 8 and Figure 9, the minimum and the maximum total load
reduction when IEP is used are the same for both 12-bus system and IEEE 14-bus system. This
could be due to the fact that both systems are not very much different.
Figure 9. Effect of optimal PV injection on reactively loaded IEEE 14-bus system using IEP
technique
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In terms of network stability, adding the optimal PV to cater for increasing demand
would also improve the voltage level of the network as shown in Table 1. Although IEP is slightly
better than EP in maintaining the voltage stability, adding optimal PV certainly improve the
voltage compared to the network without PV. As the objective function of the optimization
technique in this work is loss minimization, the slight improvement of minimum voltage profile is
acceptable.
Table 1. Minimum Voltage profile obtained when bus 5 was reactively loaded using Load Flow,
EP and IEP technique in the 12-Bus System
Reactive load, Qd (MW) Load Flow (pu) EP (pu) IEP (pu)
5 0.8273 0.8775 0.8776
15 0.7684 0.8267 0.8269
25 0.6938 0.7676 0.7677
35 0.5701 0.6936 0.6938
As mentioned, the results presented earlier are found when the reactive load is varied
only at one bus,i.e. bus 5.To see the capability of IEP technique to determine the optimal size
and location of PV to the 12-bus transmission system in order to control the system loss, the
reactive load is then incrementally added to other busses. Hence case II is simulated, where the
reactive load at two load busses, bus 5 and bus 7, are uniformly increased. The maximum
reactive load that can be uniformly added to each bus in case-II and case-III is 20MVar each.
Figure 10 depicts the ability of IEP to control the loss of the network in case-II by finding
the optimal PV size and optimal PV location. The loss is reduced by at least 48.51%. As the
reactive load is increased, so does the loss reduction percentage.
The computation of optimal size and optimal placement of PV by IEP technique is
continued with case III; the reactive load is increased uniformly on bus 5, 7 and bus 10 in the
12-bus system. The network performance based on total system loss is graphically presented in
Figure 11. Again, IEP is able to reduce the system loss with multi-increment load by determining
the optimal PV location and optimal PV size.
Figure 8, Figure 10 and Figure 11 illustrate that the loss reduction increases as the load
is reactively incremented. This is because the extra power supplied by the optimal PV is put to
the better usage by the network to cater for incremental demand. It is to be noted that the
optimal PV sizing and the location are found to be the same for all reactive load subjectedto the
system. Hence, the PV may have provided unnecessary extra energy that is wasted at lightly-
loaded instances. The comparisons of load reduction between case-I, case-II and case-III at
three reactive loading points are tabulated in Table 2.
Figure 10. Effect of Optimal PV injection on two-reactively loaded bus of 12-bus system using
IEP technique
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Figure 11. Effect of Optimal PV injection on three-reactively loaded bus of 12-bus
system using IEP technique
In case-IV, the contingency scenario is considered, where one of the transmission lines
is taken-out. The line connecting bus-3 and bus-4 is chosen for this case. Without the
compensation scheme, the 12-bus network quickly collapses. But, with optimal PV injected to
the system, the network is able to be recovered. Table 3 tabulates the results for this case.
From the table, installation of PV to the system during contingency condition (line removal)
managed to revive the system. This is also optimized using IEP.
Table 2. Loss Reduction Comparison Between Case-I, Case-II and Case-III
Reactive Load, Qd
(Mvar)
Total Loss Reduction (%)
Case I Case II Case III
0 48.62 48.51 48.56
10 49.57 49.74 49.43
20 50.41 52.46 57.36
Table 3. Network Performance of 12-Bus System with IEP during Contingency Scenario
Reactive Load, Qd
(Mvar)
PV Location PV size (MW) Loss (MW) Vmin (p.u.)
10 7 42.00 6.33 0.86
20 10 39.76 8.81 0.77
25 7 41.55 7.60 0.77
4. Conclusion
This paper has presented immune-evolutionary programming technique for loss-control
in transmission system by optimizing the size and the location of a PV to be assimilated to
existing system. Results of IEP outperform the EP in finding the optimal solution of the size and
location of the PV while minimizing the loss. It is concluded that injecting correct size of PV at
the right location would reduce the network loss, when there is multi-load increment. The
optimal PV size and location calculated by IEP is also able to support the 12-bus system during
contingency case.
Acknowledgement
The authors would like to acknowledge The Institute of Research Management and
Innovation (IRMI) UiTM, Shah Alam, Selangor, Malaysia and Ministry of Higher Education
(MOHE) for the financial support of this research. This research is supported by MOHE under
the Research Acculturation Grant Scheme (RAGS) with project code: 600-RMI/RAGS 5/3
(187/2014).
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IJEECS Vol. 6, No. 3, June 2017 : 737 – 748
747
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