In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
ANFIS used as a Maximum Power Point Tracking Algorithmfor a Photovoltaic Syst...IJECEIAES
Photovoltaic (PV) modules play an important role in modern distribution networks; however, from the beginning, PV modules have mostly been used in order to produce clean, green energy and to make a profit. Working effectively during the day, PV systems tend to achieve a maximum power point accomplished by inverters with built-in Maximum Power Point Tracking (MPPT) algorithms. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS), as a method for predicting an MPP based on data on solar exposure and the surrounding temperature. The advantages of the proposed method are a fast response, non-invasive sampling, total harmonic distortion reduction, more efficient usage of PV modules and a simple training of the ANFIS algorithm. To demonstrate the effectiveness and accuracy of the ANFIS in relation to the MPPT algorithm, a practical sample case of 10 kW PV system and its measurements are used as a model for simulation. Modelling and simulations are performed using all available components provided by technical data. The results obtained from the simulations point to the more efficient usage of the ANFIS model proposed as an MPPT algorithm for PV modules in comparison to other existing methods.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
ANFIS used as a Maximum Power Point Tracking Algorithmfor a Photovoltaic Syst...IJECEIAES
Photovoltaic (PV) modules play an important role in modern distribution networks; however, from the beginning, PV modules have mostly been used in order to produce clean, green energy and to make a profit. Working effectively during the day, PV systems tend to achieve a maximum power point accomplished by inverters with built-in Maximum Power Point Tracking (MPPT) algorithms. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS), as a method for predicting an MPP based on data on solar exposure and the surrounding temperature. The advantages of the proposed method are a fast response, non-invasive sampling, total harmonic distortion reduction, more efficient usage of PV modules and a simple training of the ANFIS algorithm. To demonstrate the effectiveness and accuracy of the ANFIS in relation to the MPPT algorithm, a practical sample case of 10 kW PV system and its measurements are used as a model for simulation. Modelling and simulations are performed using all available components provided by technical data. The results obtained from the simulations point to the more efficient usage of the ANFIS model proposed as an MPPT algorithm for PV modules in comparison to other existing methods.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Energy Splitting for SWIPT in QoS-constraint MTC Network: A Non-Cooperative G...IJCNCJournal
This paper studies the emerging wireless energy harvesting algorithm dedicated for machine type communication (MTC) in a typical cellular network where one transmitter (e.g. the base station, a hybrid access point) with constant power supply communicates with a set of users (e.g. wearable devices, sensors). In the downlink direction, the information transmission and power transfer are conducted simultaneously by the base station. Since MTC only transmits several bits control signal in the downlink direction, the received signal power can be split into two parts at the receiver side. One is used for information decoding and the other part is used for energy harvesting. Since we assume that the users are without power supply or battery, the uplink transmission power is totally from the energy harvesting. Then, the users are able to transmit their measured or collected data to the base station in the uplink direction. Game theory is used in this paper to exploit the optimal ratio for energy harvesting of each user since power splitting scheme is adopted. The results show that this proposed algorithm is capable of modifying dynamically to achieve the prescribed target downlink decoding signal-to-noise plus interference ratio (SINR) which ensures the high reliability of MTC while maximizing the uplink throughput.
Active Distribution Grid Power Flow Analysis using Asymmetrical Hybrid Techni...IJECEIAES
A conventional distribution power flow analysis has to be improved regards the changes in distribution network. One of the changes is a grid operation because a new grid concept, e.g. micro-grid and aggregation, is aimed to be operated based on area itself. Consequently, each area can be actively operated in either grid connected mode or islanding mode. Hence, this paper proposes an asymmetrical power flow analysis using hybrid technique to support this flexible mode change. The hybrid technique offers an opportunity to analyze power flow in a decoupling way. This means that the power flow analysis can be performed separately in each grid area. Regards the distributed generation, this paper also introduces a model based on inverter-based operation, i.e. grid forming, grid supporting and grid parallel. The proposed asymmetrical hybrid load flow method is examined in three case studies, i.e. a verification study with the DIgSILENT PowerFactory, a demonstration of decoupling analysis approach and a performance study with the Newton-Raphson method.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
Network Reconfiguration in Distribution Systems Using Harmony Search AlgorithmIOSRJEEE
This manuscript explores feeder reconfiguration in distribution networks and presents an efficient method to optimize the radial distribution system by means of simultaneous reconfiguration. Network Reconfiguration of radial distribution system is a significant way of altering the power flow through the lines. This assessment presents a modern method to solve the network reconfiguration problem with an objective of minimizing real power loss and improving the voltage profile in radial distribution system (RDS). A precise and load flow algorithm is applied and the objective function is formulated to solve the problem which includes power loss minimization. HSA Algorithm is utilized to restructure and identify the optimal strap switches for minimization of real power loss in a distribution network.. The strategy has been tested on IEEE 33-bus and 69- bus systems to show the accomplishment and the adequacy of the proposed technique. The results demonstrate that a significant reduction in real power losses and improvement of voltage profiles.
Design and Implementation of Maximum Power Point Tracking in Photovoltaic Sys...inventionjournals
ABSTRACT: This paper presents an algorithm for maximum power point tracking to optimize photovoltaic systems. Beta algorithm is a type of MPPT algorithm. It is having fast tracking ability. The algorithm has been verified on a photovoltaic system modeled in Lab VIEW environment. This algorithm significantly improves the efficiency during the tracking.
SYNCHROPHASOR DATA BASED INTELLIGENT ALGORITHM FOR REAL TIME EVENT DETECTION ...IAEME Publication
The wide area measurement system (WAMS) has been installed at several locations in power system. Phasor measurements units (PMU) are considered as the building blocks of WAMS are being installed at various locations of power system. PMU is sending very large volume of data to Power system control center with the sampling rate of 50 or 25 samples per second. However there are always several events per day occurring in the system but the rate at which data is received and the volume of data to be analyzed is a big challenge for power system engineer. There is a need for developing an intelligent system to handle large volume of Synchrophasor data and identify Power system event in the present context. This paper presents an intelligent algorithm to automatically detect such events using wide area measurements in real time. In this work, Synchrophasor measurements received from PMU are fed to KNN based pattern recognition algorithm which is used to identify the Power system events. The severity and the type of the event can be judged through the change in voltage magnitude and phase angle at various buses. The developed algorithm is tested for IEEE 14 bus system and results are verified.
PV Source Integrated Micro-Grid for Power Quality Improvement using MPPT Tech...Niteesh Shanbog
The demand for Electrical energy is increasing day by day as it can be easily converted to another form of energy. All consumers expect Electrical energy with high power quality. Most of the commercial and industrial loads are inductive in nature and need power electronic circuits/ controllers to get smooth control of the equipment. This, in turn, leads to the injection of harmonics into the system, hence the power quality is affected. The above problem needs to be addressed and eliminated. In this paper, a shunt active power filter is used to mitigate the harmonics. Id-Iq control is used to analyse the performance of the filter and is simulated using MATLAB software. The MPPT controller is used to improving the power quality of the system.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
White paper - Anticipating complex network issues through the use of advanced...Ingeteam Wind Energy
Throughout the world, a broad range of grid codes are in constant evolution. As the amount of renewable power deployed globally continues to increase steadily, the critical issue of grid stability also become increasingly complex. Ingeteam’s advanced simulation models are the answer to the complex grid issues that come with a high penetration of wind power in transmission grids.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
This paper propose a new approach to determine a linear mathematical model of a PV moduel based on an accurate nonlinear model . In this study, electrical parameters at only one operating condition are calculated based on an accurate model. Then, first-order Taylor series approximations apply on the nonlinear model to estimate the proposed model at any operating conditionts. The proposed method determines the number of iteration times. This decreases calculation time and the speed of numerical convergence will be increased. And, it is observed that owing to this method, the system converged and the problem of failing to solve the system because of inappropriate initial values is eliminated. The proposed model is requested in order to allow photovoltaic plants simulations using low-cost computer platforms. The effectiveness of the proposed model is demonstrated for different temperature and irradiance values through conducting a comparison between result of the proposed model and experimental results obtained from the module data-sheet information.
This research presents tracking the maximum power of a photovoltaic to control a five-level inverter, a cascade type connecting a single-phase grid-connected system with a fuzzy logic control model. Maximum power tracking control In this research, the principle of controlling the maximum current amplitude of the photovoltaic multiplied by the sine signal per unit that used as a reference current compared to the grid current. Signal comparison with the PID controller allows the creation of five levels of PWM of cascade control of five-level inverter connects single-phase grids. The results of the simulation test using the program MATLAB/Simulink to compare with the generated prototype found that the fuzzy logic principle was used to control the maximum power tracking conditions of the P&O method, when the amount of radiation light intensity decreases suddenly, making it possible to track the maximum power of the photovoltaic. Also, when the inverter connected to the grid by controlling the power angle to compare results between the simulation and the prototype — found that the current flowing into the grid increases according to the power angle control. Resulting in a nearby waveform, sine wave and an out of phase angle to the grid voltage because the system is in the inverter mode, and the harmonic spectrum of the grid currently has total harmonic distortion (THD) is reduced as an indication of the proposed system can be developed and applications.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
Energy Splitting for SWIPT in QoS-constraint MTC Network: A Non-Cooperative G...IJCNCJournal
This paper studies the emerging wireless energy harvesting algorithm dedicated for machine type communication (MTC) in a typical cellular network where one transmitter (e.g. the base station, a hybrid access point) with constant power supply communicates with a set of users (e.g. wearable devices, sensors). In the downlink direction, the information transmission and power transfer are conducted simultaneously by the base station. Since MTC only transmits several bits control signal in the downlink direction, the received signal power can be split into two parts at the receiver side. One is used for information decoding and the other part is used for energy harvesting. Since we assume that the users are without power supply or battery, the uplink transmission power is totally from the energy harvesting. Then, the users are able to transmit their measured or collected data to the base station in the uplink direction. Game theory is used in this paper to exploit the optimal ratio for energy harvesting of each user since power splitting scheme is adopted. The results show that this proposed algorithm is capable of modifying dynamically to achieve the prescribed target downlink decoding signal-to-noise plus interference ratio (SINR) which ensures the high reliability of MTC while maximizing the uplink throughput.
Active Distribution Grid Power Flow Analysis using Asymmetrical Hybrid Techni...IJECEIAES
A conventional distribution power flow analysis has to be improved regards the changes in distribution network. One of the changes is a grid operation because a new grid concept, e.g. micro-grid and aggregation, is aimed to be operated based on area itself. Consequently, each area can be actively operated in either grid connected mode or islanding mode. Hence, this paper proposes an asymmetrical power flow analysis using hybrid technique to support this flexible mode change. The hybrid technique offers an opportunity to analyze power flow in a decoupling way. This means that the power flow analysis can be performed separately in each grid area. Regards the distributed generation, this paper also introduces a model based on inverter-based operation, i.e. grid forming, grid supporting and grid parallel. The proposed asymmetrical hybrid load flow method is examined in three case studies, i.e. a verification study with the DIgSILENT PowerFactory, a demonstration of decoupling analysis approach and a performance study with the Newton-Raphson method.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
Network Reconfiguration in Distribution Systems Using Harmony Search AlgorithmIOSRJEEE
This manuscript explores feeder reconfiguration in distribution networks and presents an efficient method to optimize the radial distribution system by means of simultaneous reconfiguration. Network Reconfiguration of radial distribution system is a significant way of altering the power flow through the lines. This assessment presents a modern method to solve the network reconfiguration problem with an objective of minimizing real power loss and improving the voltage profile in radial distribution system (RDS). A precise and load flow algorithm is applied and the objective function is formulated to solve the problem which includes power loss minimization. HSA Algorithm is utilized to restructure and identify the optimal strap switches for minimization of real power loss in a distribution network.. The strategy has been tested on IEEE 33-bus and 69- bus systems to show the accomplishment and the adequacy of the proposed technique. The results demonstrate that a significant reduction in real power losses and improvement of voltage profiles.
Design and Implementation of Maximum Power Point Tracking in Photovoltaic Sys...inventionjournals
ABSTRACT: This paper presents an algorithm for maximum power point tracking to optimize photovoltaic systems. Beta algorithm is a type of MPPT algorithm. It is having fast tracking ability. The algorithm has been verified on a photovoltaic system modeled in Lab VIEW environment. This algorithm significantly improves the efficiency during the tracking.
SYNCHROPHASOR DATA BASED INTELLIGENT ALGORITHM FOR REAL TIME EVENT DETECTION ...IAEME Publication
The wide area measurement system (WAMS) has been installed at several locations in power system. Phasor measurements units (PMU) are considered as the building blocks of WAMS are being installed at various locations of power system. PMU is sending very large volume of data to Power system control center with the sampling rate of 50 or 25 samples per second. However there are always several events per day occurring in the system but the rate at which data is received and the volume of data to be analyzed is a big challenge for power system engineer. There is a need for developing an intelligent system to handle large volume of Synchrophasor data and identify Power system event in the present context. This paper presents an intelligent algorithm to automatically detect such events using wide area measurements in real time. In this work, Synchrophasor measurements received from PMU are fed to KNN based pattern recognition algorithm which is used to identify the Power system events. The severity and the type of the event can be judged through the change in voltage magnitude and phase angle at various buses. The developed algorithm is tested for IEEE 14 bus system and results are verified.
PV Source Integrated Micro-Grid for Power Quality Improvement using MPPT Tech...Niteesh Shanbog
The demand for Electrical energy is increasing day by day as it can be easily converted to another form of energy. All consumers expect Electrical energy with high power quality. Most of the commercial and industrial loads are inductive in nature and need power electronic circuits/ controllers to get smooth control of the equipment. This, in turn, leads to the injection of harmonics into the system, hence the power quality is affected. The above problem needs to be addressed and eliminated. In this paper, a shunt active power filter is used to mitigate the harmonics. Id-Iq control is used to analyse the performance of the filter and is simulated using MATLAB software. The MPPT controller is used to improving the power quality of the system.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
White paper - Anticipating complex network issues through the use of advanced...Ingeteam Wind Energy
Throughout the world, a broad range of grid codes are in constant evolution. As the amount of renewable power deployed globally continues to increase steadily, the critical issue of grid stability also become increasingly complex. Ingeteam’s advanced simulation models are the answer to the complex grid issues that come with a high penetration of wind power in transmission grids.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
This paper propose a new approach to determine a linear mathematical model of a PV moduel based on an accurate nonlinear model . In this study, electrical parameters at only one operating condition are calculated based on an accurate model. Then, first-order Taylor series approximations apply on the nonlinear model to estimate the proposed model at any operating conditionts. The proposed method determines the number of iteration times. This decreases calculation time and the speed of numerical convergence will be increased. And, it is observed that owing to this method, the system converged and the problem of failing to solve the system because of inappropriate initial values is eliminated. The proposed model is requested in order to allow photovoltaic plants simulations using low-cost computer platforms. The effectiveness of the proposed model is demonstrated for different temperature and irradiance values through conducting a comparison between result of the proposed model and experimental results obtained from the module data-sheet information.
This research presents tracking the maximum power of a photovoltaic to control a five-level inverter, a cascade type connecting a single-phase grid-connected system with a fuzzy logic control model. Maximum power tracking control In this research, the principle of controlling the maximum current amplitude of the photovoltaic multiplied by the sine signal per unit that used as a reference current compared to the grid current. Signal comparison with the PID controller allows the creation of five levels of PWM of cascade control of five-level inverter connects single-phase grids. The results of the simulation test using the program MATLAB/Simulink to compare with the generated prototype found that the fuzzy logic principle was used to control the maximum power tracking conditions of the P&O method, when the amount of radiation light intensity decreases suddenly, making it possible to track the maximum power of the photovoltaic. Also, when the inverter connected to the grid by controlling the power angle to compare results between the simulation and the prototype — found that the current flowing into the grid increases according to the power angle control. Resulting in a nearby waveform, sine wave and an out of phase angle to the grid voltage because the system is in the inverter mode, and the harmonic spectrum of the grid currently has total harmonic distortion (THD) is reduced as an indication of the proposed system can be developed and applications.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
A NOVEL CONTROL STRATEGY FOR POWER QUALITY IMPROVEMENT USING ANN TECHNIQUE FO...IJERD Editor
The proposed system presents power-control strategies of a Micro grid-connected hybrid generation
system with versatile power transfer. This hybrid system allows maximum utilization of freely available
renewable energy sources like wind and photovoltaic energies. For this, an adaptive MPPT algorithm along with
standard perturbs and observes method will be used for the system.
The inverter converts the DC output from non-conventional energy into useful AC power for the
connected load. This hybrid system operates under normal conditions which include normal room temperature
in the case of solar energy and normal wind speed at plain area in the case of wind energy. However, designing
an optimal micro grid is not an easy task, due to the fact that primary energy carriers are changeable and
uncontrollable, as is the demand. Traditional design and optimization tools, developed for controlled power
sources, cannot be employed here. Simulation methods seem to be the best solution.
The dynamic model of the proposed system is first elaborated in the stationary reference frame and
then transformed into the synchronous orthogonal reference frame. The transformed variables are used in
control of the voltage source converter as the heart of the interfacing system between DG resources and utility
grid. By setting an appropriate compensation current references from the sensed load currents in control circuit
loop of DG, the active, reactive, and harmonic load current components will be compensated with fast dynamic
response, thereby achieving sinusoidal grid currents in phase with load voltages, while required power of the
load is more than the maximum injected power of the DG to the grid. In addition, the proposed control method
of this paper does not need a phase-locked loop in control circuit and has fast dynamic response in providing
active and reactive power components of the grid-connected loads.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
Neural Network-Based Stabilizer for the Improvement of Power System Dynamic P...TELKOMNIKA JOURNAL
This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs)
to improve the oscillation damping and dynamic performance of interconnected multimachine power
system. The scheme was based on the use of a neural network which identifies online the optimal
controller parameters. The inputs to the neural network include the active- and reactive- power of the
synchronous generators which represent the power loading on the system, and elements of the reduced
nodal impedance matrix for representing the power system configuration. The outputs of the neural
network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing
system configuration and operating condition. For a representative power system, the neural network has
been trained and tested for a wide range of credible operating conditions and contingencies. Both
eigenvalue calculations and time-domain simulations were used in the testing and verification of the
performance of the neural network-based stabilizer.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
In this paper, a detail design and description of a predictive current control scheme are adopted for three-phase grid-connected two-level inverter and its application in wind energy conversion systems. Despite its advantages, the predictive current controller is very sensitive to parameter variations which could eventually affected on system stability. To solve this problem, an estimation technique proposed to identify the value of harmonic filter parameter based on Model reference adaptive system (MRAS). Lyapunov stability theory is selected to guarantee a robust adaptation and stable response over large system parameter variation. The simulation results shows the efficiency of the proposed techniques to improve the current tracking performance.
Particle Swarm Optimization based Network Reconfiguration in Distribution Sys...theijes
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This paper proposes an improvement of the direct power control (DPC) scheme of a grid connected three phase voltage source inverter based on artificial neural networks (ANN) and fuzzy logic (FL) techniques for the renewable energy applications. This advanced control strategy is based on two intelligent operations, the first one is the replacement of the conventional switching table of a three phase voltage source inverter (VSI) by a selector based on artificial neural networks approach, and the second one is the replacement of the hysteresis comparators by fuzzy logic controllers for the instantaneous active and reactive power errors. These operations enable to reduce the power ripples, the harmonic disturbances and increase the response time period of the system. Finally, the simulation results were obtained by Matlab/Simulink environment, under a unity power factor (UPF). These results verify the transient performances, the validity and the efficiency of the proposed DPC scheme.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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Transient Stability Assessment and Enhancement in Power SystemIJMER
Power system is subjected to sudden changes in load levels. Stability is an important concept
which determines the stable operation of power system. For the improvement of transient stability the
general methods adopted are fast acting exciters, circuit breakers and reduction in system transfer
reactance. The modern trend is to employ FACTS devices in the existing system for effective utilization
of existing transmission resources. The critical clearing time is a measure to assess transient instability.
Using PSAT, the critical clearing time (CCT) corresponding to various faults are calculated. The most
critical faults were identified using this calculation. The CCT for the critical faults were found to change
with change in operating point. The CCT values are predicted using Artificial Neural Network (ANN) to
study the training effects of ANN. TCSC is selected as the FACTS device for transient stability
enhancement. Particle Swarm Optimization method is used to find the optimal position of TCSC using
the objective function real power loss minimization. The result shows that the technique effectively
increases the transient stability of the system
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Similar to Performance assessment of an optimization strategy proposed for power systems (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
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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Runway Orientation Based on the Wind Rose Diagram.pptx
Performance assessment of an optimization strategy proposed for power systems
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 5, October 2020, pp. 2729~2736
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i5.14396 2729
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Performance assessment of an optimization strategy proposed
for power systems
Harold Puin, Cesar Hernandez
Technological Faculty, Universidad Distrital Francisco Jose de Caldas, Colombia
Article Info ABSTRACT
Article history:
Received Oct 24, 2019
Revised May 15, 2020
Accepted Jun 17, 2020
In the present article, the selection process of the topology of an artificial
neural network (ANN) as well as its configuration are exposed. The ANN was
adapted to work with the Newton Raphson (NR) method for the calculation of
power flow and voltage optimization in the PQ nodes of a 10-node power
system represented by the IEEE 1250 standard system. The purpose is to assess
and compare its results with the ones obtained by implementing ant colony and
genetic algorithms in the optimization of the same system. As a result, it is
stated that the voltages in all system nodes surpass 0,99 p.u., thus representing
a 20% increase in the optimal scenario, where the algorithm took 30 seconds,
of which 9 seconds were used in the training and validation processes of
the ANN.
Keywords:
Artificial neural network
Newton Raphson
Optimization
Power system
This is an open access article under the CC BY-SA license.
Corresponding Author:
Cesar Hernandez,
Technological Faculty,
Universidad Distrital Francisco Jose de Caldas,
Calle 68D Bis A Sur # 49F – 70, Bogotá D.C., 111941, Colombia.
Email: cahernandezs@udistrital.edu.co
1. INTRODUCTION
The integration of power generation, transmission and distribution systems with the progress
evidenced in the information and communication technologies (ICT) sector has been encompassed within
the development of the concept known as smart grids. It has allowed to unite in a single management system
the areas of protection coordination, control, measure and economic dispatch of an electric network. Its main
purpose is to achieve integration through the efficient and rational use of energy as well as the increase of
reliability, security and flexibility of electric systems [1-3].
The inherent result of the development of smart grids has been the constant search for
the implementation of algorithms that optimize power systems based on technical and economic criteria as well
as energy quality. Improvement practices are also proposed such as control systems to correct voltage
unbalances in power generation units embedded within the network [4-7], multi-agent systems [8-10] to
compensate the high variability of the energy supply related to alternative sources of generation, and
the forecast of the effects of a massive connection of electric vehicles in the future [11-13]. Other proposals
have focused in the effects of environmental conditions in the projection of the economic dispatch based on
probability functions delivered by the statistical method Latin Hypercube Sampling [14]. Other strategies have
focused on developing algorithms that create usage profiles of the generators linked to a network, according to
the consumption demand [15, 16].
The calculation of the voltage values involves solving a system of simultaneous non-linear equations
in most cases. To achieve this, the Newton-Raphson (NR) and Gauss-Siedel iterative methods [4]. The NR
method consists of an iterative procedure in which non-linear equations are used involving voltage magnitude
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and angle variables as well as active and reactive power. Since there are only two power equations, the other
two variables must be calculated [8]. In [9], the NR method is used to calculate photovoltaic parameters and
carry out the modelling process. The Gauss-Seidel method is an iterative method in which the number of
unknown variables is equal to the number of equations to solve. It consists on designing a converging
succession according to a previously defined criteria [11], the convergence values are the solution of the nodal
voltages and powers of the electric system. In [12], the Gauss-Seidel method is used as the main strategy to
accelerate the solution of power flow in a high-performance reconfigurable computer.
It is evident that it is the process to be optimized within the smart grid that determines the type of
algorithm to be implemented, whether it is a linear system or not. Currently, the algorithms of the iterative [17]
(Newton method [18], conjugated gradient, interpolation, etc) and heuristic types [19] (evolutionary
algorithms, genetic algorithms, Nelder Mead, among others) are the most commonly implemented.
Furthermore, there are computational models that can be used in optimization models such as the Artificial
Neural Networks (ANN), a bio-inspired algorithm from 1943 that was relegated to the background due to
the computational capacity that it required at the time. However, with the unstoppable development in
electronics and semi-conductor materials and the manufacture of increasingly powerful processors,
the application of ANN has risen. They can be classified into iterative or heuristic methods depending on their
learning process [20, 21].
Since their reintroduction as a computational model, ANNs have been used to simulate different types
of processes [22-25], due to their swift prediction of variables. Nonetheless, their use as an optimization model
is not yet extended and needs some sort of iterative or heuristic algorithm in order to work. Hence, this article
presents the development process of an artificial neural network combined with the Newton Raphson (NR)
method for the calculation of power flow and the optimization of nodal voltages in power systems with n nodes.
This is subsequently implemented in a 10-node IEEE 1250 standardized power system with the purpose of
guaranteeing voltages over 0.98 p.u. in all system nodes. Finally, the performance will be assessed by
comparing the results obtained with the bee swarm and ant colony algorithms for the same power system.
This helps to determine its potential for implementation in smart grids as an optimization method based on
the electric energy criteria.
2. PROPOSED ALGORITHM
The proposed algorithm is developed in its totality in the MATLAB 2018b numerical computing
software. Initially, the execution of the NR method is carried out in order to obtain the magnitude and angle of
the voltage, based on the impedance matrix and the active and reactive power data. Using the power flow
calculations, the algorithm can proceed to assess the voltage in each PQ node through ANN to determine
whether the nodes are underpowered in comparison to the optimal value used for training. Thus, a capacitive
reactance of 0.1 p.u. is injected if the assessed node does not comply with this optimization parameter. When
the ANN completes the assessment of all power system nodes, the algorithm executes the NR method with
the purpose of determining the new voltages in the system, which are once again assessed by the ANN.
The process is concluded when it is established that all the PQ nodal voltages are equal or above the optimal
reference value.
In this manner, the NR and ANN methods enable the optimization of the power system, by giving
the user a final report in .xlsx format with the optimized values of voltage magnitude and angle for each node
as well as the calculation of generated active-reactive power and the demand. The user is informed on the value
of the capacitive correction required by each node to elevate the voltage to optimal values. Figure 1 shows
the flow diagram of the developed algorithm.
The neural network proposed in the algorith was developed by implementing the base codes of
the MATLAB fitnet function, establishing a structure of three layers: input layer, hidden layer and output layer.
This is illustrated in Figure 2 and the components are explained in this section.
2.1. Input variables
It is the information given to the neural network, for the traning phase as well as the validation and
testing phases. In this specific case, the nodal voltage in p.u. may vary from 0 to 1. The user is informed on
the value of the capacitive correction required by each node to elevate the voltage to optimal values. Figure 1
shows the flow diagram of the developed algorithm.
2.2. Layers
The model has three types of layers: input layer, hidden layer and output layer. The input layer has
one neuron, which individually receives the information of the voltage in each node. The hidden layer has 30
neurons, which is a number determined by considering that the number of neurons present in this layer is
3. TELKOMNIKA Telecommun Comput El Control
Performance assessment of an optimization strategy proposed for power systems (Harold Puin)
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proportional to the accuracy of the neural network regarding the classification of data but it is also proportional
to the time required to perform such task. In terms of the connections, each neuron in this layer is connected
with a neuron of the input layer depending oh the weights of the connections. It is important to clarify that
there may be more than one hidden layer. The number of hidden layers of a neural network is directly related
with how easy it is to classify the desired output according to the input. Figure 3 shows the output vs input
diagram with typical values of the optimization process of a power system. According to the diagram, for input
variables with values below 0.9, the neural network can generate an output of 0. For input variables over 0.9,
the neural network must generate an output of 0.1. This allows the perfect division of the data through a linear
curve. In case the output states cannot be linearly separated, the number of hidden layers must keep increasing.
The output layer has one neuron, corresponding to the number of output variables with a minimum of 2 states.
2.3. Weights
Weights are coefficients that alter the input value of neurons, starting from the hidden layer,
considering the periodicity in which a specific input value is transmitted compared to a desired output value.
This strengthens the connection with the transmitted neuron at the expense of the connection with other
neurons. Hence, weights also have the functionnality of interconnecting neurons in different layers.
Develop of power flow analysis with NR
RNA training process with 7000 type values
RNA validation porcess with 1500 type
values
RNA test process with 1500 type values
PQ nodes individual RNA evaluation
Power electric system initial data reading
from .xlsx file
The node have
undervoltage?
NO
Generation of the .xlsx report file
YES
Individually inyection of reactive
compensation in the all PQ nodes of the
system with undervoltage
NR algoritm ejecution
Figure 1. Flow diagram of the developed algorithm
4. ISSN: 1693-6930
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a2.1
a2.2
a2.3
a2.30
a3.1a1.1
Input
layer
Hidden
layer
Output
layer
Nodes Nodes Nodes
Weigth Weigth
Inputs
variables
Output
variables
Figure 2. Structure of the neural network implemented in the optimzzation algorithm
Figure 3. Data classification in a single layer neural network
2.4. Output variables
In (1) is the output of one neuron in the input layer. In (2) is the mathematical model for the output of
a neural network and it represents all possible combinations of the connections between the different layers of
a neural network from the hidden layer up to the output layer. For the ANN discussed in this paper, the output
variable has two states: 0 or 0.1.
𝑎𝑖
𝑘
= 𝑥𝑖 (1)
For k = 1, i > 0, where a is the neuron output and x is the input variable.
𝑎𝑖
(𝑘)
= 𝑓(∑ 𝑎𝑗
( 𝑘−1)𝑛𝑘−1
𝑗=1 ∗ 𝑤𝑗𝑖
( 𝑘−1)
) (2)
for k > 1, i > 0 and j > 0, where a is the neuron output, w are the weights of the neural connections and f(x) is
the limited function.
After establishing the topology of the ANN described in this article, Figure 4 presents the typical
results of the training, validation and testing phases, using 7000, 1500 and 1500 typical data respectively.
The system implements the Bayesian regularization function as a training function. In order to assess its
performance, the geometric mean root is used. A total of 58 iterations were carried out to train the network.
The training, validation and testing processes were stopped because the ANN attained the expected minimum
gradient in 8 seconds.
5. TELKOMNIKA Telecommun Comput El Control
Performance assessment of an optimization strategy proposed for power systems (Harold Puin)
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Figure 4. Typical data in the training, validation and testing processes of the implemented ANN
3. RESULTS
The schematic of the system to be optimized is shown in Figure 5. It is comprised of 10 nodes, from
which two nodes have a PV type, one node has a slack type and seven nodes have a PQ type. The other
characteristics and variables of the electric system are presented in Tables 1 and 2, respectively. In Table 1,
the first two columns represent the interconnection of the system nodes, the electric characteristics of these
connections are established from the third to the fifth column. In Table 2, the first two columns represent
the number of the node and its type. The third column up to the seventh column show the values of the generated
active/reactive power, the demanded active/reactive power and the voltage magnitude in each node.
The performance of the neural network during the training, validation and testing phases for the optimization
process of this specific system is presented in Figure 6. The preliminary result of the nodal voltage after
optimization and the total computation time (ANN training time plus optimization time) are shown in Figure 7.
Figure 5. IEEE 1250 power system
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Table 1. Characteristics of the IEEE 1250 power system
N start N finish R X Bsh/2
1 2 0.08379 0.1772 0.00047
1 6 0.1089 0.2304 0.00061
2 3 0.1843 0.3900 0.0010
2 5 0.1508 0.3191 000085
2 6 0.1675 0.3545 0.00094
3 4 0.2346 0.4963 0.0013
3 5 0.2094 0.4432 0.0011
5 4 0.1843 0.3900 0.0010
4 10 0.1675 0.3545 0.00094
6 7 0.1256 0.2659 0.00071
7 8 0.1005 0.2127 0.00056
7 9 0.0670 0.1418 0.00037
9 8 0.1340 0.2836 0.00075
8 10 0.1089 0.2304 0.00061
Table 2. Known system variables of the IEEE 1250 power system
Node Type P gen Q gen P dem Q dem V mag
1 Slack 0.7 0 0 0 1
2 PQ 0 0 0.15 0.2 0.9433
3 PQ 0 0 0.15 0.2 0.8939
4 PQ 0 0 0.15 0.2 0.8666
5 PV 0 0 0.15 0.2 1
6 PQ 0 0 0.15 0.2 0.9353
7 PQ 0 0 0.15 0.2 0.9335
8 PQ 0 0 0.15 0.2 0.8943
9 PV 0.5 0 0 0 1
10 PQ 0 0 0.15 0.2 0.8373
Figure 6. Training, validaiton and testing of the ANN used in
the optimization of the IEEE 1250 power system
Figure 1. Preliminary results of the nodal voltages and total execution time of the optimization algorithm
7. TELKOMNIKA Telecommun Comput El Control
Performance assessment of an optimization strategy proposed for power systems (Harold Puin)
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4. COMPARATIVE ASSESSMENT
Figure 7 shows the report generated by the algorithm in which the optimization results can be
thoroughly detailed for the 10-node IEEE 1250 standard power system. In Table 3, the node type is coded in
the second column, with 1 for Slack, 2 for PV and 3 for PQ. Table 4 presents the results of the nodal voltage
magnitudes obtained with the genetic algorithm (GA) and the ant colony optimization (ACO). Table 5 shows
the optimization percentage of the voltage magnitude for each node of the IEEE 1250 power system. Each
column represents one of the algorithms discussed in this section.
Table 3. Report of the optimization results for IEEE 1250
Bus i Type node P gen Q gen P dem Q dem Vmag Vang
1 1 0.779 -0.28 0 0 1 0
2 3 0 0.2 0.15 0.2 0.992 -5.96
3 3 0 0.3 0.15 0.2 1.002 -1126
4 3 0 0.3 0.15 0.2 1.008 -12.58
5 2 0 0 0.15 0.2 1 -10.99
6 3 0 0.3 0.15 0.2 1 -4.369
7 3 0 0.3 0.15 0.2 1.002 -5.194
8 3 0 0.3 0.15 0.2 1.004 -7.374
9 2 0,5 0 0 0 1 -2.583
10 3 0 0.3 0.15 0.2 1009 -10.996
Table 4. Results in nodal voltage magnitudes
for ga and aco
Node GA ACO
1 1 1
2 0.9984 0.9566
3 0.9963 0.9109
4 0.9972 0.9038
5 1 1
6 0.9957 0.9722
7 0.9957 0.9735
8 0.9947 0.9513
9 1 1
10 0.9955 0.9075
Table 5. Comparison of the percentages of nodal
voltage increase
Node GA ACO ANN
1 0% 0% 0%
2 5.84% 1.4% 5.16%
3 11.45% 1.9% 12.09%
4 15.07% 4.29% 16.31%
5 0% 0% 0%
6 6.45% 3.94% 6.91%
7 6.55% 4.28% 7.33%
8 11.22% 6.37% 12.26%
9 0% 0% 0%
10 18.89% 8.38% 20.5%
5. ANALYSIS OF THE RESULTS
Figures 4 and 6 exhibit the training, validation and testing process of the neural network. It is
concluded that the time lapses and iterations of convergence are similar under different performance scenarios.
This indicates that the size of the power system to be optimized does not influence the time and quality of
the ANN in detecting subvoltages in the system nodes. Going back to Figures 4 and 6, it can be determined
that the typical parameters for which the training process is stopped are tied to the minimum learning gradient
of the neural network and its performance. This means that the trained neural network guarantees a a high
accuracy in the prediction of values depending on the input variable. This is highlighted by the fact that
the output has two states only distanced by a 0.01. In case that the neural network is halted after completing
the maximum number of iterations, it is recommended to apply once again the learning process since
the network may not successfully predict the expected output values.
Table 3 showcases the report of the algorithm after the optimization process of the IEEE 1250 power
system. This table also shows that, even if the total non-capacitive reactive powers per node do not exceed
the active powers, they are significant enough to affect the power factor associated to the node. This is true not
only for the ANN algoritihm but also for the other algorithms. It is recommended to consider this parameters
for future versions of the algorithms. Furthermore, Table 3 reveals that the reactive power in the PV nodes or
the nodes connected to a generator is increased in order to compensate the capacitive reactive power injected
to the system by the ANN. This results in a normal behavior considering that these nodes must remain with
a voltage equal to 1 p.u. The results of the optimization of the IEEE 1250 power system exhibited by the genetic
algorithm and the ANN are fairly competitive. The minimum difference is 0.46% in the sixth node and
the maximum difference is 1.26% in the last node. It is suggested to assess the optimization time to find
an arguable difference.
6. CONCLUSIONS
The topology of the implemented ANN in the optimization algorithm has shown a stable behaviour
in terms of the parameters used for training, validation and testing, guaranteeing accuracy and swiftness in
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most cases. A neural network that adopts a number of inputs equal to the number nodes in the power system
to be optimized is not viable since it would require a large amount of data to assure a correct training process.
The corresponding computing power would need to surpass the capacity of an average computer. It is
recommended to include the power factor as an optimization parameter keeping in mind that optimization must
be based on the principle of energy quality. The optimization results obtained with both the genetic algorithm
and the implemented ANN are similar.
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