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A Heuristic Dynamic Programming based Power System Stabilizer for a Turbogenerator in a Single Machine Power System
"... Abstract—Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on ..."
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Abstract—Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique.
An Indirect Adaptive Fuzzy Power System Stabilizer for a Multi-machine Power System
"... Abstract:- An indirect adaptive fuzzy power system stabilizer (IDFPSS) is proposed in this paper. It consists of A fuzzy identifier and a feedback linearizing controller. The objective is to damp local and inter-area oscillations that occur following power system disturbance. The effectiveness of th ..."
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Abstract:- An indirect adaptive fuzzy power system stabilizer (IDFPSS) is proposed in this paper. It consists of A fuzzy identifier and a feedback linearizing controller. The objective is to damp local and inter-area oscillations that occur following power system disturbance. The effectiveness of the proposed technique is illustrated by applying the IDFPSS to a two-area four-machine system that is typically used in the literature to test the performance of power system stabilizers. A comparison between the proposed IDFPSS and a welltuned conventional power system stabilizer (CPSS) confirms the superiority of the IDFPSS.
An Improved Shuffled Frog Leaping Algorithm for Simultaneous Design of Power System Stabilizer and Supplementary Controller for SVC
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"... Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term ..."
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Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Oscillation Control in a Synchronous Machine
"... How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System ..."
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How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System
A Multi-layer Artificial Neural Network Architecture Design for Load Forecasting in Power
"... Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term ..."
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Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Voltage Profile Analysis in Power Transmission System based on STATCOM using Artificial Neural Network in MATLAB/SIMULINK
"... shown that trained Neural Network developed has excellent capabilities of forecasting which can be very useful in research. Voltage control and reactive power compensation in a weak distribution networks for integration of wind power is also represented in this paper. For dynamic reactive power comp ..."
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shown that trained Neural Network developed has excellent capabilities of forecasting which can be very useful in research. Voltage control and reactive power compensation in a weak distribution networks for integration of wind power is also represented in this paper. For dynamic reactive power compensation, when,STATCOM (Static Synchronous Compensator) is a used at a point of interconnection of wind farm and the network; the system absorbs the generated wind power for maintaining its voltage level. Voltage level of the system changes on changing the values of resistive loads connected to transmission line and using these voltages on bus 1 and bus2 on different values of loads a neural network is developed after training which can forecast voltage on bus 1 and bus 2 of the transmission line on any values of the resistive load connected to transmission line.
SOEBAGIO,
"... This paper presents the settings of automatic voltage regulator (AVR) and power system stabilizer (PSS) on a single machine infinite bus (SMIB) to improve the dynamic stability of the power system. This setting is done by determining the fitness function of AVR (KA) and PSS (KPSS) gain using Particl ..."
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This paper presents the settings of automatic voltage regulator (AVR) and power system stabilizer (PSS) on a single machine infinite bus (SMIB) to improve the dynamic stability of the power system. This setting is done by determining the fitness function of AVR (KA) and PSS (KPSS) gain using Particle Swarm Optimization (PSO) algorithm. The main purpose of this setting is to minimize the oscillation frequency so that it would improve the stability of electric power. Simulations are conducted by inputting step function with 5 % load fluctuations as a representation of dynamic load. Simulation results show that the proposed method is very effective for improving the damping of electromechanical oscillations of the power system. The proposed method shows that the power system produces a reduced rate of 11 % overshoot and settling time 40%.
Improvement of Dynamic Stability of a SMIB using Fuzzy logic based Power System Stabilizer
"... Abstract—In this paper a design of an optimal fuzzy Proportional Integral derivative (PID) power system stabilizer for single machine infinite bus power system (SMIB) is presented. The aim of the control is to enhance the stability and to improve the dynamic response of the SMIB operating at differe ..."
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Abstract—In this paper a design of an optimal fuzzy Proportional Integral derivative (PID) power system stabilizer for single machine infinite bus power system (SMIB) is presented. The aim of the control is to enhance the stability and to improve the dynamic response of the SMIB operating at different conditions. Speed deviation and rate of change of speed deviation of the synchronous machines are chosen as input signals to the fuzzy controllers. These variables take significant effects on damping of the generator shaft mechanical oscillations. The three parameters (Kp, Ki, Kd) of PID controller are computed using the fuzzy membership functions depending on these variables. The inference mechanism of the fuzzy PID controller is represented by three (7x7) decision tables. Simulation results of Fuzzy PID power system stabilizer are compared with Conventional Power System Stabilizer (CPSS) and Fuzzy power system stabilizer in order to show effectiveness of the proposed controller.
unknown title
"... Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term ..."
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Abstract—In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
