Application of neural networks to load-frequency control in power systems

Beaufays, Françoise; Abdel-Magid, Y.L.; Widrow, Bernard

doi:10.1016/0893-6080(94)90067-1

Cited by 163 publications

(52 citation statements)

References 3 publications

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“…For this reason, many control approaches have been developed for the load frequency control. Among them, PID controllers [2], optimal [3], nonlinear [4] and robust [5] control strategies, and neural and/or fuzzy [6][7] approaches are to be mentioned. An industrial plant, such as a power system, always contains parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust and Adaptive Load Frequency Control of Multi-area Power Networks with System Parametric Uncertainties Using Temporal Difference Based MLP Neural Networks

Rashidi¹,

Rashidi²

2004

Artificial Intelligence: Methodology, Systems, and Applications

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Abstract. In this paper a robust and adaptive Temporal Difference learning based MLP (TDMLP) neural network for power system Load Frequency Control (LFC) is presented. Power systems, such as other industrial processes, are nonlinear and have parametric uncertainties that for controller design had to take the uncertainties into account. For this reason, in the design of LFC controller the idea of TDMLP neural network is being used. Some simulations with two interconnections are given to illustrate proposed method. Results on interconnected power system show that the proposed method not only is robust to increasing of load perturbations and operating point variations, but also it gives good dynamic response compared with traditional controllers. It guarantees the stability of the overall system even in the presence of generation rate constraint (GRC). To evaluate the usefulness of proposed method we compare the response of this method with RBF neural network and PID controller. Simulation results show the TDMLP has the better control performance than RBF neural network and PID controller.

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Section: Introductionmentioning

confidence: 99%

Robust and Adaptive Load Frequency Control of Multi-area Power Networks with System Parametric Uncertainties Using Temporal Difference Based MLP Neural Networks

Rashidi¹,

Rashidi²

2004

Artificial Intelligence: Methodology, Systems, and Applications

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“…Once the current reaches the rated value, it is maintained constant by reducing the voltage across the inductor to zero since the coil is [24][25] superconducting. Neglecting the transformer and the converter losses, the DC voltage is given by equation (11) where E d is DC voltage applied to the inductor (KV),…”

Section: Smes Systemmentioning

confidence: 99%

“…As ANN configuration will be used to control the non linear system. So back propagation through time algorithm is preferred in the ANN controller to cope with the continuous time dynamics [11]- [13]. This algorithm in a way gives control rule.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis and Effect of CES on ANN Based AGC for Frequency Excursion

Raja¹,

Rajan²

2010

Journal of Electrical Engineering and Technology

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-This paper presents an application of layered Artificial Neural Network controller to study load frequency control problem in power system. The objective of control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed controller has been designed for a two-area interconnected power system. Only one artificial neural network controller (ANN), which controls the inputs of each area in the power system together, is considered. In this study, back propagation-through time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and ANN controller, separately. For the first time comparative study has been carried out between SMES and CES unit, all of the areas are included with SMES and CES unit separately. By comparing the results for both cases, the performance of ANN controller with CES unit is found to be better than conventional controllers with SMES, CES and ANN with SMES.

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“…This is a time consuming and trial and error method. Different numbers of approaches such as optimal, classical, artificial neural network (ANN), fuzzy logic and genetic algorithm (GA) have been used for optimization of controller parameters [13]- [15]. Many authors [16], [17] tried to use genetic algorithm (GA) for designing of controller more efficiently than the controller based on classical approach.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation Control of an Interconnected Power System Before and After Deregulation

Nain¹,

Parmar²,

Singh³

2013

IJCA

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This paper presents the particle swarm optimization (PSO) technique to optimize the integral controller gains for the automatic generation control (AGC) of the interconnected power system before and after deregulation. Each control area includes the dynamics of thermal systems. The AGC in conventional power system is studied by giving step load perturbation (SLP) in either area. The AGC in deregulated environment is studied for three different contract scenarios. To simulate bilateral contracts in deregulated system, the concept of DISCO participation matrix (DPM) is applied.

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Application of neural networks to load-frequency control in power systems

Cited by 163 publications

References 3 publications

Robust and Adaptive Load Frequency Control of Multi-area Power Networks with System Parametric Uncertainties Using Temporal Difference Based MLP Neural Networks

Robust and Adaptive Load Frequency Control of Multi-area Power Networks with System Parametric Uncertainties Using Temporal Difference Based MLP Neural Networks

Stability Analysis and Effect of CES on ANN Based AGC for Frequency Excursion

Automatic Generation Control of an Interconnected Power System Before and After Deregulation

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