Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

Sabahi, Kamel; Teshnehlab, Mohammad; shoorhedeli, Mahdi Aliyari

doi:10.1016/j.enconman.2008.12.028

Cited by 59 publications

(16 citation statements)

References 25 publications

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“…For example, as the most popular control technique, proportional-integral-derivative (PID) controller and its various variations have been widely applied to the LFC issue [3][4][5][6][7][8]. Moreover, some researchers have paid more attention to the advanced control theories based LFC methods recently, such as robust control theories [9], model predictive control [10][11][12][13][14], sliding mode control [15,16], neural network control [17], internal model control [18], and differential games [19]. It should be noted that there are different evolutionary algorithms based PID or proportional-integral (PI) control methods for the LFC issue of multi-area power systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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Abstract:As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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“…In the aforementioned research work, some studies make tentative consideration on the GRC problem. In [1,7,8,12], GRC is considered in the simulation, but neglected in the controller design. Therefore, the validity of these methods to deal with GRC lacks theoretical support.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain better performance of the PI-type LFC, the parameter optimization methods of the PI-type controller are proposed in [3][4][5]. To enhance the robustness and reliability of the control system, some fuzzy-logic-based LFC methods are introduced in [6][7][8][9]. In addition, some advanced control technologies are utilized to improve LFC performance, such as sliding mode methods [10][11][12], optimal or suboptimal feedback control methods [13][14][15][16], and robust control methods [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Anti-Windup Load Frequency Controller Design for Multi-Area Power System with Generation Rate Constraint

et al. 2016

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Abstract:To deal with the problem of generation rate constraint (GRC) during load frequency control (LFC) design for a multi-area interconnected power system, this paper proposes an anti-windup controller design method. Firstly, an H ∞ dynamic controller is designed to obtain robust performance of the closed-loop control system in the absence of the GRC. Then, an anti-windup compensator (AWC) is formulated to restrict the magnitude and rate of the control input (namely power increment) in the prescribed ranges so that the operation of generation unit does not exceed the physical constraints. Finally, the anti-windup LFC is tested on the multi-area interconnected power systems, and the simulation results illustrate the effectiveness of the proposed LFC design method with GRC.

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“…Adaptive control studies also take advantage of error learning which is called feedback error learning (FEL) [31–33]. FEL was proposed to establish a computational model of the cerebellum for learning motor control with interval models in the central nervous system [33].…”

Section: The Proposed Ridge Polynomial Neural Network With Error Feedmentioning

confidence: 99%

“…FEL was proposed to establish a computational model of the cerebellum for learning motor control with interval models in the central nervous system [33]. …”

Section: The Proposed Ridge Polynomial Neural Network With Error Feedmentioning

confidence: 99%

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

2016

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Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

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Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

Cited by 59 publications

References 25 publications

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

Anti-Windup Load Frequency Controller Design for Multi-Area Power System with Generation Rate Constraint

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

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