Frequency Regulation of Nonlinear Power Systems using Neural Network Observer-Based Optimized Resilient Controller

Patel, Vivek Kumar; Guha, Dipayan; Purwar, Shubhi

doi:10.1155/2022/6286500

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…This illustrates the diverse applications of PID-based control methods within the PS domain. Additionally, intelligent control techniques based on PI controllers, including fuzzy logic, AI, model predictive control, genetic algorithms, adaptive neuro-fuzzy inference systems, and Harris Hawks optimization (HHO), have been employed in various PS applications, including wind energy grid connection systems [22][23][24][25][26][27][28]. More recently, the honey badger algorithm (HBA) was introduced by Hashim et al [29] that outperformed other meta-heuristic algorithms based on the CEC'17 serial benchmark functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

Tran,

Duong,

Pham

et al. 2024

IET Generation Trans & Dist

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This article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

Tran,

Duong,

Pham

et al. 2024

IET Generation Trans & Dist

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“…ANNs have a strong ability to approximate nonlinear functions, the current power system is highly nonlinear, and the use of nonlinear controllers to control nonlinear systems is very advantageous [74]. The classical neural network structure is shown in figure 11, which is generally composed of the input layer, output layer and hidden layer.…”

Section: Neural Network Controlmentioning

confidence: 99%

A review of control strategies for automatic generation control in power systems with renewable energy

Kuang,

Tian,

Liu

et al. 2024

Prog. Energy

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This review presents a state-of-the-art literature review of Automatic Generation Control (AGC) control strategies for power systems containing renewable energy sources. The incorporation of renewable energy into the power system has a large impact on the stability, reliability, economy and security of the power system. To mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is a limited amount of literature available on the review of AGC in renewable energy power systems., so a review of AGC control strategies for renewable energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for renewable energy-containing power systems, such as Proportional Integral Derivative (PID) control, fuzzy control, artificial neural network control, etc., and compares and considers these different control methods, while this paper summarises the power system models with/without renewable energy. In addition, this paper summarises and discusses the application of intelligent optimization algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with renewable energy sources are also discussed.

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“…Artificial neural networks have a strong ability to approximate nonlinear functions, the current power system is highly nonlinear, the use of nonlinear controllers to control nonlinear systems is very advantageous [72].…”

Section: Neural Network Controlmentioning

confidence: 99%

A Review of Control Strategies for Automatic Generation Control of Power Systems Containing New Energy Sources

Kuang,

Tian,

Liu

et al. 2023

Preprint

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This review presents a state-of-the-art literature review of automatic generation control (AGC) control strategies for power systems containing new energy sources. The incorporation of new energy into the power system causes a large impact on the stability, reliability, economy and security of the power system. In order to mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is no review of AGC control strategies specifically for new energy-containing power systems, so a review of AGC control strategies for new energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for new energy-containing power systems, such as PID control, fuzzy control, artificial neural network control, etc., and compares and considers these different control methods, while this paper summarises the power system models with/without new energy. In addition, this paper summarises and discusses the application of intelligent optimisation algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with new energy sources are also discussed.

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Frequency Regulation of Nonlinear Power Systems using Neural Network Observer-Based Optimized Resilient Controller

Cited by 3 publications

References 37 publications

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

A review of control strategies for automatic generation control in power systems with renewable energy

A Review of Control Strategies for Automatic Generation Control of Power Systems Containing New Energy Sources

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