Variable structure fuzzy gain scheduling based load frequency controller for multi source multi area hydro thermal system

Chandrakala, K.R.M. Vijaya; Balamurugan, S.; Sankaranarayanan, K.

doi:10.1016/j.ijepes.2013.05.009

Cited by 131 publications

(114 citation statements)

References 20 publications

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“…Fuzzy gain tuning has been an effective way to tune parameters of a controller online with respect to parameter changes. It has been applied recently to tune PID controller for multiple input multiple output (MIMO) systems [14], continuous stirred-tank reactor (CSTR) systems [15], maximum power point tracking in a photovoltaic system [16], load frequency control [17,18] and many other control applications [19][20][21][22].…”

Section: Wireless Sensor Network -Insights and Innovationsmentioning

confidence: 99%

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Hassan

Ibrahim²,

Saad³

et al. 2017

Wireless Sensor Networks - Insights and Innovations

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Gain range limitation of conventional proportional-integral-derivative (PID) controllers has made them unsuitable for application in a delayed environment. These controllers are also not suitable for use in a Wireless Highway Addressable Remote Transducer (WirelessHART) protocol networked control setup. This is due to stochastic networkinduced delay and uncertainties such as packet dropout. The use of setpoint weighting strategy has been proposed to improve the performance of the PID in such environments. However, the stochastic delay still makes it difficult to achieve optimal performance. This chapter proposes an adaptation to the setpoint weighting technique. The proposed approach will be used to adapt the setpoint weighting structure to variation in WirelessHART network-induced delay through fuzzy inference. Result comparison of the proposed approach with both setpoint weighting and proportional-integral (PI) control strategy shows improved setpoint tracking and load regulation. For the first-, second-and third-order systems considered, analysis of the results in the time domain shows that in terms of overshoot, undershoot, rise time, and settling times, the proposed approach outperforms both the setpoint weighting and the PI controller. The approach also shows faster recovery from disturbance effect.

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Section: Wireless Sensor Network -Insights and Innovationsmentioning

confidence: 99%

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Hassan

Ibrahim²,

Saad³

et al. 2017

Wireless Sensor Networks - Insights and Innovations

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“…Vijaya Chandrakalam et al [27]. The area frequency and tie line power fluctuates during load deviations were managed by primary governor controller and secondary Proportional Integral controller.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neural Network-Bacterial Foraging Algorithm to Control the Load Frequency of Power System

Palanisamy¹,

Mahadevan²

2014

ijeei

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Abstract:In this paper, a neural network (NN)-bacterial foraging algorithm (BFA) is proposed to control the load frequency of the power system. Traditionally, the tumbling decision element of BFA is defined by the stochastic values. But, these values are changed as per the variations of chemotactic step size so that the convergence time is increased. Here, NN is used to ensure the distribution of stochastic value with respect to the chemotactic step size and thus the performance of BFA is enhanced. The feed forward NN is used here with back propagation training algorithm. Using the proposed controller, the controller error, load changes, and speed changer position are tuned and the stability of the interconnected power system is improved. The proposed tuning controller is implemented in MATLAB/Simulink platform and the load frequency control responses are evaluated. The performances of proposed controller are compared with those of the PID controller and the BFA-PID controller.

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“…Artificial intelligence (AI) and Evolutionary algorithms based controllers have been applied to LFC problem by a wider section of researchers [5, [9][10][11][12][13][14][15][16]. Modern intelligent methods and Evolutionary algorithms, such as Genetic algorithms(GAs), Artificial neural networks (ANNs), Fuzzy logic, Particle swarm optimization (PSO), Modified PSO and its hybrids based optimization have helped in the design of variety of controllers for the LFC systems [5, [9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Modern intelligent methods and Evolutionary algorithms, such as Genetic algorithms(GAs), Artificial neural networks (ANNs), Fuzzy logic, Particle swarm optimization (PSO), Modified PSO and its hybrids based optimization have helped in the design of variety of controllers for the LFC systems [5, [9][10][11][12][13][14][15][16][17]. GA/particle swarm intelligence based optimization technique has been applied to the automatic generation control [13].…”

Section: Introductionmentioning

confidence: 99%

“…The GA optimization method faces some deficiencies like premature convergence which degrades its efficiency and the search capability [5,17]. LFC study of multi area interconnected power systems using type-2 fuzzy approach [14] and Variable structure fuzzy gain scheduling [15] has also been appeared in the literature. PSO is very important and powerful tool to solve the various engineering problems [5, 13,17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Load Frequency Control of Multi-Source Power System with Redox Flow Batteries: An Analysis

Parmar¹

2014

IJCA

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This paper presents the Load frequency control (LFC) of a multi-source power system with Redox flow batteries (RFB). The power system comprises non-reheat thermal, hydro and gas generating units. RFB is integrated to the LFC system to improve the dynamic responses. The proportional plus integral (PI) controller is designed using the PSO technique. Dynamic responses are obtained by giving 1% step load perturbation (SLP) in the area. Further, to show the robustness of the controller, the dynamic responses are obtained by varying the SLP from 1% to 3%. Dynamic responses obtained in all the cases, satisfy the LFC requirements. PSO technique gives good convergence characteristics and promising computational results.

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Variable structure fuzzy gain scheduling based load frequency controller for multi source multi area hydro thermal system

Cited by 131 publications

References 20 publications

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

A Neural Network-Bacterial Foraging Algorithm to Control the Load Frequency of Power System

Load Frequency Control of Multi-Source Power System with Redox Flow Batteries: An Analysis

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