A Robust Method of Tuning the Feedback Gains of a Variable Structure Load Frequency Controller Using Genetic Algorithm Optimization

Huddar, Anupama; Kulkarni, P. S.

doi:10.1080/15325000802258380

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…The results are also analyzed with 0.03 p.u MW load disturbance in area 1 as presented in the previous study . The dynamic responses of frequency and tie‐line power has approximate peak undershoots of −0.071, −0.038, and −0.028 using GA .…”

Section: Resultsmentioning

confidence: 96%

“…Examining the performance parameters from Table and dynamic responses of control inputs and deviations in frequency from Figure to Figure , it is obvious that the DOGSA‐optimized SMC controller stabilizes the system and performs better as compared to GA, PSO, and TLBO‐optimized sliding mode controller . To achieve this, the following are the feedback gains ( ρ ) and switching vector ( S T ) values of SMC optimized by DOGSA in order to study the dynamic response and the chattering phenomenon in the control signal while solving interconnected power system load frequency control problem:

ρ = ([],, \begin{array}{l} 0.3541 ‐ 0.0842 ‐ 0.2130 0.6921 0.1334 ‐ 0.5382 ‐ 0.0130 ‐ 0.0384 ‐ 0.0079 \\ 0.0354 0.3333 0.2242 ‐ 0.1872 ‐ 0.0390 0.0009 1.1628 0.0524 ‐ 0.4181 \end{array}),

S^{T} = ([],, \begin{array}{l} 0.3284 0.0096 ‐ 0.8492 0.2055 0.0612 ‐ 0.3109 ‐ 0.0091 0.0227 ‐ 0.4436 \\ ‐ 0.0923 0.5390 ‐ 0.0100 ‐ 0.1928 ‐ 0.0611 0.1773 0.7981 0.2706 ‐ 0.7252 \end{array}) .

…”

Section: Resultsmentioning

confidence: 99%

“…These algorithms have become a popular choice for solving complex optimization problems because of their flexibility, generality, and ease of use. The GA and PSO techniques although are reported to minimize the chattering but they sometimes get trapped in the local minima . The other algorithms may also suffer from difficulties such as premature convergence and more computational time in case of the large dimension system .…”

Section: Introductionmentioning

confidence: 99%

“…The GA and PSO techniques although are reported to minimize the chattering but they sometimes get trapped in the local minima. 1,[8][9][10][11][12][13] The other algorithms may also suffer from difficulties such as premature convergence and more computational time in case of the large dimension system. 17 The gravitational search algorithm (GSA) based on Newton's gravitational law is reported to be efficient in terms of computational time and gives more accuracy while solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

Dahiya

Sharma

Naresh

2017

Optim Control Appl Methods

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Summary In this paper, the load frequency regulation problem of 2‐area interconnected power system is resolved using the sliding mode control (SMC) methodology. Interconnected 2‐area power systems with and without doubly fed induction generator wind turbines are considered for implementing the proposed optimal control methodology. Here, a heuristic gravitational search algorithm (GSA) and its variants such as opposition learning–based GSA (OGSA), disruption‐based GSA (DGSA), and disruption based oppositional GSA (DOGSA) are employed to optimize the switching vector and feedback gains of SMC. In order to overcome the inherent chattering problem in SMC, the control signals are considered in the objective function. The robustness of optimized SMC is analyzed by the inclusion of nonlinearities such as generation rate constraint (GRC), governor deadband, and time delay during the signal processing between the control areas, which are present in the real‐time power system. The insensitiveness of the optimal controller is shown by variation in system parameters like loading condition, speed governor constant, turbine constant, and tie‐line power coefficient. Further, the optimal SMC has been studied with significant load variations and wind power penetration levels in the control areas. The potential of proposed SMC design with chattering reduction feature is shown and validated by comparing the results obtained with the other reported methods in the literature.

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Section: Resultsmentioning

confidence: 96%

ρ = ([],, \begin{array}{l} 0.3541 ‐ 0.0842 ‐ 0.2130 0.6921 0.1334 ‐ 0.5382 ‐ 0.0130 ‐ 0.0384 ‐ 0.0079 \\ 0.0354 0.3333 0.2242 ‐ 0.1872 ‐ 0.0390 0.0009 1.1628 0.0524 ‐ 0.4181 \end{array}),

S^{T} = ([],, \begin{array}{l} 0.3284 0.0096 ‐ 0.8492 0.2055 0.0612 ‐ 0.3109 ‐ 0.0091 0.0227 ‐ 0.4436 \\ ‐ 0.0923 0.5390 ‐ 0.0100 ‐ 0.1928 ‐ 0.0611 0.1773 0.7981 0.2706 ‐ 0.7252 \end{array}) .

…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

Dahiya

Sharma

Naresh

2017

Optim Control Appl Methods

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“…Output feedback sliding mode control optimized with teaching learning‐based optimization implemented for AGC problems . Variable structure controller optimized with genetic algorithm (GA) was employed for AGC problem …”

Section: Introductionmentioning

confidence: 99%

Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

Simhadri

Mohanty

Panda

2018

Optim Control Appl Methods

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Summary This paper presents the design of two‐degree‐of‐freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two‐area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional‐integral, proportional‐integral‐derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system.

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Optimal load frequency control of multi-area multi-source deregulated power system with electric vehicles using teaching learning-based optimization: a comparative efficacy analysis

Kumar,

Prasad

2023

Electr Eng

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A Robust Method of Tuning the Feedback Gains of a Variable Structure Load Frequency Controller Using Genetic Algorithm Optimization

Cited by 18 publications

References 12 publications

Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

Optimal load frequency control of multi-area multi-source deregulated power system with electric vehicles using teaching learning-based optimization: a comparative efficacy analysis

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