Firefly Algorithm Optimized Load Frequency Controller for Multi-Source Power System

Ranjitha, K; Sivakumar, P.; Elavarasu, R; Monica, M.; Rajapandiyan, A

doi:10.1109/eti4.051663.2021.9619304

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Krill herd algorithm (Guha et al, 2016) is used to get optimized constraints of integral controller for the LFC of a three different two area systems. Bat algorithm (Abd-Elazim and Ali, 2016), firefly algorithm (FA) (Padhan et al, 2014;Ranjitha et al, 2021), grey wolf optimizer (GWO) (Guha et al, 2015), bacterial foraging optimization algorithm (BFOA) (Ali and Abd-Elazim, 2011), etc. have shown to be competent in resolving the LFC issue.…”

Section: Load Frequency Controlmentioning

confidence: 99%

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

Ranjitha¹,

Sivakumar²,

Monica³

2022

COMPEL

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Purpose This study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system. Design/methodology/approach This work was adopted by IChimp to optimize proportional integral derivative (PID) controller parameters used for the LFC of a two area interconnected thermal system. Findings The supremacy of proposed IChimp tuned PID controller over Chimp optimization, direct synthesis-based PID controller, internal model controller tuned PID controller and recent algorithm based PID controller was demonstrated. Originality/value IChimp has good convergence and better search ability. The IChimp optimized PID controller is the proposed controlling method, which ensured better performance in terms of converging behaviour, optimizing controller gains and steady-state response.

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Section: Load Frequency Controlmentioning

confidence: 99%

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

Ranjitha¹,

Sivakumar²,

Monica³

2022

COMPEL

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“…Teir prominence stems from their capacity to successfully tackle real-world global optimization problems [29]. Diferent swarm intelligence algorithms such as Ant Colony Optimization (ACO) [30], Ant Lion Optimizer (ALO) [31], Particle Swarm Optimization (PSO) [32], Firefy Algorithm (FA) [33], and Chimp Optimization (CO) [34] are based on simple notions related to physical phenomena and evolutionary psychology. Tese algorithms have drawn a lot of interest since they are derivative-free, robust, and can be used to solve a variety of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

Sundararajan

Sivakumar²

2022

International Transactions on Electrical Energy Systems

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Due to the unpredictable and stochastic nature of renewables, current power networks confront operational issues as renewable energy sources are more widely used. Frequency stability of modern power systems has been considerably harmed by fast and unpredictable power variations generated by intermittent power generation sources and flexible loads. The main objective of the power system frequency control is to ensure the generation demand balance at all times. In reality, obtaining precise estimates of the imbalance of power in both transmission and distribution systems is challenging, especially when renewable energy penetration is high. Electric vehicles have become a viable tool to reduce the occasional impact of renewable energy sources engaged in frequency regulation mainly because of vehicle-to-grid technologies and the quick output power management of EV batteries. The rapid response of EVs enhances the effectiveness of the LFC system significantly. This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. The new approach assesses power fluctuations from a real-time observed frequency signal precisely and quickly. The observed power fluctuations can be used as a control reference, allowing automatic generation control to maintain better system frequency and ensure optimum generation cost with the use of demand management techniques. To validate the suggested method and compare it with several classical methods, a realistic model of the Indian power system integrated with distributed generation technology is used. The simulation results clearly indicate the importance of power fluctuation identification as well as the benefits of the proposed strategy. The results clearly show a considerable improvement in response performance indices, as the maximum peak overshoot was decreased by 21.25% to 51.2%, and settling time was lowered by about 23.34% to 65.40% for the suggested control technique compared to other controllers.

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Firefly Algorithm Optimized Load Frequency Controller for Multi-Source Power System

Cited by 2 publications

References 15 publications

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

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