Swarm Intelligence based Load Frequency Control of Two Area Thermal System – Comparative Analysis

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

doi:10.1109/iceca49313.2020.9297432

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The response provided by BESS when sudden change in load occurs is recorded in the Figures 16 and 17. The frequency deviancies in the existence of wind energy are (Shiva et al, 2015) 0.12 2.55 3.12 4.43 GWO-PID (Guha et al, 2015) 0.13413 1.06 3.17 3.34 EDSDE-PID (Guha et al, 2015) 0.1497 2.88 3.37 3.56 CLPSO-PID (Guha et al, 2015) 0.1569 1.89 3.6 3.8 hFA-PS-PID (Sahu et al, 2015) 0.2782 2.8 4.5 4 FA-PID (Padhan et al, 2014) 0.4714 4.25 5.49 4.78 BFOA-PID (Sahu et al, 2015) 0.4788 4.7 6.4 5.1 GA-PID (Sahu et al, 2015) 0.5513 6.9 8 5.7 ZN-PID (Sahu et al, 2015) 0.604 8.1 0.2 6.7 ABC-PID (Ranjitha et al, 2020) 0 IChimp provided improved convergence rate and search capability that performed well in LFC issue. The controller sustains dynamic performance and system stability by diminishing the consequence of numerous uncertainties and conflicts.…”

Section: Test System 2 -Ieee-39 Bus Systemmentioning

confidence: 99%

“…Change in load is considered in Area 1 and in Area 2 separately in Figures 9 and 10; the corresponding response is revealed in Figures 11 and 12 To explain the supremacy of the projected controlling methodology, the performance is compared with some of the published works for the same test system with similar objective function. GWO-PID (Guha et al, 2015), FA-PID) (Padhan et al, 2014), BFO-PID) (Sahu et al, 2015), genetic algorithm (GA-PID), hFA-PS-PID) (Sahu et al, 2015), dragonfly algorithm (DA) (Kouba et al, 2018), artificial bee colony (ABC) (Ranjitha et al, 2020) optimization, quasi-oppositional harmony search algorithm (QOHS) (Shiva et al, 2015), comprehensive learning PSO (CLPSO) (Guha et al, 2015), differential evolution with collaborative of mutation and crossover strategies (EPSDE) (Guha et al, 2015) and ZN-PID (Sahu et al, 2015) is implemented for the LFC of the same two-area system with similar objective function. Hence, the results obtained by the above methodologies were taken to prove the effectiveness of the proposed method of control where DP L is the load change; D is load damping constant; and R is speed droop.…”

Section: Test System 1 Two-area Interconnected Thermal Systemmentioning

confidence: 99%

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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: Test System 2 -Ieee-39 Bus Systemmentioning

confidence: 99%

Section: Test System 1 Two-area Interconnected Thermal Systemmentioning

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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“…Swarm intelligence systems imitate the social behavior of birds, bees, and ants. 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.…”

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

Ranjitha¹,

Sivakumar²,

Elavarasu³

et al. 2021

2021 Emerging Trends in Industry 4.0 (ETI 4.0)

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Swarm Intelligence based Load Frequency Control of Two Area Thermal System – Comparative Analysis

Cited by 5 publications

References 13 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

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

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