A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

Nireekshana, Namburi; Ramachandran, Raj; Narayana, G. V.

doi:10.37391/ijeer.110218

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…The rate and intensity of the electricity must remain constant, as intended. Therefore, load frequency control is a crucial component of the power system if it is to deliver reliable electricity [2]. The most important goal of load frequency control (LFC) issues is to keep the frequency and two-area tieline power within a reasonable range in order to manage fluctuations in demand and disturbances [2].…”

Section: Introductionmentioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.

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Section: Introductionmentioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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“…Several different types of new control methods, such as the grey wolf optimization based integral (I) [6], the ICA tuned PID [7] , the combination of pattern search -adaptive gravity search method tuned PID [8], tuning of PIDN (PID with filter) by the wind-driven optimization (WDO) algorithm [9], particle Swarm Optimization [10] and the firefly-algorithm (FA) tuned PID/PI/I [10] is studied. However, they reveal several flaws in realistic PS implementations.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

A Novel Black Widow Optimized Controller Approach for Automatic Generation Control in Modern Hybrid Power Systems

Wadhwa,

Gupta

2023

IJEER

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This research paper demonstrates an application of the Black Widow Optimization (BWO) approach to address the issue of load-frequency control (LFC) in networked power systems. BWO is an innovative metaheuristic method that quickly suggests technique is initially evaluated on a non-reheat thermal-thermal (NRTT) power system spanning two areas of interconnection, and then it is applied to two different actual power systems: (a) a two-area thermal-thermal considering Generation Rate Constraint (GRC); and (b) a two-area having thermal, hydro, wind, solar, and gas systems. The BWO method uses two fitness functions based on integral time multiplied absolute error (ITAE) and integral square error (ISE) to optimize controller gains. The suggested BWO algorithm's performance has been compared to that of existing meta-heuristic optimization methods, such as grey wolf optimization (GWO), comprehensive learning particle swarm optimization (CLPSO), and an ensemble of parameters in differential evolution (EPSDE). The simulation results show that BWO's tuning skills are better than other population-based planning methods like CLPSO, EPSDE, and GWO. The ITAE value is enhanced by 33.28% (GWO), 40.28% (EPSDE), and 43.27% (CLPSO) when the BWO algorithm is used in conjunction with the PID Controller for thermal system.

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Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

Ogar,

Hussain,

Gamage

2023

Energies

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Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 Ki, 0.7741 Kp, and 0.1850 Kd. The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network.

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A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

Cited by 7 publications

References 16 publications

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

A Novel Black Widow Optimized Controller Approach for Automatic Generation Control in Modern Hybrid Power Systems

Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

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