Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

• Published in: International journal of circuit theory and applications Vol. 50; no. 9; pp. 3231 - 3250
• Main Authors: Saxena, Nitin Kumar, Gao, Wenzhong D., Kumar, Ashwani, Mekhilef, Saad, Gupta, Varun
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.09.2022
• Subjects: Control systems design; Controllers; Distributed generation; distributed generator; Electric power distribution; Electrical loads; Frequency deviation; Genetic algorithms; Harmonic distortion; Induction generators; Iterative methods; Matlab; Particle swarm optimization; Perturbation; Power control; Power management; PSO; Reactive power; reactive power compensation; SCIG; STATCOM; Static synchronous compensators; Wind turbines; Windpowered generators
• ISSN: 0098-9886; 1097-007X
• DOI: 10.1002/cta.3319

This paper presents the genetic algorithm (GA) and particle swarm optimization (PSO) based frequency regulation for a wind‐based microgrid (MG) using reactive power balance loop. MG, operating from squirrel cage induction generator (SCIG), is employed for exporting the electrical power from wind turbines, and it needs reactive power which may be imported from the grid. Additional reactive power is also required from the grid for the load, directly coupled with such a distributed generator (DG) plant. However, guidelines issued by electric authorities encourage MGs to arrange their own reactive power because such reactive power procurement is defined as a local area problem for power system studies. Despite the higher cost of compensation, static synchronous compensator (STATCOM) is a fast‐acting FACTs device for attending to these reactive power mismatches. Reactive power control can be achieved by controlling reactive current through the STATCOM. This can be achieved with modification in current controller scheme of STATCOM. STATCOM current controller is designed with reactive power load balance for the proposed microgrid in this paper. Further, gain values of the PI controller, required in the STATCOM model, are selected first with classical methods. In this classical method, iterative procedures which are based on integral square error (ISE), integral absolute error (IAE), and integral square of time error (ISTE) criteria are developed using MATLAB programs. System performances are further investigated with GA and PSO based control techniques and their acceptability over classical methods is diagnosed. Results in terms of converter frequency deviation show how the frequency remains under the operating boundaries as allowed by IEEE standards 1159:1995 and 1250:2011 for integrating renewable‐based microgrid with grid. Real and reactive power management and load current total harmonic distortions verify the STATCOM performance in MG. The results are further validated with the help of recent papers in which frequency regulation is investigated for almost similar power system models. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library, (ii) designing of STATCOM current controller with PI controller, (iii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes and their interfacing with proposed simulink model, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management between STATCOM and grid, and (vii) validation of results with available literature. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library with designing of STATCOM current controller with PI controller, (ii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management, and (vii) validation of results.