Frequency control of power system with electric vehicles using hybrid african vultures optimization algorithm and pattern search tuned fuzzy PID controller

Authors

  • P M Dash Bhubaneswar Engineering College
  • A K Baliarsingh Government College of Engineering, Kalahandi
  • Sangram K Mohapatra Government College of Engineering, Keonjhar image/svg+xml

DOI:

https://doi.org/10.4108/ew.135

Keywords:

African Vultures Optimization Algorithm, Automatic Generation Control, Fuzzy PID Controller, Electric Vehicles, Governor Dead Band, Generation Rate Constraint, Pattern Search

Abstract

This work suggests a hybrid African Vultures Optimization Algorithm (AVOA) and Pattern search (hAVOA-PS) based Fuzzy PID (FPID) structure for frequency control of a nonlinear power system with Electric Vehicles (EVs). To illustrate the dominance of the projected hAVOA-PS algorithm, initially PI controllers are considered and results are compared with AVOA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. To further enhance the dynamic performance, PID and FPID controllers are considered. The dominance of FPID over PID and PI controllers is shown. In the next step, EVs are incorporated in the test system and a comparative analysis of hAVOA-PS based PI/PID/FPID and FPID+EV is presented. To exhibit the superiority of projected frequency control scheme in maintaining the stability of system under different disturbance conditions like load increase in area-1 only, load decrease/increase in all areas, and large load increase in all areas are considered. It is noticed that the suggested hAVOA-PS based FPID controller n presence of EV is able to maintain system stability for all the considered cases where as other compared approaches fail to maintain stability in some cases.

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Published

30-10-2023

How to Cite

1.
Dash PM, Baliarsingh AK, Mohapatra SK. Frequency control of power system with electric vehicles using hybrid african vultures optimization algorithm and pattern search tuned fuzzy PID controller . EAI Endorsed Trans Energy Web [Internet]. 2023 Oct. 30 [cited 2024 Jun. 25];10. Available from: https://publications.eai.eu/index.php/ew/article/view/135