Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)-Reference-Cited by-同舟云学术

Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)

Published:2021-12-14 Issue:20 Volume:49 Page:1513-1524
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Hakimuddin Nizamuddin¹,Khosla Anita²,Garg Jitendra Kumar²

Affiliation:

1. New Okhla Industrial Development Authority, Noida, Government of Uttar Pradesh, Uttar Pradesh, India

2. EEE Department, Manav Rachna International Institute of Research and Studies, Haryana, India

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2022.2134510

Reference72 articles.

1. Optimal AGC with redox flow batteries in multi-area restructured power systems

2. AGC of a two-area multi-source power system interconnected via AC/DC parallel links under restructured power environment

3. AGC of two area power system interconnected by AC/DC links with diverse sources in each area

4. Optimal control strategy-based AGC of electrical power systems: A comparative performance analysis

5. Optimal automatic generation control of two-area power systems with energy storage units under deregulated environment

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