Performance evaluation of microgrid with extreme learning machine based PID controller

Isha Rajput, Jyoti Verma, Hemant Ahuja


The enhanced penetration of the renewable energy sources (RES) is dependent on microgrid (MG) to a power system is impact stability of the system due to a variation in dynamic properties of the MG from a traditional generator. As a result, analyzing the new issues with dynamic stability and controlling the operation of the power system in the connection of rising MG penetration becomes critical. This paper contains a MG system with renewable energy assisted, superconducting magnetic energy storage (SMES) storage and an extreme learning machine (ELM) based proportional integral derivative (PID) controller. The effect of renewable-based MG penetration on a dynamic stability and control of the multi machine multi area system under varied operating situations is comprehensively investigated in this study. Non-linear time-domain simulations and several performance indicators are used to evaluate the controller's ability with the different MG penetration percentages under various disturbances and operational conditions.


Extreme learning machine; Microgrid; PID controller; Renewable energy sources

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).