SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

Ahmed Eldessouky; Hossam Gabbar; Ahmed Eldessouky; Hossam Gabbar

doi:10.3934/energy.2016.2.363

AIMS Energy

2016, Volume 4, Issue 2: 363-378. doi: 10.3934/energy.2016.2.363

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Research article Topical Sections

SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

Ahmed Eldessouky ^1,3,
Hossam Gabbar ^{1,2
,
,}

1.
Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, L1H 7K4 ON, Canada
2.
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, L1H 7K4 ON, Canada
3.
Electrical Engineering Department, Canadian, International College, Cairo, Egypt

Received: 12 December 2015 Accepted: 09 March 2016 Published: 16 March 2016

Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs) is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC) with Fuzzy Model Reference Learning Controller (FMRLC). The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.
- Microgrid,
- static VAR compensator,
- learning control,
- fuzzy control,
- Model reference control
Citation: Ahmed Eldessouky, Hossam Gabbar. SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid[J]. AIMS Energy, 2016, 4(2): 363-378. doi: 10.3934/energy.2016.2.363

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Abstract

Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs) is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC) with Fuzzy Model Reference Learning Controller (FMRLC). The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

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