Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modeling and Control of Power and Energy Produced by a Synchronous Generator Using Polynomial Fuzzy Systems and Sum-of-Squares Approach

Version 1 : Received: 11 March 2021 / Approved: 12 March 2021 / Online: 12 March 2021 (11:26:53 CET)
Version 2 : Received: 25 August 2021 / Approved: 27 August 2021 / Online: 27 August 2021 (11:28:24 CEST)

How to cite: Ansari Bonab, P.; Hosseini Rostami, S.M.; Jafari, A.; Sheikhi, B.; Wang, J.; Yu, X. Modeling and Control of Power and Energy Produced by a Synchronous Generator Using Polynomial Fuzzy Systems and Sum-of-Squares Approach. Preprints 2021, 2021030339 (doi: 10.20944/preprints202103.0339.v1). Ansari Bonab, P.; Hosseini Rostami, S.M.; Jafari, A.; Sheikhi, B.; Wang, J.; Yu, X. Modeling and Control of Power and Energy Produced by a Synchronous Generator Using Polynomial Fuzzy Systems and Sum-of-Squares Approach. Preprints 2021, 2021030339 (doi: 10.20944/preprints202103.0339.v1).

Abstract

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

Keywords

Synchronous generator; Polynomial fuzzy controller; Polynomial fuzzy system; Polynomial Lyapunov function; Stability; Sum of squares (SOS)

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