Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems. (December 2021)

• Record Type: Journal Article
• Authors: Patel, Vivek; Guha, Dipayan; Purwar, Shubhi
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Frequency regulation -- Chebvshev neural network -- Wind turbine generator -- Fractional-order integral sliding mode controller -- Estimation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107534
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Design and assess the fractional-order integral sliding mode control (FOISMC) performance in frequency regulation of interconnected power system (IPS). The effects of the proposed controller is tested on an IPS model comprising thermal and wind power plants. The impact of power system nonlinearities, such as generation rate constraints and governor dead-band, on the controller performance has been inspected. A neural network (NN) is developed and applied to estimate lumped unknown nonlinearities and plant uncertainties and subsequently used to refine the FOISMC's control law. Abstract: In this work, the performance of a fractional-order integral sliding mode controller (FOISMC) has been assessed for frequency regulation of interconnected power systems considering inherent nonlinearities, such as generation rate constraint and governor dead-band. The performance of the proposed controller has been measured on two- and three-area interconnected power systems integrated with a wind turbine generator. A Chebyshev neural network (NN)-based estimator is designed to estimate the lumped system uncertainties, including nonlinearities, unknown external disturbances, and parameter uncertainty. Afterwards, an improved FOISMC is developed, augmenting the estimated output of the NN-based estimator to cope with system disturbances effectively. To corroborate the potential benefits, the results obtained with NN-aided FOISMC (NN-FOISMC) are compared with the outputs of FOISMC and results reported in the literature. The simulation study confirms the superiority of NN-FOISMC over its other counterparts in terms of damping of power-frequency oscillations, weaker chattering, and a high degree of robustness. Graphical abstract: Fig. 1 Block diagram of two-area interconnected power system Fig. 2 Structure of neural network (NN) Image, graphical abstract