Adaptive Neuro Sliding Mode Control of Superconducting Magnetic Energy Storage System

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES Smart Science Pub Date : 2022-05-22 DOI:10.1080/23080477.2022.2074659

Zahid Afzal Thoker, S. A. Lone

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Abstract

ABSTRACT In this paper, an adaptive RBF neural network-based sliding mode controller is developed for superconducting magnetic energy storage (SMES) installed in a wind–diesel power system. Due to sudden load and wind power variations in a wind–diesel power system, power imbalances may force the system frequency to deviate from its nominal value and drive the system to operate in an unstable mode. Therefore, in a wind–diesel power system using a converter interface, a fast-acting and high power density SMES device is interconnected to carry the required power exchange whenever power imbalances occur. Based on switching manifold design, a sliding mode controller is developed to control the charging and discharging operations of SMES coil as per the power requirements, and the same is achieved by controlling the converter. A neural network using a radial basis function (RBF) is developed to estimate the unknown function of the system. Lyapunov stability analysis is conducted to guarantee the asymptotic stability of the system. MATLAB simulations are carried out and are presented to show the improved performance with the system exposed to disturbances in load and wind power. Graphical abstract

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超导磁储能系统的自适应神经滑模控制

摘要本文针对安装在风电-柴油发电系统中的超导磁储能（SMES），开发了一种基于RBF神经网络的自适应滑模控制器。由于风电-柴油发电系统中的突然负载和风功率变化，功率失衡可能会迫使系统频率偏离其标称值，并导致系统在不稳定模式下运行。因此，在使用转换器接口的风电-柴油发电系统中，快速作用和高功率密度SMES设备相互连接，以便在出现功率失衡时进行所需的功率交换。在开关管汇设计的基础上，开发了一种滑模控制器，根据功率要求控制SMES线圈的充电和放电操作，并通过控制转换器来实现。开发了一种使用径向基函数（RBF）的神经网络来估计系统的未知函数。为了保证系统的渐近稳定性，进行了李雅普诺夫稳定性分析。进行了MATLAB仿真，并展示了系统在负载和风电干扰下的改进性能。图形摘要

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来源期刊

Smart Science Engineering-Engineering (all)

CiteScore

4.70

自引率

4.30%

发文量

期刊介绍： Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials