A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs)

B. Singh, Sunil Kumar Goyal, S. A. Siddiqui, D. Shrivastava, Satyendra Singh, Majed A. Alotaibi, Hasmat Malik, Fausto Pedro García Márquez, Asyraf Afthanorhan
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Abstract

Effective microgrid control for system recovery and restoring normal operation necessitates fast event detection and implementation of remedial action (if need arises). However, fast and reliable event detection in microgrids is challenging because of low observability and inconsistencies in measurements. A novel technique is proposed in the present work for the real-time event detection and to identify the various emerging abnormalities in the microgrid. The continuous energy signature using TKEO (Teager-Kaiser Energy Operator) of the continuous varying voltage and frequency signal are extracted through μPMU. REII (Robust Event Identification Index) is constructed from these energy signatures and based on its abrupt post-event deviation from the nominal values an event is flagged in the proposed method. The proposed method is data–driven and only depends on the real-time inputs through μPMUs thus it automatically adapts the uncertainties associated with the intermittent sources of energy in the microgrid under different operating conditions. The traditional event detection techniques fail in identification of abnormalities for a microgrid connected to the transmission systems and equipped with multiple DERs such as PVDG, WG etc. To address this challenge, an integrated microgrid with multiple DERs viz. PVDG, WG and a SG (Synchronous Generator) is first developed in this work. The complexity of simultaneous operation of a static generator i.e. PVDG along with a rotor-based generator such as WG and SG is handled by the modeling the dynamic controllers of PVDG and WG for their frequency and voltage control. The simulation results depict the efficiency, accuracy and robustness of the proposed technique in terms of estimation time, event accuracy and applicability in all types of events. Moreover, the presented methodology is also compared with the four AI/ML based methods to highlight the superiority of the method.
为具有多种分布式能源资源 (DER) 的并网微电网提供增强型实时事件诊断的新方法
要有效控制微电网以恢复系统和正常运行,就必须快速检测事件并采取补救措施(如有必要)。然而,由于可观测性低和测量不一致,在微电网中进行快速可靠的事件检测具有挑战性。本研究提出了一种新技术,用于实时事件检测和识别微电网中新出现的各种异常情况。使用 TKEO(Teager-Kaiser Energy Operator,泰格-凯撒能量运算器)对连续变化的电压和频率信号进行连续能量特征提取,并通过 μPMU 进行分析。根据这些能量特征构建 REII(鲁棒性事件识别指数),并根据事件发生后与额定值的突然偏差,在拟议方法中标记出一个事件。所提出的方法是数据驱动的,只依赖于通过 μPMU 的实时输入,因此它能在不同运行条件下自动适应与微电网中间歇性能源相关的不确定性。传统的事件检测技术无法识别连接到输电系统并配备多个 DER(如 PVDG、WG 等)的微电网的异常情况。为了应对这一挑战,本研究首先开发了一个集成微电网,其中包含多个 DER,即 PVDG、WG 和 SG(同步发电机)。静态发电机(即 PVDG)与转子发电机(如 WG 和 SG)同时运行的复杂性是通过对 PVDG 和 WG 的动态控制器进行建模,以控制其频率和电压来解决的。仿真结果表明了所提技术在估计时间、事件准确性和所有类型事件的适用性方面的效率、准确性和鲁棒性。此外,还将所提出的方法与四种基于人工智能/ML 的方法进行了比较,以突出该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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