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Development and Validation of Artificial Neural Network-Based Tools for Forecasting of Power System Inertia With Wind Farms Penetration
IEEE Systems Journal ( IF 4.4 ) Pub Date : 2020-09-02 , DOI: 10.1109/jsyst.2020.3017640
E. S. N. Raju Paidi , Hesamoddin Marzooghi , James Yu , Vladimir Terzija

Increased penetration of power electronics interfaced renewable energy sources-based generation, for instance, wind farms, that displaces much of the conventional synchronous generation, has a profound effect on the inertia of modern/future power system networks. This article presents the development and validation of artificial neural network (ANN)-based tools, utilizing the power system variables measured by phasor measurement units through wide-area measurements systems, for estimation/forecasting of power system inertia with high penetration of wind farms. The development stage involves the correlation analysis to identify the best power system variables that can be nominated as inputs, and the training of the proposed inertia forecasting tools with the best-nominated inputs that are highly correlated with the power system inertia. Whereas, in the validation stage, the functionality of the trained ANN-based inertia forecasting tools have been validated using the hardware-in-the-loop testing facility developed at the University of Manchester. The development and validation procedures of the proposed inertia forecasting tools have been demonstrated on the IEEE 9-bus modified test system. The validation results revealed the effectiveness of the proposed inertia forecasting tools in estimating the inertia of modern/future power system networks with high penetration of wind farms.

中文翻译:

基于人工神经网络的风电场穿透力电力系统惯性预测工具的开发与验证

电力电子接口式可再生能源发电(例如风电场)的普及率不断提高,这取代了许多传统的同步发电,对现代/未来电力系统网络的惯性产生了深远影响。本文介绍了基于人工神经网络(ANN)的工具的开发和验证,该工具利用相量测量单元通过广域测量系统测量的电力系统变量,来估算/预测高风场的电力系统惯性。开发阶段涉及相关性分析,以识别可以提名为输入的最佳电力系统变量,并使用与电力系统惯性高度相关的最佳提名输入来训练拟议的惯性预测工具。鉴于,在验证阶段,已使用曼彻斯特大学开发的硬件在环测试设施对经过训练的基于ANN的惯性预测工具的功能进行了验证。建议的惯性预测工具的开发和验证程序已在IEEE 9总线改进的测试系统上进行了演示。验证结果表明,所提出的惯性预测工具在估算风电场普及率较高的现代/未来电力系统网络的惯性方面是有效的。建议的惯性预测工具的开发和验证程序已在IEEE 9总线改进的测试系统上进行了演示。验证结果表明,所提出的惯性预测工具在估算风电场普及率较高的现代/未来电力系统网络的惯性方面是有效的。建议的惯性预测工具的开发和验证程序已在IEEE 9总线改进的测试系统上进行了演示。验证结果表明,所提出的惯性预测工具在估算风电场普及率较高的现代/未来电力系统网络的惯性方面是有效的。
更新日期:2020-09-02
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