PMU-ANN based real time monitoring of power system electromechanical oscillations

2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) Pub Date : 2016-07-04 DOI:10.1109/ICPEICES.2016.7853073

Abhilasha Gupta, K. Verma

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引用次数: 13

Abstract

Power system oscillations monitoring is a vital issue in operation of modern interconnected power systems. The existing methods for identifying the electromechanical modes are time-consuming and require modelling of the entire system that includes a large number of states and are performed offline. In this paper, an integrated Phasor Measurement Unit and Artificial Neural Network (PMU-ANN) based approach for online and real time monitoring of power system electromechanical oscillations is proposed. The placement of PMU is obtained using Integer Linear Programming (ILP). The data obtained from PMU is given as input to a multilayer Feedforward Neural Network (FFNN) and its output gives all the information related to the modes of the system and the mode ranking. The effectiveness of the proposed approach is investigated on IEEE 39-bus test system. The results show that the proposed approach is fast with less computational burden and is suitable for online and real time oscillations monitoring of the power systems under varying operating conditions.

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基于PMU-ANN的电力系统机电振荡实时监测

电力系统振荡监测是现代互联电力系统运行中的一个重要问题。现有的机电模式识别方法耗时长，并且需要对包含大量状态的整个系统进行建模，并且需要离线执行。本文提出了一种基于相量测量单元和人工神经网络(PMU-ANN)的电力系统机电振荡在线实时监测方法。采用整数线性规划(ILP)方法求解PMU的位置。PMU获得的数据作为多层前馈神经网络(FFNN)的输入，其输出给出了与系统模式和模式排序相关的所有信息。在IEEE 39总线测试系统上验证了该方法的有效性。结果表明，该方法速度快，计算量小，适用于各种运行工况下电力系统的在线和实时振荡监测。

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2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

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