电压暂降
可观测性
人工神经网络
变压器
工程类
计算机科学
电能质量
电子工程
断层(地质)
控制理论(社会学)
模式识别(心理学)
实时计算
电压
人工智能
电气工程
数学
控制(管理)
地质学
地震学
应用数学
作者
Yaping Deng,Xinghua Liu,Rong Jia,Qi Huang,Gaoxi Xiao,Peng Wang
标识
DOI:10.35833/mpce.2020.000528
摘要
Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.
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