基于长短期记忆网络的PMU不良数据检测方法

同步相量测量单元(Phasor Measurement Units, PMUs)因其同步性、快速性和准确性,已成为复杂电力系统状态感知的最有效工具之一。但是,现场的复杂环境导致PMU数据存在数据丢失、数据损坏、同步异常、噪声影响等质量问题,严重影响其在系统中的各类应用,甚至威胁电网安全稳定运行。提出了一种基于长短期记忆(Long Short-Term Memory,LSTM)网络的PMU不良数据检测方法。首先分析了LSTM在不良数据检测中的优势。然后基于LSTM网络对时间序列选择记忆的特性,构造了一种双层LSTM网络架构,提出了对原始数据的分解重构方法。在此基础上,定义了两种目标函数,以获得不同的误差特征。提出了一种基于决策树的不良数据阈值确定方法,实现了不良数据的有效检测。通过大量仿真与实测数据验证了该方法的可行性和准确性,可提高PMU数据质量,使其更好地应用于电力系统的各个方面。

PMU bad data detection method based on long short-term memory network

Phasor Measurement Units (PMUs) have become one of the most effective tools for state awareness of complex power systems due to their synchronization, speed and accuracy. However, the complex on-site environment causes data loss, data corruption, synchronization anomaly, noise and other quality problems of the PMU data, which seriously affects various applications in the power system and even threatens the safe and stable operation of the grid. This paper proposes a PMU bad data detection method based on Long Short-Term Memory (LSTM) network. First, the advantages of LSTM in bad data detection are analyzed. Based on the characteristics of time sequence selection and memory of the LSTM network, a two-layer LSTM network architecture is constructed, and the decomposition and reconstruction method of original data is proposed. On this basis, two objective functions are defined to obtain different error characteristics. A method for determining the threshold of bad data based on decision tree is proposed, which realizes the effective detection of bad data. The feasibility and accuracy of the proposed method are verified by a large number of simulations and field data. The quality of PMU data is improved, which makes it better applied to all aspects of the power system. This work is supported by National Key Research and Development Program of China (No. 2017YFB0902900 and No. 2017YFB0902901).