基于小波KPCA与Bi-LSTM的特高压换流站测控装置健康评估和预测

特高压换流站测控装置作为模拟量非线性、传输转换高要求的二次设备，目前的评估和预测方法不完全适用于测控装置的健康分析。提出了一种基于小波核主元(kernel principal component analysis, KPCA)分析和双向长短期记忆网络(bi-directional long short-term memory, Bi-LSTM)结合的健康评估和预测方法。通过引入小波核函数，以提高KPCA对健康状态影响因素进行特征提取的能力。通过第一核主元建立健康指数，以评估测控装置状态变化。通过构建Bi-LSTM网络模型以输入特征信息达到健康预测目的。以浙江某换流站采集到的真实数据作为样本，通过实验数据进行了对比分析。结果表明，该方法可以提升多维健康监测数据的准确评估和预测精度，为检修人员制定检修策略提供科学参考。

Health status assessment and prediction of operational condition of a measurement and control device in a UHV converter station based on KPCA and Bi-LSTM

The measurement and control device of a UHV converter station is a piece of secondary equipment with nonlinear analog and high requirements for transmission and conversion. The current evaluation and prediction methods are not fully suitable for the analysis of such a device. A health assessment and prediction method based on wavelet kernel principal component analysis (KPCA) and a bidirectional long-term and short-term memory network (Bi-LSTM) is proposed. The wavelet kernel function is introduced to improve KPCA''s feature extraction of influencing factors on the state of health. A health index is established through the first nuclear principal component to evaluate the state change of the device. The purpose of health prediction is achieved by constructing a Bi-LSTM network model to input characteristics information. Taking the real data collected by a converter station in Zhejiang as the sample, the experimental data are compared and analyzed. The results show that this method can improve the accurate evaluation and prediction accuracy of multidimensional health monitoring data, and provide a scientific reference for maintenance personnel in formulating maintenance strategies. This work is supported by the National Natural Science Foundation of China (No. 51777119).