DOI:10.35833/MPCE.2020.000526 Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders Page view: 0        Net amount: 165 Author: Haosen Yang1,Robert C. Qiu1,2,Houjie Tong1 Author Affiliation: 1.Department of Electrical Engineering, Center for Big Data and Artificial Intelligence, Shanghai Jiaotong University, Shanghai 200240, China;2.School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430000, China Foundation: Abstract: Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states, or even unstable state. However, in practice, the data of insecure or unstable state is very rare, as the power system should be guaranteed to operate far away from voltage collapse. Under this circumstance, this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system. The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data, while it fails to rebuild data of insecure states. Thus, the residual of reconstruction is effective in indicating VSA. Besides, to develop a more accurate and robust algorithm, long short-term memory (LSTM) networks combined with fully-connected (FC) layers are used to build the autoencoder, and a moving strategy is introduced to bias the features of testing data toward the secure feature domain. Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method. Keywords: Reconstruction loss ; autoencoders ; voltage stability ; long-short-term memory (LSTM) ; feature moving strategy Received:July 27, 2020               Online Time:2020/12/03 View Full Text       Download reader