Journal
NEURAL COMPUTING & APPLICATIONS
Volume 31, Issue 10, Pages 6719-6731Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3490-5
Keywords
Data mining; Fault diagnosis; Power system fault; Neural networks
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Funding
- Zhejiang Provincial Natural Science Foundation of China [LZ15F030001]
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Artificial intelligence methods have been applied to power system fault diagnosis based on the switch values such as relay protection actions and electrical component actions. These methods have their own problems including availability of data, state exponential explosion, difficulty modeling, etc. This paper deals with the application of stacked sparse autoencoder (SSAE) for power system line trip fault diagnosis based on the analog quantity of operation data. SSAE, a deep learning structure, is a more effective approach to solve problems mentioned above due to its network structure and layer-wise training mechanism. It also covers the detection of incipient faults based on the strong data mining capability. In the paper, an SSAE-based network with support vector machine (SVM) and principal component analysis (PCA) is proposed to improve the accuracy of fault diagnosis in power systems. The real-world simulation experiments prove the improvement and practical application of the proposed method. The process steps and parameter selection are elaborated in detail.
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