Assessment of PD severity in gas‐insulated switchgear with an SSAE. Issue 4 (1st July 2017)

• Record Type: Journal Article
• Authors: Tang, Ju; Jin, Miao; Zeng, Fuping; Zhang, Xiaoxing; Huang, Rui
• Is Part Of: IET science, measurement & technology. Volume 11:Issue 4(2017)
• Page Start: 423
• Page End: 430
• Publication Date: 2017-07-01
• Subjects: partial discharge measurement -- gas insulated switchgear -- statistical analysis -- neural nets -- learning (artificial intelligence) -- encoding -- feature extraction -- computerised instrumentation; PD severity assessment -- gas‐insulated switchgear -- SSAE -- partial discharge severity assessment -- discharge time -- discharge amplitude -- deep‐learning neural network model -- stacked sparse autoencoder -- feature extraction -- soft‐max classifier -- unsupervised greedy layer‐wise pre‐training method -- supervised fine‐tuning method -- support vector machine algorithm; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0326
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas‐insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep‐learning neural network model called stacked sparse auto‐encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft‐max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer‐wise pre‐training and supervised fine‐tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.