A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data Scenarios

• Published in: Sustainability Vol. 16; no. 8; p. 3110
• Main Authors: Liu, Yanzheng, Sun, Chenhao, Yang, Xin, Jia, Zhiwei, Su, Jianhong, Guo, Zhijie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Accuracy; Aging; Algorithms; Analysis; Case studies; Electric power systems; Electric transformers; Environmental sustainability; Fault diagnosis; Information management; Machine learning; Methods; Neural networks; Pattern recognition; Power supply; Taiwan; Taiwan
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su16083110

As a crucial component of power systems, distribution transformers are indispensable to ensure the sustainability of power supply. In addition, unhealthy transformers can lead to wasted energy and environmental pollution. Thus, accurate assessments and predictions of their health statuses have become a top priority. Unlike assumed ideal environments, however, some complex data distributions in practical scenarios lead to more difficulties in diagnosis. One challenge here is the potential imbalanced distribution of data factors since sparsely occurring factors along with some Unusual High-Risk (UHR) components, whose appearance may also damage transformer operations, can easily be neglected. Another is that the importance weight of data components is simply calculated according to their frequency or proportion, which may not always be reasonable in real nonlinear data scenes. With such motivations, this paper proposes a novel integrated method combining the Two-fold Conditional Connection Pattern Recognition (TCCPR) and Component Significance Diagnostic (CSD) models. Initially, the likely environmental factors that could result in distribution transformer heavy overloads were incorporated into an established comprehensive evaluation database. The TCCPR model included the UHR time series and factors that are associated with heavy overload in both spatial and temporal dimensions. The CSD model was constructed to calculate the risk impact weights of each risky component straightforwardly, in line with the total risk variation levels of the whole system caused by them. Finally, the results of one empirical case study demonstrated their adaptation capability and enhanced performance when applied in complex and imbalanced multi-source data scenes.