Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, ma...

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Bibliographic Details
Published in:arXiv.org 2017-11
Main Authors: Mahoor, Mohsen, Amin Khodaei
Format: Article
Language:English
Subjects:
Adaptive systems
Ambient temperature
Artificial intelligence
Asset management
Basis functions
Data integration
Fuzzy systems
Kalman filters
Machine learning
Multisensor fusion
Radial basis function
Transformers
Online Access:Get full text
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Summary:Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process is proposed based on hourly transformer loading and ambient temperature values. This synthesized data is employed to estimate transformer loss of life by using Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function (RBF) network, which are further fused together with the objective of improving the estimation accuracy. Among various data fusion techniques, Ordered Weighted Averaging (OWA) and sequential Kalman filter are selected to fuse the output results of the estimated ANFIS and RBF. Simulation results demonstrate the merit and the effectiveness of the proposed method.
ISSN:2331-8422