SP139-Machine Learning For Predictive Maintenance Of Transformer


The research paper presents a methodology to carry out predictive maintenance of power transformers. The core concept of the methodology is to monitor some of the parameters and predict the condition of transformer. This helps to prevent catastrophic failure and unnecessary testing and maintenance thus reducing the overall operating cost and time required for maintenance. The common methods of predictive maintenance of transformer include dissolve gas analysis, artificial neural network, support vector machine, multi-class least square support vector machine. The paper is based on methodology for predictive maintenance of power transformers by using Linear Regression and Principal Component Analysis (PCA). The electrical parameters used for conducting this research includes phase voltage’s magnitude, phase voltage angles, phase current’s magnitude, phase current’s angles, active power, frequency and reactive power. Two different approaches are used for this study. First approach makes use of Phase A voltage’s magnitude and Phase A current’s magnitude to train the linear regression model. The applied model is trained and then tested to predict the value of Phase A voltage’s magnitude for the given values of Phase A current’s magnitude. Where as in second approach, PCA is applied on the whole data set and its output was given to the linear regression model as input. The model is trained and tested to predict Phase A voltage’s magnitude for the given principal components (input). To obtain the result, the data collecting device i.e. PMU was installed at Sheikh Muhammadi Grid with an 11KV distribution transformer. The device records the values of parameter (phase voltage, phase current, reactive power, active power and frequency) at time interval of 5 minutes. Keywords: Predictive maintenance, Transformer, Machine learning, Linear Regression, Principal Component Analysis (PCA), Dissolved gas analysis (DGA), Support vector machine (SVM)


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