A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Qunli Wu and Hongjie Zhang
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Qunli Wu: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
Hongjie Zhang: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China

Sustainability, 2019, vol. 11, issue 6, 1-19

Abstract: The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

Keywords: Expertise-Guided Machine Learning model; fault diagnosis; power transformer; genetic algorithm; mind evolutionary algorithm; robustness test (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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