Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers

Abstract

The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra’s algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations.

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

“…Initially, the radiometric method was applied to monitor a gas-insulated substation (GIS) [19,[23][24][25][26]. With the consolidation of the radiometric method for the detection, classification, and localization of PD in GIS, researchers expanded their studies to the application of the radiometric method in other high-voltage equipment, such as power transformers [27][28][29][30][31]. Thus, the radiometric method established itself as a method implemented in the detection, location, and classification of PD in electrical equipment [15,16,27,28].…”

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

“…The experimental findings demonstrate that the proposed approach achieves a high level of classification accuracy and provides precise health assessments for power transformers. Topics related to the application of artificial neural networks (ANNs) in the analysis of the operating conditions of power transformers and electrical energy systems have currently received attention [13][14][15]. In recent years, a series of studies and research have been published in this field, demonstrating the growing interest and relevance of these approaches [16][17][18][19].…”

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

“…Such tiny discharges can erode the insulation structure, leading to a localized increase in the temperature of the power equipment insulation and causing fires. Partial discharges in power equipment, if not resolved or detected in time, will continue to expand the deterioration area and eventually lead to overall insulation breakdown [ 4 , 5 , 6 , 7 ]. Partial discharges pose a serious threat to the safe operation of power equipment, and the ability to accurately detect localized discharge signals promptly is a critical step.…”