Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply

Human-Centered Explainable Artificial Intelligence for Anomaly Detection in Quality Inspection: A Collaborative Approach to Bridge the Gap Between Humans and AI

In the quality inspection industry, the use of Artificial Intelligence (AI) continues to advance to produce safer and faster autonomous systems that can perceive, learn, decide, and act independently. As observed by the researcher interacting with the local energy company over a one-year period, these AI systems’ performance is limited by the machine’s current inability to explain its decisions and actions to human users. Especially in energy companies, eXplainable-AI (XAI) is critical to achieve speed, reliability, and trustworthiness with human inspection workers. Placing humans alongside AI will establish a sense of trust that augments the individual’s capabilities at the workplace. To achieve such an XAI system centered around humans, it is necessary to design and develop more explainable AI models. Incorporating these XAI systems centered around human workers in the inspection industry brings a significant shift in conducting visual inspections. Adding this explainability factor to the AI intelligent inspection systems makes the decision-making process more sustainable and trustworthy by bringing a collaborative approach. Currently, there is a lack of trust between the inspection workers and AI, creating uncertainty among inspection workers about the use of the existing AI models. To address this gap, the purpose of this qualitative research study was to explore and understand the need for human-centered XAI systems to detect anomalies in quality inspection in energy industries