Fault location and isolation technology for power grid automation based on intelligent algorithms

Abstract

Background

Power grid automation is critical for maintaining the stability and reliability of electrical grids. A major challenge in power grid management is identifying and isolating faults quickly and accurately to avoid widespread disruptions. Traditional fault detection and isolation methods rely on rule-based diagnostics, which frequently struggle for speed, precision, and adaptability to changing fault conditions. As power grids become more complex, intelligent algorithms are critical for improving the efficiency of fault localization and isolation.

Problem statement

Conventional fault management methods, like rule-based and heuristic methods, have limitations in both accuracy and real-time adaptability. To address these issues, this study proposes and assesses two intelligent algorithms: the Fault Localization Algorithm (FLA) and the Fault Isolation Algorithm (FIA). Unlike conventional methods, FLA incorporates machine learning methods to improve fault detection, whereas FIA provides an optimized isolation strategy, decreasing operational delays and reducing power disruption.

Methodology

The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. The FIA algorithm then uses the FLA output to evaluate fault severity and select the best fault isolation strategy. This approach combines an SVM-based fault localization algorithm with a decision-tree-based fault isolation strategy to guarantee quick and accurate fault identification, reducing system downtime and enhancing fault resolution efficiency. The proposed system is validated with the PowerGrid Fault Localization Dataset (PGFLD), which contains practical power grid fault data.

Results

Experimental findings show that the proposed FLA algorithm achieves 92% accuracy, outperforming traditional techniques like Decision Tree (85%), KNN (82%), and Logistic Regression (78%). Furthermore, FIA achieves 95% accuracy, outperforming current rule-based (89%) and heuristic (85%) methods. These findings show a significant enhancement in fault detection accuracy and isolation effectiveness, which reduces false positives and improves power grid resilience.

Conclusion

This study provides an innovative method of power grid fault management that employs intelligent algorithms for fault localization and isolation. The use of SVM for fault localization and decision-tree-based fault isolation improves fault detection accuracy while reducing operational delays. The proposed methods improve grid resilience and offer actionable isolation tactics, making them extremely effective for contemporary power grid automation.