ENHANCING SMART GRID RELIABILITY THROUGH MACHINE LEARNING-BASED ANOMALY DETECTION

Abstract

Background: Advanced monitoring and control have been added by the development of smart grids, but they have also raised the complexity and susceptibility of systems to anomalies like faults, cyber-attacks, and data inconsistencies. Conventional rule-based anomaly detectors are becoming less and less sufficient to sustain high volume, real-time data, and thus more intelligent and adaptive solutions are required. Objective: This paper will evaluate the performance of machine learning (ML) to enhance the reliability of smart grids, through anomaly detection, and identify the key issues, performance standards, and deploying barriers, from the perspective of industry practitioners. Methodology: A quantitative survey-based approach was adopted, with a sample of 250 professionals including grid operators, data scientists, academicians, and policy makers. The survey aimed to gather information on current practices, required ML approaches, challenges in deployment and anticipated performance levels. Data analysis was performed using descriptive statistics (mean, standard deviation, frequency, percentage). Results: The results indicate that there is a high agreement that the current traditional approaches cannot work, mainly because of the growing complexity of data and the presence of anomalies that cannot be identified. The most preferred methods are supervised, unsupervised, and hybrid ML techniques. The high performance expectations were established, and the majority of respondents are expected to be precise and recall with an accuracy rate of above 90 percent and detection latency of less than 10 seconds. The main difficulties are a shortage of labeled data, class imbalance, cybersecurity issues, and compatibility with the legacy systems. Nonetheless, despite these obstacles, ML is seen to have a strong beneficial influence on predictive maintenance, fault minimization, and cyber-attack control. Moreover, most respondents demonstrated a favorable attitude to using ML in the future.Conclusion: Machine learning is a revolutionary chance to improve the reliability of smart grids with the help of precise and real-time detection of anomalies. Nevertheless, to achieve a successful large scale implementation, one has to deal with data limitations, enhance model interpretability, enhance infrastructure integration and acquire skilled human resources.