In an era where the energy sector relies increasingly on digital technologies, the reliability and security of Electric Power and Energy Systems (EPES) infrastructure are vital. Utilizing cutting-edge machine learning models for fault diagnosis to reinforce cybersecurity and mitigate risks is of high importance to the EPES secure operation. While machine learning has significantly improved the efficiency of diagnosing faults, it often falls short in terms of “explainability” – the ability to make model outcomes interpretable for humans. However, bridging the gap between advanced algorithms and human understanding is of paramount importance.
Machine learning models can bring substantial benefits to fault diagnosis in EPES infrastructure. They’ve slashed computational time and demonstrated remarkable adaptability to various system parameters. Yet, their drawback has been their lack of transparency. When a machine learning model makes a decision, it’s often challenging to comprehend the reasoning behind it. This lack of transparency has posed challenges in terms of trust and decision-making.
To that end an expanding field within machine learning, XAI (Explanable AI) aims to make machine learning outcomes comprehensible and interpretable for humans. XAI answers critical questions about why a specific fault diagnosis was made. This transparency empowers decision-makers to gain deeper insights and confidence in the diagnostic process.
The explainability aspect is crucial, in critical domains like EPES, where the consequences of decisions can be far-reaching. This transparency ensures not only better decision-making but also the ability to continuously improve and fine-tune the required actions.
Towards providing effective solutions addressing the above EPES needs, the scientific community is working to exploit XAI related research (initially applied to other fields and sectors, like financing, manufacturing, etc.) in the risk assessment processes of energy assets [1]. One such approach is Netcompany-Intrasoft’s Quantitative Association Rule Mining Algorithm (QARMA) which is a rule-based learner that excels in both accuracy and explainability. In various scenarios and tests, QARMA showcased an accuracy rate of 98%, surpassing other explainable schemes [2]. It also worth mentioning that satisfactory performance is achieved even with highly noisy data. This robustness is invaluable when dealing with real-world, complex EPES infrastructure.
As the ELECTRON project forges ahead, its data-driven risk assessment, will investigate innovative approach that harnesses the potential of machine learning while placing transparency and interpretability at its core. The goal is not just a more efficient system, but a safer and more resilient energy infrastructure that we can rely on in an era where energy and cybersecurity are inseparably linked.
References
[1] A. Beattie et al., “A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics,” 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Singapore, Singapore, 2022, pp. 296-301, doi: 10.1109/SmartGridComm52983.2022.9961002.
[2] Gutierrez-Rojas, D., Christou, I.T., Dantas, D. et al. Performance evaluation of machine learning for fault selection in power transmission lines. Knowl Inf Syst 64, 859–883 (2022). https://doi.org/10.1007/s10115-022-01657-w