Detection of Bad PMU Data using Machine Learning Techniques

Karpilow, Alexandra; Cherkaoui, Rachid; D’Arco, Salvatore; Duong, Thuc Dinh

doi:10.1109/isgt45199.2020.9087782

Cited by 10 publications

(1 citation statement)

References 4 publications

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“…It is inevitable that faults will occur seldomly while processing a large number of data points such as PMUs (Karpilow et al 2020). The term "Bad Data" can include missing data where a problem occurred during measurement recording or data that is unrepresentative of the real situation of the analyzed power system.…”

Section: Missing and Incorrect Datamentioning

confidence: 99%

Trustworthy cyber-physical power systems using AI: dueling algorithms for PMU anomaly detection and cybersecurity

Cali,

Catak,

Halden

2024

Artif Intell Rev

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Energy systems require radical changes due to the conflicting needs of combating climate change and meeting rising energy demands. These revolutionary decentralization, decarbonization, and digitalization techniques have ushered in a new global energy paradigm. Waves of disruption have been felt across the electricity industry as the digitalization journey in this sector has converged with advances in artificial intelligence (AI). However, there are risks involved. As AI becomes more established, new security threats have emerged. Among the most important is the cyber-physical protection of critical infrastructure, such as the power grid. This article focuses on dueling AI algorithms designed to investigate the trustworthiness of power systems’ cyber-physical security under various scenarios using the phasor measurement units (PMU) use case. Particularly in PMU operations, the focus is on areas that manage sensitive data vital to power system operators’ activities. The initial stage deals with anomaly detection applied to energy systems and PMUs, while the subsequent stage examines adversarial attacks targeting AI models. At this stage, evaluations of the Madry attack, basic iterative method (BIM), momentum iterative method (MIM), and projected gradient descend (PGD) are carried out, which are all powerful adversarial techniques that may compromise anomaly detection methods. The final stage addresses mitigation methods for AI-based cyberattacks. All these three stages represent various uses of AI and constitute the dueling AI algorithm convention that is conceptualised and demonstrated in this work. According to the findings of this study, it is essential to investigate the trade-off between the accuracy of AI-based anomaly detection models and their digital immutability against potential cyberphysical attacks in terms of trustworthiness for the critical infrastructure under consideration.

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Section: Missing and Incorrect Datamentioning

confidence: 99%