Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems

Bhatnagar, Kaustubh; Sahoo, Subham; Iov, Florin; Blaabjerg, Frede

doi:10.1109/egrid52793.2021.9662148

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…As the measured data has varying scales, and the splines do not make any assumption about their distribution, it is normalized between 0 and 1 to identify the distribution using the minmax approach. Then, we employ a weakly physics-informed gradient-based optimization to pre-train the network using D and the candidate library Φ in (5). We call it "weakly physics-informed" because we have not included (8) into the optimization yet.…”

Section: Resultsmentioning

confidence: 99%

“…1(d) that the generated cyber attack replicates the fault accurately. This problem, usually addressed by fully data-driven discriminators to distill the underlying dynamics [3]- [5], still remains a big challenge due to the necessary requirements of high computational resources and observational data. Moreover, considering the data-privacy restraints, distilling the analytical equations from scarce data, commonly seen in practice, adds to this intractable challenge [6].…”

Section: Introductionmentioning

confidence: 99%

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Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach

Kurukuru

Khan

Sahoo

2022

IEEE Trans. Power Electron.

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Cyber attacks can be strategically counterfeited to replicate grid faults, thereby manipulating the protection system and leading to accidental disconnection of grid-tied converters.To prevent such setbacks, we propose a physics-informed spline learning (PiSL) approach based anomaly diagnosis mechanism to distinguish between both events using minimal data for the first time in the realm of power electronics. This methodology not only provides compelling accuracy with limited data, but also reduces the training and computational resources significantly. We validate its effectiveness and accuracy under experimental conditions to conclude how data availability problem can be handled.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach

Kurukuru

Khan

Sahoo

2022

IEEE Trans. Power Electron.

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“…As it is clear, supervised learning techniques like linear regression, support vector machines, and neural networks are valuable tools for power electronics control and optimization. They enable the prediction of system behavior based on input data and the fine-tuning of system parameters to achieve specific goals in power electronics applications [42][43][44][45].…”

Section: Neural Networkmentioning

confidence: 99%

Review of Machine Learning Techniques for Power Electronics Control and Optimization

Bahrami,

Khashroum

2023

CRPASE

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In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integration of machine learning techniques assumes paramount significance. This article endeavors to present an extensive and comprehensive review of the machine learning techniques that find application in power electronics control and optimization. Through meticulous exploration, we aim to elucidate the profound potential of these methods in shaping the future of power electronics control and optimization.

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“…Furthermore, there is a growing need for real-time anomaly detection and classification systems in power electronic systems due to the integration of various technologies like renewable energy sources, smart grids, and cyber-physical systems [10]. This integration adds complexity to electronic power systems, leading to challenges in real-time anomaly detection and necessitating the advancement of sophisticated anomaly detection and classification systems [13].…”

Section: Introductionmentioning

confidence: 99%

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

Hossein Rahimighazvini,

Zeyad Khashroum,

Maryam Bahrami

et al. 2024

Int. J. Sci. Res. Arch.

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Power electronics pertains to the conception, regulation, and utilization of electronic power circuits to proficiently administer and transform electrical energy. Power electronics play a crucial role in maintaining the reliability, efficiency, and security of complex production systems. Also, increasingly important in various applications such as renewable energy systems, electric vehicles, and industrial automation. However, modern power electronics systems are vulnerable to both cyber and physical anomalies due to the integration of information and communication technologies. So far, different methods have been used to detect abnormalities. This survey provides an overview of the state-of-the-art in anomaly detection in power electronics using machine learning and deep learning methods. It highlights the potential of these techniques in addressing the growing complexity and vulnerability of power electronics systems.

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Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems

Cited by 5 publications

References 30 publications

Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach

Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach

Review of Machine Learning Techniques for Power Electronics Control and Optimization

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

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