Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

Kummerow, André; Dirbas, Mohammad; Monsalve, Cristian; Nicolai, Steffen; Bretschneider, Peter

doi:10.1016/j.segan.2022.100803

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Autoencoder neural networks represent a specific subspecies of ANNs [23,24]. An autoencoder is an unsupervised neural network that, by utilizing an unlabeled dataset, learns an effective method to compress and encode input data.…”

Section: Autoencodermentioning

confidence: 99%

Artificial Intelligence for Energy Theft Detection in Distribution Networks

Žarković,

Dobrić

2024

Energies

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The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks.

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Section: Autoencodermentioning

confidence: 99%

Artificial Intelligence for Energy Theft Detection in Distribution Networks

Žarković,

Dobrić

2024

Energies

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Influence of nuisance variables on the PMU-based disturbance classification in power transmission systems

Kummerow,

Bretschneider

2023

At - Automatisierungstechnik

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The online classification of grid disturbances in power transmission systems has been investigated since many years and shows promising results on measured and simulated PMU signals. Nonetheless, a practical deployment of machine learning techniques is still challenging due to robustness problems, which may lead to severe misclassifications in the model application. This paper formulates an advanced evaluation procedure for disturbance classification methods by introducing additional measurement noise, unknown operational points, and unknown disturbance events in the test dataset. Based on preliminary work, Siamese Sigmoid Networks are used as classification approach and are compared against several benchmark models for a simulated power transmission system at 400 kV. Different test scenarios are proposed to evaluate the disturbance classification models assuming a limited and full observability of the grid with PMUs.

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Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

Cited by 2 publications

References 16 publications

Artificial Intelligence for Energy Theft Detection in Distribution Networks

Artificial Intelligence for Energy Theft Detection in Distribution Networks

Influence of nuisance variables on the PMU-based disturbance classification in power transmission systems

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