A survey of anomaly detection methods for power grids

Madabhushi, Srinidhi; Dewri, Rinku

doi:10.1007/s10207-023-00720-z

Cited by 3 publications

(1 citation statement)

References 208 publications

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“…The problem is especially important from the tuning point of view of power systems and energy price optimization. Outlier detection was performed using the hybrid model as a combination of Long Short-Term Memory (LSTM) and the K-means algorithm [16]. The LSTM is the most commonly used variety of Recurrent Neural Networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Karpio,

Łukasiewicz,

Ząbkowski

2024

Energies

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In this article, the Leading Point Multi-Regression model was applied to identify days with anomalous energy consumption profiles. The data for the analysis come from the German energy system and they represent the hourly energy demand observed between 2006 and 2015. Days with abnormal daily profiles were identified based on the statistical analysis of the errors observed for the model. The accuracy of the model is very high and comparable with other models, e.g., LSTM, K-means, Recurrent NN, and tree-based ML methods. However, these methods rely on external factors (e.g., humidity, temperature, and sunshine) impacting energy consumption while our model uses only the energy consumption at specific fixed hours, regardless of external factors, thus being universal. Days with anomalous energy consumption profiles were identified as days related to celebration of New Year’s Eve and the New Year. Also, anomalies were identified for some other days, which were not that obvious, including Good Friday, National Day of Mourning, and, interestingly, the day of the Germany–Turkey match during the European Championship in 2008.

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Section: Introductionmentioning

confidence: 99%

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Karpio,

Łukasiewicz,

Ząbkowski

2024

Energies

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Cyber-physical metropolitan area digital substations test bench for evaluating intrusion detection systems

Sanchez-Acevedo,

Zerihun,

Koshutanski

et al. 2024

2024 6th Global Power, Energy and Communication Conference (GPECOM)

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Detecting Aberrations in Renewable Energy With the One-Class Support Vector Machine Model

Moroke,

Makatjane

2024

Machine Learning and Computer Vision for Renewable Energy

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The traditional energy time series forecasting methods use accurate input data for prediction and then make single-step or multi-step predictions based on the established regression model. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. Unsupervised anomaly detection techniques operate directly on an unseen dataset, under the assumption that outliers are sparsely present in it. A one-class support vector machine (SVM) is an extension of support vector machines for unlabeled data and can be used for anomaly detection. Even though outliers are accounted for in one-class SVMs, they greatly influence the learned model. Hence, one modification to one-class SVMs is presented here: eta one-class SVMs. For evaluation, eta one support vector machine-based algorithm is compared using four kernels. The results affirmed the proficiency of the RBF kernel, with the eta one-class SVM performing best. In regards to subspace division, the preliminary results showed a decreasing nonlinear trend.

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A survey of anomaly detection methods for power grids

Cited by 3 publications

References 208 publications

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System

Cyber-physical metropolitan area digital substations test bench for evaluating intrusion detection systems

Detecting Aberrations in Renewable Energy With the One-Class Support Vector Machine Model

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