Positive Active Power Outlier Detection based on One-Class SVM

Ma, Bin; Long, Yuan; Xu, Shaozhe; Zheng, Kuanyun; Huang, Fuxing; Li, Runlong; Yuan, Peng

doi:10.1109/appeec48164.2020.9220568

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Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Grid1

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2022

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Cited by 2 publications

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“…Ma et.al [49] proposed a One Class SVM based anomaly detection to identify outliers in large scale energy data. In [50] One R, Random Forest, Naive Bayes and JRipper models were analyzed on a three class dataset for anomaly detection.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

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Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%