Short-term voltage instability prediction using pre-identified voltage templates and machine learning classifiers

Dharmapala, Kalana; Rajapakse, Athula

doi:10.1016/j.ijepes.2023.109758

Cited by 2 publications

(1 citation statement)

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“…Assessing shortterm voltage stability of a power system is a time sensitive process which requires trajectory feature identification within few seconds therefore feature engineering approaches can be seen in Zhu et al (2016, Yang et al (2018), Dharmapala et al (2020), and Dharmapala and Rajapakse (2024). These features are Time Series Shapelets (TSS) in Zhu et al (2016) and , preidentified templates in Dharmapala and Rajapakse (2024) and online induction motor slip in Yang et al (2018). On the other hand, deep learning algorithms such as LSTM can grasp features without pre-processing (feature computation).…”

Section: Voltage Stabilitymentioning

confidence: 99%

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage,

Dharmapala,

Chaves

et al. 2024

Front. Smart Grids

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Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.

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