Application of PMU to detect high impedance fault using statistical analysis

Abdelsalam, Hany A.; Makram, Elham B.

doi:10.1109/pesgm.2016.7741454

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Kantra et al proposed statistical analysis to detect high impedance faults [98] . It applies a null-and-alternative hypothesis test to the gaussian distribution of the mean of PMU data samples obtained from utility substations to detect frequency deviation from a nominal value.…”

Section: ) Statistics Based Methodsmentioning

confidence: 99%

“…Similar to signal processing techniques, many of the statistical analysis based methods also rely on data from multiple buses in the network [95], [96], [98], [102], [110], [121] entailing communication cost. The performance of many of these techniques is also dependent on user-defined threshold [105], [106], [109], [112], [116], [121].…”

Section: ) Statistics Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

Farooq

Bass

2022

IEEE Access

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Modern power systems characterized by complex topologies require accurate situational awareness to maintain an adequate level of reliability. Since they are large and spread over wide geographical areas, it is inevitable that failures will occur. Various generation and transmission disturbances, such as generator and transmission line tripping and load disconnection, give rise to a mismatch between generation and demand, which manifest as frequency events. These events can take the form of negligible frequency deviations or more severe emergencies that can precipitate cascading outages, depending on the severity of the disturbance and efficacy of remedial action schema. The impacts of such events have become more critical recently due to increased levels of renewable penetration and distributed energy resources, which have caused a decline in system synchronous inertia. Due to the repercussions, it is indispensable to arrest such disturbances on time by activating primary frequency control measures. In this paper, a comprehensive systematic literature review is presented on the techniques used for event detection in power systems and the methods of primary frequency response in modern power systems. The paper also highlights the impacts of severe frequency events within power systems.

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Section: ) Statistics Based Methodsmentioning

confidence: 99%

Section: ) Statistics Based Methodsmentioning

confidence: 99%

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

Farooq

Bass

2022

IEEE Access

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“…In this case, we make a comparison on our RMT-based approach with some other existing approaches, including the maximum in statistical analysis approaches [9], the PCA in signal processing approaches [15] and the SAE in supervised machine learning approaches [18,21]. The IEEE 57-bus test system was used to generate the simulation data.…”

Section: Case IV -Comparing With Other Existing Approachesmentioning

confidence: 99%

“…The data-driven approaches for anomaly analysis in power systems can be broadly categorised into three classes: (1) statistical analysis approaches, (2) signal processing approaches, and (3) artificial intelligence approaches. The statistical approaches often use some statistics for anomaly detection, such as maximum (minimum), mean, variance or higher moments [7][8][9], etc. The signal processing approaches, frequently used in recent years, mainly include Fourier analysis, wavelet transform, principal component analysis (PCA) [10][11][12][13][14][15][16], etc.…”

Section: Introductionmentioning

confidence: 99%

Early anomaly detection and localisation in distribution network: a data‐driven approach

Shi

Qiu

et al. 2020

IET Generation, Transmission & Distribution

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“…Kantra et al . [3] propose an approach to detect an HIF using static analysis with a null and alternative hypothesis. In the approach given in [4], artificial neural networks is used to detect the occurrence of an HIF.…”

Section: Introductionmentioning

confidence: 99%