Mining Spatial Frequency Time Series Data for Event Detection in Power Systems

Lavand, Swati A.; Gajjar, Gopal; Soman, S. A.; Gajbhiye, Rajeev

doi:10.1049/cp.2016.0083

Cited by 7 publications

(5 citation statements)

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“…Gajjar and Soman [105], and Lavand et al [106] based their work on a statistical analysis technique called the Ellipsoid Method, which uses PCA and an error ellipsoid for detection of minor and major events in a power system. The idea is that every power system disturbance gives rise to electromagnetic oscillations in the network.…”

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]. To reduce computational complexity, statistical indices such as correlation, variance, mean, maximum, and minimum are calculated over a window.…”

Section: ) Statistics Based Methodsmentioning

confidence: 99%

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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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“…bolted or single phase), the spikes in all three sequence components of voltages can be monitored. A similar approach to event detection is also proposed in [25], where events are detected using the Hodrick-Prescott filtering technique.…”

Section: Capturing a Spike Signaturementioning

confidence: 99%

Real‐time spatio‐temporal trend and level (T&L) filtering scheme for early detection of voltage instability

Nadkarni¹,

Soman²

2019

IET Generation, Transmission & Distribution

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“…The first step is to utilize many measurements and apply feature engineering to extract relevant features for events under consideration. Some examples are raw time-series, energy function approach [1]- [3], ellipsoid characteristics [4]- [7], frequency-domain details such as wavelets or shapelets [1], [7]- [11], principal component analysis [4], [12]- [14]. Extraction of features involve computations for every new data window.…”

Section: Introductionmentioning

confidence: 99%

Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing

Mahapatra¹,

Lu²,

Sengupta³

et al. 2020

Preprint

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Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring. Although many conventional machine learning techniques are very successful in classifying events, they rely on extracting information from PMU data at control centers and processing them through CPU/GPUs, which are highly inefficient in terms of energy consumption. To solve this challenge without compromising accuracy, this paper presents a novel methodology based on event-driven neuromorphic computing architecture for classification of power system disturbances. A Spiking Neural Network (SNN)-based computing framework is proposed, which exploits sparsity in disturbances and promotes local eventdriven operation for unsupervised learning and inference from incoming data. Spatio-temporal information of PMU signals is first extracted and encoded into spike trains and classification is achieved with SNN-based supervised and unsupervised learning framework. Moreover, a QR decomposition-based selection technique is proposed to identify signals participating in the low rank subspace of multiple disturbance events. Performance of the proposed method is validated on data collected from a 16machine, 5-area New England-New York system.

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Mining Spatial Frequency Time Series Data for Event Detection in Power Systems

Cited by 7 publications

References 0 publications

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

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

Real‐time spatio‐temporal trend and level (T&L) filtering scheme for early detection of voltage instability

Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing

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