A Novel Event Detection and Classification Scheme Using Wide-Area Frequency Measurements

Shaw, Priyabrata; Jena, Manas Kumar

doi:10.1109/tsg.2020.3039274

Cited by 32 publications

(12 citation statements)

References 22 publications

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“…The existing work on event detection Complexity and speed of operation are the two important features to be considered for an event detection algorithm. Shaw and Jenna proposed a simple and effective event detection and classification algorithm using wide area frequency measurement system [70]. Discrete Wavelet Transform (DWT) is used to remove noise from PMU data and Kalman filtering is used to estimate the ROCOF as well as phase angle difference for frequency measurements.…”

Section: B Rq2: What Are the Current Methods Used For Event Detection...mentioning

confidence: 99%

“…For reliable operation, most of the signal processingbased techniques discussed above, such as [70], [72], [83], [86], rely on data from multiple buses in the network, which involve communication costs. Moreover, the results depend highly on changes in the window size, since window size shapes the range of coefficient energy.…”

Section: B Rq2: What Are the Current Methods Used For Event Detection...mentioning

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: B Rq2: What Are the Current Methods Used For Event Detection...mentioning

confidence: 99%

Section: B Rq2: What Are the Current Methods Used For Event Detection...mentioning

confidence: 99%

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

Farooq

Bass

2022

IEEE Access

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“…Existing research in event detection can be divided into four categories: rule-based, statistic-based, conventional machine learning, and deep learning. For rule-based methods, (Shaw and Jena, 2020), observes an event if the standard deviation (std.) of the phase angle difference or the rate of change of frequency (ROCOF) exceeds a given threshold.…”

Section: Introductionmentioning

confidence: 99%

“…The rule-based approach for event detection has lower model complexity and requires less computation time (Shaw and Jena, 2020). However, it has three main disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

Li¹,

Liang²,

Zhao³

et al. 2021

Front. Energy Res.

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Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.

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“…Hence, this indicator plays an essential relationship in the stability and resilience of the grid [6]. Moreover, the use of RoCoF is not only crucial for the synthetic inertia control [7], but for other applications such as the inertia estimation [8][9][10], the RoCoF sharing for enhancing primary controllers [11], Wide-Area frequency measurements [12], load shading schemes [13], frequency stability margins [14], and possibly, to determine the inertia placement [15].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Inertia Response and Rate of Change of Frequency in Low Rotational Inertial Scenario of Synchronous Dominated System

et al. 2021

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The shift to a sustainable energy future is becoming more reliant on large-scale deployment of renewable and distributed energy resources raising concerns about frequency stability. Rate of Change of Frequency (RoCoF) is necessary as a system inertia metric in order for network operators to perform control steps to preserve system operation. This paper presents in a straightforward and illustrative way several relevant aspects of the inertia response and RoCoF calculation that could help to understand and explain the implementation and results of inertial response controllers on power converter-based technologies. Qualitative explanations based on illustrative numerical experiments are used to cover the effects on the system frequency response of reduced rotational inertia in synchronous dominated power systems. One main contribution of this paper is making evident the importance of the governor action to avoid the synchronous machine taking active power from the system during the recovering period of kinetic energy in an under frequency event.

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A Novel Event Detection and Classification Scheme Using Wide-Area Frequency Measurements

Cited by 32 publications

References 22 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

A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

Investigation of Inertia Response and Rate of Change of Frequency in Low Rotational Inertial Scenario of Synchronous Dominated System

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