Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid

Mukherjee, Sayak; Darvishi, Atena; Chakrabortty, Aranya; Fardanesh, B.

doi:10.1109/pesgm40551.2019.8973632

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…A prototype study of dynamic event classification for the New York state power grid was presented by Mukherjee et al [128]. A full-scale transmission model of the Eastern Interconnection was simulated in PSS/E for generation of simulation data.…”

Section: ) Machine Learning/deep Learning Based Methodsmentioning

confidence: 99%

“…Event Detection Category Applied Technique [70], [80], [83], [85], [86], [136] Signal Processing Wavelet Transform [70], [72], [75], [79], [90]- [92], [138], [139] Filtering [81], [89], [140], [141] Fourier Transform [94], [100], [104]- [107], [116], [120], [142] Statistical Analysis PCA [95], [99], [103], [110], [113], [115], [121] Clustering and Correlation [123], [127], [131]- [133] Machine Learning/Deep Learning CNN [125], [128], [134] LSTM Among these requirements, frequency response and active power control are critically important. Figure 6 shows the deterioration of the U.S Eastern Interconnection (EI) frequency response to a 4.5 GW generator loss under different levels of PV penetration [148]*.…”

Section: Referencementioning

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: ) Machine Learning/deep Learning Based Methodsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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

Farooq

Bass

2022

IEEE Access

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“…For the classification of transients, several methods have been studied in the literature such as Decision Trees (DT) [3][4][5], Support Vector Machines (SVM) [4,5], Discrete Wavelet Transform [5,7] and Artificial Neural Networks [8]. Moreover, since the success of deep learning (DL)-based studies in automatic classification has been demonstrated, DL-based algorithms have recently been used frequently for classification of PQ Events and transients [9][10][11][12][13][14][15][16]. A novel framework for Transient Stability Assessment (TSA) based on Convolutional Neural Network (CNN) including Stacked Autoencoders is proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Gok

Salor

Taplamacıoğlu

2023

IET Generation Trans & Dist

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This paper presents a research work which focuses on generating synthetic data to enrich the training-set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved trainingset is shown to increase the classification performance compared to the case when only the actual-data training-set is used. The proposed classification system helps to reveal high-frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training-data exists. Synthetic PMU frequency data is generated based on the DFT analysis statistics on the limitedsize PMU frequency data. Generation of the synthetic data is achieved by re-synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training-set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training-set.

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“…In this case, data-driven approaches seem to be more suitable, since they are directly based on the measurement data reflecting the actual status of electrical quantities in power grids. Machine learning-based event classification has been proposed and developed in the past two decades exhibiting promising results [2][5] [6] [7]. Even regardless of data quality issues, a major hurdle to apply these machine-learning based classifiers is usually a lack of a sufficient data set for training.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the PMU measurements at t + e are the first data point of the post-event time series, which are used as the initial state of the Neural ODE model. On the other hand, the Neural ODE model is trained independently by a supervised learning to minimize the scalar-valued loss function in (7).…”

mentioning

confidence: 99%

Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

Zheng

Wang

Kalathil

et al. 2021

IEEE Open J. Power Energy

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A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this paper. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent datadriven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.

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Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid

Cited by 4 publications

References 9 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

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

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