An overview of PMU-based Electrical Power Systems modelling for Power Quality enhancement

Anton, Nicolae; Bulac, Constantin; Sănduleac, Mihai; Gemil, Eda-Elif; Dobrin, Bogdan; Ion, Vladut-Andrei

doi:10.1109/upec55022.2022.9917615

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The block diagram of the model is shown in Figure 5, where S represents the convolutional step length, and the S of each convolutional layer is 2. C i represents the number of channels after the signal passes through each convolutional layer, and the corner mark i represents the index number of the convolutional layer (i.e., [1][2][3][4][5][6][7][8][9][10][11]. F represents the input gradient signal length and, after convolution, the eigenvector dimension of each layer is F/S i .…”

Section: Compressed Sensing Based On Dynamic Threshold Of Depthmentioning

confidence: 99%

“…Accurate short-term load forecasting is one of the key technologies to ensure the safe and stable operation of an NPS. However, the traditional forecasting method faces difficulties, such as large number of users, a high load heterogeneity, a high volatility, and a high randomness, meaning that it is difficult to meet the requirements of load forecasting in an NPS [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

Wang,

Wei,

Zeng

et al. 2023

Energies

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The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

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Section: Compressed Sensing Based On Dynamic Threshold Of Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

Wang,

Wei,

Zeng

et al. 2023

Energies

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Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

Qin,

Wang,

Guddanti

et al. 2024

2024 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

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Compatibility Analysis of Frequency Containment Reserve and Load Frequency Control Functions

Ramūnas,

Miglė,

Konstantinas

2024

IgMin Res

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The study focuses on investigating the behavior of Electric Power Systems (EPS) during desynchronization from a synchronous area, its stability in island mode operation, and its synchronization with Continental Europe (CE). The primary aim is to ensure and maintain the stability and reliable operation of self–operating individual EPSs by analyzing the technical requirements and prequalification procedures for Frequency Containment Reserves (FCR) and Load Frequency Control (LFC) reserves. The study employed numerical simulations to analyze the behavior of isolated EPSs after losing a certain amount of active power, to ensure reliable operation and avoid the effects of Under Frequency Load Shedding (UFLS). It examined the technical requirements for LFC reserve units, such as the ability to remain connected to the network during disturbances and provide necessary support. The research investigated the procedures for prequalifying reserve units to provide FCR and LFC services. This investigation contributes to ensuring that EPSs operate reliably during desynchronization and that reserve providers meet the necessary technical and operational standards.

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An overview of PMU-based Electrical Power Systems modelling for Power Quality enhancement

Cited by 3 publications

References 17 publications

Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

Compatibility Analysis of Frequency Containment Reserve and Load Frequency Control Functions

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