Capacitive voltage transformer measurement error prediction by improved long short-term memory neural network

Zhou, Feng; Zhao, Peng; M, Liu; Yue, Changxi; Yu, Jicheng; Liang, Siyuan

doi:10.1016/j.egyr.2022.05.262

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…As demonstrated in the figure above, adaptive decomposition effectively segments the original ratio error and load sequences into distinct subcomponents. The parameters (K, α) are configured to round towards zero, where the optimal decomposition values are (13,18,271). The decomposed IMF components are then screened using the Spearman rank correlation coefficient.…”

Section: Mvmd Decomposition and Reconstructionmentioning

confidence: 99%

“…By making short-term predictions based on monitoring data, these models can provide state information for the future sampling moment, supporting risk warning, fault detection, and maintenance planning. For instance, reference [13] uses a BiLSTM network to directly predict the measurement errors of voltage transformers, while reference [14] employs GRU and MTL to predict the ratio error of voltage transformers. Reference [15] uses VMD to decompose the measurement error signals of voltage transformers and inputs features for prediction, but this approach overlooks the impact of decomposition residuals on model stability and lacks interpretability in empirical reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations

Li,

Cui,

Rocha

et al. 2024

WEVJ

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The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method’s robustness and superiority in accurately forecasting VT measurement error trends.

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Section: Mvmd Decomposition and Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations

Li,

Cui,

Rocha

et al. 2024

WEVJ

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show abstract

“…Li et al selected the main influencing factors of ratio difference and angle difference by transferring entropy and then realized transformer error prediction through a wavelet neural network [4] . Zhou et al improved the LSTM model by introducing strategies such as bidirectional memory and depth feature extraction to improve the prediction accuracy of voltage transformer measurement errors [5] . The existing forecasting methods do not comprehensively consider the error cycle and environmental factors, resulting in low accuracy and insufficient stability of error prediction.…”

Section: Introductionmentioning

confidence: 99%

The measurement error prediction method of voltage transformer based on TCN-RBF

Gao,

Jiang,

Liu

et al. 2024

J. Phys.: Conf. Ser.

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Accurate prediction of voltage transformer error is an important guarantee for the stable and economic operation of the power grid. This paper presents a measurement error prediction method for voltage transformers based on time convolution and radial basis neural networks (TCN-RBF). Firstly, the ensemble empirical mode decomposition is used to decompose the original error sequence into multiple components. Secondly, a TCN prediction model was established based on each component and historical error sequence to achieve error prediction. To further improve the prediction progress, the RBF model was used to further fit the predicted results with the environmental parameter data and confirm the final predicted value. The operation error of the transformer in a substation is tested. The test results show that compared with LSTM, SVM, BP, and other prediction models, this method has better performance in error prediction.

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“…The literature [13] reduces the condition assessment problem to a feature classification problem and uses machine learning algorithms, such as support vector machines, to achieve a CVT insulation condition fault diagnosis, but the method requires a large number of negative samples to train the model. The literature [14,15] estimates the measurement error by the analysis of multi-source heterogeneous data. The literature [16] proposes a supervised learning method for feature information analysis using principal component analysis and random forest to achieve the CVT internal insulation status evaluation.…”

Section: Introductionmentioning

confidence: 99%

Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning

He,

Zhou,

Tong

et al. 2023

Energies

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A capacitive voltage transformer (CVT) is one of the electrical quantities measurement devices, and the state of its internal insulation is the key factor for ensuring the accuracy of its measurement of electrical energy. In view of the fact that the traditional real-time evaluation method of a CVT internal insulation anomaly mainly relies on empirical rules and prior knowledge and lacks the ability to independently mine effective features, an online evaluation method of a CVT internal insulation anomaly based on self-supervised learning is proposed. Firstly, an autoencoder is constructed to extract the residual sequence of the CVT secondary voltage and eliminate the influence of primary voltage fluctuation and power system voltage regulation. Without any prior knowledge, the complex dependence of the residual sequences in time and feature dimensions is learned by using a parallel graph attention layer (GATv2). Finally, a joint optimization based on the prediction and reconstruction model is introduced to obtain the abnormal inference score at each timestamp and realize the evaluation of the CVT internal insulation status. Experimental analysis shows that this method can effectively eliminate the influence of primary voltage fluctuation and power system voltage regulation on the online evaluation of the CVT internal insulation status and independently excavate the abnormal characteristics of the CVT secondary voltage to realize real-time monitoring and early warning of the CVT internal insulation status.

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Capacitive voltage transformer measurement error prediction by improved long short-term memory neural network

Cited by 12 publications

References 12 publications

A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations

A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations

The measurement error prediction method of voltage transformer based on TCN-RBF

Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning

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