Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang, Xi; Chen, Zixuan; Tang, Xin; Li, Zewen; Zhang, Xiangjun

doi:10.1049/gtd2.12364

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Among them: y k is the observation signal; ω, A 1,k and ϕ 1 are angular frequency, the amplitude, and the initial phase angle of the fundamental component [15]; ϕ r and A r,k (r = 2, • • • , M) are the initial phase angle of the rth harmonic component and the amplitude; v k is a Gaussian white noise signal with zero mean covariance [19]; M is the maximum number of harmonics; T s is the sampling interval; and the sampling frequency f s can be obtained.…”

Section: Adaptive Maximum Likelihood Kalman Filter 21 Space State Modelmentioning

confidence: 99%

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Zhao,

Liu,

Chen

et al. 2024

ENERGY

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In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN architecture is conducted. The multi-scale features of the signal are captured by convolution kernels of different sizes so that the model can obtain diverse feature expressions. The attention mechanism (ATT) is introduced to adaptively allocate the extracted features, and the features are fused and selected to obtain the new main features. The Softmax classifier is employed for the classification of power quality disturbances. Finally, by comparing the recognition accuracy of the convolutional neural network (CNN), the model using the attention mechanism, the bidirectional long-term and short-term memory network (MS-Bi-LSTM), and the multi-scale convolutional neural network (MSCNN) with the attention mechanism with the proposed method. The simulation results demonstrate that the proposed method is higher than CNN, MS-Bi-LSTM, and MSCNN, and the overall recognition rate exceeds 99%, and the proposed method has significant classification accuracy and robust classification performance. This achievement provides a new perspective for further exploration in the field of power quality disturbance classification.

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Section: Adaptive Maximum Likelihood Kalman Filter 21 Space State Modelmentioning

confidence: 99%

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Zhao,

Liu,

Chen

et al. 2024

ENERGY

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“…They achieved an overall classification accuracy of 93.7%. Finally, Xi et al [100] proposed a KF-ML-aided DBN method for identification of multiple types of PQ disturbances and time locations. The proposed method achieved an overall accuracy of 97.5% for type identification and 97.3% for time location.…”

Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

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“…KF-ML, Kalman filter based on maximum likelihood; PQD, power quality disturbance; UKF, unscented Kalman filter. To improve the noise immunity performance of the proposed classification method, the Kalman filter based on maximum likelihood (KF-ML) 7,33 is used for denoising the raw PQD signals in this paper. For demonstrating the efficacy of KF-ML in reducing noise, Figure 5 gives the comparison of PQD waveforms before and after denoising.…”

Section: C19mentioning

confidence: 99%

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

Zhou

et al. 2023

Energy Science & Engineering

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Aiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms.

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Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Cited by 8 publications

References 36 publications

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

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