A Novel Recognition Method for Complex Power Quality Disturbances Based on Visualization Trajectory Circle and Machine Vision

Yuan, Ding; Liu, Yulong; Lan, Mingyang; Jin, Tao; Mohamed, Mohamed A.

doi:10.1109/tim.2022.3204985

Cited by 13 publications

(4 citation statements)

References 24 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%

“…Although the improved fast independent component analysis method and the improved random forest classifier can provide a basis for the detection of potential PQ problems and help to control and manage signal distortion, the proposed recognition model is not universal [14]. Although the trajectory circle image based on the improved Hilbert transform makes up for the shortcomings of unclear time series features [15], the starting time of a single disturbance signal is close to the end of the sequence, which accounts for a small proportion of the whole sequence, so that it is not detected in this sequence. Moreover, under the sampling error, the existence of some serious noises causes some trajectory circle distortion, which leads to recognition errors [16].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…With the deepening of the reform of the power system and the formation of the new normal of the macro economy, the competition in the energy consumption market has become increasingly fierce, and the external environment has gradually increased the service requirements of power grid operators and power supply companies (Kaur et al, 2020;Yuan et al, 2022;Liu et al, 2023). The existing management and monitoring mode of the power room is based on sensors, video monitoring, manual analysis, robot inspection, etc.…”

Section: Introductionmentioning

confidence: 99%

Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network

Chen¹,

Dai²,

Wang³

et al. 2023

Front. Energy Res.

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In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning.

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“…Correctly wearing safety helmets and tooling can protect the personal safety of operators to a large extent (Jacob and Darney, 2021). However, due to the slack and negligence of the operators themselves and the relaxed vigilance of the management personnel, safety risks in the construction process have occurred from time to time (Yuan et al, 2022). To this end, a deep learning-based power worker safety-wearing recognition method is proposed to identify operators who do not wear tooling correctly and remind them in time, which can improve the effectiveness of supervision, enhance the safety awareness of operators, reduce potential safety risks, and ensure that construction safety is of great significance.…”

Section: Introductionmentioning

confidence: 99%

Research on the identification method of safety wearing of electric power workers based on deep learning

Chen¹,

Ma²,

Ren³

et al. 2023

Front. Energy Res.

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Aiming at the difficulties of manual monitoring and compliance with the current wear identification of electric power workers, the detection and identification of safety helmets, work clothes, and insulating gloves are used to carry out normative identification and warning, and a deep learning-based power worker safety wear identification method is proposed in this paper. The AlexNet and Inception are introduced to increase the width and depth of the artificial neural network. At the same time, the ReLU activation function with better performance is used to reduce the amount of network computation, and the Global Average Pooling layer is used to replace the fully connected layer with more parameters. The improved convolution neural network model has a total of 13 layers. In order to prevent the network from overfitting, the Early-stopping mechanism and the L2 regularization method are used to improve the performance of the network model. The experimental results show that the algorithm can achieve a good recognition effect on the staff who do not wear safety according to the regulations in the video, and the feasibility and effectiveness of the algorithm in practical application are verified.

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A Novel Recognition Method for Complex Power Quality Disturbances Based on Visualization Trajectory Circle and Machine Vision

Cited by 13 publications

References 24 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

Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network

Research on the identification method of safety wearing of electric power workers based on deep learning

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