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power quality disturbance Identification Basing on Adaptive Kalman Filter andMulti-Scale Channel Attention Fusion Convolutional Network

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Energy Engineering 2024年第7期121卷 1865-1882页

作者： Feng Zhao Guangdi Liu Xiaoqiang Chen Ying Wang School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

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 *** and composite-disruption signals are generated through *** 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 *** multi-scale features of the signal are captured by convolution kernels of different sizes so that the model can obtain diverse feature *** attention mechanism(ATT)is introduced to adaptively allocate the extracted features,and the features are fused and selected to obtain the new main *** Softmax classifier is employed for the classification of power quality ***,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 *** 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 *** achievement provides a new perspective for further exploration in the field of power quality disturbance classification.

关键词： power quality disturbance kalman filtering convolutional neural network attention mechanism

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A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for power quality disturbance Detection and Classification

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CSEE Journal of power and Energy Systems 2024年第2期10卷 584-592页

作者： Zhi Ding Tianyao Ji Mengshi Li Q.H.Wu School of Electric power Engineering South China University of Technology(SCUT)Guangzhou 510641China

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

关键词： Hadamard matrix mathematical morphology morphological clustering power quality disturbance Walsh theory

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A Hybrid Compression Method for Compound power quality disturbance Signals in Active Distribution Networks

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Journal of Modern power Systems and Clean Energy 2023年第6期11卷 1902-1911页

作者： Xiangui Xiao Kaicheng Li Chen Zhao State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China College of Mechanical and Electrical Engineering Fujian Agriculture and Forest UniversityFuzhou 350002China

In the compression of massive compound power quality disturbance(PQD) signals in active distribution networks, the compression ratio(CR) and reconstruction error(RE) act as a pair of contradictory indicators, and traditional compression algorithms have difficulties in simultaneously satisfying a high CR and low RE. To improve the CR and reduce the RE, a hybrid compression method that combines a strong tracking Kalman filter(STKF), sparse decomposition, Huffman coding, and run-length coding is proposed in this study. This study first uses a sparse decomposition algorithm based on a joint dictionary to separate the transient component(TC) and the steady-state component(SSC) in the PQD. The TC is then compressed by wavelet analysis and by Huffman and runlength coding algorithms. For the SSC, values that are greater than the threshold are reserved, and the compression is finally completed. In addition, the threshold of the wavelet depends on the fading factor of the STKF to obtain a high CR. Experimental results of real-life signals measured by fault recorders in a dynamic simulation laboratory show that the CR of the proposed method reaches as high as 50 and the RE is approximately 1.6%, which are better than those of competing methods. These results demonstrate the immunity of the proposed method to the interference of Gaussian noise and sampling frequency.

关键词： Signal compression power quality disturbance Huffman coding run-length coding wavelet analysis sparse decomposition

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The application of WPT in compress power quality disturbance

The application of WPT in compress power quality disturbance

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2010 Chinese Control and Decision Conference

作者： Wang Wen-jun,Fu Jian-hua,Liu Wan-shan Water Conservancy and Hydropower College,Hebei University of Engineering,Handan,China,056021

＜正＞According to characteristic of the transient disturbance data of power quality whose frequency is very high, this paper put forward a method based on optimum wavelet packet for the compression of transient disturbance data of power *** packet transform can decompose the signal into different frequency bands,which chooses frequency bands adaptively to overcome the disadvantages of wavelet *** compressions for voltage sag signal and transient pulse signal are simulated and the compression results with wavelet transform are *** prove its feasibility and effectiveness.

关键词： Wavelet Packet Transform Data Compression power quality disturbance

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A Review on Intelligent Detection and Classification of power quality disturbances:Trends,Methodologies,and Prospects

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1345-1379页

作者： Yanjun Yan Kai Chen Hang Geng Wenqian Fan Xinrui Zhou School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more *** is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent *** order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse *** paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly ***,comments on the future evolution of PQD detection techniques are *** the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

关键词： power quality disturbance renewable energy feature extraction and optimization intelligent classification signal processing smart grids

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Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第5期12卷 20-42页

作者： Rajendra Mahla Baseem Khan Om Prakash Mahela Anup Singh Department of Electrical Engineering National Institute of Technology Kurukshetra India Department of Electrical and Computer Engineering Hawassa University Ethiopia power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Limited JaipurIndia Department of Electrical Engineering Apex Institute of Engineering and Technology JaipurIndia

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)*** are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared *** algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and *** work has been performed using the MATLAB *** of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).

关键词： Fast kurtogram power quality disturbance ruled-based decision tree wavelet packet transform

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power quality disturbances Detection and Classification Using Complex Wavelet Transformation and Artificial Neural Network

Power Quality Disturbances Detection and Classification Usin...

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第二十六届中国控制会议

作者： Liu Hua,Wang Yuguo,Zhao Wei Hebei University of Engineering,Handan 056038,P.R.China

This paper presents a novel power quality disturbance detection and classification method of distribution power system based on complex wavelet transform （WT） and radial basis function （RBF） neural *** complex sup- ported orthogonal wavelets is employed to extract the feature information of disturbance signal,and finally proposed to ex- plore several novel wavelet combined information （CI） to analyze the disturbance,superior to real wavelet analysis *** feature obtained from WT coefficients are inputted into RBF network for power quality disturbance pattern *** power quality disturbance classification model is established and the synthesized method of recursive orthogonal least squares algorithm （ROLSA） with improved Givens transform is used to fulfill the network structure *** means of choos- ing enough samples to train the recognition model,the type of disturbance can be obtained when signal representing fault is inputted to the trained *** simulation results demonstrate that the complex WT combined with RBF network are more sensitive to signal singularity,and found to be significant improvement for acquiring signal feature information.

关键词： Complex wavelet signal detection power quality disturbance power system

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Application of Wavelet Network for Automatic power quality disturbances Recognition in Distribution power System

Application of Wavelet Network for Automatic Power Quality D...

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第二十六届中国控制会议

作者： Liu Hua,Zhao Baoqun,Wang Guangjian Hebei University of Engineering,Handan 056038,P.R.China

power quality（PQ）has attracted considerable attention from both utilities and users due to the use of many types of sensitive electronic *** paper proposed a novel approach for the PQ disturbances classification based on the wavelet *** transform is utilized to extract feature vectors for various PQ disturbances based on the multi-resolution analysis（MRA）.These feature vectors then are applied to wavelet network for training and *** signal containing noise is de-noised by wavelet transform to obtain a signal with higher signal-to-noise ratio（SNR）.The synthesized method of recursive orthogonal least squares algorithm（ROLSA）and improved Givens transform is used to fulfill the network *** fundamental component of the signal is estimated to extract the mixed information using wavelet network,and then the disturbance is acquired by subtracting the fundamental *** simulation results demonstrate that the proposed method is *** with conventional methods,the simulation results show accurate discrimination,fast learning,good robustness,and faster processing time for detecting PQ disturbing.

关键词： power quality disturbance Wavelet transform Signal de-noise Singularity detection disturbance localization power system

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Neural Network-based Detection and Recognition Method for power quality disturbances Signal

Neural Network-based Detection and Recognition Method for Po...

引用

2010 Chinese Control and Decision Conference

作者： Wei Liao~1,Hua Wang~1,Pu Han~2 1.Hebei University of Engineering,Handan 056038,China 2.North China Electric power University,Baoding 071003,China

＜正＞With widespread use of various kinds of electric devices,the demand for clean power supply has been increasing in the past decades,which has great effect on the energy *** order to improve supply quality of power system,the sources and causes of power quality disturbances must be known before appropriate mitigating *** paper used wavelet network-based neural classifier to automatically detect,localize,and classify the transient disturbance pattern,which can acquire the qualitative and quantitative *** wavelet transform can offer a better compromise in terms of time-frequency domain, which decomposes the transient signal into a series of wavelet coefficients,corresponding to a specific octave frequency band containing more detailed *** acquire the original information of transient signal, the wavelet-based denoising technology is discussed in a low signal noise ratio *** improved training algorithm is utilized to complete the neural network parameters initialization and classification *** order to satisfy power system observation,the power quality monitor configuration method is *** testing results and analysis indicate that the proposed method is feasible and practical for analyzing power quality disturbances.

关键词： Energy market power quality disturbance wavelet transformation neural classifier signal noise ratio power system observation

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power quality Event Detection and Analysis in power System Using Neural Network

Power Quality Event Detection and Analysis in Power System U...

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第二届国际测量仪器与测试自动化学术会议

作者： Liao Wei~1 Wang Hua~1 Han Pu~2 1 Hebei University of Engineering Handan 050038 China 2 Noah China Electric power University Baoding 071003 China

＜正＞A novel approach for the power quality（PQ） disturbances classification based on the wavelet transform（WT） and neural network is *** transform is utilized to extract feature vectors for various PQ disturbances and the WT can accurately localizes the characteristics of a signal both in the time and frequency *** feature vectors then are applied to a neural network for training and disturbance pattern *** comparing with a classic neural network,it is concluded that the proposed neural network has better data driven learning and local interconnections *** research results between the proposed method and the other existing method are discussed and the proposed method can provide accurate classification *** the basis of hypothesis test of the averages,it is shown that corresponding to different wavelets selection,there is no statistically significant difference in performance of PQ disturbances classification and the relationship between the wavelet decomposition level and classification performance is *** simulation results demonstrate the proposed method gives a new way for identification and classification of dynamic power quality disturbances.

关键词： power quality disturbance self-organizing learning array wavelet transform classification performance

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