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第35届中国过程控制会议

作者： Chengcheng Xu Aimin An Jinhu Niu Bo Yu Chanyun Shao College of Electrical Technology and Information Engineering Lanzhou University of Technology Key Laboratory of Gansu Advanced Control for Industrial Processes

In the ultra-high voltage(UHV) network, the operational status of gas-insulated switchgear(GIS) equipment is closely related to the stable, reliable and safe operation of the entire UHV substation. Aiming at the problem that it is difficult to realize accurate and reliable partial discharge detection of GIS equipment due to the high voltage level and strong interference involved in GIS equipment, the study proposes a filtering algorithm based on vmd for electromagnetic wave interference signal of GIS equipment. Firstly, the local discharge signal is collected, secondly, the electromagnetic wave interference signal is superimposed to the discharge signal, and finally, the Variable Difference Modal Decomposition(vmd) signal filtering process is implemented on the MATLAB platform, and the validity of the vmd algorithm is verified by comparing it with the second-order Butterworth high-pass filtering method.

关键词： gis partial discharge detection electromagnetic wave interference signal filtering vmd algorithm

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BCSSA-vmd and ICOA-ELM based fault diagnosis method for analogue circuits

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2025年第2期123卷 1-22页

作者： You, Dazhang Liu, Shan Yuan, Ye Zhang, Yepeng Hubei Univ Technol Coll Mech Engn Wuhan 430068 Peoples R China Hubei Prov Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagnose and maintain faults in analog circuits. However, due to the tolerance, high nonlinearity, and susceptibility to environmental interference of analog circuit components, the development of related research on fault diagnosis has been hindered, and it cannot meet the current practical requirements for high safety and reliability of electronic devices. With the continuous increase in circuit scale and integration level, how to effectively and as much as possible extract more discriminative fault features is the key research direction of analog circuit fault diagnosis. Therefore, this article proposes a variational model decomposition (vmd) feature extraction method that combines Butterfly and Cauchy Sparrow search algorithms (BCSSA) and relies on an improved crayfish optimization algorithm (COA) to optimize the Extreme Learning Machine (ELM). Decomposition (vmd) feature extraction method, and rely on Improved Crayfish Optimization algorithm (COA) Optimized Extreme Learning Machine (ELM) to complete the classification of faults. Firstly, the BCSSA algorithm is used to optimize the number of vmd decomposition modes K and the penalty factor alpha to achieve the optimal vmd decomposition of the original fault signal, obtain a series of Intrinsic Mode Function (IMF) and calculate its envelope entropy, determine the optimal IMF component by selecting the IMF component with the lowest envelope entropy., and calculate its time-domain parameter, then normalize and reduce the dimensionality to construct the vector that contains the characteristics of the fault. The normalized dimensionality reduction process constitutes the fault feature vector;secondly, the ICOA algorithm is introduced to optimize the ELM;Ultimately, the fault feature vector is fed into the ELM

关键词： Analogue circuits Fault diagnosis Sparrow algorithm vmd algorithm Crayfish algorithm Extreme learning machine

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Precision Prediction Strategy for Renewable Energy Power in Power Systems-A Physical-Knowledge Integrated Model

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PROCESSES 2025年第4期13卷 1049-1049页

作者： Hua, Shenbing Jin, Shuanglong Song, Zongpeng Liu, Xiaolin Wang, Shu Ma, Hengrui China Elect Power Res Inst State Key Lab Renewable Energy Grid Integrat Beijing 100192 Peoples R China Wuhan Univ Hubei Engn & Technol Res Ctr AC DC Intelligent Dis Sch Elect Engn & Automat Wuhan 430072 Peoples R China Wuhan Univ Sch Elect & Automat Wuhan 430072 Peoples R China

To promote energy transformation and upgrading and achieve the dual carbon strategic goals, the proportion of renewable energy generation in China has been increasing annually. Among them, wind and photovoltaic power generation play a significant role in renewable energy generation due to their widely distributed energy sources, and their development has been particularly rapid. However, renewable energy generation exhibits strong volatility, and the integration of a large amount of renewable energy into the power grid poses a threat to the stable operation of the power system. Therefore, renewable energy power prediction is of great significance for the safe and stable operation of the power system. This paper proposes a physical-knowledge integrated renewable energy power prediction model. Firstly, the Fuzzy C-Means (FCM) method is used to handle missing data. Then, the variational mode decomposition (vmd) algorithm is applied to decompose the renewable energy power into high-frequency and low-frequency components. The high-frequency components are input into a Convolutional Neural Network (CNN) model, while the low-frequency components are input into a Long Short-Term Memory (LSTM) neural network for training and prediction. Finally, the effectiveness and feasibility of the proposed method in this paper are verified through real data from a certain actual power grid. After analysis, the proposed method demonstrates a prediction accuracy improvement of up to 5.71% compared to conventional approaches. Simultaneously, in high-noise interference environments, the prediction error can be reduced by up to 11.16%.

关键词： renewable energy Fuzzy C-Means method vmd algorithm CNN model Long Short-Term Memory neural network

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Research on non-intrusive load identification based on vmd-LSTM 24

Research on non-intrusive load identification based on VMD-L...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Hou, Baoyu Luo, Dan Zhang, Jiajun Ren, Bin Wang, Jie Mao, Zhixiang Quzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Quzhou 324000 Zhejiang Peoples R China Kaihua Cty Power Supply Co State Grid Zhejiang Elect Power Co Ltd Quzhou 324300 Zhejiang Peoples R China

ISBN: (纸本)9798400716638

Non-intrusive load identification technology is important for optimising the energy structure, improving power efficiency and saving resources by installing smart meters at the main power supply connector to collect and analyse the user's electricity information in real time. For the different characteristics of household load changes in the power system, a non-intrusive load identification approach based on vmd-LSTM is proposed. The acquired data were first preprocessed by decomposing the signal into K IMF components using the variational modal decomposition (vmd). Secondly, the relevance coefficient of each component with the normalized signal are calculated, and the three components with the largest correlation coefficients are selected to form the load feature. Then, it is input into the trained LSTM neural network for identification. Finally, the experimental results indicate that the recognition rate of the proposed means in the public data set is more than 96 %, and the accuracy of load recognition is significantly improved.

关键词： Non intrusive load monitoring Power system vmd algorithm LSTM

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Variational mode decomposition based differentiation of fatigue conditions in muscles using surface electromyography signals

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IET SIGNAL PROCESSING 2020年第10期14卷 745-753页

作者： Krishnamani, Divya Bharathi Karthick, P. A. Swaminathan, Ramakrishnan Indian Inst Technol Madras Dept Appl Mech Noninvas Imaging & Diagnost Lab Biomed Engn Grp Chennai Tamil Nadu India Natl Inst Technol Tiruchirappalli Dept Instrumentat & Control Engn Physiol Measurements & Instrumentat Lab Tiruchirappalli India

Surface electromyography (sEMG) signals are stochastic, multicomponent and non-stationary, and therefore their interpretation is challenging. In this study, an attempt has been made to develop an automated muscle fatigue detection system using variational mode decomposition (vmd) features of sEMG signals and random forest classifier. The sEMG signals are acquired from 103 healthy volunteers during isometric (45 subjects) and dynamic (58 subjects) muscle fatiguing contractions and preprocessed. The band-limited intrinsic mode functions (BLIMFs) are extracted from non-fatigue and fatigue segments of the signals using the vmd algorithm. Hjorth features, such as activity, mobility and complexity are extracted from each BLIMF and are given to the random forest classifier. The performance of these features is evaluated using leave-one-subject-out cross-validation. The results show that the complexity feature performs better than others and it has resulted in an accuracy of 83% in dynamic contractions and 80% in isometric contractions. The performance is increased by about 8% in a dynamic condition when the most significant complexity features (p < 0.001) are used and by about 12% for isometric when the authors use all significant features. Therefore, the proposed approach could be used to detect fatigue conditions in various neuromuscular activities and real-time monitoring in the workplace.

关键词： fatigue medical signal processing biomechanics electromyography feature extraction muscle dynamic condition significant complexity features fatigue conditions variational mode decomposition based differentiation surface electromyography signals automated muscle fatigue detection system variational mode decomposition features sEMG signals random forest classifier 103 healthy volunteers 45 subjects dynamic muscle fatiguing contractions 58 subjects band-limited intrinsic mode functions BLIMF fatigue segments vmd algorithm hjorth features mobility leave-one-subject-out cross-validation complexity feature dynamic contractions isometric contractions

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Research on short-term output power forecast model of wind farm based on neural network combination algorithm

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WIND ENERGY 2022年第10期25卷 1710-1734页

作者： Qu, Boyang Xing, Zuoxia Liu, Yang Chen, Lei Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

Reliable wind farm short-term output power prediction plays a crucial role in the safety and economy of power system operation. By embedding multiple neural network methods, such as long short-term memory (LSTM) neural network, multiple optimized BP neural network and wavelet neural network (WNN), combined with multiple methods based on signal decomposition and reconstruction to evaluate the short-term prediction effect. In this study, the variational modal decomposition (vmd) method and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are used to reduce the fluctuation of the original wind power signal. Besides, the intrinsic mode function (IMF) signals obtained by vmd and CEEMDAN methods are used. By predicting the IMF signals, the prediction error of each signal can be reduced, and the accuracy of wind power prediction can be indirectly improved. In order to test the validity of the proposed composite model, the validation prediction and rolling prediction of 72 h with a resolution of 10 min were carried out, respectively, using the data of Caijiagou wind farm in 2018. Through experiments, the relationship between the two predictions is analyzed, which provides an important reference for the selection of the prediction model. Corresponding results show that the proposed vmd-LSTM and CEEMDAN-LSTM methods significantly outperform other benchmark methods and provide very satisfactory results in both the accuracy and stability of wind farm power prediction.

关键词： BP neural network CEEMDAN algorithm intrinsic mode function long short-term memory vmd algorithm wind power forecasting

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Vibration detection method for core loosening degree of high-voltage direct current converter valve saturable reactor based on variational mode decomposition-symmetrized dot pattern and image matching

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2022年第8期16卷 454-466页

作者： Liu, Yunpeng Lai, Tingyu Liu, Jiashuo Wei, Xiaoguang Pei, Shaotong Wang, Chenghao Zhang, Xiaolong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding Peoples R China North China Elect Power Univ Hebei Prov Key Lab Power Transmiss Equipment Secu Baoding Peoples R China Jining Power Supply Bur Jining Peoples R China State Key Lab Adv Power Transmiss Technol Beijing Peoples R China

Saturable reactor is the key equipment in converter valves;it is important to evaluate the core condition. In this paper, a recognition model of saturable reactor core loosening based on variational mode decomposition (vmd)-symmetrized dot pattern (SDP) feature fusion images and scale-invariant feature transform (SIFT) was proposed. Firstly, the saturable reactor vibration test under high frequency pulse excitation was carried out, and the vibration signals in different core loosening degrees were collected. Secondly, the vmd algorithm was used to decompose the broadband vibration signal into multiple narrowband functions, which were used to reflect the characteristics of each frequency band. Thirdly, each function and the original signal were transformed by SDP, the generated spiral arms were fused into a new image, and the typical templates in different loosening degrees were selected. Finally, the improved SIFT algorithm was used to obtain the matching results between test sets and templates. The results show that the recognition accuracy of the proposed model for core loosening is 97.5%, which is better than traditional algorithms. It can find the core loosening defect early and avoid further failures such as water pipe break and discharge, which can provide an important basis for saturable reactor monitoring.

关键词： saturable reactor monitoring scale-invariant feature transform vibrations saturable reactor core loosening saturable reactor vibration test water pipe break vmd algorithm power convertors broadband vibration signal high frequency pulse excitation image matching recognition model variational mode decomposition-symmetrized dot pattern feature extraction vibrational signal processing reactors (electric) transforms core loosening degree image fusion HVDC power convertors feature fusion images vibration detection method multiple narrowband functions SIFT algorithm SDP high-voltage direct current converter valve saturable reactor

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Frequency division and optimizing capacity technology for hybrid energy storage system considering load smoothing

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第3期22卷 983-995页

作者： Bian, Haihong Zhao, Xiu Fu, Yun Peng, Shan Nanjing Inst Technol Sch Power Engn Nanjing 211100 Jiangsu Peoples R China

There is a problem that how to optimize the allocation of hybrid energy storage system (HESS) economically on user-side. To solve this, a planning and scheduling method of HESS considering load smoothing is proposed. Firstly, an optimization model of energy storage system (ESS) considering energy storage life and peak-shaving rate is established, which is used to determine the rated power, rated capacity of ESS, and the charge and discharge power at each moment of the day. Then, this paper constructs a HESS consisting of lead-carbon battery (Pb-C) and supercapacitor. Variational mode decomposition (vmd) algorithm was used to extract peak load. The peak load is smoothed by super capacitor, and the composite load after peak load optimization is smoothed by Pb-C. Finally, solving the optimization model of HESS by mixed integer linear programming (MILP). And the results of the calculation examples show that the optimization method of HESS proposed in this paper not only reduces peak load effectively, but also improves benefits of HESS under the background of time-of-use electricity price.

关键词： Optimization hybrid energy storage system user-side vmd algorithm

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Hybrid model based on vmd decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform

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ENERGY 2022年第PartB期261卷

作者： Sun, Zexian Zhao, Mingyu Zhao, Guohong North China Univ Sci & Technol Coll Elect Engn Qinhuangdao Peoples R China

Carving out the stochastic wind speed is still a challenge due to its intrinsic nature. With the wide spread of Internet of Things technology, the amount of data have presented explosive growth, thereby enhancing the difficulty of capturing its inherent characteristics. Therefore, the improvement of training efficiency requires keeping abreast of the forecasting accuracy and stability, which still has the large promotion space. These drawbacks motivate the propose of the hybrid model based on the variational mode decomposition(vmd), clustering analysis, LSTM network, stacking ensemble learning and error complementation for wind speed forecasting in which all the components are performed on Flink platform to ensure the forecasting efficiency. More specifically, the vmd module is employed to disintegrate the wind speed series into a primary trend and several fluctuate sub-series;Next, kmeans clustering and LSTM networks are conducted to deduce the latent characteristics of the primary trend and the stacking ensemble learning consisting of two stages is applied to infer the fluctuate abstractions of the other sub-series;Furthermore, the error complement is incorporated for assessing the error sequence created by the preliminary results. Finally, the experimental results have demonstrated that the proposed model exceeds the contrastive models on forecasting accuracy and efficiency.

关键词： Wind speed forecasting vmd algorithm K -means clustering Long short term memory network Error sequence Flink platform

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Research on Long-Term Tidal-Height-Prediction-Based Decomposition algorithms and Machine Learning Models

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REMOTE SENSING 2023年第12期15卷 3045-3045页

作者： Ban, Wenchao Shen, Liangduo Lu, Fan Liu, Xuanru Pan, Yun Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316022 Peoples R China Zhejiang Ocean Univ Sch Naval Architecture & Maritime Zhoushan 316022 Peoples R China Zhoushan City Land Reserve Ctr Zhoushan 316022 Peoples R China

Tidal-level prediction is crucial for ensuring the safety and efficiency of offshore marine activities, port and channel management, water transportation resource development, and life-saving operations. Although tidal harmonic analysis is among the most prevalent methods for predicting tidal water level fluctuations, it relies on extensive data, and its long-term prediction accuracy can be limited. To enhance prediction performance, this paper proposes a model that combines the variational mode decomposition (vmd) algorithm with the long short-term memory (LSTM) neural network. The initial step involves decomposing the original data using the vmd algorithm, followed by applying the LSTM to each decomposition component. Finally, all prediction results are superimposed and summed. The model is tested using the 2018 tidal time series data from the Lvsi station in Zhoushan City and the 2020 tidal time series data from the Ganpu station. The results are compared with those from the classical harmonic analysis model, the traditional machine learning model, and the decomposition-based machine learning method. The experimental outcomes demonstrate the superior predictive capabilities of the proposed model.

关键词： tide level prediction harmonic analysis vmd algorithm LSTM neural network combined prediction model

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