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A Harmonic Current Self-Compensation Control Strategy of DC Active power Filter

IEEE Journal of Emerging and Selected Topics in power Electronics 2025年

作者： Li, Gaoxiang Wu, Huanchao Chen, Jining Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical En-gineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Guangxi Uni-versity Nanning530004 China

Due to the diversity of nonlinear loads, the harmonic frequency of the DC microgrid is unpredictable, which poses a challenge to traditional DC active power filters (DC-APF). Firstly, the harmonic compensation mechanism and characteristics of the DC-APF with classical control strategy are analyzed. For the classical DC-APF, the voltage control and harmonic extraction are independent of each other, which may lead to their conflict and reduce the harmonic compensation performance. To address this issue, a harmonic current self-compensation control strategy of DC-APF is proposed, which uses a first-order inertia controller in the voltage loop and can simultaneously achieve voltage control and harmonic extraction. Then, the mechanism, characteristics, and parametric design method of the proposed harmonic current self-compensation control are introduced in detail. Compared with the classical DC-APF, the proposed DC-APF does not require any harmonic extraction filter and has better harmonic current compensation performance. Finally, the proposed control strategy and theoretical analysis are verified by simulation and experiment. © 2025 IEEE. All rights reserved.

关键词： Active filters

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Prediction the Thermal of Compression Dead-end and Jumper Terminal for Electrical Equipment Based on High Voltage Transmission Lines, Case Study of Electricity Du Cambodia (EDC)

Prediction the Thermal of Compression Dead-end and Jumper Te...

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IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： Singhen Suos Lixiao Cong Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning Guangxi Zhuang Autonomous Region China Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Intelligent Manufacturing College Nanning University Nanning Guangxi Zhuang Autonomous Region China

ISBN: (纸本)9781665482097

The high voltage overhead transmission line system is one of the methods of delivering electric power at a high voltage of Electricity Du Cambodia (EDC) is the largest company in Cambodia’s location, which transmitted from one source to another distribution, especially over a long distance to the customers. In an electrical power system, compression dead end and jumper terminal is one of the most important pieces of equipment and play important roles in the transmission system high voltage as well. Moreover, a bad or loose connection can be made a hot spot for generating the thermal that affecting for transmitting power, for the survey thermal energy of electrical equipment that they can to observed and quantified as thermography by applying infrared imaging and measuring the camera techniques. This method of detecting heat output is known as infrared thermography (IRT). Infrared thermography is not only a cost-effective technology but also powerful diagnostic equipment for improving system efficiency and dependability, power quality, and worker safety, and preventing outages, expensive equipment failure, and line losses. Using the Neural Network (NN) in the comparison to the Linear Regression (LR) model. This paper regarded schedule working actual base on the history of transmission line reports of using IRT which was noted by EDC, and the input parameters were the weather factors, wind speed, duration of operation, and load on the line. The prediction accuracy of these two methodologies was calculated with the use of the mean absolute percentage error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Based on the act result of the two models, the Neural Network (NN) model is better performance accuracy.

关键词： power transmission lines Wind speed Linear regression Thermal engineering High-voltage techniques Artificial neural networks Thermal conductivity

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Diff-MSVAE: A Generative Prediction system for Reliable Long-Term Multivariate Air Quality Forecasting

SSRN

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SSRN 2025年

作者： Jia, Pengfei Su, Xun Zhu, Zhengyang Zhang, Yiyi Liu, Jiefeng Xu, Min School of Electrical Engineering Guangxi University Nanning China Institute of Science and Technology for Carbon Peak & Neutrality Guangxi University Nanning China College of Food Science and Bioengineering Xihua University Chengdu China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

As air pollution becomes an increasingly serious issue, accurately predicting the long-term trends of pollutants has become an important task in global environmental management. Although various statistical and deep learning methods have been proposed for air quality prediction, most of these methods focus on short-term forecasts. This study proposes a new generative forecasting model, Diff-MSVAE, for long-term multivariate air quality prediction, focusing on six major pollutants including PM2.5, PM10, and sulfur dioxide. The dataset is from Xi'an, China, covering a variety of meteorological factors and pollutant concentration information in the region. By introducing feature engineering, using the Phik correlation coefficient for feature selection, and incorporating multi-scale encoding and decoding units, the model can effectively capture the interactions and dynamic changes between pollutants, demonstrating strong stability and high accuracy in long-term predictions. Experimental results show that Diff-MSVAE reduces the mean squared error by approximately 71.3% compared to its baseline model D3VAE in 32-day predictions. Diff-MSVAE also shows significant advantages when compared with other advanced models such as DVA and Informer, and its advantages become more pronounced as the prediction time increases. These results indicate that Diff-MSVAE can provide more reliable long-term predictions, offering strong technical support for air quality management and policy formulation. © 2025, The Authors. All rights reserved.

关键词： Sulfur dioxide

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Research on Digital Twin Modeling Method for Combustion Process Based on Model Reduction

SSRN

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SSRN 2024年

作者： Zhang, Yue Li, Jiale Department of Automation North China Electric Power University Hebei Baoding071000 China Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System China

In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by establishing a twin model of the combustion temperature field. Initially, a finite element method is employed to analyze the combustion system of a 600MW subcritical boiler under various operating conditions. Subsequently, cross-sectional data from burner nozzle positions at each operating condition are extracted. These data are subjected to Proper Orthogonal Decomposition (POD), Spectral Proper Orthogonal Decomposition (SPOD), and Wavelet Transform-POD (WT-POD) for dimensionality reduction to obtain modal data. Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. The average absolute error of the reconstructed temperature fields from three methods under different operating conditions is then compared. Finally, the model is refined using clustering segmentation, resulting in an improvement of approximately 0.6% in reconstruction accuracy. This enhancement demonstrates that the clustered POD-SVR-GA model achieves higher accuracy in reconstructing combustion temperature fields after clustering-based improvements. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Small Signal Stability Assessment of power system Based on Sade-ELM Algorithm

Small Signal Stability Assessment of Power System Based on S...

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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Yude Yang Xiu Zhang Quan Wen Key Laboratory of Power System Optimization and Energy Saving Technology Guangxi University School of Electric Power Engineering Guangxi University Nanning China

ISBN: (纸本)9781665434263

Along with the increasing scales of power system, the traditional numerical method to calculate the damping ratio of system to evaluate the small signal stability cannot meet the requirements of real-time analysis. Therefore, we propose a small signal stability assessment method for power system based on Extreme Learning Machine (ELM) optimized by Differential Evolution Algorithm with Strategy-Adaptation (SaDE). The node power, generator power and branch power are taken as inputs of the model in this method. Meanwhile, the minimum damping ratio of the system is taken as the output, and fits input data and output data. The results of 3-machine 9-node and 10-machine 39-node systems show that the method can estimate the minimum damping ratio of the system after off-line training of historical data without considering the change of control parameters. Compared with the prediction results of Back Propagation (BP) and ELM models, SaDE-ELM has the lowest error, the highest prediction accuracy and the strongest generalization ability.

关键词： Damping Training system integration power system stability Predictive models Prediction algorithms Stability analysis

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Intelligent Terminal Monitoring system of Distribution Network for 5G Network 4

Intelligent Terminal Monitoring System of Distribution Netwo...

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4th International Conference on power and energy Engineering, ICPEE 2020

作者： Wu, Wenxia Zhu, Yun Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University NanNing China

ISBN: (纸本)9781728191256

This paper proposes an intelligent terminal monitoring system of distribution network for 5G network. With features such as larger bandwidth, higher transmission rate and lower delay, 5G communication technology can be used to solve problems such as automatic positioning and isolation of distribution network faults and business expansion of high definition video transmission. This paper uses socket technology to realize the communication between the wireless communication module and the data acquisition server, and realizes the data transmission based on IEC 60870-5-101 protocol by balanced transmission, so as to solve the problem that the master station can only request to the substation through polling. © 2020 IEEE.

关键词： 5G mobile communication systems

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Trend-guided Small Hydropower system power Prediction Based on Extreme Learning Machine 4

Trend-guided Small Hydropower System Power Prediction Based ...

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4th International Conference on power and energy Engineering, ICPEE 2020

作者： Lian, Chunjie Wei, Hua Qin, Shengchao Li, Zongsheng Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning China

ISBN: (纸本)9781728191256

A trend-guided extreme learning machine prediction model (TG-ELM) is proposed, which enriches the physical mechanism of the input and output of the traditional extreme learning machine (ELM), and solves the problems that a large amount of raw data in the traditional hydropower prediction model depends on a single data normalization process, which causes the prediction accuracy of the entire prediction model to decrease. The model first smoothed and repaired abnormal output data of the small hydropower group, and then extracted the trend of power change from the processed power curve, and used this trend as a new feature input for the extreme learning machine model to provide a correct and unique prediction trend for the prediction of the extreme learning machine. The model is applied to the power generation forecast of small hydropower groups in Guangxi, which greatly improves the accuracy of the power generation forecast and shortens the calculation time of the forecast. Based on the historical data of the power generation of the small hydropower group in Guangxi in August 2019 for simulation analysis, the root mean square error and average absolute error of the prediction are 4.16 WM and 6.11%, respectively. Compared with prediction methods such as backpropagation neural network and support vector machine, the proposed prediction model has distinct advantages and is more suitable for industrial applications. © 2020 IEEE.

关键词： Forecasting

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Research and Implementation of Dispatcher power Flow Based on Cloud Computing technology 4

Research and Implementation of Dispatcher Power Flow Based o...

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4th International Conference on power and energy Engineering, ICPEE 2020

作者： Lyu, Zhongliang Lian, Chunjie Qin, Shengchao Li, Zongsheng Wei, Hua Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning China

ISBN: (纸本)9781728191256

This paper presents a solution to construct the dispatcher power flow software based on service-oriented architecture (SOA) and cloud computing platform (CS-DPF). Take advantage of the characteristics of SOA and cloud computing technology, the proposed dispatcher power flow service not only runs more reliably but also has the advantages of the cross-platform, light application, and updating and maintaining easily, which solves the problems of the traditional dispatcher power flow software. The proposed approach has been implemented in a real power system. The operation results for more than one year indicate that the proposed approach has a broad application value. © 2020 IEEE.

关键词： Cloud computing

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A Bi-Level Model for DG and ESS integrated planning in distribution system 4

A Bi-Level Model for DG and ESS integrated planning in distr...

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4th IEEE Conference on energy Internet and energy system Integration, EI2 2020

作者： Li, Ruowen Chen, Biyun Bai, Xiaoqing Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology Nanning China

ISBN: (纸本)9781728196060

The output of intermittent distributed generation (DG) is characterized by intermittently and randomness, and its access makes distribution system planning and operation management more difficult. The rapid response characteristic of energy storage system (ESS) can deal with the problems brought by the intermittent DG. A bi-level model for DG and ESS integrated planning is proposed, which optimizes the location and capacity of DG and ESS to achieve minimum investment, power loss, and voltage deviation in the upper level, and optimizes the daily operation strategies of ESS to maximize the daily profit in the lower level. An improved multi-objective particle swarm optimization (PSO) algorithm and YALMIP solver are used to solve the model. Case study on IEEE 33-bus distribution system shows the effectiveness and feasibility of the proposed model. © 2020 IEEE.

关键词： Distributed power generation

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An Empirical Evaluation of Meta Adaptive Residual Network for Classifying Sensor Drift Samples

SSRN

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SSRN 2023年

作者： Zhu, Zhengyang Ling, Haikui Zhang, Yiyi Liu, Jiefeng Shuang, Feng Xu, Min Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China College of Food Science and Bioengineering Xihua University Chengdu China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

With the advancements in materials and pattern recognition algorithms, electronic nose (E-nose) has been continuously developing and finding increasingly widespread applications. However, due to factors including but not limited to variations in temperature and humidity, as well as chemical and physical interactions among analytes and gases, E-nose is prone to drift during practical usage, leading to decreased accuracy of pattern recognition algorithms. But now, this challenge can be overcome with the help of meta-learning. We applied the model-agnostic meta-learning (MAML) architecture from meta-learning and designed a residual classifier with learnable per-parameter learning rates. Leveraging the ability of ‘learning to learn’, our method do not require a large amount of target domain data and there is no need to repeat multiple training iterations when transferring to any other target domain compared to other unsupervised algorithms, resulting it highly deployable. Additionally, we present a Gini value-based approach to enhance the certainty of model output labels in a multi-task setting. Through the evaluation of multiple experiments, we demonstrate the feasibility of our approach and achieve very high classification accuracy on a publicly available long-term drift dataset compared with other methods. © 2023, The Authors. All rights reserved.

关键词： Electronic nose

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