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Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization

IEEE Transactions on Smart Grid 2025年第3期16卷 2695-2698页

作者： Lyu, Ruike Guo, Hongye Strbac, Goran Kang, Chongqing Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom

The intricate mixed-integer constraints in industrial load models not only pose challenges for their direct integration into economic dispatch or market clearing processes but also render current analytical dimension-reduction methods ineffective. We propose a novel data-driven dimension-reduction approach for industrial load modeling, which uses the optimal energy usage data from industrial loads to train a dimension-reduced model that best fits the original constraints. Our approach, implemented by the adjustable load fleet model, outperformed analytical methods across three industrial load datasets. © 2010-2012 IEEE.

关键词： Load modeling Energy consumption Training Dimensionality reduction Biological system modeling Optimization Computational modeling Electricity Steel Cement industry

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Energy-carbon pricing-guided collaborative optimization for local integrated energy communities with multi-vector electrification consumers

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Applied Energy 2025年 392卷

作者： Xi, Yufei Zuo, Lujie Chen, Meng Zhang, Jiansheng Cheng, Lin Lestas, Ioannis State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China Department of Energy and Power Engineering Tsinghua University Beijing100084 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

The integration of multi-energy services and CO2 reduction measures is crucial for advancing the transition to low-carbon energy. This study proposes a novel Integrated Energy Community (IEC) framework that synergizes Electric Vehicles (EVs), power-to-Chemical (P2C), and power-to-Heat (P2H) electrification consumers to optimize renewable energy utilization and carbon management. The framework incorporates Electrochemical CO2 Reduction (ECO2R) and Carbon Capture and Storage (CCS) technologies, enabling carbon trading and chemical product commercialization. Meanwhile, an energy-carbon pricing-guided collaborative optimization model is developed, utilizing Nash bargaining game theory to coordinate electricity, gas, and heat operations under limited communication. Case studies validate the model's effectiveness, with numerical results demonstrating its superior performance in operational efficiency, renewable energy utilization, and carbon reduction compared to conventional community systems. © 2025 The Author(s)

关键词： Energy utilization

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LCL Resonance Analysis and Damping in Single-Loop Grid-Forming Wind Turbines

arXiv

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arXiv 2025年

作者： Chen, Meng Xi, Yufei Blaabjerg, Frede Cheng, Lin Lestas, Ioannis Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China AAU Energy Aalborg University Aalborg9220 Denmark

A dynamic phenomenon known as LCL resonance is often neglected when stability analysis is carried out for grid-forming (GFM) control schemes by wind turbine systems, due to its high frequency. This paper shows that this simplification is not always valid for single-loop (SL) control schemes. A detailed small-signal analysis reveals that reactive power (RAP) control significantly influences the resonant modes, which may be dominant in determining overall system stability, even if the resonant frequency is high. The underlying mechanism via which the LCL resonance may dominate the overall system stability is systematically analyzed. Furthermore, various RAP control strategies are compared to assess their different effects on resonant modes. An active damping (AD) strategy favorable for SL-GFM control is then designed. We also provide a comparison between SL-GFM and well-studied grid-following control schemes, highlighting quite different resonance features between them. Finally, case studies associated with a 14-bus, 5-machine IEEE test system are presented. These show that instability originates from the LCL resonance rather than low-frequency interactions among multiple machines, validating the theoretical analysis and the proposed AD strategy. © 2025, CC BY-NC-ND.

关键词： Wind turbines

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Application of Polarization and Depolarization Current Measurement for Rotating Machine Insulation Analysis 3

Application of Polarization and Depolarization Current Measu...

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3rd International Conference on High Voltage Engineering and power systems, ICHVEPS 2021

作者： Pattanadech, N. Nimsanong, P. Worthong, T. King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering School of Engineering Bangkok Thailand Metropolitan Electricity Authority Power System Operation and Control Section 2 Power System Control Dept. Bangkok Thailand Global Power Synergy Public Co. Ltd. Maintenance Electrical Section Manager Maintenance Electrical Phase 3-5 Coal Fired Units Rayong Thailand

ISBN: (纸本)9781665443548

Rotating machine is a vital asset of the power plants. For the motors and generators in service, the stator winding insulation is generally operated under thermal, electrical, ambient, and mechanical stresses. The combination of these stresses causes insulation degradation that can lead to early aging or severe damage and, finally, the failure of such machines. The failure of rotating machines may result in immense financial loss and other aspects. Polarization and depolarization current (PDC) measurement has been introduced as one of the time-domain dielectric spectroscopy test techniques applied for insulation condition testing. This paper represents the application of PDC measurement to evaluate the insulation for the stator bars/coil insulation experienced with various kinds of stresses. Moreover, the PDC field test of generators and high voltage motors used in the power plants is introduced. From the experiments, the dielectric parameters obtained from PDC measurements are clearly useful parameters to evaluate the electrical insulation of such rotating machines. © 2021 IEEE.

关键词： Time domain analysis

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Online PD Measurement by Detecting the Pulsed Compensating Current in High Voltage Equipment 8

Online PD Measurement by Detecting the Pulsed Compensating C...

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8th International Conference on Engineering, Applied Sciences, and Technology, ICEAST 2022

作者： Srinangyam, Chissanupong Pannil, Pittaya Nimsanong, Phethai Chuayin, Chaitawat Thungsook, Kittisak Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand Power System Operation and Control Section 2 Power System Control Dept. Metropolitan Electricity Authority Bangkok Thailand School of Engineering Rajamangala University of Technology Rattanakosin Electrical Engineering Dept. Bangkok Thailand

ISBN: (数字)9781665459198

ISBN: (纸本)9781665459198

This paper discusses the pulsed compensating current caused by partial discharge (PD) phenomena that flows inside the electrical equipment installed in the power system. Such electrical equipment like to be the capacitive coupler in the partial discharge test circuit. In order to study the pulsed compensating current of the PD measurement, artificial PD source such as corona, surface, and internal was used in this experiment. The electrical equipment like surge arrester combined with HFCT sensor utilized to detect the PD pulse current. Furthermore, this proposed technique has been applied for online PD measurement in substations. It was found that the surge arrester could compensate for the pulse current arising from the PD source. This study will be helpful information for online PD measurement. © 2022 IEEE.

关键词： Partial discharges

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Partial discharge classification using principal component analysis combined with self-organizing map

Partial discharge classification using principal component a...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section Power System Control Dept Metropolitan Electricity Authority Bangkok Thailand

This document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with self-organizing map (SOM) for a classification purpose, so called, PCA-SOM model compared with SOM model to classify partial discharge pattern (PD) into four categories listed as corona at high voltage side, corona at low voltage side, surface discharge, and internal discharge. PD signals were investigated by using ultra high frequency (UHF) measurement technique. 12 independent parameters, skewness and kurtosis of each period of the measured electromagnetic signal, were calculated. 80 experiments in total were performed. PCA-SOM PD classification model was constructed. Then, 60% of the experimented data was used as a training data for the PD classification model. Another 40% experimented data was utilized to evaluate the performance of the designed PD classification model. Besides, noise signals were generated with a computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed SOM model and PCA-SOM model can predict PD patterns without noise signal with the accuracy 100% of classification. The prediction ability of SOM for PD classification models decreased sharply when this model was tested by the mixed-PD signals with the noise level of 30% of the peak value of the PD signal. Whereas the PCA-SOM provided some degree accuracy reducing for PD pattern classification when it was verified with such data.

关键词： Partial discharges Noise Discharges (electric) Corona Mathematical model Data models Vectors

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Partial discharge classification using learning vector quantization network model

Partial discharge classification using learning vector quant...

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IEEE Region 10 International Conference TENCON

This paper represents a partial discharge (PD) classification technique by using learning vector quantization (LVQ) network model. LVQ model was not only implemented with the PD test data but also combined with the principal component analysis (PCA), so called, PCA-LVQ for a data reduction. In this research work, both LVQ model and PCA-LVQ model were investigated. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models both LVQ and PCA-LVQ models. Besides, noise signals were generated with computer program for testing the efficiency of these models. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed LVQ model can predict PD patterns without noise signal with the accuracy 100% whereas the PCA-LVQ model provided more than 87.5% accuracy of PD classification. The prediction ability of LVQ PD classification models decreased sharply especially for CHV when this model was tested by the mixed noise-PD signals. Whereas the prediction accuracy of PCA-LVQ model was more robust to the noise signals than LVQ model.

关键词： Partial discharges Mathematical model Noise Discharges (electric) Predictive models Data models Corona

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The Bandwidth Verification of VHF Antenna and Apply for Partial Discharge Measurement

The Bandwidth Verification of VHF Antenna and Apply for Part...

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2022 9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Kittisak Thungsook Norasage Pattanadech Phethai Nimsanong Chissanupong Srinangyam Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Control Dept. Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

ISBN: (数字)9784886864314

ISBN: (纸本)9781665470155

This research explains the bandwidth verification and application of the VHF antenna for partial discharge measurement. Three different VHF antennas were used for the experiment. In the research, the measuring bandwidth of such antennas was evaluated using a network analyzer. The antenna properties such as return loss and voltage standing wave ratio were analyzed. Then, the partial discharge in the air was simulated using the surface discharge model. The physical phenomena of PD occur like pulse current transients and electromagnetic waves. The pulse current transients were detected using a coupling capacitor and HFCT sensor, while the electromagnetic wave was detected using a VHF antenna. five test positions of the VHF antenna were designated for partial discharge measurements namely 1, 2, 3, 4, and 5 meters away from the partial discharge source. The PRPD pattern, PD magnitude, and waveform obtained from the VHF antenna were compared with coupling capacitors and HFCT sensors and presented in this paper. It is found that the VHF antenna, which has higher return loss and lower voltage standing wave ratio, provides high performance to detect the electromagnetic wave of partial discharge.

关键词： Antenna measurements Partial discharge measurement Partial discharges Couplings Surface waves Electromagnetic scattering Capacitors

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Effect of training methods on the accuracy of PCA-KNN partial discharge classification model

Effect of training methods on the accuracy of PCA-KNN partia...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.

关键词： Partial discharges Training Noise Accuracy Discharges (electric) Training data Testing

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Effect of noise signals on partial discharge classification models

Effect of noise signals on partial discharge classification ...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

This document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated and measured in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF, and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of experimented data was used as a training data for the PD classification model. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. Besides, noise signals were generated with computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed KNN, PNN, PCA-KNN and PCA - PNN model can predict PD patterns without noise signal with the accuracy 100%. Noise signals with amplitude of 20% or more of peak value of PD signal have obviously influence the accuracy of PD pattern classification. The combination of PCA with KNN model can improve the ability of PD classification compared with KNN PD classification model. However, it seems that PCA provided negative effect when PCA was c

关键词： Partial discharges Noise Principal component analysis Discharges (electric) Atmospheric modeling Data models Training

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