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IEEE International Conference on power and Energy Applications (ICPEA)

作者： Haoda Lei Kuan Cao Runjia Sun Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350356113

ISBN: (纸本)9798350356120

In blackout scenarios of new power systems, wind farms (WF) and photovoltaic power stations (PVS) configured with scaled energy storage system (ESS) can be used as sources to support the black-start of thermal power units. Aiming at the disturbance of system voltage and frequency during black-start, a voltage and frequency control coordination strategy of Wind-PV-ESS system for black-start is proposed. The voltage control strategy includes normal control mode and correction control mode. When the bus voltage keeps within the limit, the former coordinately distributes the reactive power of each WT and PVA to maximize the dynamic reactive power reserve. After the bus voltage is disturbed beyond the limit, the latter makes full use of the fast dynamic reactive power response characteristics of SVG and ESS to provide voltage support. In the frequency control strategy. the reference power generated by the PI controller is allocated according to the proportion of the reserve power of each wind turbine (WT) or photovoltaic array (PVA) to the total reserve to eliminate the frequency deviation. Simulation results show the effectiveness of the proposed strategy.

关键词： Photovoltaic systems Reactive power PI control Simulation power system dynamics Wind farms Wind turbines Voltage control Frequency control Radio spectrum management

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Low-carbon economic dispatch strategy for renewable integrated power system incorporating carbon capture and storage technology

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ENERGY REPORTS 2022年 8卷 251-258页

作者： Yu, Fei Chu, Xiaodong Sun, Donglei Liu, Xiaoming Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan Peoples R China

With the global warming and climate change, the low-carbon power system has attracted increasing attention. A low-carbon optimal dispatch model incorporating carbon capture and storage technology and the uncertainty of wind power is proposed. An accurate calculation of carbon dioxide emission is established, and the carbon tax mechanism is incorporated into the traditional economic dispatch taking into account the economy and low-carbon of power production. The effectiveness of the proposed method is verified by simulation results of modified IEEE 24-bus system. The case study shows that the proposed model can significantly reduce carbon emissions of coal-fired units and encourage coal-fired units to improve their emission performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Carbon tax Economic dispatch Carbon capture and storage (CCS) Carbon emission

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Research on Stepwise Clustering and Fitting Method for Wind power Based on Discrete Wavelet Transform 3

Research on Stepwise Clustering and Fitting Method for Wind ...

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3rd power system and Green Energy Conference, PSGEC 2023

作者： Zhang, Haijing Yu, Hong Wei, Lin Wang, Yuxi Wang, Peng Zhang, Letian Yang, Yang State Grid Shandong Electric Power Company Jinan China State Grid Jinan Power Supply Company Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9798350340099

The participation of new energy reseurces in market trading needs accurate metering of new energy power generation. In order to establish a verification method for wind power generation metering data, a step-by-5tep k-means clustering algorithm based on discrete wavelet transform is proposed to explore the variation laws of wind power fluctuation, so that a basis for missing data fitting and abnormal data correction of wind power generation curves can be built. The frequency domain analysis of wind power generation data is carried out by using discrete wavelet transform, and the generation curve is decomposed into different frequency ranges. For the frequency domain results after multiple wavelets transforms, step-by-tep k-means clustering is carried out from the lowest frequency component to the highest frequency component. This processing method can etfetively ensure that the information of stroke fluctuation is not lost in the clustering process. Typical curves extracted based on clustering are used to fit the missing data. The proposed method is simulated by using the wind power generation data of some wind farms in East China, and the effectiveness of the proposed algorithm is verified. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Short-Term Wind power Forecasting Based on VMD-QPSO-LSTM

Short-Term Wind Power Forecasting Based on VMD-QPSO-LSTM

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power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peiqi Xin Hongtao Wang Haobo Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

The unpredictable and highly variable nature of wind power generation demands advanced predictive models to enhance the operational stability of power systems under non-stationary wind conditions. This study presents an innovative approach for short-term wind power prediction, ingeniously integrating Variational Mode Decomposition (VMD), Quantum Particle Swarm Optimization (QPSO), and Long Short-Term Memory (LSTM) network into a cohesive forecasting methodology. Initially, VMD is applied to deconstruct wind power data into distinct sub-modes, thus reducing data complexity and facilitating more accurate analysis. The LSTM model is then fine-tuned using QPSO to refine its hyperparameters, thereby optimizing forecast performance. Each sub-mode is forecasted through a specialized LSTM model, and the collective forecasts are compounded to form the final prediction. Comparative analyses with conventional LSTM, PSO-enhanced LSTM, and QPSO-LSTM models reveal that our proposed VMD-QPSO-LSTM approach yields significantly higher accuracy in forecasting, highlighting its potential to efficiently navigate the uncertainties associated with wind power generation.

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Virtual Oscillator Grid-Forming Inverter Stability Analysis Based on Impedance Model

Virtual Oscillator Grid-Forming Inverter Stability Analysis ...

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China International Electrical and Energy Conference (CIEEC)

作者： Shuiyuan Guo Hua Ye Jialun Yang Xiaodong Chu Qianying Mou Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Grid-forming (GFM) inverters are DC-AC converters that regulate their AC terminal frequency and voltage in response to real-time measurements. Virtual oscillator control (VOC) is an emerging strategy of grid-forming controllers. Unlike conventional phasor-based control methods such as droop control and virtual synchronous machine control, VOC is a time-domain controller that global convergence can be almost guaranteed by adding a nonlinear element. In this article, the state space model of a VOC-based inverter system connected to an infinite bus is established to access the steady-state values. In order to study the effect of nonlinear terms on the system stability, a detailed impedance model of the VOC controller is proposed. Numerical simulations validate the impedance model. Through the impedance-based method, how the nonlinear term can influence system stability is analyzed.

关键词： Analytical models Voltage measurement Stability criteria power system stability Numerical models Impedance Voltage control

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An Improved Conditional Generative Adversarial Method for Wind Turbine Gearbox Fault Diagnosis With Imbalance Data

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IEEE SENSORS JOURNAL 2024年第23期24卷 38727-38739页

作者： Zeng Xiangjun Xia Lingqin Feng Chen Yang Ming China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydr Yichang 443002 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

The problem of data imbalance is widely present in wind turbine (WT) gearbox fault diagnosis, resulting in significant differences in diagnostic accuracy for majority and minority classes. To address this challenge, an improved conditional generative adversarial network (ICGAN) was proposed to mitigate the impact of imbalanced data on fault diagnosis. The ICGAN uses a 1-D convolutional neural network (CNN) to extract feature vectors rich in detailed information from real samples as conditional signals. This enhances the learning ability of the generator for intrinsic patterns and structures in the data, ensuring that the generated data not only contain the intrinsic features of real samples but also meet specific condition requirements. In addition, considering that Gaussian mixture models (GMMs) can effectively capture the multimodal characteristics of signals, ICGAN uses the signals synthesized by GMM as the generator inputs, enabling the generator to cover more sample space and increase the diversity of generated samples. Moreover, the GMM can adaptively adjust parameters according to the feedback of the discriminator to improve the adaptability of the generator to the data, thereby enhancing the generalization performance of the ICGAN. The Wasserstein distance and the Bayesian optimization algorithm are also used to improve the stability and efficiency of model training. The experiments show that the data generated by ICGAN are more similar to real samples compared to other generative adversarial network (GAN)-based models. Using the vibration data generated by ICGAN for multiclass fault diagnosis of real WT gearbox, the average diagnostic accuracy can reach 94%, with the accuracy for minority classes also reaching 92%.

关键词： Data imbalance fault diagnosis Gaussian mixture model (GMM) improved conditional generative adversarial network (ICGAN) wind turbine (WT) gearbox

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Current Differential Protection for Active Distribution Networks Based on Improved Fault Data Self-Synchronization Method

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 166-178页

作者： Zhou, Chenghan Zou, Guibin Zang, Lindong Du, Xiaogong Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Xunfengdianzi Co Ltd Design Inst Jinan 250200 Peoples R China

The uncertainty in the direction and magnitude of fault current is a thorny issue for active distribution network (ADN) protection. Current differential protection (CDP) is suitable for ADN with complex operating modes and multiple types of DGs. However, the data synchronization conditions in transmission lines are usually not available in distribution systems with relatively low investment levels. Fault data self-synchronization (FDSS) algorithm is a low-cost data synchronization method specially used for the protection of distribution networks, but there will be large time synchronization errors under some extreme fault conditions. In this paper, the inherent defect of the conventional FDSS algorithm is first analyzed. Then an improved FDSS algorithm is proposed, which uses the current zero-crossing time and current slope polarity to estimate the starting delay difference at both terminals. On this basis, a corresponding CDP scheme is proposed. Compared with traditional CDP, the proposed scheme does not require additional synchronization equipment and high communication bandwidth. Finally, an ADN simulation model was built using PSCAD, and the improved FDSS algorithm program was implemented in MATlab. Test results prove that the improved FDSS algorithm can effectively reduce the time synchronization error, and the corresponding CDP scheme is effective under various fault conditions.

关键词： Relays Synchronization Current measurement Delays Fault currents Global Positioning system Distribution networks Active distribution network current differential protection distributed generation fault data self-synchronization slope polarity zero-crossing-point

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Online Monitoring Method for Transformer Winding Deformation Based on Leakage Magnetic Induction Coefficient Identification

Online Monitoring Method for Transformer Winding Deformation...

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

作者： Mengfei Wu Guoxin Lu Shiming Liu Yongqi Kang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

In response to the shortcomings of existing transformer winding deformation detection methods, this paper proposes an online monitoring scheme for winding deformation based on changes in leakage magnetic induction coefficients. Using the Roth method, an analytical calculation model for leakage magnetic fields under normal and deformed conditions of the windings is established, which is then validated against finite element simulations, with an error margin of less than 2%. The Recursive Least Squares algorithm is employed to extract the leakage magnetic induction coefficients in real time, which are then compared with the coefficients obtained from the analytical model to derive characteristic parameters that represent the health status of the windings. Corresponding threshold calibration methods are established, resulting in a high-sensitivity and high-reliability online monitoring scheme capable of accurately identifying various winding deformations of 5% or more, as well as determining the location of the deformed windings. Finally, finite element simulation models for both normal and deformed transformer windings are constructed in ANSYS-Maxwell, confirming the effectiveness of the online monitoring scheme.

关键词： Deformable models Analytical models Parameter estimation Deformation Windings system integration Real-time systems Finite element analysis Magnetic fields Monitoring

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Current Reference control Scheme of Voltage Source Converters to Ensure the Existence of Stable Equilibrium Points During Grid Fault

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IEEE TRANSACTIONS ON power ELECTRONICS 2023年第9期38卷 10750-10765页

作者： Wang, Zhihao Zhu, Guofang Wu, Heng Gao, Xuesong Ding, Lei Wang, Xiongfei Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Aalborg Univ AAU Energy DK-9220 Aalborg Denmark KTH Royal Inst Technol S-10044 Stockholm Sweden

Maintaining synchronization with the grid during fault ride-through (FRT) is essential for grid-connected voltage source converters (VSCs). However, improper current references can lead to a loss of synchronization during FRT due to the nonexistence of stable equilibrium points (SEPs). While the mechanism of SEPs has been explained, the development of control schemes ensuring their existence remains challenging due to the difficulty of obtaining real-time grid parameters during FRT. To address this issue, this article proposes a novel current reference control scheme that only requires prefault grid parameters. By analyzing the changes in grid parameters caused by faults and characterizing the correspondence between the during-fault and prefault grid parameters, a voltage-dependent current reference control scheme is proposed. Since no real-time grid parameter estimation is needed during FRT, the proposed control scheme is practical and easy to implement. Simulation studies using DIgSILENT powerFactory demonstrate that the proposed control scheme enables the VSC to maintain synchronization with the grid under the conditions of short-circuit ratio equaling 1.3 and grid voltage dropping to 0.1 p.u. Moreover, experiments further verify the effectiveness and feasibility of the proposed control scheme.

关键词： Fault ride-through (FRT) loss of synchronization (LOS) stable equilibrium point (SEP) voltage source converter (VSC)

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Soft Fault Location and Imaging Using Residual Voltage Inversion in Cable Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Chuanxu Guan, Quansheng Guan, Quanxue Jin, Xin Shi, Zhan South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Sun Yat sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China China Southern PowerGrid Co Ltd Elect Power Res Inst Guangzhou 510700 Peoples R China Guangdong Prov Key Lab Intelligent Measurement & A Guangzhou 510700 Peoples R China Guangdong Power Grid Co LTD Elect Power Dispatch & Control Ctr Guangzhou 510030 Peoples R China

Locations and parameters monitoring for soft faults in a cable network is of importance to prevent hard faults at an early stage and maintain the stability of the power system. The existing fault detection methods often identify faults using the fixed parameters in a reference cable model to locate the faults. However, the changes of the reference model by faults bring model mismatch, for example, the signal propagation speed is different in the line-like soft faults. The model mismatch will lead to inaccurate fault location and not to mention parameter imaging for faults. To this end, this article proposes a residual voltage inversion (RVI) method to learn the model of the cable network with unknown faults. RVI uses the residual voltages, that is, the difference between the scattering voltages measured at the ports and those generated by the current model, as the gradient to update the multiple parameter distributions of the cable network iteratively. The learned model can then be used to calculate the precise location, imaging of the length, capacitance, and resistance for line-like soft faults. The simulation results show that RVI locates the range of line-like soft faults with an accuracy over 90%, and achieves insulation layer and core conductor imaging with accuracies larger than 95% and 80%, respectively. In addition, the experimental tests are carried out to verify the feasibility and performance of RVI.

关键词： power cables Circuit faults power cable insulation Resistance Capacitance Reflectometry Voltage measurement Scattering Conductors Junctions Cable networks capacitance and resistance imaging residual voltage inversion (RVI) soft fault location

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