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A review of bearing fault diagnosis for wind turbines

IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Bing Liu Baixin Liu Qingbin Dai Huaping Liu AnHui JEP New Energy Co LTD AnHui 230000 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin 132012 China

Bearings are the core components of the wind turbine's mechanical transmission system, but the bearings of wind turbines are prone to wear and failure during vibration, and it is often difficult to determine the fault-bearing status and fault location. Therefore, the monitoring and fault diagnosis of wind turbine bearing status are very necessary. This paper introduces the types of bearings of wind turbines and common faults of bearings, analyzes from the aspects of bearing vibration data and supervisory control and data acquisition (SCADA) systems, summarizes the existing fault diagnosis methods and systems of wind turbine bearings, and points out the basics of these methods thought. Finally, the research focus of future bearing fault diagnosis is elaborated.

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Ultra-Short-Term Prediction of Wind power Based on Chaos Theory and ABC Optimized RBF Neural Network

Ultra-Short-Term Prediction of Wind Power Based on Chaos The...

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China International Electrical and energy Conference (CIEEC)

作者： Yang Cui Shi Yan Huiquan Zhang Siyu Huang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Ji Lin China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

Wind energy has strong randomness and volatility, which makes the prediction accuracy of wind power low. In an attempt to improve the accuracy and robustness of ultra-short term prediction of wind power, a wind power prediction method in ultra-short term combining chaos theory and artificial bee colony(ABC) algorithm to optimize RBF neural network is studied in this paper. Firstly, based on chaos theory, the historical wind power data with the highest correlation is found, which is used for RBF neural network training after phase space reconstruction. Then, the artificial bee colony algorithm is used to solve the optimal RBF neural network parameters through multiple iterations to construct the optimized neural network model. Finally, considering the uncertainty of the prediction model and data noise, the prediction results are interval, and the robustness of the prediction results is evaluated by calculating the confidence of the corresponding interval. The results of the example show that the method proposed in this paper has good accuracy and robustness.

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power grid frequency regulation participated by photovoltaic generation to adapt to different control modes of grid-side AGC

Power grid frequency regulation participated by photovoltaic...

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8th renewable power Generation Conference (RPG 2019)

作者： Gangui Yan Wenbo Hu Qi Jia Dan Wang Guoqing Zhou Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

Large-scale grid-connection of photovoltaic (PV) generation causes the power system to face the problem of insufficient inertia and insufficient frequency modulation capability. One of the important means to solve this problem is to take part in grid frequency regulation actively. On the basis of spare certain active power, this paper puts forward the active power-frequency droop control strategy. By modifying the original control structure of the inverter, PV generation actively takes part in the grid frequency primary regulation. In order to achieve non-error adjustment of frequency, this paper further proposes a secondary frequency regulation control strategy that adapts to different grid-side AGC control modes (constant frequency control mode, constant tie line power control mode and tie line power frequency deviation control mode). The effectiveness and feasibility of the control strategies are verified based on the PSCAD/EMTDC simulation platform.

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A novel frequency regulation control strategy based on the external characteristics of PV generation

A novel frequency regulation control strategy based on the e...

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8th renewable power Generation Conference (RPG 2019)

作者： Gangui Yan Dan Wang Qi Jia Wenbo Hu Guoqing Zhou Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

Large-scale grid-connected PV generation results in the power system facing with the problem of reduced inertia and inadequate frequency regulation capability, which is urgent for PV generation to take participate in frequency regulation. In this paper, the research object is the double-stage PV generation, a novel frequency regulation control strategy based on the external characteristics of PV generation is proposed. f-U PV compensation is introduced into DC low-voltage control loop, which makes the DC low-voltage vary with the grid frequency, thus controlling the output power of PV generation. The influence mechanism of the control strategy on the frequency dynamic process of power system is also analysed based on the frequency response model (SFR). The results show that: the larger the ΔU PV generated by the f-U PV compensation unit, the more power of PV generation can be generated; the control strategy essentially affects the damping coefficient of the system, which not only affects the transient process of the system frequency, but also the steady-state frequency deviation.

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Research on Aggregation Model of Wind Farm Based on SSO Complex Torque Coefficient Analysis

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Journal of Physics: Conference Series 2021年第1期1754卷

作者： SU Xunwen CUI Hanqing PEI Yuming ZHANG Dongni AN Pengyu Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China School of Electrical & Control Engineering Heilongjiang University of Science & Technology Harbin Heilongjiang China

In order to study the influence of the clustering aggregation of doubly-fed wind turbines (DFIG) in the wind-fire bundling system on the system subsynchronous oscillation (SSO), the expression of the DFIG output active power on the electrical damping De of thermal power units is first derived. Then the wind-thermal bundling system is built based on the IEEE first bench mark, and the wind farm is aggregated to several groups according to the rated power. The complex torque coefficient method is used to analyze the two cases of the same running state of the wind turbine and the difference of wind speed between the turbine and verified by time-domain simulation. The results show that the difference in wind speed within the DFIG groups has little effect on the system oscillation frequency before and after the aggregation of the wind farm, but there may be a slight error in the electrical damping coefficient before and after the aggregation,but the error does not affect the trend and analysis of damping characteristics of system. When studying the influencing factors of wind-fire bundling system SSOthe aggregated model can be uesd to improve simulation efficiency.

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XLPE cable health assessment based on Relief-F feature weighted FSVM

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IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Xiaohuan Wu Yang Liu Liqun Wang Xinzhuo Ren Xianjun Tan Hangzhou Power Equipment Manufacturing Co. Ltd. Hangzhou 310018 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education(Northeast Electric Power University) Jilin 132012 China

For the cross-linked polyethylene (XLPE) cable health status assessment process, there are noise data for each feature parameter and the importance of each dimension feature is difficult to determine, which leads to deviations in the cable status assessment results. An XLPE cable health assessment algorithm based on Relief-F feature weighted fuzzy support vector machine is proposed. The Relief-F algorithm is used to calculate the importance of each dimension feature. The feature quantity that is weakly related to the evaluation of the health status of the XLPE cable is deleted. Furthermore, the fuzzy support vector machine is used to evaluate the health status of the XLPE cable, which gives the noise data a smaller degree of membership and reduces the influence of the noise data on the evaluation results. Experimental analysis shows that this method can accurately assess the health status of XLPE cables. It can provide a reference value for relevant departments to carry out cable maintenance and replacement work and save the human resources, and material resources required for maintenance and replacement of cables.

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Prediction of dissolved gas in power transformer oil based on LSTM-GA

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IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Xin Zhang Shengyuan Wang Yijun Jiang Feifei Wu Chenming Sun Hangzhou Power Equipment Manufacturing Co. Ltd. Hangzhou 310018 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education(Northeast Electric Power University) Jilin 132012 China

Because the change process of the content of dissolved gas in transformer oil is fluctuating and is affected by many factors such as oil temperature and external environment, the gas content does not increase exponentially and is nonlinear and non-stationary. This feature determines the current Prediction technology cannot accurately predict the concentration of dissolved gas in transformer oil. To improve the prediction accuracy of the dissolved gas content in the transformer oil, more accurately evaluate the transformer status and customize the entire preventive plan under the alarming development trend of the transformer, to minimize the prediction error, this paper proposes a transformer combining genetic algorithm and long short-term memory (LSTM)neural network Prediction model of dissolved gas content in oil. The genetic algorithm (GA) is used to optimize look back(lb), lstm nets(ls), epochs(ep), and the dropout(dp), and then the genetic algorithm is combined with the long short-term memory neural network. The gas content is predicted. This model overcomes the problem of low prediction accuracy caused by selecting parameters based on experience. The analysis result of the calculation example shows that compared with the traditional prediction algorithm, the proposed method can better track the change law of the dissolved gas concentration in the oil, improve the prediction accuracy, and provide a strong guarantee for the safe and stable operation of power transformers.

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Dynamic Security Assessment Method of power system Based on Improved XGBoost

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Journal of Physics: Conference Series 2021年第1期1750卷

作者： Jihong Ren Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin Jilin 132012 China

With the deployment of wide-area measurement systems (WAMS) equipment, dynamic security assessment (DSA) based on artificial intelligence (AI) has more and more advantages. For the power system, the number of transient stable cases is much larger than that of unstable cases, which leads to insufficient learning of unstable cases and affects the evaluation effect of the model. In addition, during the stability assessment process of the power system, the misjudgment cost of unstable case is much higher than that of stable case. Based on the above two points, we proposed a DSA method of power system based on improved XGBoost. The proposed method introduces the weight adjustment factor into the loss function to balance the quantity variance of two type cases and the difference of misjudgment cost, which greatly reduces the misjudgment rate of unstable samples. The test results on IEEE 16-machine 68-bus test system and IEEE 50-machine 145-bus test system show that the proposed method can have higher recognition of the unstable samples and can be applied to DSA.

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Short-term Optimized Operation of Multi-energy power system Based on Complementary Characteristics of power Sources

Short-term Optimized Operation of Multi-energy Power System ...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Xiao, Bai Zheng, Jia Yan, Gangui Shi, Yonggang Jiao, Mingxi Wang, Yao Dong, Ling Wang, Maochun Yang, Hongzhi Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Tonghua Power Supply Company State Grid Jilin Electric Power Co. Ltd. Tonghua China Changchun Power Supply Company State Grid Jilin Electric Power Co. Ltd. Changchun China State Grid Qinghai Electric Power Company Xining China

ISBN: (纸本)9781728135205

Aiming at the problem that uncertain power sources bring fluctuation to power system and cause lots of wind and photovoltaic curtailment, a short-term optimized operation method of multi-energy power system based on complementary characteristics of power sources is presented. The complementary mechanism of multi-energy power system is established. Based on the complementary mechanism, mathematical models of complementary objectives and complementary demands are established. Define renewable energy complementary power source. To minimize complementary demands, the capacity matching ratio of hydropower in renewable energy complementary power source is determined. According to the complementary objectives, a hierarchical optimal operation strategy for multi-energy power system is proposed. Particle swarm optimization is used to solve the optimization objective function. Taking a provincial power grid in northwest China as an example, the results show that the strategy makes full use of the complementarity of power sources and has the benefit of energy saving. © 2019 IEEE.

关键词： energy conservation

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Improved active damping control strategy and closed-loop parameter design for LCL grid-connected inverter

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Journal of Physics: Conference Series 2021年第1期2030卷

作者： Qi Chen Hongbo Liu Qingxiang Hao Yikun Yin Yuanhong Li Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China Zaozhuang Power Supply Company State Grid Shandong Electric Power Company Zaozhuang 277000 China Changchun Institute of Technology ChangChun 130012 China

In this paper, an active damping control strategy based on capacitance and voltage feedback is proposed to solve the resonance problem of three-phase LCL inverter grid-connected system. To solve the problem that the parameters of active damping control strategy are complicated and need to be adjusted repeatedly, a parameter design method is proposed in this paper, which is simple to implement and does not need to be adjusted repeatedly. Finally, the effectiveness of the control strategy and parameter design method is verified by experiments.

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