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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Li Li Qirong Lin Hanbing Qu Bo Liu Yuanzhen Zhu Changgang Li Yutian Lui State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143040

With the development of HVDC transmission and changes of load composition and characteristics, the short-term voltage problem seriously threatens the safety and stable operation of power systems. A short-term voltage stability assessment method based on deep learning neural network for AC/DC receiving-end power grid is proposed in this paper. The stacked denoising autoencoder (SDAE) and adaptive moment estimation (ADAM) algorithms are used to build a rapid evaluation model, and the training sample set is used to train the transient voltage stability rapid evaluation model. The steady-state power flow features are used as inputs and an index of quantifying short-term voltage stability of commutation bus is utilized as the output. simulation results of real multi-infeed AC/DC power grid demonstrate the effectiveness of the proposed method.

关键词： power system stability power grids Transient analysis Stability criteria Training

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Preventive control optimization of Commutation Failure Based

Preventive Control optimization of Commutation Failure Based

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Li Li Qirong Lin Hanbing Qu Bo Liu Yuanzhen Zhu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143040

In the hybrid AC/DC systems, AC system failures may cause the commutation failure of DC system, which will lead to DC blocking and affect the safety and stability of the system. This paper proposes a reactive power optimization method to reduce the risk of system commutation failure. This method aims at minimizing the amount of reactive power control and the number of failure nodes that trigger commutation failure, which takes into account steady-state safety constraints and optimizes generator reactive power output and reactive power compensation capacity. Aiming at this multi-objective optimization problem, an improved particle swarm optimization (PSO) algorithm with parameter adaptation is proposed. When calculating the particle fitness value, a surrogate model based on the support vector machine (SVM) to evaluate the commutation failure is used to replace the time-domain simulation to improve the particle fitness value calculation speed. An example of a provincial power grid is used to verify the effectiveness and rapidity of the proposed optimization method.

关键词： Optimization Reactive power Commutation Support vector machines Generators Reactive power control Particle swarm optimization

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

作者： Jiaqi Qu Qiang Sun Zheng Qian Lu Wei Hamidreza Zareipour School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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Energy Storage Sizing Based on Asymmetric Copula for Wind Farm to be Black-Start Source

Energy Storage Sizing Based on Asymmetric Copula for Wind Fa...

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Sustainable power and Energy Conference (iSPEC)

作者： Weipeng Liu Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

In local power grid with high wind power penetration, using wind farm (WF) with energy storage system (ESS) as black-start (BS) source will significantly accelerate the restoration after blackout. For enabling WF to be a reliable BS source, an ESS sizing method based on asymmetric copula function is proposed, aiming mainly at smoothing WF output power during the process of starting up generator unit. To model the asymmetric correlation between ESS power sizing and capacity sizing, the joint probability distribution is built by asymmetric copula function. The most suitable copula function is determined by maximum likelihood estimation and a goodness of fit test. Considering sample variation, the parametric bootstrap method is applied for the confidence intervals of dependence parameter estimation. A case study with real WF data demonstrates that the proposed asymmetric copula function is more accurate and ESS sizing cost is lower consequently.

关键词： Maximum likelihood estimation power smoothing Wind farms Wind power generation Probability distribution Generators Energy storage

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Performance evaluation of CFB unit combustion system based on comprehensive economics and entropy weight method

Performance evaluation of CFB unit combustion system based o...

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2019 International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2019

作者： Qin, Ce Gao, Mingming Zhang, Hongfu North China Electric Power University State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781728101996

performance evaluation method for the combustion system in the variable load process of circulating fluidized bed (CFB) unit based on the minimum variance control is proposed. Each operating parameter are initially weighted according to the dynamic economic index of the operating parameters, and entropy weight method is used to adjust the weights of operating parameter under different operating conditions. Then, the minimum variance index based on comprehensive economic and entropy weight method is proposed to evaluate the performance of the combustion system of CFB unit, which reduces the polarization effect generated by the single weighting performance evaluation method and improves the objective scientificality of the operating parameter weighting. Finally, a case study on the combustion system of a 300MW CFB unit reveals that the minimum variance index based on the comprehensive economy and entropy weight method can better evaluate the performance of the combustion system in the dynamic process of the unit, compared with the traditional performance evaluation method. It is of great significance to the operational safety and economy of CFB boiler units. © 2019 IEEE.

关键词： Economics

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A Resilience Enhancement Scheme of Cyber-Physical power system for Extreme Natural Disasters

A Resilience Enhancement Scheme of Cyber-Physical Power Syst...

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Sustainable power and Energy Conference (iSPEC)

作者： Tianhao Liu Runjia Sun Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

Extreme disasters seriously threaten power system security and stable operation. power system resilience is an important means to evaluate the system response to low probability and high impact events. This paper proposes a resilience enhancement strategy based on cyber physical power system to cope with extreme natural disasters and restore the power system. At first, a defense framework for natural disaster is given to show stages and techniques used in resilience enhancement strategy. Secondly, a zone division based power flow adjustment method is proposed to adjust the transmission power with high failure probability, which can reduce the risk of cascading failures. Finally, two resilience evaluation functions considering load restoration and equipment restoration are proposed respectively to guild collaborative communication lines and transmission lines restoration. simulation results of the New England 39-bus test system demonstrate the effectiveness.

关键词： power transmission lines simulation power system protection Collaboration power system stability power system faults Resilience

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Research on the Substitution Ability of Battery Energy Storage system for Thermal power Units in Frequency Regulation

Research on the Substitution Ability of Battery Energy Stora...

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Sustainable power and Energy Conference (iSPEC)

作者： Huan Zhu Kaifeng Wang Ying Qiao Lirong Xie Guojing Liu State Grid Jiangsu Electric Power Company Economic Research Institute Nanjing China Xinjiang University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering (Tsinghua University) Urumqi and Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering Tsinghua University Beijing China Xinjiang University Urumqi China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

When penetration levels of renewable energy of power system are increased, the proportion of thermal power units will gradually decrease. This will cause the frequency regulation capacity of the power system to gradually decline. To this end, battery energy storage systems are widely used to frequency regulation in the power system. However, the frequency regulation ability of battery energy storage system to substitute thermal power units has not yet been quantified. In this paper, it introduces two frequency regulation method models. In the proposed models, the influence of different energy storage participation degrees on the effect of frequency regulation is analyzed, and a power and energy indicators system for quantifying energy storage substitution ability is proposed based on the advantages of frequency regulation with energy storage. The simulation results indicate that the ability of energy storage to substitute thermal power units is about 20-35 in terms of rapid energy supply when the installed capacity is same.

关键词： Renewable energy sources simulation power system stability Stability analysis Batteries Frequency control Thermal stability

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Wide Frequency Range Suppression of Subsynchronous Oscillation for DFIG Wind Farm Based on Optimal control

Wide Frequency Range Suppression of Subsynchronous Oscillati...

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

作者： Xingyuan Li Peng Zhang Chenyang Han State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

The subsynchronous oscillation of wind turbines is affected by many factors in the working environment and conditions and the oscillation frequency has the characteristic of broadband oscillation, which puts forward new requirements for the design of suppressing the oscillation. This paper designs a suppression method of wide frequency range for subsynchronous oscillation of doubly-fed induction generator(DFIG) wind farm based on optimal control. First, the small signal linearized model of the DFIG is established in this paper. Then based on the quadratic index in the optimal control theory, the optimal time control theory is used to determine the weight coefficient of the control variables and the state variables of the quadratic index, then the optimal feedback controller is designed. Finally the simulation shows that the feedback controller designed in this paper has a good suppression effect on the broadband subsynchronous oscillation generated by the DFIG under complicated working conditions. For a more complicated multi-machine system, the controller also has a significant suppression effect on the subsynchronous oscillation.

关键词： Doubly fed induction generators Optimal control Broadband communication Indexes Adaptive control Oscillators Frequency control

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Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

Cogging Force Identification Based on Self-Adaptive Hybrid S...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks

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Method for Single-phase Electric Meter Phase Discrimination Based on Multiple Linear Regression 8

Method for Single-phase Electric Meter Phase Discrimination ...

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8th IEEE International Conference on Advanced power system Automation and Protection, APAP 2019

作者： Zhang, Liqiang Cong, Wei Dong, Gang Sun, Yun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781728117225

The phase information of single-phase electric meter is crucial to improve the level of load management and the quality of modeling. A method for single-phase electric meter phase discrimination based on multiple linear regression (MLR) is proposed. The MLR equation takes the single-phase electric meter voltage as the dependent variable. Meanwhile, it takes the gateway meter voltage, the gateway meter current and the single-phase electric meter current as the independent variables. Each single-phase meter forms a regression equation with the gateway meter A, B and C respectively. The determination coefficients of the three regression equations are calculated. According to the value of the coefficients, the phase of single-phase electric meter is discriminated. An example is given to analyze the proposed method with actual energy meter reading data. The analysis results show that the proposed method has higher accuracy and reliability. © 2019 IEEE.

关键词： Multiple linear regression

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