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Ultra-Short-Term Wind power Interval Prediction Based on Fluctuating Process Partitioning and Quantile Regression Forest

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Sun, Yong Huang, Yutong Yang, Mao Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Jilin Electric Power Company Ltd. Changchun China

As errors in point forecasts of wind power are unavoidable, interval forecasts can adequately describe the uncertainty in wind power and thus provide further guidance to dispatchers in their decision making. Current interval prediction methods are still incomplete in terms of tapping into the physical variability of wind power, especially for the specific time scale of the ultra-short term. This paper therefore proposes a new framework for interval forecasting of ultra-short-term wind power that incorporates the power fluctuation process. Firstly, a fluctuating process of wind power series is defined and a Kalman-SOM method for clustering the fluctuating processes of wind power is constructed. Secondly, a quantile regression forest interval prediction model is constructed for multiple fluctuation processes for ultra-short-term time scales. Finally, the effectiveness of the overall framework is validated at a wind farm in Jilin Province, China. Compared with the traditional interval prediction method. The interval bandwidth is reduced by 0.86% on average, and the interval coverage is increased by 1.4% on average. The results demonstrate the effectiveness and feasibility of the method in this paper.

关键词： wind power ultra-short-term prediction self-organizing map fluctuation quantile regression forest

Bald Eagle Search Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Yang, Bo Li, Danyang Zeng, Chunyuan Han, Yiming Li, Junhui Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Faculty of Electric Power Engineering Kunming University of Science and Technology Kunming China

A precise and reliable proton exchange membrane fuel cell (PEMFC) parameter identification performs an essential function in simulation analysis, optimal control, and performance research of actual PEMFC systems. Unfortunately, achieving an accurate, efficient, and stable parameter identification can sometimes be problematic for traditional optimization methods, owing to its strong coupling, inherent nonlinear, and multi-variable characteristics. Therefore, an advanced bald eagle search (BES) algorithm is designed to dependably identify the unknown parameters of the electrochemical PEMFC model in this work. For evaluating and analyzing the overall optimization performance of the BES comprehensively, it is compared with the genetic algorithm (GA) based on MATLAB under three cases. According to the simulation results, the optimum root mean square error (RMSE) achieved by BES is 96.27% less than that achieved by GA in parameter identification, which fully indicates that the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of BES.

关键词： proton exchange membrane fuel cell parameter identification bald eagle search algorithm metaheuristic algorithm MATLAB

Optimization Charging Method of Lithium-Ion Battery Based on Multi-Objective Bbo Algorithm

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SSRN 2024年

作者： Duan, Shuangming Xia, Kuifeng Li, Junhui Zhao, Zhiqiang Liu, Haojun Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology Ministry of Education Northeast Electricity Power University Jilin Jilin132012 China State Grid Hami Power Supply Company Xinjiang Uygur Autonomous Region Hami839000 China

Currently, the charging strategy of lithium-ion battery is usually an open-loop method, which lacks feedback in the charging process. In order to obtain a suitable charging strategy for lithium-ion battery, a simple and easy PID closed-loop constant temperature control system is proposed, which takes the temperature of lithium-ion battery as the feedback quantity, and improves the charging speed under the condition that the maximum temperature rise of lithium-ion battery is consistent. Secondly, SOC-based Multistage Constant Current (SMCC) based on SOC segmentation is proposed and orthogonal experiments are constructed by Taguchi method to determine the constant current values of each SMCC segment. Then, taking charging time, charging efficiency and SOH (State of Health) decay as the charging objectives to be optimized, multi-objective biogeographic-based optimization (BBO) is used. The MBBO algorithm optimizes each charging current under different weight coefficient combinations. According to the optimization results, Pareto optimal frontier is used to select the most balanced charging strategy from the six proposed charging strategies. Finally, SMCC charging current optimized by MBBO algorithm is connected to PID closed-loop constant temperature control system, and compared with SMCC charging strategy without constant temperature control and traditional 1C-CCCV method, the results show that compared with 1C-CCV charging strategy, The algorithm optimized SMCC can significantly reduce the charging time and SOH loss of lithium-ion batteries, and the SMCC charging current can be connected to PID closed-loop constant temperature control to further improve the charging speed, and keep the charging efficiency basically equal to the SOH attenuation. © 2024, The Authors. All rights reserved.

关键词： Taguchi methods

Long-Foresight-Term power Prediction Considering the Evolution of Significant Weather Information for Wind Farm Cluster

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SSRN 2023年

作者： Yang, Mao Jiang, Renxian Zhang, Xiaobin Huang, Yutong Guo, Yunfeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Gansu Electric Power Company Gansu730030 China

The current wind power prediction horizon extends up to 7 days, and the research on power prediction for the 8 to 15 day-ahead is still lacking. However, with the increasing proportion of renewable energy in novel power systems, breaking through the long-foresight-term power prediction barrier is crucial support for guaranteeing the secure and stable operation of the power system. To address the aforementioned issues, this study presents a power prediction method for wind farm cluster (WFC) in long-foresight-term, based on the poor availability of existing numerical weather prediction (NWP) in long-foresight-term. Firstly, a significant wind process identification method considering autocorrelation analysis is employed to predict future power trends, and an optimal method is explored for power generation (PG) prediction in the 8 to 15 day-ahead. Secondly, under the constraint of predicted PG, the power corresponding to the historical similar wind process is selected as the predicted value. Finally, the proposed methodology is applied to a WFC in Gansu Province, China. The results indicate that the proposed method reduces the average RMSE and MAE by 4.15% and 3.3%, respectively, effectively tracking the changing trends of wind power. Moreover, it achieves long-foresight-term prediction and enhances the reliability of the prediction. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

Partial Discharge Fault Diagnosis in power Transformer Based on Symplectic Geometry Mode Decomposition and Optimized Convolutional Neural Networks

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SSRN 2023年

作者： Shang, Haikun Liu, Zhidong Huang, Tao Zhang, Ranzhe Li, Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Electric Power Research Institute Xinjiang830011 China

To address challenges in feature extraction, such as difficulty in accurately extracting valuable information and low recognition accuracy during pattern recognition, this paper proposes a novel method for power transformer PD fault diagnosis based on symplectic geometry mode decomposition (SGMD) and optimized Convolutional Neural Networks (CNN). First, three different types of power transformer PD fault signals are extracted, namely floating discharge (FD), corona discharge (CD), and bubble discharge (BD), in a laboratory environment. Second, PD signals are decomposed using SGMD to obtain different symplectic geometric components (SGCs). Afterwards, approximate entropy (ApEn) values of each SGC are calculated as PD feature parameters. Subsequently, the sparrow search algorithm (SSA) algorithm is utilized to optimize and configure the CNN parameters to enhance the diagnostic accuracy and stability. Finally, the obtained feature parameters are fed into the optimized CNN for fault diagnosis. simulation and experimental analysis verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

An Optimized control Strategy for Distributed Energy Storage system to Reduce the Peak-valley Difference of Distribution Network 4

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An Optimized Control Strategy for Distributed Energy Storage...

4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Saren, Gaowa Sun, Zhebin Liang, Tao Li, Hengzhen Liu, Pengxuan Ma, Dexuan Zhang, Hao Sun, Dapeng Yan, Jiaqi Li, Cuiping Li, Junhui Inner Mongolia Power Technology Research Institute Hohhot China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9781728171494

Accompanied by energy structure transformation and the depletion of fossil fuels, large-scale distributed power sources and electric vehicles are accessed to distribution network that result in the load peak-valley gap increasing. Energy storage system (ESS) possessed the characteristics such as quick response, precise control and energy bidirectional flow. Therefore, the configuration of ESS in grid is a feasible measure to reduce the difference between peak load and valley load. This paper presents a superior control strategy that uses distributed energy storage to smooth load fluctuation of grid. Set the time scale to one day, constraints such as energy storage capacity, power and state of charge are taken into account, and the calculation examples are designed based on the proposed the evaluation index compares the effect of optimized control strategy given in this paper with the current common constant power strategy, which proves the feasibility and effect of the presented method. © 2021 IEEE.

关键词： simulation platform

Exergic Perspective of Low Carbon Economy Dispatch of Carbon Capture power Plant and power to Gas Synergistic Operation

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SSRN 2023年

作者： Wang, Xiuyun Zhang, Wanyu Wang, Rutian Wen, Xiangyun Cui, Benwang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

In order to improve the system economy and low carbon, the power to gas-carbon capture power plants joint operation system is introduced in the integrated energy system to reduce carbon emissions while increasing the consumption of renewable energy. From the perspective of exergy, both the quantity and quality of energy were taken into account, the calculation methods of different exergy values of energy flow were refined, and the sub-model of exergy flow calculation was established. To further achieve low carbon emission reduction, the costs of carbon emission trading, carbon capture energy consumption and carbon storage were included in the non-energy costs, and the exergoeconomic cost distribution model was established. Compared with the traditional pricing method, this paper considers the impact of carbon cost and energy grade difference on unit exergic cost of cooling, heating and electricity in the system, reflecting the principle of higher quality and higher price. Finally, the constructed model is solved with the total cost as the objective function. The simulation example shows that the proposed scheduling method can improve the low-carbon emission reduction capacity of the system and achieve reasonable cost distribution. © 2023, The Authors. All rights reserved.

关键词： Carbon capture

The Development of simulation Mirror system and its Application in HVDC

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The Development of Simulation Mirror System and its Applicat...

HVDC (HVDC), International Conference on

作者： Kaijian Ou Wencong Wu Haiqing Cai State Key Laboratory of HVDC Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory for Power System Simulation National Energy Power Grid Technology R&D Centre Electric Power Research Institute China Southern Power Grid Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798350387759

ISBN: (纸本)9798350387766

The testing of HVDC new control function and training of HVDC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&DC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVDC new control function testing and HVDC simulation training cabinet are also introduced in this paper.

关键词： Training Automation HVDC transmission power system simulation High-voltage techniques Dispatching Real-time systems Mirrors Personnel Testing

A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

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Journal of modern power systems and Clean Energy 2020年第6期8卷 1060-1069页

作者： Jun An Jiachen Yu Zonghan Li Yibo Zhou Gang Mu the Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University) Ministry of EducationJilin 132012China China Electric Power Research Institute Beijing 100192China

Transient stability assessment(TSA)based on security region is of great significance to the security of power *** this paper,we propose a novel methodology for the assessment of online transient stability *** with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency *** network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar *** proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.

关键词： Security region computer-vision-based power flow image(CVPFI) transient stability margin convolutional neural network(CNN) comprehensive network