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Anomalous data detection methods for new power systems WANG Liang, DENG Song Integrated Intelligent Energy    2024, 46 (5): 12-19.   DOI: 10.3969/j.issn.2097-0706.2024.05.002 Abstract （289）   HTML （10）    PDF （1719KB）（923）       Knowledge map    With the rapid development of new power systems， massive data with various types are generated from the power systems. The complicated data conditions bring new challenges to anomaly data detection for power systems. In a summary on commonly used methods for anomalous power data in detecting， traditional technology-based， machine learning-based and deep learning-based detection methods are introduces. The working principle， characteristics and shortcomings of the three types of detecting methods are analysed. In the end， the challenges and development trends of anomalous data detection in new power systems are looked forward. Table and Figures | Reference | Related Articles | Metrics

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Research on security and privacy protection of electric power data sharing XING Huidi, GONG Gangjun, ZHAI Mingyue, LIU Xuesong, WANG Haomiao, YANG Shuang Integrated Intelligent Energy    2024, 46 (5): 30-40.   DOI: 10.3969/j.issn.2097-0706.2024.05.004 Abstract （346）   HTML （10）    PDF （1839KB）（861）       Knowledge map    Electric power data can reflect the development status of society and is promising in the opening and integrated applications. In order to create and release the value of power data， power companies need to build a data sharing service system for all industries. However， there are security issues such as privacy leakage， data tampering and shortage of data security aggregation method in the process of power data sharing. At present， there are three main technologies that can deal with the security problems above， blockchain， privacy computing and desensitization. Based on reviews on relevant literatures， a demand model for and privacy data protection in power data sharing is proposed. The three security and privacy protection technologies for electric power data sharing are summarized，and their integration and applications in different scenarios are comparatively analyzed. Different security and privacy protection schemes provide references for promoting electric power data opening and sharing. Table and Figures | Reference | Related Articles | Metrics

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Optimized scheduling on large-scale hydrogen production system for off-grid renewable energy based on DDPG algorithm ZHENG Qingming, JING Yanwei, LIANG Tao, CHAI Lulu, LYU Liangnian Integrated Intelligent Energy    2024, 46 (6): 35-43.   DOI: 10.3969/j.issn.2097-0706.2024.06.005 Abstract （193）   HTML （3）    PDF （3212KB）（750）       Knowledge map    To improve the renewable energy consumption， reduce the investment on rectifiers and grid connection equipment， cut down the cost of water electrolysis for hydrogen production through powering hydrogen production by renewable energy， an islanded renewable energy large-scale hydrogen production system is constructed. An intelligent energy management platform can improve the economy and safety of the system. Firstly， a simulation model of the renewable energy large-scale hydrogen production system is established and its control strategy is formulated. Secondly， an energy optimization scheduling strategy based on deep deterministic policy gradient （DDPG） algorithm is proposed. Through long-term trainings， the agent obtained from the DDPG algorithm can achieve intelligent dynamic optimized scheduling on energy. Comparing the performances of the proposed strategy with deep Q network （DQN）， Particle Swarm Optimization （PSO） and traditional control methods in terms of economy and safety， it is shown that applying the DDPG algorithm in energy optimization and management can get higher economic returns and utilization rates of renewable resources， and ensure the safe operation of the system. Table and Figures | Reference | Related Articles | Metrics

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Review on impedance modeling of grid-connected inverters under weak grid conditions ZHUO Chaoran, XIN Jie, HONG Hanzhuo, AN Bang, LI Ning Integrated Intelligent Energy    2024, 46 (6): 88-101.   DOI: 10.3969/j.issn.2097-0706.2024.06.010 Abstract （195）   HTML （4）    PDF （1245KB）（486）       Knowledge map    The impedance analysis method has become an important means of studying the stability of the interaction system between grid-connected inverters and the power grid. To make further investigations， two control modes for grid-connected inverters are reviewed， and the impact of impedance modeling on the power system stability and broadband oscillation suppression are analyzed， providing a theoretical basis for solving the broadband stability problem in new power systems.Then，grid-connected inverters' impedance modeling methods including dq linearization，harmonic linearization， as well as the impedance model identification method based on neural networks are expounded. The comparison on the modeling methods facilitates their practical applications. The summaries on the advantages，challenges and opportunities of impedance modeling methods for grid-connected inverters in existing power electronic systems provide guidance for improving the stability of the interaction system. Table and Figures | Reference | Related Articles | Metrics

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Advancement in multi-objective optimization for building energy use based on genetic algorithms FAN Yanbo, XIONG Yaxuan, LI Xiang, TIAN Xi, YANG Yang Integrated Intelligent Energy    2024, 46 (9): 69-85.   DOI: 10.3969/j.issn.2097-0706.2024.09.009 Abstract （144）   HTML （6）    PDF （1609KB）（431）       Knowledge map    Currently， fossil energy consumption accounts for a high proportion of energy use in buildings in China， which is not conducive to achieving the "dual-carbon" goals. This paper discusses the current status of green building energy systems and highlights the potential of technologies such as renewable energy integration， waste heat recovery， and energy storage in improving building energy efficiency and reducing carbon emissions. Researchers often focus on optimizing building energy consumption， indoor comfort， and construction costs by building physical models using simulation software and selecting appropriate algorithms for multi-objective optimization. The paper explores the advantages and disadvantages of existing technologies and algorithms in multi-objective optimization of building energy use， emphasizing that genetic algorithms can achieve good optimization results in terms of building energy consumption， indoor comfort， and construction costs， thus providing strong support for building design and renovation decisions. In the future， there is a need to develop new multi-objective optimization algorithms and establish a comprehensive big data platform for intelligent energy management to expand all-scenario applications and achieve the perfect integration of building energy use and intelligent management. Table and Figures | Reference | Related Articles | Metrics

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Application and prospect of federated learning in new power systems LYU Yongsheng, ZHANG Xiaoyu, WANG Xirong, GUO Peiqian Integrated Intelligent Energy    2024, 46 (11): 54-64.   DOI: 10.3969/j.issn.2097-0706.2024.11.007 Abstract （207）   HTML （6）    PDF （1033KB）（363）       Knowledge map    New power systems aiming to make clean， low-carbon， safe， flexible and efficient power supply is a key measure to achive the "dual carbon" target. However， with the widespread access of renewable energy， the fusion of artificial intelligence technologies and the rapid development of smart microgrids and distributed energy sources， such as electric vehicles， traditional centralized data processing methods fall short in protecting data privacy and enabling intelligent management. Federated learning （FL）， an innovative distributed machine learning technology， offers an effective efficiency optimization solution for new power systems due to its data privacy protection capability and intelligence. In reviews on the applications of FL in new power systems， basic principles and main algorithms of FL are expounded， practical cases of FL applied in load forecasting， anomaly detection， distributed power control and energy management under data privacy protection are analysed. Then the current technical challenges encounter by FL are also discussed. Finally， the prospects of FL in new power systems are made. Table and Figures | Reference | Related Articles | Metrics

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Photovoltaic power forecasting model based on probabilistic TCN-Transformer SHENG Ruixiang, ZHANG Xiaoyu Integrated Intelligent Energy    2024, 46 (11): 10-18.   DOI: 10.3969/j.issn.2097-0706.2024.11.002 Abstract （174）   HTML （5）    PDF （1450KB）（346）       Knowledge map    A short-term PV power prediction method based on a temporal convolutional network （TCN） and a Transformer structure is proposed. Firstly， the main factors affecting PV power generation，such as wind speed， rainfall， light intensity and cloudiness， are analysed. Then， TCN is used to extract the global spatial features of the sequence， and Transformer is used to extract the temporal features of long-term dependencies in the sequence， so that a TCN-Transformer composite model with a high prediction precision is applied to PV power deterministic and probabilistic prediction. Simulation analyses are performed on the dataset from DKASC（Australia）， and the results show that the improved TCN-Transformer model exhibits excellent prediction performance under different weather conditions， improving the short-term prediction accuracy on PV power. Table and Figures | Reference | Related Articles | Metrics

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Impact of iron-manganese modified Camellia oleifera shell-based biochar on the anaerobic digestion performance and microbial community structure of sludge LUO Kun, ZHU Yi, HUANG Jing, LI Hui Integrated Intelligent Energy    2024, 46 (8): 41-49.   DOI: 10.3969/j.issn.2097-0706.2024.08.006 Abstract （140）   HTML （7）    PDF （3483KB）（308）       Knowledge map    Hydrolysis process limits the anaerobic digestion （AD） rate of sludge. Supplementing exogenous biochar （BC） can effectively boost methane production by overcoming the limitation in hydrolysis. The iron-manganese modified biochar （Fe-Mn-BC） derived from the residual shells of woody oil crops， specifically Camellia oleifera shells， is studied. SEM， FTIR， XPS and XRD are employed to characterize the material， and its impacts on sludge AD performance， methane yield and microbial community structure are explored. The study results demonstrate that since Fe-Mn-BC possesses a porous structure， iron and manganese particles can load onto the BC surface in various forms of oxides. The addition of Fe-Mn-BC elevates methane production. When the total solid mass fraction of Fe-Mn-BC reaches 80 mg/g， cumulative gas production peaks at 301.59 mL/g， marking a 45.27% increase compared to that of the control group. Microbial community analysis reveals that Fe-Mn-BC enriches the abundance of archaeal communities， including Crenarchaeota， Candidatus_Methanomethylicus and Candidatus_Methanofastidiosum. These communities play crucial roles in promoting the hydrolysis of organic matters and enhancing the methane production， indicating that Fe-Mn-BC not only enriches functional microbial communities such as methanogenic bacteria， but also effectively improves the efficiency of sludge AD. Furthermore， this method presents a resource utilization solution for Camellia oleifera shells. Table and Figures | Reference | Related Articles | Metrics

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Research on transformer fault diagnosis method based on improved TCN model XU Bo, WEI Yijun, DENG Fangming Integrated Intelligent Energy    2024, 46 (11): 38-45.   DOI: 10.3969/j.issn.2097-0706.2024.11.005 Abstract （114）   HTML （1）    PDF （1419KB）（289）       Knowledge map    Transformer diagnosis methods based on traditional machine learning have been limited by low accuracy， single-source data， and a scarcity of fault samples. This paper proposes a transformer fault diagnosis model utilizing multi-source heterogeneous data fusion and the Mud Ring Algorithm （MRA） to optimize the Temporal Convolutional Network （TCN）. Oil chromatography data， infrared high-voltage bushing detection images， ultrasonic discharge detection images， and ultra-high frequency partial discharge detection images were selected as input information for the transformer fault diagnosis model. The Informer network and ResNet （Residual Network） were applied to extract and learn features from different data types， followed by feature fusion of multi-type data. The MRA algorithm was used to optimize the parameters of the TCN network， and the integrated results were used for fault classification. Experimental results showed that the proposed method achieved a Nash efficiency coefficient of 0.82 and an accuracy of 94.83%， with faster convergence， demonstrating its effectiveness in enhancing transformer fault diagnosis performance. Table and Figures | Reference | Related Articles | Metrics

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Economic dispatch and profit distribution strategy for multi-agent virtual power plants considering risk preferences YU Haibin, LU Wenzhou, TANG Liang, ZHANG Yuchen, ZOU Xiangyu, JIANG Yuliang, LIU Jiabao Integrated Intelligent Energy    2024, 46 (6): 66-77.   DOI: 10.3969/j.issn.2097-0706.2024.06.008 Abstract （161）   HTML （2）    PDF （1087KB）（271）       Knowledge map    Virtual power plants （VPPs） are playing an increasingly prominent role in new power systems， while the operational strategies and revenues of a VPP with distributed energy resources（DERs）are affected by the fluctuated outputs of wind turbines and PV generators in the plant. A fair， reasonable and transparent benefit distribution mechanism is the key to maintaining the cooperation between different DERs in a VPP. To motive various entities in a VPP to participate in trades， the profit is allocated among different entities based on their own characteristics and contributions though consultations. An optimal scheduling and profit distribution strategy is proposed for the VPP integrating wind turbines， PV generators， controllable distributed power suppliers and flexible loads considering their risk preferences. The multi-agent VPP participates in the electricity market （EM），and its profit distribution model is built based on Nash-Hassanyi bargaining solution. A numerical analysis is carried out，aiming to maximize the operational efficiency， guide the operation and reduce the operation risk of the VPP. The results verify that the proposed benefit distribution method can effectively balance the interests of all parties， quantify the actual economic contribution of each DER to the VPP， and improve the willingness of each entity to participate in market competition. The benefit allocation mechanism using Nash-Hassanyi solution is superior to that using Shapley value method in terms of allocation rationality and applicability. Table and Figures | Reference | Related Articles | Metrics

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Comprehensive benefit analysis on the cascade utilization of a power battery system HUANG Xiaofan, LI Jiarui, LIU Hui, TANG Xiaoping, WANG Ziyao, WANG Tong Integrated Intelligent Energy    2024, 46 (7): 63-73.   DOI: 10.3969/j.issn.2097-0706.2024.07.008 Abstract （185）   HTML （3）    PDF （1113KB）（236）       Knowledge map    With the development of clean energy， new energy vehicles gradually entered the market. As an energy storage device and an important component of a new energy vehicle， the power battery will see its performance degradation with the extension of time and changes in working conditions until its decommissioning. The retired power battery can be applied to other fields to improve its full-life cycle value. A life-cycle assessment（LCA） model and a life-cycle cost（LCC） model for the cascade utilization of a power battery system are developed. The environmental impacts of a pack of lithium iron phosphate batteries' five stages from production to recycling on global warming potential （GWP） are calculated by the LCA. The GWP， fine particulate matter formation （FPMF）， terrestrial acidification （TA）， marine eutrophication （MEP）and fossil resource scarcity （FRS） of the battery system under four scenarios are analyzed， and sensitivity analyses on parameters such as energy consumption and charging and discharging efficiency are conducted. LCC assessment analyses the net present value （NPV） and levelized cost of electricity （LCOE） of the battery system， and sensitivity analyses are performed on parameters that affected LCOE， such as energy storage efficiency and discharge depth. The results show that retired batteries processed by wet recycling applied to wind energy storage have favorable social benefits， leading to a smallest GWP of 194. The NPV and LCOE of the system with a 15-year service time are -42.066 million yuan， 2.44 yuan/（kW·h）， respectively. Making quantitative analyses on the social and economic benefits of the cascade utilization of power battery energy storage systems is of great significance for comprehensive utilization of resources and environmental protection in China. Table and Figures | Reference | Related Articles | Metrics

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Modeling and control optimization of photovoltaic-thermal heating system based on MPC WANG Zhe, CHENG Gang, XING Zuoxia, FU Qitong, FU Changtao Integrated Intelligent Energy    2024, 46 (7): 21-28.   DOI: 10.3969/j.issn.2097-0706.2024.07.003 Abstract （135）   HTML （3）    PDF （2268KB）（235）       Knowledge map    To reduce the energy consumption and fight against environmental pollution crises caused by heating in northern China， and to improve the insufficient light utilization efficiency in areas with abundant or relatively abundant solar resources， a distributed energy system combining solar energy and building heating is proposed，taking the advantages of off-peak electricity and sufficient illumination. The model of the proposed photovoltaic-thermal heating system is built based on TRNSYS dynamic modeling and numerical modeling. Then， considering the time-lag of the heat-supply system for a small area and the output of each device in the system， a model predictive control（MPC） strategy is developed based on Matlab， and an MPC-based optimization control strategy which can realize real-time error correction is made. According to the analysis results： the MPC-based optimization control can keep the maximum error of tracking heat load within 4.16%，and decrease the average error by 2.79%； and the optimization control can keep the maximum deviation of indoor temperature within 1.2 ℃，which is 0.2 ℃ lower that without the control； under a solar radiation intensity approaching 800 W/m2，the difference between solar energy utilization rates with and without the optimization control goes up to a maximum of 8.9%.The results indicate that the MPC can track heat load fluctuations in buildings quickly and accurately，suppress indoor temperature fluctuations effectively and increase the utilization rate of clean energy. Table and Figures | Reference | Related Articles | Metrics

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Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning LI Mingyang, DOU Mengyuan Integrated Intelligent Energy    2024, 46 (6): 27-34.   DOI: 10.3969/j.issn.2097-0706.2024.06.004 Abstract （172）   HTML （6）    PDF （1102KB）（207）       Knowledge map    Disorderly charging behaviors of massive electric vehicles （EVs） may cause violent fluctuations in power loads， affecting the security and stability of the power grid. With the application of vehicle to grid（V2G） technology， the scheduling method can be optimized by aggregating EV charging stations and surrounded distributed renewable energy generators into a virtual power plant（VPP）. The aggregation can improve the economy of charging behaviors and satisfaction of EV users， raise the utilization rate of distributed renewable energy， and mitigate load fluctuations in the grid. However， the overall charging or discharging load is the aggregation result of random charging or discharging behaviors of massive individual EVs， which is difficult to be accurately described by mathematical models. Thus，an interactive optimal scheduling framework based on deep reinforcement learning is presented for a VPP including EVs， with the objective of maximizing the benefit of all EV users in the VPP. The VPP control center，serving as an intelligent agent， can decide the charging and discharging of individual EVs without their detailed models. The agent continuously learns and updates its strategies through interactions with regional grids， overcoming the limitations of centralized optimal scheduling. The framework is solved by Deep Deterministic Policy Gradient（DDPG） algorithm. Simulation results show that， compared with the centralized scheduling， the proposed method improves the benefits of individual EV users， and the coordinative scheduling of EV charging/discharging loads and renewable energy outputs shaves the peak loads in the grid， and boosts the overall performance of the VPP. Table and Figures | Reference | Related Articles | Metrics

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Capacity optimization of wind-solar-nuclear-energy storage hybrid system considering wind and solar energy consumption NIE Xueying, CHENG Maosong, ZUO Xiandi, DAI Zhimin Integrated Intelligent Energy    2025, 47 (1): 51-61.   DOI: 10.3969/j.issn.2097-0706.2025.01.007 Abstract （339）   HTML （47）    PDF （4751KB）（206）       Knowledge map    The capacity configuration optimization of a wind-solar-nuclear-energy storage hybrid energy system was performed through a multi-objective evolutionary algorithm in this research. The hybrid energy system included photovoltaics（PV），wind turbines（WT），small modular thorium molten salt reactor（smTMSR），and thermal energy storage（TES）. The optimization objectives were to improve the stability of the electricity supply，reduce the electricity generation cost，reduce the electricity curtailment probability，and increase the fraction of renewable energy in the total power supply system（renewable energy fraction）. The PV capacity，WT capacity，and TES capacity were selected as the optimization parameters，while the local meteorological data of Wuwei city were used as input parameters. By comparing the performance of the nondominated sorting genetic algorithm（NSGA-Ⅱ，NSGA-Ⅲ）and the strength Pareto evolution algorithm（SPEA-SDE），the optimal algorithm was selected to solve the multi-objective optimization problem and obtain the Pareto solution set. The Criteria Importance Through Intercriteria Correlation（CRITIC）method was used to determine the objective weights，and the Technique for Order Preference by Similarity to Ideal Solution（TOPSIS）method was used to sort the obtained Pareto solutions，from which the best compromise solution was selected. The results demonstrated that NSGA-Ⅱ had the fastest convergence speed compared to other algorithms，but its solution set was less uniform. NSGA-Ⅲ，although slower to converge，had the most uniform solution set compared to other algorithms. The optimization results showed that the optimal capacity configuration resulted in a deficiency of power supply probability of 0.968 6%，a levelized cost of energy of 0.085 7 dollars/（kW·h）. an electricity curtailment probability of 4.898 6%，and a renewable energy share of 21.258 9%. The electricity curtailment mainly came from nuclear power，with minimal renewable energy curtailment. The sensitivity analysis results showed that the PV capacity had the most significant impact on the probability of power supply deficiency，electricity curtailment probability，and renewable energy fraction，while the WT capacity had the most significant impact on the levelized cost of energy. The wind-solar-nuclear-energy storage hybrid energy system can effectively promote renewable energy consumption and ensure the reliability of the power supply. Table and Figures | Reference | Related Articles | Metrics

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Key technologies of the evaluation on distributed wind-storage systems' frequency and voltage regulation capacities ZHAO Changwei, WANG Hui, GU Zhicheng, LIU Xubin, ZHU Guangming, GE Leijiao Integrated Intelligent Energy    2024, 46 (6): 78-87.   DOI: 10.3969/j.issn.2097-0706.2024.06.009 Abstract （152）   HTML （1）    PDF （992KB）（203）       Knowledge map    The wide and scattered connection of wind power to medium/high voltage distribution networks is getting popular，resulting in insufficient frequency and voltage support of weak power grids. The evaluation on the frequency and voltage regulation capacities of distributed wind-storage systems is helpful for the scheduling and management of grids，detecting and avoiding potential problems that could destroy the stability of grids timely，but the construction of an evaluation system is thorny due to its complex and variable structure. To fulfill the construction， the influence factors， comprehensive evaluation indicators and comprehensive evaluation method for the systems' frequency and voltage regulation capacities have to be considered comprehensively in analyzing existing technical methods and pointing out the key techniques and challenges. The study focuses on the following contents：multi-time scale high-precision wind power output forecasting technology，adjustment capacities of different wind-storage system configuration schemes under different conditions;calculation and improvement methods of the frequency-voltage regulation capacity and contribution index of a distributed wind-storage system under multi-modal coupling condition; improvement on the robustness of evaluation method under uncertain conditions. Finally， new ideas on frequency and voltage regulation capability evaluation for distributed wind-storage systems are presented， to provide references for the development of wind power in China. Table and Figures | Reference | Related Articles | Metrics

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Spatiotemporal distributed parameter modeling of solid oxide electrolysis cells DOU Zhenlan, LI Jiawen, ZHANG Chunyan, CAI Zhenqi, YUAN Benfeng, JIA Kunqi, XIAO Guoping, WANG Jianqiang Integrated Intelligent Energy    2024, 46 (7): 53-62.   DOI: 10.3969/j.issn.2097-0706.2024.07.007 Abstract （134）   HTML （1）    PDF （4047KB）（197）       Knowledge map    There are complex thermoelectric couplings in solid oxide electrolysis cells （SOEC）operating under high temperature environment. Thus， temperature control and voltage control are crucial to the stable and safe operation of SOEC stacks. In view that SOEC stacks are complex， nonlinear and spatiotemporal distributed systems， a spatiotemporal distributed model for the temperature and voltage of an SOEC is constructed based on the spatiotemporal least square support vector machine（LS-SVM）. A kernel function is adopted to represent the spatial correlations at different locations along the flow channel， and dynamic regression equation is used to represent the temporal features of the temperature and voltage of the SOEC stack. A mechanism model of the SOEC stack is established in Simulink to obtain the sample data，which are used for the training and testing of the spatiotemporal distributed model. The simulation results show that the developed model can effectively predict the spatiotemporal distributions of the temperature and voltage in SOEC and has an excellent generalization ability， which can guide the further optimization and control of electrolysis cells. Table and Figures | Reference | Related Articles | Metrics

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Research on a wind power operation and maintenance Q&A system based on large language models and knowledge graphs CHEN Qing, LIU Yusheng, DUAN Lianda, LIANG Hao, SUN Qitao, LU Nana Integrated Intelligent Energy    2024, 46 (9): 61-68.   DOI: 10.3969/j.issn.2097-0706.2024.09.008 Abstract （163）   HTML （9）    PDF （2216KB）（186）       Knowledge map    The operation and maintenance of wind farms heavily rely on on-site practical experience， while the high turnover rate in the industry poses challenges to the impartment of such experience. Traditional knowledge bases and Q&A systems are increasingly revealing their limitations in this regard. To enhance the applicability and reliability of Q&A systems in professional domains， this paper designs a wind farm operation and maintenance Q&A system that integrates large language models （LLMs） with knowledge graphs. Through semantic understanding and correlation analysis， the system combines both structured and unstructured data to provide comprehensive and accurate professional responses. Both subjective and objective evaluations indicate that the accuracy， coherence， and informativeness of this specialized Q&A model surpass those of a certain Chinese LLM and the ChatGLM model. This not only improves the efficiency of wind farm operation and maintenance but also offers a solution for knowledge transfer and updating within the industry. Table and Figures | Reference | Related Articles | Metrics

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Research on vulnerability of distribution networks with distributed photovoltaic under cyber attacks LI Yinuo, LIU Wei, WEI Xingshen, WANG Qi Integrated Intelligent Energy    2024, 46 (5): 50-57.   DOI: 10.3969/j.issn.2097-0706.2024.05.006 Abstract （171）   HTML （3）    PDF （4634KB）（182）       Knowledge map    With the rapid development of network technologies， a large number of intelligent measurement terminals and communication devices are widely connected to power systems， introducing new intrusion paths for cyber attacks and intensifying the exposure risk of power systems to the attacks. To ensure the safe operation of power systems and evaluate the vulnerability of distribution systems with distributed photovoltaic under cyber attacks， a data tampering attack model for distributed photovoltaic is established from the perspective of attackers， describing the specific forms of cyber attacks. Then， in attack scenarios， the power flow correction equation for the distribution system suffering from photovoltaic load losses is solved， and the node vulnerability is proposed based on the sensitivity of power flow to characterize the vulnerability of the distribution system. Finally， the correlations between the vulnerability of a distribution network and the impact degree of an attack， the topology and time series are verified through a simulation test. The research results can provide references for power system cyber security defenders to accurately formulate defense plans and reasonably allocate defense resources under cyber attacks. Table and Figures | Reference | Related Articles | Metrics

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Switching method for distribution network feeder automation system based on 5G communication delay ZHU Weiwei, ZHU Qing, GAO Wensen, LIU Caihua, WANG Luze, LIU Zengji Integrated Intelligent Energy    2024, 46 (5): 1-11.   DOI: 10.3969/j.issn.2097-0706.2024.05.001 Abstract （216）   HTML （6）    PDF （2545KB）（171）       Knowledge map    Since data transmission time is difficult to predict due to the uncertain delay of 5G communication， the fault response timeliness and decision-making accuracy of a feeder automation （FA） system are affected. Thus， a distribution network FA switching method based on 5G communication delay is proposed. Initially， the topological relationship between feeder terminals is established， and the real-time communication delay of the FA system is calculated based on the maximum communication delay in each branch of the FA system. Subsequently， a stacked Long Short-Term Memory （LSTM） neural network model is trained by the historical data of fault processing time under different FA strategies and various delays， to obtain the FA strategies with the fastest fault handling speed under different communication delays. Finally， based on the learning outcomes of the layer-stacked LSTM model， the FA strategy with the shortest fault handling time under a certain communication delay is selected. Experimental results demonstrate that the proposed method effectively mitigates the impact of uncertain delays in 5G communication on FA systems， ensuring their reliable operation. Moreover， compared to other machine learning methods， the layer-stacked LSTM model shows advantages in prediction accuracy and prediction delay， effectively enhancing the adaptive capacity and fault response speed of feeder terminals. Table and Figures | Reference | Related Articles | Metrics

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Security protection for photovoltaic data acquisition and storage LIU Xu, LU Jun, GONG Gangjun, HOU Zanyu, ZHANG Chunmeng, LIU Bo Integrated Intelligent Energy    2024, 46 (5): 73-80.   DOI: 10.3969/j.issn.2097-0706.2024.05.009 Abstract （147）   HTML （2）    PDF （2284KB）（171）       Knowledge map    With the energy structure transformation and upgrading， photovoltaic power， a representative emerging new energy， gets increasing attention. However， with the continuous development of photovoltaic systems， the security protection of photovoltaic data faces great challenges. Based on this， a security protection method for photovoltaic data collection and storage is proposed. First，a two-way trusted authentication technology is implemented at the photovoltaic collection end to ensure that the collected photovoltaic data is complete and reliable. Secondly， in terms of photovoltaic data storage， distributed transmission protocols and consistent hashing algorithm are used to complete grouping storage of photovoltaic data. In order to prevent photovoltaic data leakage and illegal theft， a trusted security architecture and a privacy protection method are used to protect stored photovoltaic data. Finally， the feasibility of the method is verified through simulation examples. Table and Figures | Reference | Related Articles | Metrics