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检索条件"机构=Grid Laboratory of Grid Advanced Computing and Applications"

共 144 条记录，以下是61-70 订阅

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Modeling of Evaluation Index System for Coordinated Operation of Wind-solar-hydro Multi energy Complementary Systems at Multiple Time Scales

Modeling of Evaluation Index System for Coordinated Operatio...

引用

IEEE International Conference on Power and Energy Systems (ICPES)

作者： Danni Shi Jie Li Bo Chai Siyan Liu Yishen Wang Xinghua Shi State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

The coordinated operation of multiple energy complementary power generation is gradually becoming the development direction of future power systems, and accurate analysis of their complementary characteristics is also particularly important. Although the current research on the characteristics of multi energy complementary power generation systems mainly focuses on calculating simple single evaluation indicators and has achieved fruitful results, there are problems such as a single evaluation scale and chaotic indicator classification. In response to this issue, this article constructs an indicator framework that comprehensively reflects the complementary characteristics of wind-solar-hydro. The entropy weight theory is used to scientifically assign and recombine different evaluation indicators to reveal the operational characteristics of wind-solar-hydro multi energy complementary power generation systems at different time scales. Based on this indicator system modeling, taking a hydroelectric power plant, photovoltaic power plant, and wind farm in a certain area in southwestern China as an example, the multi time scale characteristics of wind-solar-hydro multi energy complementary power generation were analyzed. The results indicate that the power output of the wind-solar-hydro complementary power generation system can exhibit different load tracking performance in different time scale scenarios. Increasing the time scale will cause a decrease in load tracking. Understanding this law can provide reference for the formulation of power system scheduling plans.

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Federated Learning Based Privacy-Preserving Renewable Energy Consumption for the Local Community

Federated Learning Based Privacy-Preserving Renewable Energy...

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International Conference on Power and Energy Technology (ICPET)

作者： Renjie Wei Yishen Wang Xiaohui Wang Ningyu An Xiao Liang Yuqiang Yang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Zhejiang Electric Power Co. Ltd. Zhejiang China

With the growing demand for sustainable energy, there is an increasing need for efficient management and distribution of renewable energy resources. However, collecting and sharing energy consumption data in a centralized manner poses a significant privacy risk for individuals. To address this issue, Federated Learning can be leveraged to train a predictive model on decentralized data sources while preserving data privacy. In this approach, each participant can train a local model using their own energy consumption data and share the model updates with a central server. The central server then aggregates the updates to generate a global model, which can be used to predict energy consumption patterns for the local community. The proposed approach can enable effective management of renewable energy resources while preserving the privacy of individual energy consumption data. It has the potential to provide a sustainable solution to the growing demand for renewable energy consumption in local communities.

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A Transformer Health State Assessment Method Based on Multi-Dimensional Deep Correlation Relationship Analysis

A Transformer Health State Assessment Method Based on Multi-...

引用

Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Guoliang Zhang Peng Zhang Weishang Xia Jinrui Gan Shunli Lv Long Chen Xin Liu Huan Ma Jian Ge State Grid Laboratory of Grid Advanced Computing and Applications(State Grid Smart Grid Research Institute Co.Ltd.) Beijing China NARI Group Corporation/State Grid Electric Power Research Institute Jiangsu China State Grid Anhui Electric Power Co.LTD Anhui China State Grid Anhui Ultra High Voltage Company Anhui China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

As one of the core equipments of the power system, the stability of transformer health state is directly related to the secure and stable operation of the power system. At present, most of the transformer health assessment only relies on the single dimension of the target transformer's own state quantity. Neglecting the different correlations between different operating states of neighbouring transformers on the changes in the transformer state, it is impossible to carry out an accurate state assessment of the transformer. In this paper, we propose a transformer health state assessment method based on multidimensional in-depth correlation analysis. Combine the primary wiring diagram of the station to mine the relationship between the nodes of each transformer. Find transformers that have adjacent relationships with the target transformer, and analyse the multi-dimensional connections between them to assess the transformer health state in depth.

关键词： Wiring Power engineering Correlation Accuracy Power system stability Transformer cores Transformers

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A Transformer Operation State Assessment Method Based on Virtual and Real Symbiotic Data of Power grid and Transformer

A Transformer Operation State Assessment Method Based on Vir...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Peng Zhang Guoliang Zhang Weishang Xia Jinrui Gan Shunli Lv Long Chen Xin Liu Huan Ma Jian Ge State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Co. Ltd.) Beijing China Electric Power Research Institute NARI Group Corporation/State Grid Jiangsu China State Grid Anhui Electric Power Co. LTD Anhui China State Grid Anhui Ultra High Voltage Company Anhui China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

Transformer is one of the core of the power system. The assessment of transformer operating condition is important to understand the operating condition of the transformer and the power system operation. Most of the current transformer operating condition assessments rely only on transformer-related monitoring condition quantities, such as dissolved gases in the oil, micro-water in the oil, oil temperature, etc., or a combination of transformer environmental data, test-type condition quantities, and so on. However, the information of the transformer grid environment is ignored, and the transformer is independent from the grid. This leads to the problem of ignoring the actual operating conditions of the transformer when analyzing its operating status. This paper proposes a transformer operating state assessment method based on virtual and real symbiotic data of power grid and transformer. It integrates the virtual and real data related to power grid and transformer. Taking into account the transformer's own operating conditions and the actual operating conditions of the power grid, the accuracy of the transformer condition assessment is improved.

关键词： Symbiosis Wiring Temperature distribution Accuracy Current transformers Oils Oil insulation

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Optimization model of distributed energy supply system based on multi-energy complementarity

Optimization model of distributed energy supply system based...

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12th International Conference on Renewable Power Generation (RPG 2023)

作者： J. Li B. Chai S. Liu Y. Wang X. Shi State Grid Laboratory of Grid Advanced Computing and Applications Beijing People's Republic of China State Grid Zhejiang Eletric Power Co. Ltd. Hangzhou People's Republic of China

The distributed energy supply system based on multi-energy complementarity is currently a research hotspot in the field of energy engineering, and the key issue faced by this system is operation optimization. The reasonable configuration or not directly affects the economic benefits and operational efficiency of the system. This article focuses on this key issue faced by distributed energy supply systems with multiple complementary energy sources. Taking a cultural tourism park as the research object, considering factors such as economic indicators, energy consumption, and carbon emissions, a multi-objective optimization model for distributed energy supply systems with multiple complementary energy sources is established by gradually decomposing and introducing hourly energy constraints. Experiments have shown that compared to traditional systems, this model can achieve hourly operation optimization and has good economic efficiency and energy-saving and emission-reduction benefits. Therefore, it provides a good reference and reference for the energy supply and comprehensive energy planning of multi-energy complementary distributed energy supply systems.

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Pre-trained Language Model for Chinese Frame Semantic Parsing based on Electric Power Dataset

Pre-trained Language Model for Chinese Frame Semantic Parsin...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Xiaohui Wang Yundan Liang Chenwei Gui Boya Wang Gaozhou Wang State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Corporation of China Beijing China Lab of Intelligent Social Computing University of International Relations Beijing China Information and Telecommunications Company State Grid Shandong Electric Power Company Shandong China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

In the operational landscape of the electric power industry, a wealth of events, operations, and fault reports generates substantial textual data. The application of frame semantic parsing to the power grid system presents challenges, especially given the distinctive characteristics of Chinese text, including entity ambiguity, word segmentation issues, and a misalignment with knowledge in the power systems field. In response to these challenges, we introduce an innovative pipeline specifically designed for Chinese semantic analysis based on an electric power dataset, showcasing significant enhancements over the baseline. This research strives to drive technological advancements, fostering a more sophisticated understanding of textual data within the intricate domain of the Chinese electric power industry, with a particular focus on natural language processing.

关键词： Industries Computational modeling Semantics Pipelines Big Data Power grids Natural language processing

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Seasonal Forecasting Model of Data Center Net Load Based on LSTM-Attention Fusion Neural Network

Seasonal Forecasting Model of Data Center Net Load Based on ...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Zhichao Li Shuya Lei Qifeng Huang Fei Zhou Meimei Duan Yixuan Huang Kaijie Fang Lan Ren State Grid Corporation Laboratory of Power Grid Advanced Computing and Application Technology (State Grid Smart Grid Research Institute Co. Ltd) Beijing China State Grid Jiangsu Electric Power Company Marketing Service Center Nanjing China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

Accurate load forecasting of data centers is an important supporting means for them to participate in demand response or power market. In view of the problems such as large errors and poor stability existing in current load forecasting methods of data centers, and considering the differences of cooling loads in different seasons, in this paper, a LSTM-Attention fusion neural network model based on Attention mechanism is proposed for the net load prediction of data centers. LSTM neural network is used to extract the time series characteristics of data center loads, and then the Attention mechanism is added to capture the fluctuation characteristics to improve the prediction accuracy. Based on the data set provided by the National Renewable Energy laboratory (NREL), seasonal forecasting is carried out in this paper. The results show that the introduction of the Attention mechanism in the model can effectively improve the accuracy of data center load forecasting, and the model has transferability.

关键词： Data centers Load forecasting Cooling Neural networks Time series analysis Predictive models Data models

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Exploitability Analysis of Public Component Library Vulnerabilities Based on Taint Analysis 7

Exploitability Analysis of Public Component Library Vulnerab...

引用

7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yuan, Huijie Wang, Yunchao Zong, Guoxiao Lv, Zhuo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Grid Electric Power Research Institute Zhengzhou China

ISBN: (纸本)9781665478571

The reuse of public component libraries has contributed to the field of computer science because of its low implementation time and high production efficiency. However, the vulnerabilities in libraries can be more serious than those in real-world software because they can affect various software which uses these libraries. Therefore, the technologies of vulnerability mining of public component libraries have received extensive attention, such as fuzzing. But the number of crashes generated by fuzzing is huge, and only an exceedingly small number of public component library vulnerabilities are exploitable in real-world software. To solve this problem, we use taint analysis techniques to analyze the exploitability of library vulnerabilities in real-world software. We first use Pin binary instrumentation technology to instrument the consumer program, analyze the process of calling the library in the consumer program through the taint analysis, and convert the extracted execution path and parameter information into an adjacency matrix. Then we analyze the execution path and crash scene of the crash file, convert the exploitability analysis into path reachability analysis, and determine whether the crash can reach the vulnerable pointer of the software through reachability. Finally, we divide the library vulnerabilities into three levels: directly exploitable, indirectly exploitable, and unexploitable. We design and implement a prototype tool, LibExp-T, to analyze nine public component libraries and four real-world software containing multiple attack surfaces such as images, audio, video, fonts, etc. And we compare them with automatic exploit generation tools CRAX and REX. The results show that LibExp-T can effectively verify the exploitability of component library vulnerabilities in real-world software with low overhead. © 2022 IEEE.

关键词： Libraries

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Power Transformer Health State Prediction Based on Multi-attention Coupling Learning

Power Transformer Health State Prediction Based on Multi-att...

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China International Electrical and Energy Conference (CIEEC)

作者： Peng Zhang Jinrui Gan Chunpeng Wu Peng Wu Bo Qi Fei Zhou State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Transformer is the key apparatus of the power system, and its operational reliability is directly related to the stability of the power grid. Accurately predicting the future health state of transformers can reserve enough time for formulating outage maintenance and power dispatching plans. The existing transformer health state prediction methods are based on the structure of “predict-diagnose”, which is the superposition of two local optimal methods, with error superposition. To fill this gap, a transformer health state prediction method based on multi-attention coupling learning is proposed in this paper. The “situational quantity” integrating the transformer operation state change relationship, the correlation relationship between state quantities, the mapping relationship between the state quantity and the state, the fuzzy relationship between the evaluation result and the actual state, and the traditional expert experience is proposed. Then, the structural optimization attention (SOA) model applicable to “situational quantity” is proposed, and various relationships are treated as individual attention. All kinds of attention are coupled and constrained in the SOA to realize the unification of the prediction process and diagnosis process, obtain the global optimization of prediction and diagnosis, and the problem of error superposition is solved. The actual fault case verification results show that the accuracy of the proposed SOA prediction model can reach 96.33%, which is significantly better than the traditional methods.

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A Short-term Net Load Forecasting Method Based on Two-stage Feature Selection and LightGBM with Hyperparameter Auto-Tuning

A Short-term Net Load Forecasting Method Based on Two-stage ...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Shuya Lei Xiao Liang Xiaohui Wang Jingyang Ding Xinxin Ge Fei Wang Jia Feng State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Co. Ltd) Beijing China Department of Electrical Engineering North China Electric Power University Baoding China State Grid Zhejiang Electric Power Company Ltd Hangzhou China

Reliable short-term net load forecasting (STNLF) is important for the stable operation of power systems and assists utilities in unit commitment and demand side management. With the increasing penetration of invisible behind-the-meter photovoltaic systems (BTM-PVs) installed close to customers, accurate STNLF is becoming more challenging. The intermittent of PV output is carried into the load due to the coupling of PV output and actual load, which makes the net load complex and difficult to forecast. In addition, the related influencing factors of BTM-PV also have a great impact on load characteristics based on the original features of load demand. To eliminate the redundant features is also one of the difficulties. This paper proposes a machine learning method to improve the accuracy of 24-hourahead net load forecasting, including PV-Load decoupling, feature selection and light gradient boosting machine (LightGBM) with Bayesian optimization. Firstly, the net load is decomposed into PV output and actual load using the data of small amount of observable PV customers. Then a two-step feature selection method is used to select key features for load demand and PV output, respectively, based on the forecasting accuracy of different feature combinations in the validation dataset. The selected features are used to train LightGBM model, and in order to fully maximize the performance of the forecasting model, the parameters of the model are optimized by Bayesian. Compared with other benchmark models, the proposed model has the best forecasting accuracy. The proposed framework is conducted on smart meter dataset in Ausgrid.

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