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Short-term Photovoltaic Power Prediction Based on Feature Optimization and Hybrid Improved Grey Wolf Algorithm-optimized BiLSTM Network

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Dianwang Jishu/Power System Technology 2025年第1期49卷 209-222页

作者： Zhao, Ruyi Wang, Xiaohui Zheng, Bihuang li, daoxing Gao, Yi Guo, Pengtian China Electric Power Research Institute Haidian District Beijing 100192 China Economic and Technological Research Institute State Grid Tianjin Electric Power Company Hedong District Tianjin 300171 China

To address the challenges posed by strong noise interference in photovoltaic (PV) sequences and the issues of low accuracy and poor generalization of a single model in PV power prediction, we propose a short-term PV power prediction method. This method uses feature optimization and a hybrid improved Grey Wolf algorithm to optimize the BiLSTM network. Firstly, the mutual information algorithm is employed for variable selection in input data to eliminate redundant variables. Subsequently, the Complementary Ensemble Empirical Mode Decomposition and an improved wavelet threshold algorithm are applied to the selected data for feature reconstruction to reduce noise interference in the data and optimize input variable features. Finally, the standard Grey Wolf Optimizer algorithm underwent hybrid optimization by integrating improved Tent chaotic mapping, nonlinear decreasing factors, dynamic weight strategies, and the differential evolution algorithm. This process aimed to ascertain the optimal hyperparameter combination for the BiLSTM network. The attention mechanism was also employed to extract key temporal information from the data, developing a novel short-term photovoltaic power prediction model. Simulation experiments demonstrate that, compared to the least squares support vector machine, long short-term memory network, and bidirectional long short-term memory neural network, the proposed model achieves an average reduction of 12.45%, 7.95%, and 5.37% in root mean square error under various weather conditions such as sunny, cloudy, overcast, and rainy, showcasing excellent predictive performance, good generalization ability, and promising engineering application value. © 2025 Power System Technology Press. All rights reserved.

关键词： Mean square error

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Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

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Global Energy Interconnection 2024年第1期7卷 94-105页

作者： Pengtian Guo Kai Xiao Xiaohui Wang daoxing li China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic *** the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application ***,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security *** address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal *** further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

关键词： Power Internet of Things Object model High concurrency access Zero trust mechanism Multi-source heterogeneous data

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Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

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Global Energy Interconnection 2023年第5期6卷 614-633页

作者： daoxing li Xiaohui Wang Jie Zhang Zhixiang Ji China Electric Power Research Institute Co. LtdHaidian DistrictBeijing 100192P.R.China Sichuan Electric Power Research Institute SGCC Chengdu 610041P.R.China

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

关键词： Transmission line inspection Deep learning Automated machine learning Image analysis and processing

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Modeling and application of marketing and distribution data based on graph computing

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Global Energy Interconnection 2022年第4期5卷 448-460页

作者： Kai Xiao daoxing li Xiaohui Wang Pengtian Guo China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy *** energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution *** existing unified data model and the coordinated application of marketing and distribution suffer from various *** a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data ***,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and *** this manner,a novel identification algorithm is proposed based on the collected data for marketing and ***,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution ***,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.

关键词： Marketing and distribution connection Graph data Graph computing Knowledge graph Data model

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Global Energy Interconnection 2023年第1期6卷 15-25页

作者： Kai Xiao daoxing li Pengtian Guo Xiaohui Wang Yong Chen China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable ***,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently *** investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing *** on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is *** with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are ***,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are *** neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector *** model is verified on the operating section of the power distribution system of a provincial grid *** results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

关键词： Neural information retrieval Power distribution Graph data Operating section Similarity matching

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Towards sparse matrix operations:graph database approach for power grid computation

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Global Energy Interconnection 2023年第1期6卷 50-63页

作者： daoxing li Kai Xiao Xiaohui Wang Pengtian Guo Yong Chen China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)***“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion *** graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing ***,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power *** this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the *** on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,***,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix *** on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph *** promotes the efficiency of PIoT when handling multi-source heterogeneous *** comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB *** results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.

关键词： Graph database Graph description Matrix Parallel computing Power flow

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中浓纸浆泵内部气液分离数值模拟与试验

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排灌机械工程学报 2017年第4期35卷 309-318页

作者：叶道星李红张翔西华大学流体及动力机械教育部重点实验室四川成都610039 江苏大学国家水泵及系统工程技术研究中心江苏镇江212013

为了研究中浓纸浆泵内部的气液分离特征,基于欧拉-欧拉多相流模型,对泵内的流动进行三维非定常数值模拟,分析了转速、抽气真空度和流量对气液分离的影响及瞬态气液分离过程.搭建了基于声呐流量计的泵性能测试系统,研究了中浓纸浆泵运行... 详细信息

为了研究中浓纸浆泵内部的气液分离特征,基于欧拉-欧拉多相流模型,对泵内的流动进行三维非定常数值模拟,分析了转速、抽气真空度和流量对气液分离的影响及瞬态气液分离过程.搭建了基于声呐流量计的泵性能测试系统,研究了中浓纸浆泵运行性能特点.结果表明:随着转速的增大,较高的气体体积分数区域集中于湍流发生器叶片的背面,叶轮内的气体含量减小;随着流量的增大,湍流发生器内气体体积分数较高的区域面积逐渐减小,大流量工况下气体集聚在湍流发生器的背叶片根部.叶轮背面叶片的作用使气液得到了进一步的分离;转速、抽气真空度和流量的增大可以提高叶轮的气液分离能力.叶轮的排气孔附近产生并聚集了大量气体,并伴随着强烈的气液两相流相互作用变化.通过中浓纸浆泵的性能试验研究,发现提高泵的转速和抽气真空度有利于泵内的气液分离,验证了采用数值模拟方法研究中浓纸浆泵内部气液分离的正确性.研究气液分离特征有助于认识中浓纸浆泵内部气液分离过程的演变机理,为中浓纸浆泵的气液分离结构和排气系统提供设计参考.

关键词：中浓纸浆泵气液分离多相流数值模拟试验验证

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城镇化中的市场化运作思考

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中共福建省委党校学报 2002年第4期 43-47页

作者：李道兴闽北日报社

用市场经济的方式方法促进城镇化建设,是城乡资源在全社会范围内合理配置和有效利用的内在要求,是城镇历史发展和应对经济全球化的必然选择。这在各地取得了一定成就,但缘于产权制度、评估机制、参与渠道、传统理念等的缺陷,尚存在建设... 详细信息

用市场经济的方式方法促进城镇化建设,是城乡资源在全社会范围内合理配置和有效利用的内在要求,是城镇历史发展和应对经济全球化的必然选择。这在各地取得了一定成就,但缘于产权制度、评估机制、参与渠道、传统理念等的缺陷,尚存在建设主体二元化、方式方法单一、不讲效益活力等非市场化问题。为此,要明确政府、农户、社会的角色定位构造市场化运作新格局,促进农村资源流转、城镇活力增强、参与渠道畅通,在市场经济框架内提高城镇化建设成效。

关键词：城镇化市场化运作思考

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中原文化探源之“钧文化”

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黄河科技学院学报 2024年第12期26卷 14-18,23页

作者：张向阳李道兴黄河科技学院河南郑州450063 中国国土经济学会北京101100

“钧文化”是夏王朝的主流文化,源于今河南中西部地区,具有明显的文化地理位置标识,蕴含华夏民族重要的思想观念和政治意识,一直在传承发展。“钧文化”的钧地观是中原文化“中原”概念的肇始,钧天观是远古人类宇宙自然认知的重要阶段,... 详细信息

“钧文化”是夏王朝的主流文化,源于今河南中西部地区,具有明显的文化地理位置标识,蕴含华夏民族重要的思想观念和政治意识,一直在传承发展。“钧文化”的钧地观是中原文化“中原”概念的肇始,钧天观是远古人类宇宙自然认知的重要阶段,“钧”之节律观与中原农耕文明相伴而生,“钧台之享”是中原王权政治制度的发端,钧天广乐是华夏民族礼乐制的先声,钧瓷是钧文化传承与创新的重要载体。

关键词：钧文化中原文化根脉探源

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面向电力无人机巡检图像分析处理的自动化深度学习系统:架构设计与关键技术

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电力信息与通信技术 2024年第4期22卷 38-54页

作者：李道兴王晓辉李黎季知祥中国电力科学研究院有限公司北京市海淀区100192

当前电力无人机巡检图像处理模型存在适用范围小、研发成本高、研发周期长等问题,文章提出一种面向无人机巡检图像分析处理的自动化深度学习系统,明确该系统设计的泛化性、可拓展性、自动化三要素,综述与三要素密切相关的技术进展,设计... 详细信息

当前电力无人机巡检图像处理模型存在适用范围小、研发成本高、研发周期长等问题,文章提出一种面向无人机巡检图像分析处理的自动化深度学习系统,明确该系统设计的泛化性、可拓展性、自动化三要素,综述与三要素密切相关的技术进展,设计系统架构,构建原型系统。实验表明,在绝缘子自爆识别和鸟巢识别2项电力无人机巡检图像分析处理上,系统自动化构建的模型全类平均精度分别可达91.36%和86.13%,表明系统设计理念合理且系统架构可行。

关键词：输电线路巡检深度学习自动化训练图像分析处理

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