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Optimal Design of Heliostat Field Using Random Search Algorithm 6

Optimal Design of Heliostat Field Using Random Search Algori...

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6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data Based on FCM and Wavelet Neural Network 14th

Research on Abnormal Power Consumption Behavior Detection an...

引用

14th international conference on Frontier computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet Neural Network

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Classification of False Data Injection Attacks in Smart grid using Multi Label Deep Learning 2

Classification of False Data Injection Attacks in Smart Grid...

引用

2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sikri, Alisha Singh, Anurag Greater Noida India Galgotias University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350347456

The traditional electricity structure is displaced by a new physical structure brought about by the smart grid, which is brought about by the creative use of information that leads to the building of a new structure. When applied to a sector of the economy like this one, the dissemination of bad information has the potential to have substantial repercussions. The conventional residual methods that are used in fake data identification will never be in a position to identify accurate FDI attacks. The linearized power system model DC has served as the foundation for the vast bulk of the research that has been conducted on the subject of the detection of FDI attacks. The AC model is not the source of any of the assaults that are uncovered by this study. In addition, the overwhelming majority of the currently available DIA identification algorithms focus on FDIA, despite the fact that significant injection site data cannot be gathered. In light of the ongoing advancements in deep learning, we present a Deep Learning based Locational Detection approach as a means of continually identifying the various parts of FDIA. Solver Gap Happiness is a False Data Detector (FDD) that was developed in the realm of software engineering. It makes use of a Recurrent Neural Network (RNN). The FDD has been around long enough to be able to spot false information. The subsequent RNN is employed here as a multi-label classifier in order to analyse the irregularity and co-occurrence dependence of power flow computations which may have been caused by potential assaults. Due to the fact that they are "model-free,"the suggested design does not include any prior statistical assumptions. In addition to this, it is "cost-accommodating,"since it does not change the existing FDD structure and the identification method only takes a few microseconds on a typical home computer. Through our extensive work in the IEEE 14, 30, 57, and 118 bus systems, we have shown that ANN-MLP, SVM-RBF, CNN and RNN are cap

关键词： Cyber Physical System

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Integrative Optimization Framework for Strategic distributed Generation Placement and Sizing to Enhance Loss Minimization and Voltage Stability in Power Distribution Networks

Integrative Optimization Framework for Strategic Distributed...

引用

2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Prasad, Kashi Sai Aravinda, K. Singh, Jay Singla, Atul Kumari, Savita Ziara, Sajjad Karuna, Gotlur Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India New Horizon College of Engineering Bangalore India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Hyderabad India

ISBN: (纸本)9798331527495

The location and scale of distributed generation (DG) units are critical for minimizing losses and enhancing the performance of distribution networks. This research establishes the optimization approach for establishing the optimal location of the dg, especially solar PV and wind turbines using the IEEE 37-bus system as the test feeder. Having this in mind, this work applied a unified approach based on Pandapower, a versatile Python-based power systems tool, combining optimization and power flow computations. The algorithm analyzes a range of DG technologies and penetration levels to determine the effectiveness of decreasing loss, considering variability in renewable generation and load demand to increase realism. The single-tool forms presented in the paper show practical application potential and offer a better understanding of the best approaches to DG integration that would benefit grid stability and energy efficiency in the distribution networks of the future. © 2025 IEEE.

关键词： distributed Wind

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distributed computing Agriculture Water Spraying System using LORA Communication

Distributed Computing Agriculture Water Spraying System usin...

引用

2022 IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Lee, Kyung Mog Semyung University School of Computer Science Jecheon Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The distributed computing system of the client-server architecture in a small-size computing grid was used in the water spraying system in the agriculture field. The grid was built across Long Range(LoRa) communication networks with the same network ID and different addresses. The computing system at a collaboratively working level was built over autonomous microprocessors, which consist of a server and five client microprocessors. A smartphone was used as the server of a central controller. Four moisture sensing modules and a water spraying system module were used as autonomous clients. The server was interconnected with the clients through star type topology network in the polling process. Each client module autonomously analyzed the measured digital voltage of the moisture sensor plugged into the soil. When the server asked about the status of the water request condition, the client sent the request signal to the server in the LoRa communication technology. The communication between the server and the clients was in the LoRa communication by a LoRa-to-Bluetooth converter, which was interpreted between the Bluetooth communication and the LoRa communication signals. The field test was performed in about 6600 square meters of watermelon field. © 2022 IEEE.

关键词： Agriculture

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A Novel Cluster Control Method for Effectively Improving the Hosting Photovoltaic Capacity of Distribution Networks 8

A Novel Cluster Control Method for Effectively Improving the...

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8th IEEE international conference on Energy Internet, ICEI 2024

作者： Li, Haobin Li, Yiquan Zhao, Ruifeng Guo, Ruifeng Chen, Zhiwei Pan, Kaiyan Wu, Haiyong Guangdong Power Grid Co. Ltd Electric Power Dispatching and Control Center Guangzhou China Dongfang Electronics Cooperation Yantai China

ISBN: (纸本)9798331523558

To handle the problem of not considering the photovoltaic hosting capacity for cluster control in distribution networks, this paper proposes an effective cluster control strategy to improve the distributed photovoltaic (PV) hosting capacity. Firstly, based on a "cloud-cluster-end"architecture, a cloud optimization model aiming at minimizing PV curtailment and the voltage deviation of dominant nodes is established to improve PV accommodation. Secondly, a cluster optimization model aiming at minimizing power loss is established to implement coordinated optimization between cloud and clusters. Different from previous studies, the PV outputs from the cloud optimization are used as the constraints for the cluster optimization. Finally, the proposed model is converted from the nonlinear programming into a linear programing problem using the second-order cone relaxation technique combined with the polygon interior approximation method (PIAM). The accuracy and efficiency of the proposed method compared to existing methods are verified through validation on the actual IEEE151-bus system. © 2024 IEEE.

关键词： Cloud computing architecture

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User Interest Profiling Techniques for Targeted Web Advertisements: Leveraging Social Networking Data

User Interest Profiling Techniques for Targeted Web Advertis...

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international conference on Parallel distributed and grid computing (PDGC)

作者： Geetanjali Goyal Rajneesh Randhawa Department of Computer Science Punjabi University Patiala India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

These days, social networking sites play a major role in digital interactions. They produce vast amounts of data that may be utilized to create detailed user interest profiles. Understanding user behavior, customisation, and targeted advertising all depend on this process. This study offers a thorough analysis of the different methods for creating user interest profiles from social networking data, classifying approaches into four categories: behavior-based, content-based, network-based, and hybrid. We also examine the difficulties with profiling techniques, including scalability, data sparsity, and privacy issues. The study concludes by highlighting recent developments and possible avenues for future investigation in this field.

关键词： Data privacy Social networking (online) Scalability distributed databases grid computing Advertising

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Risk Assessment of Power grid Infrastructure Operation under the Background of Big Data Era

Risk Assessment of Power Grid Infrastructure Operation under...

引用

distributed computing and Optimization Techniques (ICDCOT), international conference on

作者： Shiyun An Wenjie Li Xiaoqi Zhang Jianning Liu Famin Cheng Qiurui Zhang State Grid Shandong Electric Power Company Dongying Power Supply Company Dongying Shandong China

With the continuous progress of The Times and economic development, China's electric power industry is also undergoing profound changes. In the process of the continuous expansion of the scale of the power grid infrastructure construction project, the risk management for the safety of the infrastructure operation is becoming more and more important. This paper mainly studies the risk assessment of power grid infrastructure operation under the background of big data era. This paper first gives a systematic overview of risk management, and then builds a risk assessment model based on big data and random set theory. In this paper, the risk assessment model is used to evaluate the equipment risk and environmental risk in the operation of power grid infrastructure. In this paper, through the construction of the operation risk assessment model, various production risk factors are quantitatively evaluated, the key and high-risk factors of operation risk are determined, and the overall production risk level of infrastructure operation is obtained, which solves the subjective problem of risk assessment. This helps identify the failure modes and trends of power grid equipment. By analyzing historical data, typical fault modes of equipment can be identified, and a prediction model can be constructed using machine learning algorithms to predict potential future faults. In this way, power grid operators can take maintenance measures or replace equipment in advance to avoid the impact of faults on the power grid.

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A Clustering Method of Electric Power Customers Based on DBSCAN Algorithm 3

A Clustering Method of Electric Power Customers Based on DBS...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Fan, Liping Peng, Linfang Li, Huangqiang Du, Fengyi Huang, Yu Liu, Yunfei Wang, Zhixin Zheng, Yuelin State Grid Yichang Electric Power Supply Company Hubei Yichang China College of Computer and Information China Three Gorges University Hubei Yichang China

ISBN: (纸本)9798350380989

Providing personalized services for electricity users with different needs has become an important part of deepening the reform of the electricity sales side market system, and achieving efficient and accurate electricity customer clustering is an important foundation for improving personalized services. In order to achieve efficient and accurate power customer clustering, a power customer clustering method based on DBSCAN algorithm is proposed in the paper. Firstly, the data in the power dataset is preprocessed and feature values are extracted from it. Secondly, a power customer clustering model based on the DBSCAN algorithm is constructed. Finally, the results obtained from the clustering model are visualized and analyzed. The experiment shows that when the parameter Eps of DBSCAN clustering algorithm is 0.006 and MinPts is 10, the overall clustering effect is good. At this time, the number of clusters is 5, and the profile of these 5 customer groups is obtained. This provides strong support for personalized power supply services for power enterprises and meeting the different needs of power customers. © 2024 IEEE.

关键词： Clustering Analysis DBSCAN Clustering Algorithm distributed computing Power Customers Clustering

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Insulator Micro-Defect Recognition Based on Improved YOLOv7 Model

Insulator Micro-Defect Recognition Based on Improved YOLOv7 ...

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2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Liu, Zhili Tang, Hong Zheng, Weigang Lu, Xuchen Huang, Fucun Yu, Bingbing Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd. Liaoning Shenyang110006 China Shenyang Agricultural University Liaoning Shenyang110000 China

ISBN: (纸本)9798350375275

In modern electric power systems, insulators, as a crucial component of transmission lines, have their condition directly linked to the safe and stable operation of the entire power grid. However, insulators are prone to minor defects under extreme weather and long-term operational conditions. These defects, if not detected and addressed in time, could evolve into serious safety hazards. Traditional insulator inspection methods rely on manual visual inspections or simple image processing techniques, which are not only inefficient but also struggle to meet the growing maintenance demands of high-voltage transmission lines in terms of accuracy and reliability. With the rapid development of deep learning technology, target detection models based on deep convolutional neural networks, such as the YOLO series, have demonstrated superior performance in many fields. This study aims to propose a more efficient and accurate method for detecting minor defects in insulators by improving the YOLOv7 model. The core of the research involves enhancing the model's ability to recognize minor defects against complex backgrounds through algorithm optimization, network structure adjustment, and improved training strategies. This paper will first review the research history and current status of insulator defect detection, analyzing the shortcomings of existing methods;then, it will detail the design and implementation of the proposed improved YOLOv7 model;followed by a series of experiments to verify the model's performance, comparing it with current advanced detection methods;and finally, discussing the practical application prospects and potential research expansion directions of the findings. The significance of this study lies not only in advancing insulator defect detection technology but also in providing new research ideas and technical support for the application of deep learning in other industrial fields. © 2023 IEEE.

关键词： Defects

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