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D-PSO: An Optimization Algorithm for Wireless Sensor Network Layout in power Systems 9

D-PSO: An Optimization Algorithm for Wireless Sensor Network...

引用

9th International conference on automation, Control and Robotics engineering (CACRE)

作者： Ma, Pengwei Zhu, Yuanjiao Sun, Zheng Wei, Jingyi Luo, Yunchen Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this paper proposes a novel method named the Distributed Particle Swarm Optimization algorithm (D-PSO). This method mitigates the premature convergence issue of heuristic algorithms by introducing a regional operator. Additionally, considering the high-interference environment in power systems, relay node strategy (RNS) is incorporated to ensure communication quality. Simulation results validate the effectiveness and superiority of the D-PSO method and demonstrate the necessity of the RNS. Compared to some advanced particle swarm algorithms, this method better balances energy consumption, coverage, and communication quality, thereby significantly enhancing the overall performance of the wireless sensor network.

关键词： wireless sensor network power systems Particle Swarm Optimization algorithm

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A Flexible Charging Pile Scheduling Optimization Platform Based on Ant Colony Algorithm 7

A Flexible Charging Pile Scheduling Optimization Platform Ba...

引用

7th International conference on automation Electronics and Electrical engineering

作者： Li, Guoqing Hu, Hanghua Miao, Leying Wu, Hao Li, Shenna Huang, Yihua Liu, Jiaxin Wang, Linyan Cai, Xingpu Guo, Xiongbai Xiao, Yiyao Shangcheng Power Supply Branch 158 Jianguo South Rd Hangzhou Peoples R China

ISBN: (纸本)9798350377040;9798350377033

In this paper, taking the load of electric vehicle charging piles as the cutting point, by analyzing various constraints such as the access state of electric vehicles and the characteristics of charging piles, a refined single equipment model and a participation demand response adjustment model are established, an optimal scheduling strategy and incentive compensation measures are proposed, and a charging station heuristic insertion algorithm is designed to quickly calculate the insertion position of charging stations. At the same time, an improved ant colony algorithm was proposed by designing a local search algorithm and integrating it into the iterative process of ant colony algorithm. Experimental results show that the local search embedded in the ant colony algorithm has significant advantages in improving the quality of the solution. It provides theoretical and practical support for the application of demand-side resources in the power market, and provides new ideas and methods for the balance of supply and demand in the power system and the development of new energy.

关键词： Flexible charging Ant colony algorithm Charging load

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A Distributed and Self-powered Soil Monitoring Network for Smart Agriculture 16

A Distributed and Self-powered Soil Monitoring Network for S...

引用

16th International conference on Computer and automation engineering (ICCAE)

作者： Yang, Jianzhi Luo, Yichen Hou, Yixuan Wu, Zihang Li, Jianxing Guo, Yanxue Hu, Yifei You, Haotao Zhu, Xiaohui Yue, Yong Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou Peoples R China

ISBN: (纸本)9798350370058;9798350370164

Real-time soil fertility monitoring can help evaluate soil quality in time for soil improvement, which in turn improves agricultural yields. This paper presents a self-powered and distributed soil monitoring network for smart farms. Each monitoring node is powered by a battery (5000 mAh) and a small photovoltaic panel to continuously charge the battery, which enables the monitoring nodes to be widely distributed in the farm and continuously monitor soil data with soil fertility parameters, such as nitrogen, phosphorus, potassium, pH, moisture and temperature. All monitoring data can be transferred to a remote data server in the cloud via a 4G/5G network and MQTT protocol. This allows users to analyse the soil monitoring data via an APP to support further operational decisions, such as watering or fertilising. To minimise power consumption and maximise the duration of the power supply, a power management circuit is designed to support the sleep mode, which can turn off the sensors and 4G module during the sleeping period and turn them back on for data monitoring and transmission. Experimental results show that even without charging from the photovoltaic panel, the battery can support the continuous operation of the monitoring node for about eight days, which means that the designed system can be widely used in wild farms for long-term soil monitoring.

关键词： IoT MQTT 4G Soil monitoring Low power consumption Software design

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Self-supervised Learning-based Algorithm for Chinese Image Caption Generation in Electric Robot Inspection Processing 7

Self-supervised Learning-based Algorithm for Chinese Image C...

引用

7th International conference on automation Electronics and Electrical engineering

作者： Qian Ping Zhang Yong Cao Yang Zhou Gang Qi Zhongyi State Grid Dongtou Power Supply Co Dept Management Hangzhou Peoples R China State Grid Zhejiang Elect Power Dept Equipment Hangzhou Peoples R China State Grid Jiaxing Power Supply Co Dept Operat & Maintenance Jiaxing Zhejiang Peoples R China State Grid Zhejiang Elect Power Jiaxing Power Sup Dept Operat & Inspect Jiaxing Zhejiang Peoples R China State Grid Zhejiang Elect Power Jiaxing Power Sup Dept Operat Jiaxing Zhejiang Peoples R China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040;9798350377033

Electric robot will obtain a large amount of image information during inspection, and if these images are checked whether there are faults in power inspection is time-consuming and labor-intensive. There is an urgent need for power image Chinese title generation technology to solve it. However, existing image Chinese title generation methods face the problems of small training data sets, differences in specific applications, and few methods for generating Chinese titles for power images. To this end, this paper proposes a self-supervised learning-based image Chinese title generation algorithm for fault detection in electric robot inspection. Specifically, a contrastive learning-based model to automatically capture the semantic relationship between images and text. Then, we propose an end-to-end encoding-decoding model combined with an attention mechanism to obtain Chinese title generation for inspection images. The effectiveness of the proposed algorithm is experimentally verified on two real datasets.

关键词： self-supervised learning attention mechanism encoder-decoder image caption generation electric robot Semantic feature

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A Method for Maximizing Transmission power in Omnidirectional Wireless power Transfer Based on Particle Swarm Optimization 7

A Method for Maximizing Transmission Power in Omnidirectiona...

引用

7th International conference on automation Electronics and Electrical engineering

作者： Zhao, Yusheng Xu, Lian Tang, Fucan Ye, Zhaohong Zhu, Wanting Chongqing Univ Coll Automat Chongqing Peoples R China Chongqing Univ Elect Engn & Automat Chongqing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

Currently, few theoretical analysis models focus on irregularly shaped receiving coil structures in the wireless power transfer field. On this basis, this paper conducts a theoretical analysis of the issues related to load posture angles and irregularly shaped receiving coil structures. It proposes an optimization method for a full-range, all-attitude spatial wireless power transfer system based on the particle swarm optimization algorithm. Firstly, a three-coil orthogonal spherical structure is used at both the transmitter and receiver end to realize full-space energy transfer. Secondly, the realization method proposed in this paper is not limited by the specific structure and attitude angle of the receiving end and can realize wireless energy transmission in space under general environments. Moreover, it does not impose restrictions on the number of receiving coils, making it applicable to models with multiple receiving coils. Finally, an experimental prototype with a transmission distance of 40cm and transmission power of 4W was constructed to validate the above theory. The experimental results show that the system can accurately achieve spatial positioning of the load and enable omnidirectional WPT. Furthermore, the system's control optimization theory can serve as a theoretical reference for other multi-load or irregular-load systems.

关键词： Omnidirectional wireless power transfer Particle Swarm Optimization Maximum power

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An Ultra-High-Speed MMC-HVDC Fault Location Algorithm Based on Regression Neural Network 16

An Ultra-High-Speed MMC-HVDC Fault Location Algorithm Based ...

引用

16th International conference on Computer and automation engineering (ICCAE)

作者： Zhang, Yunqi Yu, Yue China Elect Power Res Inst State Key Lab Secur & Energy Saving Beijing Peoples R China

ISBN: (纸本)9798350370058;9798350370164

A new ultra-high-speed fault location algorithm based on regression neural network (RNN) is proposed for modular multilevel converter-based high-voltage DC (MMC-HVDC) grids, which is suitable for MMC-HVDC grids equipped with quick-action protections and hybrid DC circuit breakers (HDCCBs) utilizing 2.5 ms postfualt data window. Firstly, the analyses based on the lumped RLC equivalent circuit demonstrate that the delay time, the first negative peak time and its value all have exact relationships with the fault location and the transition resistance. Nevertheless, considering the actual parameters and topology of the MMC-HVDC grid, three characteristic variables could not be extracted accurately within the allowed time window. Thus, RNN is utilized to estimate fault locations through the three characteristic variables. The test statistics on a four-terminal MMC-HVDC grid for 2020 different fault cases excluded in the training dataset validate the proposed algorithm's fairly high accuracy and robustness for different fault locations and tolerance to transition resistance up to 1005 Omega.

关键词： fault location MMC-HVDC transient voltage response extraction of characteristic variables regression neural network

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power Equipment Fault Prediction and Diagnosis Algorithm Driven by power Big Data 7

Power Equipment Fault Prediction and Diagnosis Algorithm Dri...

引用

7th International conference on automation Electronics and Electrical engineering

作者： Shu, Lu Ji, Tingting Zhang, Longxin Li, Wenda State Grid Info Telecom Great Power Sci & Technol Fuzhou 350003 Fujian Peoples R China

ISBN: (纸本)9798350377040;9798350377033

The fault of power equipment must be dealt with in time, otherwise the damage degree may continue to increase, which will not only greatly increase the complexity and cost of maintenance, but also have a chain reaction to the surrounding equipment, thus leading to the expansion of the fault range. In this paper, Transformer model is introduced to deeply mine and analyze the historical operation data of power equipment. In the concrete implementation of the algorithm, the multi-dimensional operation data of power equipment are collected comprehensively and continuously through the sensor network;then, these original data are strictly cleaned and preprocessed, such as noise removal, missing value filling and standardization. Then, the carefully selected feature sets are input into the Transformer model and trained with rich historical fault data. Finally, the trained Transformer model is deployed to the power system to realize real-time monitoring and fault early warning of power equipment. When the potential failure risk is detected, the model immediately triggers the early warning mechanism and informs the operation and maintenance personnel to take corresponding preventive measures or maintenance plans. The experimental data show that the average prediction accuracy rate of the Transformer model proposed in this paper is 97.19%, and the prediction accuracy rate of fault types is above 90.2%, which all show better prediction performance than SVM. This paper not only shows the outstanding advantages of Transformer model in the field of financial forecasting, but also provides new ideas for future research.

关键词： power Equipment Fault Prediction power Big Data Transformer Model Self-Attention Mechanism

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Defect Detection Method for power Communication Fibre Optic Cables Based on Improved YOLOv8 7

Defect Detection Method for Power Communication Fibre Optic ...

引用

7th International conference on automation Electronics and Electrical engineering

作者： Wang, Wanxin Hou, Guangwei Zhou, Hang Zhang, Zhifei Li, Wenyue Liu, Peng State Grid Jilin Elect Power Co Ltd Tonghua Power Supply Co Tonghua Peoples R China

ISBN: (纸本)9798350377040;9798350377033

In response to the problems of limited accuracy, inability to effectively identify subtle defects, and insufficient ability to handle different input resolutions and aspect ratios in defect detection of power overhead communication optical cables, this paper proposes a defect detection method for power communication optical cables based on improved YOLOv8. This method introduces a Global Connectivity Part (GCP) module into the Neck network to enhance intra-layer feature interaction and enrich feature details. By replacing it with a dynamic detection head, it automatically adapts to the input shape to generate anchor boxes, significantly improving the recognition accuracy and flexibility of small targets such as line damage, loose fittings, and other defects. The experimental results show that compared with the original YOLOv8 and other mainstream algorithms, this method not only performs well in reducing false positives on the dataset provided by the State Grid Jilin Electric power Co., Ltd. Tonghua power Supply Company, but also effectively detects the vast majority of defects, reducing the possibility of missed detections. Its accuracy, recall rate, and mAP@50 have all been significantly improved, demonstrating good practical application potential.

关键词： power overhead communication optical cable improvement of the YOLOv8 model GCP module dynamic detection head

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Fault diagnosis and disposal platform for power grid automation system based on RPA 4

Fault diagnosis and disposal platform for power grid automat...

引用

4th International conference on Energy, power and Electrical engineering, EPEE 2024

作者： Xu, Jiong Sha, Yichuan Lao, Yingying Peng, Cheng Guo, Dongsheng Wang, Jili Ji, Huiying Nari Group Corporation Nanjing China

ISBN: (纸本)9798331518066

In light of the development trends of computer technology and the pain points in the intelligent operation and maintenance of power and grid automation system, this paper introduces a universal platform for fault diagnosis and disposal of power grid automation system that caters to diverse scenarios and business needs. This platform allows users to create customized RPA (Robotic Process automation) processes tailored to specific application scenarios, supports multiple execution methods, and offers flexible expansion of atomic operations through a plugin-based approach. Furthermore, it supports distributed deployment, enabling seamless adaptation to the expansion of power grid automation systems. With its strong versatility and scalability, this platform can provide support to various departments and businesses, significantly enhancing the accuracy and efficiency of fault diagnosis and disposal while reducing labor costs. © 2024 IEEE.

关键词： power grids

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Method for Modeling Photovoltaic Uncertainties Based on Improved Conditional Generative Adversarial Networks 7

Method for Modeling Photovoltaic Uncertainties Based on Impr...

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7th International conference on automation Electronics and Electrical engineering

作者： Ge, Wen Liu, Tianma Li, Junlin Han, Chang Guangzhou Elect Power Design Inst Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the extensive adoption of renewable energy, the uncertainty of photovoltaic (PV) output poses significant challenges to power grid scheduling and planning. To address the issue of PV output uncertainty, this paper proposes an improved Conditional Generative Adversarial Network (CGAN)-based modeling method for PV uncertainty. The method incorporates the characteristic data of PV output as conditional information for the CGAN and employs the Wasserstein distance as the discriminator's loss function. Subsequently, through adversarial training of the CGAN, the generator learns the mapping relationship between random noise and the real historical dataset, enabling the efficient generation of scenarios closely resembling the real distribution. Comparative analysis and validation through case studies demonstrate that the proposed method accurately captures the uncertainty of renewable energy output.

关键词： Photovoltaic Output Uncertainty Data Generation Conditional Generative Adversarial Network Deep Learning

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