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检索条件"机构=Key Laboratory of Intelligent Dispatching and Control of Power Grid"

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Unbalanced few-shot classification based on ME-MAML framework

Unbalanced few-shot classification based on ME-MAML framewor...

引用

第43届中国控制会议

作者： Zherun Liao Zheng Lv Qiao Sun Jun Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Electric Operations Control Center State Grid Dalian Power Supply Company

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the few-shot problem of lack of sufficient samples and uneven samples, which is common in medical image classification tasks, this paper proposes a meta-learning framework ME-MAML(Medical ModelAgnostic Meta-Learning) for few-shot medical image classification tasks. The framework aims to train a set of parameters with good generalization performance for the multi-class medical image classification task by learning the base class of medical images, and complete the generalization for the new class of medical image classification task by learning a small number of new classes of medical images. ME-MAML improves the MAML(ModelAgnostic Meta-Learning) meta-learning framework from the aspects of the feature extraction network, the loss function and the algorithmic framework, which makes the framework more suitable for few-shot medical image classification tasks. The experimental results demonstrate that for few-shot medical image classification task, the framework has better classification performance compared to supervised learning frameworks as well as advanced meta-learning frameworks.

关键词： Few-shot Learning Meta-learning Medical Image Classification

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An Integrated Energy Optimized Scheduling Strategy for VPPs Considering Electric Heating Conversion

An Integrated Energy Optimized Scheduling Strategy for VPPs ...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Ning Wu Siyang Sun Jianbin Lu Shuai Han Wenchuan Meng Zaimin Yang Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning China China Southern Power Grid Energy Development Research Institute Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper proposes an integrated energy optimized scheduling strategy for virtual power plants (VPPs) considering electric heating conversion to meet the needs of VPPs participating in multiple types of electricity market transactions. This strategy takes the regulation characteristics of energy conversion equipment, distributed energy, energy storage equipment, and user demand response as relevant constraints, with the objective function of minimizing the comprehensive operating cost of VPP, including operation, energy purchase, and demand response. The simulation results show that the proposed model can improve economic efficiency through load response strategy, and is a feasible method to solve the internal integrated energy optimization scheduling problem of virtual power plants.

关键词： Simulation Energy conversion Virtual power plants Electricity supply industry Demand response Regulation power grids Optimization Resistance heating Energy storage

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Industrial Smoke Images Generation Network Based on Bidirectional Recurrent Network

Industrial Smoke Images Generation Network Based on Bidirect...

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第43届中国控制会议

作者： Kai Chen Zheng LV Qiao Sun Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology) Ministry of Education Electric Operations Control Center State Grid Dalian Power Supply Company

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The monitoring of smoke emissions serves as a crucial reference for guiding operations in the steel industry. However,due to the scarcity of industrial smoke datasets, most deep learning methods encounter difficulties when addressing this *** address the issue of limited smoke data, this paper proposes a novel data augmentation method called the bidirectional recurrent generative network. This algorithm combines the concepts of recurrent generative networks and Laplacian pyramid networks to learn smoke generation from smoke-free blast furnaces and eliminate smoke from smoke blast furnaces. In order to overcome the limitation of recurrent generative networks in accurately depicting target contours, this paper introduces contour feature vectors, which provide precise characterization of the target contours through constraint-based feature vectors. Furthermore, to alleviate the difficulty of generation, the Laplacian pyramid model is introduced, which reduces parameters and learning time by learning from low-resolution images. To validate the effectiveness of the proposed algorithm, extensive ablation experiments and comparative trials are conducted on four different smoke image datasets from industrial scenarios with limited quantities. The results demonstrate significant improvements achieved by this paper's algorithm. Compared to other generation methods, the proposed algorithm exhibits superior performance in smoke generation tasks, offering an effective solution to address the challenges of scarce industrial smoke data and the training difficulties faced by related deep learning algorithms.

关键词： Bidirectional Recurrent Generative Network Data Augmentation Smoke Generation Laplacian Pyramid

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Design and Implementation of a Cross-Platform Real-Time power System Simulation Software for the New power system

Design and Implementation of a Cross-Platform Real-Time Powe...

引用

intelligent power and Systems (ICIPS), International Conference on

作者： Ying Huang Yuanhong Lu Jingyue Zhang Jie Zhan Yanjun Zhao Yufeng Hu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou 510663 China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou 510663 China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou 510663 China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou 510663 China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

With the rapid increase in renewable energy installation nodes and the substantial enhancement of switching frequencies in the new power system, this paper proposes a design scheme for a cloud simulation software that employs the Latency Insertion Method (LIM) to decompose large-scale new power system models into several subsystems, enabling multi-platform integrated parallel computing. The self-developed platform meets the demands of ultra-large simulation scales, ultra-small simulation time steps, and massive simulation computations in power systems. Experimental results demonstrate that the accelerated simulation technology, which integrates model segmentation using LIM with multi-core parallel execution, can enhance the simulation speed by up to 858 times while maintaining simulation accuracy. This significantly improves simulation efficiency and is suitable for large-scale simulations of new power systems.

关键词： Renewable energy sources Accuracy Computational modeling Switching frequency Software algorithms Random access memory Parallel processing Software Real-time systems power systems

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Real-time power Transmission and Transformation Online Monitoring based on Convolutional Neural Network Algorithm

Real-time Power Transmission and Transformation Online Monit...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Zhang, Yubo Feng, Yubin Huang, Chengwei Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi NanNing530000 China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Guangxi Hezhou542899 China Guangxi Key Laboratory of Intelligent Control and Maintenance Power Equipment Guangxi NanNing530000 China

ISBN: (纸本)9781665489621

The convolution operation is vectorized by using single instruction multiple data stream (SIMD). The SIMD instruction in the CPU is used to realize the vectorized convolution operation, eliminating the synchronization overhead of parallel computing, and at the same time multiplexing the data in the register, reducing the number of memory reads and writes. Relying on the power production management information system (PMS), it aims to research and establish a set of A complete and unified online monitoring system for the status of power transmission and transformation equipment. The Internet of Things system architecture for power transmission and transformation equipment at the information integration layer and the intelligent application layer, and a detailed discussion of several key technologies involved in the system architecture, mainly including integrated intelligence Monitoring device, coding and identification system, communication technology, panoramic information modeling. The original input data is grouped and allocated to different data node groups to realize data parallelism;for the data of each data node group, pipeline parallelism is adopted. © 2022 IEEE.

关键词： Convolution

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Design and Implementation of power System Simulation Platform Based on Cloud Computing

Design and Implementation of Power System Simulation Platfor...

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Energy, power and Electrical Engineering (EPEE), International Conference on

作者： Yuanhong Lu Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control.

关键词： Cloud computing Data privacy Accuracy Federated learning Data security power system simulation power system dynamics power system stability power grids Testing

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Neural Network Technology Applied in Fault Detection and Simulation of power Systems

Neural Network Technology Applied in Fault Detection and Sim...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Zeqi Hong Libin Huang Yufeng Hu Haiping Guo Yuanhong Lu Binjiang Hu Jingyue Zhang Hui Luo State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the escalating demand for power inspection and equipment maintenance, developing effective algorithms for power system fault detection is crucial for ensuring the safety and stability of power systems. This paper leveragesthe Trellis network, enhanced with an attention module, to significantly improve the Trellis network’s capability in recognizing power fault images, thus better addressing fault identification tasks within power systems. Through testing on a self-constructed dataset and comparative experiments with models such as DCNN, RCNN, and DenseNet, the proposed attention mechanismenhanced Trellis network algorithm has demonstrated its effectiveness and superiority in fault detection.

关键词： Fault diagnosis Accuracy Attention mechanisms Fault detection Neural networks power system stability Predictive models Feature extraction Electrical fault detection Maintenance

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Study on Capacity Ratio Optimization of Multi-Energy power System 5

Study on Capacity Ratio Optimization of Multi-Energy Power S...

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5th International Conference on Energy, Electrical and power Engineering, CEEPE 2022

作者： Li, Wensheng Yang, Bin Yuan, Zhenhua Huang, Dan Cheng, Peifen Yun, Jiangyang State Grid Shandong Electric Power Company Economic & Technology Research Institute Jinan China China Electric Power Research Institute Beijing China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9781665479059

In order to achieve the goal of "double carbon", it is urgent to build a new power system with new energy as the main body. It is vital for the construction and planning of new power system to reasonably optimize the installed ratio of various power supplies. To this end, this paper proposes a capacity allocation optimization method of multi energy power system based on complementary equilibrium theory, and establishes the capacity optimization models of multi-energy system such as wind power, thermal power, nuclear power and energy storage with the objective of minimizing the operation cost of the whole network. The model adopts the load characteristics, the output characteristics of new energy and the operation characteristics of energy storage power stations in the target power grid. The annual output time series of wind/photovoltaic and the load series are used as the input of the model. Through the annual time series production simulation calculation, the optimal capacity ratio of wind-photovoltaic-thermal-nuclear-storage system is determined to meet the conditions. Finally, a regional power grid in China is demonstrated as an example to verify the effectiveness and practicability of the proposed model and method. © 2022 IEEE.

关键词： Costs Time series analysis Optimization methods Production power grids power systems Planning

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power System Robust Operation Considering the Extreme Output of Renewable power Generation

Power System Robust Operation Considering the Extreme Output...

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IEEE Asia power and Energy Engineering Conference (APEEC)

作者： Yue Chen Yinguo Yang Tianyuan Xu Zhiren Zhang Pingping Xie Shuangxi Wu Power Dispatching Control Center Guangdong Power Grid Co. Ltd Guangdong China State Key Laboratory on Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an China

The topic of power system robust operation has been widely explored in recent years. However, the detailed reason why considering renewable energy extreme output is so important is seldom discussed. This paper employs a SCUC (Security Constrained Unit Commitment) model to study the influence of potential extreme output of renewable power generation on power system operation. The results are revealed through a series of case studies via the comparisons of load curtailment. The SCUC model is formulated as a MILP (Mixed Integer Linear Programming) problem and solved by customized C&CG (Column and Constrained Generation) algorithm which is implemented on a modified IEEE 24 node system.

关键词： Electrical engineering Renewable energy sources Asia Scheduling power systems Mixed integer linear programming Security

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Robustness Trend of power Transmission and Transformation Monitoring System based on Multidimensional Data Anomaly Detection Algorithm 1

Robustness Trend of Power Transmission and Transformation Mo...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Zhang, Wei Zhang, Yubo Huang, Chengwei Feng, Yubin Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi NanNing530000 China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Guangxi Hezhou542899 China Guangxi Key Laboratory of Intelligent Control and Maintenance Power Equipment Guangxi Nanning530000 China

ISBN: (纸本)9781665460842

A data anomaly detection method based on fuzzy isolated forest algorithm is proposed. By selecting some valuable attributes and building them into isolated forests, starting from multiple dimensions, the membership degree of the detection results of each dimension attribute is judged. The influence of several key factors on CPPS was investigated. The proposed weak interdependence model considers protection mechanisms such as realistic redundancy backup, which makes the model more close to the actual system operation. Great trend of change. Subsequently, the strategy of edge-break protection is further proposed to improve the robustness of the power grid, and the simulation results are shown by computer graphics, which show that it has practical value for the analysis of optimal control systems with accurate mathematical models. © 2022 IEEE

关键词： power transmission

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