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Clustering and Equivalent Modeling Method for direct-drive Wind Farms Based on Particle Swarm Neural Network 4

Clustering and Equivalent Modeling Method for Direct-Drive W...

引用

4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Tu, Liang Huang, Libin Guo, Haiping Hu, Binjiang Zhang, Jingyue Luo, Hui State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&d Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

In the realm of large-scale integration of wind farms or clusters into power grids, the fault transient characteristics of these grids have experienced significant transformations. Unfortunately, the prevalent single-machine equivalent models are inadequate in accurately depicting the fault transient behavior of wind farms or clusters. Moreover, their simulation computations are exceedingly intensive, resulting in suboptimal simulation efficiency. To address these challenges, this paper introduces a clustering and equivalent modeling approach tailored for direct-drive wind farms, leveraging the capabilities of a particle swarm neural network. Initially, a transient model for direct-drive wind turbines is formulated, and fault characteristic influence factors are established through theoretical analysis. These factors encompass the distance between the wind turbine and the point of common coupling (PCC), the implementation of dC-side current limiting measures, wind speed, and reactive power at the outlet. Following this, the Euclidean distance of the influence factor set is computed, and the initial cluster centers of wind turbines are derived using an enhanced max-min distance method. Ultimately, an equivalent clustering model is formulated by utilizing the influence factors and initial cluster centers as the training set. Simulation outcomes reveal that, in comparison to the single-machine equivalent model, this method enhances simulation efficiency by a range of 62% to 67% and reduces the steady-state error in fault current simulation by 0.8% to 1.9%. © 2024 IEEE.

关键词： Windmill

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A Networked Method Applied to Sensitivity Analysis of a VSG controlled BESS system 5

A Networked Method Applied to Sensitivity Analysis of a VSG ...

引用

5th International Conference on Power Engineering, ICPE 2024

作者： Sun, Zhiyuan Luo, Chao Zheng, Kun Zhu, Yihua Yu, Jiawei Guangxi Power Grid Electric Power Research Institute China S. Power Grid Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc Guang zhou China National Energy Power Grid Technology R&d Centre Guang zhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guang zhou China

ISBN: (纸本)9798331531140

The grid-forming battery energy storage system (BESS) plays important role in constructing new energy system. This paper presents a frequency domain modal-based stability analysis of virtual synchronous generator (VSG) controlled BESS. Both ac and dc side interactive dynamics of the grid-tied inverter system are considered, by establishing a hybrid ac/dc impedance network. Then, the frequency domain modal analysis (FMA) method for networked converter systems is applied to the BESS system, covering the eigen-sensitivity from the node level to parameter level. Using this method, key parameters that influence the BESS system's weakly/negative damping modes most can be better analyzed and identified. The analysis results are validated via the time-domain simulations. © 2024 IEEE.

关键词： Frequency domain analysis

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Technical research on Simulation and Predictive Analysis of Wind Farms in Power systems 4

Technical Research on Simulation and Predictive Analysis of ...

引用

4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

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

ISBN: (纸本)9798331518066

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. Simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance. © 2024 IEEE.

关键词： Wind forecasting

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The development of Simulation Mirror system and its Application in HVdC 6

The Development of Simulation Mirror System and its Applicat...

引用

6th International Conference on HVdC, HVdC 2024

作者： Ou, Kaijian Wu, Wencong Cai, Haiqing State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre. Guangzhou China Guangdong Prov. 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: (纸本)9798350387759

The testing of HVdC new control function and training of HVdC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&dC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVdC new control function testing and HVdC simulation training cabinet are also introduced in this paper. © 2024 IEEE.

关键词： automatic generation control (A GC) automatic HVdC control (AdC) dispatching automation high voltage direct curren t(HVd C) real-time digital simulator(rTdS)

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Application of deep reinforcement learning in power system scheduling strategy simulation 4

Application of deep reinforcement learning in power system s...

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4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

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

ISBN: (纸本)9798331518066

With the increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using dQN (deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime. © 2024 IEEE.

关键词： deep reinforcement learning

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design and Implementation of Power system Simulation Platform Based on Cloud Computing 4

Design and Implementation of Power System Simulation Platfor...

引用

4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue 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: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Cloud platforms

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distributed Power system Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MAdrL 4

Distributed Power System Simulation and Monitoring Supported...

引用

4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid 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 National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

This study explores the optimization of distributed Generation systems (dGS) within a Multi-Agent deep reinforcement Learning (MAdrL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources. © 2024 IEEE.

关键词： Cloud platforms

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Large-scale non-real-time Simulation Platform for Power systems Based on Cloud Computing 4

Large-scale non-real-time Simulation Platform for Power Syst...

引用

4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Zhang, Jingyue Lu, Yuanhong Hu, Yufeng Zhang, Jie Guo, Haiping Huang, Libin Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China China Southern Power Grid Co. Ltd Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

The rapid evolution of smart grids has increased the complexity of power system structures and operational characteristics, presenting new challenges in system management and reliability. Traditional power system simulation tools struggle with non-real-time data processing and scalability due to limitations in their architectural designs. Moreover, existing cloud simulation platforms are constrained by closed hardware architectures and lack sufficient non-real-time capabilities. As a solution, we propose a novel large-scale non-real-time simulation platform for power systems based on cloud computing, designed with a modular architecture. By leveraging advanced technologies in cloud computing, parallel computing, and non-real-time simulation, our platform addresses critical needs for scalability, efficiency, and cost-effectiveness. We discuss how integrating these technologies enhances computational performance and supports complex simulation tasks, ultimately improving decision-making and operational efficiency in power system management. © 2024 IEEE.

关键词： Cloud platforms

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Adaptive Identification of Power system Operational States Based on Spatio-Temporal dynamic Graph Neural Networks 4

Adaptive Identification of Power System Operational States B...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Zhang, Qingqing Ma, Qinfeng Liu, Mingshun Zhang, Jie Zhu, Yihua Liang, Zhuohang An, Su Pu, Qingxin dai, Jiang Power Dispatching and Control Center of Guizhou Power Grid Co. Ltd. Guizhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou510663 China National Energy Power Grid Technology R&D Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China

ISBN: (纸本)9798350361643

The state identification of the operational status of power systems plays a crucial role in ensuring the safety, efficiency, and reliable operation of power systems, forming the foundation for the management and control of power system operations. In the practical operation of power systems, the duration of fault states is short, while the system remains in a safe and stable state for a significantly longer period, leading to an imbalance in the training samples available. This imbalance can cause traditional machine learning classification algorithms to favor the majority categories of power system operational states, resulting in inaccurate evaluation metrics and reduced model generalization capability. To address this issue, this paper introduces a spatiotemporal dynamic graph neural network model for the identification of power system operational states. By incorporating class cost coefficients, focal loss, and cross-entropy in a joint loss function, the model adaptively focuses on the less represented operational state categories during training, improving classification accuracy on imbalanced and challenging samples. Moreover, the integration of temporal information and spatial topology information within the graph neural network enhances the model's robustness and generalization ability in state identification. Experimental results on the standard IEEE 68-bus system demonstrate that, compared to traditional machine learning methods, the proposed joint loss with the spatiotemporal dynamic graph neural network model achieves higher accuracy in the identification of power system operational states, significantly improving classification performance on imbalanced samples with a 4.8% increase in F1-score, reaching a peak F1-score of 98.6%. © 2024 IEEE.

关键词： Graph neural networks

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Mathematical Modeling of the Impact of a 5 day Interim relaxation at 21 days in 42 days Lockdown Strategy on COVId-19 Transmission in India 10

Mathematical Modeling of the Impact of a 5 Day Interim Relax...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Immaculyne, Flavia Bakiyaambikapathy Krishnamurthy, Kamalanand de Britto, Lourduraj Benoi Cresent Schlumberger- Reda Production System Singapore Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Anna University Department of Instrumentation Engineering MIT Campus Chennai India Vector Control Research Centre Indian Council of Medical Research Puducherry India

ISBN: (纸本)9798350350951

As of the first week of May 2020, the COVId-19 pandemic is present in 212 nations globally. Many countries have enforced lockdown measures strategically to slow down the epidemic's spread, with the objective of relieving pressure on healthcare systems. Mathematical modelling serves as a useful tool for the prediction of the future numbers of the infected population and for analyzing the effects of various intervention strategies. In this work, a mathematical model based on ordinary differential equations has been developed for analyzing the impact of an interim relaxation of 5 days at 21 days in a 42 days lockdown period. results of the simulation demonstrate that the relaxation of 5 days during the 42 days lockdown does not result in any adverse effects if the transmission rate remains the same. However, the model-based analysis suggests that an increase in the transmission rate by a factor of 1.2 times during the relaxation period due to the possible aggregation of the susceptible with the infected, it could shift the growth pattern of infected cases from sub-exponential to exponential. © 2024 IEEE.

关键词： COVId-19

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