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Voltage-Coupled Cascading Trip-off within Permanent Magnet Synchronous Generator Based Wind Farm during Low Voltage Ride Through 1

Voltage-Coupled Cascading Trip-off within Permanent Magnet S...

引用

2024 1st International Conference on Smart Grids and Power systems, ICSGPS 2024

作者： Zhang, Yujiang Ding, Zewen Zhang, Pengcheng Zou, Xin Yao, Ruizhe Wang, Fei Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Shandong University Jinan China State Grid Economic and Technological Research Institute CO. Ltd State Grid Corporation of China Beijing China

With the increasing penetration of renewable energy, power electronic interfaced devices have become dominant in power system. It is necessary to investigate the dynamic process of the power grid in a smaller time scale. In case of failing in low voltage ride through (LVRT), the traditional method that treats the response of permanent magnet synchronous generator (PMSG) based wind farm as a one-time trip-off cannot accurately model the cascading failures in such a new-type power system. This paper focuses on this issue and conducts a detailed study of the dynamic process in a PMSG wind farm during LVRT. Firstly, we reveal an evolution mechanism of voltage-coupled cascading trip-offs in PMSG wind farm. Then, the wind farm based on PMSG is modeled in DIgSILENT/PowerFactory. The model takes into account the voltage losses on the power collection line inside the wind farm, and each wind turbine includes an independent control model. Finally, by simulation, we obtain the principle on cascading failures of internal wind turbines in the wind farm, and confirm the interaction mechanism between turbines as well as the detailed process of cascading failures. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Wind turbines

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An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision

An Adversarial Defense Algorithm Based on Triplet Network an...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Zheng, Haonan Zhao, Jiajia Tan, Wei School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In the field of artificial intelligence, neural network is one of the key technologies used for image classification and recognition. However, recent work has demonstrated that deep neural networks are easily attacked by adversarial examples to make misjudgments. Adversarial examples are almost indistin-guishable from normal examples and yet cannot be classified correctly by neural networks. The existence of adversarial examples is a major obstacle to the practical application and deployment of neural networks, so the research on adversarial defense algorithms is an important topic in the field of AI security. This paper proposes an adversarial example defense algorithm based on a triplet network and voting decision mechanism. Firstly, two neural networks with different structures are trained based on normal dataset. Secondly, the first network is fine-tuned using the adversarial examples generated by these two networks, resulting in a third neural network. Then, these three neural networks are used as sub-networks in parallel to construct a triplet network. Through adversarial training and differences in structures, the transferability of adversarial examples among the three sub-networks is weakened. Finally, the final classification result is obtained by majority voting, based on the parallel output results of the three sub-networks. Through the complementarity between these three sub-networks, the defense against adversarial examples is realized. The experimental results demonstrate the effectiveness of this algorithm. © 2022 IEEE.

关键词： Deep neural networks

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Optimal Economic Dispatch Strategy for Microgrids Considering Distributed Energy Storage Aggregation 7

Optimal Economic Dispatch Strategy for Microgrids Considerin...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Kan, Changtao Luo, Xin Liang, Shuang State Grid Jinan Power Supply Company Jinan China State Grid Shandong Electric Power Company Maintenance Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350366945

User-side distributed energy storage, as a flexible demand response resource, possesses excellent source-load interaction characteristics and can effectively interact with the power grid. However, currently in China, user-side distributed energy storage resources often face difficulties in directly participating in grid regulation due to factors such as dispersed geographical distribution, small individual installed capacity, and uncertain output time. To address this issue, this paper proposes an optimal economic dispatch strategy for microgrids that considers distributed energy storage aggregation. First, a distributed energy storage aggregator is introduced to manage scattered user-side distributed energy storage resources in a scaled manner, and the scheduling potential of distributed energy storage in each time period is assessed based on consumer psychology theory. Subsequently, a multi-objective optimal dispatch model for microgrids is constructed, which takes into account both economic and environmental aspects. The multi-objective optimization problem is transformed into a single-objective optimization problem using the weighted assignment method, and the CPLEX solver is employed to solve the problem. Finally, the effectiveness and feasibility of the proposed method in this paper are verified through a microgrid test system. © 2024 IEEE.

关键词： consumer psychology theory distributed energy storage resources economic dispatch

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Feasibility Evaluation and Application Practice of Distributed Photovoltaic Power Generation in Large Commercial Buildings 17th

Feasibility Evaluation and Application Practice of Distribut...

引用

17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Sun, Liucun Lou, Zhongkai Li, Jingjing Su, Weijiang China Green Development Investment Group Co. Ltd. Beijing100010 China Key Laboratory for Intelligent Control and Decision on Complex Systems School of Automation Beijing Institute of Technology Beijing100029 China

ISBN: (纸本)9789819904075

Large commercial complex projects have the characteristics of large roof area and high electricity price, and the development of distributed photovoltaic power generation has great potential. In this paper, a feasibility evaluation model of distributed photovoltaic power generation in large commercial buildings is established from the aspects of solar energy resources, power generation, economics and environmental benefits. A commercial complex photovoltaic project in Jinan is used as an example. In the case of fully considering the project operating cost and environmental benefits, the feasibility of the project under different power consumption modes is analyzed. The research results show that the economy of large-scale commercial distributed photovoltaic power generation projects in class III solar resource areas such as Jinan is directly related to the power consumption mode. When the "spontaneous self-use" mode is adopted, the economic benefits of the project are good and the feasibility is strong;When the "excess electricity goes into the power grid" mode is adopted, the economic benefits gradually deteriorate with the increase of the proportion into the power grid, but considering the environmental protection benefits of photovoltaic power generation projects, it is still feasible;When the "full the power goes into the power grid" mode is adopted, the economic benefit is poor, and the project is basically not feasible. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Electric power utilization

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Variational Autoencoder Based Network Embedding Algorithm for Protein Function Prediction 2022

Variational Autoencoder Based Network Embedding Algorithm fo...

引用

14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Cao, Guansong Zhu, Yuan Yi, Ming School of Automation China University of Geosciences China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System China University of Geosciences Wuhan China School of Mathematics and Physics China University of Geosciences Wuhan China

ISBN: (纸本)9781450395700

The development of high-throughput technology has produced a large number of protein-protein interaction datasets, which provide an effective way to infer the functional annotation of proteins. However, how to make proper use of these datasets to extract effective low-dimensional feature representation of proteins for functional prediction is a challenge. Most existing network integration methods for protein function prediction have some limitations to capture complex and highly non-linear network structure information due to their design architecture. Therefore, we propose a novel multi-network embedding method deepVAE based on deep variational autoencoder (VAE), which uses the variational autoencoder to extract low-dimensional features of proteins from multiple various interactive network datasets and then trains a SVM classifier to predict protein function. Particularly, we denoise the original networks before network embedding, thus the new proposed method is called deepVAE-NE. The experiments are conducted on the yeast and human protein-protein interaction datasets and the experimental performance shows that our methods perform better than the other four compared advanced approaches, which greatly improves the accuracy of functional prediction. © 2022 ACM.

关键词： Proteins

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Coordinated control Strategy of Grid-Connected Photovoltaic Generation system 8

Coordinated Control Strategy of Grid-Connected Photovoltaic ...

引用

8th Asia Conference on Power and Electrical Engineering, ACPEE 2023

作者： Zhang, Jian Zhang, Hengxu Cao, Yongji Liu, Xiaoming Zou, Shihao Ma, Ruicong Shandong University Key Laboratory of Power System Intelligent Dispatch Control of the Ministry of Education Jinan China Shandong University Academy of Intelligent Innovation Jinan China Economic and Technological Research Institute State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350345520

The development and utilization of renewable energy is imperative, and photovoltaic power generation has broad development prospects. However, photovoltaic power generation systems are limited by many external conditions, which bring more problems to power output and grid connected power quality. Therefore, in order to make better use of photovoltaic power generation, this paper proposes a coordinated control strategy of the grid-connected photovoltaic generation system. The operating principle, mathematical model, and output characteristics of photovoltaic cells are analyzed. Then, the MPPT algorithm and the operating principle of DC/DC converters are studied. Moreover, the structure and control strategy of grid connected inverters are analyzed. A simulation model of a three-phase photovoltaic grid-connected inverter system is established, and a control strategy based on grid voltage orientation is adopted. A case study is conducted to verify the proposed control strategy. © 2023 IEEE.

关键词： Maximum power point trackers

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Multi-Agent Reinforcement Learning with Region Division and Leader-Follower Communication

Multi-Agent Reinforcement Learning with Region Division and ...

引用

Chinese Automation Congress (CAC)

作者： Chao Wang Xiang Xu Shenzhen Key Laboratory of Control Theory Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving ef-ficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL frame-work. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of fol-lower agents and handling inter-region communication. This hier-archical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle en-vironments demonstrate RDLF's superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment.

关键词： Automation Scalability Heuristic algorithms Collaboration Reinforcement learning Problem-solving complex systems Multi-agent systems

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Anomaly Detection of Wind Turbine Generator Based on LSTM and Kshape Clustering

Anomaly Detection of Wind Turbine Generator Based on LSTM an...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Zheyu Jin Ming Yang Xiangjun Zeng Chen Feng Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

Anomaly detection of wind turbine generators helps to detect faults of related components in advance, thus improving the economy and reliability of wind farm operation. This paper presents a generator anomaly detection method using long short-term memory neural network (LSTM) and Kshape clustering algorithm. This work first considers the characteristics of wind turbine supervisory control and data acquisition(SCADA) data, proposes a feature analysis method combining correlation coefficients and feature ranking, then the Kshape clustering algorithm is used to select wind turbines with similar operating states to expand the training dataset. Anomaly detection state estimation model is constructed by combining well-performanced LSTM models trained by selected units' dataset. Finally, the feasibility and accuracy of the proposed method are verified based on the SCADA data of actual wind farms.

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Short-Term Load Forecast Based on Feature Reconstruction and Bidirectional LSTM

Short-Term Load Forecast Based on Feature Reconstruction and...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Xudong Zheng Ming Yang Yixiao Yu Menglin Li Chuanqi Wang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

Short term load forecasting is a guarantee for the safe, stable, and economic operation of the power system. With the large-scale integration of distributed new energy, load fluctuations increase, and the input feature data dimension increases during forecasting. Therefore, it is particularly important to screen and process input features reasonably when using deep neural networks for prediction. In response to the varying degrees of influence of various feature factors at different time points, this paper proposes a feature screening and input feature reconstruction method based on the maximum information coefficient of MIC, which achieves feature impact analysis, screening, and input feature reconstruction at sampling point granularity. The BI-LSTM neural network with stronger information mining ability is introduced to achieve high-precision short-term load forecasting. The results show that using the proposed input feature reconstruction strategy and assigning input feature weights according to the degree of influence significantly improves the prediction accuracy of deep neural networks. The method proposed in this paper has good adaptability to load fluctuations..

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