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Optimal Robust control Scheme for SSSC System Based on H∞ to Improve Power System Small Signal Stability

Optimal Robust Control Scheme for SSSC System Based on H∞ t...

引用

Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International conference on

作者： Mahsa Kashani Zhenyu Yang Mohammad Ali Lasemi AAU Energy Aalborg University Denmark

Static Series Synchronous Compensator (SSSC) is one of the controllable reactive power series compensators, which is applied to modify the power flow in the transmission line and enhance the electrical grid’s small-signal stability. In this paper, a novel damping controller for the electrical network equipped with SSSC is presented considering H ∞ control theory and Teaching Learning Based optimization (TLBO) algorithm based on a fixed structure control scheme to reduce the controller order and minimize the system error. The suggested mechanism guarantees both meet the robustness criteria and move the undamped and lightly damped nodes toward the specified stability region. Therefore, a fixed structure controller is employed and designed by proposing a novel optimization problem. Moreover, SMIB connected to the SSSC system is applied to analyze the electrical network’s small-signal stability. Finally, the proposed controller performance is compared to other conventional robust control methods and the simulation results show the controller achieves both excellent damping and acceptable robust performance compared to other methods.

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Emergency Material Supply for Power Enterprises Based on Internet of Things under Short-Term Prediction of Echo State network 3

Emergency Material Supply for Power Enterprises Based on Int...

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3rd International conference on Advanced Electrical and Energy Systems, AEES 2022

作者： Zhao, Xiaowei Yang, Hui Li, Shuaibing Li, Yixuan Yi, Longlong Zhang, Daqi Linxia Power Supply Company State Grid Gansu Electric Power Company Lanzhou China School of New Energy and Power Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9781665472081

The electric power industry is one of the major national infrastructures. Affected by the geographical environment, weather, sudden natural disasters, cost control, and other factors, and with the improvement of people's living standards and the requirements of large equipment for power supply quality, power supply enterprises put forward higher requirements on how to resist risks before accidents and rapid emergency response after accidents. The establishment of emergency material supply system for power supply enterprises based on the internet of things mode is conducive to the reform of supply side structure and the optimization of resource allocation. At the same time, it is of constructive significance for the development of intelligent and automatic emergency material package management. With the help of the analysis steps of prognostics and health management (PHM), driven by the internet of things of emergency material reserves, and starting from the four structural factors of storage, cost, risk, and technology, the data acquisition, transmission, preprocessing and short-term prediction systems of emergency material packages are established respectively, to improve the operation, maintenance, and management level of power supply enterprises. © 2022 IEEE.

关键词： Data acquisition

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Prediction and optimization of Power Grid Cross Section Exceeding Limits based on Generative Adversarial networks

Prediction and Optimization of Power Grid Cross Section Exce...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Bao Li Yinsheng Su Xin Zhou Ligang Zhao Hongyue Zhen Yuan Xu Power Dispatch and Control Center of China Southern Power Grid Guangzhou Guangdong China Power Dispatch and Control Center of China Southern Power Grid Guangzho Guangdong China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Exceeding the cross-sectional power limit of the power grid poses significant risks to the safety and stability of power system operations. Traditional prediction methods based on models and historical data face challenges in accurately capturing the nonlinear characteristics of power grids under complex environments. This study proposes a novel method using Generative Adversarial networks (GANs) to predict and optimize cross-sectional power violations. GANs are utilized to train a generator network to learn the distribution of power grid operation data and simulate realistic scenarios, combined with a discriminator network in an adversarial framework to enhance prediction accuracy. Additionally, a power optimization scheduling strategy is developed to minimize the probability of exceeding limits using the prediction results. Experimental findings reveal that the GAN model significantly outperforms ARIMA and ANN models in cost optimization, achieving an average reduction of 1919 yuan/month and a maximum of 1984 yuan/month. These results demonstrate the superior performance of GAN models in predicting and optimizing power grid operations.

关键词： Training Costs Predictive models Power system stability Generative adversarial networks Power grids Stability analysis Generators Data models optimization

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Performance Evaluation of First Hop Redundancy Protocols in IPv4 and IPv6 networks

Performance Evaluation of First Hop Redundancy Protocols in ...

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Sciences and Techniques of Automatic control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the conference on

作者： Najia Ben Saud Mahmud Mansour Department of Network University of Tripoli Tripoli Libya

To enhance IP network availability, First Hop Redundancy Protocol (FHRP) is a necessary tool that used to achieve this goal. FHRPs are group of protocols that used to manage and maintain network first hop router (i.e. default router) failure. This work compares and evaluates different FHRPs which are Hot Standby Router Protocol (HSRP), Virtual Router Redundancy Protocol (VRRP), and Gateway Load Balancing (GLBP), in both IPv4 and IPv6 networks. The comparison among the three mentioned protocol was accomplished based on convergence time, packet loss and CPU utilization, after implementation, testing and optimization. GNS3 emulation tool was used. The comparison results highlights which protocol performed the best in each scenario and which protocol can be considered as the best among the three FHRPs.

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Proceedings - 2022 International conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022

Proceedings - 2022 International Conference on Industrial Io...

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3rd International conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022

ISBN: (纸本)9781665454551

The proceedings contain 62 papers. The topics discussed include: design and implementation of best master clock selection algorithm based on FPGA;design of rural road lighting system based on Internet of things and deep learning;new energy vehicle full plastic body advanced manufacturing technology;on the optimization of new energy automobile power system;technological evolution of lithium batteries for new energy vehicles;research on deployment scheme of aggregation control gateway for hybrid network of virtual power plant;design and implementation of internet of things monitoring system based on neural network algorithm and cloud edge collaborative architecture;state sensing and timely delay measurement method for virtual power plant communication network;and research and implementation of medium and high voltage electric intelligent topology editing tool considering distributed new energy access.

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Research on Precise Timing Method of Airborne Wireless Sensor network for Flight Test 3

Research on Precise Timing Method of Airborne Wireless Senso...

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3rd International conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Li, Tielin He, Jing Institute of Testing Chinese Flight Test Establishment Xi'an China

ISBN: (纸本)9781665492812

Aiming at the problem of the precise timing of airborne wireless sensor networks under the constraints of size and power consumption, a two-step method for precise timing is proposed. The first step is to complete the initial timing by broadcasting the time information. In the second step, the clock's precise alignment method based on improved PTP time deviation calculation and calculation optimization of frequency difference based on least squares and coefficient weighting are proposed. The proposed method achieves efficient and precise clock alignment between the wireless sensor and the main clock unit. Finally, the precise timing of the wireless sensor network is better than 10μs in the airborne test environment of the flight test, which meets the requirements of practical engineering application. © 2022 IEEE.

关键词： airborne wireless sensor networks flight test precise timing

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network Attack Strategy Generation Based on Multiagent Reinforcement Learning

Network Attack Strategy Generation Based on Multiagent Reinf...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International conference on

作者： Renhua Liu Chaohao Liao Ruidong Chen Shihe Zhang School of Computer Science and Engineering University of Electronic Science and technology of China Chengdu China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

Cyberspace is the main battlefield of future war, how to command and control network operations in a timely and effective manner in the highly complex and rapidly changing network environment is very necessary. Intelligent network attack and defense is the future development direction, aiming to study intelligent network combat methods, realize the strategic game of both red and blue, and assist in guiding the optimization of attack strategy selection. We analyze a series of representative network simulation environments, in order to solve the adversarial lack problem, we first model the behavior of both red and blue parties and construct an adversarial validation environment (MLWorld) based on numerical simulation. Secondly, we proposed MADQN algorithm to solve the problem of unsteady state in multi-agent training environment, and use the penalty decay strategy and the exploration rate decreasing mechanism to improve the training efficiency. The MADQN performs better than the traditional multi-agent algorithm in MLword, and the effectiveness of the agent strategy and the antagonism between agents are verified in environments of different scales.

关键词： Training Intelligent networks Costs Process control Reinforcement learning Games Numerical simulation Stability analysis Numerical models optimization

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Volt/Var control of Active Distribution network Based on Unified Power Quality Conditioners: A Receding Horizon control Strategy

Volt/Var Control of Active Distribution Network Based on Uni...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Xiaoguang Zhu Juning Zhang Ao Li Pengcheng Sha Shurong Li Junbo Deng School of Electrical Engineering Xi'an Jiaotong University Xi'an China

The Volt/Var control (VVC) of active distribution network (ADN) is challenged by uncertainty of renewable energy and load. This paper proposes a receding horizon control (RHC) strategy for the optimized VVC of ADN based on multiple unified power quality conditioners (UPQCs). A virtual branch based UPQC power injection model is proposed by combining the equivalent models of series unit (SEU) and parallel unit (SHU). On this basis, a uniform framework of optimal power flow (OPF) is established to achieve the VVC in the ADN with UPQCs. The proposed RHC strategy of UPQCs includes four stages: model prediction, rolling optimization, error correction, and feedback iteration. A case study is carried out in the modified IEEE 33-node ADN, which shows the effectiveness, progressiveness and the potential of online application of the proposed strategy.

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Research application of BP neural network algorithm based on genetic algorithm optimization

Research application of BP neural network algorithm based on...

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Data Science and Computer Application (ICDSCA), IEEE International conference on

作者： Haodong Chi Jingde Xie Qinghai University Xining China

In this paper, based on the ACS dataset, the original data were firstly preprocessed by excluding abnormal data, classification and interpolation operations to obtain complete, effective, intuitive and concise data. Secondly, the correlation relationship model of hybrid genetic BP neural network was proposed, and the training accuracy reached 0.99994. Then, the unit step method was used to discretize each independent variable in the chemical reaction, and the network relationship after training using BP neural network was obtained by the control variable method, and the data of the dependent variable changed with them were derived separately, and the trend analysis of the dependent variable when the independent variable changed was obtained by the different independent variables on the dependent variable. The prediction error percentage of the nine sample data in the test set was 0.06% at maximum, and the error analysis results were good.

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Local Self-adaptive Volt-var Optimal control in Distribution network

Local Self-adaptive Volt-var Optimal Control in Distribution...

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Energy, Power and Electrical Technology (ICEPET), International conference on

作者： Minghui Chen Zhaoyu Wu Zhenyong Niu Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd Guangzhou China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

This paper proposes a self-adaptive volt-var optimal control strategy to address the challenges that traditional control faces in adapting to changing distribution network operating conditions and the difficulty that local control faces in achieving equipment and photovoltaic coordination. To balance the reactive power of solar and compensators from overall situation demand, the local optimum control develops an adaptive coordination factor and a self-control optimization factor. Using the reactive power margin coefficient as a guide, the local self-adaptive optimum control maximizes reactive power with the goal of reducing power losses or the danger of voltage overrun. The techniques described in this study are simulated using an actual feeder as an example. The outcomes validate the suggested approach’s validity and demonstrate its superior performance in maximizing reactive power production, lowering power loss, and enhancing voltage quality.

关键词： Photovoltaic systems Reactive power Adaptive systems Optimal control Distribution networks Production Regulation

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