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robustness assessment of Wind Power Generation Considering Rigorous Security Constraints for Power System: A Hybrid RLO-IGDT Approach

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 518-529页

作者： Lianyong Zuo Shengshi Wang Yong Sun Shichang Cui Jiakun Fang Xiaomeng Ai Baoju Li Chengliang Hao Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430073China State Grid Jilin Electric Power Company Changchun 130021China

Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)*** increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted by fluctuation and intermittence of renewable *** this paper,information gap decision theory(IGDT)is adapted to handle uncertainty of wind power *** on conventional IGDT method,linear regulation strategy(LRS)and robust linear optimization(RLO)method are integrated to reformulate the model for rigorously considering security *** a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of ***,a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming(MILP)problem for convenient optimization without robustness ***,results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without *** Terms-Hybrid RLO-IGDT approach,information gap decision theory(IGDT),operation security,robustness assessment,robustness security region(RSR).

关键词： IGDT approach information gap decision theory(IGDT) operation security robustness assessment robustness security region(RSR)

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robustness assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View

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Journal of Computer Science & Technology 2021年第5期36卷 1002-1021页

作者： Tong Chen Ji-Qiang Liu He Li Shuo-Ru Wang Wen-Jia Niu En-Dong Tong Liang Chang Qi Alfred Chen Gang Li Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing 100044China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Donald Bren School of Information and Computer Sciences University of CaliforniaIrvine 92697U.S.A. Centre for Cyber Security Research and Innovation Deakin UniversityGeelongVIC 3216Australia

Reinforcement learning as autonomous learning is greatly driving artificial intelligence(AI)development to practical *** demonstrated the potential to significantly improve synchronously parallel learning,the parallel computing based asynchronous advantage actor-critic(A3C)opens a new door for reinforcement ***,the acceleration's influence on A3C robustness has been largely *** this paper,we perform the first robustness assessment of A3C based on parallel *** perceiving the policy's action,we construct a global matrix of action probability deviation and define two novel measures of skewness and sparseness to form an integral robustness *** on such static assessment,we then develop a dynamic robustness assessing algorithm through situational whole-space state sampling of changing *** experiments with different combinations of agent number and learning rate are implemented on an A3C-based pathfinding application,demonstrating that our proposed robustness assessment can effectively measure the robustness of A3C,which can achieve an accuracy of 83.3%.

关键词： robustness assessment skewness sparseness asynchronous advantage actor-critic reinforcement learning

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robustness assessment and Adaptive FDI for Car Engine

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International Journal of Automation and computing 2008年第2期5卷 109-118页

作者： Mahavir Singh Sangha J.Barry Gomm Control Systems Research Group School of EngineeringLiverpool John Moores University

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

关键词： On-board fault diagnosis automotive engines adaptive neural networks (ANNs) fault classification robustness assessment

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