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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

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ieee/CAA Journal of Automatica Sinica 2019年第3期6卷 838-849页

作者： Zhiming Lv Linqing Wang Zhongyang Han Jun Zhao Wei Wang ieee the Faculty of Electronics Information and Electrical Engineering Dalian University of Technology

For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.

关键词： Multiobjective optimization Pareto active learning particle swarm optimization (PSO) surrogate

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Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP

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ieee/CAA Journal of Automatica Sinica 2019年第3期6卷 657-666页

作者： Ruizhuo Song Liao Zhu ieee the School of Automation and Electrical Engineering University of Science and Technology Beijing

Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.

关键词： Adaptive dynamic programming (ADP) fixedpoint tracking optimal control

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Simplified Deep Reinforcement Learning Based Volt-var Control of Topologically Variable Power System

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1396-1404页

作者： Qing Ma Changhong Deng ieee School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on ieee 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.

关键词： Volt-var control(VVC) deep reinforcement learning(DRL) topologically variable power system transfer learning

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SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

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ieee/CAA Journal of Automatica Sinica 2022年第12期9卷 2121-2137页

作者： Linfeng Tang Yuxin Deng Yong Ma Jun Huang Jiayi Ma Electronic Information School Wuhan UniversityWuhan 430072China ieee

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision *** study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art *** source code and pre-trained model are publicly available at https://***/Linfeng-Tang/Super Fusion.

关键词： Global spatial attention image fusion image registration mutual promotion semantic awareness

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Hosting Capacity Evaluation Method for Power Distribution Networks Integrated with Electric Vehicles

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1564-1575页

作者： Wei Dai Cheng Wang Hui Hwang Goh Jingyi Zhao Jiangyi Jian ieee School of Electrical Engineering Guangxi UniversityNanning 530000China

The large-scale deployment of electric vehicles(EVs) poses critical challenges to the secure and economic operation of power distribution networks(PDNs). Therefore, a method for evaluating the hosting capacity that enables a PDN to determine the EV chargeable area(EVCA) to satisfy the charging demand and ensure the secure operation is proposed in this paper. Specifically, the distribution system operator(DSO) serves as a public entity to manage the integration of EVs by determining the presence of the charging load in the EVCA. Hence, an EVCA optimization model is formulated on the basis of the coupling effect of the charging nodes to determine the range of the available charging power. In this model, nonlinear power flow equations and operational constraints are considered to maintain the solvability of the power flow of the PDN. Subsequently, a novel multipoint approximation technique is proposed to quickly search for the boundary points of the EVCA. In addition, the impact of the demand response(DR) mechanism on the hosting capacity is explored. The results show that the presence of the DR significantly enlarged the EVCA during peak hours, thus revealing the suitability of the DR mechanism as an important supplement to accommodate the EV charging load. The examined case studies demonstrate the effectiveness of the proposed model and show that the unmanaged allocation of the charging load impedes secure operation. Finally, the proposed method provides a reference for the allocation of the EV charging load and a reduction in the risk of line overloading.

关键词： Capacity evaluation demand response(DR) electric vehicle(EV) electric vehicle chargeable area(EVCA)

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Deep Active Learning for Solvability Prediction in Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1773-1777页

作者： Yichen Zhang Jianzhe Liu Feng Qiu Tianqi Hong Rui Yao Argonne National Laboratory LemontIL 60439USA ieee

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow *** this letter,we propose a deep active learning framework for solvability prediction in power *** with passive learning where the training is performed after all instances are labeled,active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for *** the active learning framework,the acquisition functions,which correspond to different sampling strategies,are defined in terms of the on-the-fly posterior probability from the ***,the ieee 39-bus system is employed to validate the proposed framework,where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical ***,the Northeast Power Coordinating Council(NPCC)140-bus system is used to validate the performance on large-scale power systems.

关键词： Active learning deep learning power flow solvability power flow equation

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Harmonic Amplification Analysis of Cable Lines with Distributed Parameters Based on Kalman Filter and Convolution Inversion

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1804-1813页

作者： Yang Pu Qin Shu Fangwei Xu College of Electrical Engineering Sichuan UniversityChengdu 610065China ieee

Harmonic amplification phenomena could appear at the point of common connection(PCC) of the cable line terminal. However, the distributed parameter model of the cable line contains hyperbolic functions with plural variables, which makes it challenging to obtain the harmonic amplification factor(HAF). Hence, a time-domain method combining the Kalman filter and convolution inversion(KFCI) methods is proposed to address this problem. First, the Kalman filter method optimizes the square wave pulse response(SWPR) with measurement error. Then, the optimized SWPR data are used to get the HAF by the convolution inversion method. Next, the harmonic amplification characteristics of cable lines are explored. Finally, an experimental simulation model is built on the PSCAD software, verifying the optimization effectiveness of Kalman filter for the SWPR with error and the accuracy of the HAF calculated by the proposed method. The analysis rationality of harmonic amplification properties is also demonstrated.

关键词： Cable line distributed parameter Kalman filter convolution inversion harmonic amplification

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A Hybrid State Estimation Approach for Integrated Heat and Electricity Networks Considering Time-scale Characteristics

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Journal of Modern Power Systems and Clean Energy 2020年第4期8卷 636-645页

作者： Tongtian Sheng Guanxiong Yin Qinglai Guo Hongbin Sun Zhaoguang Pan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China ieee

State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is ***,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the ***,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative *** show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and *** hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.

关键词： Integrated heat and electricity network measurement resolution state estimation(SE) time-scale characteristics

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Coordination of Networked Nonlinear Multi-Agents Using a High-Order Fully Actuated Predictive Control Strategy

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ieee/CAA Journal of Automatica Sinica 2022年第4期9卷 615-623页

作者： Guo-Ping Liu ieee the Department of Electronic and Electrical Engineering Southern University of Science and Technology

This paper is concerned with the coordinative control problem of networked nonlinear multi-agents(NNM)with communication delays.A high-order fully actuated(HOFA)model is introduced to describe the nonlinear *** on this model,a HOFA predictive coordination method is proposed to compensate for the communication delays actively and achieve simultaneous stability and *** method largely simplifies the design of networked nonlinear multi-agents and makes the control performance be same for networked nonlinear multi-agents with and without communication *** analysis on the closed-loop systems derives the simultaneous stability and consensus criteria of networked nonlinear multiagents using the HOFA predictive coordination *** the presented way of designing HOFA predictive coordination controllers,a simulated example demonstrates the advantages of the proposed method.

关键词： method nonlinear coordination

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Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

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ieee/CAA Journal of Automatica Sinica 2019年第3期6卷 789-806页

作者： Mayank Agarwal Santosh Biswas Sukumar Nandi ieee the Department of Computer Science and Engineering Indian Institute of Technology Guwahati

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system *** to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

关键词： Fault diagnosis instrasion detection system (IDS) measurement inconsistent discrete event system (DES) rogue dynamic host configuration protocol (DHCP) server

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