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Convex Hull Based Economic Operating Region for Power Grids Considering Uncertainties of Renewable Energy Sources

Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1419-1430页

作者： Huating Xu Bin Feng Gang Huang Mingyang Sun Houbo Xiong Chuangxin Guo the College of Electrical Engineering in Zhejiang University HangzhouChina the Department of Control Science and Engineering in Zhejiang University HangzhouChina

The increasing integration of renewable energy sources(RESs)presents significant challenges for the safe and economical operation of power *** the critical need to assess the effect of RES uncertainties on optimal scheduling schemes(OSSs),this paper introduces a convex hull based economic operating region(CH-EOR)for power *** CHEOR is mathematically defined to delineate the impact of RES uncertainties on power grid *** propose a novel approach for generating the CH-EOR,enhanced by a big-M preprocessing method to improve the computational *** on four test systems,the proposed big-M preprocessing method demonstrates notable advancements:a reduction in average operating costs by over 10%compared with the box-constrained operating region(BC-OR)derived from robust ***,the CH-EOR occupies less than 11.79%of the generators'adjustable region(GAR).Most significantly,after applying the proposed big-M preprocessing method,the computational efficiency is improved over 17 times compared with the traditional big-M method.

关键词： Convex hull big-M preprocessing method economic operating region(EOR) renewable energy source(RES) uncertainty

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Exact Box-Constrained Economic Operating Region for Power Grids Considering Renewable Energy Sources

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 514-523页

作者： Huating Xu Bin Feng Chutong Wang Chuangxin Guo Jian Qiu Mingyang Sun College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The growing integration of renewable energy sources manifests as an effective strategy for reducing carbon emissions. This paper strives to efficiently approximate the set of optimal scheduling plans(OSPs) to enhance the performance of the steady-state adaptive cruise method(SACM) of power grid, improving the ability of dealing with operational uncertainties. Initially, we provide a mathematical definition of the exact boxconstrained economic operating region(EBC-EOR) for the power grid and its dispatchable components. Following this, we introduce an EBC-EOR formulation algorithm and the corresponding bi-level optimization models designed to explore the economic operating boundaries. In addition, we propose an enhanced big-M method to expedite the computation of the EBCEOR. Finally, the effectiveness of the EBC-EOR formulation, its economic attributes, correlation with the scheduling plan underpinned by model predictive control, and the significant improvement in computational efficiency(over twelvefold) are verified through case studies conducted on two test systems..

关键词： Exact box-constrained economic operating region(EBC-EOR) big-M method intelligent scheduling steady-state adaptive cruise uncertainty renewable energy source

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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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An efficient and robust system for human following scenario using differential robot

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IET Cyber-Systems and Robotics 2024年第1期6卷 15-23页

作者： Jiangchao Zhu Changjia Ma Chao Xu Fei Gao College of Control Science and Engineering Zhejiang UniversityHangzhouChina

A novel system for human following using a differential robot,including an accurate 3‐D human position tracking module and a novel planning strategy that ensures safety and dynamic feasibility,is *** authors utilise a combination of gimbal camera and LiDAR for long‐term accurate human *** the planning module takes the target's future trajectory as a reference to generate a coarse path to ensure the following *** that,the trajectory is optimised considering other constraints and following *** demonstrate the robustness and efficiency of our system in complex environments,demonstrating its potential in various applications.

关键词： human‐robot interaction trajectory optimization visual tracking

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Internal and external disturbances aware motion planning and control for quadrotors

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IET Cyber-Systems and Robotics 2024年第3期6卷 23-30页

作者： Xiaobin Zhou Miao Wang Can Cui Yongchao Wang Chao Xu Fei Gao Institute of Cyber‐Systems and Control College of Control Science and EngineeringZhejiang UniversityHangzhouChina Huzhou Institute Zhejiang UniversityHuzhouChina

Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of *** development of a motion planning and control architecture for quadrotors,considering both internal and external disturbances(i.e.,motor damages and suspended payloads),is ***,the authors introduce the use of exponential functions to formulate trajectory *** choice is driven by its ability to predict thrust responses with minimal computational ***,a reachability analysis is incorporated for error dynamics resulting from multiple *** analysis sits at the interface between the planner and controller,contributing to the generation of more robust and safe spatial–temporal ***,the authors employ a cascade controller,with the assistance of internal and external loop observers,to further enhance resilience and compensate the *** authors’benchmark experiments demonstrate the effectiveness of the proposed strategy in enhancing flight safety,particularly when confronted with motor damages and payload disturbances.

关键词： Internal and external disturbances reachability analysis resilient motion planning and tracking

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Toward Universal controller: Performance-Aware Self-Optimizing Reinforcement Learning for Discrete-Time Systems With Uncontrollable Factors

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3249-3260页

作者： Zhang, Jianfeng Zhang, Haoran Zhao, Chunhui Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

The industrial system usually contains not only controllable variables (CVs) but also uncontrollable variables (unCVs), e.g., weather conditions and friction. These unCVs have a direct impact on system control performance. Despite the success of current deep reinforcement learning (DRL) control algorithms, most of them neglect the impact of unCVs, which can cause the deterioration of control performance and instability of the system. To perceive and eliminate the impact of unCVs, a performance-aware self-optimizing universal controller (PASOUC) is designed in this article. The PASOUC aims at integrating the representation of unCVs and controller design to perceive and eliminate the impact of unCVs under different conditions, which goes beyond most existing control methods. Technically, a historical trajectory-inspired control performance perceptron is developed to perceive the impact of unCVs on system control performance under different conditions. Subsequently, a new performance-aware reward is designed to integrate the representation of unCVs and controller design while training the DRL controller. In addition, the domain randomization (DR) training strategy is employed to learn a universal control policy, which can access the approximate optimal trajectory under nonideal conditions. In this way, the impact of unCVs can be eliminated. To handle the low efficiency of the DR training, the policy improvement-policy proximal optimization (PI-PPO) is proposed to enhance the convergence speed of the DR training by performing explicit policy improvement. Finally, illustrative examples are presented to demonstrate the superiority of the proposed method. © 2013 IEEE.

关键词： Deep reinforcement learning

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A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

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Chinese Journal of Chemical engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong Yang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace ***, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis Gaussian mixture model Process monitoring Ensemble Process control

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Solving multiple travelling salesman problem through deep convolutional neural network

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IET Cyber-Systems and Robotics 2023年第1期5卷 67-78页

作者： Zhengxuan Ling Yueling Zhou Yu Zhang College of Control Science and Engineering Zhejiang UniversityHangzhouChina

The multiple travelling salesman problem(mTSP)is a classical optimisation problem that is widely applied in various *** the mTSP was solved using both classical algorithms and artificial neural networks,reiteration is inevitable for these methods when presented with new *** meet the online and high-speed logistics requirements deploying new information technology,the iterative algorithm may not be reliable and *** this study,a deep convolutional neural network(DCNN)-based solution method for mTSP is proposed,which can establish the mapping between the parameters and the optimal solutions directly and avoid the use of *** facilitate the DCNN in establishing a mapping,an image representation that can transfer the mTSP from an optimisation problem into a computer vision problem is *** maintaining the excellent quality of the results,the efficiency of the solution achieved by the proposed method is much higher than that of the traditional optimisation method after ***,the method can be applied to solve the mTSP under different constraints after transfer learning.

关键词： convolutional neural network deep learning path planning

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A Novel Visual Transformer for Long-Distance Pipeline Pattern Recognition in Complex Environment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2933-2945页

作者： Zhu, Chengyuan Pu, Yanyun Yang, Kaixiang Yang, Qinmin Chen, C.L. Philip Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310007 China Huzhou Institute of Zhejiang University Zhejiang Huzhou313000 China South China University of Technology College of Computer Science and Engineering Guangdong Guangzhou510006 China

The effective warning of dangerous events along long-distance pipelines is critical to ensure the safety of oil and gas transportation. Distributed optical fiber sensing (DOFS) technology can assist operators to identify threat vibration signals. However, due to the complex and changeable environmental background noise along long-distance pipeline, most of the existing methods only extract one-dimensional features of the signal, making it difficult to distinguish various types of environmental noise, strong interference, and dangerous events. Besides, the samples of different classes in the actual scene are unbalanced. The sample size of dangerous events is often smaller than others. To address these problems, we use image encoding to transform the time series signals collected by the DOFS system into image data, and fully extract the time dependence and the correlation between different elements in the signal. Moreover, a visual transformer model PipelineADWinT is proposed. The self-attention mechanism of diagonal-axial window designed in this model can perfectly combine image encoding features, and obtain local to global multiscale features through hierarchical structure. By optimizing the loss function, the model's ability to handle the class imbalance problem is enhanced. The experimental results show that PipelineADWinT has more comprehensive classification performance and fewer false alarms than all the baseline models, which proves the effectiveness and superiority of the model. © 2020 IEEE.

关键词： Pattern recognition

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An Accurate and Interpretable Framework for Trustworthy Process Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2241-2252页

作者： Wang, Hao Wang, Zhiyu Niu, Yunlong Liu, Zhaoran Li, Haozhe Liao, Yilin Huang, Yuxin Liu, Xinggao Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

Trustworthy process monitoring seeks to build an accurate and interpretable monitoring framework, which is critical for ensuring the safety of energy conversion plant (ECP) that operates under extreme working conditions, such as high pressure and temperature. Contemporary self-attentive models, however, fall short in this domain for two main reasons. First, they rely on stepwise correlations that fail to involve physically meaningful semantics in ECP logs, resulting in suboptimal accuracy and interpretability. Second, attention matrices are frequently cluttered with spurious correlations that obscure physically meaningful ones, further impeding effective interpretation. To overcome these issues, we propose AttentionMixer, a framework aimed at improving both accuracy and interpretability of the existing methods, and establish a trustworthy ECP monitoring framework. Specifically, to tackle the first issue, we employ a spatial adaptive message passing block to capture variatewise correlations. This block is coupled with a temporal adaptive message passing block through a mixing operator, yielding a multifaceted representation of ECP logs accounting for both the stepwise and variatewise correlations. Concurrently, to tackle the second issue, we employ a sparse message passing regularizer to filter out spurious correlations. We validate the efficacy of AttentionMixer using two real-world datasets from the radiation monitoring network for Chinese nuclear power plants. © 2020 IEEE.

关键词： Accident prevention

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