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The main strategies for soil pollution apportionment:A review of the numerical methods

Journal of Environmental Sciences 2024年第2期136卷 95-109页

作者： Bin Shi Jing Meng TieyuWang Qianqian Li Qifan Zhang Guijin Su Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention Shantou UniversityShantou 515063China

Nowadays,a large number of compounds with different physical and chemical properties have been determined in soil.Environmental behaviors and source identification of pollutants in soil are the foundation of soil pollution control.Identification and quantitative analysis of potential pollution sources are the prerequisites for its prevention and control.Many efforts have made to develop methods for identifying the sources of soil pollutants.These efforts have involved the measurement of source and receptor parameters and the analysis of their relationships via numerical statistics methods.We have comprehensively reviewed the progress made in the development of source apportionment methodologies to date and present our synthesis.The numerical methods,such as spatial geostatistics analysis,receptor models,and machine learning methods are addressed in depth.In most cases,however,the effectiveness of any single approach for source apportionment remains limited.Combining multiple methods to address soil quality problems can reduce uncertainty about the sources of soil pollution.This review also constructively highlights the key strategies of combining mathematical models with the assessment of chemical profiles to provide more accurate source attribution.This review intends to provide a comprehensive summary of source apportionment methodologies to help promote further development.

关键词： Soil pollutants Source attribution Methodologies Chemical profiles mathematical models

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Estimation of Equivalent Model of Photovoltaic Array Using Unscented Kalman Filters

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 819-827页

作者： M.A.González-Cagigal José A.Rosendo-Macías A.Gómez-Expósito the Department of Electrical Engineering University of Seville IEEE ENGREEN Laboratory of Engineering for Energy and Environmental Sustainability

This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic(PV) array, using external measurements of current and voltage at the inverter level. The estimated model is of interest to predict the power output of PV plants, in both planning and operation scenarios, and thus improves the efficient operation of power systems with high penetration of renewable energy. The proposed technique has been assessed in several simulated scenarios under different operating conditions. The results show that accurate estimates are provided for the model parameters, even in the presence of measurement noise and abrupt variations under the external conditions.

关键词： mathematical models Estimation Power systems Power system dynamics Kalman filters Parameter estimation Voltage measurement

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Distributed Hybrid Model Predictive Secondary Control of DC Microgrids with Random Communication Disorders

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 925-935页

作者： Meysam Yaribeygi Zeinab Karami Qobad Shafiee Hassan Bevrani the Department of Electrical Engineering K.N.Toosi University of Technology the Sportradar Company the Department of Engineering Cybernetics (ITK) Norwegian University of Science and Technology (NTNU) IEEE the Smart/Micro Grids Research Center Department of Electrical Engineering University of Kurdistan

A reliable and robust communication network is essential to exchange information between distributed generators(DGs) and accurately calculate their control actions in microgrids(MGs). However, the integration of the communication network and MGs poses challenges related to the flexibility, availability, and reliability of the system. Furthermore, random communication disorders such as time delays and packet loss can negatively impact the system performance. Therefore, it is essential to design a suitable secondary controller(SC) with a fast dynamic response to restore voltage and appropriate powersharing, while ensuring that the effects of random communication disorders are eliminated. In this regard, an optimal distributed hybrid model predictive secondary control method is presented in this paper. Realistic simulations are carried out in a mixed simulation environment based on MATLAB and OMNET++, by considering IEEE 802.11(Wi Fi) using the recently developed Internet networking(INET) framework. In the implemented application layer, the recovery Unit is responsible for reducing the impact of random communication disorders. The effectiveness and performance of the proposed method in comparison with a conventional model predictive control are verified by simulation results.

关键词： Voltage control Delays mathematical models Communication networks Power system stability Microgrids Matlab

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Large-disturbance Stability Analysis of Power Systems with Synchronous Generator and Converter-interfaced Generation

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 997-1002页

作者： Lei Chen Yong Min Liangshuai Hao Guangzheng Xing Yalou Li Shiyun Xu IEEE Department of Electrical Engineering Tsinghua University China Electric Power Research Institute

This letter studies large-disturbance stability of the power system with a synchronous generator(SG) and a converter-interfaced generation(CIG) connected to infinite bus. The power system is multi-timescale and first simplified. It is shown that the boundary of region of attraction(ROA) of the simplified model is composed of stable manifolds of unstable equilibrium point(UEP) or semi-singular point(SSP), named anchor points, and singular surface pieces. The type of anchor point determines the dominant instability pattern of the power system. When the anchor point is UEP or SSP, the dominant instability pattern is the instability of rotor angle of SG or the instability of phase-locked loop and outer control loop(OCL) of CIG, respectively. Transition of dominant instability pattern can be analyzed with the relative position relationship between UEP and SSP. The effect of OCL is discussed. When the OCL is activated, the ROA becomes smaller and the system is more prone to instability of CIG. It is necessary to consider the OCL when studying the large-disturbance stability of the power system.

关键词： Power system stability mathematical models Phase locked loops Stability criteria Transient analysis Power system dynamics Rotors

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Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 719-729页

作者： Qing Ma Changhong Deng IEEE the School of Electrical Engineering and Automation Wuhan University

Volt-var control(VVC) is essentially a non-convex optimization problem due to the non-convexity of power flow(PF) constraints, resulting in the difficulty in obtaining the optimum without convexity conversion. The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables, which in turn increases the optimization difficulty or even leads to optimization failure. This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF). This method uses the input convex neural network(ICNN) to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment, which can ensure the global optimum with extremely fast computation speed. To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC, this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF), which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval. Combining DLIPF with DLPF, this method decreases the modeling and optimization difficulty of robust VVC significantly. Test results on 30-bus, 118-bus, and 200-bus systems prove the correctness and rapidity of the proposed methods.

关键词： Optimization Reactive power mathematical models Deep learning Uncertainty Load flow Programming

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Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 886-899页

作者： Jie Xu Hongjun Gao Renjun Wang Junyong Liu the College of Electrical Engineering Sichuan University

The increasing integration of intermittent renewable energy sources(RESs) poses great challenges to active distribution networks(ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL), which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources(DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing(PS) and prioritized experience replay(PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118-bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

关键词： Real-time systems Switches Scalability Reinforcement learning Deep learning Topology mathematical models

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mathematical Modeling of Cell Polarity Establishment of Budding Yeast

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Communications on Applied Mathematics and Computation 2024年第1期6卷 218-235页

作者： Yue Liu Jun Xie Hay-Oak Park Wing-Cheong Lo Department of Mathematics City University of Hong KongTat Chee AvenueKowloonHong KongChina Department of Molecular Genetics The Ohio State UniversityColumbusOHUSA

The budding yeast Saccharomyces cerevisiae is a powerful model system for studying the cell polarity establishment.The cell polarization process is regulated by signaling molecules,which are initially distributed in the cytoplasm and then recruited to a proper location on the cell membrane in response to spatial cues or spontaneously.Polarization of these signaling molecules involves complex regulation,so the mathematical models become a useful tool to investigate the mechanism behind the process.In this review,we discuss how mathematical modeling has shed light on different regulations in the cell polarization.We also propose future applications for the mathematical modeling of cell polarization and morphogenesis.

关键词： Budding yeast Cdc42 Morphogenesis Septin mathematical models

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Energy-Efficient Driving Strategy for High-Speed Trains with Considering the Checkpoints

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Chinese Journal of Electronics 2024年第3期33卷 847-858页

作者： Zixuan ZHANG Yuan CAO Shuai SU State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

With rising energy prices and concerns about environmental issues,energy-efficient driving strategies(EEDS) for high-speed trains have received a substantial amount of attention.In particular,energy-saving schemes play a huge role in reducing the energy and operating costs of trains.This article studies the EEDS of high-speed trains at a given time.A well-posed model is formulated,in which the constraints of the checkpoints,in addition to the speed limits,vehicle dynamics,and discrete control throttle,are first considered.For a given control sequence,the Karush-Kuhn-Tucker(KKT) conditions are used to obtain the necessary conditions for an EEDS.According to several key equations of the necessary conditions,the checkpoint constraints are satisfied.Some case studies are conducted based on the data of the Beijing-Shanghai high-speed line to illustrate the effectiveness of the proposed approach.

关键词： Energy consumption Costs Simulation mathematical models Energy efficiency Vehicle dynamics Optimization

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Weighted Linear Loss Large Margin Distribution Machine for Pattern Classification

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Chinese Journal of Electronics 2024年第3期33卷 753-765页

作者： Ling LIU Maoxiang CHU Rongfen GONG Liming LIU Yonghui YANG School of Electronic and Information Engineering University of Science and Technology Liaoning

Compared with support vector machine,large margin distribution machine(LDM) has better generalization performance.The central idea of LDM is to maximize the margin mean and minimize the margin variance simultaneously.But the computational complexity of LDM is high.In order to reduce the computational complexity of LDM,a weighted linear loss LDM(WLLDM) is proposed.The framework of WLLDM is built based on LDM and the weighted linear loss.The weighted linear loss is adopted instead of the hinge loss in WLLDM.This modification can transform the quadratic programming problem into a simple linear equation,resulting in lower computational complexity.Thus,WLLDM has the potential to deal with large-scale datasets.The WLLDM is similar in principle to the LDM algorithm,which can optimize the margin distribution and achieve better generalization performance.The WLLDM algorithm is compared with other models by conducting experiments on different datasets.The.experimental results show that the proposed WLLDM has better generalization performance and faster training speed.

关键词： Training Support vector machines Pattern classification Transforms Fasteners mathematical models Vectors

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A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

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CES Transactions on Electrical Machines and Systems 2024年第2期8卷 227-239页

作者： Yutong Zhu Yaohua Li Institute of Electrical Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited (Beijing Zongheng Electromechanical Technology Co.Ltd) IEEE

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

关键词： Rotors Stators Optimization Torque Iron mathematical models Resistance

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