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Distributed Riemannian Manifold optimization for Unimodular Waveform Set Design Toward AAF/CAF Shaping

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2854-2870页

作者： Qiu, Xiangfeng Jiang, Weidong Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

The unimodular waveform set with desired autoambiguity functions (AAFs) and cross-ambiguity functions (CAFs) have extensive applications in assessing interference suppression performance across range and Doppler dimensions. In this article, we focus on designing low computational complexity but theoretically-guaranteed algorithms to achieve these kinds of waveforms. Specifically, we first formulate the waveforms set designing problem as a quartic polynomial minimization problem under constant modulus constraint, aiming at minimizing the response values within the interested areas of the AAFs and CAFs. Then, the established optimization problem is equivalently transformed into a series of constrained subproblems by introducing auxiliary variables. To reach its good approximate solution efficiently, we further convert the constrained subproblem into an unconstrained one over manifold space, which is further addressed by employing the Riemannian conjugate gradient method. In addition, we also theoretically prove that the proposed approach can converge to the stationary point of the original nonconvex problem. Experiments based on the numerical simulations and hardware systems are conducted to demonstrate the effectiveness and superiority of the proposed method and explore the influence of the nonlinear instruments.

关键词： optimization Radar Radar polarimetry Manifolds Interference Aerospace and electronic systems Shape Convergence Vectors optimization models Ambiguity function (AF) distributed optimization integrated sidelobe level (ISL) Riemannian manifold optimization (RMO) unimodular waveform set waveform optimization

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Local Uncertainty Management of Distribution System Based on Multi-Stage Dispatch of Flexible Resources

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 918-929页

作者： Zhu, Junpeng Meng, Dexin Lu, Peng Guan, Qingwei Kong, Peng Yuan, Yue Hohai Univ Sch Elect & Power Engn Nanjing 210098 Peoples R China State Grid Shandong Elect Power Co Ltd Weihai Power Supply Co Weihai 264200 Peoples R China

This paper proposes a two-stage energy management strategy for distribution network (DN) operation, addressing the power uncertainty of renewable energy (RE) generation and loads through flexible resource (FR) regulation. In day-ahead (DA) stage, both the flexibility margin of FRs and power uncertainty of RE and load are modeled into interval form, based on which the uncertainty of the interaction power in the grid supply point (GSP) can be quantitatively evaluated. The objective of DA stage optimization is to reduce the uncertainty level of GSP power and the DN operation costs by simultaneously formulating the expected FR scheduling plan and allocating the sequential flexibility margins of FRs. During the intra-day (ID) operation process, as the operating scenarios of the preceding time periods are determined, some flexibility margins that were not actually utilized may be released. To fully exploit the regulation potential of FRs, a rolling optimization-based margin redistribution model are proposed for ID operation, considering the feasibility of pre-scheduled plans in the subsequent time periods. The effectiveness of proposed method in local uncertainty management is demonstrated based on case studies on a real-world distribution system.

关键词： Uncertainty optimization Regulation Stochastic processes Power systems optimization models Load modeling Active distribution network flexible resources interval modeling local uncertainty management

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Game Behavior-Driven Consensus models With Maximum Linear-Quadratic Payoffs and Minimum Adjustment

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3929-3940页

作者： Li, Shengli Sang, Yuzheng Rodriguez, Rosa M. Qin, Jindong Wei, Cuiping Taiyuan Normal Univ Sch Math & Stat Jinzhong Peoples R China Taiyuan Normal Univ Shanxi Key Lab Intelligent Optimizat Comp & Blockc Jinzhong Peoples R China Univ Jaen Dept Comp Sci Jaen 23071 Spain Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Yangzhou Univ Coll Math Sci Yangzhou 225002 Peoples R China

Interaction behaviors play a core role in the process of reaching a consensus. In this article, a network game is employed to model the interplay between the behaviors of decision makers (DMs) and Stackelberg game architecture is used to design an interactive mechanism between the DMs and the moderator. An optimization model based on these two games results in a consensus model with maximum linear-quadratic payoffs and minimum adjustment (MPMACM). In the proposed MPMACM, the moderator provides compensation strategies and feedback suggestions to guide the DMs to reach the desired consensus level with minimum adjustment, while the DMs adjust their opinions aiming to obtain their maximum payoffs. We present the equilibrium analysis for the MPMACM, and an adaptive differential evolution algorithm is offered to enact this optimization model. Finally, an example application is conducted to illustrate and justify the performance of the MPMACM.

关键词： Games Costs Biological system modeling optimization models Heuristic algorithms Analytical models Social networking (online) Phase measurement Investment Entropy Consensus reaching process (CRP) group decisions and negotiations maximum payoff minimum adjustment

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Gradient Boosting Feature Selection for Integrated Fault Diagnosis in Series-Compensated Transmission Lines

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IEEE ACCESS 2025年 13卷 63640-63670页

作者： Nawaz, Rab Wadood, Abdul Khalid Mehmood, Khawaja Basit Ali Bukhari, Syed Albalawi, Hani Mohammed Alatwi, Aadel Sajid, Muhammad Mirpur Univ Sci & Technol Dept Elect Engn Mirpur 10250 Azad Jammu & Ka Pakistan Univ Tabuk Fac Engn Dept Elect Engn Tabuk 47913 Saudi Arabia Univ Tabuk Renewable Energy & Environm Technol Ctr Tabuk 47913 Saudi Arabia Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands Univ Azad Jammu & Kashmir Dept EE Muzaffarabad 13100 Azad Jammu & Ka Pakistan

Series-compensated power transmission lines are integral to modern power grids and enhance the system reliability and stability. However, they introduce challenges such as voltage inversion, harmonic distortion, and nonlinear dynamics, complicating fault diagnosis in contemporary power systems. This study introduces an innovative method to analyze the waveforms of fault signals, utilizing advances in Internet and sensor technologies that provide extensive voltage and current data in a time-series form. By optimizing each phase of data processing from feature extraction to model learning, the proposed system effectively addresses fault detection, classification, and localization as a multi-classification problem. The integration of feature extraction with efficient gradient-boosting feature selection ensures high accuracy, speed, and computational efficiency, thus outperforming techniques that require heavy preprocessing. The methodology is implemented using four ensemble classifiers: Adaptive Boosting (AB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Extreme Gradient Boosting (XGB), and is rigorously evaluated on various standard models of differing complexity and system configurations under dynamic conditions. This approach achieves high accuracy, adaptability, and robustness, overcoming practical constraints, such as high noise immunity, resource efficiency, and scalability for real-world applications. The results showed that RF achieved the highest accuracy of 99.94%, whereas XGB exhibited the fastest computational time of 0.2790 s for binary classification. RF achieved perfect accuracy (100%) for binary fault classification, whereas XGB achieved perfect accuracy for tri-class fault localization. RF achieved 99.89% accuracy in the five-class classification. LGBM demonstrates the highest noise immunity with 99% accuracy at 10 dB in ten-class multi-classification, whereas RF maintains 94.52% accuracy under extreme noise conditions (5 dB) and unf

关键词： Feature extraction Noise Accuracy Fault diagnosis Computational efficiency Voltage Computational modeling Circuit faults Boosting Training feature extraction fault diagnosis gradient methods hyperparameter optimization noise cancellation optimization models power system faults supervised learning time series analysis

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A Unified Model for Large-Scale Inexact Fixed-Point Iteration: A Stochastic optimization Perspective

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2435-2449页

作者： Hashemi, Abolfazl Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Calculating fixed points of a nonlinear function is a central problem in many areas of science and engineering with applications ranging from the study of dynamical systems to optimization and game theory. Fixed-point iteration methods provide a simple way to calculate the fixed point of nonexpansive mappings and they have been studied extensively. Emerging applications, however, necessitate the study of fixed-point calculation under various perturbations. For instance, in the data-driven identification of dynamical systems, the learning is typically erroneous, which in turn impacts the subsequent fixed calculations, which itself is an essential step for control. Motivated by such settings, in this work, we establish a general mathematical modeling framework for the study of inexact fixed-point iteration (FPI) algorithms. In doing so, we leverage and extend the recent advances in the stochastic optimization literature to derive new methods and convergence analysis results. In particular, adopting this view enables us to present a unified mathematical model to study the impact of inexact computations in both expansive and nonexpansive scenarios, a new technical approach for the analysis of inexact FPI methods, and a new inexact FPI method, which under certain assumptions, enjoys a faster convergence rate than traditional FPI algorithms in the expansive case.

关键词： Stochastic processes optimization Convergence Mathematical models Control systems Complexity theory Vectors Signal processing algorithms Reinforcement learning Noise Gradient methods iterative algorithms iterative methods optimization methods optimization models

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Research on Container Multimodal Transportation Multi-Objective Path optimization From Hinterlands to Shanghai Port

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IEEE ACCESS 2025年 13卷 32794-32807页

作者： Huang, Chuan Sun, Hanbing Liu, Changjian Zhang, Xiaoqing Gao, Tianhang Tian, Jia Minist Transport Transport Planning & Res Inst Beijing 100028 Peoples R China Digital Lab Integrated Transportat Planning Beijing 100028 Peoples R China Wuhan Univ Technol Wuhan 430062 Peoples R China

Given Shanghai Port's proximity to the sea and the Yangtze River, this article investigated the optimization of Shanghai Port's foreign trade container multimodal transport path. First and foremost, because the range of the port hinterland generating container is large, Sichuan Province, Chongqing Municipality, Hubei Province, Hunan Province, Jiangxi Province, Anhui Province, and Jiangsu Province are chosen as the major container generating hinterland. And the container intermodal transportation network is centered on these cities and provinces. Then, using minimizing total transportation cost and total transportation time as objective functions, a multi-objective container multimodal transportation path optimization model is built. The improved multi-objective particle swarm approach is used to solve the mathematical mode. Finally, the efficient multimodal container transportation routes from Yibin, Luzhou, Chongqing, Yi-chang, Wuhan, Jiujiang, and Yueyang to Shanghai Port are identified. The acquired results are used to discuss the port competitiveness and freight organization games. It closes by verifying the usefulness of the approaches and models presented in this study. It is intended that the study presented in this paper will serve as a model and method reference for optimizing container multimodal transportation patterns.

关键词： Transportation Containers Seaports Costs optimization Biological system modeling Linear programming optimization models Roads Pricing Shanghai port container multimodal transportation path optimization multi-objective particle swarm optimization

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Joint optimization of Layout and Electrolyzer Capacity for Decentralized Offshore Wind-Powered Hydrogen Production

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 4158-4168页

作者： Du, Yunfei Shen, Xinwei Lu, Boan Ding, Xiaochi Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Recently, there has been a surge in interest globally for offshore wind-powered hydrogen production (OWPHP) as a sustainable and environmentally friendly approach. To cut the levelized cost of hydrogen (LCOH) of the decentralized OWPHP, we propose a joint optimization model that simultaneously optimizes the wind farm layout and electrolyzer capacity, accounting for the wake effect among turbines. Further, to address the computational complexities, a bi-level distributed decomposition method (BLDDM) is developed, which could decompose the primary problem into several subproblems in a hierarchical and distributed manner, allowing the efficient solution with alternating direction method of multipliers. Case studies are conducted based on actual data to validate the effectiveness of the model, demonstrating that the joint optimization approach would significantly reduce the LCOH compared to the individual approach. Meanwhile, the proposed BLDDM has a better computational performance than the commonly used nonlinear optimization algorithms.

关键词： Hydrogen Turbines optimization Costs Wind turbines Wind farms Finance Production optimization models Layout Alternating direction method of multipliers (ADMM) decomposition method joint optimization levelized cost of hydrogen (LCOH) offshore wind-powered hydrogen production (OWPHP)

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Two-Stage Integrated Planning of Energy-Saving Operations of Metro Trains Using MOJS and GWO Algorithms

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10713-10727页

作者： Jiao, Xiangmeng Zhou, Yonghua Fujita, Hamido Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Princess Nourah Bint Abdul Rahman Univ Coll Sci Riyadh 11564 Saudi Arabia Univ Hradec Kralove Fac Sci Hradec Kralove 50003 Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Iwate Prefectural Univ Reg Res Ctr Takizawa 0200693 Japan

In recent years, with a remarkable increase in urban rail transit operations, the issue of energy efficiency in train operations has attained increasing attention. In this study, a two-stage optimization model is proposed to optimize driving strategies and schedules. We comprehensively consider the optimization of train running curves, running time allocations to a whole line, and utilization of regenerative braking energy, to reduce the net energy consumption of train operations. In the first stage, a multi-objective jellyfish search (MOJS) optimization algorithm is used to optimize a switching sequence at each inter-station, and Pareto fronts are obtained corresponding to energy-saving train running curves. In the second stage, a grey wolf optimizer (GWO) is adopted to optimize running times between adjacent stations, dwell times at stations, and headway time. This stage aims to coordinate the operations of multiple trains, to reduce the traction energy consumption of a whole line, and to increase the utilization of regenerative braking energy. The optimality is discussed for the proposed two-stage optimization processes. Numerical experiments are conducted based on train and infrastructure data of the Beijing Yizhuang metro line. The results show that the proposed optimization model and solution algorithms have a considerable energy-saving effect. Note to Practitioners-The motivation of this work is to reduce the energy consumption of a metro line by optimizing control profiles and scheduling schemes of multiple trains, including three main steps. Firstly, a multi-objective optimization model is constructed with inter-station running time and traction energy consumption as optimization objectives, and the time-energy Pareto fronts between stations are obtained by optimizing the inter-station running curves of trains. Secondly, an objective function considering multi-train regenerative-energy synergistic utilization is established, with derived regenerative-energy

关键词： optimization Energy consumption Heuristic algorithms optimization models Genetic algorithms Energy efficiency Resource management Schedules Time factors Switches Energy saving multi-train coordination multi-objective optimization regenerative braking energy

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Accuracy Assessment for Multibaseline Phase Unwrapping Without Using External Reference Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ye, Xin Yu, Hanwen Yan, Yan Yang, Taoli Univ Elect Sci & Technol China UESTC Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China

Accuracy assessment of interferometric synthetic aperture radar (InSAR) products without external reference data (ERD) has been a long-standing challenge because traditional phase unwrapping (PU) is an ill-posed problem. The limitations in accuracy assessment make it impossible to verify the precision of the PU results under actual observation conditions, so that the reliability of InSAR products in practical applications is unknowable. However, multibaseline (MB) PU is well-posed, so its accuracy can be evaluated in a statistical sense without using ERD, i.e., even if there are no in situ data, the accuracy of the products generated by MB InSAR can still be assessed theoretically. In this article, by obtaining the closed form optimality condition of the Chinese remainder theorem (CRT) optimization model, the new independent quantitative index for MB PU accuracy evaluation was mathematically established. Interestingly, we found that: 1) the optimality condition is a sufficient and necessary condition for the accuracy assessment of the MB PU and 2) it is affected by the normal baseline lengths of the MB InSAR system and the interferogram noise intensity. To practically apply this mathematical condition, a deep convolutional neural network (DCNN) was developed to refine MB InSAR product accuracy. The validity and effectiveness of the proposed approach have been systematically verified using simulated and acquired interferometric datasets.

关键词： Accuracy Synthetic aperture radar Deformation Indexes Upper bound Training Search methods Reviews Phase noise optimization models Accuracy assessment interferometric synthetic aperture radar (InSAR) multibaseline (MB) phase unwrapping (PU)

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Optimal Deployment of Overhead Catenary Charging for Electric Bus Transit Systems

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第2期11卷 6559-6571页

作者： Shehabeldeen, Ali Mohamed, Moataz McMaster Univ Dept Civil Engn Hamilton ON L8S 4L7 Canada Tanta Univ Fac Engn Engn Phys & Math Dept Tanta 31733 Egypt

Integrating in-motion charging into battery electric bus (BEB) transit systems offers a promising solution to address challenges associated with stationary charging, including resource limitations, extended charging durations, and higher battery costs. This study develops a generic optimization model integrating overhead catenary charging (OCC) facilities with overnight charging to minimize BEB system costs (capital and operational). Capital costs are reduced by optimizing OCC deployment, BEB battery capacity, and depot charging configurations. Simultaneously, operational costs are minimized by optimizing charging schedules considering electricity time-of-use (ToU) tariffs, greenhouse gas (GHG) emissions intensity (tCo2e), and BEB battery degradation costs. Application of our model to a real-world transit network highlights substantial reductions in on-peak hours electricity demand (56%), GHG emissions (13%), and overall charging costs (27%). Furthermore, sensitivity analysis explains the impact of OCC infrastructure costs on the total system cost. However, increasing the charging power of OCC facilities yields notable cost savings (28%).

关键词： Batteries Costs Degradation optimization models Electricity Schedules Power system dynamics Energy consumption Transportation Tariffs Battery electric bus (BEB) battery degradation electric bus system configuration in-motion charging deployment optimization overhead catenary charging (OCC) system transit electrification

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