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An analytical approach for reducing k-line failure analysis and load shed computation

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第13期16卷 2623-2641页

作者： Sarkar, Ranadhir Univ Cent Florida Elect & Comp Engn Orlando FL 32816 USA

In this paper, the problem of identification of critical k-line contingencies that fail one after another in quick succession that render large load shed in the power system is addressed. The problem is formulated as a mixed-integer non-linear programming problem (MINLP) that determines total demand that cannot be satisfied under various k-line removal scenarios. Due to the large search space of the problem, the solution through enumeration is intractable. Two algorithms are proposed using a proposed power flow sensitivity and a topological metric to identify a reduced number of k-line contingencies that initiate cascading overload failure and islanding of power system respectively, that are used to solve the MINLP iteratively for identification of critical k-line contingencies. The algorithms identify a reduced number of k-line contingencies in linear time as compared to the exponential time complexity of brute-force search for solutions of the MINLP. Case studies show that the proposed algorithms significantly reduce the search space and the computation time of the MINLP problem to find the most critical k-line contingency in the IEEE 30 and 118 bus systems at 2 <= k <= 4$2 \le k \le 4$ that are also obtained in the list of k-line contingencies identified using the proposed algorithms.

关键词： failure analysis line contingency search space line failure analysis power system load flow mixed-integer nonlinear programming problem line removal scenarios MINLP problem computational complexity integer programming linear programming nonlinear programming

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On the Convergence of Overlapping Schwarz Decomposition for nonlinear Optimal Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第11期67卷 5996-6011页

作者： Na, Sen Shin, Sungho Anitescu, Mihai Zavala, Victor M. Univ Chicago Dept Stat Chicago IL 60637 USA Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA

We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

关键词： Decomposition methods nonlinear programming optimal control overlapping parallel algorithms

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Indirect Optimization of Power-Limited Asteroid Rendezvous Trajectories

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第5期45卷 962-971页

作者： Wang, Yang Topputo, Francesco Polytech Univ Milan Dept Aerosp Sci & Technol Via La Masa 34 I-20156 Milan Italy

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Optimization approach for planning hybrid electrical energy system: a Brazilian case

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ELECTRICAL ENGINEERING 2022年第2期104卷 587-601页

作者： Kitamura, D. T. Rocha, K. P. Oliveira, L. W. Oliveira, J. G. Dias, B. H. Soares, T. A. Fed Univ Juiz De Fora UFJF Juiz De Fora MG Brazil Fed Technol & Educ Ctr Minas Gerais Belo Horizonte MG Brazil INESC TEC Ctr Power & Energy Syst P-4200465 Porto Portugal

The continuous proliferation of distributed generation is leading end users to look for new tools that help to design hybrid electrical energy systems (HEES). Thus, this work proposes a novel approach for optimal planning of HEES, which comprises the optimization of the type and capacity of distributed generation connected to the end user. The main objective is to minimize the project's total cost, considering the net metering scheme. To this end, the bioinspired meta-heuristic artificial immune system is proposed to optimally determine the number and type of photovoltaic panels. In addition, a nonlinear programming model is proposed to optimize the diesel generator and BESS capacity, considering the energy supply to the consumer by the HEES and the main distribution grid. Case studies involving commercial and residential customers in Brazil are introduced considering the normative resolutions from ANEEL, the Brazilian Regulatory Agency. Comparative analyses are made concerning an exhaustive search procedure and the commercial software Homer Pro, designed to optimize the operation of HEES systems. An important conclusion is that the proposed approach is as effective as the cutting-edge tools, with reasonable computational effort.

关键词： Artificial immune system Hybrid electrical energy system nonlinear programming Optimization Renewable energy sources

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Grey wolf optimization algorithm based on dynamic weighting strategy 4

Grey wolf optimization algorithm based on dynamic weighting ...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Yang, Shipin Chen, Yu College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9781510681637

Grey wolf optimization (GWO) algorithm, when applied to multidimensional and nonlinear optimization problems, often encounters the problems of getting stuck into local optimums and slow rate of convergence. Aiming at above two problems, we propose the grey wolf optimization algorithm on the basis of dynamic weights (DGWO) in this paper. This strategy can dynamically adjust the position updates of other individuals depending on the three best search agents' own locations, thus improving the search precision and speed of GWO. Additionally, to validate the usefulness of our proposed algorithm in treating multidimensional and nonlinear optimization projects, six classical benchmark functions are selected as test targets and some classical algorithms are compared with them in this paper. The results of simulation experiments indicate that DGWO exhibits superior optimization search performance compared with other competing algorithms. © 2024 SPIE.

关键词： nonlinear programming

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Modeling design and control problems involving neural network surrogates

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2022年第3期83卷 759-800页

作者： Yang, Dominic Balaprakash, Prasanna Leyffer, Sven Univ Calif Los Angeles Los Angeles CA 90095 USA Argonne Natl Lab Lemont IL USA

We consider nonlinear optimization problems that involve surrogate models represented by neural networks. We demonstrate first how to directly embed neural network evaluation into optimization models, highlight a difficulty with this approach that can prevent convergence, and then characterize stationarity of such models. We then present two alternative formulations of these problems in the specific case of feedforward neural networks with ReLU activation: as a mixed-integer optimization problem and as a mathematical program with complementarity constraints. For the latter formulation we prove that stationarity at a point for this problem corresponds to stationarity of the embedded formulation. Each of these formulations may be solved with state-of-the-art optimization methods, and we show how to obtain good initial feasible solutions for these methods. We compare our formulations on three practical applications arising in the design and control of combustion engines, in the generation of adversarial attacks on classifier networks, and in the determination of optimal flows in an oil well network.

关键词： Mixed-integer programming nonlinear programming Complementarity constraints Machine learning Neural networks

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Optimal placement of fixed series compensation and phase shifting transformer in the multi-year generation and transmission expansion planning problem at the pool-based market for maximizing social welfare and reducing the investment costs

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第15期16卷 2959-2976页

作者： Nourizadeh, Heshmatollah Mosallanejad, Ali SetayeshNazar, Mehrdad Shahid Beheshti Univ Dept Elect Engn Tehran Iran

Multi-year generation and transmission expansion planning (MY-G&TEP) is a critical issue in power systems. The present paper considers the optimal placement of Fixed Series Compensation (FSC) and Phase Shifting Transformer (PST) proposes a pool-market-based mathematical model (MY-G&TEP) for maximizing social welfare (SW) and reducing investment costs of transmission lines and new power plants. Following the determination of optimal strategy, the present paper compares the usefulness of PSTs and FSCs in the MY-G&TEP problem in three scenarios. Since MY-G&TEP is a complex hybrid, mixed-integer linear programming (MILP), and nonlinear optimization problem, the YALMIP toolbox and CPLEX solver have been applied to find the optimal solution, a globally optimized solution is obtained. For evaluation, the proposed model has been tested on the IEEE 24-bus and IEEE 57-bus systems, and the simulation results indicate that the installation of PST and FSC not only improves market conditions but also increases the flexibility of MY-G&TEP, and adding these FACTS devices to the studied system leads to an increase in the network's performance and enhancement of objectives of the proposed model.

关键词： PST transmission lines flexible AC transmission systems cost reduction social welfare CPLEX solver pool-based market phase shifting transformer compensation power transmission economics integer programming linear programming power markets power system simulation power transmission planning FSC MY-G&TEP problem MILP investment costs fixed series compensation IEEE 24-bus systems power generation economics power systems mathematical analysis mixed-integer linear programming power transmission lines IEEE 57-bus systems pool-market-based mathematical model power plants nonlinear programming FACTS devices power generation planning transmission expansion planning problem investment nonlinear optimization problem power transformers

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Sequencing paths of optimal control adjustments determined by the optimal reactive dispatch via Lagrange multiplier sensitivity analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期301卷 373-385页

作者： Barros, Rafael Martins Lage, Guilherme Guimaraes Lira Rabelo, Ricardo de Andrade Univ Fed Piaui UFPI Dept Comp Campus Minist Petronio Portela BR-64049550 Teresina PI Brazil Univ Fed Sao Carlos UFSCar Dept Engn Elect Rod Washington LuisSP 310Km 235 BR-13565905 Sao Carlos SP Brazil

Optimal power flows play a key role in power system operation planning. While most papers in the literature focus on attaining optima, sequencing paths of optimal control adjustments that lead the sys-tem from an initial operating point towards the optimum remain scarcely accounted for. Thus, this work proposes a practical framework based upon power system steady-state analysis for sequencing strictly feasible paths of optimal control adjustments determined by the Optimal Reactive Dispatch (ORD) via Lagrange multiplier sensitivity analysis. The proposed framework is methodologically founded on the re-formulation of the ORD in terms of optimal control adjustments rather than optimal control values, suc-cessive Newton's power flow calculations to assure a strictly feasible path from the initial operating point towards the optimum, and successive resolutions of the reformulated ORD's associated dual problem to determine Lagrange multipliers along such sequence path. Thus, pondering optimal control adjustments by their respective Lagrange multipliers indicates which control action must be realised. Numerical re-sults for IEEE test-systems with up to 300 buses with an increased number of controllable variables are obtained to validate and illustrate the efficiency and robustness of the proposed framework.(c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in energy nonlinear programming Optimal power flow Sensitivity analysis Sequence of control adjustments

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Box-Jenkins Model of Elastic Drive System Using Levenberg-Marquardt Algorithm

Box-Jenkins Model of Elastic Drive System Using Levenberg-M...

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Future of Information and Communication Conference, FICC 2024

作者： Jafari, Reza Virginia Tech University Falls ChurchVA22043 United States

ISBN: (纸本)9783031539596

This paper explains the derivation of Box-Jenkins model for the elastic drive system using Levenberg-Marduardt algorithm. The Box-Jenkins model which is the most flexible linear model has been chosen to identify the elastic drive system. The GPAC analysis has been used for the preliminary identification and the Maximum Likelihood Estimator (Levenberg-Marduardt) is used for the parameter estimations. Several models have been developed for the elastic drive system and the simplest model has been chosen. The accuracy of the final model, residual analysis, has been checked using CHI-Square test. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： nonlinear programming

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Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第5期59卷 1660-1670页

作者： Isaji, Masafumi Takubo, Yuji Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance;however, this integrated optimization problem results in a large-scale mixed-integer nonlinear programming (MINLP) problem, which is challenging to solve. In response to this challenge, this paper proposes a new solution approach to this problem based on decomposition-based optimization via augmented Lagrangian coordination. The proposed approach leverages the unique structure of the problem that enables its decomposition into a set of coupled subproblems of different types: a mixed-integer quadratic programming (MIQP) subproblem for mission planning, and one or more nonlinear programming (NLP) subproblem(s) for spacecraft design. Because specialized MIQP or NLP solvers can be applied to each subproblem, the proposed approach can efficiently solve the otherwise intractable integrated MINLP problem. An automatic and effective method to find an initial solution for this iterative approach is also proposed so that the optimization can be performed without a user-defined initial guess. The demonstration case study shows that, compared to the state-of-the-art method, the proposed formulation converges substantially faster and the converged solution is at least the same or better given the same computational time limit.

关键词： Spacecraft Design Lunar Exploration MDO nonlinear programming Quadratic programming Propellant Space Exploration Environmental Control and Life Support System Particle Swarm Optimization Space Logistics Optimization

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