The Experts below are selected from a list of 43815 Experts worldwide ranked by ideXlab platform

Yoshikazu Fukuyama - One of the best experts on this subject based on the ideXlab platform.

  • parallel genetic algorithm for service restoration in Electric Power Distribution systems
    International Journal of Electrical Power & Energy Systems, 1996
    Co-Authors: Yoshikazu Fukuyama, Hsiaodong Chiang, Nan K Miu
    Abstract:

    Abstract This paper develops a coarse-grain parallel genetic algorithm for solving a service restoration problem in Electric Power Distribution systems. Service restoration is performed to restore Electricity for out-of-service areas. Developing effective service restoration procedures is a cost-effective approach to improving service reliability and enhancing customer satisfaction. The main objective in service restoration procedures is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting Distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described. The proposed method is implemented on transputers for parallel computations. The feasibility of the developed algorithm for service restoration is demonstrated on several Distribution networks with promising results.

  • a parallel genetic algorithm for service restoration in Electric Power Distribution systems
    IEEE International Conference on Fuzzy Systems, 1995
    Co-Authors: Yoshikazu Fukuyama, Hsaiodong Chiang
    Abstract:

    This paper develops a coarse-gain parallel genetic algorithm for solving a service restoration problem in Electric Power Distribution systems. A Power utility performs service restoration in order to restore out-of-service areas at fault. Developing an effective service restoration procedure is a cost-effective approach to improve service reliability and to enhance customer satisfaction. The main objective in service restoration procedure is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting Distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described. The proposed method is implemented on transputers for parallel computation. The feasibility of the developed algorithm for service restoration is demonstrated on several Distribution networks with promising results. >

  • A reactive tabu search for service restoration in Electric Power Distribution systems
    1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1
    Co-Authors: S. Toune, Yoshikazu Fukuyama, H. Fudo, T. Genji, Yosuke Nakanishi
    Abstract:

    This paper presents a reactive tabu search for service restoration in Electric Power Distribution systems. Service restoration is an emergency control in Distribution control centers to restore out-of-service area as soon as possible when a fault occurs in Distribution systems. Therefore, it requires fast computation time and high quality solutions for customers' satisfaction. The problem can be formulated as a combinatorial optimization problem to divide the out-of-service area to each Power source. The effectiveness of the proposed method is demonstrated on typical service restoration problems. It is compared favorably with conventional tabu search, genetic algorithm, and parallel simulated annealing. The results reveal the speed and effectiveness of the proposed method for solving the problem.

R. Gholizadeh Roshanagh - One of the best experts on this subject based on the ideXlab platform.

  • On optimal multistage Electric Power Distribution networks expansion planning
    International Journal of Electrical Power & Energy Systems, 2014
    Co-Authors: S. Najafi Ravadanegh, R. Gholizadeh Roshanagh
    Abstract:

    Abstract The optimal expansion planning of Electric Power Distribution network to meet system load growth and overcome to pseudo dynamic behavior of network parameters considering a large number of constraints is a hard satisfactory multiobjective optimization problem. This paper implements new developed Imperialist Competitive Algorithm (ICA) for the optimal expansion planning of Distribution network. The topology of medium voltage (MV) Distribution network as backbone of Electric Power Distribution systems is designed by optimal sizing, siting and timing of medium voltage network components such as HV substation and MV feeders’ routes. A multistage expansion planning is proposed to consider dynamic behavior of the system parameters asset management and geographical constraints. In order to reach the global solution an efficient coding is developed for ICA parameters. The Greedy algorithm is used to solve the minimum spanning tree problem to construct a radial configuration of the mesh network. At each stage of the problem the results are fully illustrated either by figures or by tables. A sensitivity analysis is used to show the robustness of the results with respect to ICA parameters variation. The obtained results are compared with GA as well known heuristic optimization tool. The efficiency and capability of the methodology has been tested on an under developed relatively large-scale Distribution network.

Hsaiodong Chiang - One of the best experts on this subject based on the ideXlab platform.

  • a parallel genetic algorithm for service restoration in Electric Power Distribution systems
    IEEE International Conference on Fuzzy Systems, 1995
    Co-Authors: Yoshikazu Fukuyama, Hsaiodong Chiang
    Abstract:

    This paper develops a coarse-gain parallel genetic algorithm for solving a service restoration problem in Electric Power Distribution systems. A Power utility performs service restoration in order to restore out-of-service areas at fault. Developing an effective service restoration procedure is a cost-effective approach to improve service reliability and to enhance customer satisfaction. The main objective in service restoration procedure is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting Distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described. The proposed method is implemented on transputers for parallel computation. The feasibility of the developed algorithm for service restoration is demonstrated on several Distribution networks with promising results. >

Nan K Miu - One of the best experts on this subject based on the ideXlab platform.

  • parallel genetic algorithm for service restoration in Electric Power Distribution systems
    International Journal of Electrical Power & Energy Systems, 1996
    Co-Authors: Yoshikazu Fukuyama, Hsiaodong Chiang, Nan K Miu
    Abstract:

    Abstract This paper develops a coarse-grain parallel genetic algorithm for solving a service restoration problem in Electric Power Distribution systems. Service restoration is performed to restore Electricity for out-of-service areas. Developing effective service restoration procedures is a cost-effective approach to improving service reliability and enhancing customer satisfaction. The main objective in service restoration procedures is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting Distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described. The proposed method is implemented on transputers for parallel computations. The feasibility of the developed algorithm for service restoration is demonstrated on several Distribution networks with promising results.

Haralambos Sarimveis - One of the best experts on this subject based on the ideXlab platform.

  • Predictive control algorithms for congestion management in Electric Power Distribution grids
    Applied Mathematical Modelling, 2020
    Co-Authors: Ioannis Kalogeropoulos, Haralambos Sarimveis
    Abstract:

    Abstract In this paper, model predictive control methodologies are developed to address two main issues which arise in Electric Power Distribution systems, namely the congestion of the Distribution lines and the balancing problem. Consumer energy demand is divided into an uncontrollable part, a controllable part that can be either stored in energy storage devices in order to be consumed at later times or shifted in time in the form of hourly consumption or a consumption that maintains a pattern. Demand – response strategies involve consumers actively in the balancing effort and are part of the MPC methodologies, which are formulated as Mixed Integer Quadratic Program optimization problems involving both continuous and binary variables. Finally, these new developments are tested on the IEEE European Low Voltage Test Feeder which highlights the performance of the proposed control schemes.