The Experts below are selected from a list of 36168 Experts worldwide ranked by ideXlab platform

Jinhua She - One of the best experts on this subject based on the ideXlab platform.

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

  • Trust-tech based parameter estimation and its application to power system load modeling
    2009 IEEE Power & Energy Society General Meeting, 2009
    Co-Authors: Byoung-kon Choi, Hsiao-dong Chiang
    Abstract:

    Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple Local Optimal Solutions may exist and Local methods can be trapped in a Local Optimal Solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization is applied to tackle the multiple Local Optimal Solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using the Trust-Tech in a deterministic manner. Numerical studies indicate that the Trust-Tech along with conventional Local methods can be successfully applied to power system load model parameter estimation in measurement-based approach.

  • Multiple Solutions and Plateau Phenomenon in Measurement-Based Load Model Development: Issues and Suggestions
    IEEE Transactions on Power Systems, 2009
    Co-Authors: Byoung-kon Choi, Hsiao-dong Chiang
    Abstract:

    In the measurement-based approach, given a load model structure, its parameter values are derived using actual measurements. The issue of multiple Local Optimal Solutions may arise in parameter estimation when the parameter estimation task is formulated as a nonlinear least squares problem. In this paper, the multiple Local Optimal Solutions issue and a plateau phenomenon in developing load models are addressed in detail and linked to the Local identifiability problem. Examples of multiple Local Optimal Solutions in estimating parameter values of several static, dynamic and composite load models are demonstrated. In order to address the Local identifiability at an obtained Local Optimal Solution, output sensitivity and correlation between parameters are analyzed. Finally, several suggestions are proposed to resolve these issues.

  • Trust-Tech based Parameter Estimation and its Application to Power System Load Modeling
    Journal of Electrical Engineering and Technology, 2008
    Co-Authors: Byoung-kon Choi, Hsiao-dong Chiang
    Abstract:

    Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple Local Optimal Solutions may exist and Local methods can be trapped in a Local Optimal Solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple Local Optimal Solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional Local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

  • A systematic search method for obtaining multiple Local Optimal Solutions of nonlinear programming problems
    IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1996
    Co-Authors: Hsiao-dong Chiang, Chia-chi Chu
    Abstract:

    We propose, in this paper, a systematic method to find several Local Optimal Solutions for general nonlinear optimization problems. We have developed some analytical results for quasi-gradient systems and reflected gradient systems, applying these results to derive topological and geometric properties of the critical points of the underlying objective function. A mechanism has also been devised to escape from a Local Optimal Solution and proceed into another Local Optimal Solution via decomposition points. By properly switching between quasi-gradient systems and reflected gradient systems, our proposed method can attain a set of Local Optimal Solutions. The proposed method is applied to two test examples with promising results.

  • ISCAS - A systematic search method for obtaining multiple Local Optimal Solutions of nonlinear programming problems
    Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1
    Co-Authors: Hsiao-dong Chiang, Chia-chi Chu
    Abstract:

    We propose a systematic method to find several Local Optimal Solutions for a general nonlinear optimization problem. Analytical results for quasi-gradient systems and reflected gradient systems are developed and applied to explore the topological and geometric aspects of the critical points of the objective function. A mechanism is devised to escape from a Local Optimal Solution and proceed into another Local Optimal Solution by locating the decomposition point. By properly switching between quasi-gradient systems and reflected gradient systems, our proposed method can obtain a set of Local Optimal Solutions and decomposition points. This algorithm also can find the global Optimal Solution. It depends on its ability to find all the decomposition points. The main algorithm contains two levels: the lower level is continuous while the upper level is discrete in nature. Further improvements in the algorithm to locate all decomposition points are desirable. The proposed method is applied to one test example with encouraging results. >

Zhi-yong Feng - One of the best experts on this subject based on the ideXlab platform.

Keiichiro Yasuda - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Combinatorial optimization method with search strategy based on hierarchical interpretation of Solution space
    2017 IEEE International Conference on Systems Man and Cybernetics (SMC), 2017
    Co-Authors: Masatoshi Hashimoto, Kenichi Tamura, Junichi Tsuchiya, Keiichiro Yasuda
    Abstract:

    A "basin of attraction" is a set of Solutions arriving at the same Local Optimal Solution by Best-improvement Local Search. By utilizing the concept of basin of attraction in the Solution space of combinatorial optimization problem, the Solution space is interpreted as a higher structure, which is a set of basin of attraction, and a lower structure, which is a set of Solutions, in this paper. Based on the hierarchical interpretation of the Solution space, which consists of the higher structure and the lower structure, and basic strategy in metaheuristics, an optimization method with search strategy to find a basin of attraction to which superior Local Optimal Solution belongs is proposed. The search performance of this method was evaluated through numerical experiments using benchmark problems. In addition, the search situation and influence on search by parameter of this method are considered.

  • IEEE Congress on Evolutionary Computation - Particle swarm optimization based on the concept of tabu search
    2007 IEEE Congress on Evolutionary Computation, 2007
    Co-Authors: Shinichi Nakano, Atsushi Ishigame, Keiichiro Yasuda
    Abstract:

    This paper presents a new Particle Swarm Optimization based on the concept of Tabu Search (TS-PSO). In PSO, when a particle finds a Local Optimal Solution, all of the particles gather around the one, and cannot escape from it. On the other hand, TS can escape from the Local Optimal Solution by moving away from the best Solution at the present. The proposed TS-PSO is the method for combining the excellence of both PSO and TS. In this method, particles are divided into two categories called swarm1 and swarm2. And they play the key roles of intensification and diversification respectively. Swarm1 playing roles of intensification searches the area around the best Solution at the present, and swarm2 playing roles of diversification intends to avoid Local Optimal Solutions and to find global Optimal one. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to the conventional PSO methods.

  • Global optimization method using intermittency chaos
    Proceedings of the 36th IEEE Conference on Decision and Control, 1
    Co-Authors: Tokumitu Fujita, Takao Watanabe, Keiichiro Yasuda, Ryuichi Yokoyama
    Abstract:

    A method of unconstrained global optimization is proposed which uses intermittency chaos. Through convergence (nonperiodically) to the Local Optimal Solution, the global Optimal Solution is searched without trapping into the Local Optimal Solution.

Chia-chi Chu - One of the best experts on this subject based on the ideXlab platform.

  • A systematic search method for obtaining multiple Local Optimal Solutions of nonlinear programming problems
    IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1996
    Co-Authors: Hsiao-dong Chiang, Chia-chi Chu
    Abstract:

    We propose, in this paper, a systematic method to find several Local Optimal Solutions for general nonlinear optimization problems. We have developed some analytical results for quasi-gradient systems and reflected gradient systems, applying these results to derive topological and geometric properties of the critical points of the underlying objective function. A mechanism has also been devised to escape from a Local Optimal Solution and proceed into another Local Optimal Solution via decomposition points. By properly switching between quasi-gradient systems and reflected gradient systems, our proposed method can attain a set of Local Optimal Solutions. The proposed method is applied to two test examples with promising results.

  • ISCAS - A systematic search method for obtaining multiple Local Optimal Solutions of nonlinear programming problems
    Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1
    Co-Authors: Hsiao-dong Chiang, Chia-chi Chu
    Abstract:

    We propose a systematic method to find several Local Optimal Solutions for a general nonlinear optimization problem. Analytical results for quasi-gradient systems and reflected gradient systems are developed and applied to explore the topological and geometric aspects of the critical points of the objective function. A mechanism is devised to escape from a Local Optimal Solution and proceed into another Local Optimal Solution by locating the decomposition point. By properly switching between quasi-gradient systems and reflected gradient systems, our proposed method can obtain a set of Local Optimal Solutions and decomposition points. This algorithm also can find the global Optimal Solution. It depends on its ability to find all the decomposition points. The main algorithm contains two levels: the lower level is continuous while the upper level is discrete in nature. Further improvements in the algorithm to locate all decomposition points are desirable. The proposed method is applied to one test example with encouraging results. >