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POWER-SYSTEM OPTIMAL REACTIVE POWER DISPATCH USING evolutionary programming

IEEE TRANSACTIONS ON POWER SYSTEMS 1995年第3期10卷 1243-1249页

作者： WU, QH MA, JT Department of Mathematical Sciences Loughborough University of Technology Leicestershire UK

This paper is concerned with application of evolutionary programming (EP) to optimal reactive power dispatch and voltage control of power systems. Practical implementation of the EP for global optimization problems of large-scale power systems has been considered. The proposed EP method has been evaluated on the IEEE 30-bus system. Simulation results, compared with that obtained using a conventional gradient-based optimization method, are presented to show the potential of applications of the proposed method to power system economical operations.

关键词： evolutionary programming REACTIVE POWER DISPATCH

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Momentum coefficient for promoting accuracy and convergence speed of evolutionary programming

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APPLIED SOFT COMPUTING 2012年第6期12卷 1765-1786页

作者： Alipouri, Yousef Poshtan, Javad Alipouri, Yagub Alipour, Mohammad Reza Tabriz Univ Med Sci TB & Lung Res Ctr Tabriz Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Civil Engn Tehran Iran

Many practical problems culminate with solving optimization problems. Thus, many methods have been introduced for solving these types of problems. The need for algorithms that are fast and more accurate at finding global minimums is ever increasing. One of the promising methods is a heuristic and iterative method called evolutionary programming (EP). It is one of the computational methods used in optimization that is implemented for many practical applications. Many papers have shown the capability of this algorithm for addressing a variety of optimization problems. These studies have opened a vast new and interesting field of research. Recently, many methods have been proposed for promoting the performance of EP when finding the optimum point of functions or applications;however, EP has some shortcomings that cause slow convergence on some functions, especially multimodal functions. By overcoming these shortcomings, EP could be more effective in the optimization research field. This paper introduces new methods for overcoming these disadvantages and promoting the performance of EP. One of these methods, which has the best results on cost functions, changes the searching procedure by adding a new factor to produce offspring and pulling offspring toward a gathering point (the mean value of the parents). This method was tested on 50 well-known test functions discussed in the literature and was compared with state-of-the-art algorithms on twenty-two new cost functions. Finally, a hybrid method of CEP and MCEP (Momentum Coefficient evolutionary programming) called IMCEP (Improved Momentum Coefficient evolutionary programming) is introduced. The results of the calculations reported here show the efficiency of MCEP and IMCEP. (C) 2012 Elsevier B. V. All rights reserved.

关键词： evolutionary programming Gathering point Mean value Momentum Coefficient evolutionary programming

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Diversity Guided evolutionary programming: A novel approach for continuous optimization

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APPLIED SOFT COMPUTING 2012年第6期12卷 1693-1707页

作者： Alam, Mohammad Shafiul Islam, Md. Monirul Yao, Xin Murase, Kazuyuki Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka 1000 Bangladesh Univ Sci & Technol China Dept Comp Sci Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England Univ Fukui Dept Human & Artificial Intelligence Syst Fukui 9108507 Japan

Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving population is considered necessary. Several studies have focused to devise techniques to control and preserve population diversity throughout the evolution. Since mutation is the major operator in many evolutionary systems, such as evolutionary programming and evolutionary strategies, a significant amount of research has also been done for the elegant control and adaptation of the mutation step size that is proper for traversing across the locally optimum points and reach for the global optima. This paper introduces Diversity Guided evolutionary programming, a novel approach to combine the best of both these research directions. This scheme incorporates diversity guided mutation, an innovative mutation scheme that guides the mutation step size using the population diversity information. It also takes some extra diversity preservative measures to maintain adequate amount of population diversity in order to assist the proposed mutation scheme. An extensive simulation has been done on a wide range of benchmark numeric optimization problems and the results have been compared with a number of recent evolutionary systems. Experimental results show that the performance of the proposed system is often better than most other algorithms in comparison on most of the problems. (C) 2012 Elsevier B. V. All rights reserved.

关键词： evolutionary programming Population diversity Exploitation and exploration Continuous optimization

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Global minimum routing in evolutionary programming using fuzzy logic

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INFORMATION SCIENCES 2015年第0期292卷 162-174页

作者： Alipouri, Yousef Poshtan, Javad Alipour, Hasan Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Islamic Azad Univ Dept Elect & Elect Engn Shabestar Branch Shabestar Iran

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. (C) 2014 Elsevier Inc. All rights reserved.

关键词： evolutionary programming Weighted mean point Fuzzy logic Routing toward global minimum Ozone layer data VAR model optimization

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Long-period fiber grating filter synthesis using evolutionary programming

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FIBER AND INTEGRATED OPTICS 2004年第4期23卷 249-261页

作者： Lee, CL Lai, YC Natl Chiao Tung Univ Inst Electroopt Engn Hsinchu Taiwan Natl United Univ Dept Electroopt Engn Miaoli Taiwan

An innovative algorithm based on the evolutionary programming (EP) method is developed for the synthesis of long-period fiber gratings (LPGs). The proposed method exhibits a number of attractive features that prove to be effective for solving the inverse design problems of LPGs. The basics of EP are reviewed and the detailed programming procedures of the proposed algorithm are presented. A new mutation process using the concepts of leveled adjustment and adaptive weighting factor is proposed and verified. Comprehensive numerical results on designing practical LPG filters are presented to demonstrate the feasibility and the effectiveness of the proposed algorithm.

关键词： long-period fiber gratings inverse design evolutionary programming

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Stable grasp planning by evolutionary programming

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2001年第4期48卷 749-756页

作者： Katada, Y Svinin, M Ohkura, K Ueda, K Kobe Univ Dept Mech Engn Kobe Hyogo 657 Japan RIKEN Biomimet Control Res Ctr Nagoya Aichi 4630003 Japan

This paper deals with the problem of synthesis of stable grasp by multifingered hands. First, a mathematical description of the problem is formulated. The grasp to be synthesized should satisfy equilibrium conditions and unilateral frictional constraints. In addition, it should be stable against disturbances applied to the object. Two types of stability conditions, contact stability and Lyapunov stability, are taken into consideration. Contact points, contact forces, and joint stiffnesses are considered as the problem variables. The objective function maximizes admissible linear and rotational disturbances applied to the object. Since the dimension and the complexity of the resulting constrained optimization problem is high enough, the evolutionary programming (EP) approach is explored. Two EP techniques, a conventional one and a specially designed robust technique with a genetic drift, are discussed. The feasibility of these techniques is verified for the synthesis of stable grasp by a three-fingered robotic hand.

关键词： evolutionary programming grasping multifingered hands optimization stability

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Application of evolutionary programming to reactive power planning - Comparison with nonlinear programming approach

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IEEE TRANSACTIONS ON POWER SYSTEMS 1997年第1期12卷 198-204页

作者： Lai, LL Ma, JT Energy Systems Group Department of Electrical Electronic and Information Engineering City University London UK

This paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). Several techniques have been developed to make EP practicable to solve a real power system problem and other practical problems. The proposed approach has been used in the IEEE 30-bus system and a practical power system. For illustration purposes, only results for the IEEE 30-bus system will be given. Simulation results, compared with those obtained by using a conventional gradient-based optimization method, Broyden's method, are presented to show that the present method is better for power system planning. In the case of optimization of noncontinuous and non-smooth function, EP is much better than nonlinear programming. The comprehensive simulation results show a great potential for applications of EP in power system economical and secure operation, planning and reliability assessment.

关键词： power systems reactive power planning artificial intelligence evolutionary programming

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Economic dispatch with prohibited operating zones using fast computation evolutionary programming algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2004年第3期70卷 245-252页

作者： Somasundaram, P Kuppusamy, K Devi, RPK Anna Univ Sch Elect & Elect Engn Power Syst Engn Div Madras 600025 Tamil Nadu India

This paper presents an efficient and simple approach for solving the economic dispatch (ED) problem with units having prohibited operating zones. The operating region of the units having prohibited zones is broken into isolated feasible sub-regions which results in multiple decision spaces for the economic dispatch problem. The optimal solution will lie in one of the feasible decision spaces and can be found using the conventional lambda-delta iterative method in each of the feasible decision spaces. But, this elaborate search procedure is time consuming and not acceptable for on-line application. In this paper, a simple and novel approach is proposed. In this approach, the optimal solution and the corresponding optimum system lambda are determined using an efficient fast computation evolutionary programming algorithm (FCEPA) without considering the prohibited operating zones. Then, a small set of advantageous decision spaces is formed by combining the feasible sub-re-ions of the fuel cost curve intervening the prohibited zones in the neighbourhood of the optimal system lambda. A penalty cost for each advantageous decision space is judiciously computed using participation factor. The most advantageous decision space is found out by comparing the penalty cost of the decision spaces. The optimal solution in the most advantageous decision space is obtained using the FCEPA. The proposed algorithm is tested on a number of sample systems with units possessing prohibited zones. The study results reveal that the proposed approach is computationally efficient and would be a competent method for solving economic dispatch problem with units having prohibited operating zones. (C) 2004 Elsevier B.V. All rights reserved.

关键词： prohibited operating zones decision space economic dispatch evolutionary programming

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OPTIMAL NETWORK PARTITIONING FOR FAULT-TOLERANT NETWORK MANAGEMENT USING evolutionary programming

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INFORMATION PROCESSING LETTERS 1994年第3期50卷 145-149页

作者： KUMAR, GP BABU, GP INDIAN INST SCI DEPT COMP SCI & AUTOMATBANGALORE 12INDIA INDIAN INST SCI DEPT ELECT COMMUN ENGNBANGALORE 12INDIA

This paper explores the applicability of clustering methods for obtaining an optimal partition of a network. In order to make the network management fault-tolerant, more than one management center is assigned to each cluster of nodes in the partition. Gradient descent partition methods converge to locally optimal partitions. In contrast, a stochastic search method called evolutionary programming is employed to search for a globally optimal partition that minimizes the communication cost.

关键词： FAULT-TOLERANCE COMBINATORIAL OPTIMIZATION NETWORK MANAGEMENT CLUSTERING evolutionary programming

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A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

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ELECTRICAL ENGINEERING 2015年第1期97卷 1-12页

作者： Kahourzade, Solmaz Mahmoudi, Amin Bin Mokhlis, Hazlie Univ Malaya UMPEDAC Kuala Lumpur Malaysia Univ Malaya Dept Elect Engn Kuala Lumpur Malaysia

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered.

关键词： evolutionary programming Genetic algorithm Multi-objective optimal power flow Particle swarm Pareto optimal method

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