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real coded genetic algorithm optimization of long term reservoir operation

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2003年第5期39卷 1157-1165页

作者： Chen, L Chung Hua Univ Dept Civil Engn Hsinchu 300 Taiwan

An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work applies an RGA to obtain the 10-day (the traditional period of reservoir operation in Taiwan) operating rule curves for the proposed reservoir system. The RGA is combined with an effective and flexible scheme for coding the reservoir rule curves and applied to an important reservoir in Taiwan, considering a water reservoir development scenario to the year 2021. Each rule curve is evaluated using a complex simulation model to determine a performance index for a given flow series. The process of generating and evaluating decision parameters is repeated until no further improvement in performance is obtained. Many experiments were performed to determine the suitable RGA components, including macro evolutionary (ME) selection and blend-a crossover. Macro evolution (ME) can be applied to prevent the premature problem of the conventional selection scheme of genetic algorithm. The purpose of adjusting alpha of a crossover scheme is to determine the exploratory or exploitative degree of various subpopulations. The appropriate rule curve searched by an RGA can minimize the water deficit and maintain the high water level of the reservoir. The results also show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system. It is also very efficient for optimizing other highly nonlinear systems.

关键词： long term reservoir operation real coded genetic algorithm rule curves macro evolutionary selection blend-alpha crossover

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real coded genetic algorithm based transmission system loss estimation in dynamic economic dispatch problem

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 3535-3547页

作者： Jethmalani, C. H. Ram Simon, Sishaj P. Sundareswaran, K. Nayak, P. Srinivasa Rao Padhy, Narayana Prasad Natl Inst Technol Dept Elect & Elect Engn Pondicherry India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India

Estimation of transmission loss is vital in scheduling, optimization and planning of power systems. The conventional transmission loss evaluation methods used in power system scheduling problems are not accurate as the transmission network parameters in the system operator database are erroneous and not updated periodically. The conventional techniques rely on the precise network model. Moreover, loss evaluation gains significant importance as it affects the revenues of several utilities. In this context, this article proposes a method to evaluate transmission losses in a scheduling problem without relying on the network model. The proposed method uses samples of real power generation, consumption and losses collected at various operating conditions. From these data, genetic algorithm based loss coefficients (GALCs) are obtained by minimizing the mean absolute error between actual and calculated loss values using real coded genetic algorithm. Then, GALCs are used to evaluate losses in a dynamic economic dispatch problem and its performance is compared with conventional loss estimation techniques. The proposed GALC is validated on the IEEE 30 bus system and using the real time data of the Ontario power system. The performance analysis is also carried out for change in system operating conditions, transmission network modifications and outages. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Sample based transmission system loss coefficients real coded genetic algorithm Transmission network model Dynamic economic dispatch

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real coded genetic algorithm for Optimal Parameter Estimation of Solar Photovoltaic Model 1

Real Coded Genetic Algorithm for Optimal Parameter Estimatio...

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1st International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Ulaganathan, M. Devaraj, D. Kalasalingam Univ Dept Elect & Elect Engn Krishankoil 626126 India

ISBN: (纸本)9781467367257

The modeling of Photovoltaic (PV) cell plays a vital role in evaluating the performance and fault diagnosis of solar PV system. The parameters of the Solar PV model depend on the input parameters namely temperature and irradiance. The parameter estimation of Solar Photovoltaic module is one of the challenging research areas. In this work, the parameter estimation of the PV cell is formulated as an optimization problem and real coded genetic algorithm is applied to seek the optimal parameter of the solar cell model. The mathematical model of the solar PV cell is expressed explicitly using Lambert function. The estimated PV cell parameters are up scaled to PV module based on the PV module internal cell connection. In the proposed algorithm, the variable of the optimization problem are directly represented as floating point to overcome the drawbacks of representing variables as binary string in the conventional genetic algorithm. Under varying temperature and irradiance conditions, the parameters are estimated and the result is also presented in this paper. The simulation results are compared with binary coded genetic algorithms based parameter estimation in terms of accuracy and computational time in a PC with Core 2 Duo processor with 3 GB RAM.

关键词： Lambert Function Parameter Estimation Photovoltaic Cell Model real coded genetic algorithm

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real coded genetic algorithm for permanent magnet motor mass minimization for electric vehicle application

Real coded genetic algorithm for permanent magnet motor mass...

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3rd International Symposium on Computational Intellegence and Intelligent Informatics

作者： Ben Hadj, N. Tounsi, S. Neji, R. Sellami, F. Ecole Natl Ingn Sfax LETI BP W Sfax 3038 Tunisia

This paper presents the application of real coded genetic algorithms in the optimization of permanent magnet motor mass in order to reduce simultaneously the cost and the consumption of electric vehicles (EVs).

ISBN: (纸本)9781424411573

关键词： real coded genetic algorithm motor mass optimisation electric vehicle

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Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2007年第10期29卷 738-747页

作者： Kumar, Sushil Naresh, R. Natl Inst Technol Dept Elect Engn Hamirpur 177005 HP India

A simple and efficient optimisation procedure based on real coded genetic algorithm is proposed for the solution of short-term hydrothermal scheduling problem with continuous and non-smooth/non-convex cost function. The constraints like load-generation balance, unit generation limits, reservoir flow balance, reservoir physical limitations and reservoir coupling are also considered. The effectiveness of the proposed algorithm is demonstrated on a multichain-cascaded hydrothermal system that uses non-linear hydro generation function, includes water travel times between the linked reservoirs, and considers the valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint-handling technique, which eliminates the need for penalty parameters. A simple strategy based on allowing infeasible solutions to remain in the population is used to maintain diversity. The same problem is also solved using binary coded genetic algorithm. The features of both algorithms are same except the crossover and mutation operators. In real coded genetic algorithm, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in binary coded genetic algorithm. The comparison of the two genetic algorithms reveals that real coded genetic algorithm is more efficient in terms of thermal cost minimisation for a short-term hydrothermal scheduling problem with continuous search space. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： short-term hydrothermal scheduling constraint-handling valve point loading effect real coded genetic algorithm simulated binary crossover polynomial mutation

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Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

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MEASUREMENT 2014年第1期47卷 558-568页

作者： Manikandan, S. Ramar, K. Iruthayarajan, M. Willjuice Srinivasagan, K. G. Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti 628503 Tamil Nadu India Einstein Coll Engn Dept Comp Sci & Engn Tirunelveli Tamil Nadu India Natl Engn Coll Dept Elect & Elect Engn Kovilpatti 628503 Tamil Nadu India Natl Engn Coll Dept Comp Sci & Engn Kovilpatti 628503 Tamil Nadu India

Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder-Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： MRI brain image Entropy Thresholds Image segmentation real coded genetic algorithm

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A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 617-629页

作者： Fang, Na Zhou, Jianzhong Zhang, Rui Liu, Yi Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China

The short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a series of hydraulic and electric system constraints. This paper presents a hybrid algorithm for solving SHS problem by combining real coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA), which takes advantage of their complementary ability of global and local search for optimal solution. real coded genetic algorithm (RCGA) is applied as global search, which can explore more promising solution spaces and give a good direction to the global optimal region. Artificial fish swarm algorithm (AFSA) is used as local search to obtain the final optimal solution for improving the exploitation capability of algorithm. The water transport delay between connected reservoirs is taken into account in this paper. Moreover, new coarse and fine adjustment methods without any penalty factors and extra parameters are proposed to deal with all equality and inequality constraints. To verify the feasibility and effectiveness of RCGA-AFSA, the proposed method is tested on two hydrothermal systems. Compared with other methods reported in the literature, the simulation results obtained by hybrid RCGA-AFSA are superior in fuel cost and computation time. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Short-term hydrothermal scheduling real coded genetic algorithm Artificial fish swarm algorithm Constraints handling

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Optimal coordination of directional over current relays using a modified real coded genetic algorithm: A comparative study

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 82卷 484-495页

作者： Thakur, Manoj Kumar, Anand IIT Mandi Sch Basic Sci Mandi India

In a large distribution network, the coordination of relays is a highly constrained optimization problem with the objective to minimize the overall operating time of each primary relay. For proper functioning of the power system with distributed power generating stations, appropriate coordination of protection devices is crucial. The present work incorporates bounded exponential crossover and power mutation (BEX-PM) into real coded genetic algorithm (RCGA) in order to find optimal settings for the Directional Overcurrent Relays (DOCRs). Optimal settings are obtained to minimize the overall action time of all the primary relays as well as to get rid of miscoordination among the backup primary relay pairs. Another objective of the work is to maintain the difference of response time of backup relay and corresponding primary relay to possible minimum. Results obtained are compared with various approaches available in the literature. The results of this work evidences the compatibility of the proposed strategy in solving complex real-world optimization problems and applicability of the obtained results. (C) 2016 Published by Elsevier Ltd.

关键词： Overcurrent relay Power system protection Optimization real coded genetic algorithm Constraint handling

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Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第2期79卷 374-381页

作者： Subbaraj, P. Rajnarayanan, P. N. Kalasalingam Univ Srivilliputhur Tamil Nadu India PSR Engn Coll Sivakasi Tamil Nadu India

In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is introduced by applying the simulated binary crossover (SBX) operator. The binary tournament selection and polynomial mutation are also introduced in real coded genetic algorithm. The problem formulation involves continuous (generator voltages), discrete (transformer tap ratios) and binary (var Sources) decision variables. The stochastic based SARGA approach can handle all types of decision variables and produce near optimal solutions. The IEEE 14- and 30-bus systems were used as test systems to demonstrate the applicability and efficiency of the proposed method. The performance of the proposed method is compared with evolutionary programming (EP) and previous approaches reported in the literature. The results show that SARGA solves the ORPD problem efficiently. (C) 2008 Elsevier B.V. All rights reserved.

关键词： real coded genetic algorithm Simulated binary crossover Evolutionary programming Optimal reactive power dispatch

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A new technique for computation of closest saddle-node bifurcation point of power system using real coded genetic algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第5期33卷 1203-1210页

作者： Phadke, A. R. Fozdar, Manoj Niazi, K. R. Malaviya Natl Inst Technol Jaipur Rajasthan India

Estimation of voltage stability margin is essential for operation of the system with an adequate security margin. In this paper, a new technique to determine the worst case loading margin, i.e., shortest distance to voltage instability is developed. The problem of determining the closest saddle-node bifurcation point (CSNBP) is formulated as an optimization problem and solved using real coded genetic algorithm (RCGA). The method is capable of handling various operational constraints and can determine the CSNBP accurately even if the transfer limit surface is not smooth. The proposed approach has been applied on a simple radial system, IEEE 14-bus and IEEE 57-bus systems. The developed method has been compared with the method based on Particle Swarm Optimization. Simulation results show the validity and feasibility of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Voltage stability real coded genetic algorithm Saddle-node Bifurcation Minimum loading margin

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