• Title/Summary/Keyword: genetic algorithms (GAs).

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An Optimization Method using Evolutionary Computation in Large Scale Power Systems (진화연산을 이용한 대규모 전력계통의 최적화 방안)

  • You, Seok-Ku;Park, Chang-Joo;Kim, Kyu-Ho;Lee, Jae-Gyu
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.714-716
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    • 1996
  • This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

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An Optimization Method of Series Condenser for Improvement of Transient Stability (과도안정도 향상을 위한 직렬콘덴서의 최적화 방안)

  • You, Seok-Ku;Moon, Byoung-Seo;Kim, Kyu-Ho
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.890-892
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    • 1996
  • This paper presents a method for optimal placement of series condenser in order to improve the power system transient stability using genetic algorithms(GAs). In applying GAs, this approach utilizes two kinds of strings, one is coded by a binary finite-length for the selection of lines to install series condenser, the other is coded by a real value for the determination of injected condenser capacitance. For the formulation. this paper considers multi-objective function which is the critical energy as decelerating energy in power systems and the total injected condenser capacitance. The proposed method is applied to 9-bus, 18-line, 3-machine model system to show its effectiveness in determining the locations to install series condenser and the series condenser capacitance to be injected, simultaneously.

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An Optimal Real and Reactive Power dispatch using Evolutionary Computation (진화연산을 이용한 유효 및 무효전력 최적배분)

  • You, Seok-Ku;Park, Chang-Joo;Kim, Kyu-Ho
    • Proceedings of the KIEE Conference
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    • 1996.11a
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    • pp.166-168
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    • 1996
  • This paper presents an power system optimization method which solves real and reactive power dispatch problems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods, applied to the IEEE 30-bus system, were run for 12 other exogenous parameters. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

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Optimization of Fuzzy Set-Fuzzy Systems based on IG by Means of GAs with Successive Tuning Method

  • Park, Keon-Jun;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.101-107
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    • 2008
  • We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

A Study on the PID Order tuning by GAs for Velocity Control of DC Servo Motor (DC 서보모터의 속도제어를 위한 GAs의 PID 계수조정에 관한 연구)

  • Park Jae-Hyung;Kim Seong-Kon;Lee Sang-kwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.8
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    • pp.1840-1846
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    • 2005
  • In this paper, does by purpose DC servo motor speed controller design about PID coefficient tuning techniques that use genetic algerian. DC servo motor is used in application field of a peat many control machine or robot etc. and in this field, selection of controller parameters requires user's expert knowledge. Therefore, general amount of work engineers must continuously iteration tuning in controller parameters by trial and error. With this, when must tuning parameter coefficient about change of dynamic system or disturbance, can improve the efficiency according to following that is more precised and parameter coefficient value that is optimized by using genetic algorithm. In this paper, from dynamic character modeling get in analyze dynamic character of DC motor desist controller drive control possible that is fast response character md improved speed precision using a Genetic Algorithms.

Optimum cost design of frames using genetic algorithms

  • Chen, Chulin;Yousif, Salim Taib;Najem, Rabi' Muyad;Abavisani, Ali;Pham, Binh Thai;Wakil, Karzan;Mohamad, Edy Tonnizam;Khorami, Majid
    • Steel and Composite Structures
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    • v.30 no.3
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    • pp.293-304
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    • 2019
  • The optimum cost of a reinforced concrete plane and space frames have been found by using the Genetic Algorithm (GA) method. The design procedure is subjected to many constraints controlling the designed sections (beams and columns) based on the standard specifications of the American Concrete Institute ACI Code 2011. The design variables have contained the dimensions of designed sections, reinforced steel and topology through the section. It is obtained from a predetermined database containing all the single reinforced design sections for beam and columns subjected to axial load, uniaxial or biaxial moments. The designed optimum beam sections by using GAs have been unified through MATLAB to satisfy axial, flexural, shear and torsion requirements based on the designed code. The frames' functional cost has contained the cost of concrete and reinforcement of steel in addition to the cost of the frames' formwork. The results have found that limiting the dimensions of the frame's beams with the frame's columns have increased the optimum cost of the structure by 2%, declining the re-analysis of the optimum designed structures through GA.

Genetic algorithm-based design of a nonlinear PID controller for the temperature control of load-following coolant systems (부하추종 냉각수 시스템의 온도 제어를 위한 유전알고리즘 기반 비선형 PID 제어기 설계)

  • Yu-Soo, LEE;Soon-Kyu, HWANG;Jong-Kap, AHN
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.58 no.4
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    • pp.359-366
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    • 2022
  • In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

A Study on Compressor Map Identification using Artificial Intelligent Technique and Performance Deck Data (인공지능 및 성능덱 데이터를 이용한 압축기 성능도 식별에 관한 연구)

  • Kong Chang-Duck;Ki Ja-Young;Lee Chang-Ho
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2005.11a
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    • pp.149-153
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    • 2005
  • In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. In this study a component map generation method which may identify compressor map conversely from a performance deck provided by engine manufacturer using genetic algorithms was newly proposed. As a demonstration example for this study, the PW 206C turbo shaft engine for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle). In ordo to verify the proposed method, steady-state performance analysis results using the newly generated compressor map was compared with them performed by EEPP(Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method.

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A Genetic Algorithm with Local Competing (지역적으로 경정하는 유전자 알고리즘)

  • Kang, Tae-Won
    • Journal of KIISE:Software and Applications
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    • v.29 no.6
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    • pp.396-406
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    • 2002
  • On the whole, the simple GAs with just one population set is effective in finding one optimal solution. However, many real world problems have a lot of optimal solutions, and often it is important to find all of them. In this paper, we propose a GA that has a population set containing multiple optimal solutions. In the proposed GA, each of the individuals in population set has its own geological neighbors, and they exchange their genes globally as well as compete with others locally. The paper then evaluates the proposed GA along with many multimodal problems including a 30bit, order-six bipolar-deceptive function. Finally, we present some improvement directions of the proposed GA.

Design of FNN architecture based on HCM Clustering Method (HCM 클러스터링 기반 FNN 구조 설계)

  • Park, Ho-Sung;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2821-2823
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    • 2002
  • In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

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