• 제목/요약/키워드: Evolutionary Programming (EP)

검색결과 26건 처리시간 0.028초

EP Based PSO Method for Solving Multi Area Unit Commitment Problem with Import and Export Constraints

  • Venkatesan, K.;Selvakumar, G.;Rajan, C. Christober Asir
    • Journal of Electrical Engineering and Technology
    • /
    • 제9권2호
    • /
    • pp.415-422
    • /
    • 2014
  • This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

진화 프로그래밍의 전원개발계획에의 적용 연구 (Application to Generation Expansion Planning of Evolutionary Programming)

  • 원종률
    • 대한전기학회논문지:전력기술부문A
    • /
    • 제50권4호
    • /
    • pp.180-187
    • /
    • 2001
  • This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

  • PDF

진화 프로그래밍을 이용한 안정지수 결정 (Finding Stability Indices Using Evolutionary Programming)

  • 신진욱;김인택;강환일
    • 한국지능시스템학회:학술대회논문집
    • /
    • 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
    • /
    • pp.39-42
    • /
    • 2000
  • 진화 프로그래밍은 유전자 알고리즘과 함께 진화 연산 분야에 속하며 넓은 탐색공간에 존재하는 해를 찾는데 유용한 방법으로 알려져 있다. 본 논문에서는 이 두 가지 방법을 비교하기 위해서 Manabe 표준형을 기준으로 사용자의 요구사항에 맞는 스텝응답을 만족하는 계수, 즉 안정지수를 이 두 가지 방법을 적용하였다.

  • PDF

진화 프로그래밍기법을 적용한 단기 수화력 운용 (A Short Term Hydro-Thermal Scheduling using Evolutionary Programming)

  • 김재철;백영식
    • 대한전기학회논문지:전력기술부문A
    • /
    • 제48권8호
    • /
    • pp.917-923
    • /
    • 1999
  • This paper proposes an efficient method of hydro-thermal scheduling in coordination with head variation and hydraulically coupled plants using Evolutionary Programing(EP). Based on the EP technique, the proposed algorithm is capable of determining the global optimal solutions. The constraints such a power balance condition, water available condition and transmission losses are embedded and satisfied throughout the proposed EP approach. The effectiveness of the proposed approach is demonstrated on the test systems and compared to those of other method. The results show that the new approach obtains a more highly optimal solutions than the conventional other methods such as newton-raphson method, Dynamic Programming(DP), LU factorization.

  • PDF

진화 연산의 성능 개선을 위한 하이브리드 방법 (A Hybrid Method for Improvement of Evolutionary Computation)

  • 정진기;오세영
    • 한국지능시스템학회논문지
    • /
    • 제12권4호
    • /
    • pp.317-322
    • /
    • 2002
  • The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

전력계통의 전력조류제어를 위한 진화연산의 비교 (Comparison of Evolutionary Computation for Power Flow Control in Power Systems)

  • 이상근
    • 전기학회논문지P
    • /
    • 제54권2호
    • /
    • pp.61-66
    • /
    • 2005
  • This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

방향성 벡터를 갖는 개선된 진화프로그래밍 (The Improved Evolutionary Programming with Direction Vectors)

  • 박진현;배준경
    • 한국지능시스템학회논문지
    • /
    • 제10권6호
    • /
    • pp.542-547
    • /
    • 2000
  • 진화프로그래밍(Evolutionary Programming : EP)은 최적화 문제에 있어서 매우 유용한 기법으로 자연선택의 원리를 모방한 탐색알고리즘이다. EP는 기존의 최적화 알고리즘에 비하여 여러해를 동시에 탐색하는 전역탐색(global search)방법이므로 국부수렴(local convergence)의 가능성이 줄어들고, 최적화 파라메터 영역의 연속성과 미분치의 존재성과 같은 조건이 필요 없는 장점을 갖는다. 이러한 장점에도 불구하고, EP의 탐색영역이 초기조건 및 최적화 파라메터들의 랜덤 생성 그리고 최적화에 필요한 전략적 파라메터들에 의하여 탐색 영역이 결정되고, 수렴성이 느린 단점을 갖는다. 이러한 문제를 해결하기 위하여, 본 연구에서는 빠른 수렴성과 다양성을 갖는 개선된 EP을 제안하고, 제안된 방향성 벡터를 갖는 개선된 EP를 함수 최적화 문제에 적용하여 그 성능의 유용성을 보이고자 한다.

  • PDF

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

  • 유석구;박창주;김규호;이재규
    • 대한전기학회:학술대회논문집
    • /
    • 대한전기학회 1996년도 하계학술대회 논문집 B
    • /
    • pp.714-716
    • /
    • 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.

  • PDF

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

  • Prabakaran, S.;Senthilkuma, V.;Baskar, G.
    • Journal of Electrical Engineering and Technology
    • /
    • 제10권4호
    • /
    • pp.1441-1452
    • /
    • 2015
  • This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

잡음 영상에서 불균등 돌연변이 연산자를 이용한 효율적 에지 검출 (Edge detection method using unbalanced mutation operator in noise image)

  • 김수정;임희경;서요한;정채영
    • 정보처리학회논문지B
    • /
    • 제9B권5호
    • /
    • pp.673-680
    • /
    • 2002
  • 이 논문은 진화 프로그래밍과 개선된 역전파 알고리즘을 이용한 에지 검출 방법을 제안한다. 진화 프로그래밍은 알고리즘의 성능저하와 계산비용을 고려하여 교차 연산은 수행하지 않고, 선택연산자와 돌연변이 연산자를 사용한다. 개선된 역전파 알고리즘은 학습단계에서 연결강도를 변화시킬 때 이전학습단계의 연결강도를 보조적으로 활용하는 방법이다. 이 개선된 역전파 알고리즘은 학습률 $\alpha$를 작은값으로 설정하기 때문에 각 학습단계에서의 연결강도 변화량이 기존의 방법에 비해 상대적으로 줄어들게 되어 학습이 느려지는 문제점을 해결하였다. 실험결과 학습시간과 검출률에 있어서 GA-BP(GA : Genetic Algorithm BP : Back-Propagation)를 이용한 방법보다 제안한 EP-MBP(EP : Evolutionary Programming, MBP :Momentum Back-Propagation)를 이용하여 학습시킨 방법이 학습시간의 단축과 효율적인 에지 검출 결과를 얻을 수 있었다.