• Journal of Power Electronics
• /
• 제9권2호
• /
• pp.207-214
• /
• 2009

This paper presents a modem approach for speed control of a PMSM using the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the PI-Controller. The overall system simulated under various operating conditions and an experimental setup is prepared. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. Comparison between different controllers is achieved, using a PI controller which is tuned by two methods, firstly manually and secondly using the PSO technique. The system is tested under variable operating conditions. Implementation of the experimental setup is done. The simulation results show good dynamic response with fast recovery time and good agreement with experimental controller.

• Nuclear Engineering and Technology
• /
• 제50권6호
• /
• pp.877-885
• /
• 2018

Various controllers such as proportional-integral-derivative (PID) controllers have been designed and optimized for load-following issues in nuclear reactors. To achieve high performance, gain tuning is of great importance in PID controllers. In this work, gains of a PID controller are optimized for power-level control of a typical pressurized water reactor using particle swarm optimization (PSO) algorithm. The point kinetic is used as a reactor power model. In PSO, the objective (cost) function defined by decision variables including overshoot, settling time, and stabilization time (stability condition) must be minimized (optimized). Stability condition is guaranteed by Lyapunov synthesis. The simulation results demonstrated good stability and high performance of the closed-loop PSO-PID controller to response power demand.

• 전기학회논문지
• /
• 제57권8호
• /
• pp.1334-1341
• /
• 2008

This paper introduces an approach of Hybrid Particle Swarm Optimization(HPSO) for a security-constrained economic dispatch(SCED) with line flow constraints. To reduce a early convergence effect of PSO algorithm, we proposed HPSO algorithm considering a mutation characteristic of Genetic Algorithm(GA). In power system, for considering N-1 line contingency, we have chosen critical line contingency through a process of Screening and Selection based on PI(performance Index). To prove the ability of the proposed HPSO in solving nonlinear optimization problems, SCED problems with nonconvex solution spaces are considered and solved with three different approach(Conventional GA, PSO, HPSO). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed algorithm.

• 지식경영연구
• /
• 제10권4호
• /
• pp.17-30
• /
• 2009

This study is concerned with suggesting a new clustering algorithm to evaluate the value of papers which were supported by research grants by Korea Research Fund (KRF). The algorithm is based on an extended version of a conventional PSO (Particle Swarm Optimization) mechanism. In other words, the proposed algorithm is based on integration of k-means algorithm and simulated annealing mechanism, named KASA-PSO. To evaluate the robustness of KASA-PSO, its clustering results are evaluated by research grants experts working at KRF. Empirical results revealed that the proposed KASA-PSO clustering method shows improved results than conventional clustering method.

• International Journal of Control, Automation, and Systems
• /
• 제6권4호
• /
• pp.488-494
• /
• 2008

This paper proposes an effective approach based on binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration. The proposed method considers the advantages and disadvantages of existing particle swarm optimization method and redefined the operators of PSO algorithm to fit the application field of distribution systems. Shift operator is proposed to construct the binary coding particle swarm optimization for feeder reconfiguration. A typical distribution system of Taiwan Power Company is used in this paper to demonstrate the effectiveness of the proposed method. The test results show that the proposed method can apply to feeder reconfiguration problems more effectively and stably than existing method.

• 한국건축시공학회지
• /
• 제17권4호
• /
• pp.369-375
• /
• 2017

본 연구는 제설작업의 효율성을 높일 수 있도록 제설차량의 작업구간 할당 및 제설기지 위치를 최적화할 수 있는 PSO 알고리듬을 제시하였다. 기존의 PSO 알고리듬을 개선하여 해공간의 탐색 성능을 높일 수 있는 개선된 알고리듬을 제시하였으며, 제설차량의 작업구간 할당 문제에 적용할 수 있도록 개체의 표현 및 적합도 합수값을 제시하였다. 또한 제시한 알고리듬의 타당성을 검증하기 위하여 지자체의 실제 사례에 적용하였으며, 기존 알고리듬과 개선된 알고리듬을 비교하였다. 그 결과 개선된 PSO의 경우 기존 알고리듬보다 폭넓게 해공간을 탐색하여 지역해에 빠지지 않고 더 우수한 해를 도출하는 것을 알 수 있다. 또한 개별 제설차량의 작업부하가 평준화될 수 있도록 작업구간을 할당할 수 있으며, 할당된 작업구간에 가장 가까운 지점을 도출하여 제설전진기지의 위치를 결정하는데 활용될 수 있음을 알 수 있었다.

• 한국정보전자통신기술학회논문지
• /
• 제16권4호
• /
• pp.203-209
• /
• 2023

Harmony search(HS)는 새로운 하모니를 구성할 때 HM을 참조하는 경우 개별 하모니의 평가를 이용하지 않지만 PSO(particle swarm optimization)는 개별 입자의 평가와 모집단의 평가를 이용하여 해를 찾아간다. 그러나 본 연구에서는 HS와 PSO의 유사점을 찾아 PSO의 입자 개선 과정을 HS에 적용하여 알고리즘의 성능을 향상시키고자 하였다. PSO 알고리즘을 적용하기 위해서는 개별 입자의 local best와 떼(swam)의 global best가 필요하다. 본 연구에서는 HS가 harmony memory(HM)에서 가장 나쁜 하모니을 개선하는 과정을 PSO와 매우 유사한 과정으로 보았다. 이에 따라 HM의 가장 나쁜 하모니를 입자의 PSO의 local best로, 가장 좋은 하모니는 PSO의 global best 최고로 간주하였다. 이와 같이 PSO의 입자 개선과정을 HS 하모니 개선과정에 도입하여 HS의 성능을 향상시킬 수 있었다. 본 연구의 결과는 다양한 함수에 대한 최적화 예시를 통해 비교 확인하였다. 그 결과 정확성과 일관성에 있어 기존 HS보다 제안한 HS-PSO가 매우 우수함을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1568-1589
• /
• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2008년도 추계학술대회 논문집 전력기술부문
• /
• pp.261-263
• /
• 2008

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

• Journal of Information Processing Systems
• /
• 제15권1호
• /
• pp.116-126
• /
• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.