• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.45-53
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• 2010

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

• 한국정보통신학회논문지
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• 제17권8호
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• pp.1796-1803
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• 2013

본 논문에서는 IE3D를 기반으로 하는 새로운 PSO(Particle Swarm Optimization) 알고리즘을 사용해 이동통신용마이크로스트립 패치 안테나를 설계하였다. 본 논문의 목적은 이동통신용 급전 사각형 마이크로스트립 안테나를 설계하고, 대역폭의 복사 매개변수에 대한 안테나 크기(길이, 폭) 및 기판 매개변수 상대 유전 상수(${\varepsilon}r$), 기판 두께의 영향을 연구하여 이동통신용에 적합한 안테나를 설계하는 것이다. 안테나 설계 프로그램인 IE3D에 PSO알고리즘을 적용하여 안테나의 낮은 입력저항, 대역폭, 안테나의 이득 등을 개선하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.682-685
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• 2014

본 논문에서는 센서노드용 원형편파 안테나의 설계하였다. 확률론적 방법인 Particle Swarm Optimization(PSO) 알고리즘과 Adaptive Particle Swam Optimization(APSO) 알고리즘을 구현하고 성능비교를 통해 안테나 최적설계에 적합한 알고리즘을 결정하였다. PSO는 41번, APSO는 27번의 계산 결과 수렴을 하였다. 두 알고리즘 모두 최적설계에서 목표값을 모두 만족을 하였으나 수렴도에서 APSO가 빠르게 수렴한 것을 확인할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1586-1592
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• 2017

Voltage sourced converter (VSC) based direct-current (DC) grid has the ability to control power flow flexibly and securely, thus it has become one of the most valid approaches in aspect of large-scale renewable power generation, oceanic island power supply and new urban grid construction. To solve the optimal power flow (OPF) problem in DC grid, an adaptive particle swarm optimization (PSO) algorithm based on fuzzy control theory is proposed in this paper, and the optimal operation considering both power loss and voltage quality is realized. Firstly, the fuzzy membership curve is used to transform two objectives into one, the fitness value of latest step is introduced as input of fuzzy controller to adjust the controlling parameters of PSO dynamically. The proposed strategy was applied in solving the power flow issue in six terminals DC grid model, and corresponding results are presented to verify the effectiveness and feasibility of proposed algorithm.

• ETRI Journal
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• 제35권6호
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• pp.1168-1171
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• 2013

We propose an improvement of particle swarm optimization (PSO) based on the stabilization of particle movement (PM). PSO uses a stochastic variable to avoid an unfortunate state in which every particle quickly settles into a unanimous, unchanging direction, which leads to overshoot around the optimum position, resulting in a slow convergence. This study shows that randomly located particles may converge at a fast speed and lower overshoot by using the proportional-integral-derivative approach, which is a widely used feedback control mechanism. A benchmark consisting of representative training datasets in the domains of function approximations and pattern recognitions is used to evaluate the performance of the proposed PSO. The final outcome confirms the improved performance of the PSO through facilitating the stabilization of PM.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.211-219
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• 2018

This paper designs a nonlinear PI-type controller for the robust control of a boost DC-DC converter using a particle swarm optimization (PSO) algorithm. Based on the common knowledge that the transient responses can be improved if the P and I gains increase when the transient error is big, a nonlinear PI-type control design method is developed. A design procedure to autotune the nonlinear P and I gains is given based on a PSO algorithm. The proposed nonlinear PI-type controller is implemented in real time on a Texas Instruments TMS320F28335 floating-point DSP. Simulation and experimental results are given to demonstrate the effectiveness and practicality of the proposed method.

• 융합신호처리학회논문지
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• 제19권2호
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• pp.77-88
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• 2018

본 논문은 입력 포화를 가지는 불확실한 전기 구동 로봇 시스템에 대해 입자 군집 최적화(PSO)를 이용한 방사형 기저 함수 신경망(RBFNN) 기반 분산 적응 추종 제어 기법을 제안한다. 실제적으로 로봇 시스템에서는 구동기의 포화로 인해 입력 전압과 전류 신호 크기가 제한된다. 제안된 제어기는 이러한 입력 포화를 극복하며, 어떠한 로봇 링크 및 구동기의 모델 파라미터들을 요구하지 않는다. 제시된 PSO 기법에서 쓰인 적합도 함수는 추종 오차만이 아니라 전압과 전류의 크기를 포함하는 다중 목적 함수로 표현된다. PSO 기법을 이용하여 제어 이득과 방사형 기저 함수의 개수가 자동으로 조정되어 제어 시스템의 성능이 개선된다. 리아푸노프 안정도 해석에 의해 전체 제어 시스템의 안정도가 보장된다. 제안된 제어 기법의 타당성과 강인성이 시뮬레이션 결과를 통해 검증된다.