• Title/Summary/Keyword: particle swarm optimization

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Strategy based PSO for Dynamic Control of UPFC to Enhance Power System Security

  • Mahdad, Belkacem;Bouktir, T.;Srairi, K.
    • Journal of Electrical Engineering and Technology
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    • v.4 no.3
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    • pp.315-322
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    • 2009
  • Penetration and installation of a new dynamic technology known as Flexible AC Transmission Systems (FACTS) in a practical and dynamic network requires and force expert engineer to develop robust and flexible strategy for planning and control. Unified Power Flow Controller (UPFC) is one of the recent and effective FACTS devices designed for multi control operation to enhance the power system security. This paper presents a dynamic strategy based on Particle Swarm Optimization (PSO) for optimal parameters setting of UPFC to enhance the system loadability. Firstly, we perform a multi power flow analysis with load incrementation to construct a global database to determine the initial efficient bounds associated to active power and reactive power target vector. Secondly a PSO technique applied to search the new parameters setting of the UPFC within the initial new active power and reactive power target bounds. The proposed approach is implemented with Matlab program and verified with IEEE 30-Bus test network. The results show that the proposed approach can converge to the near optimum solution with accuracy, and confirm that flexible multi-control of this device coordinated with efficient location enhance the system security of power system by eliminating the overloaded lines and the bus voltage violation.

Design of Real-time Face Recognition Systems Based on Data-Preprocessing and Neuro-Fuzzy Networks for the Improvement of Recognition Rate (인식률 향상을 위한 데이터 전처리와 Neuro-Fuzzy 네트워크 기반의 실시간 얼굴 인식 시스템 설계)

  • Yoo, Sung-Hoon;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1952-1953
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    • 2011
  • 본 논문에서는 다항식 기반 Radial Basis Function(RBF)신경회로망(Polynomial based Radial Basis function Neural Network)을 설계하고 이를 n-클래스 패턴 분류 문제에 적용한다. 제안된 다항식기반 RBF 신경회로망은 입력층, 은닉층, 출력층으로 이루어진다. 입력층은 입력 벡터의 값들을 은닉층으로 전달하는 기능을 수행하고 은닉층과 출력층사이의 연결가중치는 상수, 선형식 또는 이차식으로 이루어지며 경사 하강법에 의해 학습된다. Networks의 최종 출력은 연결가중치와 은닉층 출력의 곱에 의해 퍼지추론의 결과로서 얻어진다. 패턴분류기의 최적화는 PSO(Particle Swarm Optimization)알고리즘을 통해 이루어진다. 그리고 제안된 패턴분류기는 실제 얼굴인식 시스템으로 응용하여 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출, 특징 추출 등과 같은 처리 과정을 포함하여 서로 다른 등록인물의 n-클래스 분류 문제에 적용 및 평가되어 분류기로써의 성능을 분석해본다.

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Optimized Design of Intelligent White LED Dimming System Based on Illumination-Adaptive Algorithm (조도 적응 알고리즘 기반 지능형 White LED Dimming System의 최적화 설계)

  • Lim, Sung-Joon;Jung, Dae-Hyung;Kim, Hyun-Ki;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1956-1957
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    • 2011
  • 본 연구는 White LED를 이용하여 주변 밝기 변화에 빠르게 적응하는 퍼지 뉴로 Dimming Control System을 설계한다. 본 논문에서는 방사형기저함수 신경회로망(Radial Basis Function Neural Network: RBFNN)을 설계하여 실제 White LED Dimming Control System에 적용시켜 모델의 근사화 및 일반화 성능을 평가한다. 제안한 모델에서의 은닉층은 방사형기저함수를 사용하여 적합도를 구현하였고, 후반부의 연결가중치는 경사하강법을 사용한다. 이때 멤버쉽 함수의 중심점은 HCM 클러스터링 (Hard C-Means Clustering)을 적용하여 결정한다. 연결가중치는 4가지 형태의 다항식을 대입하여 출력을 평가하였다. 최종 출력의 최적화를 위하여 PSO(Particle Swarm Optimization)을 이용하여 은닉층 노드수 및 다항식 형태를 결정한다. 본 논문에서 제안한 LED Dimming Control System은 Atmega8535를 사용하여 PWM 제어 방식을 사용하고, 조도계(Cds)를 이용하여 LED의 밝기에 따른 주변의 밝기를 감지하여 조명에 적응시키는 방법을 적용하였다.

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RBFNNs-based Recognition System of Vehicle License Plate Using Distortion Correction and Local Binarization (왜곡 보정과 지역 이진화를 이용한 RBFNNs 기반 차량 번호판 인식 시스템)

  • Kim, Sun-Hwan;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.9
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    • pp.1531-1540
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    • 2016
  • In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

Design of pRBFNN Based on Interval Type-2 Fuzzy Set (Interval Type-2 퍼지 집합 기반의 pRBFNN 설계)

  • Kim, In-Jae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • 2009.07a
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    • pp.1871_1872
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    • 2009
  • 본 논문 에서는 Type-2 퍼지 논리 시스템을 설계하고, 불확실한 정보를 갖는 입력 데이터에 대하여 Type-1 퍼지 논리 시스템과 성능을 비교한다. Type-1 퍼지 논리 시스템은 외부 잡음에 민감한 단점을 가지고 있는 반면, Type-2 퍼지 논리 시스템은 불확실한 정보를 잘 표현 할 수 있다. 따라서 Type-2 퍼지 논리 시스템을 이용하여 이러한 단점을 극복하고자 2가지의 모델을 설계한다. 첫 번째 모델은 규칙의 전 후반부가 Type-1 퍼지 집합으로 구성된 Type-1 퍼지 논리 시스템을 설계 한다. 두 번째는 규칙 전 후반부에 Type-2 퍼지 집합으로 구성된 Type-2 퍼지 논리 시스템을 설계한다. 여기서 규칙 전반부의 입력 공간 분할 및 FOU(Footprint Of Uncertainty)형성에는 FCM(Fuzzy C_Means) clustering 방법을 사용하고, 입자 군집 최적화(Particle Swarm Optimization) 알고리즘을 사용하여 최적의 파라미터를 설계한다. 본 논문 에서는 또한 입력 데이터에 인위적으로 가하는 노이즈에 따른 각각 모델의 성능을 비교한다. 마지막으로 비선형 모델 평가에 주로 사용되는 NOx 데이터를 제안된 모델에 적용하고, 실험을 통하여 노이즈가 첨가되고, 불확실한 정보를 다루기에 Type-1 퍼지 논리 시스템 보다 Type-2 퍼지 논리 시스템이 효율적이라는 것을 보인다.

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Decentralized Neural Network-based Excitation Control of Large-scale Power Systems

  • Liu, Wenxin;Sarangapani, Jagannathan;Venayagamoorthy, Ganesh K.;Liu, Li;Wunsch II, Donald C.;Crow, Mariesa L.;Cartes, David A.
    • International Journal of Control, Automation, and Systems
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    • v.5 no.5
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    • pp.526-538
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    • 2007
  • This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.

Feature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Density

  • Nassuna, Hellen;Kim, Jaehoon;Eyobu, Odongo Steven;Lee, Dongik
    • IEMEK Journal of Embedded Systems and Applications
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    • v.15 no.3
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    • pp.119-127
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    • 2020
  • The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

Non-equal DC link Voltages in a Cascaded H-Bridge with a Selective Harmonic Mitigation-PWM Technique Based on the Fundamental Switching Frequency

  • Moeini, Amirhossein;Iman-Eini, Hossein;Najjar, Mohammad
    • Journal of Power Electronics
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    • v.17 no.1
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    • pp.106-114
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    • 2017
  • In this paper, the Selective Harmonic Mitigation-PWM (SHM-PWM) method is used in single-phase and three-phase Cascaded H-Bridge (CHB) inverters in order to fulfill different power quality standards such as EN 50160, CIGRE WG 36-05, IEC 61000-3-6 and IEC 61000-2-12. Non-equal DC link voltages are used to increase the degrees of freedom for the proposed SHM-PWM technique. In addition, it will be shown that the obtained solutions become continuous and without sudden changes. As a result, the look-up tables can be significantly reduced. The proposed three-phase modulation method can mitigate up to the 50th harmonic from the output voltage, while each switch has just one switching in a fundamental period. In other words, the switching frequency of the power switches are limited to 50 Hz, which is the lowest switching frequency that can be achieved in the multilevel converters, when the optimal selective harmonic mitigation method is employed. In single-phase mode, the proposed method can successfully mitigate harmonics up to the 50th, where the switching frequency is 150 Hz. Finally, the validity of the proposed method is verified by simulations and experiments on a 9-level CHB inverter.

Enhanced Absorption Efficiency of Solar Cells Using Guided-mode Resonance (도파모드 공진을 이용한 태양전지의 흡수효율 증대)

  • Kim, Doo-Sung;Kim, Sang-In;Lee, Jae-Jin;Lim, Han-Jo
    • Korean Journal of Optics and Photonics
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    • v.21 no.1
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    • pp.1-5
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    • 2010
  • In this study, we propose a grating structure using guided-mode resonance (GMR) to increase the absorption efficiency of a silicon solar cell. The proposed solar cell design consists of a one-dimensional diffraction grating and a planar waveguide layer of poly-silicon deposited on a silver reflector. We investigate the influence of structure parameters such as grating period, waveguide thickness, grating width and grating depth. Optimal parameters are found using the particle swarm optimization (PSO) algorithm. In the optimized GMR-assisted solar cell, absorption efficiency up to 65.8% is achieved in the wavelength range of 300 nm~750 nm.

Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques (영상처리 기법을 통한 RBFNN 패턴 분류기 기반 개선된 지문인식 시스템 설계)

  • Bae, Jong-Soo;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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    • pp.1060-1069
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    • 2016
  • In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.