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Power System Oscillations Damping by Optimal Coordinated Design Between PSS and STATCOM Using pso and ABC algorithms 13

Power System Oscillations Damping by Optimal Coordinated Des...

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13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Shahgholian, Ghazanfar Fazeli-Nejad, Saeid Moazzami, Majid Mahdavian, Mehdi Azadeh, Manijeh Janghorbani, Mohammadreza Farazpey, Saeed Islamic Azad Univ Najafabad Branch Dept Elect Engn Esfahan Iran Islamic Azad Univ Naein Branch Dept Elect Engn Naein Isfahan Iran Islamic Azad Univ Cent Tehran Branch Young Researchers & Elite Club Tehran Iran

ISBN: (纸本)9781467397490

Coordinated design of the power system stabilizer (PSS) and static synchronous compensator controller parameters (STATCOM) using artificial bee colony algorithm (ABC) to improve power system stability as an optimizations problem has been proposed in this study. ABC algorithm is a collective inteligence based on optimization algorithm inspired by the feeding behavior of bees in finding food. Rapid convergence and high precision are the capabilities of this algorithm which in this study to demonstrate its effectiveness and robustness, a two zone - four machines system is used and analyzed through non-linear simulation of time-domain and the results are compared with particle swarm optimization (pso) algorithm. The optimization after a large turbulence shows that the coordinated design of the STATCOM and PSS controllers' parameters using the ABC algorithm considerably improves the system stability together with rapid damping of the system fluctuations as compared to the state without optimization. On the other hand, the optimization results using the ABC algorithm shows the superiority of this method over the pso algorithm.

关键词： PSS STATCOM ABC algorithm pso algorithm transient stability

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A pso optimization algorithms Based on the Priori probability

A PSO optimization algorithms Based on the Priori probabilit...

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World Automation Congress (WAC)

作者： Zhao, Wei Cai, Xingsheng JiLin Agr Univ Coll Informat Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781467344975

pso algorithm is easy to operate and to realize, and it has got many scholars' attention once proposed. In recent years there has appeared many improved pso algorithm, but it cann't get the global optimal answer in probability 1. Thinking over the problem using probability theory, this paper can achieve the optimal answer as far as possible big Priori probability, and experiments show that the improved pso algorithm has avoided local minima successfully and got higher search rates.

关键词： pso algorithm Priori probability Optimization

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Optimization of Spiking Neural Networks with Dynamic Synapses for Spike Sequence Generation using pso

Optimization of Spiking Neural Networks with Dynamic Synapse...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mohemmed, Ammar Matsuda, Satoshi Schliebs, Stefan Dhoble, Kshitij Kasabov, Nikola Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand Nihon Univ Coll Ind Technol Dept Math Informat Engn Tokyo 102 Japan

ISBN: (纸本)9781424496365

We present a method that is based on Particle Swarm Optimization (pso) for training a Spiking Neural Network (SNN) with dynamic synapses to generate precise time spike sequences. The similarity between the desired spike sequence and the actual output sequence is measured by a simple leaky integrate and fire spiking neuron. This measurement is used as a fitness function for pso algorithm to tune the dynamic synapses until a desired spike output sequence is obtained when certain input spike sequence is presented. Simulations are made to illustrate the performance of the proposed method.

关键词： Atmospheric measurements Biological information theory Biological system modeling Computational modeling Neurons pso algorithm Particle measurements dynamic synapses fire spiking neuron fitness function neural nets particle swarm optimisation particle swarm optimization spike sequence generation spiking neural network time spike sequence

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Controlling Chaos by pso-based Neural Network

Controlling Chaos by PSO-based Neural Network

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International Conference on Intelligent Human-Machine Systems and Cybernetics

作者： Sun Xuegang Yun Chao Cui Yihui An Zhengang Beihang Univ Coll Mech Engn & Automat Beijing Peoples R China Liaoning Guide S&T Co Ltd Dept Automat Anshan Peoples R China

ISBN: (纸本)9780769537528

In this paper, a method of neural network control system which is based on Particle Swarm Optimization (pso) learning algorithm is proposed to control chaotic dynamical systems. Under the condition of chaotic model unknown, the control system may stabilize a chaotic orbit into an unstable fixed point based on the technique of small perturbations. Structurally, the control system is composed of two integrated feed-forward neural networks: a predict neural network which help to adjust a control neural networks. Both the predict network and the control network use the pso to adapt itself according to the fitness defined by predict and previous control. The proposed method is an adaptive search for the optimum control technique. Simulations show that, by defining proper fitness function, the control system is suitable for chaos stabilization.

关键词： Chaos pso algorithm predict control

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A pso-based Weighting Method to Enhance Machine Learning Techniques for Cooperative Spectrum Sensing in CR Networks 1

A PSO-based Weighting Method to Enhance Machine Learning Tec...

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1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Ghazizadeh, Elham Nikpour, Bahareh Moghadam, Dariush Abbasi Nezaniabadi-pour, Hossein Shahid Bahnoar Univ Kerman Kerman Province Iran

ISBN: (纸本)9781467387378

Cognitive radio (CR) is a recent technology to tackle the problem of radio spectrum scarcity. Successful spectrum sensing is fundamental in performance of CR networks;hence, a pso-based weighting method is proposed in order to improve the functionality of machine learning techniques which are used with the aim of detecting the activity of secondary users in cooperative cognitive radio (CCR) networks. Regarding classification methods, three supervised classifiers which are supported vector machines (SVM), K-nearest neighbors (K-NN) and naive Bayes are used for pattern classification. Since our goal is spectrum sensing in CCR networks, the vector of energy levels in radio channel which is considered as a feature vector is fed into the classifier to determine the availability of the channel. The classifier labels each feature vector as two classes: the "channel available class" or the "channel unavailable class". In our proposed method, first, the three mentioned classifiers go through a training phase. Next, for new feature vectors, a label is assigned to the feature vector by each classifier and the final decision about the availability of the channel is made by a weighted voting method based on the pso algorithm in an online fashion. The performance of our technique is measured in terms of the classification error. Also, the comparative results show twofold merit over previous methods since it not only reduces the error rate but also decreases the error of the channel available class.

关键词： cognitive radio cooperative spectrum sensing K-NN Naive Bayes SVM pso algorithm

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Development of Hybrid Models for Forecasting Time-Series Data Using Nonlinear SVR Enhanced by pso

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2015年第4期9卷 699-711页

作者： Kumar, T. L. Mohan Prajneshu Indian Agr Res Inst Lib AvePusa Campus New Delhi 110012 India

Real-world time-series are rarely purely linear or nonlinear and often contain both these patterns. Therefore, in this article, we have developed four hybrid models by combining linear seasonal autoregressive integrated moving average (SARIMA) and nonlinear support vector regression (NLSVR) models for time-series forecasting. Further, particle swarm optimization (pso), which is a very efficient population-based global stochastic optimization technique, is employed to estimate the hyperparameters of resultant models. A relevant computer program is written in MATLAB function (m file). The SAS and MATLAB software packages are used for carrying out data analysis. Subsequently, as an illustration, the models are applied to all-India monthly marine products export time-series data. Superiority of hybrid models over individual SARIMA and NLSVR models is demonstrated for the data under consideration using root mean square error (RMSE) and mean absolute error (MAE) criteria.

关键词： Hybrid models Indian marine products export NLSVR model pso algorithm SARIMA model SAS and MATLAB software packages

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Experimental Parameter Identification of Lead-Acid Batteries using pso Optimization 2

Experimental Parameter Identification of Lead-Acid Batteries...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Bouharchouche, Abderrezzaq Dali, Ali Kaabeche, Abdelhamid Berkouk, El Madjid Ctr Dev Energies Renouvelables CDER Algiers 16340 Algeria Ecole Natl Polytech Lab Commande Proc LCP Algiers Algeria

ISBN: (纸本)9798350349740;9798350349757

This study proposes a technique for parameter identification of lead-acid batteries using the Particle Swarm Optimization (pso) algorithm. The methodology uses a detailed mathematical model of the batteries to accurately estimate the parameters representing their actual behavior within a predefined margin of error. Experimental validation demonstrates the effectiveness of the proposed approach in achieving precise parameter identification.

关键词： Lead-Acid Batteries Parameter Identification Battery Modelling pso algorithm

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Chiller System Modeling using pso Optimization based NARX approach 12

Chiller System Modeling using PSO Optimization based NARX ap...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Song, Zilong Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing 100124 Peoples R China Commun Univ China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9798350321050

The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (pso) algorithm are used to establish a nonlinear autoregressive neural network model (pso-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the pso-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.

关键词： NARX Modeling chiller system Data-driven pso algorithm

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A NOMA-pso Based Cooperative Transmission Method in Satellite Communication Systems 9

A NOMA-PSO Based Cooperative Transmission Method in Satellit...

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9th International Conference on Wireless Communication and Signal Processing (WCSP)

作者： Wan, Ruimin Zhu, Lina Li, Tian Bai, Lin Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781538620625

Due to the limited frequency and energy resources, cooperative satellite communication (SATCOM) systems have been widely studied. To improve the spectrum efficiency further, we propose to combine non-orthogonal multiple access (NOMA) with cooperative SATCOM systems in this paper. Under the condition of fixed total transmit power, an optimization problem is formulated to maximize the sum-rate with the variables of power allocation factors. In order to solve this non-convex optimization problem, the particle swarm optimization (pso) algorithm is applied with an optimal power allocation strategy. Simulation results show that the proposed NOMA-pso scheme outperforms existing orthogonal multiple access (OMA) approach.

关键词： Non-orthogonal multiple access (NOMA) cooperative satellite communication systems pso algorithm power allocation

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IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON pso

IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BA...

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1st International Joint Conference on Computational Intelligence

作者： Zheng, Liping Li, Guangyao Liang, Jing Pan, Quanke Liaocheng Univ Sch Comp Sci Liaocheng Shandong Peoples R China

ISBN: (纸本)9789896740146

Image segmentation plays an important role in the field of image processing. Threshold segmentation is a simple and important method in image segmentation. Maximum Entropy is a common threshold segmentation method. In order to adequately utilize gray information and spatial information of image, an improved 2D entropy computation method is proposed. Otherwise, Particle Swarm Optimization(pso) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two CT images were segmented in experiment. Experimental results show that this method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and save computation time.

关键词： Image segmentation Entropy pso algorithm Gray probability

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