Nowadays wind energy is one of the most important source of renewable energy worldwide. Wind power generation is an important form of wind energy utilization. The energy problem has become increasingly prominent, whic...
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ISBN:
(纸本)9781467371063
Nowadays wind energy is one of the most important source of renewable energy worldwide. Wind power generation is an important form of wind energy utilization. The energy problem has become increasingly prominent, which requires to speeding up the development of wind energy industry. However, the existing wind speed forecasting using grey model is inaccurate. Direct prediction of original wind speed sequence produces large error because of the randomness of wind power. To solve the above problems, a novel method for short term wind speed forecasting based on grey model is proposed in this paper. In order to reduce the error of short term wind speed forecasting, one of the most successful approaches is particle swarm optimization algorithm, which chooses the parameters of grey model to avoid the man-made blindness and enhances the efficiency and capability of forecasting. In the present paper, the wavelet decomposition and reconstruction are used to separate the high frequency signal and the low frequency signal. To verify its efficiency, this proposed method is applied to a wind farm's wind speed forecasting in China. The result confirms that the performance of the method proposed in this paper is much more favorable in comparison with the original methods studied.
作者：
Tan AilingZhao YongWang SiyuanYanshan Univ
Sch Informat Sci & Engn Key Lab Special Fiber & Fiber Sensor Hebei Prov Qinhuangdao 066004 Peoples R China Yanshan Univ
Inst Elect Engn Qinhuangdao 066004 Peoples R China
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel an...
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ISBN:
(数字)9781510607712
ISBN:
(纸本)9781510607705;9781510607712
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter sigma of gasoline, diesel and kerosene model were 48.1418 and 0.1067;53.2820 and 0.1095;59.1689 and 0.1000 respectively;The decision coefficient R-2 of the prediction model were 0.9983. 0.9907 and 0.9942 respectively;RMSEP values were 0.0753, 0.1539 and 0.0789 respectively;For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176 +/- 0.0636 mu L/mL, -0.0084 +/- 0.1941 mu L/mL, and 0.00338 +/- 0.0726 mu L/mL respectively. The results showed that each component's concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.
This paper introduces two instances of deceptive problems of particleswarmoptimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** add...
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This paper introduces two instances of deceptive problems of particleswarmoptimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** addition,we empirically verified the correctness of our theoretical *** order to solve the two deceptive problems,we propose an algorithm ISA-PSO (inverse search area-PSO),which can change the particles' search direction and expand their search area at the appropriate *** experimental results show the effectiveness of our algorithm ISA-PSO.
Power Systems are inherently non-linear systems that are frequently subjected to various disturbances causing oscillations at low frequencies that may lead to instability. Generators are usually provided with power sy...
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ISBN:
(纸本)9781467385879
Power Systems are inherently non-linear systems that are frequently subjected to various disturbances causing oscillations at low frequencies that may lead to instability. Generators are usually provided with power system stabilizers minimize the effect of these oscillations. The objective of this paper is find the optimal parameters for a conventional lead-lag compensator based Power System Stabilizer (PSS) for a system comprising of a generator connected to an infinite bus and containing a STIA type excitation system. The tuning of the parameters of the Power System Stabilizer is accomplished using the particleswarmoptimization (PSO) algorithm. In this paper, a Fuzzy Power System Stabilizer (FPSS) where the optimal values of the parameters of the FPSS are decided using the PSO algorithm is also designed. The particleswarmoptimization based conventional PSS and the particleswarmoptimization based Fuzzy PSS are also incorporated in a system containing multiple machines to check the system responses under different loading conditions and faults of different types. The simulation results clearly prove the efficiency of the PSO based conventional and fuzzy power system stabilizers in damping the low frequency speed and power oscillations occurring in the power system due to various disturbances.
In this paper small signal stability improvement of two-area, four-machine Kundur power system stabilizer is presented using Cuckoo Search algorithm (CSA), particle swarm optimization algorithm (PSOA) and Genetic Algo...
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ISBN:
(纸本)9781467399685
In this paper small signal stability improvement of two-area, four-machine Kundur power system stabilizer is presented using Cuckoo Search algorithm (CSA), particle swarm optimization algorithm (PSOA) and Genetic algorithm (GA). For this the propose, the problem is formulated using an eigenvalue based multiobjective function that shift unstable or poorly damped modes to specific D-shape region in the left-half of the s-plane by controlling the damping ratio and damping factor. To show the effectiveness and superiority of the proposed CSA based PSS (CSAPSS), the non-linear time domain simulations are compared with GA based PSS (GAPSS), PSO based PSS (PSOAPSS) for different line outages and different scenarios of severe disturbances. The robustness of proposed CSAPSS is evaluated by performances indices for wide range of loading conditions with severe faults. Moreover, the lower value of performances indices for proposed CSAPSS than to GAPSS, PSOAPSS exhibit its relative stability.
Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in...
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Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in this *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** discussed and analyzed the impact factor of intrusion *** the ability of strong self-learning and faster convergence,this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic *** the character that rough set can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train neural network,which increase the detection *** apply this technique on KDD99 data set and get satisfactory *** experimental result shows that this intrusion detection method is feasible and effective.
The reasonable capacity configuration of distributed generation in a micro-grid is the prerequisite of its reliable and economical operation. This paper establishes a optimal configuration model of micro grid power su...
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ISBN:
(纸本)9781509030682
The reasonable capacity configuration of distributed generation in a micro-grid is the prerequisite of its reliable and economical operation. This paper establishes a optimal configuration model of micro grid power supply aiming to reach the goal of the optimal comprehensive benefits of micro grid, and then solve the configuration model by using particle swarm optimization algorithm.
An important application field of swarm intelligence algorithms is fuzzy rule ***,their limitations are showed in two *** one hand,it takes a long process to create fuzzy rules during the iterations;on the other,the s...
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ISBN:
(纸本)9781509001668
An important application field of swarm intelligence algorithms is fuzzy rule ***,their limitations are showed in two *** one hand,it takes a long process to create fuzzy rules during the iterations;on the other,the swarm intelligence algorithms obtain local optimal solution at *** overcome these disadvantages,a dynamic hybrid swarm intelligence approach is proposed to generate fuzzy rules from *** this approach,the dynamic adjustment strategy accelerates convergence rate of the swarm intelligence algorithms,meanwhile,the hybrid particles are introduced to avoid being trapped in the local *** adopt the Chinese Longitudinal Healthy Longevity Survey data to prove the effectiveness of the *** to the experiments,the dynamic hybrid swarm intelligence approach provides a competitive results comparing with the differential evolution algorithm,particle swarm optimization algorithm,social emotional optimizationalgorithm,and Wang Mendel method.
This paper presents a way of combining BP (Back Propagation) neural network and an improved PSO (particleswarmoptimization) algorithm to predict the earthquake magnitude. It is known that the BP neural network and t...
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ISBN:
(纸本)9781467397148
This paper presents a way of combining BP (Back Propagation) neural network and an improved PSO (particleswarmoptimization) algorithm to predict the earthquake magnitude. It is known that the BP neural network and the normal PSO-BP neural network have some defeats, such as the slow convergence rate, easily falling into local minimum values. For improving the properties of PSO, some proposed the linear decreasing inertia weight strategy. Furthermore, this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal solutions. Compared with the linear decreasing strategy, the inertia weight in our nonlinear method has a faster declining speed in the early iteration, which can enhance the searching precision. In the late iteration, the inertia weight has a slower declining speed to avoid trapping in local minimum value. Then we apply the improved PSO to optimize the parameters of BP neural network. In the end, the improved PSO-BP neural network is applied to earthquake prediction. The simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.
The optimization of pipe path planning plays an important role in a building system. The three-dimensional building environment model is established, because of complicated building pipeline systems. Also this paper p...
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ISBN:
(纸本)9781509027323
The optimization of pipe path planning plays an important role in a building system. The three-dimensional building environment model is established, because of complicated building pipeline systems. Also this paper proposed a new method of pipe routing layout-design a quick method of generating orthogonal path. particleswarmoptimization (PSO) is then used for optimizing the pipe path with a fixed-length code for the path, and for it's vulnerable to local problems, a mutation operator is adding to increase the diversity of the population. Finally, simulation results are presented and prove the feasibility and effectiveness of this method.
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