Papers by Keyword: Improved Particle Swarm Optimization

Abstract: According to the number of constraints of the reactive power control equipment, this paper considers the constraints of the voltage in the load of extreme points of the segmentation program period, and uses dynamic optimization of sub-segmentation in the load curve. It solves the optimization based on applying improved particle swarm optimization into load curve segments, and uses particle swarm optimization through adaptive inertia weight and acceleration factor, phase initialization and mutation operations in order to improve the ability of global optimization.

Abstract: When larger scale of small hydropower and wind power exist in power system, the traditional reserve capacity algorithm is no longer applicable at the same time of reducing the carbon emission. Aiming at the volatility and non-regulatory of small hydropower and wind power, considering the forecast error of small hydropower, wind power and load,the unit outage rate and other factors, combined with the actual operation parameters of one regional power system in Southwest of China, the wind-hydro – thermal power coordinated operation of multiple objective function optimization calculation model of reserve capacity is established. Using the weighted coefficient method of unified objective method, it transforms the multi-objective optimization problem into a single objective optimization one. Applying the exterior penalty function method, the constrained optimization problem is changed into a non-constrained optimization one. The improved particle swarm optimization algorithm is got by introducing the particle concentration cognition into traditional particle swarm optimization algorithm. The simulation results verify the validity of the model. To Compare them under different strategies, this method can get less fuel expenses of thermal power units in the case of lower loss of load probability (L_OLP).With the small hydropower and wind power output size to optimize the spinning reserve capacity. The model and the algorithm are helpful for drawing up optimization strategies of the reserve capacity in those regions in which larger scale small hydropower and wind power exists.

Abstract: Because wind power can reduce the total generation costs under certain conditions, in order to minimize the system production cost, optimize the power output of thermal unit, a dynamic economic dispatching (DED) model containing wind power based on energy-saving and emission reduction benefits is proposed. Such DED model has a nonlinear objective function and the characteristic of high dimension, and is restricted by many constraints. An improved particle swarm optimization (IPSO) is proposed for solving the problem. Feasible regulation scheme is adopted in equality constrains. On the premise that the speed of iterative convergence is assured, the diversity and the excellence of solution are improved by introducing the differential mutation, which enhances the algorithm in breaking away from the local optimum and speeds up the iterative convergence of the algorithm.

Abstract: To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hybrid technique of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) learning algorithm is simple conceptually derived and its implementation process is faster with better scaling properties for complex problems with non linearity and load variations but performance factor related to accuracy has a declined value in case of correlations implicit . In order to obtain better fault diagnosis to improve the service of the power transformer, SVM is optimized with Improved PSO technique to achieve high interpretation accuracy for Dissolved Gas Analysis (DGA) of power transformer through the extracting positive features from both the techniques. Primary SVM is applied to establish classification features for faults in the transformer through DGA. The features are applied as input data to Autonomous optimized Technique for faults analysis. The proposed methodology obtains the DGA data set from diagnostic gas in oil of 500 KV main transformers of Pingguo Substation in South China Electric Power Company. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 4 GB RAM PC. The result obtained by Autonomous optimized Technique (IPSO-SVM) is compared against PSO-SVM to estimate the performance of the classifiers in terms of execution time and quality of classification for precision. The test results indicate that the Autonomous optimization of IPSO-SVM approach has significantly improved the classification accuracy and computational time for power transformer fault classification. Keywords: Transformer Fault Analysis, Improved Particle Swarm Optimization, Hybrid Optimization, Dissolved Gas Analysis, Support Vector Machine

Abstract: The paper focus on an improved particle swarm optimization (IPSO) used to solve nonlinear optimization problems of steering trapezoid mechanism. First of all, nonlinear optimization model of steering trapezoid mechanism is established. Sum of absolute value of difference between actual rotational angle of anterolateral steering wheel and theoretical rotational angle of anterolateral steering wheel is taken as objective function, bottom angle and steering arm length of steering trapezoid mechanism are selected to be design variables. After that, an improved particle swarm optimization algorithm is proposed by introducing Over-flow exception dealing functions to deal with complicated nonlinear constraints. Finally, codes for IPSO are programmed and parameters of steering trapezoid mechanism for different models are optimized, and numerical result shows that errors of objective function's ideal values and objective function's optimization values are minimal. Performance comparison experiment of different intelligent algorithms indicates that the proposed new algorithm is superior to Particle swarm algorithm based on simulated annealing (SA-PSO) and traditional particle swarm optimization (TPSO) in good and fast convergence and small calculating quantity, but a little inferior to particle swarm algorithm based on simulated annealing (SA-PSO) in calculation accuracy in the process of optimization.

Abstract: An energy optimizing operation model of BCHP distributed energy supply system is proposed in this paper. The objective function and its constraint conditions are determined so as to minimize the total cost. Not only the cooperation between BCHP and electric air conditioner, but also the influence of spot price of electricity is considered in this model. According to the demand forecast of cooling, heating and power load, operation schemes of some different typical days of BCHP system are optimized. Improved particle swarm optimization algorithm is adopted to solve the energy optimizing operation model for BCHP. Taken an actual commercial building as an example, this paper makes a comparison between BCHP system and separate supply system under the condition that electricity micro turbine generated is allowed to be sold to utility.

Abstract: Core-drilling is the traditional pavement thickness detection method. However, this way is low efficient and destructive. The Ground Penetrating Radar (GPR) is a continuous, high efficient, non-destructive pavement quality testing tool. The dielectric constant, thickness and other information of pavement structure layer are obtained by inverse analysis of GPR echo signal. In this paper, the improved Particle Swarm Optimization (IPSO) is developed to analyze the dielectric constant and thickness of the pavement structure. Compared with inverse analysis results of theoretical model, the inverse precision of IPSO is higher than that of PSO. In addition, the measured echo signal of GPR is analyzed by the IPSO. The errors between inverse results and the actual core-drilling measurements are less than 3%.

Abstract: This paper utilized the Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of PID and the bandwidth of zero-phase Butterworth Filter of an Iterative Learning Controller (ILC) for precision motion. Simulation results show that IPSO-ILC-PID controller without adaptive bandwidth filter tuning have the chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. However the learnable and unlearnable error signals should be separated for bettering control process. Thus the adaptive bandwidth of a zero phase filter in ILC-PID controller with IPSO tuning is applied to one single motion axis of a CNC table machine. Simulation results show that the developed controller can cancel the errors efficiently as repetition goes. The frequency response of the error signals is analyzed by the empirical mode decomposition (EMD) and the Hilbert-Huang Transform (HHT) method. Errors are reduced and validated by ILC with adaptive bandwidth filtering design.

Abstract: This paper utilizes the Improved Particle Swarm Optimization (IPSO) with bounded constraints technique for adjusting the gains of a Proportional-Integral-Derivative (PID) and Iterative Learning Control (ILC) controllers. This study compares the conventional ILC-PID controller with proposed IPSO-ILC-PID controller. A cycloid trajectory for mimicking the real industrial motion profile is applied. Two system plants with nonminimum phase are numerically simulated. Proposed IPSO with bounded constraints technique is evaluated on one axis of linear synchronous motor (LSM) with a PC-based real time controller. Simulations and experiment results show that the proposed controller can reduce the error significantly after two iterations.

Abstract: In order to meet varied requirements of gas and electricity in petrochemical enterprises, as well as reducing the operation cost and minimizing energy use, optimal operation of utility systems is a must. This paper establishes a model, which accounts for expenses of operation, maintenance, depreciation, and changeover cost. The model is validated against operative data. The results show optimal control strategy can be quickly obtained with improved PSO algorithm, accompanied with considerable cost saving.