Elimination of lower order harmonics in Voltage Source Inverter feeding an induction motor drive using Evolutionary Algorithms

Selective Harmonic Elimination technique is one of the control methods applied in Voltage Source Inverters to eliminate the harmonics. However, finding the solutions for the harmonic reduction is a difficult problem to be solved. This paper presents an efficient and reliable Evolutionary Algorithms based solution for Selective Harmonic Elimination (SHE) switching pattern to eliminate the lower order harmonics in Pulse Width Modulation (PWM) inverter. Determination of pulse pattern for the elimination of lower order harmonics of a PWM inverter necessitates solving a system of nonlinear transcendental equations. Evolutionary Algorithms are used to solve nonlinear transcendental equations for PWM–SHE. In this proposed method, harmonics up to 19th are eliminated using Evolutionary Algorithms without using dual transformer. The experimental results are obtained and are validated with simulations using PSIM 6.1 and MATLAB 7.0.     

An application of PSO technique for harmonic elimination in a PWM inverter

Harmonic elimination problem in PWM inverter is treated as an optimization problem and solved using particle swarm optimization (PSO) technique. The derived equation for computation of total harmonic distortion (THD) of the output voltage of PWM inverter is used as the objective function in the PSO algorithm. The objective function is minimized to contribute the minimum THD in the voltage waveform and the corresponding switching angles are computed. The method is applied to investigate the switching patterns of both unipolar and bipolar case. While minimizing the objective function, the individual selected harmonics like 5th, 7th, 11th and 13th can be controlled within the allowable limits by incorporating the constraints in the PSO algorithm. The results of the unipolar case using five switching angles are compared with that of a recently reported work and it is observed that the proposed method is effective in reducing the voltage THD in a wide range of modulation index. The simulated results are also validated through suitable experiments.     

Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms.     

Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-ABC algorithm

The harmonious appearance in multilevel inverter output voltage is more for the case of unequal DC sources. In this paper, a hybrid technique incorporating fuzzy inference system (FIS) and artificial bee's colony (ABC) algorithm is proposed. FIS is a rule-based artificial intelligent technique which is used for generating the data set in terms of switching angle, harmonic voltage and harmonic distortion. The data set is generated as per the behaviour of the multilevel inverter without using any harmonic elimination technique. In the generated data set, the switching angle and the harmonic voltage are categorised as SMALL, MEDIUM and LARGE. Then, the ABC algorithm is used to optimise the selection of switching angles from the training data set. The performance of the proposed hybrid technique is tested on a 7-level cascade H-bridge inverter for different voltage levels of unequal DC sources using MATLAB/SIMULINK platform. The effectiveness and superiority of the proposed technique is evaluated by comparing the reduction capacity of total harmonic distortion for different voltage levels of unequal DC sources with particle swarm optimisation (PSO) algorithm and fuzzy-PSO algorithm.     

Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources

Programmed pulse width modulation is an optimized pulse width modulation which is particularly applicable for high-power applications where the power losses must be kept below firm limits. Based on the offline estimation, it is capable of pre programming the harmonic profile of the output waveform over a range of modulation indices by eliminating some lower order harmonics. In this paper, improved firefly algorithm (FA) is applied to determine the optimum switching angles for the 11- level cascaded H bridge multilevel inverter (MLI) with adjustable DC sources in order to eliminate pre specified lower order harmonics and to achieve the desired fundamental voltage. Though number of optimization algorithms is available for the estimation of switching angles, Firefly algorithm takes least computation time and surpasses all other 11 metaheuristic Algorithms. The algorithm and the model are developed using MATLAB and the validity of the simulation is confirmed by an experimental setup using FPGA Spartan 6A DSP. Results are compared with the results obtained using particle swarm optimization (PSO) and artificial bee colony algorithm (ABCA) and it is proved that the proposed method offers reduced total harmonic distortion (THD) with less computation period.     

基于EEMD-ICA与SVD的电网谐波检测方法研究

论文将EEMD-ICA技术与SVD相结合,提出基于EEMD-SVD-ICA算法的单通道电网电压谐波分离方法,与现有单通道谐波分离方法相比具有无需源信号先验信息,可分离非平稳信号谐波,算法简单等优点。EEMD方法将单通道信号分解为多路互相正交的本征模态函数分量(IMFs),然后采用SVD代替PCA方法进行数据降维,再运用基于负熵的固定点独立成分分析(FastICA)方法提取IMFs独立分量,实现单通道电网电压谐波分离。对模拟信号进行谐波分离,验证所提方法在该领域的应用的可行性,仿真结果表明论文所提方法不仅能够分离电网电压的谐波,并且对频率小于50Hz的间谐波也有很好的分离效果。     

基于DSP的特定消谐脉宽调制波的实现

特定谐波消除是一种以消除某些特定谐波为目的的优化脉宽调制方法。与其它脉宽调制技术相比,具有消谐性好,输出波形质量高,电力电子器件开关频率低,开关损耗小,电压利用率高等特点。本文主要讨论利用DSP芯片TMS320F2812来实现特定脉宽调制波的方法并给出实现了的波形。     

基于粒子群优化的光伏系统MPPT控制方法

局部遮阴条件下光伏阵列P-V特性引起的多个极值点使常规的最大功率点跟踪(MPPT)算法失效。针对上述问题,提出一种基于粒子群优化算法的控制方法,以解决局部遮阴下的最大功率跟踪问题。实验结果显示,光伏模板的输出电压被稳定地控制在最大功率点附近,证明算法是有效的。     

Particle swarm optimization technique for multilevel inverters in solar harvesting micro grid system

In on grid solar generation system the irradiance causes voltage mismatch as a result of this the nonlinearities increase in the output voltage of the multilevel inverter. The sunlight is not uniform at all places, variation in irradiance is inevitable when it comes to solar power. The Solar power is not similarly circulated there is a change in sun orientation with respect to geometrical positions. The un symmetrical voltage results in unbalancing and introduces more harmonics. The article proposed here analyses a topology where a DC-DC converter is utilized ahead of the multilevel inverter so as to overcome the voltage varieties, thereby reducing the harmonics in the system. Particle swarm optimization is carried out in minimizing the Harmonics. Different observation and studies working stages their simulations and results for both are studied and recorded. Since solar energy is an essential, a complete analysis of this is done. A variety of initial calculations in designing the converter are also carried out. Similar calculations are also carried out informing the solar panel and harvesting the energy. By facilitating MATLAB simulation of particle swarm optimization for firing angles employed in multilevel inverter along with the converter. The measure of total harmonic distortion THD obtained is taken as a measure of evaluation for the performance of this combined system.     

基于改进磷虾群算法的服务组合优化

面向服务的架构（SOA）下,针对服务组合优化过程中易陷入局部最优、时间开销大的问题,提出一种加入自适应交叉算子和随机扰动算子的改进磷虾群算法PRKH。首先基于服务质量（QoS）建立了服务组合优化模型,并给出不同结构下QoS的计算公式和归一化处理方法。然后在磷虾群（KH）算法的基础上加入自适应的交叉概率和基于实际偏移量的随机扰动,从而在磷虾群的全局搜索能力和局部搜索能力之间达到良好平衡。最后通过仿真,把所提算法与KH算法、粒子群优化（PSO）算法、人工蜂群（ABC）算法和花朵授粉算法（FPA）进行对比,实验结果表明,PRKH算法能够更快找到QoS更优的复合服务。     

基于EEMD模糊熵和PSO-KELM的NPC三电平逆变器故障诊断

功率开关器件是逆变器的核心部件,但其易发生开路故障,故对其进行故障诊断方法研究很有必要。针对中点钳位型（Neutral Point Clamped,NPC）三电平逆变器功率开关管器件的开路故障,提出一种基于总体经验模态分解（Ensemble Empirical Mode Decomposition,EEMD）模糊熵和粒子群算法（Particle Swarm Optimization,PSO）优化的核函数极限学习机（Kernal Extreme Learning Machine,KELM）的故障诊断方法。首先采样功率开关器件的桥臂输出端的三相电压作为故障信号以区分各种故障类型,然后利用EEMD模糊熵提取故障特征向量,最后将其划分为训练集和测试集送入PSO-KELM中,识别故障类型并输出诊断结果。经MATLAB平台仿真实验得到该方法的故障诊断率超过98%,通过与其他方法的对比实验分析,该方法的有效性与优势得到验证。     

Simulation modelling and analysis of modular cascaded multilevel converter based shunt hybrid active power filter for large scale photovoltaic system interconnection

Modular multilevel inverters are promising candidates for next generation of efficient, robust and reliable inverters in large scale photovoltaic system. A modular cascaded multilevel inverter based shunt hybrid active power filter (SHAPF) for three phase grid-connected large scale PV systems is represented in this paper. The main contribution of this paper is to model and control of grid interfaced large scale photovoltaic system with embedded hybrid active power filter functions. In proposed system, the features of hybrid active power filter have been amalgamated in the control circuit of the voltage controlled voltage source inverter interfacing the photovoltaic system to the grid. As a result, the same inverter part of SHAPF is utilized to inject power generated from photovoltaic source to the grid and also to act as hybrid active power filter to compensate harmonics and reactive power demand. With using compensation ability, the grid currents are sinusoidal and in phase with grid voltages. The whole system is modeled in PSCAD/EMTDC. The simulation results are demonstrated to verify the operation and the control system of the proposed system.     

Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.     

基于混沌PSO算法的选择性神经网络集成方法

提出基于十进制粒子群优化算法（DePSO）和二进制PSO算法（BiPSO）的选择性神经网络集成(NNE)方法,通过PSO算法合理选择组成神经网络集成的各个神经网络,使个体间保持较大的差异度,减小"多维共线性"和样本噪声的影响。为有效保证PSO算法的粒子多样性,在迭代过程中加入混沌变异。试验表明,混沌PSO算法是组合优化权值的有效方法,同已有方法比较可以有效提高神经网络集成的泛化能力。     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

具有快速收敛和自适应逃逸功能的粒子群优化算法

为了克服标准粒子群优化算法(PSO)后期收敛速度慢、容易陷入局部最优等缺点,借鉴人工蜂群算法的思想,提出了一种提高收敛速度并且带有自适应逃逸功能的粒子群优化算法(FAPSO)。算法中每进化一次粒子搜索两次：一次全局搜索,一次局部搜索。当粒子陷入局部最优时,通过逃逸功能使粒子重新搜索。8个经典基准测试函数仿真结果表明,改进的粒子群优化算法在收敛速度和寻优精度上均有提高,相对于目前常用的改进粒子群优化算法如CLPSO等,t检验结果说明,新算法具有明显的优势。     

A novel SOSMC based SVPWM control of Z-source inverter for AC microgrid applications

In this paper, analysis and control of Single stage Z-Source Inverter (ZSI) using Particle Swarm Optimization (PSO) tuned Proportional Integral (PI) based Space Vector Pulse Width Modulation (SVPWM) and Second Order Sliding Mode Control (SOSMC) based SVPWM for harmonic reduction and load voltage regulation are presented. To increase the reliability and to enhance the output voltage of ZSI, the Shoot-Through (ST) state is implemented. To decrease the number of sensors and to simplify the controller design, sixth order model of ZSI is transformed into second order model using Pade's approximation method. To analyse the steady state and transient response of the proposed system, the closed loop implementation is carried out using proposed control techniques. PSO tuned PI controller is utilized for outer voltage control to obtain the Shoot Through Duty Ratio (STDR). Inner current loop utilizes PSO tuned PI controller based SVPWM/SOSMC based SVPWM techniques. MATLAB/SIMULINK software tool is used to simulate the proposed system. From the simulation results, it is inferred that the SOSMC based SVPWM technique offers fast transient response, low % Total Harmonic Distortion (THD) and regulated output voltage when compared to PSO tuned PI based SVPWM control scheme. Hence, an experimental prototype model of 2 kW controlled by the SOSMC based SVPWM using Field Programmable Gate Array (FPGA) is constructed to validate the simulation results with the experimental results.     

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading.     

Multilevel thresholding using grey wolf optimizer for image segmentation

Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.     

基于RBF神经网络的SVPWM过调制

线性调制状态下的逆变器,存在不能充分利用直流母线电压的问题,为了获得尽可能大的输出电压,一般对逆变器进行过调制控制.由于过调制的非线性,计算复杂,提出新的基于神经网络的SVPWM逆变器,采用线性调制和过调制2种模式,通过限定轨迹双调制模式法,实现在整个调制范围内线性控制.采用资源分配法确定径向基函数的网络结构和参数,设计出较精简的网络实现SVPWM;并将这种逆变器应用于异步电动机控制系统中.最后在Matlab环境下建立基于神经网络的SVPWM逆变器供电的异步电机控制系统仿真模型, 仿真结果表明,该方法简单、高效、控制效果良好,能提高直流母线电压利用率,降低输出电流谐波含量和电机转矩脉动.