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.     

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.     

Bacterial foraging optimization and adaptive version for economically optimum sitting,sizing and harmonic tuning orders setting of LC harmonic passive power filters in radial distribution systems with linear and nonlinear loads

This paper presents bacterial foraging optimization (BFO) algorithm and its adaptive version to optimize the planning of passive harmonic filters (PHFs).The important problem of using PHFs is determining location, size and harmonic tuning orders of them, which is reach standard levels of harmonic distortion with applying minimum cost of passive filters.In this study to optimize the PHFs location, size and setting the harmonic tuning orders in the distribution system, considered objective function includes the reduction of power loss and investment cost of PHFs. At the same time, constraints include voltage limits, number/size of installed PHFs, limit candidate buses for PHFs installation and the voltage total harmonic distortion (THDv) in all buses. The harmonic levels of system are obtained by current injections method and the load flow is solved by the iterative method of power sum, which is suitable for the accuracy requirements of this type of study. It is shown that through an economical placement and sizing of PHFs the total voltage harmonic distortion and active power loss could be minimized simultaneously.The considered objective function is of highly non-convex manner, and also has several constraints. On the other hand due to significant computational time reduction and faster convergence of BFO in comparison with other intelligent optimization approach such as genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) the simple version of BFO has been implemented. Of course other versions of BFO such as Adaptive BFO and combination of BFO with other method due to complexity of harmonic optimization problem have not considered in this research.The simulation results for small scale test system with 10 buses, showed the significant computational time reduction and faster convergence of BFO in comparison with GA, PSO and ABC. Therefore in large scale radial system with 34 buses, the proposed method is solved using BFO.The simulation results for a 10-bus system as a small scale and 34-bus radial system as a large scale show that the proposed method is efficient for solving the presented problem.     

Design and implementation of cascaded H-Bridge multilevel inverter using FPGA with multiple carrier phase disposition modulation scheme

The paper deals with design of Cascaded H-Bridge Multilevel Inverter (CHB-MLI) with separate DC source to each inverter using multiple carrier Phase Disposition PWM such as symmetric disposition, phase opposition disposition and alternative phase opposition disposition to reduce the Total Harmonic Distortion (THD) and to improve the power quality of the supply voltage and current. In early days, the multilevel inverters are controlled with high frequency PWM method. This is not suitable for high power applications due to the high switching losses. With the help of multiple carrier PWM switching losses in the inverter circuit are reduced, so it can be used for high switchi ng frequency applications. The simulation of proposed system is developed using MATLAB Simulink software. The Performance simulation results are validated through experimental test setup using SPARTAN - 6 FPGA. Simulation results and effectiveness of the proposed method is proved and validated by experimental data.     

Design of output filter for inverters using fuzzy logic

This paper proposes a design method to improve the harmonic of output voltage of a single phase inverter with an L-C output filter using fuzzy logic controller (FLC). In practice, the harmonic characteristics of circuits are complicated and entangled. There are two kinds of harmonic sources that cause inverter output voltage waveform distortion: One is the PWM switching of inverter and the other is the nonlinear characteristics of the load. In general, PI feedback control by coefficient diagram method (CDM) is used to design the output voltage filter. The relation between the L-C value and the system time constant are described with the closed form and the filter values must be calculated repeatedly to satisfy the prescribed voltage total harmonic distortion (THD) of the system. Therefore, the MATLAB Fuzzy Logic Toolbox for the fuzzy logic control algorithm is proposed. The L-C value of the filter can be set to a fixed range in the nonlinear characteristic of the practical condition, to improve the harmonic of output voltage more effectively and to avoid repeated calculation.     

Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

This paper proposes an optimal power control strategy for inverter-based Distributed Generation (DG) units in autonomous microgrids. It consists of power, voltage, and current controllers with Proportional-Integral (PI) regulators. The droop concept is used for the power control strategy. Static parameters in PI regulators may not ensure the most optimal solution due to inevitable changes happening in microgrid configuration and loads. In the proposed method, after occurring a load change in a standalone microgrid, parameters of the PI controller are dynamically adjusted to get the most optimal operating point that satisfies objective functions. The optimization problem is formulated as a multi-objective programming with objective functions of minimizing overshoot/undershoot, settling time, rise time, and Integral Time Absolute Error (ITAE) in the output voltage. These objective functions are combined using fuzzy memberships. The Hybrid Big Bang-Big Crunch algorithm (HBB-BC) is used to solve the optimization problem. The proposed methodology is simulated on a case study and according to obtained results, the suggested tuning of PI parameters leads to a better voltage response than previous methods. The case study is also solved using the Particle Swarm Optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms and it is found that the HBB-BC gives a better solution than the PSO and BB-BC.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

基于混合粒子群算法的烧结配料优化

在引入惩罚函数和对目标函数进行适当修改的前提下,充分利用粒子群优化算法的全局搜索能力和约束条件下共轭梯度法的局部搜索能力,设计了烧结配料优化算法．利用惩罚函数方法将约束条件优化问题转化为无约束条件优化问题,然后利用粒子群优化算法进行寻优．当群体最优信息陷入停滞时将目标函数进行适当变化,继续利用共轭梯度法进行寻优．计算结果表明,采用该方法能够在提高混合料中的有用成分、降低有害成分的前提下,更多地降低生产成本．     

基于粒子群优化算法的无线电 频谱分配方法研究

粒子群(PSO)算法在认知无线电频谱分配问题上发挥着重要的作用,但是在连续无约束条件下基本的PSO 算 法才能得以运用,并且在此条件下,早熟收敛和收敛速度不够快等问题仍然无法得到效解决。为了优化这些问题,本文将对粒 子群算法的早熟收敛问题进行分析并加以改进,成功地将统一的粒子群算法应用于解决频谱分配问题。在综合考虑系统的总 宽带收益及用户接入公平性的基础上,建立了相应的目标函数,并验证了该算法的可行性和优越性。     

基于T细胞调节的免疫遗传优化算法对逆变器PWM控制序列的优化

针对遗传算法(GAs)收敛速度慢、易于陷入局部最优等不足,基于单相全桥逆变器输出电流与参考电流的误差模型,提出一种改进的免疫遗传优化算法(IGOAs)用于逆变器PWM最优控制序列优化。算法采用0、1编码,自适应突变概率及T细胞调节算子增强算法的快速收敛性和种群的多样性。数值实验中考虑逆变器负载端电阻为定值和受随机扰动两种情形,将GAs和IGOAs用于此两种情形的PWM控制序列优化,仿真结果统计表明：IGOAs具有较好的收敛性和稳定性,负载电阻受随机扰动时,IGOAs较GAs能快速跟踪参考电流,获得较小的THD电流波。     

Neural network based switching control of AC–AC converter with DC–AC inverter for voltage sags, harmonics and EMI reduction using hybrid filter topology

A neural network based AC–AC voltage restorer is designed for voltage sags and PWM type active power filter with compound trap passive filter as a new hybrid filter are simultaneously used for voltage harmonics compensation and electromagnetic interference (EMI) reduction. First objective is to apply the neural network based switching control technique for the AC–AC voltage restorer to reduce time delays during the switching conditions and switching losses. The aim of the IGBTs used in the AC–AC voltage restorer is to test and to find the best switching frequency–power combination in the steps of the simulation. Thus, the proposed AC–AC voltage restorer has important advantages such as fast switching response, simplicity and more intelligent structure, better output waveform. The transient condition of the AC–AC voltage restorer is improved via the neural network based control technique. The second objective is the proposed strategy for elimination of voltage harmonics using PWM type DC–AC inverter part of the system as an active power filter. The last objective of the system is EMI reduction with using hybrid filter and voltage restorer together. Three problems which are voltage sags, harmonics and EMI are solved with the proposed system simultaneously.     

A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION

This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.     

异步粒子群优化算法在QoS组播路由中的应用

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。为适应下一代IP网络对实时信息传输的要求,在异步模式粒子群优化算法基础上,给出包含延迟、延迟抖动、带宽、丢包率和最小花费5个约束条件在内的QoS组播路由算法。该算法首先给出数学模型,设计适应度函数,再给出受限的网络模型,通过粒子群优化(PSO)算法最大化适应度函数来求解最优Steiner树。算法仿真实验结果表明:与遗传算法和同步模式的粒子群优化算法相比,该算法有较好的收敛速度和寻优效果。     

空间矢量调速方法研究

介绍了一种电压空间矢量PWM控制的方法。SVPWM能有效的减少输出电流失真及切换损耗,它与目前广泛使用的正弦式PWM比较,可以显著的增加有效输出电压及降低谐波含量。该方法输出电压较一般SPWM逆变器提高15%,每次状态切换只涉及一个元件,开关损耗降低,且模型简单,适用于各种PWM调速装置。     

基于改进粒子群算法的行李条码阅读器布局优化

针对航空旅客托运行李时,检测行李条码的阅读器数量、位置、姿态存在很多不确定性问题,提出了动态种群-双适应值粒子群优化(DPDF-PSO)算法。首先,建立行李条码检测数学模型;然后,转化为约束优化问题;其次,通过标准粒子群优化(PSO)算法求解此优化问题;最后,依照模型特点对标准粒子群算法进行改进。仿真结果表明,与标准PSO算法相比,DPDF-PSO算法仿真时间降低了23.6%,目标函数值提高了3.7%。DPDF-PSO算法克服了标准粒子群优化算法中仿真时间慢、边界最优解难处理的缺点,阅读器布局方案能以较低的成本准确快速读取行李身份信息。     

Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

In particle swarm optimization (PSO) each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming to find the global or local best positions in case of complex problems. To overcome this problem, we propose a new multi-objective variant of PSO called attributed multi-objective comprehensive learning particle swarm optimizer (A-MOCLPSO). In this technique, we do not use global or local best positions to modify the velocity of a particle; instead, we use the best position of a randomly selected particle from the whole population to update the velocity of each dimension. This method not only increases the speed of the algorithm but also searches in more promising areas of the search space. We perform an extensive experimentation on well-known benchmark problems such as Schaffer (SCH), Kursawa (KUR), and Zitzler–Deb–Thiele (ZDT) functions. The experiments show very convincing results when the proposed technique is compared with existing versions of PSO known as multi-objective comprehensive learning particle swarm optimizer (MOCLPSO) and multi-objective particle swarm optimization (MOPSO), as well as non-dominated sorting genetic algorithm II (NSGA-II). As a case study, we apply our proposed A-MOCLPSO algorithm on an attack tree model for the security hardening problem of a networked system in order to optimize the total security cost and the residual damage, and provide diverse solutions for the problem. The results of our experiments show that the proposed algorithm outperforms the previous solutions obtained for the security hardening problem using NSGA-II, as well as MOCLPSO for the same problem. Hence, the proposed algorithm can be considered as a strong alternative to solve multi-objective optimization problems.     

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

In this research, an Artificial Bee Colony (ABC) algorithm based Selective Harmonics Elimination (SHE) technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system. Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics. A high gain DC-DC-SEPIC converter keeps the photovoltaic (PV) panel’s output voltage constant. The Grey Wolf Optimization (GWO) filter removes far more Photovoltaic panel energy from the sunlight frame. To eliminate voltage harmonics, this unique inverter architecture employs a multi-carrier duty cycle, a high-frequency modulation approach. The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization (PSO) and Flower Pollination Algorithm (FPA). The suggested system’s performance is simulated and measured using the MATLAB simulation tool. The proposed ABC approach has a THD level of 4.86%, which is better than the PSO and FPA methods.     

微粒群算法在产品组件布局设计中的应用

提出了一种基于微粒群算法的自适应优化布局求解算法,该算法以组件特征模型为基础,在微粒群算法中引入人机交互技术,从整体上自动优化布局方案,以满足约束条件为目标。并以手机组件的布局求解为例,对该算法进行了验证。理论和实例分析表明,该算法能有效地生成多个手机组件布局方案。     

Bang–bang control model with optimistic value criterion for uncertain switched systems

This paper studies an optimal control problem for uncertain switched linear systems with subsystems perturbed by uncertainty. A model for this problem is investigated with optimistic value criterion. The goal is to jointly design a deterministic switching law and a continuous feedback to optimize an uncertain objective function. A two-stage algorithm is applied to handle such model. In the first stage, the maximum value of the objective function and the bang–bang control are obtained under fixed switching instants, and in the second stage, GA and PSO algorithm are used to get the optimal switching instants, respectively. An example is shown to validate the method.     

一种改进PSO优化RBF神经网络的新方法

为了克服神经网络模型结构和参数难以设置的缺点,提出了一种改进粒子群优化的径向基函数(RBF)神经网络的新方法.首先将最近邻聚类用于RBF神经网络隐层中心向量的确定,同时对引入适应度值择优选取的原则对基本粒子群算法进行改进,采用改进粒子群(IMPSO)算法对最近邻聚类的聚类半径进行优化,合理的确定了RBF神经网络的隐层结构.将改进PSO优化的RBF神经网络应用于非线性函数逼近和混沌时间序列预测,经实验仿真验证.与基本粒子群(PSO)算法,收缩因子粒子群(CFA PSO)算法优化的RBF神经网络相比较,其在识别精度和收敛速度上都有了显著的提高.