An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialist competitive algorithm and genetic algorithm

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation (DG) sources and capacitors banks in parallel are increased. Determining the installation location and capacity are two significant factors affecting network loss reduction and improving network performance. This paper, proposes an efficient hybrid method based on Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA) which can greatly envisaged with problems for optimal placement and sizing of DG sources and capacitor banks simultaneously. The objective function is power loss reduction, improving system voltage profile, increasing voltage stability index, load balancing and transmission and distribution relief capacity for both utilities and the customers.The proposed method is implemented on IEEE 33 bus and 69 bus radial distribution systems and the results are compared with GA/Particle swarm optimization (PSO) method. Test results show that the proposed method is more effective and has higher capability in finding optimum solutions.     

Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach

This study proposes a new application of multi objective particle swarm optimization (MOPSO) with the aim of determining optimal location and size of distributed generations (DGs) and shunt capacitor banks (SCBs) simultaneously with considering load uncertainty in distribution systems. The multi objective optimization includes three objective functions: decreasing active power losses, improving voltage stability for buses and balancing current in system sections. The uncertainty of loads is modeled by using fuzzy data theory. This method uses Pareto optimal solutions to solve the problem with objective functions and constraints. In addition, a fuzzy-based mechanism is employed to extract the best compromised solution among three different objective functions. The proposed method is implemented on IEEE 33 bus radial distribution system (RDS) and an actual realistic 94 bus Portuguese RDS and the results are compared with methods of Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Genetic Algorithm (NSGA), Multi-Objective Differential Evolution (MODE) and combination of Imperialist Competitive Algorithm and Genetic Algorithm (ICA/GA). Test results demonstrate that the proposed method is more effective and has higher capability in finding optimum solutions in cases where DG and SCB are located and sized simultaneously in a multi objective optimization.     

Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms

In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.     

局部阴影条件下光伏阵列最大功率点跟踪方法对比研究

近年来涌现了许多针对局部阴影条件的光伏阵列最大功率点跟踪(MPPT)方法,这些方法的跟踪性能表现不一,目前也缺乏统一的评价标准。基于对大量文献的阅读与总结,选取了五种较为典型的多峰MPPT方法,对其在大量典型局部阴影案例下的跟踪性能进行Matlab仿真分析,并总结对比了各类MPPT算法的静态跟踪性能、动态跟踪性能以及MPPT效率。本文有助于光伏工程设计与装置研发人员快速了解并掌握当前最新的MPPT方法及其性能,为实际应用中MPPT方法的选取以及提高发电效率提供借鉴。     

平滑伪Wigner-Ville分布在电力系统谐波和电压变动检测中的应用

在分析比较了现有用于短时电能质量扰动信号检测的方法的基础上,提出使用平滑伪W igner-V ille分布(SPWVD)对电能质量信号进行检测和时频分析的新方法。平滑伪W igner-V ille(SPWVD)分布是一种优良的时频分析方法,能够在时域频域上对平稳谐波信号及非平稳信号进行联合时频分析。仿真表明,该方法能够较有效地检测出突变、非平稳扰动的短时电压波动,谐波和间谐波等电能质量信号的起止时间、频率信息。     

直埋XLPE电缆在不同敷设条件下的温升与载流量仿真

土壤直埋因具有施工周期短、散热性能好等优点而被广泛应用于电力电缆的敷设。埋设过程中各种敷设条件对电缆的温升和载流量都有着重要影响,文中采用Comsol有限元仿真软件,以110 kV交联聚乙烯(cross-linked polyethylene, XLPE)电力电缆为研究对象,在直埋敷设的基础上考虑回填沙土、加盖混凝土保护板、装设套管等多种因素,建立包含电磁场、热场和流体场的耦合模型,分析这3种敷设条件对电缆温度场变化及载流量的影响。仿真结果表明：回填沙土会提升电缆载流量,随着回填沙土厚度的增加电缆载流量提升速度变缓;加盖混凝土保护板对载流量的影响较小,与不加盖时相比载流量提升幅度在0.5%以下;在排管内填充高导热材料会提升电缆载流量,相比于无填充情况,三相均填充高导热材料与中间相填充高导热材料、其他两相填充低导热材料时载流量的提升幅度均高于50%;在考虑上述因素同时存在情况下,有利于电缆载流量的提升。