基于混合粒子群优化算法的PSS参数优化

将一种新的进化算法-粒子群优化算法(PSO)应用到电力系统稳定器(PSS)参数优化当中,文中使用引入交叉操作的混合粒子群优化算法(HPSO),可以获得更好的全局搜索能力和收敛速度.先以低频振荡范围内(0.1～2 Hz)PSS产生的附加阻尼转矩△Te与△ω尽可能同相位为目标优化PSS超前-滞后环节参数;再以小扰动时发电机功率和角速度振荡最小为目标整定PSS放大倍数.优化结果表明,HPSO算法可以有效地解决PSS参数优化问题.     

基于改进灰狼优化算法的多机电力系统稳定器参数优化

提出了灰狼优化算法与差分进化算法相结合的多机PSS参数设计方案。首先建立了发电机、励磁系统、PSS的线性化微分方程;其次构建了基于机电模式阻尼比的优化函数,通过DE-GWO算法进行迭代搜索,实现对不同运行方式下机电振荡模式阻尼系数的最大化寻优;最后在PSASP中搭建了云南电网滇西南地区部分电网,通过非线性时域仿真验证了基于DE-GWO优化算法的PSS在不同运行方式下抑制机电振荡的有效性和鲁棒性,提高了系统的阻尼。     

基于灰狼优化算法的多机电力系统稳定器参数最优设计

灰狼优化(grey wolf optimizer,GWO)算法作为一种新的、高效的群体智能优化算法,可应用于电力系统优化问题。提出了采用GWO算法的多机电力系统稳定器参数优化设计方案。将传统超前-滞后型电力系统稳定器(PSS)的参数设计建模为基于特征值的二次性能目标优化问题,通过向左半复平面移动机电振荡特征值实现对不同运行状态下机电模态阻尼系数的最大化进行寻优。GWO算法具有对初始取值不敏感,优化效率较高和全局寻优性能好等特点,因此被用来迭代搜索最优PSS参数值。通过IEEE New England 39节点算例的特征值分析和非线性时域仿真,验证了基于GWO算法优化整定的电力系统PSS在各种系统运行状态下抑制系统机电振荡的有效性和鲁棒性,并通过与传统相位补偿方法设计的PSS阻尼性能对比,表明所提GWO算法优化PSS参数具有明显优越性。进一步的算法性能分析表明,GWO算法具有对初值不敏感和稳健性强等优点。     

基于混合粒子群优化算法的PSS参数优化

将一种新的进化算法—粒子群优化算法(PSO)应用到电力系统稳定器(PSS)参数优化当中,文中使用引入交叉操作的混合粒子群优化算法(HPSO),可以获得更好的全局搜索能力和收敛速度。先以低频振荡范围内(0.1~2Hz)PSS产生的附加阻尼转矩ΔTe与Δω尽可能同相位为目标优化PSS超前-滞后环节参数;再以小扰动时发电机功率和角速度振荡最小为目标整定PSS放大倍数。优化结果表明,HPSO算法可以有效地解决PSS参数优化问题。     

混合粒子群算法在PSS参数优化中的应用

通过采用一种新的混合粒子群算法对多机系统的电力系统稳定器(PSS)进行参数优化,以达到更好的低频振荡抑制效果.引入交叉操作的混合粒子群优化算法是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的改进粒子群算法(PSO).用Matlab软件进行仿真,结果表明,该方法设计的PSS稳定性有较大提高.     

基于粒子群优化算法的PSS参数优化

粒子群算法(PSO-ω)是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法.基于单机无穷大系统模型,通过采用PSO-ω算法对电力系统稳定器(PSS)进行参数优化,以抑制低频振荡.该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力(ITAE准则).用Matlab软件进行仿真,结果表明,利用该方法设计的PSS,它的稳定性有了较大的提高.     

PSS与储能装置的协调控制及参数优化研究

针对电力系统中多种控制器间参数配合不合理的问题,结合电力系统低频振荡的抑制问题,对电力系统稳定器(PSS)与储能装置的协调优化进行研究。基于电力系统分析综合程序(PSASP),分析PSS、储能装置的控制逻辑,并基于PSASP的用户自定义模块搭建储能装置控制模型。针对PSS与储能装置对应的参数优化模型,利用细菌群体趋药性(BCC)算法,进行PSS和储能控制器的参数优化分析。以四机两区系统模型为例对协调优化方法进行验证,特征值分析结果及时域仿真测试结果表明,经协调优化后的控制器参数能够有效抑制低频振荡,提高电力系统动态稳定性。     

基于增强连续禁忌算法的PSS参数优化

多机系统中,电力系统稳定器PSS(Power System Stabilizer)的参数配置是一个复杂的非线性优化问题。描述了电力系统模型、PSS模型及参数优化目标函数和约束条件。介绍了增强连续禁忌算法ECTS(Enhanced Continuous Tabu Search)的主要组成部分及算法流程图。并以WSCC3机9节点系统为例进行PSS参数优化,通过发电机在不同方式下(正常、负荷高峰、负荷低谷)的动态性能仿真,结果表明：用ECTS算法参数优化后的PSS动态性能优于常规方法设计的PSS,具有一定的鲁棒性。     

电力系统稳定器参数优化的研究

电力系统稳定器(PSS)的性能受其参数影响很大,如何对其参数进行协调优化是一个值得深入研究的问题.基于单机无穷大系统和4机2区域系统模型,通过采用 SFPSO算法对电力系统稳定器进行参数的协调优化,以抑制低频振荡.随机聚焦粒子群算法SFPSO(Stochastic focusing particle swarm optimization)是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的改进粒子群算法(PSO).通过仿真测试以及不同算法优化结果的对比,结果表明,利用该方法设计的PSS,在不同的干扰下都具有良好的性能,对系统的稳定性提升有较大帮助.     

相量测量装置（PMU）动态测量精度在线检验

粒子群算法(PSO-ω)是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法。基于单机无穷大系统模型,通过采用PSO-ω算法对电力系统稳定器(PSS)进行参数优化,以抑制低频振荡。该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS 参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力（ITAE准则）。用Matlab软件进行仿真,结果表明,利用该方法设计的PSS,它的稳定性有了较大的提高。     

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.     

基于群灰狼优化算法的双馈感应电机最优控制

设计了一种新颖的群灰狼优化算法(Gathered Grey Wolf Optimizer, GGWO),用于整定双馈感应电机(Doubly-fed Induction Generator, DFIG)的比例-积分控制器(Proportional-integral, PI)最优参数,从而实现变风速下的最大功率点跟踪(Maximum Power Point Tracking, MPPT)并提高系统的故障穿越能力(Fault Ride-through, FRT)。GGWO在原始灰狼优化算法(Grey Wolf Optimizer, GWO)的基础上引入分组机制,将灰狼分为相互独立的合作狩猎组和随机侦察组。其中,随机侦察组中的灰狼负责进行广泛的全局搜索,而合作狩猎组的灰狼实现深度的局部探索。同时,设计狼群间的角色互换机制,可根据当前适应度函数,在下次迭代中对不同分工的狼进行角色互换,进而平衡全局搜索和局部探索的矛盾。通过阶跃风速、随机风速和电网电压跌落三个算例对GGWO的优化性能进行了研究。仿真结果表明,与遗传算法、粒子群算法、飞蛾扑火算法和GWO相比,所提算法具有更好的全局收敛性、MPPT精确性和FRT能力。     

基于灰狼优化聚类算法的日负荷曲线聚类分析

针对模糊C-均值聚类算法(Fuzzy C-Means,FCM)应用于日负荷曲线聚类分析时存在易受初始聚类中心影响,易收敛于局部最优值以及日负荷曲线的内在特性难以通过距离得到充分反映的问题,利用日负荷特征值指标对日负荷曲线进行数据降维处理。提出了基于灰狼算法(Grey Wolf Optimizer,GWO)优化的模糊C-均值聚类算法(GWO-FCM)。该算法利用GWO为FCM优化初始聚类中心,结合了GWO的全局搜索能力和FCM的局部搜索能力。算例结果表明所提方法可有效提高日负荷曲线聚类效果,算法鲁棒性好。     

Grey Wolf Optimizer for Optimal Sizing and Siting of Energy Storage System in Electric Distribution Network

One of the most important issues that must be taken into consideration during the operation of distribution network is improving the network reliability. This objective can be achieved by connecting energy storage systems (ESSs) to the network; the correct size and location of these ESSs cause enhancing of the system reliability. This paper proposes an efficient methodology based on the Grey Wolf Optimizer (GWO) to determine the optimal size and location of ESSs in a distribution network so as to minimize the total annual cost of system comprising the cost of energy not supplied (ENS), the ESSs' investment costs and operating costs. The proposed methodology is applied to two radial distribution systems, a 30-bus, 11-kV system and a 69-bus, 12.6-kV system. The results obtained via the proposed GWO are compared to those obtained via classical approaches, dynamic programming (DP), and meta-heuristic algorithms (PSO). In the case of a 30-bus network, the total cost is saved by 14.12% from the base case, network without ESSs; and, in the case of a 69-bus system, the cost is saved by 39.03%. Additionally, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms are programmed and their results are compared with those obtained via the proposed GWO. The obtained locations and sizes of ESSs encourage the usage of the proposed methodology due to its ease and efficiency in solving the optimization problem under study.     

基于IGWO-K-means的风电场动态等值建模

风电场等值建模是分析风电系统的前提和基础,为了提高风电场动态等值建模精度,降低等值难度,本文基于风(风速和风向)、风机本体、风电输出效果和风机工作环境等4个方面,从内蒙古某风电场24台机组实际采样的运行数据中选取了14个变量作为分群指标,全面描述了风电场特性。其次提出了收敛因子非线性策略和动态参考率策略两个控制策略,改进了灰狼优化算法(GWO),并结合K-means聚类算法寻找最佳聚类中心,输出聚类结果,建立风电场动态等值模型。最后在MATLAB/Simulink平台上建立风电场聚类模型,验证该模型的可行性。结果表明,该方法提高了风电场等值建模的精度,能够更好地描述风电场的动态特性。     

Grey wolf optimization for combined heat and power dispatch with cogeneration systems

The Combined Heat and Power Dispatch (CHPD) is an important optimization task in power system operation for allocating generation and heat outputs to the committed units. This paper presents a Grey Wolf Optimization (GWO) algorithm for CHPD problems. The effectiveness of the proposed method is validated by carrying out extensive tests on three different CHPD problems such as static economic dispatch, environmental-economic dispatch and dynamic economic dispatch. Valve-point effects, ramp-rate limits and spinning reserve constraint along with network loss are considered. Standard test systems containing 4, 7, 11 and 24 units are used for demonstration purpose. To validate the performance of the GWO, statistical measures like best, mean, worst, standard deviation, epsilon, iter and sol-iter over 50 independent runs are taken. The simulation experiments reveal that GWO performs better in terms of solution quality and consistency.     

基于微粒群优化算法的最优电力系统稳定器设计

传统电力系统稳定器的性能受其参数影响很大,为提高电力系统机电暂态模型的阻尼,文中提出了一种优化电力系统稳定器参数的新方法。该方法以两个特征值基目标函数为基础,采用改进的微粒群优化技术对电力系统稳定器进行参数优化。特征值分析和非线性仿真结果表明,经过参数优化的电力系统稳定器能有效抑制本地和区域间振荡,提高系统的鲁棒性。     

基于风速局部爬坡误差校正的风电功率优化预测

准确的风电功率预测对于电力系统安全稳定运行具有重要意义,滞后性是产生风电功率预测误差的主要原因,尤其是风速变化较快时,滞后性引起的预测误差较大。考虑到风速波动与风电功率的变化息息相关,提出一种基于风速局部爬坡(LR)误差校正的方法来改善预测风速的滞后性,并将校正后的预测风速及历史功率数据作为输入进行风电功率预测。提出利用灰狼优化(GWO)算法对最小二乘支持向量机(LSSVM)的参数进行优化,以提高风电功率预测的准确性。算例结果表明,所提方法能够有效提高风电功率预测精度。     

Knowledge domain states mapping concept for controller tuning in an interconnected power network

This paper presents novel power system stabilizers (PSS) tuning approach with particles swarm optimization (PSO) driven knowledge domain states mapping for multi area power system. Tuning of PSS parameters has been done by PSO technique offline at different operating conditions as load dynamics change. The objective function for PSS parameters tuning is integral time multiplied by absolute error (ITAE). The sets of tuned parameters at different operating conditions thus obtained are termed as knowledge domain. The process is viewed as knowledge domain mapping. The dynamical system response has been linked with states, and if there is any violation from desired limits, control action is initiated and thus retuned control parameters obtained straight away from the respective knowledge domain helps to provide quick response and precise damping of signals of interest. Proposed concept also demonstrates that if one controller (PSS) fails to stabilize the system at certain operating condition then another controller (UPFC) connected in the system acts as supplementary controller in automation which assists PSS functioning and thus entire system operation improves by way of modulating the signal dynamics at interface points which quickly damps oscillations. The system study has been performed on a sample six area power systems comprising of UPFC connected between area two and three and PSS to all areas. The proposed concept demonstrates auto tuning of PSS for quick oscillation damping as the operating conditions change and also under situations of PSS failure due to relatively larger perturbation, UPFC acts as supplementary controller by assisting the entire system PSS to recover ensuring stability.     

Sliding mode controller design for frequency regulation in an interconnected power system

In this paper, a Sliding mode controller design method for frequency regulation in an interconnected power system is presented. A sliding surface having four parameters has been selected for the load frequency control (LFC) system model. In order to achieve an optimal result, the parameter of the controller is obtained by grey wolf optimization (GWO) and particle swarm optimization (PSO) techniques. The objective function for optimization has been considered as the integral of square of error of deviation in frequency and tie-line power exchange. The method has been validated through simulation of a single area as well as a multi-area power system. The performance of the Sliding mode controller has also been analyzed for parametric variation and random loading patterns. The performance of the proposed method is better than recently reported methods. The performance of the proposed Sliding mode controller via GWO has 88.91% improvement in peak value of frequency deviation over the method of Anwar and Pan in case study 1 and similar improvement has been observed over different case studies taken from the literature.