基于改进蝙蝠算法的PSS参数优化研究

发布时间：2018-12-10 08:46

【摘要】：随着电网规模的日趋扩大,以及在大型发电机组中采用具有较高放大倍数的快速励磁系统,经常会在电力系统中产生低频振荡,通常使系统的阻尼有较大的削弱,对电力系统的动态稳定性产生了很大的影响,因此,低频振荡已经成了严重制约电力系统进一步发展的重要因素。在励磁系统中加装PSS(Power system stabilizer,电力系统稳定器)作为一种附加装置是为了抑制低频振荡而广泛使用的措施。PSS被用来控制发电机的励磁以改善局部模式振荡的阻尼,研究证明,通过对PSS参数的合理地整定,能使局部和区间这两种模式振荡的阻尼得到改善。然而,由于在多机系统中会产生PSS的负阻尼效应,所以在多机系统中,PSS参数的配置是一个多参数的协调优化问题。为了抑制故障发生时,在多机电力系统中的低频振荡,在分析了目前优化PSS参数常用算法所存在不足的基础上,文中提出了一种协调优化PSS参数的IBA算法(Improved Bat Algorithm,改进的蝙蝠算法)。BA算法(Bat Algorithm,蝙蝠算法)是将全局搜索与局部搜索相结合的新型智能优化算法,它通过对蝙蝠的回声定位行为进行数学化处理,有较高的精度和效率,已经被应用到求解各种复杂函数,多目标优化中,而且在许多工程问题上也得到了应用。并且对目前低频振荡的分析方法进行比较分析,确定了一种Prony算法,并运用算例进行仿真,验证其所采用方法在低频振荡分析中的有效性。因此,文中首先采用Prony算法辨识系统低频振荡的机电模式,计算出衡量系统整体阻尼性能的指标,而且此优化的目的是使系统阻尼比最大化,其次,采用IBA算法协调优化PSS参数,最后,通过单机无穷大系统和四机两区域系统算例证明了该算法的合理性。结果表明:IBA算法可以有效的适用于多种运行方式下的阻尼控制器参数优化,且具有良好的鲁棒性,可以有效地抑制低频振荡。此外,通过比较多机电力系统基于IBA算法、PSO算法和基本BA算法的PSS参数优化结果,可知基于IBA算法设计的PSS在抑制低频振荡方面具有最佳的效果,显著地提高了电力系统的稳定性,且该算法克服了基本BA算法容易陷入局部最优、后期收敛速度慢等缺点。
[Abstract]:With the increasing expansion of power network scale and the use of fast excitation system with high magnification in large generator sets, low frequency oscillation is often produced in power system, which usually weakens the damping of the system. It has a great influence on the dynamic stability of power system. Therefore, low frequency oscillation has become an important factor restricting the further development of power system. As an additional device, PSS (Power system stabilizer, is widely used to suppress low frequency oscillation. PSS is used to control the excitation of generator to improve the damping of local mode oscillation. It is proved that the damping of local and interval oscillations can be improved by reasonable tuning of PSS parameters. However, because of the negative damping effect of PSS in multi-machine systems, the configuration of PSS parameters is a multi-parameter coordination optimization problem in multi-machine systems. In order to suppress the low frequency oscillation in the multi-machine power system when the fault occurs, this paper presents a IBA algorithm (Improved Bat Algorithm, which harmonizes the optimization of PSS parameters, based on the analysis of the shortcomings of the current common algorithms for optimizing PSS parameters. The improved bat algorithm). BA (Bat Algorithm, bat algorithm) is a new intelligent optimization algorithm which combines global search with local search. It has high precision and efficiency through mathematical processing of the echolocation behavior of bats. It has been applied to solve various complex functions and multi-objective optimization, and has also been applied to many engineering problems. By comparing and analyzing the current analysis methods of low frequency oscillation, a kind of Prony algorithm is determined, and a simulation example is used to verify the validity of the method used in the analysis of low frequency oscillation. Therefore, Prony algorithm is used to identify the electromechanical mode of the low frequency oscillation of the system, and the index to measure the damping performance of the system is calculated, and the purpose of this optimization is to maximize the damping ratio of the system. The IBA algorithm is used to coordinate and optimize the PSS parameters. Finally, the rationality of the algorithm is proved by the examples of single-machine infinite bus system and four-machine two-region system. The results show that the IBA algorithm can be applied to the parameter optimization of damping controller under various operation modes, and has good robustness, and can effectively suppress the low frequency oscillation. In addition, by comparing the optimization results of PSS parameters based on IBA algorithm, PSO algorithm and basic BA algorithm in multi-machine power system, we can see that the PSS based on IBA algorithm has the best effect in suppressing low frequency oscillation. The stability of power system is improved significantly and the algorithm overcomes the shortcomings of the basic BA algorithm which is easy to fall into local optimum and slow in the later stage of convergence.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM712

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