共查询到20条相似文献,搜索用时 15 毫秒
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Ebrahim Rahimpour Vahid Rashtchi Mahmood Pesaran 《Electrical Engineering (Archiv fur Elektrotechnik)》2007,89(7):547-552
In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise
parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models
of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded
genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters
are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor. 相似文献
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针对基本遗传算法(SGA)收敛速度慢、易早熟等缺陷,从初始群体的生成、精英个体的保留、自适应的交叉率和变异率、早熟现象的防止策略等方面对SGA进行了改进,提出了一种基于改进遗传算法(IGA)的将电力系统稳定器(PSS)原模型转换成仿真软件下标准模型的参数辨识方法.通过建立含PSS的励磁系统原模型和标准模型传递函数结构框图,输入相同的扰动信号,以原模型和标准模型的输出误差最小作为辨识目标,利用IGA对PSS标准模型参数进行优化调整,最终得到满足要求的PSS标准模型参数.实际PSS参数辨识结果表明,该方法计算速度快,精度高,具有很好的效果. 相似文献
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The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which is used for the subsequent model parameters identification with a novel BSA. The newly introduced BSA, being developed upon a bacterial foraging behavior, is described in detail. Simulations and discussions are presented to explore the potential of the proposed approach using simulated and experimentally measured FRA responses taken from two transformers. The BSA identification results are compared with those using genetic algorithm. It is shown that the proposed BSA delivers satisfactory parameter identification and improved modelling can be used for FRA results interpretation. 相似文献
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提出了一种基于改进遗传算法的将发电机励磁系统原模型转换为仿真软件下标准模型的参数辨识方法,以原模型和标准模型的输出误差最小作为辨识目标,利用改进遗传算法不断优化调整标准模型中的参数,最终得到满足要求的励磁系统标准模型参数。与传统的励磁系统参数辨识方法相比较,该方法很好地解决了励磁系统非线性环节难以有效辨识的问题,方便可靠,精度高。实际发电机励磁系统参数辨识结果表明,该方法具有很好的效果。 相似文献
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为提高动态工况模拟下动力电池等效电路模型参数辨识准确性、稳定性,本文提出一种动态工况模拟下动力锂电池建模与参数辨识方法,采用准确度较高、辨识难度适中Thevenin二阶等效电路模型对动力电池进行状态空间描述;设计动力锂电池模型参数辨识算法总体流程,开展OCV-SOC曲线的工况测试获取曲线的拟合系数;提出含噪声干扰动力锂电池等效电路模型参数的DEKF+GA算法辨识方法,增加GA算法,在DEKF求解初始值处进行最优解搜索,提升DEKF算法准确性、鲁棒性。实验表明,应用DEKF+GA算法比DEKF算法,URMSE、UMAX分别平均减少29mV、27.73mV。 相似文献
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针对标准遗传算法收敛速度慢、易早熟等缺陷,通过对遗传策略的综合改进,提出了一种基于改进遗传算法的参数辨识方法。通过建立励磁系统原模型和标准模型,给原模型和标准模型施加相同的激励信号,以模型输出误差最小作为辨识目标,利用改进遗传算法对标准模型参数进行优化调整,最终得到满足误差要求的励磁系统标准模型参数。该方法的优点在于解决了传统的辨识方法无法对励磁系统非线性环节进行有效辨识的问题,实际励磁系统参数辨识结果表明,该方法具有较快的收敛速度和较高的辨识精度。 相似文献
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Toshiro Kumon Tatsuya Suzuki Makato Iwasaki Motoaki Matsuzaki Nobuyuki Matsui Shigeru Okuma 《Electrical Engineering in Japan》2003,142(4):45-55
The requirement for the high‐quality control of complex and/or structure‐unknown plant is growing in the real‐world industrial machine. Indirect Adaptive Control (IAC), which identifies model and updates the controllers automatically, is one promising way expected to meet this requirement. The conventional IAC, however, is required to know the structure of the controlled plant, that is, the order of its transfer function, in advance. This paper presents a new IAC scheme which makes use of Genetic Algorithm (GA) in its identification part. In the proposed framework, the information on the order of the plant is not required since the genetic algorithm searches both the structure of the plant dynamics and its parameters autonomously. A two‐degree‐of‐freedom Internal Mode Control (IMC) is adopted as a basic control architecture since the indirect adaptation can be harmoniously embedded in it. The effectiveness of the proposed scheme is verified through numerical simulations and experiments applied to a velocity control of multimass systems. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 142(4): 45–55, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10103 相似文献
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针对当前电力变压器故障诊断效率低、误差大的难题,提出了基于参数优化的电力变压器故障诊断模型。首先提取电力变压器故障的特征,将其作为最小二乘支持向量机输入,电力变压器故障类型作为输出,然后采用最小二乘支持向量机对电力变压器的故障诊断样本进行学习,构建电力变压器故障识别的分类器,并引入混沌粒子群算法对最小二乘支持向量机的参数进行优化,最后进行了电力变压器故障诊断的仿真对比测试。测试结果表明,本文模型可以准确辨识各种类型的电力变压器故障,获得较高正确率的变压器故障诊断结果,电力变压器故障诊断的速度,而且电力变压器故障诊断整体性能要优于当前其它电力变压器故障诊断模型。 相似文献
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基于遗传算法和并行组合模拟退火算法的洪水过程缩放模型研究 总被引:6,自引:0,他引:6
针对泄洪风险和施工导流风险计算对洪水缩放的要求和传统洪水同频率放大方法中手工修匀任意性较大等方面的不足,提出采用计算机优化方法来实现洪水过程的自动放大。在满足洪峰流量约束和分时段洪量约束条件下,本文建立了以洪水过程模式尽量相似为目标的洪水过程放大优化模型,并采用具有全局搜索能力的遗传算法和并行组合模拟退火算法求解该模型。通过实例计算可以看出,这两种算法计算结果均能较好的满足洪峰洪量约束要求,并有效保持了典型洪水的模式,避免了人工修匀的任意性。 相似文献
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低压载波通信的发展要求有适合于信道仿真性能分析的传输模型。在已有传输模型结构的基础上,以实测数据为样本,将遗传算法应用于低压载波通信信道模型的多参数辨识,实现了较好的多参数辨识效果,为低压载波通信电力网的研究和设计打下了较好的基础。 相似文献
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将遗传算法思想引入水轮发电机组调速系统的辨识工作中,解决了目前电力系统中常用的辨识方法无法对非线性环节进行有效辨识的问题;为提高辨识效率,对遗传算法初始群体的选取方法以及子代选择方法进行了改进。对实际发电机组调速系统的辨识结果说明该方法具有快速、精度较高等优点。该思想为水轮发电机组调速系统的辨识提供了一种新方法,具有重要的工程实用价值。 相似文献
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本文构造了基于分布估计算法(Estimation of Distribution Algorithm,EDA)和遗传算法(GeneticAlgorithm,GA)融合的神经网络(Neural Network,NN)故障诊断模型。传统的GA看作是对生物进化"微观"层面上的模拟,则EDA是对生物进化"宏观"层面上的建模,是一种全新的进化模式。EDA与GA融合的实质是在解空间"宏观"和"微观"两个层面进行寻优,可克服NN陷入局部最小,提高NN的泛化能力,使故障诊断的容错性能得到有效改善。将该模型用于高压输电线系统的故障诊断,并作容错性能的评估。由仿真测试表明,研究模型的容错性能要优于传统的BP-NN模型和单纯GA优化NN模型。因此,新诊断模型是有一定的理论和实用价值的。 相似文献
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基于FVM-HGA的河流水质模型多参数识别 总被引:3,自引:0,他引:3
针对解析法求解河流水质模型的不足和传统方法在求解河流水质模型多参数识别问题遇到的困难,提出了采用有限体积法结合混合遗传算法(FVM-HGA)的参数识别算法。由于水质模型中源项的广泛存在,对流项的离散采用了满足有界性条件的非线性高精度格式-MINMOD格式。由于优化算法对参数识别反问题具有的普遍适用性,因而反演算法采用了全局寻优能力很强的遗传算法;为了克服传统遗传算法局部搜索能力较弱的缺点,采用了遗传算法与最速下降法相结合的混合遗传算法。两个算例的计算结果表明,采用FVM-HGA算法对常系数河流水质模型和变系数水质模型都能给出较好的反演结果。 相似文献
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由于电力需求侧负荷形态各异、特性多变,种类繁多,采用传统方法进行负荷辨识时存在识别率不高、模型建立困难、难以推广应用等问题。为此,基于智能负荷控制器(SRLC)的用电参数检测功能和非侵入式负荷监测(NILM)原理,提出一种基于多参量隐马尔可夫模型(MPHMM)的负荷辨识方法。该方法采用4个负载特性参数(电流、有功功率、无功功率、功率因素)作为模型的观测向量,通过模型学习和多次迭代计算,求得与MPHMM模型隐藏状态相匹配的观测序列的最大输出概率和最优状态序列,再采用辅助判别算法对结果进行修正,完成对负荷的最终辨识。通过搭建实验平台对所提方法进行验证。结果表明,该方法辨识准确率可达95%以上,特别是对小功率负荷具有较好的识别效果。 相似文献
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自适应局部微调遗传算法 总被引:1,自引:1,他引:0
针对遗传算法在有限时间内难于给出高精确度解的问题,在传统遗传操作的前期与后期分别采用自适应与局部微调相结合的方法,给出了一种自适应局部微调的遗传算法,即将遗传代数划分为自适应概率搜索阶段和局部微调阶段,在交叉操作中分别采用自适应概率算术交叉和部分确定性诱导交叉;在变异操作中分别采用自适应随机扰动变异操作和最优个体诱导变异操作.应用该算法对全局最优解领域进行搜索,能在较短的时间内找到高精确度的数值解.对6个典型测试函数的优化问题实验表明,该方法具有快速、稳定和易于实现的优点. 相似文献
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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. 相似文献
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This paper presents a method to compute the parameters of a transformer model with saturation using the voltage and current waveforms of an inrush test and a no-load test. The transformer is modeled with their electric and magnetic equivalent circuits and a single-valued function that characterizes its non-linear magnetic behavior. A 3-kVA single-phase transformer and a 5-kVA three-phase three-legged transformer have been tested in the laboratory. The method to obtain the parameters of the non-linear flux–current relation that characterize the saturation has been described in the paper. The analytical function used to adjust the experimental measurements fits them very well. 相似文献