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Synchronous machines parameter estimation using artificial neural networks.

机译：使用人工神经网络的同步电机参数估计。

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Economic factors are constantly pushing the operation of power systems to their optimal capacity. For this to be possible, sophisticated models and accurate parameters of the electric components of the systems are required. This work presents an alternative approach that can be used to deal with the parameter estimation problem.; This thesis presents a new on-line based method to estimate the electric parameters of synchronous generators. The solution has been devised similar to that of solving a pattern recognition problem but in a manner which is an entirely new concept in the field.; The strategy is based on the following concept. A synchronous machine operating under different boundary conditions will have a unique response determined by its physical characteristics, which in mathematical terms are expressed by its electric parameters. Alternatively, given the behaviour of the synchronous generator, expressed in currents, voltages, and rotor position, it is possible to think of the existence of an inverse model that will be able to provide the parameters of the machine under consideration. This is a pattern classification problem.; The inverse model is obtained using a feedforward artificial neural network which has excellent properties as a pattern classifier. Artificial neural networks have been used in many areas of power systems, but this is the first time that they have been applied for parameter estimation purposes.; The proposed method has the capability to estimate all the electric parameters of a synchronous generator that govern its steady state and dynamic behaviour, including the saturation characteristics. The method has been extensively tested in a salient pole microalternator. On-line data from a round rotor generator has been used to successfully estimate the steady state characteristics of the synchronous machine and simulation studies have confirmed that the proposed method can also be applied to estimate the dynamic characteristics of round rotor synchronous machines.
机译：经济因素不断将电力系统的运行推向最佳状态。为了做到这一点，需要系统电气组件的精密模型和精确参数。这项工作提出了一种可用于处理参数估计问题的替代方法。本文提出了一种基于在线的同步发电机电气参数估计方法。该解决方案的设计类似于解决模式识别问题的解决方案，但是其方式是本领域中的全新概念。该策略基于以下概念。在不同边界条件下运行的同步电机将具有由其物理特性决定的独特响应，该响应在数学上由其电气参数表示。或者，给定同步发电机的行为，以电流，电压和转子位置表示，则可以考虑存在一个反模型，该模型将能够提供所考虑的电机参数。这是一个模式分类问题。逆模型是使用前馈人工神经网络获得的，该模型具有出色的模式分类器性能。人工神经网络已在电力系统的许多领域中使用，但这是它们首次用于参数估计目的。所提出的方法具有估算同步发电机的所有电参数的能力，这些电参数控制着同步发电机的稳态和动态行为，包括饱和特性。该方法已在凸极微型交流发电机中进行了广泛测试。圆转子发电机的在线数据已成功地用于估计同步电机的稳态特性，并且仿真研究证实，该方法也可用于估计圆转子同步电机的动态特性。

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