Abstract
An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples.
Highlights
It is a matter of fact that the actual ever-demanding environmental policies are forcing the worldwide power grids to integrate a rising amount of renewable sources, leading the grids themselves to become more and more interconnected, complex, and prone to be stressed in their ordinary functioning.Modern power systems have the fundamental need to deliver the largest electrical power over long distances
When the NaN value comes from the original phasor measurement units (PMUs) input data, the related data sample is excluded from the sliding window to which it belongs; When the NaN values come from the dynamic mode decomposition (DMD) estimation procedure, the related target vector is treated as a “don’t care” target, meaning that the network performance function is not updated during the training process for that specific target value
The overall data exploited to validate the effectiveness of the artificial neural networks (ANN) approach for the estimation of the modal parameters consists three different strategy datasetsofcoming from real
Summary
It is a matter of fact that the actual ever-demanding environmental policies are forcing the worldwide power grids to integrate a rising amount of renewable sources, leading the grids themselves to become more and more interconnected, complex, and prone to be stressed in their ordinary functioning. By considering the issues induced by LFO, and in particular by the electromechanical inter-area oscillations, the possibility to monitor the power system in real-time in order to guarantee safe and reliable grid operation is a relevant need. Model-based techniques are based on the use of a proper system modeling of the power grid, and the subsequent extraction of the mode parameters by eigenvalues decomposition (EVD) analysis They can achieve a relatively high accuracy; they are not completely suitable for online monitoring purposes because of some limitations in terms of computational burden and inherent uncertainties in system modeling. In the above mentioned papers, the measurement-based estimation techniques are combined with ANN-based methods in order to identify the LFO mode parameters in real-time, or to extract other useful power grid information (like the system operating conditions or the generator coherency).
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