Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training

Carlo Olivieri, Francesco de Paulis, Antonio Orlandi, Cosimo Pisani, Giorgio Giannuzzi, Roberto Salvati and Roberto Zaottini
Additional contact information
Carlo Olivieri: UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy
Francesco de Paulis: UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy
Antonio Orlandi: UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy
Cosimo Pisani: TERNA S.p.A., V.le Egidio Galbani, 70, 00156 Rome, Italy
Giorgio Giannuzzi: TERNA S.p.A., V.le Egidio Galbani, 70, 00156 Rome, Italy
Roberto Salvati: TERNA S.p.A., V.le Egidio Galbani, 70, 00156 Rome, Italy
Roberto Zaottini: TERNA S.p.A., V.le Egidio Galbani, 70, 00156 Rome, Italy

Energies, 2020, vol. 13, issue 23, 1-20

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.

Keywords: inter-area oscillations; modal analysis; reduced order modeling; dynamic mode decomposition; machine learning; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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