Intelligent Passive Islanding Detection Scheme For Microgrids Through a State Observer with Artificial Intelligence

Mumtaz, Faisal; Shah, Maqsood Ahmad; Khan, Haseeb Hassan; Qureshi, Hammad; Iqbal, Sohail; Asadullah,

doi:10.1109/icept58859.2023.10152365

Cited by 1 publication

(1 citation statement)

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“…For instance, in [16] and [17] some applications for facing different challenges introduced by MGs, such as PV Generation and Load Forecasting, Power System Problems, Energy Management and Fault detection and Protection in MGs are reviewed. In [18] a recurrent neural network (RNN) is proposed for performing islanding detection at MGs. Likewise, in [19] a technique based on applying Deep Learning (DL) to the the harmonic content of the voltage signals is proposed, considering a Long short-term memory (LSTM) network to classify the types of faults and to detect the presence of faults in grid-connected microgrids.…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Operation of PV Inverters Based on Regressive Prediction Models With Recursive Least Squares Training

Moreno,

Laguna,

Cipriano

et al. 2024

IEEE J. Emerg. Sel. Top. Ind. Electron.

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The large scale integration of photovoltaic (PV) power plants has launched the massive deployment of PV inverters. In fact, just a single multi-MW PV plant may have thousands of them, which can be also found isolated in low scale distributed generation applications. The number of grid-connected assets can be no longer managed and maintained effectively without using AI tools, able to analyze their operation, detect faults and support decision-making maintenance tools. In this paper, a Linear Regression method able to detect abnormal operation in PV systems, based on a Recursive Least Squares (RLS) training algorithm, which requires a low amount of data, mainly energy generation measurements and meteorological data, is proposed. In addition, two different applications of this methodology will be presented, one based on issuing a simplified model for realtime analysis, and another one consisting of a complex model for long-term diagnosis. The first one is focused on detecting faults and abnormal operation profiles in real-time, while the second one permits assessing the historical efficiency decay of PV plants in longer periods. Both will be used for detecting abnormal operation of PV inverters and panels. The performance and behavior of these algorithms will be tested using the data of 22 PV plants, placed at different climatic areas and with different peak powers. The results will show the good performance of the proposed fault detection method in both applications.

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