Deep Learning Method With Manual Post-Processing for Identification of Spectral Patterns of Waveform Distortion in PV Installations

• This paper proposes a deep learning (DL) method for the identification of spectral patterns of timevarying waveform distortion in photovoltaic (PV) installations. The PQ big data with information on harmonic and/or interharmonics in PV installations is handled by a deep autoencoder followed by feature clustering. Measurements of voltage and current from four distinct PV installations are used to illustrate the method. This paper shows that the DL method can be used as a starting point for further data analysis. The main contributions of the paper include: (a) providing a novel DL method for finding patterns in spectra; (b) guiding the manual post-processing based on the patterns found by the DL method; and (c) obtaining information about the emission from four PV installations.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

power quality

power system harmonics

electric power distribution

interharmonics

pattern analysis

unsupervised learning

deep learning

solar power

Electric Power Engineering

Elkraftteknik

Publikations- och innehållstyp

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