Automatic PV Performance Diagnosis through Inverter Data using Dual Neural Network Architecture

Ahmad Almadhor; IEEE

doi:10.1109/ROPEC.2018.8661450

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Conference proceeding

Automatic PV Performance Diagnosis through Inverter Data using Dual Neural Network Architecture

Ahmad Almadhor and IEEE

2018 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC), pp.1-4

IEEE International Autumn Meeting on Power Electronics and Computing

01/01/2018

DOI: https://doi.org/10.1109/ROPEC.2018.8661450

Abstract

Energy & Fuels

Engineering

Engineering, Electrical & Electronic

Science & Technology

Technology

The main challenge associated with solar Photovoltaic power generation is its intermittent nature which highly dictates its performance. Since PV generation is mainly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing the state of performance of the system at any given instance. To address this challenge, a deep neural network architecture is presented for instantaneous performance diagnosis. The proposed model enables to model and diagnose soiling and partial shade conditions prevalent in any PV generation system with an accuracy of 98.3%. The output of this model is used as input to another neural network model for modeling the state of performance of the system. The novelty of this work lies in creating a solely data-driven model for diagnosing PV power performance

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Title: Automatic PV Performance Diagnosis through Inverter Data using Dual Neural Network Architecture
Creators - without role: Ahmad Almadhor - University of Denver
IEEE
Publication Details: 2018 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC), pp.1-4
Series: IEEE International Autumn Meeting on Power Electronics and Computing
Publisher: IEEE
Number of pages: 4
Identifiers: 9913053108331
Academic Unit: Al Jouf University
Language: English
Resource Type: Conference proceeding