Abstract

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.

Highlights

  • With the transition from being a niche energy source to becoming mainstream, photovoltaics (PV)have to compete from an economic and a reliability point of view with established energy production techniques

  • Different data visualization tools for measured PV system data have been presented for an example PV plant, together with an overview of common data filtering approaches

  • PV system data measurements are primarily used for yield predictions, remaining-useful-lifetime estimates, performance loss calculations or other performance related studies

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Summary

Introduction

With the transition from being a niche energy source to becoming mainstream, photovoltaics (PV). A comprehensive overview of PV system monitoring and fault detection approaches is given by Livera et al [7], with clear guidelines on the required measurement parameters and their maximum uncertainties Most of these studies discuss to some extent discuss the requirements in terms of measured performance and weather parameters. We want to provide an overview of occurring problems when working on PV monitoring data, give useful examples of what faulty data look like, provide solutions on how to fill larger gaps of missing measured temperature and irradiance data and discuss the necessity of applying specific filter for statistical performance analyses. The RUL of a PV system is a date at which a pre-defined power output cannot be reached anymore, and the system reached the economic end of its lifetime All these performance evaluation studies have a similar structure including input data treatment and data filtering.

Experimental PV System
Monitoring Data Acquisition
Temperature Data
In-Plane Irradiance Imputation
In-Plane Irradiance and Power
Data Filtering
Findings
Discussion and Summary
Full Text
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