Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure
21 Nov 2016-pp 1-6
TL;DR: In this article, the authors present a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods.
Abstract: This paper presents a design and development of a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods. The approach has been validated using an experimental database of environment and electrical parameters from a 1.98 kip plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this research is, therefore, to devise a Virtual Instrument capable of simulating theoretical performances of PV systems and deploying statistical analysis of PV real-time data. The fault detection is based on the comparison between measured and theoretical output power using t-test statistical analysis. The obtained results indicate that the proposed method can detect the faults of the grid-connected PV system, and can be used for continuous monitoring of PV system status.
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Cites methods from "Grid-connected PV virtual instrumen..."
...As proposed in [2], the performance of PV systems can be monitored using virtual instrumentation software such as LabVIEW....
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"Grid-connected PV virtual instrumen..." refers background in this paper
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TL;DR: In this article, the authors present a fault detection approach for photovoltaic (PV) systems, intended for online implementation, which is based on the comparison between the measured and model prediction results of the ac power production.
Abstract: This paper presents the development of a practical fault detection approach in photovoltaic (PV) systems, intended for online implementation. The approach was developed and validated using field measurements from a Canadian PV system. It has a fairly low degree of complexity, but achieves a high fault detection rate and is able to successfully cope with abnormalities present in real-life measurements. The fault detection is based on the comparison between the measured and model prediction results of the ac power production. The model estimates the ac power production using solar irradiance and PV panel temperature measurements. Prior to model development, a data analysis procedure was used to identify values not representative of a normal PV system operation. The original 10-min measurements were averaged over 1h, and both datasets were used for modeling. In order to better represent the PV system performance at different sunlight levels, models for different irradiance ranges were developed. The results reveal that the models based on hourly averages are more accurate than the models using 10-min measurements, and the models for different irradiance intervals lead to a fault detection rate greater than 90%. The PV system performance ratio (PR) was used to keep track of the system's long-term performance.
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TL;DR: In this paper, an automatic fault detection method for grid-connected photovoltaic (GCPV) plants is presented, which generates a diagnostic signal which indicates possible faults occurring in the GCPV plant.
158 citations
TL;DR: In this paper, two methods for the fault location in PV module string were experimentally studied: earth capacitance measurement (ECM) and time-domain reflectometry (TDR).
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