Mark Dales

Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure

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.

Fault detection algorithm for multiple GCPV array configurations

In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations.

The impact of cracks on the performance of photovoltaic modules

This paper presents a statistical approach for identifying the significant impact of cracks on the output power performance of photovoltaic (PV) modules. Since there are a few statistical analysis of data for investigating the impact of cracks in PV modules in real-time long-term data measurements. Therefore, this paper will demonstrate a statistical approach which uses two statistical techniques: T-test and F-test. Electroluminescence (EL) method is used to scan possible cracks in the examined PV modules. Moreover, virtual instrumentation (VI) LabVIEW software is used to predict the theoretical output power performance of the examined PV modules based on the analysis of I-V and P-V curves. The statistical analysis approach has been validated using 45 polycrystalline PV modules at the University of Huddersfield, UK.