Abstract
This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system.
Highlights
Despite the fact that grid-connected photovoltaic (GCPV) systems have no moving parts, and usually require low maintenance, they are still subject to various failures and faults associated with the PV arrays, batteries, power conditioning units, utility interconnections and wiring [1, 2]
This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system
The developed fault detection algorithm is capable of detecting faulty PV modules and partial shading (PS) conditions which affect GCPV systems
Summary
Despite the fact that grid-connected photovoltaic (GCPV) systems have no moving parts, and usually require low maintenance, they are still subject to various failures and faults associated with the PV arrays, batteries, power conditioning units, utility interconnections and wiring [1, 2]. Some fault detection methods use an automatic supervision based on the analysis of the output power for the GCPV system. Since many fault detection algorithms use statistical analysis techniques such as [10,11,12, 19], this work proposes a fault detection algorithm that does not depend on any statistical approaches in order to classify faulty conditions in PV systems. If the theoretical curves are not capable to detect the type of the fault occurred in the GCPV system, a fuzzy logic classifier system is designed to facilitate the fault type detecting for the examined PV system. The algorithm does not depend on any statistical techniques which make it easier to facilitate and detect faults based on theoretical curves analysis and fuzzy logic classification system.
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