This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) plant. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits (high and low limit) for the VR and PR ratios obtained using LabVIEW simulation tool. The high and low detection limits are compared with real-time long-term data measurements from a 1.1kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detection 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 can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm before considering the fuzzy logic system is equal to 95.27%, however, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy logic system.

The goal of this paper is to model, compare and analyze the performance of multiple photovoltaic (PV) array configurations under various partial shading and faulty PV conditions. For this purpose, a multiple PV array configurations including series (S), parallel (P), series-parallel (SP), total-cross-tied (TCT) and bridge-linked (BL) are carried out under several partial shading conditions such as, increase or decrease in the partial shading on a row of PV modules and increase or decrease in the partial shading on a column of PV modules. Additionally, in order to test the performance of each PV configuration under faulty PV conditions, from 1 to 6 Faulty PV modules have been disconnected in each PV array configuration. Several indicators such as short circuit current (I sc ), current at maximum power point (I mpp ), open circuit voltage (V oc ), voltage at maximum power point (V mpp ), series resistance (R s ), fill factor (FF) and thermal voltage (V te ) have been used to compare the obtained results from each partial shading and PV faulty condition applied to the PV system. MATLAB/Simulink software is used to perform the simulation and the analysis for each examined PV array configuration.

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Abstract:In this work, we present a new algorithm for detecting faults in grid connected photovoltaic (GCPV) plant.There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this paper is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC-side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty Maximum Power Point Tracking (MPPT) unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, United Kingdom.

This paper analyses the impact of micro cracks on photovoltaic (PV) module output power performance and energy production. Electroluminescence imaging technique was used to detect micro cracks affecting PV modules. The experiment was carried out on ten different PV modules installed at the University of Huddersfield, United Kingdom. The examined PV modules which contains micro cracks shows large loss in the output power comparing to the theoretical output power predictions, where the maximum power loss is equal to 80.73%. LabVIEW software was used to simulate the theoretical output power of the examined PV modules under real time long term data measurements.

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.

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