This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.

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.

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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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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 parallel fault detection algorithm that can diagnose faults on the DC-side and AC-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's 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 parallel fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, faulty PV String, Faulty Bypass diode, Faulty Maximum power point tracking (MPPT) unit and Faulty DC/AC inverter unit. The parallel fault detection algorithm has been validated using an experimental data climate, with electrical parameters based on a 1.98 and 0.52 kWp PV systems installed at the University of Huddersfield, United Kingdom.

7Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up 8 significantly and degrades PV panel output power performance. High PV cell temperature due to 9 hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause 10 permanent damage to the PV panel. Therefore, the design and development of two hot spot 11 mitigation techniques are proposed using a simple, costless and reliable method. The hot spots in 12 the examined PV system was carried out using FLIER i5 thermal imaging camera. 13 Several experiments have been examined during various environmental conditions, where the PV 14 module I-V curve was evaluated in each observed test to analyze the output power performance 15 before and after the activation of the proposed hot spot mitigation techniques. One PV module 16 affected by hot spot was tested. The output power during high irradiance levels is increased by 17 approximate to 1.25 W after the activation of the first hot spot mitigation technique. However, the 18 second mitigation technique guarantee an increase of the power equals to 3.96 W. Additional test 19 has been examined during partial shading condition. Both proposed techniques ensure a decrease 20 in the shaded PV cell temperature, thus an increase in the output measured power. 21 Keywords: Hot spot protection; photovoltaic (PV) hot spotting analysis; solar cells; thermal imaging. 22 23Photovoltaic (PV) hot spots are a well-known phenomenon, described as early as in 1969 [1] and 24 still present in PV modules [2 and 3]. PV hot spots occur when a cell, or group of cells, operates 25 at reverse-bias, dissipating power instead of delivering it and, therefore, operating at abnormally 26 high temperatures. This increase in the cells temperature will gradually degrade the output power 27 generated by the PV module as explained by M. Simon & L. Meyer [4]. Hot spots are relatively 28 frequent in current PV modules and this situation will likely persist as the PV module technology 29 is evolving to thinner wafers, which are prone to developing micro-cracks during the manipulation 30 process such as manufacturing, transportation and installation [5 and 6]. 31PV hot spots can be easily detected using IR inspection, which has become a common practice in 32 current PV applications as shown in [7]. However, the impact of hot spots on operational efficiency 33 and PV lifetime have been scarcely addressed, which helps to explain why there is lack of widely 34 accepted procedures which deals with hot spots in practice as well as specific criteria referring to 35 acceptance or rejection of affected PV module in commercial frameworks as described by R. 36Moretón et al [8]. Thus, this paper demonstrates two mitigation techniques which will improve the 37 output power performance of the hot spotted PV modules.In the past, the increase in the number of bypass diodes (up to one diode for each cell) has been 39 proposed as a possible solution [9 and 10]. However, this approach has not encounte...

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.