Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up significantly and degrades PV panel output power performance. High PV cell temperature due to hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause permanent damage to the PV panel. Therefore, the design and development of a hot spot mitigation technique is proposed using a simple, low-cost and reliable hot spot activation technique. The hot spots in the examined PV system is detected using FLIR i5 thermal imaging camera. Several experiments have been studied during various environmental conditions, where the PV module P-V curve was evaluated in each observed test to analyze the output power performance before and after the activation of the proposed hot spot mitigation technique. One PV module affected by hot spot was tested. The output power increased by approximate to 3.6 W after the activation of the hot spot mitigation technique. Additional test has been carried out while connecting the hot spot PV module in series with two other PV panels. The results indicate that there is an increase of 3.57 W in the output power after activating the hot spot mitigation technique.

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.

A wireless sensor network (WSN) with the potential to monitor and locate partial discharge (PD) in high-voltage electricity substations using only received signal strength (RSS) is proposed. The advantages of an RSS-based operating principle over more traditional methods (e.g., time-of-arrival and time-difference-of-arrival) are described. Laboratory measurements of PD that emulate the operation of a PD WSN are presented. The hardware architecture of a prototype PD WSN is described and the particular challenges of an RSS-based location approach in an environment with an unknown, and spatially varying, path-loss index are discussed. It is concluded that an RSS-based PD WSN is a plausible solution for the monitoring of insulation integrity in electricity substations.

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Numerous literatures can be found on the topic of PV fault detection through the implementation of artificial intelligence. The novel part of this research is the successful development, deployment and validation of a fault detection PV system using radial basis function (RBF), requiring only two parameters as the input to the ANN (solar irradiance and output power). The results obtained through the testing of the developed ANN on a PV installation of 2.2 kW capacity, provided an accuracy of 97.9%. To endorse the accuracy of the newly developed algorithm, the ANN was tested on another PV system, installed at a remote location.The total capacity of the new system was significantly higher, 4.16 kW. A vital part of the test was to see how the proposed ANN would perform with 'scaled-up' input data, during normal operation as well as partial shading scenarios. The validation process provided an overall fault detection accuracy of above 97%. The decrease in accuracy was due to the varying nature of the two systems in terms of total capacity, number of samples and type of faults.

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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...

The impact of Photovoltaic (PV) hot-spots is assessed through the analysis of 2580 polycrystalline silicon PV modules distributed across the UK. PV hotspots were categorized into eight different groups using the percentage of power loss (PLL). All hot-spots groups were modelled using the cumulative density function (CDF), state of art geographical mapping, and performance ratio (PR) analysis. Significantly, it was found that 92.15% of the PV modules affected by hot-spotted PV string are located in northern UK, were the effect of low temperature levels, heavy snow, and hoarfrost are more significant. Lastly, it was found that the distribution of PV modules affected by only one hot-spotted solar cell, are likely (82.41%) located in coastal locations. Hence, coastal locations expect to have lower risks for causing multiple hot-spotted solar cells in PV modules, compared to central and colder locations. The PR of all examined PV modules was analysed. It was evident that the mean PR is significantly reduced due to the existence of hot-spots in the PV modules. The least difference in the PR between healthy and hot-spotted PV modules is equal to -0.83%, whereas the most difference is calculated at -15.47%.

Partial discharge is a well-established metric for condition assessment of high-voltage plant equipment. Traditional techniques for partial discharge detection involve physical connection of sensors to the device under observation, limiting sensors to monitoring of individual apparatus, and therefore, limiting coverage. Wireless measurement provides an attractive low-cost alternative. The measurement of the radiometric signal propagated from a partial discharge source allows for multiple plant items to be observed by a single sensor, without any physical connection to the plant. Moreover, the implementation of a large-scale wireless sensor network for radiometric monitoring facilitates a simple approach to high voltage fault diagnostics. However, accurate measurement typically requires fast data conversion rates to ensure accurate measurement of faults. The use of high-speed conversion requires continuous high-power dissipation, degrading sensor efficiency and increasing cost and complexity. Thus, we propose a radiometric sensor which utilizes a gated, pipelined, sample-and-hold based folding analogue-todigital converter structure that only samples when a signal is received, reducing the power consumption and increasing the efficiency of the sensor. A proof of concept circuit has been developed using discrete components to evaluate the performance and power consumption of the system.