The solar PV modules are influenced by a variety of loss mechanisms by which the energy yield is affected. A PV system is the sum of individual PV modules which should ideally operate similarly, however, inhomogeneous soiling, mismatching, and degradation, which are the main focus in this study, lead to dissimilarities in PV modules operating behavior and thus, lead to losses which will be assessed intensively in terms of energy yield. The dissimilarities in PV modules are referred to the ambient conditions or the PV modules characteristics which result in different modules’ maximum power point (MPP) and thus, different currents generated by each PV modules which cause the mismatching. However, the weakest PV module current governs the string current, and the weakest string voltage governs the voltage. Power optimizers are electronic devices connected to the PV modules which adjust the voltages of the PV modules in order to obtain the same current as the weakest module and thus, extract the modules’ MPP. Hence, the overall performance of the PV plant is enhanced. On the other hand, the power optimizers add additional cost to the plant’s investment cost and thus, the extra energy yield achieved by utilizing the power optimizers must be sufficient to compensate the additional cost of the power optimizers. This is assessed by designing three systems, a reference system with SMA inverters, a system utilizes Tigo power optimizers and SMA inverters, and a system utilizes SolarEdge power optimizers and inverters. The study considers four different locations which are Borlänge, Madrid, Abu Dhabi, and New Delhi. An Excel model is created and validated to emulate the inhomogeneous soiling and to evaluate the economic feasibility of the power optimiz ers. The model’s inputs are obtained from PVsyst and the precipitation data is obtained from Meteoblue and SMHI database. The economic model is based on the relation between Levelized Cost of Electricity (LCOE) which will be used to derive the discount rate. Graphs representing the discounted payback period as a function of the feed-in tariff for different discount rates is created in order to obtain the discounted payback period. The amount of extra energy yielded by the Tigo and the SolarEdge systems is dependent on the soiling accumulated on the PV modules. Relative to the reference system, 6.5 % annual energy gain by the systems utilizing the power optimizers in soiling conditions, up to 2.1 % in the degradation conditions, and up to 9.7 % annual energy gain at 10 % mismatching rate. The extra energy yield is dependent on the location, however, the Tigo and the SolarEdge systems have yielded more energy than the reference system in all cases except one case when the mismatch losses is set to zero. The precipitation pattern is very influential, and a scare precipitation leads to a reduction in the energy yield, in this case, the Tigo and the SolarEdge systems overall performance is enhanced and the extra energy gain becomes greater. The Tigo system yield slightly more energy than the SolarEdge system in most cases, however, during the plant’s lifetime, the SolarEdge system could become more efficient than the Tigo system which is referred to the system’s sizing ratio. The degradation of the system or the soiling accumulation decreases the irradiation and thus, a slightly oversized PV array become suitable and deliver an optimal power to the inverters. The SolarEdge system is feasible in all scenarios in terms of LCOE and discounted payback period, although its slightly lower performance relative to the Tigo system, this is referred to its low initial cost in comparison to the other systems. The Tigo system is mostly infeasible although it yields more energy than the reference and the SolarEdge systems, this is referred iii to its relatively high initial cost. However, feed- in tariffs higher than 20 € cent / kWh make all systems payback within less than 10 years. The results have overall uncertainty within ± 6.5 % including PVsyst, Excel model, and the precipitation uncertainties. The uncertainty in the degradation and the mismatching calculations is limited to PVsyst uncertainty which is ± 5 %. The uncertainties in LCOE in the location of New Delhi, since it is the worst-case scenario, are 5.1 % and 4 % for the reference and the systems utilizing power optimizers, respectively. Consequently, accommodating the uncertainties to the benefits gained by utilizing power optimizers indicates that the energy gain would oscillate in the range of 6 % - 6.9 % for the soiling calculations, 2 % - 2.2 % for the degradation simulations, and 9.2 % - 10.2 % for the mismatching simulations at 10 % mismatchrate.

In order to successfully compete against the use of fossil fuels to generate electricity, one of the challenges in the photovoltaic (PV) business currently in focus is on the asset management of large PV plants, in which developing control techniques to prognosticate and evaluate the future energy performance will be essential. Infrared thermography inspections can give meaningful support to assess the quality and performance of PV modules. However, the implementation of a cost-effective method to scan and check huge PV plants represents different challenges, such as the cost and time of detecting PV module defects with their classification and exact localization within the solar plant. In this context, it has recently been investigated the potential of a new innovative technology in the PV plants monitoring operations by using drones. The main purpose of this work is to establish a scientific basis for the interpretation of thermographic images taken by drones, in particular, regarding the influence of thermographic irregularities which will negatively influence the performance of PV plants. The drone is employed to monitor PV modules conditions by using special thermography sensors mounted on it in order to scan images. The captured images are then automatically sent to a technical office database for the image processing software. This special software receives, stores and analyses the captured images to detect the specific defect on the PV modules. Then, all information is processed and reported to the final decision-making team to decide about the best solution for the particular degraded PV module, in relation with the requirements from the operation and maintenance (O&M) services. In this particularly study project of the inspected PV plant situated in the UK, which has been carried out by trained personnel at Quintas Energy (QE), the majority of identified faults, which influence the PV module performance (especially the power output significantly), are on a sub-panel level, either individual cells or uneven hot spots. There are also some modules with bypass diode faults as well as a string fault was detected. Such faults must be repaired by the PV module manufacturer, in relation to the manufacturer’s warranties, without any cost at all since the PV modules are indeed still in warranty. It has been concluded that, in comparison with traditional manned systems by using hand-held cameras, the main functionality of using drones is the early fault diagnosis which could reduce corrective maintenance activities, since defects are easily and quickly identified and, then, repaired. This fact could reduce defects to become more serious and, thus, more difficult to be repaired, along with their correspondent production losses and costs. QE has learned by making mistakes during this project study and gained experience of this unmanned aerial vehicles (UAV) technology. Currently, they are in the process of improving this technique and will continue to implement it to all their PV plants since the efficiency of PV systems can be significantly improved by appropriate use of O&M instruments and benefit from innovative monitoring tools, such as the unmanned aerial technology.