Solar energy is considered the most abundant form of energy available on earth. However, the efficiency of photovoltaic (PV) panels is greatly reduced due to the accumulation of dust particles on the surface of PV panels. The optimization of the cleaning cycles of a PV power plant through condition monitoring of PV panels is crucial for its optimal performance. Specialized equipment and weather stations are deployed for large-scale PV plants to monitor the amount of soil accumulated on panel surface. However, not much focus is given to small- and medium-scale PV plants, where the costs associated with specialized weather stations cannot be justified. To overcome this hurdle, a cost-effective and scalable solution is required. Therefore, a new centralized cloud-based solar conversion recovery system (SCRS) is proposed in this research work. The proposed system utilizes the Internet of Things (IoT) and cloud-based centralized architecture, which allows users to remotely monitor the amount of soiling on PV panels, regardless of the scale. To improve scalability and cost-effectiveness, the proposed system uses low-cost sensors and an artificial neural network (ANN) to reduce the amount of hardware required for a soiling station. Multiple ANN models with different numbers of neurons in hidden layers were tested and compared to determine the most suitable model. The selected ANN model was trained using the data collected from an experimental setup. After training the ANN model, the mean squared error (MSE) value of 0.0117 was achieved. Additionally, the adjusted R-squared ($R^2$) value of 0.905 was attained on the test data. Furthermore, data is transmitted from soiling station to the cloud server wirelessly using a message queuing telemetry transport (MQTT) lightweight communication protocol over Wi-Fi network. Therefore, SCRS depicts a complete wireless sensor network eliminating the need for extra wiring. The average percentage error in the soiling ratio estimation was found to be 4.33%.

The role of control techniques is increasing due to the high penetration of renewable energy sources at grid level. The importance of inverters also rises as it balances the supply between the renewable source and grid power system. The issues related to penetration of renewable energy like power quality maintenance, protection against the detection of islanding and maintaining the integrity of grid; control techniques play a vital role to solve these problems. This paper proposed an internal control technique known as current based sliding mode control (SMC) for a filter less multilevel inverter (MLI). The aim of proposed research work is to achieve 27 level output voltage by using an inverter, which approaches sinusoidal wave without using any filters. To control the output of MLI, a current based SMC is implemented in order to achieve the robustness, a good dynamic response, a smaller number of voltage ripples and a smaller current THD. Moreover, a comparative analysis of SMC is done with the conventional PI controller. The response of the controller has been investigated for different cases e.g., introducing sag, swell, faults and harmonics in grid level has been implemented on MATLAB/Simulink. To validate the results of SMC for MLI, an experimental setup was also established which consists of National Instruments (NI) based hardware in loop (HIL) system and dSPACE 1202. The HIL system results show consistency with simulation results.

With the expansion of renewable energy sources worldwide, the need for developing more economical and more efficient converters that can operate on a high frequency with minimal switching and conduction losses has been increased. In power electronic converters, achieving high efficiency is one of the most challenging targets to achieve. The utilization of wideband switches can achieve this goal but add additional cost to the system. LLC resonant converters are widely used in different applications of renewable energy systems, i.e., PV, wind, hydro and geothermal, etc. This type of converter has more benefits than the other converters such as high electrical isolation, high power density, low EMI, and high efficiency. In this paper, a comparison between silicon carbide (SiC) MOSFET and silicon (Si) MOSFET switches was made, by considering a 3KW half-bridge LLC converter with a wide range of input voltage. The switching losses and conduction losses were analyzed through mathematical calculations, and their authenticity was validated with the help of software simulations in PSIM. The results show that silicon carbide (SiC) MOSFETs can work more efficiently, as compared with silicon (Si) MOSFETs in high-frequency power applications. However, in low-voltage and low-power applications, Si MOSFETs are still preferable due to their low-cost advantage.