The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each other, create solar panels, which in their turn, are connected in a solar string, and they create solar farms. These structures are extremely efficient in electricity production, but also, cells are fragile in nature and delicate to environmental conditions, which is the reason why some of them show discrepancies and are called defective. In this research, a thermal camera mounted on a drone has been used for the first time in the solar farm operating conditions of India in order to capture images of the solar field and investigate solar panels for defective cells and create an orthomosaic image of the entire area. This procedure next year will be established on an international scale as a best practice example for commercialization, providing effortless photovoltaic monitoring and maintenance planning. For this process, an open source software WebODM has been used, and the entire field was digitized so as to identify the location of defective panels in the field. This software was the base in order to provide and analyze a digital twin of the studied area and the included photovoltaic panels. The defects on solar cells were identified with the use of thermal bands, which record and point out their temperature of them, whereas anomalies in the detected temperature in defective solar cells were captured using thermal electromagnetic waves, and these areas are mentioned as hotspots. In this research, a total number of 232.934 solar panels were identified, and 2481 defective solar panels were automatically indicated. The majority of the defects were due to manufacturing failure and normal aging, but also due to persistent shadowing and soiling from aerosols and especially dust transport, as well as from extreme weather conditions, including hail. The originality of this study relies on the application of the proposed under development technology to the specific conditions of India, including high photovoltaic panels wear rates due to extreme aerosol loads (India presents one of the highest aerosol levels worldwide) and the monsoon effects. The ability to autonomously monitor solar farms in such conditions has a strong energy and economic benefit for production management and for long-term optimization purposes.

The rapid growth in the use of Solar Energy for sustaining energy demand around the world requires accurate forecasts of Solar Irradiance to estimate the contribution of solar power to the power grid. Accurate forecasts for higher time horizons help to balance the power grid effectively and efficiently. Traditional forecasting techniques rely on physical weather parameters and complex mathematical models. However, these techniques are time-consuming and produce accurate results only for short forecast horizons. Deep Learning Techniques like Long Short Term Memory (LSTM) networks are employed to learn and predict complex varying time series data. However, LSTM networks are susceptible to poor performance due to improper configuration of hyperparameters. This work introduces two new algorithms for hyperparameter tuning of LSTM networks and a Fast Fourier Transform (FFT) based data decomposition technique. This work also proposes an optimised workflow for training LSTM networks based on the above techniques. The results show a significant fitness increase from 81.20% to 95.23% and a 53.42% reduction in RMSE for 90 min ahead forecast after using the optimised training workflow. The results were compared to several other techniques for forecasting solar energy for multiple forecast horizons.

The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1%, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively.

Wind power can significantly contribute to the transition from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. The main purpose of a ground-based kite power system is to estimate the tether force for autonomous operations. The tether force of a particular kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. In this paper, we present an experimental measurement of the pulling force of an Airush Lithium 12 ${\mathrm{m}}^2$ kite with a constant tether length of 24 m in a coastal region. We obtain the position and orientation data of the kite from the sensors mounted on the kite. The flight dynamics of the kite are studied using multiple field tests under steady and turbulent wind conditions. We propose a physical model (PM) using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms to estimate the tether force in the experimental validation. The performance study using the root mean square error (RMSE) method shows that the LSTM model performs better, with overall error values of 126 N and 168 N under steady and turbulent wind conditions.

The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques.

Airborne wind energy systems (AWES) are more efficient than traditional wind turbines because they can capture higher wind speeds at higher altitudes using connected kite generators. Securing a real wind turbine or a site with favorable wind conditions is not always an assured opportunity for conducting research. Hence, the Research and Development of the Laboratory Scale Airborne Wind Energy Conversion System (LAWECS) require a better understanding of airborne wind turbine dynamics and emulation. Therefore, an airborne wind turbine emulation system was designed, implemented, simulated, and experimentally tested with ground data for the real time simulation. The speed and torque of a permanent magnet synchronous motor (PMSM) connected to a kite are regulated to maximize wind energy harvesting. A field-oriented control technique is then used to control the PMSM’s torque, while a three-phase power inverter is utilized to drive the PMSM with PI controllers in a closed loop. The proposed framework was tested, and the emulated airborne wind energy conversion system results were proven experimentally for different wind speeds and generator loads. Further, the LAWECS emulator simulated a 2 kW, 20 kW, and 60 kW designed with a projected kite area of 5, 25, and 70 square meters, respectively. This system was simulated using the Matlab/Simulink software and tested with the experimental data. Furthermore, the evaluation of the proposed framework is validated using a real-time hardware-in-the-loop environment, which uses the FPGA-based OPAL-RT Simulator.