Since the discovery of Photovoltaic (PV) effect, numerous ways of utilizing the energy that can be generated by the free everlasting solar radiation using solar panels were put forward by many researchers. However, the major disadvantage of solar panel to date is its low efficiency, which is affected by the panel temperature, cell type, panel orientation, irradiance level, etc. Though there are certain multi-junction solar panels that offer higher efficiencies, their application is very minimal due to high manufacturing cost. With the growing demand for the reduction of carbon footprint, there is a need to use and manufacture these panels in the most effective way to harness the maximum power and increase their efficiency. Another major concern is the availability of land/space for the installation of these panels. Several authors have focused on discussing the different technologies that have evolved in the manufacturing of the PV cells along with their architectures. However, there exists a gap that needs to be addressed by combining the latest PV technologies and architectures with a focus on PV applications for increasing the efficiency. Due to the technical limitations on the efficiency of PV panels, applications are to be designed that can extract the maximum power from the PV systems by minimizing the technical difficulties. Considering all these factors, this paper presents an overview of the types of silicon based solar cell architectures with efficiencies of at least 25%, and different integration methods like Building integrated PVs (BIPV), floating PVs, which can increase the efficiency by harnessing more power from a limited space. An extensive bibliography on the PV cell structures and methods of maintaining the efficiencies in real world installations are presented. The challenges with the integration of solar panels and the future work are also discussed. This work benefits the readers and researchers and serves as a basis to understand the solar panel efficiency structure and ways to improve the efficiency and associated challenges to come over in the successful implementation of these systems.

The major problem associated with the grid-connected solar photovoltaic (PV) system is the integration of the generated DC power into the AC grid and maintaining the stability of the system. With advancements in research on these PV inverters, artificial intelligence (AI)-based control models are replacing the existing linear methods. These smart PV systems are prone to a variety of attacks, ranging from physical attacks on the PV plants to data integrity attacks and communication-based attacks. This paper provides an overview of the cybersecurity issues with smart PV inverters, their impacts on the grids, and control methods that exist to detect and identify cyber-attacks on a smart PV grid system. An extensive bibliography is provided on grid-forming and grid-following inverters with a variety of control techniques like Proportional–Integral–Derivative (PID) control, fuzzy-based control, and their performances under different fault situations. Multi-level inverter design approaches with various intelligent control techniques to overcome switching failures and other system faults are reviewed. Moreover, some recommendations for future research on intelligent inverters for grid-connected PV systems are proposed. This work will benefit researchers, scientists, and engineers who are working in the field of intelligent inverters for the grid-connected PV system.

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation of the system, smart grid-forming inverters need to communicate with the Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things devices that communicate with SCADA make these systems vulnerable. Though many researchers proposed Artificial-Intelligence-based detection strategies, identification of the location of the attack is not considered by these strategies. To overcome this drawback, this paper proposes a novel Convolution extreme gradient boosting (ConvXGBoost) method for not only detecting Denial of Service (DoS) and False Data Injection (FDI) attacks but also identifying the location and component of the system that was compromised. The proposed model is compared with the existing Convolution Neural Network (CNN) and decision tree (DT) strategies. Simulation results demonstrate the effectiveness of the proposed method for both the smart PV and PV fuel cell (PV-FC) systems. For example, the proposed model is efficient with an accuracy of 99.25% compared to the 97.76% of CNN and 99.12% of DT during a DoS attack on a smart PV system. Moreover, the proposed method can detect and identify the attack location faster than other models.