Solar photovoltaic (PV) energy has shown significant expansion on the installed capacity over the last years. Most of its power systems are installed on rooftops, integrated into buildings. Considering the fast development of PV plants, it has becoming even more critical to understand the performance and reliability of such systems. One of the most common problems faced in PV plants occurs when solar cells receive non-uniform irradiance or partially shaded. The consequences of shading generally are prevented by bypass diodes. A significant number of studies and technical reports have been published as of today, based on extensive experience from research and field feedbacks. However, such material has not been cataloged or analyzed from a perspective of the technological evolution of bypass diodes devices. This paper presents a comprehensive review and highlights recent advances, ongoing research, and prospects, as reported in the literature, on bypass diode application on photovoltaic modules. First, it outlines the shading effect and hotspot problem on PV modules. Following, it explains bypass diodes’ working principle, as well as discusses how such devices can impact power output and PV modules’ reliability. Then, it gives a thorough review of recently published research, as well as the state of the art in the field. In conclusion, it makes a discussion on the overview and challenges to bypass diode as a mitigation technique.

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Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS). In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE) cost function is replaced by the Maximum Correntropy Criterion (MCC) in the traditional error backpropagation (BP) algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.