Several studies have concentrated on cooling the PV module temperature (TPV) to enhance the system’s electrical output power and efficiency in recent years. In this review study, PCM-based cooling techniques are reviewed majorly classified into three techniques: (i) incorporating raw/pure PCM behind the PV module is one of the most straightforward techniques; (ii) thermal additives such as inter-fin, nano-compound, expanded graphite (EG), and others are infused in PCM to enhance the heat transfer rate between PV module and PCM; and (iii) thermal collectors that are placed behind the PV module or inside the PCM container to minimize the PCM usage. Advantageously, these techniques favor reusing the waste heat from the PV module. Further, in this study, PCM thermophysical properties are straightforwardly discussed. It is found that the PCM melting temperature (Tmelt) and thermal conductivity (KPCM) become the major concerns in cooling the PV module. Based on the literature review, experimentally proven PV-PCM temperatures are analyzed over a year for UAE and Islamabad locations using typical meteorological year (TMY) data from the National Renewable Energy Laboratory (NREL) data source in 1 h frequency.

Wind energy is one of nature’s most valuable green energy assets, as well as one of the most reliable renewable energy supplies. Wind turbine blades convert wind energy into electric energy. Wind turbine blades range in size from 25 to 120 m, depending on the demands and efficiency necessary. Owing to ambient influences and wide structures, the blades are subject to various friction forces that might harm the blades. As a result, the generation of power and the shutdown of turbines are both affected. Downtimes are reduced when blades are detected on a regular basis, according to structural health management. On the 50-W, 12-V wind turbine, this research investigates the use of vibration signals to anticipate deterioration. The machine learning (ML) method establishes a nonlinear relationship between selected important damage features and the related uniqueness measures. The learning algorithm was trained and tested based on the excellent state of the edge. To forecast blade faults, classifier models, such as naive Bayes (NB), multilayer perceptron (MLP), linear support vector machine (linear_SVM), one-deep convolutional neural network (1DCNN), bagging, random forest (RF), XGBoosts, and decision tree J48 (DT) were used, and the results were compared according to their parameters to propose a better fault diagnostics model.