Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.

Nuclear energy is presented as a real option in the face of the current problem of climate change and the need to reduce CO2 emissions. The nuclear reactor design with the greatest global impact throughout history and which has the most ambitious development plans is the Pressurized Water Reactor (PWR). Thus, a global review of such a reactor design is presented in this paper, utilizing the analysis of (i) technical aspects of the different variants of the PWR design implemented over the past eight years, (ii) the level of implementation of PWR nuclear power plants in the world, and (iii) a life extension scenario and future trends in PWR design based on current research and development (R&D) activity. To develop the second analysis, a statistical study of the implementation of the different PWR variants has been carried out. Such a statistical analysis is based on the operating factor, which represents the relative frequency of reactors operating around the world. The results reflect the hegemony of the western variants in the 300 reactors currently operating, highlighting the North American and French versions. Furthermore, a simulation of a possible scenario of increasing the useful life of operational PWRs up to 60 years has been proposed, seeing that in 2050 the generation capacity of nuclear PWRs power plants will decrease by 50%, and the number of operating reactors by 70%.

The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available.