This paper proposes an optimisation strategy for the layout design of wave energy converter (WEC) arrays. Optimal layouts are sought so as to maximise the absorbed power given a minimum q-factor, the minimum distance between WECs, and an area of deployment. To guarantee an efficient optimisation, a four-parameter layout description is proposed. Three different optimisation algorithms are further compared in terms of performance and computational cost. These are the covariance matrix adaptation evolution strategy (CMA), a genetic algorithm (GA) and the glowworm swarm optimisation (GSO) algorithm. The results show slightly higher performances for the latter two algorithms; however, the first turns out to be significantly less computationally demanding.

This paper uses experimental data to validate a wave energy converter (WEC) array hydrodynamics tool developed within the context of linearized potential flow theory. To this end, wave forces and power absorption by an array of five-point absorber WECs in monochromatic and panchromatic waves were measured from a set of deep-water wave basin experimental tests. Unlike the few other examples of WEC array experimental campaigns, the power take-off (PTO) system of each WEC was simulated by means of advanced equipment capable of accurately reproducing linear control strategies and, thereby, reducing the uncertainty in the physical model. Experimental measurements are then compared with numerical predictions showing reasonable agreement; the measured trends are, in the same way, well captured by the numerical predictions. Further analysis demonstrates that the developed tool can predict, on the safe side, wave forces and power absorption with less than 17.5% and 23.0% error, respectively, for more than 68% of the predictions.

Solar inverters are one of the most important components in a Photovoltaic plant. Their main function is to convert the DC power produced by the solar modules into AC power that can be injected into the grid. Although inverter efficiency has reached exceptionally high values, thanks to recent technological advancements, it is typically measured at dedicated laboratories under strict testing conditions, which makes its validation after deployment extremely challenging, both from a logistic and financial point of view. This paper presents a methodology for the calculation of inverter field efficiency based on Bayesian neural networks. The goal of the neural network is to model inverter efficiency and its variance as a function of the inverter's operational state. Results show that an optimised Bayesian neural network can effectively model inverter efficiency with small reconstruction errors and negligible bias. Furthermore, the model has been proven useful to replicate the calculation of the European efficiency along with a full uncertainty characterisation.

The goal of this study is to monitor the performance of a photovoltaic plant by comparing its power output against others with similar characteristics, referred to as neighbours. The purpose of the 15 neighbours is to have the best reference for the performance of the plant in question, in other words, how the plant under analysis performs compared to the 15 neighbours. A machine learning model based on a feed forward Neural Network was employed to model power production as a function of environmental signals and the sun's position. Here, the data from the neighbours are used to train the model and the data from the plant under analysis are used to evaluate the model and predict the power output. Once the power is predicted, the performance ratio of the plant is calculated. The procedure has been tested and validated at several plants for three different cases and the numerical results highlight how the model is able to identify under/over performing plants. Therefore the developed strategy provides industries a valid tool on the correct functioning of a plant.