The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

Voltage dip analysis is a complex stochastic issue, since it involves a large variety of random factors: type of short-circuits in the power system, location of faults, protective system performance and atmospheric discharges. Among all categories of electrical disturbances, the voltage dip (sag) and momentary interruption are the nemeses of the automated industrial process. The dynamic voltage restorer (DVR) is a static series compensator (SSC) suitable for protecting industrial plant against voltage dip. This paper presents results about comparative analysis of three compensation techniques like in-phase compensation method, pre-dip compensation method (called too voltage difference compensation), and phase advance compensation method. To validate the effectiveness of each method, several disturbance scenarios including supply voltage drop and power factor of the load were examined. In particular, the paper analyzes the effect on necessary active power and voltage injected.