The wind energy industry is expanding in order to be able to meet the current and future energy demand, and is supported by governments in that renewable energy investment has been made. Optimal decision making (DM) in wind turbine manufacturing is required to guarantee the competitiveness of the business. This paper considers decision making for wind turbine manufacturing using a logical decision tree (LDT) and binary decision diagrams (BDD). A qualitative analysis of wind turbine manufacturing is carried out using logical decision trees. They are used for a qualitative study of the case study. Binary decision diagrams are used to obtain the Boolean function and, therefore, to carry out a quantitative analysis. Finally, an optimization of budgets is employed based on importance measures. There is no optimal method that can establish the importance measures. The following heuristic methods have been used to find a solution close to the optimal: Fussell-Vesely, Birnbaum and Criticality. The computational cost is reduced by ranking the events. The heuristic methods to establish the best rankings are: Top-Down-Left-Right, Level based method, AND based method, Breadth-First Search (BFS) and Depth First Search (DFS). A real case study is considered, in which a static and dynamic analysis is carried out.

A new hybrid meta-heuristic approach Jaya–PPS, which is the combination of the Jaya algorithm and Powell’s Pattern Search method, is proposed in this paper to solve the optimal power flow (OPF) problem for minimization of fuel cost, emission and real power losses and total voltage deviation simultaneously. The recently developed Jaya algorithm has been applied for the exploration of search space, while the excellent local search capability of the PPS (Powell’s Pattern Search) method has been used for exploitation purposes. Integration of the local search procedure into the classical Jaya algorithm was carried out in three different ways, which resulted in three versions, namely, J-PPS1, J-PPS2 and J-PPS3. These three versions of the proposed hybrid Jaya–PPS approach were developed and implemented to solve the OPF problem in the standard IEEE 30-bus and IEEE 57-bus systems integrated with distributed generating units optimizing four objective functions simultaneously and IEEE 118-bus system for fuel cost minimization. The obtained results of the three versions are compared to the Dragonfly Algorithm, Grey Wolf Optimization Algorithm, Jaya Algorithm and already published results using other methods. A comparison of the results clearly demonstrates the superiority of the proposed J–PPS3 algorithm over different algorithms/versions and the reported methods.

Supervisory Control and Data Acquisition (SCADA) systems are employed to collect data from sensors and monitor the condition of wind turbines. Thresholds are commonly used to set the alarms, generating many false alarms, downtimes, costs, etc. A real case study is presented to validate the approach. This paper proposes a novel approach based on Fuzzy Logic to analyse the main variables of the SCADA. Pearson’s correlation between variables is employed to reduce the number of variables that are used as inputs in the Fuzzy Logic system. The variables with perfect and strong correlations have been selected as inputs of the Fuzzy system. The signal is studied by considering the difference between the signal and the moving average value because it shows if the signal is close or not to the value in conditions free of faults. The thresholds are used to cluster the data into three groups by a statistical analysis of the new variables, i.e., the variables obtained by the difference between the signal and the moving average value. The approach helps decrease false alarms by using a Fuzzy system. The approach is capable of processing large datasets online. The results have been validated by employing SVM, where the MAPE is analysed between both methods.