The electricity generation policy is a strategic policy that drives development in a community. Energy policies are often analyzed with the aim of generating a reliable and affordable electricity for a community. There is a high probability of achieving this aim when energy policy is combined with a community social, technical, economic and environmental needs. This paper determines a hybrid renewable energy source (HRESs) for a rural community using technical, economic, and techno-economic criteria. The selection process combines Criteria Importance Through Inter-criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a solution method. This approach applicability was tested using six HRESs under economic and technical criteria. Ten technical and nine economic criteria were simulated for the HRESs using HOMER. The results from the HOMER software show that A5(PV/wind/battery) and A6 (PV/battery) had a renewable fraction of 1. The results obtained from the CRITIC method showed that the most important technical and economic criteria were diesel generator and total fuel cost, respectively. From an economic perspective, the best HRES for the case study was A4 (diesel/batteries), while A3 (wind/diesel generator/batteries) was the best HRES from a technical and techno-economic perspectives.

The increased role of wind turbine systems makes it important for its operational states to be con-tinuously monitored and optimized. This goal can be achieved using existing methods, which re-lies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10^-3, 4.2 ×10-3, and 4.0 × 10^-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.