The purpose of this research was to recognize the influence of electricity reliability problems such as unplanned and planned outages, and load shedding on the future plans of the textile industries of Punjab (India). An opinion on the mitigation measures to diminish the impact of power outages was also obtained. The results presented here were got using the seven-point Likert item scale from the 148 respondents which were the part of a randomly selected sample of 400 textile industries from a total population of 1163 registered textile industries covering specifically the high concentration dis-tricts throughout the Punjab. Both formal and informal interview methods including dropping the questionnaire schedules at the industrial premises were employed to record the responses. A syntax for Krippendorff’s Alpha was used in SPSS to estimate the interreliability of the different sections. A non-parametric Mann-Whitney U and Friedman Q with post hoc tests were applied using SPSS for independent samples and related sample designs, respectively, in order to know the statistical significant differences among the variables and the pairs. Respondents were found highly concerned towards the power system reliability issues than the quality issues. The effect of reliability issues on the micro scale industries were found higher than the small scale industries. Based on the one hour outage duration, unplanned power outage-1 hour scenario and dangerous advance warning, planned power outage, were found highly disruptive. The growth or expansion of existing unit, like to go for costly alternative energy sources, procurement of bank loans and moving industries to other state with good energy facilities, were the four key future plans and strategies that were found highly important to the textile industries. Majority of the industries believed that almost all the future plans and decisions were dominantly impacted by the unplanned outages followed by the load shedding and planned outages. In order to diminish the impact of power outages, improved system protection and improved local protection were found the preferred outage mitigation methods.

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.