Multi-state stochastic approach for reliability indices and sensitivity analysis of a Tidal power plant

Energy demand has been increasing at an accelerating rate due to increase in population, installation of more industrial equipment, modernization and needs of the society. The traditional source of energy e.g. coal, oil and natural gases etc. are not only limited but also very harmful for the earth due to their negative effects like greenhouse gasses, global warming, soil and water pollution etc. So, it’s time to move towards the sustainable source of energy like solar, wind, water/hydro, geothermal, tidal to minimize these negative effects. It not only minimizes these effects but also serve as a long-term solution of energy security and sustainability. Transition from traditional to sustainable energy involve various factors including reliability and performability as one of the most important factors that needs to be addressed. Therefore, in the present paper author have developed a mathematical model, with the aid of Stochastic Modeling, for a Tidal power plant to understand the behaviour of its different reliability measures such as component-wise reliability, mean time to failure, and sensitivity analysis, which did not get much attention in the literature. The different mechanical components and their interconnection inside TPP are considered for developing the mathematical model. The obtained result reflects the long-term impact of different components failure on overall reliability of the TPP. Obtained results about TPP reliability when Turbine failure is considered as 0.118, 0.122, 0.126, 0.130, 0.134, at 10 unit of time, is 0.3037, 0.3012, 0.2987, 0.2960, 0.2933 respectively. Also, when generator failure set as 0.040, 0.044, 0.048, 0.052, 0.056, the TPP reliability is obtained as 0.3152, 0.3074, 0.2999, 0.2925, 0.2854 respectively and when gearbox motor failure set as 0.120, 0.125, 0.130, 0.135, 0.140, TPP reliability is obtained as 0.3033. 0.3006, 0.2980, 0.2954, 0.2929 respectively. Also, sensitivity analysis gives the clear understanding about the critical component of TPP, which can be further used to reduce the downtime of TPP. The result can be utilized as one of the sources of information to plan effective and efficient maintenance strategy for TPP.