Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

The prominent objective of the present study is to optimize the availability of condenser units in steam turbine power plants. For this purpose, a novel stochastic model is proposed by considering a condenser as a system consisting of seven subsystems. All the time-dependent random variables associated with failure rates followed exponential distribution while repair rates are arbitrarily distributed. Furthermore, to attain the optimum value of system availability nature-inspired algorithm has been applied. Predicting the future phenomena is very difficult and most of the existing algorithms have some limitations like stuck at local minima, and slow convergence rate, and so forth. So, in this study proposed model has been optimized using a genetic algorithm (GA) and particle swarm optimization (PSO) The expressions for steady-state availability of the system have been assessed using Chapman-Kolmogorov differential-difference equations developed by applying the Markov birth-death process. The supplementary variable technique has been adopted to solve differential-difference equations For a comparative analysis, the steady-state values, GA, and PSO results are compared. It is revealed that the GA gives the maximum value of availability as 0.9886 corresponding to crossover probability 0.65, population size 50, evaluation 300, and mutation probability 0.5. While using PSO optimum availability value is 0.998152 at 27 iterations, population 55, inertia weight 1, damping ratio 0.85, personal best 1.5, and global best 2. So, PSO results outperform the steady-state and GA results. It is revealed that mixed bed filter and ejector are the most sensitive components of the condenser those influences most the system effectiveness.