Development of a new features selection algorithm for estimation of NPPs operating parameters

•A new features selection technique is developed.•The developed method is compared with PSO and Relief techniques.•The results show outperformance of the developed method.•The developed method is more appropriate for estimation of time-series. One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning algorithms are utilized for estimation of the target parameter. To evaluate the performance of the developed method, three transients of Bushehr nuclear power plant (BNPP) are examined. The results of estimation of departure from nucleate boiling ratio (DNBR) by the developed method in comparison with the final safety analysis report (FSAR) of BNPP show acceptable agreement. In general, RFMD shows outperformance (i.e. less estimation error) in comparison with the particle swarm optimization (PSO) algorithm and the Relief technique.