Genetic programming applied to the identification of accidents of a PWR nuclear power plant

The nuclear accident identification problem has been developed quite extensively in the literature, with numerous statistical methods and artificial intelligence techniques having been employed over the years to deal with this important safety matter. This article presents, for the first time, the application of genetic programming to this problem. The methodology consisted in evaluating the efficiency of the algorithm as a technique for the optimization and feature generation in a pattern recognition system for the diagnostic of accidents in a pressurized water reactor nuclear power plant. Considering the set of the time evolution of four physical variables for the three accident scenarios approached, plus normal condition, the task of genetic programming was to evolve non-linear classifiers that would provide the largest amount of discriminatory information for each of the events and, consequently, better identification rates. Genetic programming was proven to be a methodology capable of attaining success rates of, or very close to, 100%, with quite simple parameterization of the algorithm and at a very reasonable time, putting itself in levels of performance similar or even superior to other similar systems available in the scientific literature, while also having the additional advantage of requiring very little pretreatment, or a priori knowledge, of the data.