Hull consistency is a known technique to improve the efficiency of iterative interval methods for solving nonlinear systems describing steady-states in various circuits. Presently, hull consistency is checked in a scalar manner, i.e. successively for each equation of the nonlinear system with respect to a single variable. In the present poster, a new more general approach to implementing hull consistency is suggested which consists in treating simultaneously several equations with respect to the same number of variables.

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.