Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

“…The main goal of this stage is to obtain the adaptive fault detector model for the chosen IED. Here, the proposed meth-od uses a multilayer NN model to detect fault states in the ADN or MG, as presented in [15]. The NN model is formulated according to (1).…”

“…If the current iteration is smaller than the maximum number of iterations, the methodology must propose a strategy to modify the combination of features and hyperparameters. The adjustment strategy is made according to [15].…”

“…For the first and second cases, the NN is set up as follows: input features as listed in Table I, the number of hidden layers as 1, and the numbers of neurons in the hidden layer as 100. Then, for the third case, the method uses the algorithm proposed in [15] to obtain a better combination of features and hyperparameters. The maximum numbers of hidden layers and neurons per layer are set to be 3 and 150, respectively [33].…”

“…These solutions show outstanding performance in simulated environments [11], [12]. It should be noted that these methods use available local observations, e. g., current and voltage signals, to train and validate AI models [14], [15]. For example, [14] and [15] use two 60 Hz cycles of the voltage and current signals for decision making.…”

“…It should be noted that these methods use available local observations, e. g., current and voltage signals, to train and validate AI models [14], [15]. For example, [14] and [15] use two 60 Hz cycles of the voltage and current signals for decision making. The solutions are not validated in a physical system, thus no aspects regarding the implementation or physical validation are reported.…”