Transient stability evaluation of electrical power system using generalized regression neural networks

Transient stability assessment of power systems described with detailed models using neural networks

“…Therefore, timedomain simulation is still the most accurate method for transient stability analysis and it can be applied to any level of power system models, but as mentioned before, the main problem of this method is that it is very time consuming. As a result, in recent years there have been several attempts in using computational intelligence based techniques like neural networks (NNs) for transient stability assessment (TSA) of power systems, e.g., see [14][15][16][17][18][19][20][21][22][23][24][25]. Neural networks are widely used for function approximation and/or classification problems, because no rigorous mathematical system modeling is required in order to train an NN to form the underlying mapping.…”

Transient stability assessment of power system using support vector machine with generator combinatorial trajectories inputs

“…There are two ways in using ANN for power system transient stability assessment, one way is using the regression function of ANN to predict transient stability degree [12][13][14][15], such as critical clearing time and system stability margin; another way is using the classification function of ANN to directly classify the system into either stable or unstable states [16][17][18]. Although ANN is the most popular machine learning method to classify patterns, it requires an extensive training process and a complicated design procedure.…”

Wheat Grain Protein Content Estimation Based on Multi-temporal Remote Sensing Data and Generalized Regression Neural Network

“…Generalized regression neural network (GRNN) is a memory-based feed forward network. The GRNN consists four layers: input layer, pattern layer, summation layer and output layer (Specht, 1991;Chtioui, 1999;Haidar, 2011). Figure 2 shows a schematic diagram of generalized regression neural network architecture.…”