Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems

Abstract

This paper presents the ANN (Artificial Neural Networks) approach to obtaining complete P-V curves of electrical power systems subjected to contingency. Two networks were presented: the MLP (multilayer perceptron) and the RBF (radial basis function) networks. The differential of our methodology consisted in the speed of obtaining all the P-V curves of the system. The great advantage of using ANN models is that they can capture the nonlinear characteristics of the studied system to avoid iterative procedures. The applicability and effectiveness of the proposed methodology have been investigated on IEEE test systems (14 buses) and compared with the continuation power flow, which obtains the post-contingency loading margin starting from the base case solution. From the results, the ANN performed well, with a mean squared error (MSE) in training below the specified value. The network was able to estimate 98.4% of the voltage magnitude values within the established range, with residues around 10−4 and a percentage of success between the desired and obtained output of approximately 98%, with better result for the RBF (radial basis function) network compared to MLP (multilayer perceptron).

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

“…This is because it is largely unclear why these networks produce specific outcomes, as they lack explicit justifications for their predictions. Recognizing this limitation, numerous studies have focused on extracting knowledge from artificial neural networks and developing explanatory techniques to provide insights into the network's behavior in particular situations [15,27]. Hence, it should be observed that each time the network undergoes retraining, a distinct value will be obtained [13,27].…”

“…The experimental findings demonstrate that the proposed approach achieves a high level of classification accuracy and provides precise health assessments for power transformers. Topics related to the application of artificial neural networks (ANNs) in the analysis of the operating conditions of power transformers and electrical energy systems have currently received attention [13][14][15]. In recent years, a series of studies and research have been published in this field, demonstrating the growing interest and relevance of these approaches [16][17][18][19].…”

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

“…In this context, it is desirable in engineering that even well-established energy systems analysis methods are revisited and eventually improved. In recent decades, an alternative formulation based on artificial neural networks (ANN) has been widely employed [12][13][14][15][16]. In [12], two artificial neural networks (ANN) were presented, the Multi-Layer Perceptron and the Radial-Based Perceptron, with the objective of estimating the magnitudes of voltages on the buses of electrical power systems, taking into account several parameters, such as the loading factor, the real and reactive power in the slack bus, and the contingent branch number.…”

Design and Optimization of Sugarcane Spiral Fertilizer Applicator Based on Response Surface Methodology and Artificial Neural Networks

“…ANNs are distinguished by their exceptional predictive accuracy and capacity to process extensive, complex datasets. Moreover, they exhibit robustness to minor perturbations, adapting seamlessly to input variations and noise [18]. Currently, artificial neural network models are mainly used for the optimization of food and bioprocess parameters and have not been widely used in the optimization of structural parameters [19,20], but there are a few studies on the prediction of fertilizer discharge performance of fertilizer applicator [21,22].…”