Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

Section: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]mentioning

confidence: 99%

Nonlinear Technologies in Advanced Power Systems: Analysis and Control

Fortuna

Buscarino

2022

“…Finally, Volterra models extend a linear transfer function into nonlinear one [7]. A further modern approach is based on self-regressive nonlinear artificial neural network with exogenous input (NARX neural network) technique [8,9].…”

Section: Introductionmentioning

confidence: 99%

Experimental Lognormal Modeling of Harmonics Power of Switched-Mode Power Supplies

Bykhovsky

2022

Switched-mode power supplies (SMPSs) are an important component in many electrical systems. As a highly non-linear device, an unavoidable side-effect of SMPS operation is its high harmonics power. One of the ways to model the harmonic power consumption profile is in terms of a random process. This paper addresses random process modeling with a corresponding probability density function (PDF), auto-covariance function (ACF) and spectral coherence. The consumed harmonics power was evaluated under different load conditions and is based on experimental results of current consumption from SMPSs. The analysis shows that harmonics power may be modeled by a lognormal distribution that is time-domain uncorrelated, and that has spectral-domain correlation modeled by a Gaussian radial basis function. Extensive discussion on the modeling results is also provided. Moreover, random simulation approach based on the modeling results was proposed.

“…After decades of research, its theory has become quite mature. It has been widely used in various engineering fields, such as prediction, pattern recognition and image processing, control and optimization, prediction and intelligent information management, communication, space science and so on [21][22][23][24][25]. The BP neutral network is one of the most widely used models in electricity price forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

Yao

Xie

2021

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.