Bivariate stochastic model of current harmonic analysis in the low voltage distribution grid

Abstract

This paper presents a bottom-up bivariate analysis approach to estimate current harmonics by taking account of network and load variations. The current harmonics assessment in the presence of existing and future nonlinear loads is vital to study their impact on the distribution grid. The traditional harmonic analysis models consider only stable loads while neglecting the harmonic interaction among the devices. Modern nonlinear loads operate under different working modes and configurations. Thermal stability, harmonic cancellation, and dynamic network parameters influence the current harmonic estimations. In this paper, a probabilistic approach is presented to model harmonic emission in the low voltage distribution grid under network and load uncertainties. A case study is used to demonstrate effectiveness of the proposed model.

Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm

“…A bottom-up stochastic model to assess harmonic emissions in low voltage grids is presented in [25]. The model considers varying loads and their implications to the harmonic spectrum in the grid, in contrast to conventional approaches applying only stable loads.…”

DAE-LSTM neural network-based prediction model for segmental line loss rate of low-voltage power grid

“…The self-encoder operation includes decoding operation of input data and decoding operation of output data, usually preprocessing and aligning the input data, putting the data into the encoder, transforming the input data into the corresponding mapping space, using the decoder to restore the data, and obtaining the characteristics of the data, applying this way greatly reduces the time of labeling the data, and can improve the generalization and migration ability of the model. The self-coding is also achieved through the interconnection and correlation of neurons in the neural network, but it is easy to overfit the network with more parameters, and if there is noise in the data and put into the model after fitting, it will lead to the decrease of model accuracy [5].…”