Stacked denoising autoencoder based fault location in voltage source converters‐high voltage direct current

Abstract

High voltage direct current has been more and more popular in modern transmission systems. Accurate fault location could help fault clearance and fast recovery of the faulted system. A stacked denoising autoencoder based fault location method for high voltage direct current transmission systems is proposed. The local measurements are analysed, and an end-to-end stacked denoising autoencoder-based fault location is realised. Representative features are extracted with unsupervised learning and labelled as the input of the regression network for fine-tuning in a supervised manner. The trained network can precisely map the local measurements and their corresponding fault distance. The performance of the proposed method is tested on a point-to-point high voltage direct current transmission system, which is modelled on the platform of PSCAD/EMTDC. The faults on both overhead lines and cables are considered, and the location performance in different scenarios are discussed. The simulation results show that the proposed method is effective in pinpointing faults location in various cases.

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

“…Researchers used the unsupervised learning method for fault extraction trained later. Final results showed that it can accurately locate fault in various situations, making the fault system recover faster [9] . Li h et al found that the lack of damage labels made it difficult for the existing deep neural network to carry out effective damage detection.…”

“…There are linear, polynomial, radial basis, and sigmoid function. Sigmoid function is selected as the model kernel function in equation (9).…”

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

“…Numerous studies have attempted to employ machine learning techniques for HVDC system failure detection, as advancements in statistics and machine learning technology continue to progress [8,9]. In reference [10], the characteristics of DC voltage and DC current are extracted using principal component analysis (PCA), and then these features are trained and tested using support vector machine (SVM) for fault identifcation and classifcation.…”