Impact of transmission topology for protective operations in multi-terminal HVDC networks

This paper presents an outcome of a comprehensive study which evaluates the transient behaviour of point-to-point and multi-terminal high voltage direct current (MT-HVDC) networks. The behaviour of the HVDC system during a permanent pole-to-pole and pole-to-ground fault is assessed considering a range of fault resistances, fault positions along the line, and operational conditions. The emphasis of this investigation is on DC fault characteristics which would facilitate a reliable method of faulty line discrimination in a multi-terminal direct current (MTDC) system using local measurements only (i.e. assuming that no communication media is used). All the simulated waveforms (and subsequent analysis) utilise the sampling frequency of 96 kHz in compliance with IEC-61869 and IEC-61850:9-2 for DC-side voltages and currents

Machine Learning-Based Backpressure Unstart Prediction and Warning Method for Combined Cycle Engine Hypersonic Inlet-Oriented Wide Speed Range

Inlet unstart prediction and warning are strictly crucial to the operation of hypersonic engines, especially for combined cycle engines where implementation across a wide speed range poses significant challenges. This paper proposes a realization method that involves constructing the conditions of critical backpressure ratios for the inlet unstart and unstart warning states within a wide speed range and establishing the backpressure prediction models for each engine mode. The detection of the unstart and unstart warning states is achieved by predicting the backpressure ratio at the exit of the isolator and comparing it to the critical backpressure ratios. To achieve this, numerical simulations for a three-dimensional inward-turning multiducted hypersonic combined inlet at various Mach numbers and backpressure ratios are carried out to obtain the dataset of surface pressure. A 10-fold cross-validation support vector machine (10-CV SVM) is used to solve the unstart boundary of surface pressure, and an unstart margin is set to determine the unstart warning boundary. A back propagation (BP) neural network is constructed to estimate the critical backpressure ratios at each working point within a wide speed range. The data information of surface pressure on the boundaries is used as the input for the predictions. The overall average regression correlation coefficient approaches 0.99 on the test dataset at each working point. The backpressure prediction models are established by the one-dimensional convolutional neural network (1D-CNN). Only 2 to 4 measurement points of surface pressure are considered for cross-validation evaluation, and the mean absolute percentage error is between 4% and 8% with the average prediction time not exceeding 2 ms. Finally, the proposed method and prediction models are validated by wind tunnel experimental data