800kV UHVDC Transmission Line Research Articles

Identifying faults in UHVDC transmission systems using DAE and ensemble learning

Abstract The current fault identification technique for the ultra high voltage direct current transmission system has a low success rate, the process of extracting feature qualities is complex, and it is difficult to identify high resistance grounding. This research provides a defect evaluation method for UHVDC transmission system using Deep Auto Encoder (DAE) and integrated learning. Firstly, the fault voltage signal is feature extracted using DAE, and then, a series of obtained features are feature screened by XGBoost algorithm and combined with an integrated classifier consisting of Multi-Layer Perceptron (MLP), K Nearest Neighbor (KNN), and Random Forest (RF) classifiers to identify the UHVDC faults. Finally, ±800kV UHVDC transmission line models are established in MATLAB/Simulink simulation software to simulate different types of faults. The study’s findings demonstrate that the fault diagnostic method introduced in this research may significantly enhance the poor detection success rate of current fault diagnostics methods and is more resilient to excessive resistance.

Calculation Of Ion Flow Field In HVDC Transmission Lines Under The Influence Of Haze

Because of the influence of haze on the electromagnetic environment of HVDC transmission lines, this paper proposes a method for calculating the ion flow field considering the influence of haze based on the flux line method, and takes the Yunguang 800kV UHVDC transmission line as an example. A synthetic field intensity and ion flow calculation model with different haze and haze levels was established, and the resultant field strength and ion flux density under different haze conditions were obtained. The results showed that the ground synthetic field intensity and ion flux density under haze weather increased relative to normal weather, and increased with the increasing degree of haze weather pollution. This calculation method has reference value for the analysis of haze weather and corona impact factors in the planning and design of HVDC transmission lines.

Identification of Lightning Strike on ±800kV UHVDC Transmission Line Based on the PSCAD

This paper proposed an effective method of lightning stroke recognition. The characteristic of non-faulty lightning is that the correlation degree with the axis voltage approaches 1.This feature can identify lightning interference. The characteristic of the lightning failure is that when the flashover occurs, a large number of high frequency components in the lightning wave are immediately reduced; the characteristic of the ground fault is that contain less high frequency components. After wavelet transform, the ratio of high-frequency components caused by lightning stroke to middle and low frequencies components is larger than that of ground fault. This feature can be used to identify lightning fault and ground fault. This paper is based on the model of ±800kV UHVDC power transmission project from Yunnan to Guangdong, and simulation results show that the proposed method is effective.

Identification of Lightning Disturbance in UHVDC Transmission Lines Using Correlation Degree Based on Short Time Window Data

In this paper, the simulation of ±800kV UHVDC transmission line in which fault lightning stroke and non-fault lightning stroke, line short circuit occur shows: because there is fault current lumped parameter access to earth during the fault lightning stroke or ground fault, the voltage waveform decrease suddenly and cross repeatedly the zero line in 5ms time window；There is no fault current accessing to earth during non-fault lighting stroke, the disturbance caused by lightning is too less than value of the pole voltage. The voltage waveform comes from the protect position change up and down around the direct voltage line and decays to the pole voltage line in the 5ms time window at last. In the case of the fault lightning or ground fault non-lightning, the correlation degree of transient component and pole voltage line in the time window is little. On the contrary, it is large during the non-fault lighting. Above this, the correlation degree is calculated using 5ms samples. This kind of algorithm can resist random noise effectively,calculating speed is very fast. Many PSCAD simulations show that the algorithm can identify lighting disturbance correctly.

The Identification of Internal and External Faults for ±800kV UHVDC Transmission Line Based on S-Transform and SVM

There is a smoothing reactor and DC filter between the inverter and the direct current line to form a boundary in the HVDC transmission system. Since this boundary presents the stop-band characteristic to the high frequency transient voltage signals, the high-frequency transient voltage signal caused by external faults through boundary will be attenuated and the signals caused by internal faults will be unchanged. In this paper, a new technique in which the combination of the S–transform and support vector machine (SVM) is used to analyze the transient signals, is proposed for distinguishing internal faults from the external faults based on the significantly difference of high-frequency transient voltage on the point of protection.

The Identification of Internal and External Faults for±800kV UHVDC Transmission Line Using Wavelet based Multi-Resolution Analysis

The Identification of Internal and External Faults for±800kV UHVDC Transmission Line Using Wavelet based Multi-Resolution Analysis