Faults In HVDC System Research Articles

XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph

This study aims to achieve intelligent decision making in HVDC systems in the framework of knowledge graphs (KGs). First, the whole life cycle KG of an HVDC system was established by combining intelligent decision making. Then, fault diagnosis was studied as a typical case study, and an intelligent decision-making method for HVDC systems based on XGBoost that significantly improved the speed, accuracy, and robustness of fault diagnosis was designed. It is noteworthy that the dataset used in this study was extracted in the framework of KGs, and the intelligent decision making of KG and HVDC systems was accordingly combined. Four kinds of fault data extracted from KGs were firstly preprocessed, and their features were simultaneously trained. Then, sensitive weights were set, and the pre-computed sample weights were put into the XGBoost model for training. Finally, the trained test set was substituted into the XGBoost classification model after training to obtain the classification results, and the recognition accuracy was calculated by means of a comparison with the standard labels. To further verify the effectiveness of the proposed method, back propagation (BP) neural network, probabilistic neural network (PNN), and classification tree were adopted for validation on the same fault dataset. The experimental results show that the XGBoost used in this paper could achieve accuracy of over 87% in multiple groups of tests, with recognition accuracy and robustness being higher than those of its competitors. Therefore, the method proposed in this paper can effectively identify and diagnose faults in HVDC systems under different operation conditions.

A frequency spectrum-based method for detecting and classifying faults in HVDC systems

This paper proposes a method based on Artificial Neural Networks - ANNs for detecting and classifying faults in HVDC systems. An analysis of the frequency spectrum of different fault types in a HVDC system allowed finding different patterns of frequency spectrum for different fault types. As a result, the proposed method uses only the spectrum of the voltage signals to identify and classify faults in any point of the system, that is, upstream of the rectifier substation, in the DC line, or downstream of the inverter substation. The method is composed of an online step, to detect the fault, and an offline step, to classify it. The set of ANNs employed in the proposed scheme uses the same topology for all ANNs, which simplifies its practical implementation. The proposed algorithm was validated using VSC and CSC based technologies, being able to precisely detect and classify faults in HVDC systems.

Loss reduction analysis and control of AC‐voltage‐boosted FBSM MMC by injecting second harmonic circulating current

Reducing the power loss and handling short-circuit faults in HVDC systems are crucial for economical and reliable operation of modular multilevel converters (MMC). This paper proposes a loss reduction method for AC voltage-boosted FBSM-MMC by controlling the second harmonic component in the circulating current. The relationship between converter loss and second harmonic circulating current (SHCC) is first derived for AC voltage-boosted FBSM-MMC. From the analysis, it can be seen that the converter loss can be reduced by injecting the optimal SHCC into the arm current. The topology comparison between traditional FBSM-MMC and AC voltage-boosted FBSM-MMC is carried out to show the benefit of this method. Simulation results using PSCAD/EMTDC confirm the feasibility and validity of the proposed loss reduction method for AC voltage-boosted FBSM-MMC.

Locating short-circuit faults in HVDC systems using automatically selected frequency-domain features

Locating short-circuit faults in HVDC systems using automatically selected frequency-domain features

Improved Design and Control of FBSM MMC With Boosted AC Voltage and Reduced DC Capacitance

Reducing the capacitance of submodules (SM) and handling short-circuit faults in HVdc systems are crucial for economical and reliable operation of modular multilevel converters (MMC). This paper proposes an SM capacitance reduction method for full-bridge submodule (FBSM)-MMC, which combines circulating current injection and ac voltage boosting. An optimized circulating current control method considering the limitation of fixed semiconductor current rating is introduced. By adding second-harmonic voltage to the reference arm voltage, the capacitor voltage ripple can be controlled to be a targeted value under all operating conditions. To further decrease the SM capacitor voltage ripples, the negative voltage state of FBSM is fully utilized to boost the ac voltage under fixed dc voltage with the modulation index up to 1.414. Consequently, the capacitance requirement for ac voltage boosting FBSM-MMC can be significantly reduced as compared to the conventional FBSM-MMC. Experimental results confirm the feasibility and validity of the proposed SM capacitance reduction method.

Research on DC Fault of Multi-Terminal Direct Current System

This paper introduces the characteristics of VSC and MMC-MTDC and discusses the effects of different kinds of faults in HVDC systems. Special attention is given to the comparison between a pole-to-pole fault and a pole-to-ground fault occurring in the middle of the line or at the terminal of a VSC. Simulations using MATLAB are provided in this article which show the difference effects clearly when faults occur in a VSC-MTDC system or in a MMC-MTDC system. Understanding of such fault characteristics and the influence of the control system on them are important prerequisites on the way to MTDC systems.

Location of DC Line Faults in Conventional HVDC Systems With Segments of Cables and Overhead Lines Using Terminal Measurements

This paper presents a novel algorithm to determine the location of dc line faults in an HVDC system with a mixed transmission media consisting of overhead lines and cables, using only the measurements taken at the rectifier and inverter ends of the composite transmission line. The algorithm relies on the traveling-wave principle, and requires the fault-generated surge arrival times at two ends of the dc line as inputs. With accurate surge arrival times obtained from time-synchronized measurements, the proposed algorithm can accurately predict the faulty segment as well as the exact fault location. Continuous wavelet transform coefficients of the input signal are used to determine the precise time of arrival of traveling waves at the dc line terminals. Two possible input signals-the dc voltage measured at the converter terminal and the current through the surge capacitors connected at the dc line end-are examined and both signals are found to be equally effective for detecting the traveling-wave arrival times. Performance of the proposed fault-location scheme is analyzed through detailed simulations carried out using the electromagnetic transient simulation software PSCAD. The impact of measurement noise on the fault-location accuracy is also studied in this paper.

Identification of Faults in HVDC System using Wavelet Analysis

The identification and classification of faults is important for safe and optimal operation of power systems. For secure operation of a system a feasible approach is to monitor signals so that accurate and rapid classification of fault is possible for making correct protection control.To identify HVDC faults by using pure frequency or pure time domain based method is difficult. The pure frequency domain based methods are not suitable for time varying transients and the pure time domain based methods are very easily influenced by noise.Wavelet analysis is one of the methods used for providing discriminative features with small dimensions to classify different disturbances in HVDC transmission system. This paper explores the application of wavelet based Multi-Resolution Analysis (MRA) for signal decomposition to monitor some faults in HVDC system. The faults in HVDC system can be classified by monitoring the signals both on AC and DC sides of the HVDC system. The fault classifier can be developed from these monitored signals which show promising features to classify different disturbances in the HVDC system. DOI: http://dx.doi.org/10.11591/ijece.v2i2.179