Transmission Fault Research Articles

Quench recovery time of an optical fiber encapsulated resistance-type DC superconducting fault current limiter coil determined by a Raman-based distributed temperature sensing system

The superconducting fault current limiter (SFCL), as a very important power facility for high voltage DC system, can consume fault current energy and make DC breakers cut off fault transmission lines successfully. For the moment of reclosing DC breakers, the quench recovery time of the SFCL must be determined in advance. In this article, according to the design requirement of a 160 kV/1 kA resistance type DC SFCL in the Nan’ao multi-terminal flexible HVDC transmission demonstration project, the quench recovery time of a SCFL coil wound by two optical fiber encapsulated high temperature superconducting (OFE-HTS) tapes was checked by our optimized Raman-based distributed temperature sensing (Rm-DTS) system. The experimental results show that the used OFE-HTS tapes can withstand a 1500 A/100 ms AC short circuit current impact and are qualified with the SFCL. Furthermore, the quench recovery time of the SFCL coil measured by the Rm-DTS was 2930 ms. During the quench, the highest temperature zone occurred near the center of the SFCL coil.

Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid

To reduce emissions worldwide, decarbonization has also affected for power engineering and its conventional methods of power generation, distribution, and transmission. This fact affects not only the production of power and supply itself, but also other parts related to it, namely systems for the protection of electricity generation facilities and the location of faults in distribution networks and transmission systems. Is this new technology of renewable energy sources (RES) mastered enough to be able to replace traditional methods of energy generation reliably and safely? If we can answer the previous question, we could proceed to the next phase of putting these technologies into practice, considering the actual state of distribution networks and transmission systems. The aim of the article will be to evaluate the current state of systems and production sources of electricity in the Slovak Republic and to provide a better insight and suggestions for rebuilding current networks on a smart grid. The proposals will also include models of some RES in the Matlab/Simulink software.

Modeling and Analysis of Transient Reactive Power Characteristics of DFIG Considering Crowbar Circuit under Ultra HVDC Commutation Failure

Ultra high voltage direct current (UHVDC) transmission is an effective means of long-distance transmission of renewable power generation, which has obtained a lot of research and practical applications. The commutation failure is a common DC transmission fault, which will cause the voltage amplitude of the sending ac grid in UHVDC system to first decrease then increase. The existing transient mathematical models of the wind power generation system (WPGS) are difficult to apply to scenarios where the grid voltage changes continuously. A mathematical model suitable for commutation failure is established to analyze the transient reactive power characteristics of the doubly fed induction generator (DFIG)-based WPGS with the consideration of the crowbar circuit trigger. The correctness of the mathematical model is validated by an experiment based on the control hardware-in-loop (CHIL) platform. Based on the proposed mathematical model, the influence of the crowbar parameters on the reactive power output of the DFIG is analyzed, and the selection of crowbar parameters to suppress the overvoltage of the sending ac grid is investigated. A simulation model is built based on Matlab/Simulink to verify the overvoltage suppression effect of the proposed selection scheme.

Effect of excitation frequency on nonlinear vibration of crack fault in multi-stage gear transmission system

In a multi-stage gear transmission system, the motion state of the system will change with the excitation frequency, and the frequency characteristics will also change accordingly. If this change is not taken into account, there is often a great deviation in identifying and judging system faults according to unified standards, especially when the system has such early undetectable fault as crack. In this paper, the dimensionless differential equations of motion of multistage gear transmission system are established. The stiffness model of gear tooth crack is established by potential energy method. The changes of the motion state of the system with the increase of excitation frequency are obtained by calculating the displacement bifurcation diagram of cracked gear tooth. The influences of crack fault on each motion state are studied by using time domain, frequency domain, phase diagram and Poincaré cross section, and the fault frequency characteristics are summarized. By comparing the theoretical and experimental data of the vibration response characteristics of the system, the motion state of the system can be effectively determined and the crack fault can be identified.

Application of the Automatic Segmented Hilbert Huang Transform Method for the Evaluation of the Single-Event Characteristics of Voltage Sags in Power Systems

Depending on the type of the electrical network, the source of the voltage sag (or dip), and the settings of the protective devices, the magnitude, duration, and phase of the voltage sag (or dip) during an event may behave differently. Because of the nonstationary characteristics of the voltage sag, the conventional fast Fourier transform (FFT) and Hilbert Huang transform (HHT) methods show ambiguities at the starting and at the recovery points of the voltage sag waveform. This can lead to errors in the calculation of the single-event characteristics (SEC) of the voltage sag. This article investigates the application of a novel automatic segmented HHT (SHHT) method for evaluating the SEC of the voltage sag caused by different types of symmetrical and unsymmetrical faults in both transmission and distribution networks. It also investigates the influence of several factors on the three important parameters of the voltage sag-the magnitude, the phase angle, and the point-on-wave (POW) of the voltage sag waveform. These factors include the types of fault, the location of the fault, and the X/R ratio of the lines before and after the fault, the transformer and load connections, and the POW of the sag initiation. The simulation results and tests with real data validate the effectiveness of the SHHT method in the evaluation of the SEC of the voltage sag when compared with those obtained using the traditional FFT and the analytical method.

Assessment of Cable Length Limit for Effective Protection by Z-Source Circuit Breakers in DC Power Networks

This paper introduces groundbreaking research on how to assess the Cable Length Limit (CLL) to ensure effective protection by Z-source Circuit Breakers (ZCBs) in DC power networks. It has been revealed that the line parameters of power cables have a significant impact on the cutoff performance of ZCBs. The question of assessing the CLL has been raised as an unsolved problem. In this paper, a method of CLL assessment is proposed based on physical models and simulation tests. To verify the proposed method, two studies were performed to assess the Cable Length Limits depending on fault levels and power delivery levels, respectively. The ZCB parameters were specified for a simulation testing system for a 5 MW distribution line feeder. The effectiveness of ZCB protection was tested in groups of simulation tests with various impacting quantities, i.e., cable lengths, fault current levels, and power delivery levels. The effective cable lengths for the ZCB to detect and successfully interrupt a faulty branch in the DC network were assessed and analyzed. The testing results prove that the CLL decreases along with a decreasing fault current level, as well as an increasing power delivery level. Based on data analysis, an equation was derived to calculate the effective length of the ZCB for DC lines, and the equation can be used to generate new CLL curves for various load-power requirements. This study could increase the reliability of a ZCB’s response to a fault in DC transmission and distribution lines. It could also help power system designers/operators to maintain reliable protection with ZCBs in DC power system networks.

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

This paper proposes a protection scheme for multi-terminal high voltage direct current (MT-HVDC) transmission grids. The proposed scheme is based on three protection levels; di/dt overcurrent protection as the main protection, dv/dt undervoltage protection as a backup one protection. If the main and backup one fails to detect and clear the fault current, then the fault tripping and clearing is accomplished using the overcurrent protection by AC circuit breakers (ACCBs) in the AC grid side after a coordinated time. The tripping in the main and backup one protection levels are based on using fault isolation devices (FIDs) tripping. Support vector machine (SVM) is proposed for the fault detection and discrimination in the MT-HVDC using the backup two protection system. The Internet of things (IoT) has been used for monitoring and coordination between the AC grid and MT-HVDC grid. The proposed protection scheme is applied and verified on the MT-HVDC network under different transmission system faults using the MATLAB/Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> environment. The results show that the proposed scheme's effectiveness as a fast and reliable scheme in fault detection and discrimination for the MT-HVDC grids.

Study on fault identification of mechanical dynamic nonlinear transmission system

Abstract To solve the problems of large mechanical powertrain such as complex structure, serious accident, strong nonlinear characteristics of running state, bad operating environment, non-Gaussian noise, and various uncertain factors, it is difficult to make an accurate fault diagnosis. This paper proposes a method for dealing with nonlinear characteristics using nuclear waves, as well as a system, deeply conducted nuclear base fault feature extraction, classification, and decision making, such as nuclear base state trend prediction technology research, focusing on exploring and improving the accuracy of fault diagnosis under nonlinear conditions, technical method, and way to state prediction accuracy. It offers effective technical assistance for the advancement and use of mechanical power train monitoring and diagnosis technology. A fault detection method based on kernel method is proposed. Based on the characteristics of this method in dealing with nonlinear problems, the research on kernel feature extraction, kernel fault classification and decision making, and kernel state trend prediction are carried out systematically. The experimental results show that the simulation analysis of typical chaotic time series prediction and the application of the operation state prediction of a certain ship main steam turbine unit have achieved good results, among which the average relative error of the single-step prediction of the unit state is 1.7881%, and the average relative error of the 30-step prediction is 3.3983%. Proved that the nuclear methods systematically applied to mechanical power transmission system fault diagnosis and state prediction, effectively enhancing some traditional methods and techniques dealing with nonlinear feature extraction, the nonlinear prediction capability for fault identification, and nonlinear state, to deal with nonlinear fault diagnosis problems of engineering practice, a large number of explored effective solution.

Research on Transmission Fault Location Based on Scanning Data

Aiming at the problem that the conventional transmission line fault location method has a delay in positioning and cannot be timely feedback, a method for fault location of transmission lines based on 3D scan data matching is proposed. 2D feature points are used to obtain three-dimensional point pairs. Structured light projectors and 3D scanning technology are applied to match image feature points and generate mapping relationships with reconstructed 3D space points. Then use point cloud computing to solve three-dimensional coordinates and use features. Click the interpolation result to complete the data matching process. The simulation experiment applies the 3D scanning data matching technology to the operation process of transmission line fault location. The experimental results show that compared with other methods, the method of this paper has the highest detection stability and the advantage of high accuracy of intelligent fault diagnosis, and can give feedback on transmission line faults in time.

Simulation of a single-phase arc grounding fault on a 220-kV cable transmission system

Cable tunnel fires are mainly caused by single-phase arc grounding faults. The massive energy generated by arc can threaten the safety of the densely laid cable tunnels. In this study, a digital arc fault model with a 220-kV cable transmission system was established on Power Systems Computer Aided Design(PSCAD). The arc model was improved because the parameters were acquired from the experimental results. The arc fault can be divided into two stages: a primary arc at the initiation of the fault and a secondary arc after the tripping of the circuit breakers. The primary arc contains few nonlinear components because of the large arc currents. The secondary arc voltages exhibit nonlinear characteristics because of arc elongation. The secondary arc power continues to increase, however, its amplitude is considerably smaller than the primary arc. The lifetime of the primary arc is dependent on the action of the relaying protection system. Further, some limits based on the energy balance theory were introduced to obtain the extinction time of the secondary arc. In addition, the total energy generated by the arc fault in the 220-kV cable transmission system will eventually become 4891 kJ. This digital arc fault model will significantly contribute to future research with respect to the mechanism of cable tunnel fires.

Wide-Band Current Transformers for Traveling-Waves-Based Protection Applications

Owing to its system independence and unmatched speed, Traveling-wave (TW)-based relaying has become a top contender for transmission and distribution systems. However, existing measurement devices, such as Current Transformers (CTs), are incapable of capturing the full frequency content of TWs, as their bandwidths are limited. Consequently, existing TW-based schemes rely on distorted waveforms and partial-frequency spectra, i.e., low-frequency content, of TWs to perform protection tasks. Such incomplete information can lead to misoperation of protection schemes. This article investigates the impacts of the limited bandwidth of CTs on the shape and frequency content of TWs under faults in transmission and distribution lines. Additionally, it proposes a compensation technique, which utilizes an Unknown Input Kalman Filter (UIKF), to accurately estimate the primary current of a CT from the measured secondary one, i.e., to capture the waveshape and full frequency-content of TWs. The proposed method removes the bandwidth barrier from the measurement of current-TWs, and thus allows relays to access the High-Frequency (HF) spectra of TWs in order to devise more-effective TW-based schemes. Simulation results corroborate that the proposed method accurately compensates for low frequency-content and distorted waveform of TWs in the secondary currents of CTs under various fault types, resistances, inception angles, and locations.

Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots

Monitoring the transmission status of multi-joint industrial robots is very important for the accuracy of the robot motion. The fault diagnosis information is an indispensable basis for the collaborative maintenance of the robots in industry 4.0. In this paper, an attitude data-based intelligent fault diagnosis approach is proposed for multi-joint industrial robots. Based on the analysis of the transmission mechanism, the attitude change of the last joint is employed to reflect the transmission fault of robot components. An economical data acquisition strategy is performed by only installing one attitude sensor on the last joint of the multi-joint robot. Considering the characteristics of attitude data, a hybrid sparse auto-encoder (SAE) and support vector machine (SVM) approach, namely SAE-SVM, is subsequently presented to construct an intelligent fault diagnosis model by learning from the attitude dataset with multiple fault information. Experimental results show that the proposed fault diagnosis approach has promising performance in identifying different faults related to the reducer of a 6-axial multi-joint industrial robot accurately and reliably.

Coupled transmission and distribution simulations to assess distributed generation response to power system faults

The historical simulation approach of aggregating distributed generation (DG) with load for transmission simulations is proving inadequate at capturing these DG dynamics. Presented is a methodology that uses interfacing data sets to couple disparate simulations in an iterative manner. This methodology was implemented with a Positive Sequence Load Flow (PSLF) dynamic model of the Western Interconnection with 9 GW of DG. A transmission fault leading to a fault-induced delayed voltage recovery (FIDVR) is analyzed with this iterative coupled simulation method, with more than 120 substation voltage profiles extracted, representing more than 4 GW of DG, and simulated in OpenDSS to capture the ride-through dynamics. OpenDSS simulations of feeders populated with photovoltaic DG and associated time-dependent tripping and momentary cessation criteria based on the IEEE 1547 standard are performed. Coupled simulation results show significant differences in DG responses, as well as overall system responses, between single PSLF and the coupled simulations.

Hilbert‐Huangtransform based fault identification and classification technique forACpower transmission line protection

In the modern power system, the fault identification and classification is one of the challenging tasks during high fault impedance due to low change in the magnitude of fault current from steady-state to fault inception. In this paper, a high impedance fault detection technique is proposed based on Hilbert-Huang transform (HHT). The energy for each phase is computed from the intrinsic mode function (IMF) of three-phase voltage/current signals. An average relay energy index (AREI) is proposed to detect the transmission line fault in the large interconnected power system. Furthermore, a fault classification technique is also proposed based on relay energy index (REI) of each phase of the three-phase voltage/current signals. The proposed HHT based fault detection technique is validated on standard WSCC 9 bus and IEEE 14 bus test power systems using PSCAD and MATLAB software. The simulation results reveal that the proposed Hilbert-Huang transform based fault identification and classification technique can be best suitable for protection of transmission lines during high impedance faults in complex and large AC power transmission system.

Research on Fault Transmission Method of Switchgear Based on Internet of Things

In order to meet the intelligent function of the switchgear, the fault transmission method of the switchgear based on the Internet of Things is researched and designed. Firstly, use the data collection terminal and monitor to monitor the parameters in the switchgear and transmit the detected faults; Secondly, use the Internet of Things method to design a monitoring switch platform to provide an operating environment for fault monitoring; Finally, verify the accuracy of the IOT(internet of things) technology in the fault transmission of switchgear.

A Novel DC Transmission System Fault Location Technique for Offshore Renewable Energy Harvesting

Advances in renewable energy recourses technologies have paved the way for increased utilization of DC grids in modern power systems. In particular, high voltage DC (HVDC) transmission cables are used to harvest offshore energy. Since failure diagnosis and repair of the buried offshore cables under the sea bed involve quite laborious tasks, accurate fault location is of vital importance. This paper proposes a novel fault location method based on the synchronized measurement of conductors and sheaths currents in each terminal without requiring the voltage signal. Unlike the traveling-wave-based methods, the proposed method is developed for multi-terminal HVDC systems without requiring any high precision measurement infrastructure. The proposed method can be implemented for different system topologies as well as fault resistance and fault distance. The fault location method can provide accurate results even if 10 ms time window samples are imported into the proposed algorithm. In addition, accuracy of the fault location using the proposed method is not affected significantly by noisy condition. Different test systems have been simulated to evaluate performance of the proposed method in various scenarios. Obtained results confirm that the proposed practical method can provide the accurate fault location for all different scenarios and conditions.

Single-pole grounding fault location method for flexible HVDC transmission lines

One of the key problems of flexible HVDC transmission line is HVDC transmission fault location. A fault location method based on an improved traveling wave natural frequency flexible HVDC transmission line single-pole ground fault is proposed. The integrated empirical mode is used to decompose the fault signal to obtain the required traveling wave component, and then the improved MUSIC algorithm is used, according to the frequency spectrum analysis obtains the natural main frequency, and uses the relationship between the natural frequency and the fault distance to gain the fault location. Finally, use PSCAD to build simulation for simulation test and combine MATLAB fault location algorithm program to get the fault distance. The simulation results show that the proposed ranging algorithm is not affected by transition resistance and fault distance, and can accurately obtain the fault point distance, which shows the feasibility and effectiveness of the proposed fault ranging method.

Fault-tolerant communication system based on convolutional code for the control of manipulator robots

This paper presents the development of a convolutional code application for error detection and correction in a communication system. This application is based on an industrial protocol and aims at reconstructing data damaged during transmission. The Controller Area Network (CAN) protocol is used, which is widely used in the automobile industry, as well as in the automation of industrial processes. The application allows us to make a system that presents errors due to hostile conditions in the fault-tolerant industrial environment. These errors appear as corrupted data affecting the robot’s control system. The robot is a 3 Degrees of Freedom (3-DoF) manipulator robot with redundancy in the plane; data transmission takes place between the controller and such a robot. The communication system is assessed in terms of performance when facing transmission faults, comparing the response of the system with and without error correction.

Single-end current-based algorithm for fault location in series capacitor compensated transmission lines

The performance of protection functions in the series capacitor compensated transmission lines (SCCTLs) is affected by the capacitors protection system operating. One of these protection functions is the fault locator. This paper presents a highly accurate fault location algorithm in the SCCTLs for the single line to ground (SLG) faults, as the most common type of faults in transmission systems. The proposed algorithm identifies the fault location using the current phasors measured at only one side the faulty line, which makes it a low-cost and high reliable one. Therefore, the proposed algorithm can be easily implemented on numerical relays as it does not require communication channel to acquire remote data. In the proposed algorithm, after fault signal processing, the initial fault locations are obtained through moving the sampling window. Finally the exact fault location is calculated using the initial fault locations data and an outlier data removal technique. In order to evaluate the proposed algorithm, a test system is simulated in DIgSILENT software and processing process is implemented in MATLAB software. The obtained results show high accuracy and efficiency of the proposed method.