Phase Fault Research Articles

Transmission line fault classification based on spatiotemporal characteristic analysis with global and local discriminant analysis

To ensure the reliability of power supply, ultra-fast and reliable fault classification of the transmission line is the critical stage of the wide-area transmission network protection. As the distributed generation (DG) current injection to the transmission line, the amplitude characteristic of the break current protection reduces and the system reliability decreases. With the wide-area measurement generated to the power system, an online fault classification method with the global and local discriminant analysis (GLDA) is proposed to enhance the phase characteristic and decrease calculation load. In the single line to ground (SLG) fault process, the local current phase shift is analyzed to enhance the spatiotemporal characteristic. To fully measure the ordering degree of the spatiotemporal characteristic, the axiomatic definition for the characteristic entropy (CE) of the grid impedance in the individual bus in the wide-area transmission network is proposed according to the characteristic distribution. Based on the current spatiotemporal characteristic analysis, a multi-priority GLDA classifies the fault phase in a coordinating way using with the phasor measurement unit (PMU). The multi-priority GLDA-CE fault classification method reduces the computational load, ensures resilience against measurement noise and the transition resistance effect. Compared with the statistical entropy and neural network method, the classification accuracy and response speed of the GLDA-CE fault classification method are validated in the IEEE 39-bus system with 10 PMUs. Compared with the conventional statistical entropy and neural network methods, the GLDA-CE method is able to classify 226 entire power outages and 600 single phase outages within 1.621 s.

Experimental investigation of a real-time singularity-based fault diagnosis method for five-phase PMSG-based tidal current applications

This paper presents a novel real-time singularity-based fault diagnosis method for tidal current applications, specifically utilizing a five-phase permanent magnet synchronous generator with trapezoidal back electromotive forces. The proposed method incorporates an innovative orthogonal signal generator through a second-order filter, enabling the extraction of detectable singularity signatures from phase current signals. The principle of the method is elucidated through step-by-step design procedures, outlining the indicator enhancement approach and adaptive thresholds employed for enhanced robustness and adaptability. Fault detection is performed based on the improved fault indicators and an adaptive threshold law, followed by immediate fault localization that is achieved via twice average operations of the phase currents. To demonstrate the effectiveness and efficiency of the proposed method, a comparative study is carried out with a classical mean current vector-based fault diagnosis method. A small-scale experimental platform emulating a tidal current application is established for a comprehensive evaluation of both methods. The experimental results highlight the superior fault diagnosis performance of the proposed method, particularly in detecting single and multiple open circuit faults in phases or switches, while exhibiting enhanced robustness against variations in torque and speed. The simplicity of implementation and rapid detection mechanism are principal merits for the proposed method.

Hybrid wind-PV farm with STATCOM for damping & control of overall chaotic oscillations in two-area power system using hybrid technique

This manuscript proposes a hybrid wind-photovoltaic farm with static synchronous compensator(STATCOM) for damping and controlling general chaotic-oscillations in a two-area power system. The proposed hybrid method is the joint operation of Tasmanian Devil Optimization(TDO) and Aquila Optimizer (AO) and together called as TDO-AO technique. The major objective of the proposed technique is minimizing the error, optimizes the performance of the system and regulates the controller parameters of proportional-integral (PI). The STATCOM is used to regulate the voltage and improve the power-factor of the system, leading to better power quality and reduced losses. The TDO is used to optimize the location and size of the wind-PV farm. The AO is utilized to enhance the parameters of STATCOM. The hybrid wind-PV farm with STATCOM can provide a renewable source of energy and improve the stability and reliability of the power grid. While being optimally compensated, the system is vulnerable to 3-phase faults. Zero damping is one more disturbance, along with a change in reference voltage and electric torque. The disturbances are created to simulate the worst scenarios, so that the hybrid wind-PV farm may be rigorously evaluated for its effectiveness in reducing the total chaotic oscillations. In Wind-PV-STATCOM, the gate signal is managed by a controller that separately controls the AC and DC currents. Then, the performance of proposed method is implemented in MATLAB and it is contrasted with the existing methods. From the simulation analysis, the proposed method based settling time is 1.00 sec that is less than the existing one.

A Method for Fault Section Identification of Distribution Networks Based on Validation of Fault Indicators Using Artificial Neural Network

A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB.

Machine learning-based single-phase ground fault identification strategy for AC-DC transmission lines

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOA-LSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified.

A Three-Phase Phase-Modular Single-Ended Primary-Inductance Converter Rectifier Operating in Discontinuous Conduction Mode for Small-Scale Wind Turbine Applications

Small-scale wind turbines play an important role in distributed generation since customers can use their houses, farms, and business to produce electric energy. The development of the power electronics system that processes the electric energy from small-scale wind turbines is a concern due to cost, simplicity, efficiency, and performance trade-offs. This paper presents the results of applying a three-phase phase-modular single-ended primary-inductance converter rectifier to processing the energy of a small-scale wind turbine system. The rectifier was designed according to the specifications of a commercial small-scale wind turbine system and tested in an emulator workbench, providing experimental data on the operation of the rectifier in this application. The rectifier can process the energy of a non-sinusoidal three-phase system since the permanent magnet synchronous generator has trapezoidal waveforms. The results show that the rectifier has the advantages of (i) using the inductance of the generator as the input filter inductor of the rectifier, (ii) providing input currents with the same shape as the voltages and in phase without the use of a current control system, (iii) simplicity of control of the DC output voltage and PWM modulation, and (iv) phase-modular characteristics that allow operating with phase fault without any additional control techniques. Due to the operation in discontinuous conduction mode, low efficiency in high power and/or low input voltage specifications are disadvantages.

Analysing the performance of incremental quantity based directional time-domain protection near HVAC cables and VSC HVDC converters

New grid elements such as Voltage Source Converters (VSC) and cables are being increasingly used in today’s power grid. These new grid elements behave differently compared to synchronous machines and overhead lines during transients and faults, impacting the present system’s legacy protection. An increased share of underground cables affects the system’s resonance frequencies whereas VSCs introduce actively controlled current phase shifts during short-circuit faults. Existing research mainly focuses on VSC impacts on phasor-based legacy protection. This paper studies the impact of these new grid elements on incremental quantity-based directional protection, which operates in the time domain, using EMT-type models and hardware experiments. First, cables introduce low frequency oscillations for remote, single phase faults and additionally under weak grid conditions. At low frequencies, these oscillations are not filtered out by the protection, which may result in a decrease of dependability margin (23% of the tested cases) and potential security issues. For the majority of studied cases, the relays worked correctly. Second, fast reactive current injection by a VSC can cause malfunction of incremental quantity protection. Malfunction occurred in 1 case and a decrease of dependability in 10% of the tested cases. This is especially problematic for grids with relatively low synchronous infeed.

Fault Sensing of the Distribution Cable Feeders by Time-Domain Measurements

Power distribution cable feeders are prone to faults due to the poor working environment and internal defects. Considering the structure and electrical parameters of the typical distribution three-core cable, a fault sensing method is proposed. The fault sensing in this paper includes three aspects: fault cable detection, fault phase identification and fault resistance estimation. The grounding line currents of cable feeders are firstly analyzed in different neutral grounding modes. Based on the amplitudes and directions of the grounding line currents, a fault feeder detection criterion is then presented. Finally, based on the equivalent circuit of the fault cable, an algorithm for identifying the fault phase is developed by estimating the fault resistance. The experiment model of the distribution cable feeders was created by RTDS. Various fault experiments were carried out. The results show that the neutral grounding modes and fault conditions have little effect on the proposed method.

Real-Time Open-Circuit Fault Diagnosis Method for T-Type Rectifiers Based on Median Current Analysis

T-type rectifiers are widely used in DC fast chargers, super uninterrupted power supplies, renewable energy, and other low-voltage and medium-voltage rectifier applications. High equipment reliability and high-quality power are often required in these applications but are damaged by the transistor open-circuit fault. This paper proposes a novel real-time open-circuit fault diagnosis method for the T-type rectifier based on median current analysis. The method only uses the three-phase currents to obtain the median current and realize the diagnosis. In the rectifier, partial open-circuit faults result in weak fault signals, making the diagnosis challenging. To solve this, the median current obtained by the currents performs fault frequency modulation to enhance the fault signals at first. Then, the ratio of the fundamental frequency to the dc component is used to realize fault detection. The inner/outer fault is identified at the same time. At last, the fault transistor is located by simple change point detection. The change points determine the fault phase and transistor fault. The effectiveness and robustness of the proposed method are verified by the experiment results.

Instantaneous incremental current-based faulted phase selection algorithm

Instantaneous incremental current-based faulted phase selection algorithm

Detecting Bugs in Software using Supervised Machine Learning Approaches

Abstract: Software Flaw Projection (SFP) is an important issue in software development and maintenance process. Software flaws can cause significant problems for software development teams. So, projecting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust flaw projection model is a challenging task and many techniques have been proposed. Projecting the likelihood of flaws occurring in software can help developers prevent or mitigate their impact. This paper presents a software flaw projection model based on Machine Learning (ML) algorithms. Supervised ML algorithms have been used to predict future software faults based on historical data. The evaluation process proved that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance

Enhanced degradation properties of Mg-Gd-Cu alloys by introducing profuse solute segregated stacking faults phase

The Mg95.34Gd2.66Cu2 alloy containing profuse solute segregation stacking faults (SFs) phase was prepared through solution treatment and furnace cooling, and its effect on corrosion behavior and corrosion mechanism were investigated. After heat treatment, the microstructure distribution of continuous bulk-shaped Mg5(GdCu) phase at grain boundaries and profuse solute segregation SFs phases in the matrix was realized. Compared with the as-cast alloy without SFs phase, the corrosion morphology exhibited that the galvanic corrosion expanded rapidly along the length direction of SFs phase after heat treatment. The Mg95.34Gd2.66Cu2 alloy heat treated at 420 °C exhibited the optimal comprehensive performance, with the ultimate compressive strength (UCS), compressive yield strength (CYS) and degradation rate of 437 MPa, 291 MPa, 56.5 mg/(cm2·h), respectively. And the degradation rate increased by 40% compared with the as-cast alloy.

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency.

Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61×10−6, slightly lower than the desired level of 10−5.

Unsynchronized Parameter-Free Fault Location for Two or Three Terminal Double-Circuit Transmission Lines Sharing the Same Tower via Unscented Kalman Filter

This paper proposes a novel phasor domain fault location method for two or three terminal double-circuit transmission lines sharing the same tower without requiring line parameters. First, the models of the two and three terminal double-circuit lines with faults are derived. The line models fully consider the mutual coupling between the two line circuits, distributed line parameters, asymmetry of line structures, and extensive fault types. Through the modeling procedure, the fault location, line parameters, unsynchronized phase angle, fault resistance and other internal states of the line are introduced as unknown variables, resulting in a highly nonlinear problem. Afterwards, the line model is solved via the state estimation algorithm. Compared to the existing least square type algorithms such as Gauss-Newton and Levenberg Marquardt methods, the proposed Unscented Kalman Filter based algorithm greatly improves the convergence region. Compared to the existing parameter-free fault location method on double-circuit transmission lines, the proposed method more accurately presents the physical laws of the line and is compatible with common or split bus configurations. The proposed method is verified via numerous simulation experiments, proving that it can accurately locate faults for different fault resistances, locations and types. The method is also validated via field data experiments.

Fault-tolerant switched reluctance motor propulsion system for eVTOLs

eVTOLs are receiving a lot of attention as a potential solution to urban air mobility challenges. Many configurations are multirotors, which are open loop unstable, therefore very susceptible to actuator failures. Due to their usually short mission duration (20-30 min), fault-tolerance of the propulsion system is of greater importance then reliability. Thus, novel approaches to enhance this capability are required. This study proposes a new fault-tolerant propulsion system using 4-phase switched reluctance motors. It is designed for an 8-10 kg scale multirotor eVTOL, to replace redundant coaxial brushless DC motors with a single fault-tolerant drive. Acknowledging the role of fault-tolerant control algorithms, the propulsion system is validated in terms of the loss of effectiveness metric, typically used in the evaluation of control solutions. The switched reluctance motor propulsion system was found to be highly resilient to open phase and current sensor faults, but susceptible to position sensor faults. This can, however, be mitigated with sensorless control solution. Extending the findings to full-scale eVTOLs is also discussed.

Fault detection and fault tole rant control of interturn short circuit of double redundancy motor

This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor.

A Hybrid Signal Processing Technique for Identification and Categorization of Faults in IEEE-9 Bus System

A hybrid signal processing technique (HSPT) is proposed in this manuscript for identification and categorization of faults in electrical transmission network. A fault indicator (FI) is suggested by decomposition of the currents by application of Alienation coefficient (ACF), Stockwell transform (ST) and Hilbert transform (HT) for identification of faults. An indicator for ground involvement during faulty condition (SGFI) is being suggested to detect the type of fault. The categorization of faults is done by utilizing faulty phase numbers and SGFI. It is found that the proposed technique is effective in identification of faults and to classify them in different scenarios together with fault on A-phase to ground (AGF), double phase fault (ABF), fault on two phases and ground (ABGF), three phase fault (ABCF) and three phase fault including ground (ABCGF). Study is done and validated on IEEE-9 bus system using MATLAB/Simulink environment. The effectiveness and applicability of the proposed technique with respect to different parameters of faults such as Fault Incidence Angle, Fault Impedance, Line loading, Generator Supply and Noise level is also checked. The results shows that proposed scheme is able to detect and classify the faults in different faulty events.

Phase transformation behavior of aluminum under high hydrostatic pressure: A molecular dynamics study

The atomic mechanism of phase transformation under high pressure has been the focus of researches. In the present study, the roles of hydrostatic pressure (P) on phase transformation temperature and microstructural evolution in aluminum (Al) during cyclic thermal loading are studied from molecular dynamics simulations. The FCC → Liquid → FCC, FCC → liquid → FCC + BCC, FCC → BCC → Liquid → BCC and BCC → Liquid → BCC transformation pathways were observed under 0 ≤ P ≤ 14 GPa, 15 ≤ P ≤ 21 GPa, 22 ≤ P ≤ 25 GPa and 26 ≤ P ≤ 100 GPa, respectively, during cyclic thermal loading. It should be noted that the melting temperature predicted by the used potential at high P is obviously lower than experimental results. The FCC → BCC transformation involves nucleation and growth of BCC phase. The nucleation of BCC phase undergoes by the continuous deformation of FCC lattice, corresponding to the Bain transformation path. The growth of BCC phase is completed by the atomic diffusion. With increasing P, the 111<112̅> twin and {111} stacking fault in FCC phase evolve to five-fold twin and HCP phase, respectively. The microstructure of BCC phase is dominated by the {112}<110> twin. Insight obtained in this work paves the way for understanding the atomic mechanism of high pressure phase transformation.

Modal Transformation based Fault Location in Radial Distribution Network

This paper introduces the technique of fault distance estimation based on modal transformation and signal processing. The recorded faulted phase currents are applied to the Karrenbauer model transformation and these model component currents are decomposed into detail coefficients by the use of Daubechies wavelet, db6. The fault recorder installed at the terminal of the feeder records different time delays between the modal components. In order to find fault distance, the time delay values and modal components velocity are used in traveling wave theory. This paper compares two different conditions: the first condition does not use a modal transformation and the second condition uses a modal transformation. When using modal transformation conditions, three different coefficient levels (detail coefficient level 1 (D1); the combination of detail coefficient level 1+2 (D1+2) and the combination of detail coefficient level 1+2+3 (D1+2+3) ) are used to estimate the fault distance. Different fault types with different fault locations are created in MATLAB simulation.