Fault Section Research Articles

A subsystem‐based fault location method in distribution grids by sparse measurement

AbstractWith the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc.

The New Zealand Paleoseismic Site Database, Version 1.0

AbstractSite-specific paleoseismic data are key datasets underpinning the parameterization of large (Mw∼7+) earthquakes on faults in seismic hazard models. For the New Zealand National Seismic Hazard Model 2022 Revision Project (NZ NSHM 2022), a new database, the NZ Paleoseismic Site Database (NZ PSDB), was developed that contains paleoseismic (slip rate, earthquake timings, recurrence interval [RI], and single-event displacement [SED]) data for crustal (upper plate) active faults. The first edition version 1.0 (v.1.0) was modeled on datasets underpinning the Third Uniform California Earthquake Rupture Forecast (UCERF3) and builds on and significantly improves previous NZ compilations. Key improvements include documentation of the data underlying these parameters, such as offset measurements and ages for slip rate, and assignment of quality rankings for key attributes. The NZ PSDB v.1.0 contains 2811 records, subdivided into slip rate (871), earthquake timings (953), and SED (987); the earthquake timings dataset also includes 320 last event and 98 RI records. Because of time constraints, the most effort went into compiling the slip-rate records, in priority from highest to lowest slip rates, and the least amount of time was spent on the SED dataset, prioritizing records from historical earthquakes. The compilation also aimed to obtain as much geographical spread as possible, but the records only lie on a relatively small (maximum, 24%) number of faults and fault sections in the NZ Community Fault Model v.1.0. The most significant spatial gaps are offshore and in the southern and western South Island. The NZ PSDB v.1.0 contains both published and unpublished data, and many sites were relocated using high-resolution basemaps. However, radiocarbon ages were not recalibrated using a consistent calibration curve. Developing a process to efficiently recalibrate radiocarbon ages, as well as filling key data gaps, are current focuses of work toward v.2.0.

Paleoseismological Constraints on the Anghiari Normal Fault (Northern Apennines, Italy) and Potential Implications for the Activity of the Altotiberina Low‐Angle Normal Fault

AbstractThe NE‐dipping Anghiari normal fault, bounding to the west the Sansepolcro basin in the Upper Tiber Valley (northern Apennines), is thought to be a synthetic splay of the Altotiberina low‐angle normal fault (LANF), an active ENE‐dipping extensional detachment whose seismogenic behavior is debated. In order to assess the Anghiari fault capability to break the surface during strong earthquakes and be the source of historical earthquakes, we acquired high resolution topographic data, performed field survey and geophysical investigations (Seismic reflection, Ground Penetrating Radar, Electrical Resistivity Tomography) and dug three paleoseismological trenches across different fault sections of the Anghiari fault. The acquired data reveal for the first time the Late Pleistocene to historical activity of the Anghiari fault, constraining the age of seven paleo‐earthquakes over the last 25 ka, the youngest of which is comparable with one of the poorly constrained historical earthquakes of the Sansepolcro basin. The yielded slip rate is >0.2 mm/yr averaged over the last 25 ka and the recurrence interval is about 2,500–3,200 years. An analysis of the anisotropy of the magnetic susceptibility performed in one of the paleoseismological trenches revealed an extensional stress field, continuously acting during the sedimentation of the entire trenched stratigraphy. Our results confirm the ability of the Anghiari fault to generate surface faulting earthquakes. In addition, if the Anghiari fault does sole at depth into the Altotiberina low‐angle normal fault, this LANF could also be seismogenic and generateM > 6.

SLIP SOURCE MODEL OF THE 1995 NEFTEGORSK EARTHQUAKE (NORTH SAKHALIN) FROM GEODETIC DATA

The May 27, 1995 Mw=7.0 Neftegorsk earthquake occurred in the north of Sakhalin Island, rupturing the Upper Piltun fault, a secondary feature of the main Hokkaido-Sakhalin regional fault zone. The fault geometry, coseismic slip model, and Coulomb stress changes in the earthquake focal area were calculated based on a finite fault modeling. We used near-field coseismic offsets at 24 points obtained by comparison between predating triangulation and GPS observations, which were collected before and after the earthquake. Our slip distribution model shows two major slip patches. Larger slip asperity (amplitude up to 6.36 m) was characterized by right-lateral strike-slip movements, which correspond to focal mechanism of the earthquake, whereas the northern segment has reverse fault mechanism with maximum slip of 2.64 m. The fault length and width, average slip and stress drop values are estimated at 78 km, 28 km, 1.91 m and 11.3 MPa, respectively. The estimated release moment is approximately 7.49×1019 N∙m equal to Mw=7.2, which is larger than that reported by the USGS and GCMT but consistent with the values reported by other researchers. The coseismic Coulomb stress changes enhanced the stress by more than 10 MPa on the southern segment of the Gyrgylaninsky fault and middle section of the Hokkaido–Sakhalin fault. Seismic risks on the nearest faults cannot be ignored in the future despite the fact that the earthquake with a magnitude of 5.8 occurred in 2010 near the Gyrgylaninsky fault. The recent GPS rates in the surroundings of the Neftegorsk surface rupture mean that the recurrence interval for similar earthquakes may be more than a thousand years.

A Single-Ended Fault Location Method for Transmission Line Based on Full Waveform Features Extractions of Traveling Waves

Conventional single-ended traveling wave fault location methods, which commonly depend on partial features of traveling wavefronts, may lead to fault location failure, especially for weak-signal faults such as high impedance or zero-crossing faults and close-in faults. To tackle it, this paper presents a fault location method according to the extracted panoramic features of traveling wave full waveform (TWFW) in time-frequency domains. Firstly, two rules of the TWFW features are probed, that is, the wavefront arrival sequence is varied from fault sections, as well as the frequency distribution of wavefronts is strongly related to the precise fault distance. Ergo, the mapping relationships between the TWFW and fault distance are qualitatively confirmed in order to demonstrate the uniqueness of the TWFW subsequently. Next, a LeNet-5-based convolution neural network (CNN) model is constructed to quantitatively evaluate such mapping relationships. In this model, various TWFW features will be extracted to the convolution channels when the CNN parameters are optimized adaptively, and the mapping relations can be formed in light of these sensitive features to estimate the fault distance. Finally, a Grad-CAM visualization method is deployed in the case study, and the accuracy along with the robustness of the proposed method in various fault conditions can be validated consequently.

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

Fault Section Identification of Hybrid Transmission Lines by the Transients in Modal Domain Free From the Refractions and Reflections at Cross-Bonded Nodes

Compared with the homogeneous transmission lines, the hybrid ones are composed of the overhead, underground and submarine cable lines, resulting in three different sets of parameters over the transmission path and posing great challenge to fault location. This paper proposes a fault section identification method for two types of the hybrid lines based on modal-domain analysis which eliminates the effects of the refractions and reflections at the cross-bonded nodes. First, a method for decoupling the transients in the high-voltage cable is proposed to obtain the unified model of two types of the hybrid lines in modal domain. Then, the concept of the energy of the decoupled transient is defined, the reflection and refraction features of the energy of the transient signal are analyzed, and a novel fault section identification method is developed based on the transient energy ratio. Finally, the model of a 110kV hybrid transmission line is established in PSCAD/EMTDC. Various fault experiments are conducted. The results show the effectiveness of the proposed method.

Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems

This paper puts forward a fault location method combining the improved matrix algorithm and the genetic tabu algorithm based on multi-source information in view of the limitation of existing fault location methods in active distribution networks, such as long-time consumption, low accuracy, and low tolerance performance. Firstly, the fault region location method is proposed to simplify the active distribution network model and reduce the matrix calculation dimension. Secondly, the improved matrix algorithm is proposed to locate the fault section, and a positioning result verification method is proposed to improve the tolerance performance. Finally, the genetic tabu algorithm is proposed to optimize suspicious fault sections when the verification is incorrect and obtain the positioning result. The simulation results show that the fault location method proposed in this paper locates faster and performs better in both accuracy and tolerance in different fault conditions than others.

An intelligent method for TBM surrounding rock classification based on time series segmentation of rock-machine interaction data

Accurate, continuous real-time perception of surrounding rock conditions on the tunnel boring machine (TBM) tunnel face provides particularly important prior information to ensure safe, economic, and efficient tunneling. This study presents an intelligent method for assessing the TBM surrounding rock conditions using a combination of time series segmentation and a deep clustering model (SSAE-FCM). In data preprocessing, fifteen operational parameters were selected as input features. And an adaptive piecewise constant approximation method was employed for the segmentation and compression of stable phase data. Then, a total of 69,214 data samples that can characterize the stable rock-machine interaction were obtained to construct a dataset. Thereafter, the SSAE-FCM model was established and trained to classify the surrounding rock conditions into four clusters. The distribution result of the main operational parameters and composite indexes (TPI, FPI, and SE) within these clusters demonstrated that the classification results are compatible with rock-machine interaction in engineering practice. Additionally, the rock mass assessment performance of the classification results was compared to BQ classification in terms of various rock mass parameters (UCS, Jv), fault sections, and collapse sections. These results indicate that the proposed classification method accurately reflects the stability and boreability of rock mass and is more sensitive to unfavorable geology than the BQ classification. From the three clustering evaluation indexes (SC, CHI, and DBI) perspective, SSAE-FCM was superior to the other clustering models (BIRCH + k-means++, FCM, and SSAE + FCM). In conclusion, the proposed intelligent method for TBM surrounding rock classification is reasonable and applicable for the evaluation of rock mass quality, as well as accurate and sensitive in real-time perception.

Lacustrine evidence reveals spatially and temporally distinct Holocene ruptures on the Sawtooth Fault, Central Idaho, USA

ABSTRACTThe Sawtooth Fault in central Idaho, USA, is a range‐bounding normal fault with a Late Pleistocene–Holocene scarp near the up‐valley ends of several range‐front lakes. Cores from Redfish Lake, which spans the fault, exhibit evidence of catastrophic sediment re‐mobilization in two sequences consisting variably of intraclastic mud‐clast conglomerate, massive homogenite, and graded silt and clay. Event strata of the younger sequence are in unconformable contact with Pleistocene sediments in two cores on the northern lake‐basin slope, suggesting transport from the down‐valley portion of the lake toward the basin center. A silt cap associated with the event strata is preserved at all coring sites. The event sequence, distributed lake‐wide, dates to ca. 4300 cal a bp, while additional event strata in two Redfish Lake cores pre‐date the 7627 cal a bp Mazama ash. We propose seismic triggering of the sediment disruptions. Asynchronous event stratigraphy in Pettit Lake, 15 km south of Redfish Lake, suggests distinct ruptures along two portions of the Sawtooth Fault. The sedimentology and geochemistry of remobilized sediments in both lakes indicates that sources included Late Pleistocene and early Holocene lake‐floor sediments and suggests failures that cut deeply into the sedimentary sequence. Cores collected from Stanley Lake (17 km northwest of Redfish Lake) following the nearby 2020 M6.5 Stanley earthquake exhibit a thin, sandy event horizon associated with documented delta collapse. Subaerial slope failures associated with the 2020 event left no widespread disturbance stratigraphy in Redfish Lake. We conclude that the large‐magnitude lake‐floor sediment remobilization events probably required rupture of fault sections proximal to Redfish and Pettit lakes.

Two-stage fault section location for distribution networks based on compressed sensing with estimated voltage measurements

Methods for fault location in distribution networks based on compressed sensing (CS) have been investigated and the results have demonstrated some degree of feasibility over the past years. However, in the case of a fault occurring close to a line’s terminal, the existing methods typically cannot locate the fault section correctly but just locate the fault to the bus near the fault point or adjacent section rather than the fault section. To improve the accuracy of fault location, a two-stage fault section location method is proposed in this paper. In the first stage, the CS-based model is solved by using all the measured voltages, and the fault candidate sections are identified. In the second stage, the node impedance matrix of the subnetwork formed by the fault candidate sections are calculated, and the voltage amplitudes of the buses around this subnetwork are estimated, which are then used to solve the CS-based model to finally determine fault section. Validations are implemented on a modified IEEE 33 bus system and a modified 134-bus system. The results verify a satisfactory performance in fault section location under various fault conditions.

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.

Two-terminal traveling-wave-based non-homogeneous transmission-line protection

This paper presents a two-terminal traveling-wave-based protection algorithm applied to non-homogeneous transmission lines comprising any number of sections with different topologies and considering the effect of the sampling rate. Existing two-terminal traveling wave protection functions cannot protect the line under close-in faults and present limitations in non-homogeneous transmission lines. However, the effects of the sampling rate, considered in the proposed method, result in well-defined protection and unprotected zones, essential for protection security and coordination to deal with the issue of close-in faults in non-homogeneous transmission lines. Furthermore, the algorithm can accurately detect the faulted section, allowing its use in advanced protection functions such as adaptive automatic auto-reclosing and high-speed protection schemes. A protection device was modeled considering a sampling frequency equal to 1 MHz, including functions to detect traveling waves via wavelet transform, and the proposed protection algorithm to discriminate line internal from external faults, and to select the faulted section. The algorithm was evaluated using a large number of ATP fault simulations. The results show that the algorithm is robust and reliable for protection devices installed in non-homogeneous lines.

Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems

Fault section diagnosis (FSD) is extremely critical to ensure the safe and stable operation of power systems. Neural networks have become popular increasingly in solving this problem and the existing applied ones rely primarily on the traditional BP and RBF neural networks. However, their generalization is weak when diagnosing large-scale power systems. To address this issue, this study suggests a divisional FSD method based on structure adaptive hierarchical extreme learning machines (HELM). To construct an optimal HELM-based sub-module diagnosis model for each subsystem of a large-scale power system, differential evolution is applied to optimize both the number of hidden layer neurons and the regularization factor of HELM simultaneously. The optimal HELM is responsible for completing both the diagnosis of internal power sections of the subsystem and the interim diagnosis of tie lines of adjacent subsystems. Based on the interim diagnosis results, Choquet fuzzy integral is used to fuse them to obtain the final results for the tie lines. Simulation results indicate that the differential evolution algorithm is effective in finding more accurate and compact HELMs with better approximation ability and generalization performance. The proposed FSD method has higher accuracy and fault tolerance to identify various complex fault cases.

Fault Section Identification of the Power Cables in Urban Distribution Networks by Amplitude Differences Between the Zero-Sequence Currents and Those Flowing in Cable Sheaths and Armors

The power supply reliability of the urban distribution networks is being challenged by cable faults. Owing to the complex factors, such as system neutral, fault condition, and DG integration, it is difficult to detect and locate the fault cable. In this paper, the equivalent circuit model of the distribution cable is firstly created by considering the multiconductor couplings. The mathematical relation of the amplitude differences between zero-sequence currents and those flowing in cable sheaths and armors at the local end of the healthy and fault cables are then derived in theory. Finally, according to the network topology and measurement locations, a novel method for identifying the fault cable section is proposed by using the defined space-measurement matrix. The simulation model of a real urban distribution network is established in PSCAD/EMTDC. The effectiveness and robustness of the proposed method are validated. The results demonstrate that the method is barely affected by the neutral grounding mode, fault initial condition, and distributed generation (DG) penetration level.

A fuzzy-based fault section identification method using dynamic partial tree in distribution systems

In a distribution management system (DMS), fault section identification (FSI) is an essential function for the self-healing capability of smart distribution networks. Conventional FSI methods based on the flag of fault indicators (FIs) cannot be properly utilized for distribution networks with distributed energy resources (DERs) since false FIs, unintentionally activated by fault currents flowing from the downstream of a fault location due to the presence of wye-delta transformers for the DER interconnection, are expected to be generated. For more accurate FSI, this paper proposes a fuzzy-based FSI method using the fault current information measured on feeder remote terminal units (FRTUs) installed at automatic switches in distribution lines (DLs). The proposed method takes advantage of the fuzzy evaluation to provide an intuitive index for decision-making by quantifying the possibility of fault for each candidate section. Furthermore, to achieve faster FSI by reducing waiting time for FSI, the partial trees using only validated FIs with fault current information for the faulty DL are dynamically generated, thereby enabling the fault section can be identified regardless of whether the fault current information from every FRTU is available or not. Case studies on IEEE 33-bus distribution system considering various fault conditions are conducted and simulation results show that the proposed method can accurately identify correct fault sections under false FI and communication failure situations with the improved time required for the FSI.

Fault Section Location Method for Distribution Network Based on Waveform Difference Clustering

The conditions of single-phase grounding faults are variable in the resonant grounded system, and the traditional fault location methods of the distribution system based on a single characteristic quantity have their own discrimination blind area, and the reliability of the method is difficult to guarantee. In order not to be troubled by the problems, a fault section location method based on waveform difference degree clustering is proposed. According to the differential characteristics of transient and steady component waveforms of zero-sequence current at each detection point upstream and downstream of a fault point, the discrete Fréchet distance algorithm is used to quantify the transient waveform difference and steady-state waveform difference between adjacent detection points, and Fuzzy C-Means algorithm (FCM) is used to synthesize multi-feature information to distinguish the normal section from the fault section, which effectively eliminates the single feature location blind area and realizes adaptive fault section location. The PSCAD/EMTDC simulation results show the method is suitable for accurate location under different fault conditions.

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

A ground fault section location method based on active detection approach for non-effectively grounded DC distribution networks

Identifying and locating the single-pole-to-ground fault for the non-effectively grounded DC distribution networks is highly challenging due to the topology arrangement limitations and the grounding mode. To solve this problem, a ground fault section location method based on active detection approach is proposed. Firstly, an approach based on an additional control strategy in coordination with a resonant branch setup is proposed to inject the detection signals. Then, the parameters of the resonant branch and the detection signals to be injected are obtained after careful consideration to ensure the optimal distribution characteristics of the injected signals. Finally, a protection principle based on a coordinating time interval scheme is proposed to locate and isolate the fault section according to the single terminal information. Simulation results show that the proposed method achieves excellent performance.

Physics-based earthquake simulations of the Anninghe-Zemuhe-Daliangshan-Xiaojiang fault system in Southwestern China

Seismic hazard evaluation is important for urban construction and earthquake disaster prevention and mitigation. However, because of the long recurrence intervals of large earthquakes (hundreds to thousands of years), seismic hazard assessment based on relatively short-term (tens of years) observational seismic catalogs is difficult. Synthetic seismic catalogs provide a useful way to assess long-term seismic hazards. Based on the Coulomb failure criterion, we apply a quasi-static physics-based earthquake simulator (Virtual Quake) in a tectonically complicated region with several major strike-slip faults, namely, the Anninghe, Zemuhe, Daliangshan and Xiaojiang (AZDX) faults, in southwestern China. The slip rates of these major faults are constrained by GPS data, and friction is constrained by laboratory experiments. Considering the stress interactions among the fault system, a synthetic catalog over 20,000 years is simulated. The simulated catalog shows consistency with the observed seismicity in the spatial distribution of large earthquakes, b value and mean seismic rate. Based on the synthetic catalog, the mean recurrence intervals and probabilities of the recurrence interval for strong earthquakes (M ≥ 7) in each fault section are presented in detail. Moreover, qualitative analysis of seismic hazards is conducted for the AZDX fault region. Our results provide a helpful index to evaluate seismic hazards in such a complicated region. Plain language summaryEvaluating seismic hazards is important for building codes, risk prevention and mitigation. Because large earthquakes are rare, modern observed earthquake catalogs are too short to evaluate seismic hazards precisely. Thus, numerical simulations are used to obtain synthetic earthquake catalogs for seismic hazard evaluation. Here, we present a physics-based model of stress accumulation, transfer and release, and simulate an earthquake sequence in the Anninghe-Zemuhe-Daliangshan-Xiaojiang fault system, southwestern China. Thus, we obtain a synthetic catalog over 20,000 years. By comparing the spatial distributions of large earthquakes, b values, and mean seismic rates of the synthetic catalog and observed catalog, we find that the synthetic catalog is consistent with the observed catalog. Based on the synthetic seismic catalog, we summarize the mean strong seismic recurrence interval and the probability of the recurrence interval for each fault section. Qualitative analysis of seismic hazards is conducted for the AZDX fault region. Our results can help to evaluate long-term seismic hazards in a multiple-fault system.