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Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks.

This study introduces a novel approach to power system fault diagnosis by synchronised phasor measurements. Conventionally, faults are diagnosed through the status of protective relays and circuit breakers which are activated following a fault. However, the hidden failures of the protection system has itself often been among the main suspects of partial or widespread blackouts. This study proposes an alternative fault diagnosis approach independent of the function of the protection system. An analytical method is introduced for power system fault diagnosis using dispersed synchronised measurements and bus impedance matrix (Zbus). Fault inception is first detected by local phasor measurement units (PMUs). Fault diagnosis is then carried out in a hierarchical manner so that first the faulted zone of the system is diagnosed, next the faulted line in the faulted zone is diagnosed and finally the fault point along the diagnosed line is located by gradient descent. The proposed method is applied to the WSCC 9-bus, where fault incidents on all of the transmission lines are examined. Moreover, the proposed method is successfully applied to the IEEE 118-bus test system consisting of 28 PMUs, which demonstrates successful fault diagnosis and location for a large-scale power system despite the limited coverage of PMUs.

Cascade tripping of power lines triggered by maloperation of zone-3 relays during stressed system conditions, such as load encroachment, power swing and voltage instability, has led to many catastrophic power failures worldwide, including Indian blackouts in 2012. With the introduction of wide-area measurement systems (WAMS) into the grids, real-time monitoring of transmission network condition is possible. A phasor measurement unit (PMU) sends time-synchronized data to a phasor data concentrator, which can provide a control signal to substation devices. The latency associated with the communication system makes WAMS suitable for a slower form of protection. In this work, a method to identify the faulted line using synchronized data from strategic PMU locations is proposed. Subsequently, a supervisory signal is generated for specific relays in the system for any disturbance or stressed condition. For a given system, an approach to decide the strategic locations for PMU placement is developed, which can be used for determining the minimum number of PMUs required for application of the method. The accuracy of the scheme is tested for faults during normal and stressed conditions in a New England 39-bus system simulated using EMTDC/PSCAD software. With such a strategy, maloperation of relays can be averted in many situations and thereby blackouts/large-scale disturbances can be prevented. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

This paper presents a synchronized phasor measurement-based wide-area backup protection scheme which uses the magnitude of sequence voltages of buses at a system protection centre to identify the faulted bus closest to the fault and faulted line. The technique is tested for various faults including simultaneous faults in various systems with interconnections. The scheme is found to be accurate and fast with today's synchronized measurement technology. Analysis using simultaneous faults is a novel contribution in this paper. It is expected that this scheme will reduce the number of disastrous blackouts and improve the reliability and security of the power system. The required information is able to distinguish between balanced and unbalanced fault in the system. The study of new back up protection scheme is done on a WSCC-3 machine-9 bus system and an IEEE 14 bus test system. The data is simulated through EMTDC/PSCAD and MATLAB /SIMULINK softwares.

Impedance-based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model-based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model-based approach efficiently to estimate the fault location and fault resistance using artificial neural networks-based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.

This paper examines the impact of the effective stresses that develop during depletion of a faulted reservoir. The study is based on finite element modeling using 2D plane strain deformation analysis with pore pressure and elastoplastic deformation of the reservoir and sealing shale layers governed by the Drucker–Prager plasticity model. The mechanical properties and response of the rock formations were derived from triaxial test data for the sandstone reservoirs and correlation functions for the shale layers. A normal fault model and a reverse fault model were built using seismic data and interpretation of field data. The estimated tectonic in-situ stress field was transformed to the plane of the modeled geometry. Sensitivity studies were performed for uncertainties on the values of the initial horizontal stress and for the friction of the fault surfaces. It was found that the stress path during depletion is mainly controlled by the initial lateral stress ratio (LSR). The developed effective stresses with depletion are influenced by the fault geometry of the compartmentalized blocks. Plastic deformation develops for low LSR whereas for high values the system tends to remain in the elastic region. When plastic deformation takes place, it affects mainly the region near the fault. The reservoir deformation is dominated by vertical displacement which is higher near the fault region and nearly uniform in the remote area. The volumetric strain is dominated by compaction. More volatile conditions in relation to change of the friction coefficient and LSR were found for the normal fault geometry.

Hydrocarbon reservoir structures are subjected to tectonic forces along the geological time that cause rock deformation and break into faulted zones. Faulted reservoirs, enclose certain complexity in terms of the distributed effective stresses, rock plastic alteration, slipping and fault block displacement. In this study, we develop a three-dimensional (3D) geomechanical reservoir model with faulted and compartmentalized geometry, located in the offshore deepwater environment of the Levantine basin in the Eastern Mediterranean, based on non-linear finite element analysis (FEA). A regional structural and stress map was also constructed, integrating various data sources, to present the regional stress setting to enhance this work. The assessment of the geomechanical impacts on the reservoir provides important information in reservoir studies, that can analyze potential stability issues during the depletion to optimize the field production planning. Stress–strain evolution in the reservoir is primarily affected by the in situ stresses, the geometry of faults, and the degree of compartmentalization. The results demonstrate clearly the mechanism of stress transfer transmission and the impact between the fault block compartments in the reservoir. Fault contacts exhibit a higher tendency for rock displacements and deformations. Plastic yielding develops at a narrow extent along the faults. The risk of fault slipping depends on the depletion strategy, but it is low in all cases. No significant reduction in permeability was found at the end of reservoir depletion. Overall, geomechanics integration enriches and improves the dynamic reservoir models and applications. Highlights Integration of stress information and construction of a regional structural and stress map in the Eastern Mediterranean. Displacement magnitudes are controlled by structural boundary conditions, the geometrical shape of fault blocks, and the reservoir depletion strategy. Stress transfer impact to an idle fault block from the depletion of the adjacent block. Local stress anomalies along the fault are prone to stress redistribution and rotation.

During the construction of engineering projects, it is inevitable to cross fault and fractured zones, which are key geological factors that affect the stability of surrounding rock in tunnels. To study the distribution pattern of instability in surrounding rock and the optimization of synergetic support systems in fault-crossing tunnels, a comprehensive identification method integrating multi-source geological information was proposed, fully considering the geometric shape and distribution characteristics of rock fractures. The location of faults in actual projects was determined using this method, and a detailed three-dimensional numerical model was established accordingly. By simulating tunnel excavation, the spatial distribution pattern and grading characteristics of unstable blocks in surrounding rock were analyzed. Meanwhile, based on the original support methods, the effectiveness of synergetic support in stabilizing surrounding rock in tunnels was revealed, and initial support measures tailored to the characteristics of fault-crossing tunnels were proposed. The research results can provide reliable references for disaster prediction, prevention, and control in fault-crossing tunnels and underground engineering.

Hydrocarbon fluid migration through faulted cap rocks was determined by comparing the maximum connected thickness of cap rocks required for hydrocarbon fluid migration and the actual values, since cap rocks are important in the study of hydrocarbon fluid distribution in petroliferous basins based on its migration mechanism(s). The maximum connected thickness required was identified by comparing the cap rocks, fault displacement, and oil/gas distribution. The hydrocarbon fluid at the Putaohua reservoir migrated to the overlying Saertu and Heidimiao reservoirs in the Bayan Chagan Area, northern Songliao Basin. This was predicted to demonstrate the validity of the method. The results show that the adjusted Putaohua oil reservoir was distributed near the Talahai fault and Bayanchagan fault, rather than the Gulong sag in the southwest of the study area, where oil migrated vertically through the Sapu cap rocks to the overlying Saertu reservoir. Thick mudstone cap rocks in the second member of the Nenjiang Formation made it difficult for hydrocarbon fluid to migrate to the Heidimiao reservoir. This agrees well with hydrocarbon fluid distribution at the Putaohua, Saertu, and Heidimiao reservoirs in the Bayan Chagan Area, indicating that this method is feasible for predicting hydrocarbon fluid migration through faulted cap rocks.

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection.