Search Results (56)

The WieZhou12 oilfield (also known as WZ12 oilfield, the same below) is in urgent need of development using large-scale volumetric fracturing technology since it is a typical complex fault-block oilfield with low porosity, low permeability, and no natural production capacity. To study the fracturing measures with surfactants in offshore oilfields like WZ12, the surfactant fracturing fluid types were experimentally selected based on their effect of decreasing interfacial tension and enhancing matrix wettability. The water cut law and oil displacement efficiency in displacement experiments were also analyzed, according to surfactant type and fluid characteristics. Next, using the numerical simulation software CMG, the study completed the integrated simulation of volumetric fracturing in the “injection–soaking–flowback” process. Finally, some critical parameters were optimized for the block model, including the quantity of injected fluids, the soaking time, and the rate of fluid flowback. The results showed that the most suitable surfactant was 0.5% ammonium lauryl polyether sulfate (ALES), which had a low interfacial tension of 1.7 × 10⁻² mN/m, a contact angle of 20.071° with the core, and a 52% oil displacement efficiency. From the simulations, the suggested production parameters for energy storage fracturing are as follows: a daily injection volume of 600 m³/d, a soaking time of 25 days post fracturing, and a fluid production rate of 270 m³/d. The findings of this study establish a significant theoretical foundation for optimizing surfactant type and provide construction advice for the integrated measure of fracturing, well shut-in, and production in offshore oilfields. Full article

With the growing integration of renewable energy into power systems, transient stability throughout the whole fault process has become a critical issue. This process comprises three distinct stages: pre-fault, fault-on, and post-fault recovery. However, existing studies have largely overlooked the influence of active power recovery on transient stability, which leads to conservative estimates of critical fault clearing time (CCT) and potential misjudgment of stability analysis. Accordingly, this paper addresses this gap by examining a grid-following (GFL) photovoltaic (PV) converter grid-connected system. Therefore, this paper investigates the transient stability of a GFL PV converter grid-connected system during the whole fault process. Firstly, a transient stability analysis model is developed using the piecewise linearization method to represent the system behavior across the whole fault process. Secondly, based on the proposed model, the impact mechanism of the control strategy in the fault recovery stage on the transient stability of the system is revealed by using the equal area criterion (EAC). Finally, the accuracy of the theoretical analysis proposed in this paper is verified by the PSCAD/EMTDC simulation platform. The results show that a slower active power recovery rate enhances the system’s transient stability, as it creates a larger equivalent deceleration area. The critical fault clearing time calculated by the proposed model is less conservative. Full article

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources—such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems—has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system’s transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources. Full article

This paper proposes a novel multi-dimensional reliability evaluation method for distribution network under renewable energy installation and load installation, considering fault reconfiguration and multiple constraints. Firstly, the related output models of distribution network are presented. Secondly, a post-fault load restoration model of the distribution network by considering fault reconfiguration and multiple constraints under distributed generation integration is established in our study. Subsequently, the reliability evaluation indices for the distribution network, including system average interruption duration indicator, customer average interruption duration indicator, system average interruption frequency indicator, and average service availability indicator, are introduced. And the process of reliability evaluation founded on the sequential Monte Carlo simulation is also explained. Finally, simulations were performed on an improved IEEE 69-node distribution system to verify the proposed method, and the reliability of the distribution network from the distributed generation installation, types of distributed generation installation, distributed generation installation capacity, and new load installation is discussed in-depth. Full article

With the increasing penetration of renewable energy generation, large-scale voltage dips may cause significant active power deficits and threaten system frequency stability. To address the issue, this article proposes a two-stage control strategy to enhance the frequency support capability of renewable energy plants by maximizing converter utilization during asymmetric grid voltage dips. First, a qualitative analysis of converter active power capacity considering current capacity constraints under grid faults is conducted to establish the basis for mitigating system-wide active power deficits. Second, individual phase current constraints are formulated for converters under asymmetric voltage conditions to achieve full utilization of converter capacity. Based on this, a two-stage control strategy for renewable energy plants is proposed, where plant-level convex optimization models for both pre-fault and post-fault conditions are established. By optimally allocating current references of converters within the plants, the requirement of grid codes is satisfied, and the overall frequency support capability of plants is effectively improved. Simulation results demonstrate that the proposed strategy raises the system frequency nadir from 49.58 Hz to 49.66 Hz under a minor fault and from 49.06 Hz to 49.11 Hz under a severe fault, confirming its effectiveness in enhancing the frequency support capability of renewable energy plants. Full article

Earthquakes threaten the security and stability of urban integrated energy systems. Enhancing system resilience improves the ability to withstand seismic hazards. This paper proposes a coordinated post-disaster restoration strategy for integrated electricity and natural gas systems (IENGSs) that exploits natural gas line pack under seismic conditions. First, a line pack model is developed to quantify its impact on IENGS resilience. Subsequently, leveraging the load-supporting capability of line pack, we investigate how distribution network reconfiguration influences IENGS load recovery. Accounting for cross-system fault propagation during earthquakes, we formulate a post-disaster repair strategy incorporating line pack flexibility. Case studies using the IEEE 33-bus power system and a 7-node natural gas system validate the proposed strategy’s effectiveness and feasibility in enhancing seismic resilience. Full article

In the post-disaster recovery of power distribution networks (PDNs), electric vehicles (EVs) possess a great potential as mobile energy storage units. When supported by vehicle-to-grid (V2G)-enabled charging stations, EVs can provide effective supplementary power for disaster-stricken areas. However, most existing stations only support unidirectional charging, limiting the resilience-enhancing potential of V2G. To address this gap, this paper proposes a resilience-oriented restoration optimization model that jointly considers the siting of V2G-enabled charging stations and PDN topology reconfiguration. A novel metric—Resilience Entropy—is introduced to dynamically characterize the recovery process. The model explicitly describes fault propagation and circuit breaker operations, while incorporating power flow and radial topology constraints to ensure secure operation. EV behavioral uncertainty is also considered to enhance model adaptability under real-world post-disaster conditions. The optimal siting scheme is obtained by solving the proposed model. Case studies demonstrate the model’s effectiveness in improving post-disaster supply and recovery efficiency, and analyze the impact of user participation willingness on V2G-based restoration. Full article

The fracture evolution of brittle rock under 3D stress states remains poorly understood, particularly the role of deviatoric stress symmetry as characterized by the Lode angle (θ_σ). To address this knowledge gap, we systematically investigated sandstone using a true triaxial loading apparatus under a constant mean stress (100 MPa) while varying θ_σ from −30° to +30°, integrated with real-time acoustic emission (AE) monitoring and post-failure X-ray computed tomography. Our results demonstrate a critical linear reduction in peak strength with increasing θ_σ. This strength reduction is accompanied by a corresponding transition in failure mechanism, from a progressive mode dominated by dilation to an abrupt mode characterized by shear localization. Innovatively, we introduce a novel AE-based parameter (C), derived from the coupled evolution of AE energy and hit rates, which quantifies fracturing intensity in real-time without pre-defined lithological and monitoring scale. Furthermore, digital reconstruction reveals that consistent X-shaped polymodal fault networks are governed by sequential tensile–shear interactions. This key mechanistic insight prompts us to propose a new “deflected shear–tensile hybrid kinked fracture” classification, which more accurately captures the topological complexity of fractures under 3D stresses than conventional monomodal schemes. This study provides fundamental insights into 3D rock fracture mechanics and practical tools for enhancing safety and efficiency in deep geo-engineering. Full article

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in power system applications, such as transmission line inspection, fault diagnosis, and post-disaster emergency communication restoration. Nonetheless, limitations of range and battery capacity have rendered the assurance of uninterrupted task operation a critical concern. Efficient cooperation and energy replenishment solutions are crucial for effective UAV cluster scheduling to resolve this issue. This study proposes an innovative scheduling method that integrates UAV path planning with laser-based remote charging technology. Initially, a scheduling model incorporating both energy consumption and task completion time is established. Subsequently, an integrated laser-powered UAV model is proposed, unifying charging operations with mission execution processes. Furthermore, a distributed robust optimization (DRO) framework is proposed to handle spatiotemporal uncertainties, particularly those caused by weather conditions. Finally, the proposed scheduling method is applied to a disaster recovery scenario of a power system. Simulation results demonstrate that the proposed strategy significantly outperforms traditional scheduling methods without remote charging by achieving higher task completion rates and improved energy efficiency. These findings substantiate the effectiveness and engineering feasibility of the proposed method in enhancing UAV cluster operational capabilities under stringent energy constraints. Full article

Compared to the traditional unidirectional power supply system, the bidirectional traction power supply system in an electrified railway offers advantages like improved traction voltage and reduced energy losses, making it more suitable for steep gradient routes. However, its increased electrical complexity necessitates advanced catenary-rail short-circuit fault calculations and relay protection calibration. This paper proposes a fault calibration approach based on deriving electrical quantities with fault distance in the railway bidirectional traction grid system. A multi-loop circuit modeling method is used to accurately model the traction grid system and impedance parameters, incorporating real loop circuits formed by the grid transmission and return conductors for the first time. The approach is validated through real-life experiments on a Chinese railway line. A case study of a direct power supply system with a return cable is used to derive electrical quantities. Faults are categorized into two sections: between the substation and the parallel station (PS), and between the PS and the section post (SP). For each section, electrical quantities are derived under unidirectional substation excitation, and the results are superimposed to obtain fault distance variation curves for currents and voltages of substation, PS, SP, and Thévenin impedance. Finally, a calibration strategy for relay protection is presented. Full article

With the large-scale integration of renewable energy devices into the power grid, the voltage stability of the renewable energy base is becoming increasingly weak, and the problem of transient overvoltage is becoming increasingly severe. Grid-forming (GFM) converters can provide strong voltage support. When GFM converters are paralleled with grid-following (GFL) converters, they can effectively reduce transient overvoltage. However, hybrid systems involve many parameters and exhibit complex dynamics, making assessment of transient overvoltage difficult. To address this, this paper first uses Thevenin’s theorem to reduce the renewable transmission system to an equivalent model. Next, the voltage assessment of the hybrid system is analyzed across the pre-fault, mid-fault, and post-fault stages of a short-circuit fault. Then, based on the characteristics of a phase-locked loop (PLL), this paper innovatively derives an assessment method for transient overvoltage at the common coupling point (PCC) under different PLL stability conditions. Additionally, the influence of GFL converter parameters, GFM converter parameters, the GFM capacity ratio on transient overvoltage, and the external system reactance are analyzed. Finally, the proposed evaluation method and factor analysis are validated through electromechanical transient simulation using the simulation software STEPS v2.2.0. Full article

This study develops a novel transverse steel damper (TSD) to enhance the seismic performance of tall-pier girder bridges, featuring superior lateral strength and energy dissipation capacity. The TSD’s design and arrangement are presented, with its hysteretic behavior simulated in ABAQUS. Key parameters (yield strength: 3000 kN; initial gap: 100 mm; post-yield stiffness ratio: 15%) are optimized through seismic analysis under near-fault ground motions, incorporating pulse characteristic investigations. The optimized TSD effectively reduces bearing displacements and results in smaller pier top displacements and internal forces compared to the bridge with fixed bearings. Due to the higher-order mode effects, there is no direct correlation between top displacements and bottom internal forces. As pier height decreases, the S-shaped shear force and bending moment envelopes gradually become linear, reflecting the reduced influence of these modes. Medium- to long-period pulse-like motions amplify seismic responses due to resonance (pulse period ≈ fundamental period) or susceptibility to large low-frequency spectral values. Higher-order mode effects on bending moments and shear forces intensify under prominent high-frequency components. However, the main velocity pulse typically masks the influence of high-order modes by the overwhelming seismic responses due to large spectral values at medium to long periods. Full article

Coal mine disaster safety monitoring often employs microseismic technology for its high sensitivity and real-time capability. However, nonlinear, non-stationary, and multi-scale signals limit traditional time series models (e.g., ARMA, ARIMA). This paper proposes a hybrid deep learning model—VMD-SE-CEEMDAN-BO-CNNGRU—integrating variational mode decomposition, sample entropy, CEEMDAN, Bayesian optimization, and a CNN-GRU architecture. Microseismic data from the 08 working face in D mine (Weibei mining area) were used to predict daily maximum energy, average energy, and frequency. The model achieved high predictive performance with R² values of 0.93, 0.89, and 0.88, significantly outperforming baseline models lacking modal decomposition. Comparative experiments verified the superiority of the VMD-first, SE-reconstruction, and CEEMDAN-second decomposition strategy, yielding up to 13% greater accuracy than reverse-order schemes. The model maintained R² above 0.80 on another dataset from the 03 working face in W mine (Binchang mining area), demonstrating robust generalization. Although performance declined during fault disturbances, accuracy for average energy and frequency rebounded post-disturbance, indicating strong adaptability. Overall, the VSCB-CNNGRU model enhances both accuracy and stability in microseismic prediction, supporting dynamic risk assessment and early warning in coal mining. Full article

In distribution networks with high penetration of distributed generators (DGs), traditional fault reconfiguration strategies often fail to achieve maximum load recovery and encounter operational stability challenges. This paper proposes a novel two-stage fault reconfiguration strategy that addresses both the fault ride-through capability and output uncertainty of DGs. The first stage introduces a rapid power restoration reconfiguration model that integrates network reconfiguration with fault ride-through, enabling DGs to provide power support to the distribution network during faults, thereby significantly improving the recovery rate of lost loads. An AdaBoost-enhanced decision tree algorithm is utilized to accelerate the computational process. The second stage proposes a post-recovery optimal reconfiguration model that uses fuzzy mathematics theory and the transformation of chance constraints to quantify the uncertainty of both generation and load, thereby improving the system’s static voltage stability index. Case studies using the IEEE 69-bus system and a real-world distribution network validate the effectiveness of the proposed strategy. This two-stage strategy facilitates short-term rapid load power restoration and enhances long-term operational stability, improving both the resilience and reliability of distribution networks with high DG penetration. The findings of this research contribute to enhancing the fault tolerance and operational efficiency of modern power systems, which is essential for integrating higher levels of renewable energy. Full article

The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to provide resilience is not enough to justify having them installed economically. Therefore, this paper proposes a two-stage stochastic mixed-integer programming (SMIP) model for the configuration of stationary energy storage systems (SESSs) and mobile energy storage systems (MESSs) during earthquakes. The proposed model comprehensively considers both normal and disaster operation scenarios of DNs, maximizing the grid’s economic efficiency and security. The first stage is to make decisions about the location and size of energy storage, using a hybrid configuration scheme of second-life batteries (SLBs) for SESSs and fresh batteries for MESSs. In the second stage, the operating costs of DNs are evaluated by minimizing normal operating costs and reducing load loss during seismic events. Additionally, this paper proposes a scenario reduction method based on hierarchical sampling and distance reduction to generate representative fault scenarios under varying earthquake magnitudes. Finally, the progressive hedging algorithm (PHA) is employed to solve the model. The case studies of the IEEE 33-bus and 12-node transportation network are conducted to validate the effectiveness of the proposed method. Full article