With the large-scale integration of grid-forming wind turbines via LCC-HVDC transmission, the large-disturbance stability of such systems has become a critical concern due to complex interactions among multiple power electronic converters. This paper proposes a unified stability analysis framework based on the mixed potential function theory for a VSG-DFIG fed LCC-HVDC system under AC voltage sags at the point of common coupling. By incorporating the physical dynamics and constant-current control of the rectifier, and equivalently representing the PI controllers of the VSG-DFIG as RLC circuits, an equivalent circuit model of the entire system is established. The large-signal stability criterion is then derived, which explicitly reveals the influences of control parameters, circuit parameters, and the depth of AC voltage sags on the stability boundary. To enhance system stability, a virtual resistor strategy is introduced in the LCC-HVDC control loop. Finally, time-domain simulations in Simulink are carried out, and the results fully validate the theoretical analysis as well as the effectiveness of the proposed virtual resistor scheme.
The solar–air source heat pump (S-ASHP) system is used to prepare domestic hot water and is an important form of hot water preparation in many colleges and universities in China. However, the traditional design specifications and operation control strategies can cause excessive design capacity for the hot water system and energy waste. To address these issues, in this study, we integrate the S-ASHP hot water system with predictive modeling of user demand, proposing two distinct control strategies for the hot water supply system. Scheme I: The traditional hot water demand specification is used for design, and the feedback control strategy of the end hot water demand is set according to a fixed mode. Scheme II: A feedforward–feedback combined control strategy based on the ANN-LSTM-STA model is used to predict water consumption and set the end hot water demand. Compared with Scheme I, Scheme II shows significant advantages in heat supply, total energy consumption, and coefficient of performance (COP). The heat supply of the solar collector (SC) supply system is increased by 9.47%, the heat supply of the ASHP supply system is significantly reduced by 57.52%, the running time is reduced by 33.45%, the pump’s energy usage decreases by 16.65%, and the overall system energy consumption drops by 43.85%. Additionally, the coefficient of performance (COP) improves by 6.42%, and the coefficient of performance of the system (COPsys) sees a significant increase of 18.39%.
This work presents a framework for national-scale onshore wind farm screening in Brazil based on turbine technology. The approach integrates ERA5 hourly wind fields, turbine technical specifications, a Random Forest Regression (RFR)-based turbine power-output surrogate model, and GIS-based spatial exclusion analysis. The RFR model was trained using 821 empirical power curves, enabling different real turbine models to be evaluated within a unified framework. Seven representative turbines were evaluated across five wind farm configurations under capacity-factor thresholds of 0.20, 0.25, and 0.44. Results show that turbine technology substantially changes site suitability: for CF≥0.20, viable locations ranged from 116 to 1846, and for CF≥0.25, from 19 to 1175. Only 298 locations nationally reached CF≥0.44, with the two best-performing configurations accounting for 297 of them. Even between these similarly rated turbines, neither dominated nationally: one outperformed the other at 58.5% of viable locations, with the reverse holding for the remaining 41.5%. Under the cost assumptions adopted, only the best-performing configurations achieved levelized costs of energy competitive with recently contracted wind energy prices in Brazil. Validation against the Icaraizinho wind farm yielded an MAPE of 6.40%. Overall, the results demonstrate that wind potential is technology-dependent and should be assessed using turbine-specific performance models.
This study conducted a cradle-to-grave life-cycle assessment of a 1 kWp grid-connected building-applied monocrystalline silicon photovoltaic system installed on a public-building flat roof in Chizhou, Anhui Province, China. The assessment covered production, transport, 30-year operation and maintenance, and end-of-life recycling. ReCiPe 2016 Midpoint (H), IPCC AR6 100-year global warming potential characterization, cumulative energy demand accounting, and Monte Carlo simulation were applied to quantify environmental impacts, energy demand, carbon emissions, and parameter uncertainty. The system is expected to deliver 34,822 kWh/kWp of electricity over its service life. Gross life-cycle carbon emissions and cumulative energy demand, excluding recycling credits, were 1194.08 kg CO2-Eq/kWp and 4290.30 kWh/kWp, respectively. End-of-life material recovery provided avoided burdens of 773.11 kg CO2-Eq/kWp and 2309.84 kWh/kWp, reducing the net results to 420.96 kg CO2-Eq/kWp and 1980.46 kWh/kWp. Accordingly, the gross and net life-cycle carbon emission intensities were 34.29 and 12.09 g CO2-Eq/kWh, respectively. The gross and net Energy Payback Periods were 3.42 and 1.58 years, while the corresponding carbon payback times were 1.67 and 0.59 years. Production was the principal source of environmental burdens, whereas recycling substantially improved the overall performance. Monte Carlo simulation indicated coefficients of variation of 3.59–5.28% for the assessed production-stage indicators. These findings highlight the importance of decarbonizing upstream electricity, reducing material burdens from aluminium, copper, and electronic components, and improving end-of-life recovery pathways.
Offshore floating photovoltaics (OFPVs) can expand solar generation without terrestrial land-use conflicts, but marine structures, installations, and operations remain cost-intensive. This study develops a reproducible deterministic and probabilistic levelized cost of electricity (LCOE) framework for the modular 0.5 MW PV-BOS platform. The methodological contribution is the consistent integration of component-level CAPEX and OPEX ranges, route-specific logistics evidence, lifetime degradation, financing uncertainty, and scenario-dependent cost modes within one model. The deterministic calculations were reproduced using initial CAPEX at year 0 and annual energy degradation expressed as (1 − d)(t−1). For a 30-year long-term scenario, the optimized deterministic LCOE is 85.56 USD/MWh. A 100,000-trial Monte Carlo analysis gives a mean of 119.42 USD/MWh and P10/P50/P90 values of 91.64/117.11/150.23 USD/MWh under the explicitly defined baseline distributions. The route-specific Vigo–Valencia comparison shows a 58.7% reduction for the transport-and-port-assembly subtotal, but this ratio is not interpreted as a universal full T&I saving. The capacity factor is the dominant LCOE driver, followed by CAPEX. The results support modular logistics as a potentially important cost-reduction mechanism while showing that bankability depends on site-specific energy-yield, metocean design, availability, financing, and O&M validation.
To improve renewable energy utilization and reduce fossil fuel consumption in building heating systems in Northwest China, this study proposes a solar–biogas hybrid heating system for the Jiuquan region. A dynamic simulation model was developed in TRNSYS, and multi-objective optimization was conducted using the NSGA-II algorithm. The optimization objectives were to maximize the solar fraction and minimize the initial system investment. The solar collector area, thermal storage tank volume, and biogas boiler start-up temperature were selected as decision variables. The results showed that the optimized solar fraction increased from 48.98% to 78.40% as the initial investment increased from 40,000 CNY to 80,000 CNY, although the marginal return gradually decreased. The NSGA-II algorithm generated a well-distributed Pareto front, revealing a clear trade-off between economic performance and solar energy utilization. Relative to the author-defined, proportionally scaled reference configurations, the optimized solutions increased the solar fraction by 18–21% across the examined investment levels. These improvement percentages are conditional on the adopted baseline definition. Considering both energy performance and economic feasibility, the scheme with a solar fraction of approximately 70% was identified as the compromise solution. Compared with the initial design, this scheme increased the initial investment by 15.52% and improved the solar fraction by 20.69%. These results indicate that appropriate configuration of key system parameters can substantially enhance renewable energy utilization. The proposed optimization method provides a useful reference for the design and operation of solar–biogas hybrid heating systems in Northwest China.
Considering the limited information available on the use of elephant grass juice (EGJ) as a liquid co-substrate, this study aimed to evaluate its feasibility and biogas production potential for anaerobic co-digestion with dairy cattle wastewater (DCW), focusing on substrate ratios and reactor operating conditions. Initially, batch tests were conducted at different temperatures to evaluate the effects of varying substrate mixture ratios (EGJ/DCW). In the reactor with 20% EGJ/80% DCW at 39 °C, a biogas volume of 414.35 mL was generated, which was considerably higher than that in the test at 25 °C, where 258.7 mL was generated over the 30-day experimental period. Greater efficiency in organic matter removal and other analyzed parameters was also observed under these conditions. The best configuration was selected for the semi-continuous tests. To this end, an Upflow Anaerobic Sludge Blanket (UASB) reactor was operated with hydraulic retention times of 10, 6, 2, and 1 d and volumetric organic loading rates of 2.06, 3.39, 10.40, and 20.70 kg VS m−3 d−1, respectively. Volatile solids were removed by 29–58%, with biogas yields ranging from 0.78 to 2.28 m3 m−3 d−1 and a maximum CH4 concentration of 72% (v/v). The co-digestion of the analyzed agricultural waste substrates proved to be a promising approach for bioenergy recovery. These results provide useful guidelines for optimizing co-digestion systems, improving reactor performance, and waste treatment processes.
The increasing deployment of renewable energy resources in geographically remote locations has accelerated the need for efficient long-distance power transmission solutions. High-voltage direct current (HVDC) technology is widely recognized as a preferred option for transmitting large quantities of electrical power over long distances compared to conventional high-voltage alternating current (HVAC) systems. However, determining the most suitable HVDC termination point within an interconnected HVAC network remains a complex planning problem due to the presence of multiple feasible termination locations and competing selection criteria. To address this challenge, this paper presents a multi-criteria decision analysis (MCDA) methodology for the evaluation and ranking of potential HVDC termination points. The Analytic Hierarchy Process (AHP) is utilized to derive the weights of the evaluation criteria, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to assess and rank the candidate alternatives. The proposed methodology is demonstrated through a case study involving the transmission of renewable energy from a remote generation source to a distant load center. The findings indicate that the AHP-TOPSIS framework is capable of effectively distinguishing among competing alternatives and identifying the most suitable HVDC termination point. The proposed approach provides a robust, transparent, and practical decision-support tool for power system planners involved in renewable energy integration and transmission network expansion planning.
The unreliable electricity supply is a major constraint on industrial production in Myanmar, especially for garment factories, which require a reliable power supply to maintain production. Frequent outages disrupt production schedules, increase operating expense, and force factories to rely on expensive backup generation. This study aims to estimate garment factories’ willingness to pay for improvements in electricity service reliability and to assess whether a rooftop solar PV–grid–diesel hybrid system could provide electricity at a cost compatible with these preferences. To achieve this objective, a discrete choice experiment was conducted with garment factories in the Yangon Region. The estimated marginal willingness to pay was USD 0.00293/kWh for one fewer outage per month, USD 0.01207/kWh for a one-hour reduction, and USD 0.00098/kWh for one additional hour of advance notice. For the largest in-design reliability improvement, one outage per month lasting 0.5 h/event, the WTP-based demand-side affordability benchmark was USD 0.2638/kWh. This benchmark reflects respondents’ stated choices and should not be interpreted as a legal tariff, regulated price, or direct tariff recommendation. The proposed-system LCOE was USD 0.2237/kWh, compared with USD 0.2752/kWh for the current system, although the compatibility margin remained statistically uncertain. The joint-venture administration was preferred to both government and private administration. These findings highlight that the proposed rooftop solar PV–grid–diesel hybrid system may improve industrial electricity reliability, although its economic compatibility remains statistically uncertain.
This paper proposes a novel solar-assisted inter-cooling air source heat pump (SCAS–HP) system that integrates a solar collector/evaporator into the intermediate injection branch to utilize solar energy for increasing the refrigerant flow rate at intermediate pressure. A numerical model of the system was developed and validated against the literature data, and then employed to investigate performance under varying operating conditions. Three configurations were examined—two-stage throttling (T–SCAS–HP), single-stage throttling (S–SCAS–HP), and parallel evaporators (P–SCAS–HP)—and their performance was compared with a conventional vapor injection air source heat pump (VI–ASHP) across solar radiation intensities of 10–1000 W/m2, outdoor air temperatures of −20 °C to 10 °C, and outlet water temperatures of 35 °C and 55 °C. Hourly performance, economics, and CO2 reduction were further assessed for Lhasa, Beijing, and Harbin. The results show that the COPh improvement of SCAS–HP over VI–ASHP rises with increasing solar radiation intensity. Furthermore, the solar radiation threshold at which the COPh of SCAS–HP begins to exceed that of VI–ASHP lies within the range of 100–200 W/m2. Moreover, the COPh increase rate of SCAS–HP rises with larger solar collector area and higher outdoor air temperature, but declines as the outlet water temperature rises. Among the three configurations, T–SCAS–HP outperformed S–SCAS–HP and P–SCAS–HP by 0.37 and 0.42 in COPh at outlet water temperatures of 35 °C and 55 °C, respectively. In typical-day simulations, the COPh showed a relative increase of 50.9% compared to that of the VI-ASHP system, corresponding to an absolute rise from 3.73 to 5.62 with a 130 m2 collector at 35 °C outlet water temperature. The energy saving rate ranked highest in Lhasa, followed by Beijing and Harbin. The shortest payback period (5.6 years) was achieved in Harbin with a 130 m2 collector area at the outlet water temperature of 55 °C. The proposed system is particularly suitable for large heating demand and long heating seasons in cold climates with abundant solar resources.
Addressing the lack of quantitative basis for dynamic response characterization and emergency shutdown decision-making in urban high-pressure gas pipeline leaks, this study takes a JS pipeline as the engineering case and establishes a dynamic simulation model based on OLGA incorporating leak and shutoff valve modules. The effects of aperture size, leak location, inlet flow rate, and valve operation on release intensity and economic losses are systematically analyzed. Results reveal that without intervention, leak-point pressure follows a four-stage evolution—steady operation, sharp drop, gradual decline to equilibrium, and post-plugging recovery—while leakage rate exhibits positively coupled synchronous behavior. Aperture size acts as an exponential-level hazard control factor; apertures ≥150 mm and rupture cases mandate immediate valve closure. Upstream leaks primarily threaten supply continuity, whereas downstream leaks exhibit sustained high-rate venting with greater release intensity, which may lead to more severe accident consequences depending on local atmospheric conditions and dispersion patterns. Inlet flow mainly modulates pressure equilibrium with limited influence on release rate. Emergency shutoff valves achieve loss reductions of 46.9–67.6% for apertures ≥150 mm, corresponding to savings of 0.47–3.48 million CNY within 4 h. These findings provide dynamic quantitative support for leak classification, coordinated valve control strategies, and emergency repair decision-making.
In the context of global digital transformation and the “dual carbon” goals, digital infrastructure and clean energy serve as pivotal drivers of regional economic development. However, whether their synergistic effect can promote high-quality regional economic growth remains unclear, and potential regional disparities warrant investigation. Therefore, based on panel data from 30 Chinese provinces spanning 2010 to 2023, this study measures the synergy level between digital infrastructure and clean energy development using a coupling coordination degree model. It empirically examines their impact on regional economic performance through fixed-effect and mediation effect models. The findings indicate that the synergistic effect of digital infrastructure and clean energy development significantly promotes regional economic growth, with its impact exceeding their individual contributions. Such influence manifests more evidently within China’s eastern provinces, where industrial structure upgrading and green technological innovation function as two underlying mediating channels. These conclusions offer theoretical and practical insights for optimizing regional development policies and fostering the synergistic integration of digitalization and green transformation.
This study assesses roadside grass biomass harvesting and allocation to collection centers using spatially explicit methods for preliminary district-heating supply-chain planning. Road-network data combined with mowing data for county roads were used to estimate theoretical, technically recoverable, and annual energy potentials in Kołobrzeg County, Poland, while high-resolution aerial imagery was used to identify roadside sections where biomass cannot be harvested, such as bridges, paved shoulders, gravel surfaces, and bicycle paths. Dijkstra’s algorithm calculated shortest network distances, after which a custom assignment procedure allocated road nodes and associated biomass to collection centers using either the baseline distance-based rule or its capacity-constrained extension. The estimated annual energy potential ranged from 14.9 to 38.1 TJ/a among the analyzed municipalities. In the two-center scenarios, the number of nodes assigned to the southern center increased from 25 under its 3000t limit to 41 under equal 5875t limits at both centers. The results show that combining aerial-image interpretation, biomass-to-energy estimation, and network-based allocation provides a transparent framework for identifying priority collection areas and organizing roadside biomass transport. The proposed workflow can support local authorities in preliminary biomass supply-chain planning and can be adapted to other regions with suitable spatial data.
Continuous operation is increasingly expected of Internet of Things (IoT) and wireless sensor network (WSN) nodes, yet practical solar front ends must account for source variability, intermediate storage, conversion losses, sensing overhead, and battery-management constraints. This article develops and evaluates a discrete, two-stage photovoltaic front end for such nodes: a perturb-and-observe (P&O) buck stage tracks the maximum power point of a 20 W Solarland SLP020-12U module (rated 17.2 V, 1.16 A) and feeds an intermediate storage bus, while a PI-compensated SEPIC stage regulates the IoT rail to 3.2 V independently of that bus voltage. Closed-loop MATLAB/Simulink simulations are reported at 1000, 800, and 600 W/m2. The reported conversion figures originate from an idealized switching model and should therefore be interpreted as simulation-only values rather than measured prototype efficiency. A low-cost Arduino-based prototype confirms correct switching behavior and a 20.0048 kHz PWM signal, but the available captures lack synchronized, calibrated input/output power logging; consequently, no hardware efficiency, MPPT tracking efficiency, regulation error, ripple, or settling-time figure is claimed. The revised manuscript makes this simulation-to-hardware boundary explicit, adds the power cost of sensing and data conversion to the loss discussion, strengthens the battery-management and deployment caveats, and defines the measurements required for full quantitative validation.
Biomass is widely recognized as a critical renewable energy source for supporting global decarbonization and energy diversification. However, the financial viability of bioenergy investments depends heavily on how climate policies and sustainable finance mechanisms interact under environmental uncertainty. This study develops a land-constrained bioenergy valuation model to examine the joint effects of carbon pricing, climate finance arrangements, and land-use regulations on the economic sustainability of biomass producers. Environmental and financial policies enter the framework through carbon costs, land-use constraints, and financial intermediation conditions, thereby linking climate regulation to firm-level capital performance. Using a contingent-claim framework, producer equity and financing risks are evaluated to capture the complex effects of market asset volatility on energy investment incentives. The framework is evaluated through a literature-informed baseline calibration and comparative-static numerical analysis; accordingly, the reported results are model-implied comparative-static mechanisms designed to evaluate bioenergy project viability under policy and market uncertainty. The numerical analysis employs a non-region-specific representative benchmark and is intended to identify structural and comparative-static mechanisms rather than to forecast outcomes for a particular geographical market. The results show that higher carbon prices reduce producer viability by increasing operating costs, although this financial strain is partially mitigated by the option-like nature of equity under higher asset volatility. In contrast, improvements in agronomic productivity, greater land availability, and higher biomass market prices enhance investment returns and financing conditions. The findings further indicate that climate policies shape bioenergy deployment not only through physical production costs but also through financial transmission channels and risk-sharing mechanisms. The study contributes to the energy policy and climate finance literature by integrating carbon pricing, land governance, and financial options within a unified analytical framework. The results highlight the importance of policy coordination between renewable energy incentives, sustainable finance management, and carbon regulation to mitigate investment risks in the bioenergy sector.
This paper focuses on the HAZOP (Hazard and Operability) analysis applied to a dark fermentation process designed to produce biohydrogen. A pilot reactor for hydrogen production from sludge and the organic fraction of municipal solid waste was studied. With reference to the fermenter (the key process node), operating conditions both maximizing process efficiency and representing the worst-case scenario in terms of process safety have been chosen. The HAZOP analysis enabled the identification of deviations from normal operating conditions, looking for causes, consequences, and countermeasures, including the effects of microbial community variations on process efficiency. The analysis highlighted significant engineering solutions (gas detectors, temperature and flow transmitters, Distributed Control System, etc.) aimed at increasing process efficiency while simultaneously enhancing safety levels for operators and the environment. Finally, the results of the pilot reactor analysis can be applied to the development of full-scale plants.
Vehicle-to-everything (V2X) connectivity provides electrified vehicles with previews of traffic signals, road geometry, surrounding traffic, and route conditions, yet control benefit arises only when these data are converted into variables that can shape motion and powertrain decisions. This review presents a structured, framework-driven narrative synthesis organized along an information–motion–energy chain: external preview, control-oriented prediction, energy-aware speed planning, trip-level energy and state-of-charge scheduling, power-source allocation, cross-layer coordination, and staged validation. The reviewed studies are compared in terms of coupling depth, from traffic-layer optimization and sequential speed–energy management strategy (EMS) schemes to hierarchical/weakly coupled and joint/tightly coupled formulations. Across hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicle/hybrid electric vehicle (FCEV/FCHEV) platforms, the information interface is broadly shared, whereas energy-replenishment, thermal, and component-health constraints require powertrain-specific formulations. The evidence base also shows a persistent maturity gap between algorithmic simulation and hardware or vehicle validation. Key needs are uncertainty-aware closed-loop design, physically interpretable model–data fusion, fallback control under information degradation, standardized cross-layer benchmarks, and staged validation that reports both control performance and evidence level.
Conventional quasi-steady-state models treat a self-adaptive STATCOM and line-commutated converter (SLCC) station as an LCC with an external reactive-power source, which cannot fully represent valve-side coupling. This paper develops a three-phase stationary-frame differential model for the SLCC and derives quasi-steady-state expressions for the average DC voltage and fundamental displacement angle. The non-commutation equivalent voltage is decomposed into fundamental and nonfundamental components. The fundamental component is retained in the power-flow model, while a control-informed harmonic extension evaluates the corresponding average DC-voltage correction over the tested operating domain. A positive-sequence fundamental-frequency formulation calculates the commutation overlap angle, and a first-zero diagnostic identifies control-sensitive conditions associated with the fast SVG voltage response. When the fast commutation-direction voltage reaches zero or reverses before current transfer is completed, a control-equivalent effective-area formulation provides an alternative low-order representation. The station equations are incorporated into a sequential AC/DC power-flow algorithm and validated against the engineering PSCAD/EMTDC main-circuit and control model of the Yangzhou–Zhenjiang HVDC Phase II project. Across the stable tested operating points, the phase-aware EMT-derived harmonic DC-voltage correction ranges from 0.585% to 1.245%, remaining below the adopted 2% screening threshold. The control-informed estimate follows the EMT-derived correction, whereas the phase-independent conservative bound reaches 2.128% at high controller gain. Across eight cases with available PSCAD reference values, the control-equivalent formulation reduces the mean and maximum overlap-angle errors from 0.90∘ and 1.37∘ to 0.78∘ and 1.09∘. For the benchmark power-flow cases, the maximum relative errors are 1.3% for the SLCC bridge reactive power and 1.0% for the SVG reactive-power output, and the calculation converges without sustained oscillation. A representative operating-point calculation is completed in approximately 3 s with the quasi-steady-state (QSS) formulation, compared with about 15 min for the engineering EMT benchmark.
Sustained enhancement of energy utilization efficiency constitutes a pivotal lever for achieving dual carbon goals. To examine both the local effects and spatial spillovers of the Energy-use Rights Trading System (ERTS) on urban green total factor energy efficiency (GTFEE), this study employs the super-efficiency EBM-GML index to measure GTFEE across 270 prefecture-level cities in China from 2013 to 2022. Utilizing a Spatial-DID model, we find that the ERTS significantly drives local GTFEE improvements, with the positive effect intensifying over time, while concurrently exerting a negative spatial spillover on GTFEE in neighboring cities. Mechanism analysis suggests that the ERTS enhances local GTFEE through increased R&D investment, optimized fossil energy structures, and improved energy resource allocation in pilot cities, yet suppresses GTFEE improvement in adjacent areas via reduced R&D investment, a worsening fossil energy structure, and resource misallocation. These patterns are consistent with cross-regional flows of R&D resources and energy-intensive industries between pilot and non-pilot cities. Heterogeneity tests reveal that the policy’s effects on pilot city GTFEE are more pronounced in cities with larger populations, stronger industrial bases, more abundant human capital, greater resource endowments, and more transparent environmental information disclosure. Moreover, the population size of pilot cities intensifies the negative spatial spillover effect on neighboring GTFEE, whereas a well-established industrial base in pilot cities mitigates such adverse spillovers. It should be noted, however, that the Spatial-DID model is sensitive to the specification of spatial weights, and the mechanism analysis can only provide indirect evidence of cross-regional factor and industrial mobility. These findings offer spatial-effect empirical evidence to inform the further refinement of the ERTS under the dual carbon framework.
In high-voltage cable sheaths, incipient weak faults, such as jacket-damage grounding and high-resistance core–sheath breakdown, release little energy, rarely trigger protection, and can evolve into permanent faults. This study examines whether the topology-induced joint structure of multichannel sheath currents can support fault detection without fault samples. A Mahalanobis-distance-based method is formulated for the three-phase sheath circulating currents measured at a single cross-bonding box. An induction–leakage analysis relates the healthy joint structure to the bonding topology and shows how weak faults disturb it. A normalized pointwise Mahalanobis distance is combined with a threshold calibrated on separate healthy data and a K-consecutive-sample rule; the method requires no signal decomposition, and its per-sample cost is constant. On a PSCAD model of a 110 kV cross-bonded system, all 52 development fault cases are detected with confirmation delays below 6 ms; an independent sixteen-record healthy test is false-alarm-free after an envelope recalibration; boundary-grade faults under joint non-ideal conditions retain nine-fold margins; and per-line calibration extends the criterion to asymmetric and longer geometries. A twenty-seed Monte Carlo campaign shows zero noisy false alarms at all tested signal-to-noise ratios, the observable fault range being set by the disturbance-to-noise energy ratio of the acquisition chain.
