Search Results (604)

High-capacity long-distance HVDC is indispensable for the centralized integration of deep-sea offshore wind farms and sandy-region renewable energy bases. Hybrid flexible HVDC, which adopts voltage-source converters at the sending end and current-source converters at the receiving end, meets the technical needs and potentially offers better economic efficiency in terms of converter station capital cost. However, technical challenges remain, including passive sending-end charging and start-up, joint grid formation of multiple converters, commutation failure immunity at the receiving end, and minimum continuous current maintenance. This paper proposes a hybrid flexible HVDC system scheme along with its control strategy. The scheme employs modular multilevel converters (MMCs) at the sending end and hybrid commutated converters (HCCs) at the receiving end, and a small-capacity uncontrolled diode rectifier (UDR) is configured in parallel with the HCCs. Operating characteristics of the three types of converters are analyzed, and the overall system operating strategy is presented. Electromagnetic transient simulation results show that the UDR provides a system charging circuit and can automatically supply continuous current when renewable generation power fluctuates; for bipolar HVDC or hierarchical HVDC, virtual synchronous generator control of the MMCs enables the joint grid formation and realizes the power balance between multiple converters; for faults in the receiving grid, the HCCs are able to actively turn off the valves at risk of commutation failure, ensuring a smooth ride-through. Full article

The Controllable Line-Commutated Converter (CLCC) integrates fully controlled and semi-controlled devices to mitigate commutation failure. However, its application in large-capacity HVDC systems is constrained by the limited current-carrying capability of fully controlled valves in the main branch. To address the HVDC requirements under high-current conditions, this paper proposes a high-reliability cascaded H-bridge CLCC (H-CLCC) valve topology. The proposed topology employs a dual-path conduction mode for H-bridge sub-valves, reducing electrical stress on devices and enabling modular scalability. A redundant configuration, in which cascaded H-bridges are paralleled with bypass thyristors, allows faulty sub-modules to be rapidly bypassed, ensuring continuous operation. An analytical model based on the Laplace transform is developed to reveal the relationship between capacitor voltage and turn-off current, providing guidance for capacitance design. PSCAD/EMTDC simulations verify that the H-CLCC valve effectively suppresses commutation failure via active commutation, even under severe AC-side faults with currents up to 8 kA. Device-failure simulations further demonstrate strong self-healing capability, ensuring sustained forced commutation under local faults. This work provides a foundation for high-reliability UHVDC converter valve design. Full article

Compressor surge poses a critical safety risk for aero-engines. However, conventional physics-driven detection methods—relying on single-domain features and fixed empirical thresholds—struggle to adapt across varying compressor configurations, wide operating ranges, and complex interference environments. This paper proposes a multi-feature fusion adaptive surge detection method that integrates time-domain amplitude, frequency-weighted power and slope features within a joint threshold criteria, enabling reliable and adaptive surge detection according to the statistical characteristics of the signal itself. A wavelet-based preprocessing strategy is established with the db4 wavelet and four-level decomposition identified as the optimal setting through systematic evaluation. A novel feature FWP is introduced herein, which applies frequency-dependent weighting to the power spectral density to suppress noise components while amplifying energy changes within surge-relevant bands, achieving 1.7 to 6.1 times greater magnitude variation near the surge point compared with total spectral power. The slope feature is further discovered to distinguish surge from transient interferences such as rapid valve throttling, fuel stepping and rapid acceleration. Among 100 samples, the three-feature joint detection strategy integrated with adaptive threshold criteria improves accuracy from 61% to 98%. A Bayesian optimization framework using Gaussian process surrogate models is developed for efficient cross-engine hyperparameter tuning, converging to optimal solutions within merely 11 to 13 iterations across two distinct compressors. Lastly, the method is implemented on an NI cRIO-based real-time platform and validated on two distinct ten-stage high-pressure compressors, covering surge tests across a wide speed range of 45% to 98%. Comparative tests against an industry-standard reference device demonstrate earlier warning lead times of 41 to 99 ms. The results confirm that the proposed method herein achieves high accuracy, strong robustness against operational interferences, and good cross-platform adaptability for practical application. Full article

Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer from poor generalization, unphysical behaviors, and limited interpretability when facing noisy measurements and unseen operating conditions. This paper introduces a knowledge-guided physics-informed hybrid learning framework that integrates recurrent neural networks with Unscented Kalman Filter (UKF) state estimation and embedded thermodynamic constraints within a unified uncertainty-aware architecture. The proposed PI-LSTM-UKF framework achieves competitive predictive accuracy and improved physical consistency relative to the residual-learning hybrids by tightly integrating physics-informed recurrent learning, thermodynamic constraints, and sequential UKF state estimation. While the UKF provides robust recursive correction under noisy measurements during closed-loop operation, the physics-informed Long Short-Term Memory (PI-LSTM) learns nonlinear corrections and long-term dynamics that cannot be captured by the linear model alone. The proposed framework is systematically benchmarked against a hierarchy of seven modeling approaches, including Dynamic Mode Decomposition with control (DMDc), Sparse Identification of Nonlinear Dynamics (SINDy), and residual-learning variants based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). High-fidelity Simscape simulations of a Rankine-cycle steam turbine system are used as a challenging simulation-based case study. Results show that the knowledge-guided hybrid approach achieves competitive predictive accuracy, improved physical consistency, and robust performance under an unseen load profile, severe thermodynamic degradation, valve hysteresis, and substantially elevated sensor noise. The framework provides a promising simulation-based foundation for uncertainty-aware digital twins of nonlinear thermal power systems. Validation using operational plant data remains necessary before its application to real-time monitoring and predictive maintenance. Full article

This paper presents a machine-learning based strategy for accurate identification of commutation failures instances. A commutation-failure identification (CFI) block has been proposed and implemented, which utilizes the DC current and the valve-side AC currents for accurate detection. The well-established CIGRE benchmark model, implemented in PSCAD/EMTDC, is employed as the test system. Extensive simulations are performed under diverse fault scenarios, and a comprehensive dataset is generated under both commutation failure and normal operating conditions. Six different machine-learning classifier models are trained using a sufficient portion of the generated dataset and are subsequently evaluated against the remaining test dataset. The performance of the classification is assessed though confusion matrices and several statistical metrics including Accuracy, Precision, Recall and F1-Score for the base case study. Several other studies including the reduced feature cases, noisy validation scenario, five-fold cross validation, and hyperparameter sensitivity analysis have been performed to validate the effectiveness and reliability of the proposed ML-assisted approach. The results demonstrate that the proposed framework can reliably distinguish commutation failure events from normal operating conditions with high accuracy. Among all these classifier models, the Random Forest classifier has been identified as the most suitable model to classify the commutation failure instances with an Accuracy of 99.53% and Recall score of 99.63% for the base case. This accurate classification of commutation failure will ensure enhanced reliability of an HVDC system. Full article

The modernization of legacy industrial machines is a major challenge in intelligent automation, particularly when critical assets must be upgraded without replacing high-value mechanical and hydraulic infrastructure. This paper presents an industrial case study on the intelligent PLC-based retrofit of an obsolete Neyrpic Digipid speed governor installed in a Kaplan turbine. The proposed solution replaces a closed, vendor-dependent controller with an open Siemens ET 200SP architecture programmed in TIA Portal, integrating existing sensors, hydraulic actuators, redundant speed acquisition, sequential state-machine control, and a digital distributor–runner blade Cam Curve. A key technical contribution is the diagnosis and mitigation of hydraulic hunting in the distributor position loop. The instability was traced to the interaction between integral control action and the intrinsic integrating behavior of the hydraulic actuator, leading to the adoption of a proportional-only position tracking strategy. The system was validated through Factory Acceptance Tests (FATs) and Site Acceptance Tests (SATs), including signal verification, startup, synchronization, load acceptance and emergency load rejection. Quantitative results demonstrate that during initial commissioning of the new PLC-based PI position loop, the LVDT position error reached 41.59% peak-to-peak, with 351.2 servo-valve reversals per minute. Disabling the integral action reduced the peak-to-peak position error to below 1.5% and eliminated steady-state valve reversals under the tested operating conditions. Separately, the historical 0.966 V oscillation detected in the legacy analog-input chain was resolved during the retrofit. During no-load startup, the unit reached 97% of nominal speed in 49.4 s, with a maximum overshoot of 2.3% and a speed tracking standard deviation of 1.1%. The complete operational cycle was successfully validated under real industrial conditions, including a near-nominal load-rejection test (approximately 2.25 MW), during which the measured speed peaked at 126.4% of nominal speed and the shutdown sequence was completed without protection-system malfunction. The results show that open PLC-based retrofits can improve maintainability, diagnostics and operational reliability in safety-critical industrial machines, while establishing a foundation for future SCADA integration and condition-based maintenance. Full article

Securing a freshwater supply for personnel engaged in remote maritime operations remains a persistent logistical challenge. This paper presents the design, hydraulic simulation, and experimental validation of a compact, manually operated seawater desalination device based on reverse osmosis (RO) coupled with an integrated hydraulic energy recovery system. The device employs a valve-commutated piston pump (cylinder bore 6 mm, rod diameter 3.7 mm, stroke 70 mm) driven by a lever-type handle mechanism. High-pressure brine rejected by the RO membrane is redirected via a two-position, three-way directional valve into the rod-end cavity of the pump cylinder, partially offsetting the filtration resistance and achieving an energy recovery ratio of 49.5%. Hydraulic circuit dynamics were analyzed using AMESim software, yielding a simulated freshwater output of approximately 0.02 L/min (1.2 L/h). A functional prototype with overall dimensions of 200 × 128 × 63 mm was fabricated and tested under 3.57% salinity conditions. Five consecutive trials produced a mean freshwater flow rate of approximately 1.15 L/h (desalination rate exceeding 95%), confirming consistency with the simulation predictions and satisfying the design requirements for individual field use in remote maritime settings. Full article

In response to common issues with traditional automotive switches, such as poor contact of terminals, low durability, and weak vibration resistance, this paper proposes and designs a novel high-performance automotive toggle switch. Through structural design and parameter optimization, a new solution is provided to enhance the structural strength and service life of automotive electronic components. After completing three-dimensional modeling based on SolidWorks 2025, a full set of simulation analyses was carried out using ANSYS Workbench 2024 R2. After structural optimization, the maximum stress of the core valve stem decreased from 17.19 MPa to 14.877 MPa, a reduction of 13.5%; meanwhile, the fatigue life increased to 2.51 times that before optimization, indicating that for polycarbonate materials, a slight reduction in stress can significantly slow the rate of component damage accumulation. The switch’s first-order natural frequency is 1171.7 Hz, and a random vibration analysis of the switch was conducted according to the industry standard ISO 16750-3:2023. Under excitations covering the entire 2000 Hz frequency range, the switch structure did not show deformation or fatigue risks caused by resonance, indirectly confirming that vibration energy density is often more concentrated at low frequencies. This study not only completes the innovative design and performance verification of the novel toggle switch but also demonstrates that the comprehensive research methods employed provide a systematic analytical approach for developing high-performance, highly reliable automotive electronic components under stringent industry standards. Full article

Accurate valve opening prediction is important for improving the stability and control performance of wind tunnel systems. To address the challenges of strong variable coupling, nonlinear dynamics, and complex temporal dependencies, this paper proposes a mutual information-based progressive dilated temporal convolutional network, named MI-PD-TCN. The proposed method first preprocesses multi-source sensor and valve operation data, constructs candidate features such as historical lag and differential features, and selects informative variables using mutual information. Then, phase space reconstruction and a sliding-window strategy are used to generate temporal input tensors. Finally, a progressive dilated TCN is developed to extract multi-scale temporal dependencies and predict valve openings. Experimental results show that the proposed method improves prediction accuracy and generalization performance, providing an effective data-driven solution for valve opening prediction in wind tunnel control systems. Full article

This study addresses the inefficiencies and environmental concerns associated with conventional broadcast spraying in agriculture, where uniform chemical application leads to significant off-target losses and excessive agrochemical usage. A plant detection-based nozzle actuation system was developed to enable real-time, selective spraying based on canopy presence. The system integrates a LiDAR sensor for precise canopy detection, an ESP32 microcontroller for signal processing, and a solenoid valve-controlled nozzle for automated on/off spray regulation. Laboratory experiments were conducted using a controlled conveyor-based setup to simulate field conditions and evaluate the effects of forward speed, sensor–nozzle distance, and sensor–canopy distance on spray deposition. Spray performance was assessed using water-sensitive papers and image analysis techniques, while statistical analysis (ANOVA) and optimization using Response Surface Methodology (RSM) were performed. The results indicated that forward speed and sensor–nozzle distance significantly influenced spray coverage, whereas sensor–canopy distance had no significant effect. The optimized parameters (3.0 km h⁻¹ speed, 35 cm sensor–nozzle distance, and 70 cm sensor–canopy distance) achieved effective canopy coverage (~52–55%) while substantially reducing off-target deposition. Compared to continuous spraying, the developed system maintained comparable target coverage while significantly minimizing chemical losses. The findings demonstrate the potential of sensor-based precision spraying for improving input efficiency and environmental sustainability. Full article

Valve leakage in marine oil and gas pipelines is a critical failure mode that threatens operational safety, ecological integrity and production economic benefits, creating an urgent demand for accurate, real-time and robust fault diagnosis systems. Acoustic Emission (AE) technology captures transient acoustic signatures generated by leakage to enable non-intrusive online monitoring, while deep learning supports intelligent analysis through automatic signal feature extraction. Nevertheless, traditional AE-based leakage diagnosis methods rely heavily on manual feature engineering and fixed signal processing rules. Existing AE-driven deep learning methods fail to simultaneously deliver high detection accuracy, low inference latency and strong noise immunity, hindering their practical deployment on offshore platforms. To address these limitations, this paper proposes a Parameter-free Star-shaped Attention Fusion Network (SAFNet) for lightweight valve leakage localization using AE signals. Centered on the Temporal Pyramid Encoder (TPE) and Progressive Lightweight Star-shaped Attention (PLSA) module, SAFNet integrates Dual Bilinear Star Mapping (DBSM), Energy-Driven Feature Refiner (EDFR) and Multi-Scale Gated Attention Fusion (MS-GAF) modules. This architecture achieves efficient multi-scale temporal feature extraction, parameter-free nonlinear enhancement, noise-resistant refined feature processing and adaptive hierarchical feature fusion. The proposed method is applicable to valve leakage diagnosis of marine oil and gas pipelines under variable pressure and complex marine noise conditions. Comprehensive experiments are conducted on a dataset constructed by combining laboratory controlled leakage signals with real marine background noise recorded from the Liwan 3-1 offshore platform. The experimental results reveal that SAFNet balances high detection accuracy, compact model size and low inference latency simultaneously. Specifically, the network maintains a stable detection accuracy above 95% under pipeline pressures ranging from 2 MPa to 5 MPa, and exhibits excellent stability under extreme heavy noise environments. Ablation experiments further validate the synergistic performance gain brought by all core modules. The presented network delivers an efficient lightweight solution for valve leakage localization under simulated marine acoustic conditions, promotes the development of intelligent monitoring technologies for marine pipeline systems, and comprehensively improves offshore operational safety and marine ecological protection capacity. Full article

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment. Full article

Facing the demand of long-distance, high-voltage and high-power transmission, the research on MMC-UHVDC (Modular Multilevel Converter based Ultra High Voltage Direct Current) has become a hot issue. This paper focuses on the ±800 kV/8 GW UHVDC transmission system to conduct simulation modelling and insulation coordination studies. First, broadband models of the main circuit and primary equipment are established to simulate and analyse the distribution characteristics of both switching and lightning transient overvoltages under typical faults. Second, based on the overvoltage severity at critical nodes, two surge arrester configuration schemes with distinct internal valve protection topologies are proposed. Finally, an Improved Fuzzy Analytic Hierarchy Process (FAHP) is introduced to perform a quantitative techno-economic evaluation of the comparative schemes. The results demonstrate that the optimised configuration successfully suppresses extreme overvoltages at vulnerable sub-module nodes, maintaining adequate insulation margins. These research findings provide a highly reliable mathematical framework and engineering reference for the safe design of UHVDC systems. Full article

In large-scale fluid transport systems, distributed pump and valve stations must coordinate their operations to prevent overpressure while minimizing energy use and control effort. This paper presents a communication-aware, game-theoretic coordination framework in which stations act as rational agents that iteratively adjust operating setpoints based on locally computed utilities. Existing station-level pressure controllers regulate local pressures and flows, while a slower supervisory negotiation layer governs inter-station coordination using steady-state hydraulic surrogates derived from pump affinity laws and pipeline loss relationships. The proposed framework does not rely on centralized optimization or exhaustive enumeration of strategies. Instead, stations update setpoints sequentially, evaluating incremental changes in utility to determine beneficial adjustments and detect equilibrium conditions. Cooperative behavior emerges naturally when communication is available, enabling stations to internalize the hydraulic impact of their actions on neighboring stations. When communication is lost, the system transitions seamlessly to a non-cooperative mode in which each station optimizes its local objective while maintaining safe operation. Simulation studies conducted on a multi-station pipeline with mixed actuator types demonstrate measurable performance improvements over fixed-setpoint operation. Cooperative coordination reduces total system energy usage from 39.6 MW to 38.8 MW while increasing average control valve openness from 60.4% to 63.7%. Non-cooperative operation converges more rapidly but results in higher energy consumption (39.2 MW) and greater valve throttling. Under partial communication loss, the system preserves near-cooperative energy performance (38.8 MW) with a modest increase in convergence time, demonstrating robustness to degraded communication. Across all simulated scenarios, the iterative game converged to stationary operating points consistent with Nash-equilibrium behavior in non-cooperative settings and Pareto-stationary solutions in cooperative communication settings. Full article

To address the issues of thermal non-uniformity and insulation aging of converter transformers operating under long-term high electric field and high-temperature conditions in ultra-high-voltage direct current (UHVDC) transmission systems, this paper investigates the temperature field distribution characteristics of converter transformers based on electro–thermal–mechanical multiphysics coupling. By establishing a full-scale multiphysics simulation model of a ±800 kV converter transformer, the interactions among the electric field, temperature field, and mechanical stress field are comprehensively considered. The temperature gradient distribution and hotspot formation mechanisms within the valve-side winding and the lead-out structure are revealed. The results show that the internal temperature distribution of the converter transformer is non-uniform, resulting in a nonlinear distribution of material parameters in oil-paper insulation, which significantly affects the insulation performance. The research findings provide a theoretical basis and engineering reference for the structural optimization and thermal stability improvement of the main insulation system of converter transformers. Full article