Search Results (942)

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article

With the large-scale integration of wind power, transient stability issues in power systems have become increasingly prominent, among which the impact of the active power recovery rate of wind turbines on system stability cannot be ignored. This paper establishes a sensitivity analytical model between the transient stability index of the system and the active power recovery rate of doubly fed induction generators (DFIGs), revealing the influence of active power recovery rate on system stability. First, the trajectory analysis method is adopted as the transient stability assessment approach, proposing a stability index incorporating accelerating power and transient potential energy. Analytical sensitivity models for synchronous generator accelerating power and transient potential energy to the active power recovery rate of wind turbines are derived in a simplified system. Second, a sensitivity model of the stability margin index to the active power recovery rate is constructed to analyze the influence patterns of the active power recovery rate, initial active power output of wind turbines, and fault duration time on system stability. This research demonstrates that: accelerating the active power recovery rate can restore power balance more quickly but it reduces the rate of transient potential energy variation and delays the peak response of potential energy, thereby decreasing the stability margin; higher initial active power output of wind turbines suppresses the oscillation amplitude of synchronous generators but increases the risk of power imbalance; and prolonged fault duration exacerbates transient energy accumulation and significantly degrades system stability. Additionally, for each 0.1 p.u./s increase in the active power recovery rate of the wind turbine, the absolute value of the stability index of the synchronous machine in the single-machine system decreases by approximately 0.5–1.0, while the sensitivity decreases by approximately 0.01–0.02 s⁻¹. In the multi-machine system, the absolute value of the stability index of the critical machine decreases by approximately 5–10, and the sensitivity decreases by approximately 0.5–1.0 s⁻¹. Full article

This paper addresses the challenge of multicollinearity among input features in induction motor (IM) fault diagnosis, which often degrades the performance and reliability of machine learning classifiers. A novel feature selection approach based on agglomerative hierarchical clustering (AHC) is proposed to mitigate feature redundancy and enhance model generalization. The method is applied using only voltage and current signals, excluding vibration or temperature data, to improve noise immunity and facilitate practical deployment. Experimental validation demonstrates the effectiveness of the AHC framework across multiple classifiers, particularly Support Vector Classifiers (SVCs) and Artificial Neural Networks (ANNs). Compared to random forest-based feature selection, AHC yields a 2% increase in accuracy for SVCs and a 0.6% improvement for ANNs. Moreover, both classifiers exhibit enhanced balance across fault categories, with macro-average recall and F1-score improvements of approximately 1.5%. These findings highlight the ability of AHC to handle complex fault scenarios, which offer a more efficient and generalized fault diagnosis model compared to ensemble methods-based feature selection. Full article

Wireless Power Transfer (WPT) enables efficient, contactless charging for mobile devices by eliminating mechanical connectors and wiring, thereby enhancing user experience and device longevity. However, conventional WPT systems remain prone to performance issues such as coil misalignment, resonance instability, and thermal losses. Addressing these challenges involves designing coil geometries that operate at lower resonant frequencies to strengthen magnetic coupling and decrease resistance. This work introduces a WPT system with a performance-driven coil design aimed at maximizing magnetic coupling and mutual inductance between the transmitting (Tx) and receiving (Rx) coils in mobile devices. Due to the nonlinear behavior of magnetic flux and the high computational cost of simulations, exploring the full design space for coils using ANSYS Maxwell becomes impractical. To address this complexity, a machine learning (ML)-based optimization framework is developed to efficiently navigate the design space. The framework integrates a hybrid sequential neural network and multivariate regression model to optimize coil winding and ferrite core geometry. The optimized structure achieves a mutual inductance of 12.52 $\mu$H with a conventional core, outperforming many existing ML models. Finite element simulations and experimental results validate the robustness of the method, which offers a scalable solution for efficient wireless charging in compact, misalignment-prone environments. Full article

Induction in pineapples requires the targeted delivery of specific chemical solutions into the plant’s central core to enable batch management, a task currently reliant on manual operation. This study addressed this challenge by analyzing the physical characteristics of pineapple plants and establishing a perception-based mathematical model for core position localization. An integrated hardware–software system was developed, complemented by a human–machine interface for real-time operational monitoring. Comprehensive experiments were conducted to evaluate the spraying accuracy, nozzle response time, and prototype performance. The results demonstrate that the actuation system—comprising solenoid valves, pumps, and flowmeters—achieved an average spraying error of 2.72%. The average nozzle opening/closing time was 0.111 s; with a standard operating speed of 0.5 m/s, a delay compensation distance of 55.5 mm was implemented. In human–machine comparative trials, the automated system outperformed manual spraying in both efficiency and stability, with average errors of 7.1% and 6.4%, respectively. The system reduced chemical usage by over 67,500 mL per hectare while maintaining a miss-spray rate of 5–6%. Both two-tailed tests revealed extremely significant differences (p < 0.001). These findings confirm that the developed solution meets the operational requirements for pineapple floral induction, offering significant improvements in precision and resource efficiency. Full article

This paper introduces a novel field-oriented control (FOC) strategy for an open-end stator three-phase winding induction motor (OEW-TP-IM) using dual space vector modulation-pulse width modulation (SVM-PWM) inverters. This configuration reduces common mode voltage at the motor’s terminals, enhancing efficiency and reliability. The study presents a backstepping control approach combined with a mean value theorem (MVT)-based observer to improve control accuracy and stability. Stability analysis of the backstepping controller for key control loops, including flux, speed, and currents, is conducted, achieving asymptotic stability as confirmed through Lyapunov’s methods. An advanced observer using sector nonlinearity (SNL) and time-varying parameters from convex theory is developed to manage state observer error dynamics effectively. Stability conditions, defined as linear matrix inequalities (LMIs), are solved using MATLAB R2016b to optimize the observer’s estimator gains. This approach simplifies system complexity by measuring only two line currents, enhancing responsiveness. Comprehensive simulations validate the system’s performance under various conditions, confirming its robustness and effectiveness. This strategy improves the operational dynamics of OEW-TP-IM machine and offers potential for broad industrial applications requiring precise and reliable motor control. Full article

Using an inductive computational approach, our present data exploration sought to use machine learning methodology to define and identify patterns and gain insight into autism-related discussions on Reddit across three different categories of subreddits: (a) individuals who self-identify as autistic, (b) parents of individuals on the autism spectrum, and (c) behavior therapists. By doing so, we sought to review authentic autism-related discussions and identify important topics that emerged across these three demographic groups, including insights related to assessing and treating challenging behavior. Following basic and advanced preprocessing, our extraction resulted in 57 subreddits and 46,914 comments from autism spectrum subreddit members, 46 subreddits and 27,838 comments from parent subreddit members, and six subreddits with 3163 comments from behavior therapist subreddit members. Subsequent network analyses revealed interesting patterns of discussion within and across subreddit groups that may be used to inform support and resources, practice considerations, and future directions for research. Full article

Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%. Full article

Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes the use of a combination of artificial intelligence techniques in the form of shallow and convolutional structures in the diagnostics of stator winding damage from an induction motor. The proposed approach ensures a high level of defect detection efficiency while using information preserved in samples from three periods of current signals. The research presents the possibility of combining the data classification capabilities of self-organizing maps (SOMs) with the automatic feature extraction of a convolutional neural network (CNN). The system was verified in steady and transient operating states on a test stand with a 1.5 kW motor. Remarkably, this approach achieves a high detection precision of 97.92% using only 600 samples, demonstrating that this reduced data acquisition does not compromise performance. On the contrary, this efficiency facilitates effective fault detection even in transient operating states, a challenge for traditional methods, and surpasses the 97.22% effectiveness of a reference system utilizing a full 6 s signal. Full article

The sensorless control of doubly fed induction machine (DFIM) rotor magnetic flux based on a model reference adaptive system (MRAS) is proposed to improve the reliability of a large-scale variable-speed pumped storage (VSPS) system and reduce operation and maintenance costs. The existing sensorless control of doubly fed induction machines (DFIMs) is mostly focused on generator operation, making it difficult to apply to the VSPS system. The proposed strategy realizes the reversible operations of the VSPS through the design of an adaptive law under variable operating conditions, eliminating mechanical sensors, and possessing the characteristics of simple implementation and accurate identification. The mathematical model of the DFIM in a VSPS system is constructed, and an MRAS vector control strategy based on stator voltage orientation is established. The rotor angle and speed under reversible operating conditions are effectively identified by dynamically adjusting the angle error between the rotor flux reference model and the adaptive model to approach zero. Subsequently, comparative analysis with the closed-loop direct detection method verifies the advantages of the proposed strategy. The proposed control method can accurately identify rotor position and speed in the pumping and power generation conditions of the VSPS system and it demonstrates robust adaptability. Full article

This study presents a detailed investigation of the torque-speed characteristics of a WEG single-phase squirrel-cage induction motor (SPSCIM) of (1/2 hp), 110/220 V at 60 Hz. The primary objective was to derive the motor’s equivalent circuit and validate its performance curves through finite element analysis (FEA), simulation using MATLAB^®/Simulink^®, and experimental testing. Finite element simulations were conducted using the software FEMM (Finite Element Method Magnetics) to model the magnetic flux distribution within the motor’s stator and rotor. These simulations, based on the motor’s dimensions and nameplate data, provided essential insights into the electromagnetic behavior, including flux density and saturation effects, which are crucial for accurate torque-speed curve predictions. For experimental validation, tests were performed under open-circuit and locked-rotor conditions through a universal machine as a load emulator. The torque-speed characteristics were determined using the Suhr method and the classical approach, with the resulting curves compared to experimental measurements. Voltage and current were measured using AC PZEM-004T and DC PZEM-017 meters, while rotor speed was monitored with a Hall effect sensor (A3144). The results revealed strong agreement between the FEM simulations, Surh method, and experimental data, demonstrating the reliability and accuracy of the combined simulation and analytical methods for modeling the motor’s performance. The estimations using classical and Suhr methods, Simulink simulations, and FEMM yielded low error percentages, mostly below 2%. However, in the FEMM simulation, rotor resistance showed a higher error of around 20% due to unavailable data on the exact number of windings turns, a modifiable parameter that can be corrected through further adjustments in the simulation. The torque-speed curves obtained at different voltage levels showed an excellent correlation, confirming the effectiveness of the proposed approach in characterizing the motor’s operational behavior. Full article

In power inspection fields, snake-like robots are often used for transmission line inspection tasks, requiring accurate identification of various power fittings. However, traditional visual sensors perform poorly under varying light intensity and complex background conditions. This paper proposes a non-visual perception method for the high-precision classification of different power fittings (e.g., vibration dampers, suspension clamps, and tension clamps) in snake-like robot transmission line inspection for high-voltage lines. This method, unaffected by light intensity changes, uses machine learning to classify the magnetic induction electromotive force signals around the fittings. First, the Dodd–Deeds eddy current model is used to analyse the magnetic field changes around the transmission line fittings and determine the induction coil distribution. Then, the concept of condition number and singular value decomposition (SVD) are introduced to analyse the impact of detection position on classification accuracy, with optimal detection positions found using the particle swarm optimization algorithm. Finally, a BP neural network optimised by a genetic algorithm is used for power fitting identification. Experiments show that this method successfully identifies vibration dampers, tension clamps, suspension clamps, and transmission lines at detection distances of 5 cm, 10 cm, 15 cm, and 20 cm, with accuracies of 99.8%, 97.5%, 95.1%, and 92.5%, respectively. Full article

In contrast to traditional hydrogels, which are susceptible to water evaporation and structural degradation, non-hydrogel materials are engineered for superior stability and consistent performance. Here, we report an innovative piezoelectric polyvinyl chloride/multi-walled carbon nanotube polymer gel (PVC/MWCNT polymer gel, PMPG) with exceptional linearity (as low as 1.31%), high sensitivity (50–310.17 mV), rapid response (172–189 ms), and thermal stability. Under strain induction, ordered rearrangement of dipoles in PMPG and the enhancement of MWCNTs generate a potential difference. Increasing MWCNT content enhances output voltage, sensitivity, conductivity, maximum stress, Young’s modulus, and toughness, while reducing nonlinear error. Higher dibutyl adipate (DBA) content increases output voltage and slightly improves sensitivity but decreases mechanical strength. The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. It exhibits a nonlinear error as low as 1.31%, a conductivity of 25.4 μS/cm, an 80% compressive strain tolerance (273 kPa stress), and dimensional stability for 90 days in air. By integrating PMPG with machine learning algorithms, soft robotic grippers gain advanced contact perception capabilities, enabling applications in medicine, rescue, exploration, and other fields requiring fine manipulation and adaptability. This work highlights PMPG’s potential as a stable, high-performance material for soft robotics and beyond. Full article

In this paper, a winding inter-turn fault (ITF) diagnosis algorithm for electrical machines in integrated motor and drive (IMD) systems is proposed based on impedance analysis using space voltage vectors. An ITF alters the stator resistance, causing an imbalance in the motor’s impedance depending on the phase connection. This impedance asymmetry can be effectively utilized for fault diagnosis. However, in IMD systems, direct impedance measurement through mechanical terminal access is difficult due to the integrated structure. To address this, the impedance of the induction motor is analyzed electrically, without the need for physical disconnection, allowing practical implementation within integrated systems. The specific phase angle of the space voltage vector in the three-phase inverter is analyzed to replicate the electrical conditions of mechanical terminal configurations. Based on this approach, a fault diagnosis algorithm was developed by analyzing the variation in stator current and impedance with respect to space voltage vector angles. The effectiveness of the proposed method was verified through experimental validation using a 12kW three-phase induction motor and terminal box. Full article

The variable-speed operation mode of pumped storage units improves the regulation performance and endows the units with characteristics such as isolation from the power grid, thereby affecting the system stability. This study establishes a detailed mathematical model for the connection of doubly fed induction generator-based variable-speed pumped storage (DFIG-VSPS) to a single-machine infinite bus system under power generation conditions in the synchronous rotation direct-quadrature-zero coordinate system. The introduction of the eigenvalue method to analyze the small-disturbance stability of doubly fed variable-speed pumped storage units and the use of participation factors to calculate the degree of influence of each state variable on the small-disturbance stability of the units are innovations of this study. The participation factor enhances flexibility, continuity, and efficiency in doubly fed variable-speed pumped storage by optimizing dynamic power paths and enabling multi-objective control coordination. While eigenvalue analysis is not new, this study is the first to apply it with participation factors to DFIG-VSPS, addressing gaps in prior simplified models. Furthermore, based on the changes in the characteristic root trajectories, the influence of changes in the speed control system parameters and converter controller parameters on the system stability was determined. Finally, the conclusions obtained were verified through simulation. The results indicate that increasing the time constant of water flow inertia poses a risk of system instability, and the increase in proportional parameters and decrease in integral parameters of the power outer loop controller significantly affect the system stability. Full article