The paper presents an experimental verification of a method for computing losses in a magnetic core operating in a DC-DC converter with a variable duty cycle, based on losses measured under sinusoidal excitation conditions. The core operating conditions that influence the losses and the method for their measurement are presented. The measurement setup was designed for this research, its basic features are discussed in the paper. The results obtained from the analysis were compared with core loss measurements obtained from the Brockhaus MPG-200 system. The theoretical foundation for core loss calculations and the measurement error analysis are also presented. The discussion of the results takes into account additional factors, such as the effect of working temperature on loss level.
To mitigate grid load fluctuations caused by large-scale electric vehicle (EV) charging while preserving user experience, this paper proposes a satisfaction-aware orderly charging coordination framework. First, based on Kano theory, a comprehensive fitness evaluation model is developed to characterize diverse user charging demands, integrating the grid load peak-to-valley difference, total charging cost, and user satisfaction into a uni-fied scheduling objective. To efficiently solve this complex high-dimensional problem, an improved particle swarm optimization (PSO) approach is employed. By incorporating adaptive inertia weights, asynchronous learning factors, and Lévy flight perturbation, the proposed algorithm enhances global search capability and alleviates premature conver-gence. Simulation results based on the IEEE 33-node distribution system demonstrate the effectiveness of the proposed strategy. Compared with disorderly charging, the proposed method significantly reduces the grid peak-to-valley difference and total charging cost. Meanwhile, the comprehensive user satisfaction is improved and consistently outperforms the conventional PSO method. These results indicate that the proposed strategy effectively balances grid operation and user benefits.
Residual earth-fault indicators installed in medium-voltage overhead feeders operate with fixed pickup thresholds, while the available residual current decreases with feeder distance. As a result, indicator operation becomes progressively less reliable toward remote feeder sections, and deterministic short-circuit analysis alone is insufficient to cha-racterize practical detectability limits. This study investigates distance-dependent detecta-bility of fixed-threshold residual earth-fault indicators using a combined experimental, si-mulation-based, and probabilistic framework. First, the relay–RTU–SCADA event chain is verified through controlled current-injection testing in a 35 kV reserve feeder bay. Second, residual earth-fault current along the 9.4 km Prizreni 1–Zhuri overhead feeder is evaluated using IEC 60909 single-phase-to-earth simulations implemented in DIgSILENT PowerFac-tory. Probabilistic threshold-exceedance analysis is then applied to estimate detection relia-bility as a function of feeder distance. The simulated residual current decreases from 350.577 A near the source substation to 299.793 A at the feeder endpoint. For the deployed 300 A pickup threshold, the detection probability decreases from near-unity values in upst-ream feeder sections to approximately 0.49 at the remote end of the feeder. Additional sen-sitivity analysis shows that fault resistance, uncertainty level, pickup threshold, and feeder electrical length significantly influence practical detectability limits. The results demon-strate that residual earth-fault indication in long feeders should be evaluated as a probabili-stic distance-dependent problem rather than as a purely deterministic threshold-crossing condition. The presented framework provides an engineering basis for evaluating reliability limits and pickup-threshold adequacy in medium-voltage distribution.
With the deepening of the construction of the new power system, the penetration rate of new energy in the power system continues to increase significantly. However, the traditional assessment of the ability of new energy (wind, solar, and storage) to participate in the primary frequency response (PFR) of the system exhibits limitations due to the diffi-culty in fully considering their differentiated characteristics. Therefore, this study develops a comprehensive evaluation index system for the PFR performance of wind-solar-storage new energy systems based on the entropy weight method. This system fully incorporates the characteristics of wind power generation, photovoltaic power generation, and energy storage systems in terms of frequency response and operational constraints, establishing overall PFR capability evaluation indicators of the system, as well as specialized and re-fined evaluation indicators for each technology. Simulation results demonstrated the meth-odology's effectiveness, demonstrating its capability to accurately assess frequency regula-tion capacities while considering the distinct operational characteristics of wind, solar, and storage technologies. These findings provide theoretical guidance for optimizing frequency regulation resource allocation and operational strategies in renewable-dominated power systems.
This paper proposes a robust sliding-mode control strategy for efficient photo-voltaic (PV) energy extraction in island DC microgrids. A PV system is sensitive to voltage fluctuations. PV parameters are optimized, shunt resistance, saturation, reverse saturation, and photovoltaic current equations are modeled to maximize power generation. A boost converter increases PV output voltage to commercial levels, with the parameters carefully optimized for maximum performance. The converters are modeled using first-order differ-ential equations based on an average state-space representation, enabling the development of a sliding-mode control strategy with non-linear switching functions. The control strategy incorporates reachability dynamics to handle system non-linearities, with the control law formulated using surface switching and equivalent control. This ensures precise regulation, fast convergence, and robust performance. Validated in MATLAB/Simulink under nominal (0.6 mH) and perturbed (0.9 mH) converter inductance conditions. The results reveal: (i) fast convergence with settling times of 2.0 ms under nominal and perturbed conditions; (iii) robust performance with 8.5% overshoots (nominal) and 10.0% overshoots (perturbed), compared to 26.0% and 33.0% with conventional PID controllers. As converter inductance parameters vary, the system achieves optimal power extraction. This study shows how slid-ing-mode control can enhance PV system performance for microgrid applications.
This paper proposes the use of wavelet coherence to conduct a study on the im-pact of renewable energy sources' generation on voltage losses in lines. The case study of the low-voltage distribution grid equipped with seven photovoltaic panels and one wind turbine was examined to demonstrate the functionality of the proposed methodology. Based on the simulation studies, the concept of how time-varying generation from the wind turbine or photovoltaic panels can affect the voltage losses in the selected power lines over one month was discussed. The analysis results were visualized using wavelet coherence delay plots. In contrast to other related studies, this research also includes additional plots like scale-averaged coherence, scale-averaged phase shift, time-averaged phase shift, and time-averaged coherence. The conducted studies extend and enrich the current view on the im-pact of renewable energy sources' generation on the power grid. The use of wavelet coher-ence can also help highlight additional information that may be ignored or overlooked, es-pecially in fast-paced systems. This kind of research may be useful for supplementing the results of load flow calculations with additional information on the voltage losses for dis-tribution grid operators. It can be considered a general tool for conducting a more in-depth analysis of the influence of renewable energy sources on the power grid.
Accurate magnetic field measurement is vital for analysing and optimizing electric machines such as permanent magnet synchronous motors. Existing methods struggle with complex geometries, enclosed structures, and multi-axis field variations. We propose a robotic arm-based system for automated, high-resolution scanning of all three magnetic field components across the full motor geometry, including stator and rotor. The system enables precise sensor positioning on curved surfaces for reliable data acquisition where traditional setups fail. Comprehensive spatial analysis, including harmonic decomposition, reveals hidden field patterns that support improved diagnostics and motor design. This flexible, scalable approach advances both experimental research and industrial evaluation of electric machines.
Composite insulator mandrels in high-voltage transmission systems are subjected to coupled electro-thermal-mechanical stresses, making them susceptible to internal fracture damage that threatens grid safety. Traditional detection techniques exhibit significant limitations in early fracture identification, deep defect localization, and quantitative damage assessment. This paper proposes a terahertz identification method for composite insulator mandrel fractures based on terahertz time-domain spectroscopy (THz-TDS), systematically elucidating the electromagnetic response mechanisms and multi-dimensional feature extraction strategies for fracture defects. Transmission-mode non-destructive testing was performed on glass fiber reinforced epoxy resin (GFRP) mandrel specimens in the 0.1–2.5 THz frequency range using a CCT-1800 THz-TDS system. The research reveals: 1) The dielectric discontinuity interface induced by mandrel fractures alters terahertz wave propagation paths, generating distinctive frequency-domain energy spectrum characteristics in the 0.1–0.75 THz band, with significant transmitted energy attenuation in fractured regions; 2) Through multi-frequency comparative analysis, 0.458 THz was determined as the optimal characteristic frequency for fracture boundary identification, demonstrating superior imaging contrast and spatial resolution compared to 0.568 THz; 3) Time-domain pulse wave-form analysis indicates that multiple reflection effects at fracture interfaces enhance negative peak amplitudes and peak delays, with peak-to-peak imaging enabling precise fracture localization. The established two-dimensional integrated diagnostic system of "frequency-domain energy spectrum differential qualitative identification and time-domain waveform characteristic quantitative assessment" provides a novel technical approach for condition monitoring and preventive maintenance of composite insulators in power systems, offering significant engineering application value for ensuring safe and reliable operation of high-voltage transmission lines.
To address the issues of high torque ripple and unstable operation in high-power mining outer-rotor permanent magnet motors (ORPMMs), this study investigates a rotor step-skew technique aimed at enhancing operational smoothness. First, an analytical model of cogging torque that incorporates rotor skew is established based on the energy method, which clarifies the mechanism through which the skew angle influences torque attenuation. From this model, a method for determining the optimal skew angle to minimize cogging torque is derived. Second, a finite element analysis (FEA) model of the ORPMM is con-structed to compare electromagnetic characteristics such as no-load back electromotive force (EMF), cogging torque, and torque ripple under both non-skewed and variably skewed rotor conditions. The cogging torque calculated analytically is compared with FEA results, thereby validating the accuracy of the optimal skew angle determination method. Finally, prototype experiments are conducted to verify the accuracy of both the simulation and analytical models. This work provides theoretical foundations and technical support for torque ripple suppression and performance optimization in high-power mining outer-rotor motors.
The Simplified Equivalent Circuit Model (SECM) is well established as a tool for the performance analysis of the Brushless Doubly-Fed Induction Machine (BDFIM). However, the SECM typically relies on a constant-parameter approximation, which, in theory, contradicts the machine's inherent velocity-dependent nature, given its typical nested-loop rotor structure that couples magnetically with a large portion of the air-gap field harmonics' spectrum. This paper investigates the physics behind this by directly deriving its parameters from rigorous power-balance equations and a time-harmonic finite-element model. The study identifies a self-compensating mechanism: although the extracted rotor leakage reactance and effective turns ratio exhibit significant variations near the natural speed, their combined interaction minimises the impact on terminal quantities. Furthermore, the paper investigates the validity determinants of the constant-parameter SECM in double-feed mode and explains the factors of the conditional accuracy.
This paper addresses the challenge of robustness against model uncertainties as well as simplicity in controller design for position control in servo systems. More specifically, in this paper, during the modeling of a permanent magnet synchronous motor for a servo system, uncertainties of the model are considered and are introduced as a summative term in its state space equation. But to increase the controller robustness, in addition to this cumulative uncertainty term, a polytopic uncertainty is also considered in the model design for the system state matrix. The system becomes fully robust to uncertainties after modeling. A state feedback controller is then designed based on this model, ensuring that the H∞ performance criterion is satisfied. In this way, and by satisfying the H∞ condition, the controller becomes robust against uncertainties accumulated by the model. On the other hand, the considered polytopic uncertainties also create a resistance to the uncertainties of the system's transient matrix. The advantage of the method presented in this article is that it has a very simple structure and its design is very simple. It also has a low computational burden and, despite its simplicity, has very good resistance to uncertainties. In addition, the proposed method also eliminates the chattering problem that exists in the sliding mode control family. To examine the performance of the proposed method, a series of experiments was conducted in the laboratory, and the results confirmed the effectiveness of this method in the cases mentioned above.
A method is proposed for predicting and warning the dissolved gas content in transformer oil. This method combines the Granger causality test and neural basis expansion analysis with exogenous variables, addressing dynamic coupling and complex temporal correlations in gas component data. First, the Granger causality test is used to analyze temporal correlations in gas component concentrations and their relationship with transformer load, to identify mutual influence between time-series data and achieve dynamic variable selection of the prediction model. Then a dissolved gas prediction model is established using neural basis expansion analysis with exogenous variables. Finally, transformer status warning is achieved under threshold constraints by combining confidence interval distribution analysis of predicted gas component concentrations. The results of a case study show that this model can achieve a mean absolute percentage error below 5.0%. Dynamic variable screening based on Granger causality verification can significantly enhance prediction accuracy, providing a more reliable reference for early warning of transformer status.
To address the torque ripple and consequent vibration induced by inter-turn short-circuit (ITSC) faults in high-speed permanent magnet synchronous generators (HSPMSGs), an HSPMSG incorporating a single-phase correction winding configuration is employed for torque ripple suppression. This particular winding configuration enables effective mitigation of the torque ripple caused by ITSC faults. An analytical magnetomotive force (MMF) model under ITSC fault conditions is established based on the winding topology of a back-wound HSPMSG. Through this model, the critical parameters that govern torque ripple are identified, and torque ripple together with vibration characteristics under ITSC faults are experimentally measured on a prototype. Subsequently, a dedicated suppression method for torque ripple is proposed after detailed examination of the ripple components arising from the fault, and the optimal suppression strategy is derived accordingly. Finite element analysis is performed to compare torque ripple and vibration performance before and after the introduction of the correction winding. Results confirm that, upon the occurrence of an ITSC fault, the proposed configuration can effectively suppress torque ripple and significantly reduce vibration levels in the generator.
The paper deals with research on the direct and quadrature axis cross saturation effect in the synchronous reluctance machine (SynRM). The comparative analysis between the performance of the 4-pole SynRM of 3-phase and 6-phase windings have been conducted exploiting developed 2D finite element models. In the numerical models of the studied machines, the locked rotor method was adopted in which the direct (d ) and quadrature (q) axis currents iq and id were forced to determine magnetic fluxes in the d-q axes, respectively. Next, determined magnetic flux maps have been used to determine a simple analytical model taking into account the cross-saturation effect. The quality of the proposed analytical description of the cross-saturation phenomenon has been evaluated by comparison to the results of simulations in order to assess the possibility of adapting this description in the control algorithms of SynRM drives.
To suppress cogging force and thrust ripple, a novel composite magnetic slot wedge composed of hard and soft magnetic materials is proposed. A finite element model of a fractional-slot permanent magnet linear synchronous motor is established. First, the effects of non-magnetic, hard magnetic, and soft magnetic slot wedges on electromagnetic characteristics—specifically cogging force and thrust ripple—are compared and analyzed. Subsequently, the composite magnetic slot wedge structure is optimized to determine the ideal material proportion. Finally, the performance of the proposed composite magnetic slot wedge is compared with existing composite slot wedges made of magnetic and non-mag-netic materials. The results indicate that the proposed composite magnetic slot wedge pro-vides superior comprehensive optimization of motor performance.
Aiming at the problems of few samples of local demagnetization faults and high difficulty in air-gap magnetic field detection for permanent magnet flux-switching linear motors (PMFSLMs), this paper adopts the primary backplate magnetic flux leakage signal of PMFSLMs to characterize demagnetization fault, which effectively avoids air-gap magnetic field detection. A few-shot learning method based on a Siamese convolutional neural network (CNN) is adopted to diagnose and identify the demagnetization faults in PMFSLMs. Firstly, a finite element model (FEM) of the PMFSLM is established to analyze the electromagnetic characteristics of the PMFSLM, so as to obtain the backplate magnetic flux leakage data under different demagnetization conditions. Then, a dual-channel dataset of backplate magnetic flux leakage signals is constructed and thus the signals of magnetic flux density difference are reconstructed under different demagnetization faults. Finally, a few-shot learning method based on a Siamese CNN is used to establish a classification model for identifying different demagnetization faults. The finite element simulation results show that the proposed method can accurately identify different demagnetization locations and demagnetization combinations of the PMFSLM, and exhibits high accuracy under limited-sample conditions.
Abstract: This paper presents an improved sensorless scalar control (SC) scheme for in-duction motor drives, aimed at enhancing speed tracking capability in the low-speed region. The proposed method integrates rotor flux-based model reference adaptive system (RF-MRAS) with dual adaptation to estimate both rotor speed and stator resistance. The rotor flux angle obtained from the current model (CM) is used to compute the synchronous speed, while the slip component is filtered before being injected into the scalar frequency com-mand. In addition, a stator-voltage-drop compensation strategy based on estimated stator resistance is introduced to preserve the air-gap flux during low-voltage operation. Simula-tion studies confirm that the proposed method extends the stable operating range. The min-imum stable speeds are 12.23/12.28 rad/s at 20% load, 10.78/11.59 rad/s at 25% load, and 11.88/11.29 rad/s at 30% load, evaluated using EMAPE/Emax criteria. All cases satisfy EMAPE ≤ 2% and Emax ≤ 6%, showing better low-speed stability than the baseline methods. Laboratory experiments were also performed on an induction motor drive platform. At 300 rpm, online stator resistance estimation strongly reduces speed oscillations under a 20% stator resistance increase. At very low speeds, voltage compensation restores the ef-fective stator voltage and improves steady-state tracking. These results verify that the pro-posed dual RF-MRAS scheme improves robustness without requiring mechanical speed sensors.
Prof. Stanisław Fryze, by developing his power theory (PT), made a fundamental contribution to studies on energy transfer in electrical circuits. After that, these studies were continued by scientists and engineers for several decades, and several new power theories were developed. It means that despite his major contribution, his PT still has failed to provide answers to several important questions asked about physical phenomena that associate the electric energy transfer and their mathematical description, as well as the methods of increasing its effectiveness by compensation. This article discusses the causes of the failure of Fryze’s approach to PT development. One of them was the rejection of the concept of harmonics. The extension of his PT to three-phase circuits has failed because he did not try to reveal the power properties of such circuits as a single unit, but expressed them in terms of the power properties of separate supply phases of such circuits.
This study investigates the energy efficiency of an electric motor–inverter system operating under variable load conditions. The aim of the research is to develop and evaluate an AI-based supervisory method for identifying inefficient operating modes and optimizing control parameters using real-time electrical and operational data. Unlike conventional motor-control studies, which mainly focus on torque ripple, flux regulation, or speed response, the proposed approach considers the motor, inverter, load, and control algorithm as a single energy-efficiency-oriented system. The input vector of the model includes voltage, current, inverter frequency, rotational speed, electromagnetic torque, temperature, load level, power factor, and total harmonic distortion. The output variables are system efficiency, total power loss, and the classification of the operating mode as either efficient or inefficient. The proposed method was evaluated under 24 steady-state operating modes formed by four load levels and six inverter frequency values. Under the most inefficient tested operating condition, the overall system efficiency increased from 78.3% to 90.2% after AI-based optimization, while energy consumption decreased from 118 kWh to 88 kWh. In addition, the power factor improved from 0.76 to 0.94, total harmonic distortion decreased from 15% to 5%, and inverter switching losses decreased from 8% to 3%. The results confirm that the proposed method can identify inefficient operating states and support the selection of energy-efficient control parameters. The main limitation of the study is that the validation was performed using one motor - inverter configuration and mainly under steady-state conditions. Therefore, future research should include transient load changes, different motor power ratings, and embedded real-time implementation.
To more accurately obtain the feature information embedded in the acoustic pattern of transformers, a transformer fault diagnosis method is proposed based on multilevel acoustic information of 14 state types. In this method, a parallel dual-channel fault diagnosis model, CNN-BiLSTM-Transformer, is established. First, the modified Mel inversion coefficients and Mel spectrograms are extracted from the original acoustic pattern data. The modified Mel inversion coefficients and Mel spectrograms are then input into the parallel dual-channel model. In the first channel, a convolutional neural network model is used to extract the feature information of maps. In the second channel, a bidirectional long-and short-term memory network and a Transformer encoder are used to partially extract the temporal features in the MFCCs. Finally, the temporal features extracted from the two channels are fused through multimodal fusion for training. The experimental results show that the proposed diagnostic method can achieve an average accuracy of 99.5% in multiple fault diagnosis. Compared with current mainstream acoustic single-channel diagnostic models, the diagnostic rate of this model is improved by an average of 4.8%, exhibiting higher accuracy and robustness.
