Search Results (2,828)

This work examines the transient dynamics of synchronous machines under sudden fault conditions, with emphasis on short-circuit events. The study begins with a single-phase synchronous machine operating in open-circuit mode, where short-circuit current expressions are systematically derived and numerically evaluated using advanced integration algorithms. The transient evolution of stator and field currents is analyzed, highlighting their dependency on rotor position, machine parameters, and electromagnetic interactions. A SIMULINK-based model is implemented to simulate the time-domain responses and visualize the effects of parameter variations. The investigation is then extended to a three-phase synchronous machine with damper windings subjected to line-to-line faults. Generalized voltage equations in the d-q reference frame are formulated, leveraging Park’s transformation to enable detailed modeling of transient behavior. Numerical simulations confirm the analytical predictions, revealing the significant influence of rotor angle, leakage inductances, and damper circuits on fault current dynamics. The results provide valuable insights for fault analysis, power system stability assessment, and the design of synchronous machines under transient operating conditions. Full article

This paper proposes a new hybrid switched inductor and switched capacitor (HSISC) buck–boost DC–DC converter. For the duty ratio above 28%, the proposed converter operates as a boost converter; otherwise, it acts as a buck converter. Compared with conventional buck–boost converters, incorporating the hybrid switched inductor and switched capacitor (HSISC), the network yields a substantial voltage gain at lower duty ratios. Being a non-isolated topology, high-frequency transformers and the associated issues are absent. Additionally, the proposed topology has the merits of continuous input current, making it suitable for renewable energy integration and vehicle-to-grid (V2G) applications, a wide range of duty ratio for boost operation, and ease of control as there are only two modes of operation with switches operating in a complementary manner. Operational analysis for the two modes, necessary mathematical derivations for component design, and a steady-state analysis of the converter are reported. The experimental findings for the converter, which were conducted at a duty ratio of 0.05 to 0.5 at a switching frequency of 10 kHz, are reported. The presented results provide proof-of-concept validation based on analytical and simulation studies, demonstrating the feasibility and operational characteristics of the proposed converter. Full article

This paper presents a study on the development of a deep neural network (DNN)-based surrogate model for rapid induced voltage prediction in current transformer (CT)-based magnetic energy harvesting systems. CT-based magnetic energy harvesters are promising self-powered energy sources for low-power electronic devices, but their output performance is strongly affected by the nonlinear magnetic behavior of the core material. Therefore, the accurate prediction of the induced voltage is important for device design. However, evaluating the voltage response under various magnetic material characteristics and operating conditions through repeated electromagnetic simulations requires considerable computational effort. In this study, nonlinear B–H curves were parameterized using an arctangent-based model, and electromagnetic simulations were performed by varying the magnetic material parameters, primary current, and load resistance. The resulting dataset was used to train and validate the DNN surrogate model. The trained model showed high prediction accuracy, with an R² value greater than 0.99 and low prediction errors. It also reproduced the RMS induced voltage trends for different magnetic material characteristics and operating conditions and was further used for maximum power point analysis within the investigated parameter range. These results indicate that the proposed surrogate model can reduce the need for repeated electromagnetic simulations and support the efficient design exploration of CT-based magnetic energy harvesting systems. Full article

As a critical device for ensuring stable operation in power substations, an Uninterruptible Power Supply (UPS) plays a vital role in maintaining a continuous power supply, improving power quality, and enhancing system reliability. However, startup inrush current and backflow ripple voltage significantly impact the stability of DC power supply equipment. This paper proposes a DC-DC circuit-reuse method with double-line frequency ripple suppression for substation UPS systems. This approach utilizes a boost converter to transfer the double-line frequency pulsation from the DC input to the bus capacitor side, thereby reducing the double-line frequency components at the input. Notably, this Boost converter simultaneously serves as part of the power factor correction (PFC) circuit connecting the grid and DC bus, achieving functional integration that significantly reduces system costs. A 3 kVA UPS experimental platform was constructed to validate the proposed method. The test results demonstrate that while the bus voltage exhibits slight pulsation at double-line frequency, the input current transforms into an approximate DC quantity. This second-harmonic ripple suppression method effectively improves key performance indicators: Startup inrush current decreases by 38% compared to conventional designs, and backflow ripple voltage amplitude is suppressed below 0.5% of the nominal DC voltage. The proposed topology demonstrates superior compatibility with existing substation DC systems and provides a cost-optimized solution for power quality enhancement in critical infrastructure applications. Full article

Transmission power losses significantly affect the efficiency, reliability, and economic operation of modern electrical power systems. This study proposes a Hybrid Genetic Algorithm–Particle Swarm Optimization (HGAPSO) framework for transmission loss minimization in the IEEE 118-bus power system. The proposed approach combines the global exploration capability of Genetic Algorithms (GAs) with the rapid convergence characteristics of Particle Swarm Optimization (PSO) to optimize generator voltage settings, transformer tap positions, and reactive power compensation while satisfying all operational constraints. The HGAPSO framework was developed and implemented in MATLAB R2024a and evaluated using the IEEE 118-bus test system. The simulation results demonstrate that the proposed method reduced transmission losses from 132.8 MW under the base-case condition to 98.6 MW, representing a 25.75% reduction in total network losses. In addition, the optimized operating conditions improved the minimum bus voltage from 0.914 p.u. to 0.972 p.u., while the average voltage deviation decreased from 0.062 p.u. to 0.019 p.u. These voltage profile improvements were achieved as secondary benefits of the transmission loss minimization process and the enforcement of system operating constraints. Furthermore, the HGAPSO algorithm exhibited superior convergence performance, reaching the optimal solution within 82 iterations compared to 185 iterations for GA and 124 iterations for PSO. The results confirm that the proposed HGAPSO framework provides effective transmission loss reduction, faster convergence, and reliable network operation compared with standalone optimization techniques. The proposed methodology offers a robust and computationally efficient solution for large-scale power system optimization, optimal power flow studies, and smart grid applications. Full article

Partial discharge localization is a crucial step in monitoring high-voltage equipment. Over time, due to data acquisition limitations, signal strength has become a key driver of methods of localization. In the literature, the variety of testing conditions makes it challenging to identify the factors influencing localization accuracy clearly. Furthermore, there is a lack of research using real equipment as a source of partial discharge. This paper presents a meta-analysis of data from monitoring systems used for radiometric localization of partial discharge sources, employing algorithms based on the time difference in arrival and received signal strength indicator. The obtained results show that the data exhibited errors with a high standard deviation, indicating that many studies are conducted under significantly different conditions. The first method is more sensitive to noise and the sampling rate, while the second is influenced by the source type and the number of sensors. Moreover, laboratory experiments performed with a potential transformer exhibiting partial discharge suggest that signal propagation in real equipment might not be omnidirectional, which can affect accuracy. Consequently, results based on controlled sources, where source directionality and obstacles have a significant impact on performance, do not accurately reflect real-world conditions. The obtained results indicate that the more realistic the source, the higher the errors will be, due to the lower uniformity in signal propagation. Full article

Large-scale wind-farm clusters are characterized by intense power fluctuations, various turbine types and time-varying reactive power margins, along with conflicting goals of voltage security and network loss reduction. To address these issues, this paper proposes a hierarchical coordinated reactive power optimization strategy based on an Environment-Aware Particle Swarm Optimization (EAPSO) algorithm. In this study, “environment-aware” specifically refers to operating-condition awareness, including low-load degree, spatial power dispersion, and output imbalance degree of wind turbines. A three-layer framework consisting of cluster level, wind-farm level and unit level is established to coordinate wind-farm reactive power commands, SVG outputs, transformer tap positions, and turbine-level reactive power allocation. A comprehensive disturbance index is constructed from the low-load degree, spatial power dispersion, and output imbalance degree and is then mapped to the initialization strategy, learning factors, and mutation operation of EAPSO. A 24 h offline simulation is conducted using historical operating profiles of a practical large-scale wind-farm cluster in South China. The results show that the proposed strategy reduces the average absolute PCC voltage deviation from 0.013331 p.u. before optimization to 0.004120 p.u. it achieves an average network-loss reduction rate of 3.55%. Compared with Method 1, the transformer tap operation times decrease from 139 to 92, and the cumulative SVG reactive output decreases from 1077.32 Mvar to 208.39 Mvar. Full article

The probability of failure of a power transformer is difficult to quantify within a single utility because major failures are rare and operating histories are often incomplete. This paper uses large-scale CIGRE surveys (50 utilities, 26,533 transformers, 331,379 operating years) to estimate age-dependent component reliability by voltage class. In total, 1358 major failures and 991 retirements were reported for a reference period of up to 34 years. The data were treated as left-truncated and right-censored. Hazard rates were calculated for active parts, bushings, and on-load tap changers, retirements were assessed using the Kaplan–Meier estimator, and Weibull distribution models were fitted to 100–199 kV and 200–700 kV populations. The overall major failure rate was 0.41% per year. For the 100–199 kV transformers, the component hazard rates were close to constant with age (β ≈ 1), and the late-life pattern was influenced by increasing retirements. For the 200–700 kV transformers, bushing hazard showed a stronger age dependency and exceeded active-part hazard at around 50 years. The results highlight the value of component-focused risk management and show that fleet reliability should be interpreted alongside retirement and condition-management practices. Key limitations include data truncation, censoring, and the lack of categorisation of failures by technology type. Full article

Power quality disturbances (PQDs) in microgrids pose significant challenges to stable and reliable operation, particularly in tourism-oriented island systems with highly variable and uncertain load patterns. This paper proposes an interpretable PQD identification framework based on time–frequency feature extraction. The method utilizes a short-time Fourier transform to capture the nonstationary characteristics of voltage signals and constructs a compact feature set integrating time-domain, frequency-domain, and time–frequency information for disturbance classification. A supervised learning model is employed to map the extracted features to disturbance categories, while interpretability is achieved through feature contribution analysis, enabling explicit linkage between model decisions and the physical characteristics of PQDs. The proposed approach is validated using a combination of synthetic datasets, simulation data derived from MATLAB/Simulink R2024a microgrid models, and experimentally measured signals from a hardware-based platform. Case study results demonstrate that the proposed framework achieves a high overall classification accuracy of 99.50% across multiple disturbance types, including voltage sag, voltage swell, harmonic distortion, voltage flicker, transient disturbances, and hybrid disturbances. The interpretability analysis further confirms that the identified features are physically consistent with the underlying disturbance mechanisms. Overall, the proposed framework provides an accurate, robust, and interpretable solution for PQD identification, offering practical value for real-time monitoring and intelligent operation of renewable-rich microgrids. Full article

Rapid and accurate identification of various faults occurring in transmission lines is essential for restoring normal line operation. However, existing transmission line fault diagnosis methods still face challenges in terms of noise immunity and diagnostic accuracy. To address these issues, this paper proposes a deep learning method based on recurrence plots and a convolutional neural network–bidirectional gated recurrent unit–attention mechanism model. The voltage and current signals of transmission lines are transformed into recurrence plots in both the time and frequency domains. Parallel convolutional neural networks are then employed to extract local features from the two domains, while bidirectional gated recurrent units are used to capture temporal dependencies. Furthermore, multi-head self-attention and cross-attention mechanisms are introduced to enhance key features within each domain and achieve adaptive fusion of inter-domain feature information. A transmission line model is established in Simulink to collect data under various fault conditions and influencing factors, thereby verifying the effectiveness and adaptability of the proposed method. Experimental results show that the proposed method achieves fault recognition accuracies of 99.63%, 96.68%, and 75.38% under NL1, NL2, and NL3 Gaussian-noise conditions, respectively, and maintains accuracies of 99.02%, 95.93%, and 72.43% under mixed-noise conditions. Compared with other deep learning models, the proposed method demonstrates higher diagnostic accuracy and stronger robustness. Full article

Radio frequency (RF) energy harvesting offers a potential power source for low-power Internet of Things and wireless sensing nodes, but compact rectifiers must balance impedance matching, multiband response, and load-driving capability. This work presents a compact SMS7621 Schottky-diode RF rectifier for RF-powered wireless sensing applications. An 11-segment microstrip distributed-parameter collaborative optimization strategy is used to tune impedance transformation in a 3.48 cm × 1.98 cm single-layer layout while compensating for diode nonlinear impedance variation and package parasitics. Simulations show more than 40% RF-to-DC conversion efficiency from 1.90 to 2.35 GHz, with additional efficiency peaks of 40.55% at 4.45 GHz and 38.45% at 7.15 GHz. Measurements verify the 2.45 GHz output performance under controlled high-input-power excitation: with a 300 Ω load and 25 dBm input, the rectifier delivers a maximum DC voltage of 5.42 V. At 15 dBm input, the measured peak efficiency reaches 46.05% at 2 GHz and remains 35.69% at 4 GHz. These results indicate a compact rectifier front end with multiband harvesting potential and 5 V-class load-driving capability under dedicated RF powering conditions. Full article

Maximum power point tracking (MPPT) is essential for photovoltaic (PV) efficiency under dynamic environments. This paper presents a comparative analysis of three MPPT algorithms: incremental conductance (INC), Q-Learning (QL) reinforcement learning, and Transformer-inspired Machine Learning (TML) applied to a photovoltaic (PV) array configured in a 1S×3P with a DC-DC boost converter designed for a 48 V DC output. Simulations were performed under four scenarios in MATLAB/Simulink: standard test conditions (STC), irradiance variation, temperature variation, and combined wide-range variations. At STC, all three exceed 99% tracking efficiency, with QL achieving the highest efficiency, 99.914%; TML having the best output voltage regulation (48.071 V); and INC converging fastest (5.4 ms). Under dynamic irradiance variations, QL attained the highest average tracking efficiency (91.85%) and average output power (481.4 W), whereas INC converged within 36.9 ms. Under temperature variations, TML achieved the highest average tracking efficiency (97.448%) and average power output (549.30 W), while INC maintained the fastest convergence rate (68.6 ms). With wide-range combined variation, QL achieves the highest average tracking efficiency (91.32%) and output power (469.9 W), outperforming TML (84.06%) and INC (57.39%) by 7.3 and 33.9 percentage points, respectively; INC converges fastest (33.1 ms) but delivers 64.1% less output power than QL. The simulation results demonstrate that artificial intelligence-driven algorithms can significantly improve maximum power point tracking (MPPT) under dynamic conditions. To establish real-world viability, future work requires hardware-in-the-loop (HIL) testing and experimental validation. Full article

In high-step-up ratio converters for portable battery-powered devices, MOSFET switching loss limits efficiency and thermal design. This paper evaluates a multi-stage adaptive gate driver (MS-AGD) after transfer from a 900 V SiC MOSFET high-step-up converter to a 25 V Si MOSFET transformer-based boost circuit. The MS-AGD detects the Miller plateau by differential sensing and controls gate current in four stages through cascode current mirrors. The target-platform comparison combines measured switching waveforms with a temperature-based $\zeta$ coefficient and an apparent $R_{on-effective}$ indicator under a fixed device, load, fixture, pulse sequence, and thermal path. Total switching energy is not determined directly. Tests at 15 frequency points from 23.26 to 125 kHz show that drain-source voltage reaches its valley in about 500 ns with MS-AGD rather than about 1300–1450 ns with fixed-resistor drive and that the MOSFET package-temperature rise is reduced at all tested points by about 25% on average. The fitted apparent thermal-electrical indicator is also lower. These mutually consistent waveform and thermal results indirectly support a reduced turn-on switching-loss contribution while avoiding interpretation of $\zeta$ or apparent $R_{on-effective}$ as direct measurements of total switching loss or instantaneous channel resistance. Full article

This work presents an integrated methodology for modeling, parameter tuning, and experimental emulation of Dual Active Bridge (DAB) converters aimed at detailed simulation, control design, and real-time emulation. This is needed to bridge the gap between theoretical modeling and practical implementation, enabling accurate prediction of converter behavior under realistic operating conditions and facilitating the development of control strategies. The study begins with the derivation of a nonlinear model including parasitic elements and transformer characteristics, enabling accurate representation of the converter’s dynamics across operating conditions. To address deviations caused by component tolerances, the model parameters are calibrated using a multi-algorithm optimization framework based on Particle Swarm Optimization, Grey Wolf Optimizer, Secretary Bird Optimization, and Whale Optimization, where the error between predicted and experimental waveforms is minimized. The comparative analysis allows selecting the most suitable optimization strategy based on statistical analyses. The model is also discretized and implemented on a Hardware-in-the-Loop (HIL) platform based on a high-performance microcontroller, enabling real-time emulation of the converter as a digital twin. Moreover, a control-oriented version of the model is presented and used to design a voltage controller, which is subsequently tested in both the HIL environment and on a real DAB converter prototype. Experimental results report differences between HIL and real prototype below 3.9% for currents and 4.45% for voltages in multiple operation conditions, demonstrating an accurate representation of the real power system. This methodology ensures low errors between theoretical, simulated, and experimental behavior, providing a framework for accurate modeling and controller design of DAB converters. Full article

Unbalanced input voltage can significantly affect the electrical behavior of transformer rectifier units (TRUs), especially when a diode open-circuit (OC) fault breaks the original diode conduction symmetry. However, the effect of unbalanced input voltage on the diode OC fault has not been sufficiently clarified from the perspective of a conduction mechanism. This paper analyzes the effect of unbalanced input voltage on diode OC faults in TRUs by establishing a conduction-oriented mechanism. Unbalanced input voltage is divided into two forms, namely, unequal magnitude and phase-shift deviation. The effects on diode conduction boundaries, conduction angles, and conduction intervals are first derived theoretically. Then, using a 12-pulse TRU with $D_{11}$ and $D_{21}$ OC faults as representative cases, current and voltage responses are investigated in both time and frequency domains. The experimental results show that the two forms change diode conduction intervals in different ways. In particular, an unequal magnitude changes the relative driving voltage dominance near the conduction boundaries, resulting in the stretching or compression of diode conduction intervals; phase-shift deviation shifts the angular positions of the driving voltages and modifies the commutation timing. Two forms further aggravate waveform asymmetry and enhance low-order and non-characteristic harmonics under diode OC fault conditions. This effect analysis provides a more comprehensive basis for understanding diode OC fault responses in 12-pulse TRUs and supports the development of more robust diode OC fault diagnosis methods under non-ideal input voltage conditions. Full article