Gang Li

With increasing renewable penetration, virtual synchronous generators (VSGs) have become an important solution for stabilizing converter-interfaced renewable generation because of their inertia support and grid-forming capability. However, under weak-grid voltage sag conditions, the transient synchronizing stability of VSGs deteriorates significantly. In particular, virtual resistance, although effective in improving small-signal damping and suppressing synchronous resonance, may further weaken the synchronism-preserving capability under large disturbances. To address this issue, this paper investigates the effect of virtual resistance on transient synchronizing performance and proposes an additional transient damping control method. A power transfer model and a transient dynamic model are established for the VSG with virtual resistance. The results show that virtual resistance reduces the active-power transfer capability and narrows the power-angle margin, thereby weakening transient synchronizing stability. An additional damping branch is then introduced into the active power control channel, and a third-order nonlinear model is developed to analyze the stability enhancement mechanism and parameter effects. Simulation and hardware-in-the-loop results demonstrate that, with proper parameter settings, the proposed method can effectively suppress the initial post-fault acceleration process, reduce power-angle oscillations and frequency deviations, and enhance the synchronism-preserving capability during voltage sags.

Integrating energy storage and hydrogen energy into wind–solar–hydrogen storage microgrids can effectively mitigate the intermittency, volatility, and unpredictability of renewable energy. Aiming at tackling excessively idealized traditional models, this paper establishes a system model for wind–solar–hydrogen storage microgrids by adopting real-world environmental parameters such as wind speed and solar irradiance. To improve the operational performance of the system, an optimal microgrid scheduling method is proposed, which adopts the minimum daily comprehensive operating cost as its core objective. The wind and solar curtailment penalty terms are embedded into the objective function to quantify energy efficiency requirements, and hard constraints including power balance, ramp rate of hydrogen power production, and state of charge (SOC) of energy storage are introduced to guarantee system stability. An improved sparrow search algorithm (ISSA) incorporating a sine function and a golden ratio coefficient is adopted to optimize the position updating process. The objective function is solved so that it satisfies all of the system’s constraints, and the algorithm’s performance is evaluated based on typical daily simulation data. The results demonstrate that the proposed algorithm can effectively optimize the scheduling process and improve the economic efficiency, stability, and overall performance of the system. The total economic benefit generated by the system is increased by 8.48%, which constitutes a remarkable enhancement of the system’s economic efficiency.

The paper proposed a high-frequency characteristic modelling method for common-mode chokes based on a series inductance damped differential mode model. This method has been demonstrated to provide an accurate description of the electromagnetic characteristics of common-mode chokes across a wide frequency range. The model is combined with a multi-stage parameter extraction algorithm based on a frequency-adaptive weighting strategy. The algorithm has been designed to extract resistance characteristics in the low-frequency band, accurately obtain inductance characteristics in the mid-frequency band, and obtain inductance characteristics in the high-frequency band. The experimental validation process has demonstrated that the model displays excellent fitting accuracy across the frequency range of 10 Hz to 50 MHz. This finding provides a reliable theoretical foundation for the design and performance prediction of electromagnetic interference filters.

Multidominant-frequency pseudorandom waveforms are widely used in electromagnetic detection to enhance resolution and robustness. Conventional synthesis methods for such waveforms are limited by fixed spectral configurations, constrained amplitude ratios, and low adaptability to dynamic field environments. To address these critical constraints, this study introduces a novel graphical modulation method based on pulsewidth modulation (PWM) harmonic analysis. The proposed method improves adaptability and frequency diversity in PRW generation for electromagnetic detection. Theoretical analysis reveals the harmonic distribution characteristics of traditional PWM signals, leading to an innovative dual-frequency modulation strategy that combines adjacent and frequency-doubled waveforms. This breakthrough enables programmable harmonic structures, overcoming the limitations of conventional rigid or complex pseudorandom synthesis architectures. At present, no adequate model exists for a chaotic-based adaptive pseudorandom sequence generator, applied in digital encryption, that is capable of producing square-wave pseudorandom sequences while simultaneously controlling their spectral characteristics. Further research and development are needed in this area. Simulation and experimental results demonstrate the proposed method’s capability to reconfigure (1, 2, 4), (1, 3, 5), and (1, 3, 7)-type dominant frequency distributions in real time. This provides robust technical support for dynamic detection in complex electromagnetic environments, addressing the key needs of high-efficiency and high-accuracy geological surveys and energy exploration.

Driven by the rapid promotion of new energy technologies, lithium-ion batteries have found broad applications. Accurate prediction of their state of health (SOH) plays a critical role in ensuring safe and reliable battery management. This study presents a hybrid SOH prediction method for lithium-ion batteries by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a fully connected bidirectional long short-term memory network (FC-BiLSTM). ICEEMDAN is applied to extract multi-scale features and suppress noise, while the FC-BiLSTM integrates feature mapping with temporal modeling for accurate prediction. Using end-of-discharge time, charging capacity, and historical capacity averages as inputs, the method is validated on the NASA dataset and laboratory aging data. Results show RMSE values below 0.012 and over 15% improvement compared with BiLSTM-based benchmarks, highlighting the proposed method’s accuracy, robustness, and potential for online SOH prediction in electric vehicle battery management systems.

This article focuses on reactive power compensation and impedance matching of square-wave voltages. Square-wave voltage is the operating voltage in electromagnetic method (EM) exploration. The characteristics of a square-wave voltage are very different from those of a sinusoidal voltage in the power grid. Although some papers proposed impedance matching converters for square-wave voltages, they solved the high dc voltage requirement problem. The problem is caused by square-wave voltage having multiple and variable spectral components. An impedance-matching converter with an injection circuit (IMFR) for square-wave voltage is proposed. IMFR is composed of a thyristor-controlled inductor and capacitor (TCLC) part and injection circuit coupling active H-bridge part. TCLC can be controlled to resonate at the highest energy fundamental frequency for damping the largest part of the dc voltage requirement. The use of TCLC offsets the varying fundamental frequency demand on the dc voltage. First, this article gives a joint design and optimization approach for the lowest dc voltage requirement. Second, a synergistic control method of proposed IMFR is provided implementing IMFR. Third, two groups of comparative experiments show that the proposed IMFR can enlarge the current by 11.8%–38.5%. The effect on the current increase is 4.4%–7.9% higher than using only the H-bridge of the previous method when they are operated with the same dc voltage.

In the context of the increasing global demand for renewable energy and the rapid development of large-scale photovoltaic (PV) power generation, efficient grid connection of PV energy has become crucial. However, existing grid-connected inverters (GCI) face issues such as high-power losses issue of inductive GCI (IGCI) or limited operating ranges issue of capacitive GCI (CGCI). This article introduces a multiple energy conversion channels fusion GCI (MC-FGCI) for large-scale PV power injection. First, MC-FGCI is proposed based on a new topology circuit configuration. Second, an equivalent circuit model of IGCI and CGCI is introduced to calculate the critical point for channel switching. The IGCI and CGCI efficient operating boundaries are determined based on the critical point. Third, based on the efficient operating boundaries, a finite state machine based method is proposed to choose the optimal VSI for high-efficiency grid connection. Finally, experimental results show that compared to previous multiple parallel IGCI (MP-IGCI), the proposed MC-FGCI saves around 11% power loss by eliminating the lossy dc/dc link. Compared to MP-CGCI, the proposed MC-FGCI expands the active power injection range under low maximum power point (MPP) voltage.