When utilizing high-dimensional chaotic signals for frequency modulation, achieving a uniformly distributed power spectrum is a challenging task. This paper addresses this challenge by proposing a power spectrum homogenization strategy based on distribution transformation. The strategy transforms the task of achieving a uniformly distributed power spectrum in frequency modulation of high-dimensional chaotic signals to solve and equalize the probability density function of the chaotic signals, thereby further enhancing the ability of high-dimensional chaotic signals to suppress electromagnetic interference. Firstly, the difficulty of obtaining a smooth probability density function of chaotic modulation signals is solved using the kernel density estimation algorithm. Then, a distribution transformation algorithm is proposed to convert non-uniformly distributed chaotic modulation signals into uniformly distributed chaotic modulation signals. By using uniformly distributed chaotic modulation signals for frequency modulation, the objective of power spectrum equalization is achieved. Finally, taking the Chua’s chaotic signal as an example, the effectiveness of the proposed strategy is verified using an experimental platform based on a digital signal processor-controlled active clamping flyback converter.

With the large-scale integration of renewable energy sources (e.g., wind power), the system inertial response and primary frequency regulation are affected. The virtual synchronous generator technology, which makes it possible for wind power units to adjust the frequency, provides a new way of approaching this problem. In this paper, we set up the related constraints that fit the optimal dispatch framework with a primary reserve representing the primary frequency response between the conventional synchronous generator and the wind power units, and the technical parameters of the virtual synchronous generator. Meanwhile, we use the robust day-ahead optimization dispatch model considering the wind power integrated primary frequency control in the real-time operation with the scenario of the wind power output showing the uncertainty of wind power. Based on the model, we identify the key set using an iterative method and obtain the maximum power loss. Through the proposed model, we could provide the day-ahead unit output and real-time primary reserve, thus ensuring the reliable operation of the system. Finally, the effectiveness of the proposed method is verified by a computational experiment.

External steep-front transient overvoltages may introduce high-frequency electromagnetic disturbances into modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems, resulting in additional transient voltage stresses on converter valves and their sub-modules. This paper investigates the propagation, coupling, and transient voltage distribution characteristics of external high-frequency disturbances in MMC converter valves using a multi-scale wideband equivalent model covering the “sub-module–converter valve–converter station” hierarchy. A wideband equivalent model of a 4.5 kV/3 kA press-pack insulated gate bipolar transistor (IGBT)-based sub-module is developed and integrated with the distributed parasitic parameters of the valve tower and the high-frequency characteristics of converter-station components. To investigate the converter-valve response under different transient conditions, a representative lightning impulse is considered as an engineering transient condition, while a controlled fast-front impulse is employed to investigate the intrinsic high-frequency propagation and resonance characteristics of the distributed converter-valve network. Simulation results demonstrate that external transient disturbances can propagate into MMC converter valves through grounding parasitic capacitances and distributed coupling paths, resulting in additional differential-mode transient voltage stresses at sub-module terminals. Sub-modules closer to the disturbance source experience higher transient voltage peaks and larger dv/dt values. Moreover, multiple local resonance bands are identified within the valve tower, with high-frequency oscillatory components above 10 MHz being strongly influenced by the distributed parasitic network. Parameter analysis further indicates that the sub-module stray inductance has an important influence on the magnitude and oscillatory characteristics of the induced transient voltage. The proposed multi-scale modeling approach provides a practical method for evaluating high-frequency transient voltage stresses in MMC-HVDC converter valves and provides theoretical support for insulation coordination, converter-valve structural optimization, and transient reliability design of HVDC transmission systems.

In the context of the large-scale integration of high-proportion renewable energy into power grids, Power Conversion Systems (PCS) and Static Var Generator (SVG), as the core power electronic devices for ensuring grid frequency stability and power quality, demand high-level safety and reliability. To tackle challenges such as the wide range of defect sizes in PCS and SVG, the low recognition accuracy for microscopic fuzzy defects, and complex background interference, this paper presents a lightweight and high-precision defect recognition model (RD-YOLO) based on the latest YOLOv11 benchmark. First, an improved Mosaic algorithm is introduced. This algorithm utilizes conflict relationship tables to preserve physical context semantics during traditional non-discriminative data augmentation. Second, to surmount the limitations of scale-aware feature extraction, the YOLOv11 is re-engineered within the backbone network by integrating a Res2Net multi-scale cascaded mechanism. This enhances the network’s capacity to capture both fine-grained defect features and large-scale defect boundaries. Third, Focal Loss is employed for difficult sample detection. Nonlinear gradient modulation is utilized to guide the model to focus on ambiguous defect edges. Finally, the Soft-NMS post-processing strategy significantly enhances the regression accuracy in densely corroded regions. Experimental validation on a self-developed dataset consisting of 8500 high-resolution PCS and SVG defect images reveals that the enhanced RD-YOLO attains an average precision of 89.6% and a frame inference rate of 98 FPS (in RTX 3090), offering robust technical support for intelligent visual maintenance in renewable energy facilities.

Given the constraints on the invariant distribution in chaotic systems, flexibly setting the probability distribution of chaotic signals poses a significant challenge. To tackle this issue, this paper proposes a strategy that transforms the task into solving and modifying the probability density function of the chaotic intrinsic signal. Initially, kernel density estimation algorithms are employed to address the issue of obtaining smooth probability density functions for high-dimensional chaotic signals. Any chaotic signal can serve as the intrinsic signal source, with its probability density function and distribution function being solvable using this algorithm. Subsequently, a graph-based transformation algorithm is introduced for the flexible adjustment of chaotic signal probability distribution. This algorithm can convert the intrinsic signal into a chaotic signal with the desired distribution type based on the characteristics of the target distribution, providing an analytical expression for the transformation relationship. Finally, the effectiveness of this strategy is validated by generating uniform distribution chaotic signals using a Chua chaotic signal as the intrinsic source. The outstanding performance of this signal in suppressing common-mode conducted electromagnetic interference in high-frequency converters is highlighted. The experimental results demonstrate this strategy’s ability to flexibly configure probability distribution types of chaotic signals. Additionally, chaotic signals with a uniform distribution can achieve uniform power spectrum shaping, with a suppression effect on maximum common-mode conducted electromagnetic interference reaching 16.56 dB.