Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.

To address the stability of the power supply to agricultural facilities and greenhouses in remote areas, this paper proposes a solution based on the bus voltage fluctuation issue in an islanded photovoltaic-storage DC microgrid. Traditional power supply methods often struggle to meet demand due to significant fluctuations in solar irradiance and load. To resolve this, an improved sliding-mode linear active disturbance rejection control (ISMLADRC) strategy is designed, significantly enhancing the response speed of the microgrid control system while improving its adaptability in complex agricultural environments. The system integrates a hybrid energy storage system and photovoltaic power generation to optimize microgrid power compensation, ensuring the stability of the power supply to agricultural facilities and greenhouses. Simulation results demonstrate that the proposed control scheme enhances the robustness and efficiency of the original system, ensuring a reliable power supply for crop production in remote areas, advancing smart agriculture, and promoting the sustainable development of green agriculture.

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments.

With the development of semiconductor technology, the increasing number of power electronic converters and nonlinear loads has further exacerbated power-quality issues in the grid. To address this, this paper presents an improved DC-side voltage control strategy for UPQC, aiming to enhance power quality under complex conditions. First, an adaptive filter is integrated into the linear active disturbance rejection control (LADRC) to address control accuracy issues caused by noise and parameter variations. To solve the voltage sag problem resulting from the filter, the effects of fixed-step and various variable-step algorithms within the filter are analyzed, and the optimal control strategy is identified. Simulation results demonstrate that the proposed arctangent function-based variable step-size algorithm (VAV-LADRC) strategy effectively improves the UPQC system’s performance in mitigating voltage sags, swells, and harmonics under dynamic load changes, enhancing the stability of the DC-side voltage. To further validate the generalizability of the method, a co-simulation of a photovoltaic power-generation system with the UPQC is conducted, simulating variations in solar irradiance. The results show that the proposed method maintains excellent control performance under complex conditions, providing a better practical solution for the efficient use of green energy.

This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies.

With the energy transition and changes in electrical load structures, harmonic distortion in power systems is increasingly threatening grid stability, especially during the integration of renewable energy sources. Active power filters (APFs) are commonly used to improve power quality, but current fluctuations and voltage instability caused by DC-side capacitor charging and discharging hinder effective harmonic compensation. To address this, this paper presents a method combining a variable-step-size adaptive linear predictor with linear active disturbance rejection control (VALP-LADRC). By integrating an adaptive linear predictor (ALP) with LADRC, the proposed method effectively reduces the impact of disturbances on control, improving voltage regulation and harmonic compensation, and enhancing renewable energy grid integration. To address delays during the adjustment of initial parameters in the adaptive predictor, we combine it with a variable-step-size algorithm, significantly improving disturbance rejection and tracking accuracy, while solving voltage drop issues. The simulation results in a photovoltaic grid-connected system show that VALP-LADRC effectively mitigates disturbances, ensures voltage stability, and enhances power quality. This approach offers a promising solution for supporting sustainable development through better renewable energy integration and improved power quality.