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.

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.

Accurate state of charge (SOC) estimation is essential for the reliable operation and energy management of electric agricultural machinery, particularly electric tractors operating under complex field conditions. This study aims to improve SOC estimation accuracy and robustness by proposing a hybrid method that integrates a gated recurrent unit (GRU) neural network with a fractional-order extended Kalman filter (FOEKF). The GRU model is employed to capture the nonlinear behavior of lithium-ion batteries, while the FOEKF is used to mitigate noise and compensate for model uncertainties, forming a coupled data-driven and model-based framework. Experiments were conducted on lithium-ion batteries for electric tractors under hybrid pulse power characterization (HPPC) conditions at 15 °C, 25 °C, and 35 °C. These experiments can effectively simulate the dynamic power fluctuation characteristics of the battery caused by variations in electric tractor operating conditions during agricultural operations in different seasons. Experimental results demonstrate that, compared with conventional GRU and FOEKF methods, the proposed GRU-FOEKF method achieves lower estimation errors and improved robustness. In particular, at 25 °C, the proposed method achieves an MAE of 0.9% and an RMSE of 1.1%, outperforming the compared algorithms. These findings indicate that the proposed strategy is a feasible and effective solution for battery management systems in electric agricultural machinery, contributing to the development of smart and sustainable agriculture.

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.