Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently.

In the continuity of the work on design of high voltage gain DC-DC converter, used for feeding the CubeSat electrospray thrusters with a variable output voltage according to the maneuver size, this paper introduces a simulation of the converter model with MATLAB/Simulink to determine its conducted emission level. By referring to the MIL-STD-461 standard, a required passive electromagnetic interference (EMI) filter parameters are calculated to reduce, under thresholds, the converter noise. With consideration of the available volume and power constraints in this kind of satellite, the design of common-mode (CM) choke is optimized with proposed procedure optimization, so as to reduce its volume and electrical losses. Also, this optimization procedure can be generalized and applied for any passive EMI filter design.

Deep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance. Traditional methods of image capture, onboard storage, and transmission face challenges such as bandwidth limitations and the transmission of cloud-obstructed images, highlighting the need for efficient onboard artificial intelligence. However, energy consumption remains a critical consideration for onboard processing, particularly in nanosatellites where resource constraints are significant. To address these challenges, we propose an optimized SegNet-based deep learning model implemented on the STM32H7 microcontroller for real-time cloud detection, designed to operate within the nanosatellite's strict energy budget. This work, conducted within the project of the UM5-EOSat nanosatellite mission, utilized captured images from the Gecko imager for model evaluation. The customized SegNet architecture, tailored with minimal kernels and layers, achieved an accuracy of 93.50%, effectively balancing performance and computational efficiency. Quantization further optimized energy consumption, achieving a reduction of 82.2% at 280 MHz. The quantized model demonstrated a memory footprint of 304 KB RAM and 110 KB Flash memory, with an inference time of 0.21 s and an energy consumption of 31.41 mJ, ensuring compatibility with the nanosatellite's resource constraints.