Design and Development of a Scalable, Modular and Efficient Maximum Power Point Tracking Stage for a CubeSat EPS
Bibliographic record
Abstract
This paper presents a design of a modular, scalable, and efficient maximum power point tracking (MPPT) stage that can be used in 2U to 36U form-factor missions and be easily adaptable for customer power and hardware requirements. The selection process as well as the verification of the system hardware and software designs are presented. Power point tracking algorithms are systematically reviewed and compared; on this basis the Modified Perturb & Observe algorithm is selected for implementation. Simulink and Python simulations show that this algorithm can reliably acquire a global maximum power point within 6 seconds under static, dynamic, and partial shading conditions while being agnostic of the size and electrical characteristics of a solar array that is being used. Hardware for the proposed system is designed using commercial off-the-shelf components that can withstand the environmental conditions in low Earth orbit. A single power point tracker, based on a DC/DC converter, is capable of handling solar arrays with open-circuit voltages up to 60 V, corresponding to a string array of up to 22 cells. A control scheme via input regulation is proposed, allowing both input regulation and precise external operating point control by the power point tracking algorithm. This control system is analysed, verified, and tuned for stability and a low bandwidth of 2.2 kHz to reduce noise. The DC/DC converter design is simulated in LTSpice to show its feasibility; a settling time of 4 milliseconds is demonstrated for the proposed DC/DC converter, which is suitable for the proposed MPPT algorithm. Using the unregulated DC/DC converter output the ability of the system to determine the power demand of the satellite and share the load between paralleled power point trackers is shown. Through this mechanism, it is possible to scale the system to process up to 100 W of input power. Redundancy mechanisms to eliminate single points of failure are also evaluated. The combination of hardware and software forms the complete power point tracking stage, which is the outcome of this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".