Switch fault diagnosis for boost DC–DC converters in photovoltaic MPPT systems by using high‐gain observers
Bibliographic record
Abstract
Open‐ and short‐circuit faults (OSCFs) in boost dc–dc converters for photovoltaic (PV) maximum power point trackers (MPPTs) imply an inefficiency after fault is triggered, which affect the security and profitability of PV projects. Hence, fault detection and isolation (FDI) techniques have become an important issue for PV technology. In this study, a model‐based FDI technique is proposed to boost dc–dc converters in PV MPPT systems. As is well‐known, major issues of model‐based FDI techniques have always been parametric uncertainty and no‐modelled dynamics. This study focuses on how to mitigate these shortcomings by applying a high‐gain observer (HGO) as a residual generator. A striking feature of HGO's is that exponential stability is still guaranteed for bounded disturbances (or faults). As demonstrated in this study, under an integral control action in the closed‐loop control system, OSCFs are characterised for ever‐growing signals, enabling the suggested FDI scheme. Also, the FDI proposal is decoupled from PV current (irradiance changes) and load variations, thereby avoiding false alarms. Moreover, the output‐injection gain and thresholds are selected such that the fault diagnosis is achieved in eight switching cycles, enabling a fast and reliable diagnosis. Experimental results are illustrated to validate the FDI scheme proposed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".