Design, Dynamic Modelling, Simulation and Control of a Solar Powered Sucker Rod Oil Pump
Bibliographic record
Abstract
The sucker rod pump is a crucial artificial lift system widely deployed in the oil industry to extract crude oil from producing wells. Accurate modelling of the sucker rod pump has become essential as a viable strategy for optimizing performance, and ensuring both efficient and economic operation. This paper presents a comprehensive methodology for the design, dynamic modelling, simulation, and control of a solar-powered sucker rod oil pump. It combines load modelling of the sucker rod pump using SolidWorks with design, dynamic modelling, simulation, and control of the solar microgrid in Matlab's Simscape and Simulink. The model seamlessly integrates the mechanical and electrical systems with 100%renewable energy to power the sucker rod pump system. This approach combines the solar photovoltaic system, battery charge control system, battery energy storage system, step-up transformer, and the squirrel cage induction motor, which serves as the electric motor prime mover. The surface pump model is first developed in SolidWorks and then converted to Simscape, the rating of the pump is then implemented as a load in the solar-powered electrical microgrid. Environmental conditions such as solar irradiance and ambient temperature for summer and winter are obtained from data repositories and included in the modelling and analysis of the overall system performance demonstrating stable operation, robustness, and resilience to changing environmental and operational conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".