Supervisory Hybrid Control of a Wind Energy Conversion and Battery Storage System
Bibliographic record
Abstract
This thesis presents a supervisory hybrid controller for the automatic operation and control of a wind energy conversion and battery storage system.\nThe supervisory hybrid control scheme is based on a radically different approach of modeling and control design, proposed for the subject wind energy conversion and battery storage system.\nThe wind energy conversion unit is composed of a 360kW horizontal axis wind turbine\nmechanically coupled to an induction generator through a gearbox. The assembly is electrically interfaced to the dc bus through a thyristor-controlled rectifier to enable variable speed operation of the unit. Static capacitor banks have been used to meet reactive power requirements of the\nunit. A battery storage device is connected to the dc bus through a dc-dc converter to support operation of the wind energy conversion unit during islanded conditions. Islanding is assumed to occur when the tiebreaker to the utility feeder is in open position. The wind energy conversion\nunit and battery storage system is interfaced to the utility grid at the point of common coupling through a 25km long, 13.8kV feeder using a voltage-sourced converter unit. A bank of static\n(constant impedance) and dynamic (induction motor) loads is connected to the point of common coupling through a step down transformer.\nA finite hybrid-automata based model of the wind energy conversion and storage system has\nbeen proposed that captures the different operating regimes of the system during grid-connected and in islanded operating modes. The hybrid model of the subject system defines allowable operating states and predefines the transition paths between these operating states. A modular\ncontrol design approach has been adapted in which the wind energy conversion and storage\nsystem has been partitioned along the dc bus into three independent system modules. Traditional control schemes using linear proportional-plus-integral compensators have been used for each system module with suitable modifications where necessary in order to achieve the required\nsteady state and transient performance objectives. A supervisory control layer has been used to combine and configure control schemes of the three system modules to suite the requirements of system operation during any one operating state depicted by the hybrid model of the system. Transition management strategies have been devised and implemented through the supervisory control layer to ensure smooth inter-state transitions and bumpless switching among controllers.\nIt has been concluded based on frequency domain linear analysis and time domain\nelectromagnetic transient simulations that the proposed supervisory hybrid controller is capable of operating the wind energy conversion and storage system in both grid-connected and in islanded modes under changing operating conditions including temporary faults on the utility\ngrid.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".