AN EXPERT SYSTEM FOR OPTIMAL ECONOMIC OPERATION OF MIXED HYDROTHERMAL POWER SYSTEMS
Bibliographic record
Abstract
The problem of economic operation of an electrical power system is suffering from combinatorial explosiveness. Moreover the problem is distributed in nature, overwhelmed by human factors because of the fact that present day systems are spanned at very large areas. Most of the data that is transferred to the system control office is mainly useful for stability and control purposes alone, while there is a lot of data, generated by various parts of the system that can be utilized effectively for improving the economic operation but at present is lying dormant. \n \nThis thesis is addressed towards a comprehensive solution of the problem in the light of three crucial factors mentioned above. Instead of using the conventional SCADA system to transfer the data in the form of unrelated telemetry signals, techniques are developed to keep the data in distributed client server databases, where each unit of data bears a particular specified relationship with the rest of the system. Processes are developed to transform this data into information and send the database transaction over the WEB to share the distributed computing for economic operation. Several processes are identified and a higher level process map of the system operation is developed. Expert system methodology is used to couple the symbolic and numerical knowledge in the processes for better insight and management of human factor. \n \nA mathematical model is developed that represents the situation in a thermal power station running on multiple fuels. The Thermal Plants Status Development Process that has been implemented in a client-server database system for thermal power stations uses this model to infer the current status of the system. The process takes into account the human factors affecting the dynamicity of fuel cost characteristics of thermal units, and sends the transaction over the WEB, to the system control office, containing the parameters needed for economic operation. A 24 hour simulation is carried out for three thermal units having a total capacity of 1200 MW, using gas fuels from four different gas fields. The simulation recorded a net send out of 22,298.0 MWh of energy while the results show a saving in the fuel cost ranging from 0.6 Million Rupees (1$ = Rs. 60 approx) to 5.5 Million Rupees per day over the current practice. Real practical data, required for the development of various models of the thesis is obtained from Gas Thermal Power Station Guddu, which is one of the major thermal power stations in the Pakistan Electric Utility System. \n \nA portion of this work is the basis of the eight research publications. The author himself participated for presenting a paper in 38th International Universities Power Engineering Conference, Upec03, Sep-01 to Sep-03 2003, in Thessalonica, Greece, and for the second paper in IASTED, International Conference on Power and Energy Systems, EuroPES 2003, Sep-03 to September 06 2003 Marbella, Spain. Another paper has been accepted, for publication in IASTED, International Journal of Power and Energy System, Calgary, Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".