Expert system for the load management, unit commitment and optimised scheduling of power generation at hydel power plants | ||||
| The International Conference on Electrical Engineering | ||||
| Article 150, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-7 PDF (111.43 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2008.34521 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Syed Abdul Rahman Kashif1; Muhammad Asghar Saqib2 | ||||
| 1Lecturer, Department of Electrical Engineering, UET, Lahore 54890, Pakistan. | ||||
| 2Associate Professor, Department of Electrical Engineering, UET, Lahore 54890, Pakistan. | ||||
| Abstract | ||||
| Abstract: This paper presents an artificial intelligence based inference system for economic load management and scheduling of power generation. A database is developed in which the whole record of the behavior of a plant, in different situations, is available. The decisions of experts are also fed in the knowledge base. Rule base is developed on the basis of experts decisions, different conditions of load demands, unit commitment and power controlling factors such as discharge rate of water, velocity of water flow, head of water available, requirement of water for irrigation purposes and machines specifications. Then the inferences engine under different conditions fires the appropriate rules from the rule base and controls all the above-mentioned parameters. It also makes decisions to select the optimised machines for power generation to meet the peak and base load power demands. This expert system is developed in Prolog. Simulation results using the data of Mangla Power Station were compared with the actual results of the plant for this purpose and found satisfactory. | ||||
| Keywords | ||||
| expert system; knowledge base; rule base; rule adjuster; inference engine; data processor; Unit Commitment; load management and Prolog | ||||
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