Expert system for the load management, unit commitment and optimised scheduling of power generation at hydel power plants

Document Type : Original Article

Authors

1 Lecturer, Department of Electrical Engineering, UET, Lahore 54890, Pakistan.

2 Associate 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.

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