AI automates substation control

The control of a substation is a very complex task due to the great number of related problems and, therefore, the decision variables that can influence the substation performance. Under such circumstances, the use of learning control systems can be very useful. The difficulties associated with the application of artificial intelligence techniques include: selection of the magnitudes to be controlled; definition and implementation of the soft techniques; and elaboration of a programming tool to execute the control. The interest of the present work is to expose the obtained results and to present them for discussion. The objective is to show that it is possible to control the status of circuit breakers (CB) in a substation making use of a knowledge base that relates some of the operation magnitudes, mixing status variables with time variables and fuzzy sets. Even when all the magnitudes to be controlled cannot be included in the analysis (mostly due to the great number of measurements and status variables of the substation and, therefore, to the rules that would be required by the controller), it is possible to control the desired status while supervising some important magnitudes as the voltage, power factor, and harmonic distortion, as well as the present status.