ARTIFICIAL-INTELLIGENCE IN DIAGNOSIS AND SUPPLY RESTORATION FOR A DISTRIBUTION NETWORK

The development of a PC-based integrated system, to illustrate the app lication of artificial intelligence in the fault diagnosis and supply restoration for an interconnected distribution network is described. T he intelligent process utilises the post-fault network status, a list of the tripped breakers, main protection alarm, and the conventional e vent log. The fault diagnostic system is implemented by three independ ent mechanisms, namely the generic core rule, specific post-fault netw ork matching, and generic relay inference rules. The intelligent resto ration process is implemented by the switching check, the dynamic rest oration algorithm and the mechanism for restoration by record matching and learning. By linking to a PC-based distribution simulator it has been demonstrated that the developed mechanisms enable the correct ded uction of fault under different network configurations. The appropriat e restoration plan can also be generated to restore supply to the enti re restorable load for various post-fault networks. This system is cur rently used for undergraduate teaching and will be ideal for the train ing of network operation engineers. As the system developed is generic and can be used for a general network, it can be further developed fo r practical operation in a subtransmission system or an urban distribu tion system operated in any configuration.