Integration of Model-based and Heuristic Fault Diagnosis in Power Transmission Networks

This paper presents an application of Artificial Intelligence techniques for fault diagnosis in power transmission networks. The prime objective of power transmission network is to supply power to the customers and to meet the load demands. When a fault occurs in a transmission network, it must be identified and eliminated as soon as possible. Since control centres are flooded with hundreds of alarm messages during a fault, fault diagnosis, which involves the analysis of alarm messages, is a time consuming task. As part of our on going research towards the development of an intelligent system for fault diagnosis, two reasoning techniques (model-based and heuristic) were applied and their performances are compared here. Introduction Power transmission network fault diagnosis (PTNFD) is the process of detecting faults while it is in operation. This kind of diagnosis (also termed as operative diagnosis) is needed for systems which cannot be stopped for maintenance (as it is too expensive), and the diagnosis involves the consideration of symptoms and state which can change with time. In electrical power transmission networks, the diagnosis is confined to alarm readings in real time while the effects of the faults are still propagating through the network. PTNFD is heuristic in nature and often provides a challenging task for experts involved. Experts find that the pattern recognition of alarms triggered by a fault in the system is relatively easier task compared to the identification of the physical origins of the fault from a list of alarms. This difficulty could be due to several components malfunctioning at the same time within the network. 1 This Project is funded by Trans Power NZ Ltd. Power transmission networks carry power from supply utilities to the consumer and any fault in the network directly affects the consumers. The hazards of performing fault diagnosis in this domain incorrectly and too slowly result in notable accidents such as 1977 New York City blackout where the power restoration took several minutes causing inconvenience to consumers. Earlier research [2] has shown that decision-support systems can aid system controllers during emergency situations. This paper presents the proceedings of our attempt to develop an efficient fault diagnostician for power transmission network in New Zealand. Towards this development, model based and heuristic techniques are applied to the domain and the results are presented in this paper. Heuristic Fault Diagnosis Heuristic fault diagnosis is a process of troubleshooting with the help of traditional expert systems. In this type of fault diagnosis, knowledge from domain expert/s is collected and represented in a knowledge base. An Inference Engine uses this knowledge base to solve a problem by deducing inferences pertaining to the problem. The diagnostic process ends when a solution(s) is found or no solution found after an exhaustive search. Figure 1 shows the general process of heuristic fault diagnosis.