Abstract
A power transformer is an important facility in the power supply system. If a transformer unexpectedly fails or shuts down, it will cause a severe damage to the entire power supply network. Hence, establishing a good and real-time power transformer fault prognosis system to avoid unexpected break down is a critical issue to a power company. The Dissolved Gas Analysis (DGA) has been the most widely used measures to detect the hazardous gases and predict power transformers potential malfunctions. Currently, there are several approaches to interpret the fault types after certain gases are detected. However, the present methods sometimes provide conflict diagnostic outcomes, which confuse the equipment maintenance engineers. This phenomenon prevents the maintenance crews from repairing the transformers correctly and promptly and often leads to severe problems. Thus, this research integrates five sets of heuristic knowledge rules, including the Doernenburg ratio, Rogers ratio, Duval triangle, the dominant gases, and the phase analysis into an intelligent and integrated fault prognosis system to improve the accuracy and reliability of the prognosis. Further, the fault prognosis results are verified empirically by comparing with actual transformers abnormal data (in total of 65 sets). In summary, this research develops a web-based fault prognostic system, incorporating the integrated DGA knowledge base. The system, as the main module of an intelligent power transformer asset management platform, automatically predicts the fault types using the real-time sensor-collected DGA data.