Application of local memory-based techniques for power transformer thermal overload protection

Power transformers are some of the most expensive components of electrical power plant. The failure of a transformer is a matter of significant concern for electrical utilities. Not only for the consequent severe economic losses but also because the utility response to a customer during the outage condition is one of the major factors in determining the overall customer attitude towards the utility. Therefore, it is essential to predict the thermal behaviour of a transformer during load cycling and in particular in the presence of overload conditions. The authors propose a novel technique to predict the winding hottest spot temperature of a power transformer in the presence of overload conditions, as an alternative methodology to the radial basis function network (RBFN) based technique presented in a previous paper. The method proposed is based on a modified local memory-based algorithm which. Working on the load current, the top oil temperature rise over ambient temperature and taking into account other meteorological parameters, permits the recognition of the hot spot temperature pattern. In particular some corrective actions for the classical local methods are evidenced to customise it for real-time applications. Data obtained from experimental tests allow the local learning algorithm to be tested to evaluate the performance of the proposed method in terms of accuracy