A power transformer is a key unit in the transmission system, and its cut-off can impact both consumers and the general stability of the system. Therefore, it is an important tool for processing the operational and technical condition data to quantify them as the technical condition index (TCI). Based on the technical condition of the power transformer, the TCI enables objective and reasoned decisions on the future investments related to replacement or repairs of transformers. Thus, by using the TCI, the service life of the transformer can be safely extended, since the identified risks have been recognized and are being followed-up. The TCI method is useful for a power transformer park, because it allows easy identification of transformers that require most attention. A crucial precondition for this method is data availability, diversity, and regularity or frequency of data collection. These features (preconditions) may vary in different power transmission systems, and it creates the necessity for a tailored approach. The present Doctoral Thesis studies the diagnostic methods used in the transmission system in Latvia and the results thereof. Thus, it takes advantage of an already existing data set and flow to develop a TCI-based complex of algorithms for determining the risk level of high-power transformers in acceptable risk conditions. This Doctoral Thesis contains 94 pages, 40 figures and 33 tables. The Thesis consists of the introductory part, 6 chapters, and main results and conclusions.