Holistic Condition Monitoring and Replacement Strategy for Power Transformers Using Thermal Modeling, DGA Diagnostics, and LCC Analysis

Dependable methods of managing assets of power transformers are critical in the stability of the grid, especially under high load conditions in terms of temperature and seasonal loads. It is an integrated MATLAB/Simulink-based digital-twin framework of condition assessment, thermal aging prediction, fault diagnostics, and economic life-cycle planning of a fleet of 83 utility transformers. The study is unique, and unlike the previous works dedicated to single-diagnostic areas, it incorporates thermal aging, DGA, moisture/age index, and LCC-based decision engine into a single digital twin-based asset management of a fleet. The system will be a combination of IEEE C57.91 and IEC 60076-7 thermal models, dissolved gas analysis (DGA) interpretation based on the Duval method, moisture-in-paper assessment based on a Transformer Age Index Model (TAIM) to represent actual degradation with respect to service age. Simultaneously, a life-cycle cost (LCC) engine analyzes the upgrade and replacement options based on net-present-value (NPV) analysis and operational downtime. A data pipeline was then formulated to cleanse, pre-process and combine nameplate, SCADA load, climate and diagnostic data into a single MATLAB database. Thermal modelling showed realistic transformer behavior at peak season loads, hourly temperature distributions in hot-spots, acceleration with age, and total loss of life (LOL). Analyses on sample unit (TR1) showed that the overstress periods were characterized by the winding hot-spot surpassing the 110?C limit of IEEE standard operating cycle of July with indicating high rates of insulation degradation and inability to sustain high overload. The permanent thermal peaks were revealed by duration curves and the high spikes of FAA were coincided with the windows of load/weather stress. At the fleet level, it was demonstrated that maximum hot-spot temperature and LOL were variable, which allowed ranking of transformers at the technical level to assess their suitability in managed overload, and prioritize the maintenance of transformers. The last decision-layer integrates thermal margin, fault severity, moisture risk, TAIM and NPV to provide a feasibility score to categorize units into extend, retrofit, and replace operational paths. Findings show that the suggested smart framework advances predictive maintenance of assets collecting physics-based modeling, diagnostic analytics, and economic planning in a single decision-making platform. The method helps utilities to safely increase loading flexibility, minimize failures that are unplanned and