As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

This manuscript presents a reinforcement learning (RL) agent method to optimize the geometry of a coaxial magnetic gear using a 2D finite element magnetic (FEM) simulation. The proposed optimization algorithm aims to improve the maximum torque within given boundaries of the magnetic gear geometry by adjusting parameterized radii. A linear actor–critic gradient algorithm is implemented, where the actor learns a policy to adjust and discover the values of five geometric parameters of the magnetic gear model, and the critic evaluates the performance of the resulting designs. The RL agent interacts with an environment integrated with a 2D FEM simulation, which provides feedback by calculating the total torque of the new geometry discovered. The optimization algorithm uses a greedy exploration method that uses the total torque as a reward system, which the RL agent aims to maximize. The results obtained for the magnetic gear optimization demonstrate the effectiveness of the proposed RL algorithm, which can be applied to automate multiparameter geometric optimization using artificial intelligence systems.