Artificial intelligence for distributed power system management

The work aims to develop a structure and algorithms for the management of a distributed generation power system based on artificial intelligence, which has the flexibility to specify and change the control logic. A large language model was used to represent a decision center for issuing control actions using Python, SQL, and Matlab prom engineering and programming techniques. In order to speed up the calculations and reduce the amount of transmitted information, the proposed structure uses elements of the fuzzy-set theory for applying a large language model in the control algorithm. The model validation algorithm was developed to reduce errors by independently reprocessing AI results and identifying mismatches. This approach was demonstrated to overcome the known problem arising from the use of the natural language processing technology associated with inconsistency of responses. The proposed structure is tested and evaluated on a distributed generation power system model in five scenarios: switching from the island mode to parallel operation with the external system and vice versa; transition from balanced island mode of two loaded sections to the island and unbalanced mode of the first section; transition from the balanced island mode of loaded sections to island synchronous section operation. These tests have demonstrated the flexibility of the developed system, as well as its ability to take into account user commands and adapt to different power system modes. Thus, the study results illustrate the ability to control a distributed generation power system based on generic artificial intelligence. Decision-making and adaptability to changing conditions make the proposed system a valuable tool to ensure network efficiency.