Constrained search space selection based optimization approach for enhanced reduced order approximation of interconnected power system models

Metaheuristic search-based optimization strategies have recently emerged to obtain approximated models for interconnected complex power systems. However, these algorithms are frequently criticized for randomly selecting lower and upper search space boundaries and taking longer to simulate. The incorrect selection of suitable boundaries for each unknown decision variable may result in an inaccurate or unstable reduced model. This proposal introduces an interim reduced model (IRM) concept to select a tight solution space for the optimization algorithm. The balanced residualization method (BRM) obtains the IRM, and the geometric mean optimization (GMO) algorithm tunes the reduced model coefficients. The proposed method has an appealing feature: the IRM obtained by the BRM structures the solution space selection of the GMO algorithm rather than leaving it completely arbitrary. The GMO method finds the ideal reduced model coefficients by minimizing a weighted error index. The primary benefit of employing IRM-based search space limitations is that they guarantee a focused search with viable answers and lower model stability. Furthermore, maintaining the transient gain mitigates the BRM's high-frequency spectrum error disadvantage. Three complex interconnected power system models from the literature support the proposed method, contrasting with the state-of-the-art MOR methodologies.