Automatic Load Model Selection Based on Machine Learning Algorithms

Abstract

Technology development and decentralized operations create changes in conventional electric systems, where load modeling has been a challenge in dynamic analysis. Consequently, accurate dynamic load models are required to ensure the quality of the studies in current systems. This paper presents an automatic strategy based on clustering, classification, and optimization algorithms, to obtain the load models in the case of several system operating conditions. The obtained load models are helpful for the planning, operation, and protection of electric power systems. The proposed approach validation is performed using the IEEE 14-bus test system, where high performance is obtained. The average obtained cross-validation error for the load models assigned to the 13 clusters of disturbances is $5.36\times 10^{-3}$ . The cross-validation error is used as a tolerance value to determine when an online assigned load model is suitable to represent the measured disturbance. The proposed tests show the strategy’s capabilities of defining the load model online, making this approach suitable for field applications.

Keywords

• Dynamic load modeling
• generalization capability
• machine learning
• parameter identification