Dynamic Equivalent of a Power Network by Deep Artificial Neural Networks Under Varying Power System Operating Conditions

Online dynamic stability analysis plays a key role in modern power systems and is becoming even more vital with the penetration of Distributed Energy Resources (DERs). Identification of an accurate lower-order model for the ‘external system” makes it possible to carry out the stability analysis of a study system faster than the analysis for the original detailed system. However, the identified lower-order model becomes invalid when the operating state changes in the power system. ‘Black box’ representation of the ‘external system’ with the application of Deep Neural Networks (DNN) is widely discussed in the literature. In such equivalent models, the DNN response changes based on two aspects i.e. the operating point of the power system and the location/type of the fault. In this paper, a clustering algorithm is proposed to identify the clusters of the ‘external system’ models for different states of the power system, and a DNN is trained for each cluster. The clustering algorithm is based on the pre-fault steady-state power flow from the ‘external system’ to the ‘study system’, which is a reflection of changes in the system state. Mean Absolute Error (MAE) between responses of the actual fault and the predicted fault is used to evaluate the accuracy of the identified system. The proposed technique is easy to implement and requires a lesser number of DNN models compared to identifying an individual DNN model for each operating point to represent the ‘external system’.