Dynamic Composite Load Signature Detection and Classification using Supervised Learning over Disturbance Data

Load modeling that can accurately represent the dynamic behavior of generators and loads is important in the operation and planning of transmission and distribution systems. Yet, it is a complex subject in power system research communities and electric utilities. The composition of the end-use loads is changing continually based on climate zone, season, and time. The WECC composite load model has been developed recently to better represent Fault Induced Delayed Voltage Recovery (FIDVR) events, which is caused by air-conditioning stalling phenomena. The approach is based on using the information of the load class at the substation level and composition of airconditioning, induction machines, power electronics, and static loads associated with the load class. Therefore, it is important to be able to identify and classify the load class. This can be accomplished by using machine learning based signature detection since each load class has a unique signature response due to a particular disturbance in the system. The objective of this project is to implement a supervised learning, Artificial Neural Network (ANN), algorithm to detect and classify the composite load signatures in terms of residential, commercial, agriculture, and mixed load class. Furthermore, the process of creating WECC composite load model data, using the Load Model Data Tool (LMDT), to be used in time-domain dynamic simulation (PSS/E) is demonstrated. The One-Area Reliability Test System is used for the purpose of demonstration and validation of our proposed methodology. The key contribution of this research includes: 1) To demonstrate automated disturbance data creation and collection using the latest composite load model developed by WECC. 2) Introduce a new automated solution to determine the load classes by leveraging synchrophasor data and machine learning techniques.