Load Modeling using Synchrophasor Data for Improved Contingency Analysis

• Main Author: Retty, Hema
• Other Authors: Electrical and ComputerEngineering
• Format: Others
• Published: Virginia Tech 2017
• Subjects: Power Systems; Machine Learning; Load Modeling; Neural Networks; Phasor Measurement Unit; PMU
• Online Access: http://hdl.handle.net/10919/78328

For decades, researchers have sought to make the North American power system as reliable as possible with many security measures in place to include redundancy. Yet the increasing number of blackouts and failures have highlighted the areas that require improvement. Meeting the increasing demand for energy and the growing complexity of the loads are two of the main challenges faced by the power grid. In order to prepare for contingencies and maintain a secure state, power engineers must perform simulations using steady state and dynamic models of the system. The results from the contingency studies are only as accurate as the models of the grid components. The load components are generally the most difficult to model since they are controlled by the consumer. This study focuses on developing static and dynamic load models using advanced mathematical approximation algorithms and wide area measurement devices, which will improve the accuracy of the system analysis and hopefully decrease the frequency of blackouts. The increasing integration of phasor measurement units (PMUs) into the power system allows us to take advantage of synchronized measurements at a high data rate. These devices are capable of changing the way we manage online security within the Energy Management System (EMS) and can enhance our offline tools. This type of data helps us redevelop the measurement-based approach to load modeling. The static ZIP load model composition is estimated using a variation of the method of least squares, called bounded-variable least squares. The bound on the ZIP load parameters allows the measurement matrix to be slightly correlated. The ZIP model can be determined within a small range of error that won't affect the contingency studies. Machine learning is used to design the dynamic load model. Neural network training is applied to fault data obtained near the load bus and the derived network model can estimate the load parameters. The neural network is trained using simulated data and then applied to real PMU measurements. A PMU algorithm was developed to transform the simulated measurements into a realistic representation of phasor data. These new algorithms will allow us to estimate the load models that are used in contingency studies. === Ph. D.