Time Series Deep learning for Robust Steady-State Load Parameter Estimation using 1D-CNN

Availability of synchronized measurements provides an avenue for real-time measurement-based estimation of load model parameters. The presence of noise and outliers in actual PMU data makes it essential to develop estimation approaches robust to noise and outliers. Robust load parameter estimation is identified as the major goal in this research. This paper presents a time-series ML framework for robust steady-state load parameter estimation. The time-series machine learning-based approach provides the ability to intelligently estimate parameters using just voltage and power time-series data without any specific user-defined features. The convolutional neural network in 1D form (1D-CNN) is proposed for implementing the proposed time-series ML-based framework for load parameter estimation. Presented case studies demonstrate that the proposed algorithm provides robust estimation performance in the presence of realistic noise in PMU measurements. This work also introduces a bad data preprocessing framework to further enhance parameter estimation’s robustness with outliers in measurement data. A detailed statistical analysis is performed to demonstrate excellent generalizing capability and robust estimation performance of the proposed parameter estimation framework.