Deep Learning-Based Time-Varying Parameter Identification for System-Wide Load Modeling

The integration of uncertain power resources is causing more challenges for traditional load modeling research. Parameter identification of load modeling is impacted by a variety of load components with time-varying characteristics. This paper develops a deep learning-based time-varying parameter identification model for composite load modeling (CLM) with ZIP load and induction motor. A multi-modal long short-term memory (M-LSTM) deep learning method is used to estimate all the time-varying parameters of CLM considering system-wide measurements. It contains a multi-modal structure that makes use of different modalities of the input data to accurately estimate time-varying load parameters. An LSTM network with a flexible number of temporal states is defined to capture powerful temporal patterns from the load parameters and measurements time series. The extracted features are further fed to a shared representation layer to capture the joint representation of input time series data. This temporal representation is used in a linear regression model to estimate time-varying load parameters at the current time. Numerical simulations on the 23- and 68-bus systems verify the effectiveness and robustness of the proposed M-LSTM method. Also, the optimal lag values of parameters and measurements as input variables are solved.

I. Introduction

Accurate time-varying load modeling is becoming more and more important due to the increasing integration of uncertain power resources. The common load modeling structures consist of static model, dynamic model, and composite model. The composite load modeling (CLM) with specific parameters has been widely used since it considers both the static and the dynamic characteristics of static model and dynamic model [1], [2]. The more accurate load modeling can even supplement the conventional load forecasting [3]–[5] under some particular circumstance with missing data. However, due to the high frequency changes caused by uncertain power resources, parameters of CLM present more and more time-varying characteristics [6].