Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design

Abstract

In this work, we propose a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNN). It is assumed that a mechanistic model is available but is too computationally complex for estimator design and that only some target outputs are of interest and should be estimated. A reduced-order estimator that can estimate the target outputs is sufficient to address such a problem. We introduce an approach based on sensitivity analysis to determine how to select the appropriate inputs and outputs for data collection and data-driven model development to estimate the desired outputs accurately. Specifically, we consider the long short-term memory (LSTM) neural network, a type of RNN, as the tool to train the data-driven model. Based on it, an extended Kalman filter, a state estimator, is designed to estimate the target outputs. Simulations are carried out to illustrate the effectiveness and applicability of the proposed approach.

The core technique and application of knowledge graph in power grid company administrative duty

“…Some studies have proposed using one-way LSTM neural network model for named entity recognition, but one-way LSTM network can only capture the information from the front to the back, unable to achieve two-way transmission, there are certain defects. Therefore, this paper proposes to use the bidirectional LSTM network [12] for named entity recognition. The bidirectional LSTM neural network model can capture the information from front to back and from back to front at the same time, which can make better use of text information and provide recognition accuracy.…”