On-Line Determination of Salient-Pole Hydro Generator Parameters by Neural Network Estimator Using Operating Data (PEANN)

O. Shariati,B. Potter,M. R. Aghamohammadi

doi:10.1109/access.2021.3115783

Abstract

A novel application of Artificial Neural Network (ANN) to estimate and track Hydro Generator Dynamic Parameters using online disturbance measurements is presented within this paper. The data for training ANN are obtained through off-line simulation of the generators modelled in a one-machine-infinite-bus environment using the parameters sets that are representative of practical data. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANN are organized in coordination with the results obtained from the observability analysis of synchronous generator dynamic parameters in its dynamic behaviour. A collection of 10 ANNs with similar input patterns and different outputs are developed to determine a set of dynamic parameters. The trained ANNs are employed in a real-time operational environment for estimating generator parameters using online measurements acquired during disturbance conditions. The ANNs are employed and tested to identify generator parameters using online measurements obtained during different disturbances. Simulation studies demonstrate the ability of the ANNs to accurately estimate dynamic parameters of hydro-generators. The results also show the impact of test conditions on the accuracy degree of estimation for these parameters. The optimal structure of ANNs is also determined to minimize the error in estimating each dynamic parameter.

Highlights

Hydro-electric power plants constitute a major part of generation in electric power systems, which contribute to system dynamic with dominating effect on system stability
In this work, neural network-based estimators have been developed to identify the full set of dynamic parameters of hydro generators by processing sequences of measurements obtained during common transient disturbance events
The above robustness along with availability of generator measurements (Vs, Is, Pe, Qe, wm and d) used as Parameter Estimator ANNs (PEANN) inputs, and the user-friendly structure of the proposed artificial intelligence-based estimators confirm the practicality of this approach

Summary

Introduction

Hydro-electric power plants constitute a major part of generation in electric power systems, which contribute to system dynamic with dominating effect on system stability. There are powerful off-line methods such as Stand Still Frequency Repose (SSFR) [1, 2], Flux Decay [3] and Finite Element [4]. These methods can be categorized to harmless methods [5, 6] and harmful tests such as load rejection [7] and short circuit [8, 9]. There is an opportunity for presenting new harmless methods with a focus on estimating the dynamic parameter of synchronous generators. The on-line methods should be capable enough to identify

Objectives

Methods

Findings

Conclusion