# A Self-Adaptive Contractive Algorithm for Enhanced Dynamic Phasor Estimation

## TL;DR

This paper introduces a self-adaptive contractive algorithm that improves dynamic phasor estimation in power systems by transforming signals, estimating parameters with filtering, and then recovering original signals with enhanced accuracy and harmonic rejection.

## Contribution

The paper presents a novel self-adaptive contractive algorithm with convergence guarantees and superior harmonic rejection for dynamic phasor estimation in power systems.

## Key findings

- Validated with IEEE standard tests under realistic noise conditions
- Demonstrates convergence and robustness in diverse operating scenarios
- Outperforms existing methods in harmonic rejection and accuracy

## Abstract

In this paper, a self-adaptive contractive (SAC) algorithm is proposed for enhanced dynamic phasor estimation in the diverse operating conditions of modern power systems. At a high-level, the method is composed of three stages: parameter shifting, filtering and parameter unshifting. The goal of the first stage is to transform the input signal phasor so that it is approximately mapped to nominal conditions. The second stage provides estimates of the phasor, frequency, rate of change of frequency (ROCOF), damping and rate of change of damping (ROCOD) of the parameter shifted phasor by using a differentiator filter bank (DFB). The final stage recovers the original signal phasor parameters while rejecting misleading estimates. The most important features of the algorithm are that it offers convergence guarantees in a set of desired conditions, and also great harmonic rejection. Numerical examples, including the IEEE C37.118.1 standard tests with realistic noise levels, as well as fault conditions, validate the proposed algorithm.