Abstract

Aim/Background:
The increasing complexity of modern distribution networks, particularly with the integration of Distributed Energy Resources (DERs), presents significant challenges to traditional state estimation techniques. These networks require accurate and real-time monitoring to optimize operations, improve fault detection, and enhance overall system reliability. This research aims to develop a hybrid state estimation model that combines Weighted Least Squares (WLS) and Kalman Filter (KF) techniques to improve state estimation accuracy, robustness, and reliability in automated distribution systems. The primary objective is to create a method that can address the challenges posed by DERs and dynamic load conditions in distribution grids.
Methods:
The methodology includes the integration of real-time data from Supervisory Control and Data Acquisition (SCADA) systems, smart meters, and Phasor Measurement Units (PMUs), followed by the application of WLS, KF, and a hybrid approach to estimate voltage magnitudes and phase angles. The performance of each method was evaluated in terms of estimation accuracy, computational efficiency, and robustness against bad data.
Results:
The results revealed that the hybrid model outperformed both the WLS and KF methods, improving estimation accuracy by 15% compared to WLS and 7% compared to KF, particularly in systems with high DER penetration and fluctuating load. Additionally, the hybrid model demonstrated superior bad data detection and filtering.
Conclusion:
This study provides utility providers and policymakers with a more accurate and reliable method for real-time monitoring and fault detection in distribution networks. Future research should focus on reducing computational complexity and incorporating machine learning techniques to further enhance state estimation accuracy.

Key words: SCADA; State Estimation; Distribution Networks; Phasor Measurement Units (PMUs); Hybrid State Estimation; Bad Data Detection