State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS

Henrry Fernando Moyano Bojorque; Jaime Patricio Díaz Gutiérrez; Edgar Rolando Becerra Palacios

doi:10.1109/ACDSA65407.2025.11166229

State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS

Henrry Fernando Moyano Bojorque (First Author)
, Jaime Patricio Díaz Gutiérrez
, Edgar Rolando Becerra Palacios (Last Author)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The integration of renewable energy sources, decentralized generation, and increasing grid complexity have motivated the development of advanced state estimation strategies for power systems. This paper presents a distributed state estimation (DSE) framework based on the Unscented Kalman Filter (UKF) and nodal redundancy partitioning. Unlike traditional centralized estimators, the proposed method divides the electrical network into interconnected clusters, each executing a local UKF and exchanging information across boundary nodes to reconstruct the global state. The methodology was validated on the IEEE 14-bus test system and compared against centralized and distributed implementations of the WLS, EKF, and UKF estimators. Performance was evaluated in terms of estimation accuracy, convergence, and computational efficiency. Results show that the distributed UKF achieves improved accuracy in nonlinear scenarios and reduces computational time by up to 30% compared to centralized implementations. This study demonstrates the feasibility and benefits of integrating UKF with nodal redundancy for real-time, scalable state estimation in modern power systems.

Original language	English
Title of host publication	2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)
Place of Publication	Antalya, Turkiye
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
Edition	Primera
ISBN (Electronic)	979-833153562-9
ISBN (Print)	979-8-3315-3563-6
DOIs	https://doi.org/10.1109/ACDSA65407.2025.11166229
State	Published - 24 Sep 2025
Event	2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025 - Antalya, Turkey Duration: 7 Aug 2025 → 9 Aug 2025

Conference

Conference	2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025
Country/Territory	Turkey
City	Antalya
Period	7/08/25 → 9/08/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Distributed state estimation
nodal redundancy
nonlinear estimation
power systems
Unscented Kalman Filter

Access to Document

10.1109/ACDSA65407.2025.11166229

Cite this

Moyano Bojorque, H. F., Díaz Gutiérrez, J. P., & Becerra Palacios, E. R. (2025). State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS. In 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (Primera ed., pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ACDSA65407.2025.11166229

Moyano Bojorque, Henrry Fernando ; Díaz Gutiérrez, Jaime Patricio ; Becerra Palacios, Edgar Rolando. / State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS. 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). Primera. ed. Antalya, Turkiye : Institute of Electrical and Electronics Engineers (IEEE), 2025. pp. 1-6

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title = "State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS",

abstract = "The integration of renewable energy sources, decentralized generation, and increasing grid complexity have motivated the development of advanced state estimation strategies for power systems. This paper presents a distributed state estimation (DSE) framework based on the Unscented Kalman Filter (UKF) and nodal redundancy partitioning. Unlike traditional centralized estimators, the proposed method divides the electrical network into interconnected clusters, each executing a local UKF and exchanging information across boundary nodes to reconstruct the global state. The methodology was validated on the IEEE 14-bus test system and compared against centralized and distributed implementations of the WLS, EKF, and UKF estimators. Performance was evaluated in terms of estimation accuracy, convergence, and computational efficiency. Results show that the distributed UKF achieves improved accuracy in nonlinear scenarios and reduces computational time by up to 30\% compared to centralized implementations. This study demonstrates the feasibility and benefits of integrating UKF with nodal redundancy for real-time, scalable state estimation in modern power systems.",

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Moyano Bojorque, HF, Díaz Gutiérrez, JP & Becerra Palacios, ER 2025, State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS. in 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). Primera edn, Institute of Electrical and Electronics Engineers (IEEE), Antalya, Turkiye, pp. 1-6, 2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025, Antalya, Turkey, 7/08/25. https://doi.org/10.1109/ACDSA65407.2025.11166229

State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS. / Moyano Bojorque, Henrry Fernando (First Author); Díaz Gutiérrez, Jaime Patricio; Becerra Palacios, Edgar Rolando (Last Author).
2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). Primera. ed. Antalya, Turkiye: Institute of Electrical and Electronics Engineers (IEEE), 2025. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS

AU - Moyano Bojorque, Henrry Fernando

AU - Díaz Gutiérrez, Jaime Patricio

AU - Becerra Palacios, Edgar Rolando

PY - 2025/9/24

Y1 - 2025/9/24

N2 - The integration of renewable energy sources, decentralized generation, and increasing grid complexity have motivated the development of advanced state estimation strategies for power systems. This paper presents a distributed state estimation (DSE) framework based on the Unscented Kalman Filter (UKF) and nodal redundancy partitioning. Unlike traditional centralized estimators, the proposed method divides the electrical network into interconnected clusters, each executing a local UKF and exchanging information across boundary nodes to reconstruct the global state. The methodology was validated on the IEEE 14-bus test system and compared against centralized and distributed implementations of the WLS, EKF, and UKF estimators. Performance was evaluated in terms of estimation accuracy, convergence, and computational efficiency. Results show that the distributed UKF achieves improved accuracy in nonlinear scenarios and reduces computational time by up to 30% compared to centralized implementations. This study demonstrates the feasibility and benefits of integrating UKF with nodal redundancy for real-time, scalable state estimation in modern power systems.

AB - The integration of renewable energy sources, decentralized generation, and increasing grid complexity have motivated the development of advanced state estimation strategies for power systems. This paper presents a distributed state estimation (DSE) framework based on the Unscented Kalman Filter (UKF) and nodal redundancy partitioning. Unlike traditional centralized estimators, the proposed method divides the electrical network into interconnected clusters, each executing a local UKF and exchanging information across boundary nodes to reconstruct the global state. The methodology was validated on the IEEE 14-bus test system and compared against centralized and distributed implementations of the WLS, EKF, and UKF estimators. Performance was evaluated in terms of estimation accuracy, convergence, and computational efficiency. Results show that the distributed UKF achieves improved accuracy in nonlinear scenarios and reduces computational time by up to 30% compared to centralized implementations. This study demonstrates the feasibility and benefits of integrating UKF with nodal redundancy for real-time, scalable state estimation in modern power systems.

KW - Distributed state estimation

KW - nodal redundancy

KW - nonlinear estimation

KW - power systems

KW - Unscented Kalman Filter

KW - Distributed state estimation

KW - Nodal redundancy

KW - Nonlinear estimation

KW - Power systems

KW - Unscented Kalman Filter

UR - https://www.scopus.com/pages/publications/105018465590

UR - https://ieeexplore.ieee.org/document/11166229

U2 - 10.1109/ACDSA65407.2025.11166229

DO - 10.1109/ACDSA65407.2025.11166229

M3 - Contribución a la conferencia

AN - SCOPUS:105018465590

SN - 979-8-3315-3563-6

SP - 1

EP - 6

BT - 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Antalya, Turkiye

T2 - 2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025

Y2 - 7 August 2025 through 9 August 2025

ER -

Moyano Bojorque HF, Díaz Gutiérrez JP, Becerra Palacios ER. State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS. In 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). Primera ed. Antalya, Turkiye: Institute of Electrical and Electronics Engineers (IEEE). 2025. p. 1-6 doi: 10.1109/ACDSA65407.2025.11166229

State Estimation Using the Unscented Kalman Filter in Nodal Redundancy for EPS

Abstract

Conference

UN SDGs

Keywords

Access to Document

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