skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters
Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.  more » « less
Award ID(s):
2118510
PAR ID:
10439478
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Energies
Volume:
15
Issue:
19
ISSN:
1996-1073
Page Range / eLocation ID:
7105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; (, Applied Sciences)
    null (Ed.)
    Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects. 
    more » « less
  2. ; (, IEEE Transactions on Instrumentation and Measurement)
    Phasor measurement units (PMUs) are playing an increasingly important role in wide-area monitoring and the control of power systems. PMUs allow synchronous real-time measurements of voltage, phase angle, and frequency from multiple remote locations in the grid, enabled by their ability to align to global positioning system (GPS) clocks. Given that this ability is vulnerable to GPS spoofing attacks, which have been confirmed easy to launch, in this paper, we propose a distributed real-time wide-area oscillation estimation approach that is robust to GPS spoofing on PMUs and their associated phasor data concentrators. The approach employs the idea of checking update consistency with histories and across distributed nodes and can tolerate up to one third of compromised nodes. It can be implemented in a completely decentralized architecture and in a completely asynchronous way. The effectiveness of the approach is confirmed by numerical simulations of the IEEE 68-bus power system models. 
    more » « less
  3. ; (, Applied Sciences)
    State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE. 
    more » « less
  4. ; (, Applied Sciences)
    State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE. 
    more » « less
  5. ; (, IEEE Access)
    The complexity of the power system has increased due to recent grid modernization and active distribution systems. As a result, monitoring and controlling modern power systems have become challenging. Dynamic security assessment (DSA) in power systems is a critical operational situational awareness (OpSA) tool for the energy control center (ECC). State-of-the-art (SOTA) DSA has been based on traditional state estimation utilizing the supervisory control and data acquisition (SCADA) / phasor measurement units (PMU) and transmission network topology processing (TNTP) based on SCADA monitoring of relay signals (TNTP-SMRS). Due to the slow data rates of SCADA, these applications cannot efficiently support an online DSA tool. Furthermore, an inaccurate network model based on TNTP-SMRS can lead to erroneous DSA. In this paper, a distributed dynamic security assessment (D-DSA) based on multilevel distributed linear state estimation (D-LSE) and efficient and reliable hierarchical transmission network topology processing utilizing synchrophasor network (H-TNTP-PMU) has been proposed. The tool can be used in real-time operation at the ECC of modern power systems. D-DSA architecture comprises three levels, namely Level 1 - component level security assessment (substations and transmission lines), Level 2 - area level security assessment, and Level 3 - network level security assessment. D-DSA concurrently evaluates all available substations’ security in the substation security assessment (SSA) and all available transmission lines’ security in the transmission line security assessment (TSA). Under the area security assessment (ASA), all SSA and TSA in each area are separately integrated to assess the area SSI (ASI-SSI) and TSI (ASI-TSI). Subsequently, each area’s area-level security index (ASI) is calculated by fusing ASI-SSI and ASI-TSI. At the network level security assessment, network SSI (NSI-SSI) and TSI (NSI-TSI) are estimated by fusing all ASI-SSIs and ASI-TSI, respectively. Network level security index (NSI) is estimated by fusing the NSISSI and NSI-TSI in network security assessment (NSA). Typical results of D-DSA are presented for two test systems, the modified two-area four-machine power system model and the IEEE 68 bus power system model. Results indicate that the proposed D-DSA can complete the assessment accurately at the PMU data frame rate, enabling online security assessment regardless of the network size. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email