An official website of the United States government
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
This content will become publicly available on September 19, 2026
Electric Power Transmission Network Graph Models Using Quantum Approximate Optimization Algorithm
Accurate modeling of electric power transmission networks (EPTNs) is essential for real-time monitoring, operational awareness, and contingency analysis in power systems. Representing EPTNs as graphs with nodes and edges offers a powerful abstraction of the network topology. However, inferring this topology using only phasor measurement unit (PMU) data remains a challenge, especially with no prior network information. In this study, a quantum-classical hybrid approach based on the quantum approximate optimization algorithm (QAOA) to infer a transmission network graph model (TNGM) directly from PMU data is presented. The proposed approach utilizes a cost function incorporating the difference between power mismatch and mean power loss to guide one-to-one branch matching. Furthermore, the effect of quantum circuit depth is investigated to achieve 100% accuracy in TNGM construction. Typical results are presented on the two-area four-machine power system.
more »
« less
- Award ID(s):
- 2413238
- PAR ID:
- 10656840
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Location:
- Panama City, Panama
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
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
-
Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented. However, existing literature on PMU-based TLPE implicitly assumes the measurement noise to be Gaussian. Recently, it has been shown that the noise in PMU measurements (especially in the current phasors) is better represented by Gaussian mixture models (GMMs), i.e., the noises are non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian noise in the PMU measurements. The measurement noise is expressed as a GMM, whose components are identified using the expectation-maximization (EM) algorithm. Subsequently, noise and parameter estimation is carried out by solving a maximum likelihood estimation problem iteratively until convergence. The superior performance of the proposed approach over traditional approaches such as least squares and total least squares as well as the more recently proposed minimum total error entropy approach is demonstrated by performing simulations using the IEEE 118-bus system as well as proprietary PMU data obtained from a U.S. power utility.more » « less
-
Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented. However, existing literature on PMU-based TLPE implicitly assumes the measurement noise to be Gaussian. Recently, it has been shown that the noise in PMU measurements (especially in the current phasors) is better represented by Gaussian mixture models (GMMs), i.e., the noises are non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian noise in the PMU measurements. The measurement noise is expressed as a GMM, whose components are identified using the expectation-maximization (EM) algorithm. Subsequently, noise and parameter estimation is carried out by solving a maximum likelihood estimation problem iteratively until convergence. The superior performance of the proposed approach over traditional approaches such as least squares and total least squares as well as the more recently proposed minimum total error entropy approach is demonstrated by performing simulations using the IEEE 118-bus system as well as proprietary PMU data obtained from a U.S. power utility.more » « less
