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1. Multilevel Distributed Linear State Estimation Integrated with Transmission Network Topology Processing

   https://doi.org/10.3390/app14083422  

   Madurasinghe, Dulip ; Venayagamoorthy, Ganesh Kumar ( April 2024 , 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.

    

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2. Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

   Madurasinghe, Dulip ; Venayagamoorthy, Ganesh K ( July 2022 , 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM (IREP 2022))

   Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. 

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3. Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

   https://doi.org/10.1109/NAPS58826.2023.10318579  

   Moshtagh, Shiva ; Sifat, Anwarul Islam ; Azimian, Behrouz ; Pal, Anamitra ( October 2023 , IEEE)

   Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively. 

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4. A Framework to Evaluate PMU Networks for Resiliency Under Network Failure Conditions

   https://doi.org/10.1109/SmartGridComm52983.2022.9961035  

   Raj, Reuben Samson ; Jin, Dong ( October 2022 , IEEE SmartGridComm, October 2022)

   Phasor Measurement Units (PMU), due to their capability for providing highly precise and time-synchronized measurements of synchrophasors, have now become indispensable in wide area monitoring of power-grid systems. Successful and reliable delivery of synchrophasor packets from the PMUs to the Phasor Data Concentrators (PDCs) and beyond, requires a backbone communication network that is robust and resilient to failures. These networks are vulnerable to a range of failures that include cyber-attacks, system or device level outages and link failures. In this paper, we present a framework to evaluate the resilience of a PMU network in the context of link failures. We model the PMU network as a connected graph and link failures as edges being removed from the graph. Our approach, inspired by model checking methods, involves exhaustively checking the reachability of PMU nodes to PDC nodes, for all possible combinations of link failures, given an expected number of links fail simultaneously. Using the IEEE 14-bus system, we illustrate the construction of the graph model and the solution design. Finally, a comparative evaluation on how adding redundant links to the network improves the Power System Observability, is performed on the IEEE 118 bus-system. 

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5. Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise

   https://doi.org/10.1109/TPWRS.2022.3204232  

   Varghese, Antos Cheeramban ; Pal, Anamitra ; Dasarathy, Gautam ( July 2023 , IEEE Transactions on Power Systems)

   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. 

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