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1. A Multi-Level Distributed Linear State Estimator Considering Transmission Network Topology

   Madurasinghe, D; Venayagamoorthy, GK (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. Distributed Dynamic Security Assessment for Modern Power System Operational Situational Awareness

   https://doi.org/10.1109/ACCESS.2024.3475011  

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

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3. 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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4. An Efficient and Reliable Electric Power Transmission Network Topology Processing

   MADURASINGHE, D; VENAYAGAMOORTHY, G K (November 2023, IEEE access)

   The modern bulk power system operation is complex and dynamic, with rapidly increasing inverter-based resources and active distribution systems. Therefore, high-speed monitoring is required to operate the power system reliably and efficiently. Transmission network topology processing (TNTP) is vital in power system control. Today’s TNTP is based on supervisory control and data acquisition (SCADA) system monitoring of relay signals (SMRS). Due to the slow data communication rate, SMRS cannot efficiently support the modern bulk power system’s energy management system (EMS) functions. In this study, a physics-based hierarchical TNTP (H-TNTP) approach based solely on node voltages and branch currents measurements is proposed utilizing artificial intelligence algorithms. H-TNTP includes the identification of substation configuration. The reliability of the H-TNTP is guaranteed by the design with inherent verification. If required, H-TNTP is capable of operating concurrently with the TNTP-SMRS. A power system with solar photovoltaic (PV) plants is utilized as a test system to illustrate the proposed H-TNTP approach. Results indicate that H-TNTP is fast with synchrophasor measurements. Furthermore, to demonstrate the application of the reliable and fast TNTP approach in EMSs, fast automatic generation control (AGC) during contingencies is studied. Typical results show that fast reconfiguration of AGC modes and dispatch factors leads to better frequency regulation. 

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5. Deep Neural Network-based State Estimator for Transmission System Considering Practical Implementation Challenges

   https://doi.org/10.35833/MPCE.2023.000997  

   Cheeramban_Varghese, Antos; Shah, Hritik; Azimian, Behrouz; Pal, Anamitra; Farantatos, Evangelos (January 2024, Journal of Modern Power Systems and Clean Energy)

   As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system. 

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