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1. 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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2. 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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3. 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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4. Scalable Δ -AGC Logic for Enhanced Electromechanical Oscillation Damping in Modern Power Systems With Utility-Scale Photovoltaic Generators

   https://doi.org/10.1109/ACCESS.2025.3575028  

   Ratnakumar, Rajan; Venayagamoorthy, Ganesh K (May 2025, IEEE Access)

   Power systems with utility-scale solar photovoltaic (PV) can significantly influence the operating points (OPs) of synchronous generators, particularly during periods of high solar PV generation. A sudden drop in solar PV output due to cloud cover or other transient conditions will alter the generation of synchronous generators shifting their OPs. These shifted OPs can become a challenge for stability as the system may operate closer to its stability limits. If a disturbance occurs while the system is operating at the shifted OP, with reduced stability margins, it will be more vulnerable to increased oscillations, loss of synchronism of its generator(s) and system instability. This study introduces a scalable delta-automatic generation control (delta-AGC) logic method designed to address stability challenges arising from shifts in the OPs of synchronous generators during abrupt drops in PV generation. By temporarily adjusting the OPs of synchronous generators through modification of their participation factors (PFs) in the AGC logic dispatch, the proposed method enhances power system stability. The proposed delta-AGC logic method focuses on the optimal determination of delta-PFs in power systems with large number of generators, using the concept of coherency and employing a hierarchical optimization strategy that includes both inter-coherent and intra-coherent group optimization. Additionally, a new electromechanical oscillation index (EMOI), integrating both time response analysis (TRA) and frequency response analysis (FRA), is utilized as an online situational awareness tool (SAT) for optimizing the system’s stability under various conditions. This online SAT has been implemented in a decentralized manner at the area level, limiting wide-area communication overheads and any cybersecurity concerns. The delta-AGC logic method is illustrated on a modified IEEE 68 bus system, incorporating large utility-scale solar PV plants, and is validated through real-time simulation. Various cases, including high-loading conditions with and without power system stabilizers, conventional AGC logic, and delta-AGC logic, are carried out to evaluate the effectiveness of the proposed delta-AGC logic method. The results illustrate the performance and benefits of the delta-AGC logic method, highlighting its potential to significantly enhance power system stability. 

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5. An Energy-Efficient Network-on-Chip Design using Reinforcement Learning

   https://doi.org/10.1145/3316781.3317768  

   Zheng, Hao; Louri, Ahmed (June 2019, 56th ACM/IEEE Design Automation Conference (DAC))

   The design space for energy-efficient Network-on-Chips (NoCs) has expanded significantly comprising a number of techniques. The simultaneous application of these techniques to yield maximum energy efficiency requires the monitoring of a large number of system parameters which often results in substantial engineering efforts and complicated control policies. This motivates us to explore the use of reinforcement learning (RL) approach that automatically learns an optimal control policy to improve NoC energy efficiency. First, we deploy power-gating (PG) and dynamic voltage and frequency scaling (DVFS) to simultaneously reduce both static and dynamic power. Second, we use RL to automatically explore the dynamic interactions among PG, DVFS, and system parameters, learn the critical system parameters contained in the router and cache, and eventually evolve optimal per-router control policies that significantly improve energy efficiency. Moreover, we introduce an artificial neural network (ANN) to efficiently implement the large state-action table required by RL. Simulation results using PARSEC benchmark show that the proposed RL approach improves power consumption by 26%, while improving system performance by 7%, as compared to a combined PG and DVFS design without RL. Additionally, the ANN design yields 67% area reduction, as compared to a conventional RL implementation. 

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