arXiv subjects

Anamitra Pal

Publications and source records attributed to Anamitra Pal.

At least 19 recordsLinked to original sources

arXiv2026

A Constrained Formulation for Simultaneous Line Parameter Estimation and Instrument Transformer Calibration

The process of calibrating instrument transformers (ITs) has been greatly simplified by using phasor measurement unit (PMU) data since this process eliminates the need for (a) additional hardware, and (b) taking ITs offline. However, such simplification comes at the cost of knowing the line parameters, whose estimation using PMU data in turn requires calibrated ITs. To solve this interdependency problem, we propose a novel framework that incorporates power system domain knowledge as constraints to perform simultaneous line parameter estimation and IT calibration. We demonstrate the effectiveness of our approach with simulated and real PMU data as well as for a power system application that uses both PMU data and line parameter information.

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arXiv2026

Wildfire Risk-Informed Preventive-Corrective Decision Making under Renewable Uncertainty

The increasing frequency and intensity of wildfires poses severe threats to the secure and stable operation of power grids, particularly one that is interspersed with renewable generation. Unlike conventional contingencies, wildfires affect multiple assets, leading to cascading outages and rapid degradation of system operability and stability. At the same time, the usual precursors of large wildfires, namely dry and windy conditions, are known with high confidence at least a day in advance. Thus, a coordinated decision-making scheme employing both day-ahead and real-time information has a significant potential to mitigate dynamic wildfire risks in renewable-rich power systems. Such a scheme is developed in this paper through a novel stochastic preventive-corrective cut-set and stability-constrained unit commitment and optimal power flow formulation that also accounts for the variability of renewable generation. The results obtained using a reduced 240-bus system of the US Western Interconnection demonstrate that the proposed approach increases the resilience of power systems across multiple levels of wildfire risks while maintaining economic viability.

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arXiv2026

Impact of Work Schedule Flexibility on EV Hosting Capacity: Insights from Analyzing Field Data

Uncoordinated electric vehicle (EV) charging is altering residential load patterns and pushing distribution transformers to operate beyond their limits. These outcomes can be offset by exploiting the flexibility in work schedules (hybrid, remote vs. in-person) of EV owners, particularly when combined with rooftop photovoltaic (PV) generation. However, this phenomenon has not been explored in-depth yet. This paper addresses this research gap by introducing weekly work schedule-aware robust and chance-constrained optimization formulations for EV charging coordination to determine a transformer's EV hosting capacity. The results obtained using data from a residential feeder in Arizona indicate that an intelligent combination of work schedule flexibility with PV generation can help power utilities effectively manage changing grid demands.

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arXiv2025

State-Derivative Feedback Control for Damping Low-Frequency Oscillations in Bulk Power Systems

Low-frequency oscillations remain a major challenge in bulk power systems with high renewable penetration, long lines, and large loads. Existing damping strategies based on power modulation of high voltage DC (HVDC) or energy storage, are often limited by fixed control architectures, leaving some modes poorly damped. This paper introduces a state-derivative feedback (SDF) damping controller that uses both frequency and its rate of change as feedback signals. Incorporating state derivatives enhances modal damping and accelerates frequency recovery, enabling HVDC and energy storage to effectively stabilize the grid. We evaluate the SDF controller on two- and three-area systems and compare performance with a frequency difference-based damping scheme. Results show that the SDF control reproduces state-feedback performance while providing good damping of both inter- and intra-area oscillations compared to the frequency-difference method, highlighting its potential as a practical solution for stabilizing power-electronics-rich grids.

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arXiv2025

Linear State Estimation in Presence of Bounded Uncertainties: A Comparative Analysis

A variety of algorithms have been proposed to address the power system state estimation problem in the presence of uncertainties in the data. However, less emphasis has been given to handling perturbations in the model. In the context of linear state estimation (LSE), which is the focus of this paper, perturbations in the model come from variations in the line parameters. Since the actual values of the line parameters can be different from the values stored in a power utility's database, we investigate three approaches in this paper to estimate the states in the presence of bounded uncertainties in the data and the model. The first approach is based on interval arithmetic, the second is based on convex optimization, and the third is based on generalized linear fractional programming. The three algorithms are applied to multiple IEEE test systems and compared in terms of their speed and accuracy. The results indicate that the first two algorithms are extremely fast and give expected results, while the third suffers from scalability issues and is unsuitable for LSE.

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arXiv2025

Topology-Aware Graph Neural Network-based State Estimation for PMU-Unobservable Power Systems

Traditional optimization-based techniques for time-synchronized state estimation (SE) often suffer from high online computational burden, limited phasor measurement unit (PMU) coverage, and presence of non-Gaussian measurement noise. Although conventional learning-based models have been developed to overcome these challenges, they are negatively impacted by topology changes and real-time data loss. This paper proposes a novel deep geometric learning approach based on graph neural networks (GNNs) to estimate the states of PMU-unobservable power systems. The proposed approach combines graph convolution and multi-head graph attention layers inside a customized end-to-end learning framework to handle topology changes and real-time data loss. An upper bound on SE error as a function of topology change is also derived. Experimental results for different test systems demonstrate superiority of the proposed customized GNN-SE (CGNN-SE) over traditional optimization-based techniques as well as conventional learning-based models in presence of topology changes, PMU failures, bad data, non-Gaussian measurement noise, and large system implementation.

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arXiv2025

Damping LFOs: Grid Following with Power Oscillation Damping vs. Grid Forming vs. PSS

Low-frequency oscillations (LFOs) present a significant challenge to the stability and reliability of power systems, especially in grids with a high penetration of renewable energy sources. Traditional grid-following (GFL) inverters have proven less effective in damping such oscillations. This paper presents a GFL-power plant controller with an auxiliary power oscillation damping control for damping LFOs. This approach is compared with a traditional power system stabilizer (PSS) for a two-area power system. Next, the research is extended by deploying grid forming (GFM) controls, which by actively controlling the voltage and frequency dynamics emulate the behavior of traditional synchronous generators. The paper analyzes two GFM control strategies: virtual synchronous machine (VSM) and droop control, and demonstrates their effectiveness in damping LFOs in the test system. The simulation results reveal that the performance of the proposed GFM-VSM rivals that of the PSS and is better than the GFL-power oscillation damper.

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arXiv2025

A Practical Approach Towards Inertia Estimation Using Ambient Synchrophasor Data

Real-time tracking of inertia is important because it reflects the power system's ability to withstand contingencies and maintain frequency security. This paper proposes a practical approach to estimate inertia using ambient phasor measurement unit (PMU) data and a partitioned form of the swing equation. The approach accounts for (bounded) uncertainties in network parameters and PMU measurements, enabling precise estimation of inertia and damping constants, as well as mechanical power inputs. Instead of assuming constant mechanical power input throughout, the approach leverages knowledge of power system operations to determine intervals when it is actually constant to maintain estimation consistency. Simulation results on the IEEE 14-bus system and IEEE 39 bus system integrated with renewable energy sources affirm the method's accuracy and applicability.

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arXiv2025

System-wide Instrument Transformer Calibration and Line Parameter Estimation Using PMU Data

Uncalibrated instrument transformers (ITs) can degrade the performance of downstream applications that rely on the voltage and current measurements that ITs provide. It is also well-known that phasor measurement unit (PMU)-based system-wide IT calibration and line parameter estimation (LPE) are interdependent problems. In this paper, we present a statistical framework for solving the simultaneous LPE and IT calibration (SLIC) problem using synchrophasor data. The proposed approach not only avoids the need for a perfect IT by judiciously placing a revenue quality meter (which is an expensive but non-perfect IT), but also accounts for the variations typically occurring in the line parameters. The results obtained using the IEEE 118-bus system as well as actual power system data demonstrate the high accuracy, robustness, and practical utility of the proposed approach.

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arXiv2024

Exploring the Influence of Residential Electric Vehicle Charging on Distribution System Hosting Capacity -- A Case-Study in Arizona

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capacity are both costly and time-consuming. Moreover, the inherent uncertainties associated with EV charging parameters make it challenging for power utilities to accurately assess the impact of EVs added to specific locations. To address these knowledge gaps, this study (a) introduces an algorithm to coordinate residential EV charging, and (b) proposes a comprehensive framework that evaluates all transformers within a feeder. The proposed method is applied to a real-world feeder, which includes 120 transformers of varying capacities. The results demonstrate that this approach effectively manages a substantial number of EVs without overloading any of the transformers, while also pinpointing locations that must be prioritized for future upgrades. This framework can serve as a valuable reference for utilities when conducting distribution system evaluations for supporting the growing EV penetration.

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arXiv2024

A Preventive-Corrective Scheme for Ensuring Power System Security During Active Wildfire Risks

The focus of this paper is on operating the electric power grid in a secure manner when wildfire risks are high. This is a challenging problem because of the uncertain ways in which the fires can impact the operation of the power system. To address this challenge, we propose a novel preventive-corrective coordinated decision-making scheme that quickly mitigates both static and dynamic insecurities given the risk of active wildfires in a region. The scheme utilizes a comprehensive contingency analysis tool for multi-asset outages that leverages: (i) a Feasibility Test algorithm which exhaustively desaturates overloaded cut-sets to prevent cascading line outages, and (ii) a data-driven transient stability analyzer which alleviates dynamic instabilities. This tool is then used to operate a coordinated unit commitment/optimal power flow model that is designed to adapt to varying risk levels associated with wildfires. Depending on the allowed risk, the model balances economical operation and grid robustness. The results obtained using the IEEE 118-bus system indicate that the proposed approach alleviates system vulnerabilities to wildfires while also minimizing operational cost.

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arXiv2024

Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks

Traditional smart meters, which measure energy usage every 15 minutes or more and report it at least a few hours later, lack the granularity needed for real-time decision-making. To address this practical problem, we introduce a new method using generative adversarial networks (GAN) that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using convex optimization. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated historical energy data obtained from smart meters. The results demonstrate that the model can successfully create minute-by-minute temporally correlated profiles of power usage from 15-minute interval average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring and subsequently controlling such systems.

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arXiv2024

Localizing Single and Multiple Oscillatory Sources: A Frequency Divider Approach

Localizing sources of troublesome oscillations, particularly forced oscillations (FOs), in power systems has received considerable attention over the last few years. This is driven in part by the massive deployment of phasor measurement units (PMUs) that capture these oscillations when they occur; and in part by the increasing incidents of FOs due to malfunctioning components, wind power fluctuations, and/or cyclic loads. Capitalizing on the frequency divider formula of [1], we develop methods to localize single and multiple oscillatory sources using bus frequency measurements. The method to localize a single oscillation source does not require knowledge of network parameters. However, the method for localizing FOs caused by multiple sources requires this knowledge. We explain the reasoning behind this knowledge difference as well as demonstrate the success of our methods for source localization in multiple test systems.

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arXiv2024

Comparative Analysis of Information Theoretic and Statistical Methods for Line Parameter Estimation

Recent studies indicate that the noise characteristics of phasor measurement units (PMUs) can be more accurately described by non-Gaussian distributions. Consequently, estimation techniques based on Gaussian noise assumptions may produce poor results with PMU data. This paper considers the PMU based line parameter estimation (LPE) problem, and investigates the performance of four state-of-the-art techniques in solving this problem in presence of non-Gaussian measurement noise. The rigorous comparative analysis highlights the merits and demerits of each technique w.r.t. the LPE problem, and identifies conditions under which they are expected to give good results.

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arXiv2024

Cut-set and Stability Constrained Optimal Power Flow for Resilient Operation During Wildfires

Resilient operation of the power system during ongoing wildfires is challenging because of the uncertain ways in which the fires impact the electric power infrastructure (multiple arc-faults, complete melt-down). To address this challenge, we propose a novel cut-set and stability-constrained optimal power flow (OPF) that quickly mitigates both static and dynamic insecurities as wildfires progress through a region. First, a Feasibility Test (FT) algorithm that quickly desaturates overloaded cut-sets to prevent cascading line outages is integrated with the OPF problem. Then, the resulting formulation is combined with a data-driven transient stability analyzer that predicts the correction factors for eliminating dynamic insecurities. The proposed model considers the possibility of generation rescheduling as well as load shed. The results obtained using the IEEE 118-bus system indicate that the proposed approach alleviates vulnerability of the system to wildfires while minimizing operational cost.

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arXiv2024

Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on the higher voltage buses. However, this causes many of the lower voltage levels of the bulk power system to not be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but select PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show 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, scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus Synthetic Texas system.

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arXiv2024

Analyzing Cross-Phase Effects of Reactive Power Intervention on Distribution Voltage Control

Increasing photovoltaic (PV) penetration in the distribution system can often lead to voltage violations. Mitigation of these violations requires reactive power intervention from PV inverters. However, the unbalanced nature of the distribution system leads to mixed effects on the voltages of nearby nodes for each inverter injecting or absorbing reactive power. In particular, reactive power absorption to reduce over-voltage in one phase can exacerbate over-voltage in a different phase. In this paper, the factors impacting the incremental and decremental voltage effects of reactive power intervention are analyzed in detail. The result of these effects on the distribution system performance is presented to highlight their significance and the need to factor them in for any coordinated voltage control algorithm.

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arXiv2024

Design and Validation of a Very Low-Power Phasor Measurement Unit

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn increases the overall cost. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for a single-phase system at an average power consumption of only 30.8 mW -- one to two orders of magnitude lower than existing academic and commercial PMUs. This enables the proposed PMU to run for two weeks using an 11-Wh battery or indefinitely if paired with an inexpensive solar panel. A test-bench developed in accordance with the 2018 IEC/IEEE 60255-118-1 PMU Standard confirms the accuracy of this PMU. Given its low power consumption, the proposed design is expected to accelerate adoption of PMUs in modern distribution grids.

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