arXiv subjects

Kishan Prudhvi Guddanti

Publications and source records attributed to Kishan Prudhvi Guddanti.

7 recordsLinked to original sources

arXiv2024

A Comprehensive Review: Impacts of Extreme Temperatures due to Climate Change on Power Grid Infrastructure and Operation

The power grid is experiencing a multi-fold transformation while the global climate evolves with record-breaking extreme temperatures during heat domes, polar vortices, and severe ice. Over the decades, these extreme temperature events have increased in frequency, duration, and intensity. The power grid infrastructure is geographically spread over thousands of square miles with millions of small and large components, and the impact of extreme temperature operations on the grid infrastructure needs to be researched further. This paper reviews academic literature, standards, industry articles, and federal reports to identify the impacts of heat domes, polar vortices, and icing on all the T&D grid equipment, including substations (assets owned and operated by the utilities and independent system operators). This paper classifies the equipment into primary and auxiliary equipment and determines its vulnerability to extreme temperatures for a deeper analysis of a more critical and vulnerable set of grid equipment. For each equipment under consideration, its fundamental role in the system, the impact of extreme temperatures on its operation, available monitoring, and mitigation of these impacts are discussed. The paper develops insights on standards readiness and identifies gaps concerning extreme temperature definitions. The paper also develops summary tables to identify the critical failure modes for each type of equipment, failure influence diagrams, and cascading influence diagrams to highlight and aid in translating the equipment vulnerability information into power grid contingency definitions that need to be considered in grid planning and operations.

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arXiv2023

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

The fifth-generation mobile communication (5G) technology offers opportunities to enhance the real-time monitoring of grids. The 5G-enabled phasor measurement units (PMUs) feature flexible positioning and cost-effective long-term maintenance without the constraints of fixing wires. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone test-bed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of the 5G-enabled PMU communication setup is reviewed and discussed in this paper, and a generalized dynamic linear model (GDLM) based real-time synchrophasor data anomaly detection use-case is presented. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.

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arXiv2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements and linking load models with end users and their communities are the foundation for analyzing system resilience and vulnerability to natural hazards. However, power system models and geospatial data for power grid assets are often developed asynchronously without close coordination. Creating a direct mapping between the two is a challenging task, mainly due to heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect the two, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

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arXiv2022

Local Measurement Based Robust Voltage Stability Index & Identification of Voltage Collapse Onset

This paper addresses the problem of real-time monitoring of long-term voltage instability (LTVI) by using local field measurements. Existing local measurement-based methods use Thevenin equivalent parameter estimation that is sensitive to the noise in measurements. For solving this issue, we avoid the Thevenin approach by projecting the power flow equations as circles to develop local static-voltage stability indicator (LS-VSI) that is robust to measurement noises. The proposed method is an attractive option for practical implementation because of its decentralized nature, robustness to noise, and realistic modeling. Next, we utilize LS-VSI to propose a new local dynamic - VSI (LD-VSI) to identify the LTVI triggered by large disturbances and load tap changer dynamics. LD-VSI can identify not only the onset of instability with an alarm but also the voltage collapse point (VCP). Extensive numerical validation comparing LS-VSI with existing methods is presented on IEEE 30-bus and larger test systems like 2000-bus Texas synthetic grid to validate the robustness, accuracy, and situational awareness feature. We also verify the LD-VSI behavior on the Nordic power grid using PSSE dynamic simulation. When compared to existing methods, we observe that LD-VSI is not only more robust to measurement noise but also can identify VCP.

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arXiv2021

Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the 2nd place in accuracy and 1st place in speed. The developed code is open-sourced for public use.

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arXiv2021

PMU-based Distributed Non-iterative Algorithm for Real-time Voltage Stability Monitoring

The Phasor measurement unit (PMU) measurements are mandatory to monitor the power system's voltage stability margin in an online manner. Monitoring is key to the secure operation of the grid. Traditionally, online monitoring of voltage stability using synchrophasors required a centralized communication architecture, which leads to high investment cost and cyber-security concerns. The increasing importance of cyber-security and low investment cost have recently led to the development of distributed algorithms for online monitoring of the grid that are inherently less prone to malicious attacks. In this work, a novel distributed non-iterative voltage stability index (VSI) is proposed by recasting the power flow equations as circles. The online computations of VSI are simultaneously performed by the processors embedded at each bus in the smart grid with the help of PMUs and communication of voltage phasors between neighboring buses. The distributed nature of the index enables the real-time identification of the critical bus of the system with minimal communication infrastructure. The effectiveness of the proposed distributed index is demonstrated on IEEE test systems and contrasted with existing methods to show the benefits of the proposed method in speed, interpretability, identification of outage location, and low sensitivity to noisy measurements.

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arXiv2019

Power Flow as Intersection of Circles: A new Fixed Point Method

The power flow (PF) problem is a fundamental problem in power system engineering. Many popular solvers face challenges, such as convergence issues. One can try to rewrite the PF problem into a fixed point equation, which can be solved exponentially fast. But, existing methods have their own restrictions, such as the required AC network structure or bus types. To remove these restrictions, we employ the circle geometry per-bus via rectangular coordinate representation to embed our physical knowledge of operation point selection in PV curves. Each iteration of the algorithm consists of finding intersections of circles, which can be computed efficiently with high numerical accuracy. Such analysis also helps in visualizing PV curve to always select the high voltage solution. We compare the performance of our fixed point algorithm with existing state-of-the-art methods, showing that the proposed method can correctly find the solutions when other methods cannot. In addition, we empirically show that the fixed point algorithm is much more robust to bad initialization points than the existing methods.

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