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Description
Power systems are transforming into more complex and stressed systems each day. These stressed conditions could lead to a slow decline in the power grid's voltage profile and sometimes lead to a partial or total blackout. This phenomenon can be identified by either solving a power flow problem or using measurement-based real-time monitoring algorithms. The first part of this thesis focuses on proposing a robust power flow algorithm for ill-conditioned systems. While preserving the stable nature of the fixed point (FP) method, a novel distributed FP equation is proposed to calculate the voltage at each bus. The proposed algorithm's performance is compared with existing methods, showing that the proposed method can correctly find the solutions when other methods cannot work due to high condition number matrices. It is also empirically shown that the FP algorithm is more robust to bad initialization points. The second part of this thesis focuses on identifying the voltage instability phenomenon using real-time monitoring algorithms. This work proposes a novel distributed measurement-based monitoring technique called voltage stability index (VSI). With the help of PMUs and communication of voltage phasors between neighboring buses, the processors embedded at each bus in the smart grid perform simultaneous online computations of VSI. VSI enables real-time identification of the system's critical bus with minimal communication infrastructure. Its benefits include interpretability, fast computation, and low sensitivity to noisy measurements. Furthermore, this work proposes the ``local static-voltage stability index" (LS-VSI) that removes the minimal communication requirement in VSI by requiring only one PMU at the bus of interest. LS-VSI also solves the issue of Thevenin equivalent parameter estimation in the presence of noisy measurements. Unlike VSI, LS-VSI incorporates the ZIP load models and load tap changers (LTCs) and successfully identifies the bifurcation point considering ZIP loads' impact on voltage stability. Both VSI and LS-VSI are useful to monitor the voltage stability margins in real-time using the PMU measurements from the field. However, they cannot indicate the onset of voltage emergency situations. The proposed LD-VSI uses the dynamic measurements of the power system to identify the onset of a voltage emergency situation with an alarm. Compared to existing methods, it is shown that it is more robust to PMU measurement noise and can also identify the voltage collapse point while the existing methods have issues with the same.
ContributorsGuddanti, Kishan Prudhvi (Author) / Weng, Yang (Thesis advisor) / Banerjee, Ayan (Committee member) / Zhang, Baosen (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2021
Description
Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault which is very criticalin an electrical cable but very difficult to spot is incipient fault. These momentary faults are observed for very short periods however, if it persists, this would lead to consequences like insulation wear-off and even, power outages. With the advent of
machine learning in the power systems fraternity, this paper also uses a new and updated Active Learning algorithm to detect incipient fault data from a simulated test case. The objective of the paper is to detect the fault data accurately using this new and precise method. For purposes of data collection and training of the model, MATLAB Simulink and Python programming has been used respectively.
ContributorsGhosh, Kinjal (Author) / Weng, Yang (Thesis advisor) / Pal, Anamitra (Committee member) / Hedman, Mojdeh K (Committee member) / Arizona State University (Publisher)
Created2023