Theses and Dissertations at Montana State University (MSU)
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Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item VLSI synthesis of digital application specific neural networks(Montana State University - Bozeman, College of Engineering, 1992) Beagles, Grant PhilipItem Controller design for PSS and FACTS devices to enhance damping of low-frequency power oscillations in power systems(Montana State University - Bozeman, College of Engineering, 2006) You, Ruhua; Chairperson, Graduate Committee: Hashem Nehrir.Low frequency electromechanical oscillations are inevitable characteristics of power systems and they greatly affect the transmission line transfer capability and power system stability. PSS and FACTS devices can help the damping of power system oscillations. The objective of this dissertation is to design an advanced PSS and propose a systematic approach for damping controller design for FACTS devices. Intelligent control strategy which combines the knowledge of system identification, fuzzy logic control, and the neural networks are applied to the PSS design. A fuzzy logic based PSS is developed and tuned by neural network strategy. The proposed PSS improved the damping of power system oscillations over a conventional PSS. But the same control strategy is not satisfactory for the FACTS damping controller design, mainly because of the different location and role of FACTS devices in power system oscillations compared to PSS. A systematic approach is proposed to design damping controllers for FACTS devices. The problem is considered from a control point of view and treated as a feedback control problem. A low order plant transfer function is obtained by PRONY method; proper control input is selected and a damping controller is designed combining the eigenvalue sensitivity analysis and the root locus method. A gain varying strategy is proposed to change the controller gain according to the transmission line loading condition for better damping effect. This approach is successfully applied in damping controller design for SVC, TCSC, and UPFC. Simulation results demonstrate good damping effects of these controllers Another work accomplished in this dissertation is the modeling of UPFC, a voltage-sourced converter-based FACTS device who simultaneously control bus voltage and power flows on transmission lines. The UPFC brings quite a few challenges to power system simulation and study including power flow calculations, modeling of converter control and UPFC dynamics, interfacing UPFC with the power system for transient simulation program development and physical and operating constraint modeling. The proposed model accurately represented the behavior of UPFC in quasi-steady state and well demonstrated the unique capability of the UPFC to control both the load flow and the bus voltage rapidly and independently.