Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Resilience-aware management of active distribution networks(Montana State University - Bozeman, College of Engineering, 2021) Alali, Mohammad; Chairperson, Graduate Committee: Maryam Bahramipanah; Farshina Nazrul Shimim, Zagros Shahooei and Maryam Bahramipanah were co-authors of the article, 'Intelligent line congestion prognosis in active distribution system using artificial neural network' in the '2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)' which is contained within this thesis.; Zagros Shahooei and Maryam Bahramipanah were co-authors of the article, 'Resiliency-oriented optimization of critical parameters in multi inverter-fed distributed generation systems' submitted to the journal 'Sustainability journal', special Issue: 'Optimal dynamic control of active distribution power system' which is contained within this thesis.The electric power system is one of the greatest engineering achievements in the history of mankind. Electricity is integral to every part of our economy and society. Therefore, it is essential to make the electricity grid more resilient in facing different extreme events and black outs. In this work, four problems have been investigated to study and improve the resiliency of distribution networks. The first one focuses on the problem of power line congestion which can negatively harm the economy and different equipment in the grid. Two neural network models are used to predict where the congestion might happen and what would be the cause of it. Using these predictions, the problem can be alleviated in time and the resiliency of the grid will be improved. The second problem discusses power management of the distribution network under the occurrence of an extreme event. The problem is formulated as a Markov Decision Process using different agents and is solved using two Reinforcement Learning algorithms, namely, Q-Learning and Value Iteration. This approach is then tested on a benchmark system and the results show a remarkable improvement of the resiliency. The third problem studies the stability and power sharing of parallel inverters in a multi inverter-fed system. A small signal model of the power controller is studied. Further, the system's nonlinear dynamic equations are derived using accurate mathematical models. The system model is then trimmed and linearized around its operating point and the system's control parameters are optimized using Grey Wolf Optimization. Finally, improvement of stability and power sharing are verified by running time domain simulations. The last problem investigates the optimal siting and sizing of energy storage systems in a multi-microgrid system to improve the resiliency. A two-stage optimization method is used to solve the nonlinear and non-convex problem. The first stage involves an Optimal Power Flow and the second stage uses Genetic Algorithm. Further an investment cost based on the sensitivity analysis is introduced to improve the resiliency even further. The effectiveness of this ESS placement is tested on a benchmark system and validated using a fault scenario.Item Investigation of bird induced outages on Montana Power Company's 500 kV transmission lines(Montana State University - Bozeman, College of Engineering, 1996) Maehl, David RobertItem Prediction of electric heating load component of distribution feeder loads using statistical modeling(Montana State University - Bozeman, College of Engineering, 1992) Singh, VijayItem 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.