Power amplifiers and tuneable matching networks for plasma generation systems arebeing continuously advanced, and recent innovations have shown tremendous improvements in their size, efficiency, and capability. These improvements must ultimately be validated on a live plasma chamber, but this is costly and time-consuming, and debugging errors or failures is a challenge owing to the highly dynamic nature of the plasma and the experimental prototype nature of the advancements. This work addresses this challenge by developing a reactive load emulation system that can mimic the inductive reactance of a live plasma chamber. This includes a study of the saturation characteristics of low-permeability, high-frequency materials, demonstration of the suitability of this method for plasma emulation, and the design of an inductor array platform which verifies the approach.

In recent years, there has been an increasing need for effective voltage controls in power systems due to the growing complexity and dynamic nature of practical power grid operations. Deep reinforcement learning (DRL) techniques now have been widely explored and applied to various electric power operation analyses under different control structures. With massive data available from phasor measurement units (PMU), it is possible to explore the application of DRL to ensure that electricity is delivered reliably.For steady-state power system voltage regulation and control, this study proposed a novel deep reinforcement learning (DRL) based method to provide voltage control that can quickly remedy voltage violations under different operating conditions. Multiple types of devices, adjustable voltage ratio (AVR) and switched shunts, are considered as controlled devices. A modified deep deterministic policy gradient (DDPG) algorithm is applied to accommodate both the continuous and discrete control action spaces of different devices. A case study conducted on the WECC 240-Bus system validates the effectiveness of the proposed method. System dynamic stability and performance after serious disturbances using DRL are further discussed in this study. A real-time voltage control method is proposed based on DRL, which continuously regulates the excitation system in response to system disturbances. Dynamic performance is considered by incorporating historical voltage data, voltage rate of change, voltage deviation, and regulation amount. A versatile transmission-level power system dynamic training and simulation platform is developed by integrating the simulation software PSS/E and a user-written DRL agent code developed in Python. The platform developed facilitates the training and testing of various power system algorithms and power grids in dynamic simulations with all the modeling capabilities available within PSS/E. The efficacy of the proposed method is evaluated based on the developed platform. To enhance the controller's resilience in addressing communication failures, a dynamic voltage control method employing the Multi-agent DDPG algorithm is proposed. The algorithm follows the principle of centralized training and decentralized execution. Each agent has independent actor neural networks and critic neural networks. Simulation outcomes underscore the method’s efficacy, showcasing its capability in providing voltage support and handling communication failures among agents.

There has been a significant growth in the distributed energy resources (DERs) connected to the distribution networks in recent years. For a distribution system with a high penetration of DERs, a detailed modeling and representation of the distribution network is needed to accurately assess its steady-state and dynamic behavior. In this dissertation, a field-validated model for a real sub-transmission and distribution network is developed, including one of the feeders modeled with the secondary network and loads and solar PV units at their household/user location. A procedure is developed combining data from various sources such as the utility database, geoinformation data, and field measurements to create an accurate network model. Applying a single line to ground fault to the detailed distribution feeder model, a high voltage swell, with potentially detrimental impacts on connected equipment, is shown in one of the non-faulted phases of the feeder. The reason for this voltage swell is analyzed in detail. It is found that with appropriate control the solar PV units on the feeder can reduce the severity of the voltage swell, but not entirely eliminate it. For integrated studies of the transmission-distribution (T&D) network, a T&D co-simulation framework is developed, which is capable of power flow as well as dynamic simulations, and supports unbalanced modeling and disturbances in the distribution as well as the sub-transmission system. The power flow co-simulation framework is developed as a module that can be run on a cloud-based platform. Using the developed framework, the T&D system response is studied for balanced and unbalanced faults on the distribution and sub-transmission system. For some faults the resultant loss of generation can potentially lead to the entire feeder tripping due to high unbalance at the substation. However, it is found that advanced inverter controls may improve the response of the distribution feeders to the faults. The dissertation also highlights the importance of modeling the secondary network for both steady-state and dynamic studies. Lastly, a preliminary investigation is conducted to include different dynamic elements such as grid-forming inverters in a T&D network simulation.

With the emergence of electric transportation and the infrastructure for electric vehicles (EVs), numerous viable approaches and topologies have emerged. In order to improve the power quality of the grid, it is essential for Onboard Battery Chargers (OBC) for electric vehicles to maintain a power factor closer to unity. This study mainly focuses on two prominent PFC topologies, Totem-pole PFC (TPFC) and H-Bridge PFC (HPFC), which are simple to implement and capable enough of providing high operating efficiency. This study elucidates the comprehensive comparison of the TPFC and HPFC converters using the comprehensive mathematical modeling approach, simulation models, and the hardware experiments. Also, the comparison of the EMI filter requirement and design of DM EMI filter for both the topologies is also extensively illustrated in this study. Firstly, focusing the comprehensive mathematical models of TPFC and HPFC converters, which includes the mathematical formation of the duty cycle for both the converters incorporating the discretized input current controller into the mathematical model which gives more closer comparison when it is compared to simulation models and the hardware experiment model operations. The input current FFT analysis and the THD modeling are also covered in the mathematical modeling of TPFC and HPFC converters. Moreover, the EMI noise is modeled, and the corresponding EMI filter is also designed for both the PFC topologies. Further, the simulation models of TPFC and HPFC converters are also developed and the outputs of the simulation models show an input AC current is precisely following the input AC voltage and also the output voltage of constant 400V is attained for both the PFC converters. Similarly, for the experimental results, the constant 400V regulated DC output voltage is obtained and the input AC current is following the input AC voltage with the power factor of 0.983 for TPFC and 0.99 for HPFC converter. Moreover, the implementation of the EMI filter at the front end of the converter succinctly attenuates the EMI noise and complied within the FCC Class A limit for both TPFC and HPFC converters.

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to active power injection from the distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to voltage management. As guided by the previous IEEE standard 1547-2003, most of the existing photovoltaic (PV) systems in the real distribution networks are equipped with conventional inverters, which only allow the PV systems to operate at unity power factor to generate active power. To utilize the voltage control capability of the existing PV systems following the guideline of the revised IEEE standard 1547-2018, this dissertation proposes a two-stage stochastic optimization strategy aimed at optimally placing the PV smart inverters with Volt-VAr capability among the existing PV systems for distribution systems with high PV penetration to mitigate voltage violations. PV smart inverters are fast-response devices compared to conventional voltage control devices in the distribution system. Historically, distribution system planning and operation studies are mainly based on quasi-static simulation, which ignores system dynamic transitions between static solutions. However, as high-penetration PV systems are present in the distribution system, the fast transients of the PV smart inverters cannot be ignored. A detailed dynamic model of the PV smart inverter with Volt-VAr control capability is developed as a dynamic link library (DLL) in OpenDSS to validate the system voltage stability with autonomous control of the optimally placed PV smart inverters. Static and dynamic verification is conducted on an actual 12.47 kV, 9 km-long Arizona utility feeder that serves residential customers. To achieve fast simulation and accommodate more complex PV models with desired accuracy and efficiency, an integrative dynamic simulation framework for OpenDSS with adaptive step size control is proposed. Based on the original fixed-step size simulation framework in OpenDSS, the proposed framework adds a function in the OpenDSS main program to adjust its step size to meet the minimum step size requirement from all the PV inverters in the system. Simulations are conducted using both the original and the proposed framework to validate the proposed simulation framework.

With the increasing penetration levels of distributed energy resources along distribution feeders, the importance of load modeling has grown significantly and therefore it is important to have an accurate representation of the distribution system in the planning and operation studies. Although, currently, most of the power system studies are being done using positive sequence commercial software packages for computational convenience purposes, it comes at the cost of reduced accuracy when compared to the more accurate electromagnetic transient (EMT) simulators (but more computationally intensive). However, it is expected, that in the next several years, the use of EMT simulators for large-scale system studies would become a necessity to implement the ambitious renewable energy targets adopted by many countries across the world. Currently, the issue of developing more accurate EMT feeder and load models has yet to be addressed. Therefore, in the first phase of this work, an optimization algorithm to synthesize an EMT distribution feeder and load model has been developed by capturing the current transients when three-phase voltage measurements (obtained from a local utility) are played-in as input, from events such as sub-transmission faults, to the synthesized model. Using the developed algorithm, for the proposed feeder model, both the load composition and the load parameters have been estimated. The synthesized load model has a load composition which includes impedance loads, single-phase induction motor (SPHIM) loads and three-phase induction motor loads. In the second phase of this work, an analytical formulation of a 24 V EMT contactor is developed to trip the air conditioner EMT SPHIM load, in the feeder and load model developed in Phase 1 of this work, under low voltage conditions. Additionally, a new methodology is developed, to estimate and incorporate the trip and reconnection settings of the proposed EMT contactor model to trip, reconnect and stall the SPHIMs in a positive sequence simulator (PSLF) for single-line to ground faults. Also, the proposed methodology has been tested on a modified three-segment three-phase feeder model using a local utility’s practical feeder topological and loading information. Finally, the developed methodology is modified to accommodate three-phase faults in the system.

Power system robustness against high impact low probability events is becoming a major concern. About 90% of US power outages reported in the last three decades are due to Hurricanes and tropical storms. Various works of literature are focused on modelling the resilience framework against hurricanes. To depict distinct phases of a system response during these disturbances, an aggregated trapezoid model is derived from the conventional trapezoid model and proposed in this work. The model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same.A probabilistic-based Monte Carlo Simulations (MCS) approach has been proposed in this work to incorporate the stochastic nature of the power system and hurricane uncertainty. Furthermore, the system's resilience to hurricanes is evaluated on the modified reliability test system (RTS), which is provided in this work, by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Siemens Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state (both deterministic and stochastic approach) and dynamic (both deterministic and stochastic approach) analysis, resilience metrics are quantified. Finally, this work highlights the interdependency of operational and infrastructure resilience as they cannot be considered discrete characteristics of the system. The objective of this work is to incorporate dynamic analysis and stochasticity in the resilience evaluation for a wind penetrated power system.

With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more challenging task for the modern power system. This is because with higher penetration of renewable generation, the system has to bear a higher degree of variability and uncertainty. One way to address this problem is by generating realistic scenarios that complement and supplement actual system conditions. This thesis presents a methodology to create such correlated synthetic scenarios for load and renewable generation using machine learning. Machine learning algorithms need to have ample amounts of data available to them for training purposes. However, real-world datasets are often skewed in the distribution of the different events in the sample space. Data augmentation and scenario generation techniques are often utilized to complement the datasets with additional samples or by filling in missing data points. Datasets pertaining to the electric power system are especially prone to having very few samples for certain events, such as abnormal operating conditions, as they are not very common in an actual power system. A recurrent generative adversarial network (GAN) model is presented in this thesis to generate solar and load scenarios in a correlated manner using an actual dataset obtained from a power utility located in the U.S. Southwest. The generated solar and load profiles are verified both statistically and by implementation on a simulated test system, and the performance of correlated scenario generation vs. uncorrelated scenario generation is investigated. Given the interconnected relationships between the variables of the dataset, it is observed that correlated scenario generation results in more realistic synthetic scenarios, particularly for abnormal system conditions. When combined with actual but scarce abnormal conditions, the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning. The proposed scenario generation method is scalable and can be modified to work with different time-series datasets. Moreover, when the model is trained in a conditional manner, it can be used to synthesise any number of scenarios for the different events present in a given dataset. In summary, this thesis explores scenario generation using a recurrent conditional GAN and investigates the benefits of correlated generation compared to uncorrelated synthesis of profiles for the reliability planning problem of power systems.

The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a paradigm change in modeling distribution systems in more detail to maintain the reliability and efficiency while accommodating a high level of DERs. Accurate models of distribution feeders, including the secondary network, loads, and DER components must be developed and validated for system planning and operation and to examine the distribution system performance. In this work, a detailed model of an actual feeder with high penetration of DERs from an electrical utility in Arizona is developed. For the primary circuit, distribution transformers, and cables are modeled. For the secondary circuit, actual conductors to each house, as well as loads and photovoltaic (PV) units at each premise are represented. An automated tool for secondary network topology construction for load feeder topology assignation is developed. The automated tool provides a more accurate feeder topology for power flow calculation purposes. The input data for this tool consists of parcel geographic information system (GIS) delimitation data, and utility secondary feeder topology database. Additionally, a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements is presented. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification.

DC-DC converters are widely employed to interface one voltage level with another through step-up or step-down operation. In recent years, step-up DC-DC converters have been a key component in harnessing energy through renewable sources by providing an interface to integrate low voltage systems to DC-AC converters or microgrids. They find increasing applications in battery and fuel cell electric vehicles which can benefit from high and variable DC link voltage. It is important to optimize these converters for higher efficiency while achieving high gain and high power density. Non-isolated DC-DC converters are an attractive option due to the reduced complexity of magnetic design, smaller size, and lower cost. However, in these topologies, achieving a very high gain along with high efficiency has been a challenge. This work encompasses different non-isolated high gain DC-DC converters for electric vehicle and renewable energy applications. The converter topologies proposed in this work can easily achieve a conversion ratio above 20 with lower voltage and current stress across devices. For applications requiring wide input or output voltage range, different control schemes, as well as modified converter configurations, are proposed. Moreover, the converter performance is optimized by employing wide band-gap devices-based hardware prototypes. It enables higher switching frequency operation with lower switching losses. In recent times, multiple soft-switching techniques have been introduced which enable higher switching frequency operation by minimizing the switching loss. This work also discusses different soft-switching mechanisms for the high conversion ratio converter and the proposed mechanism improves the converter efficiency significantly while reducing the inductor size. Further, a novel electric vehicle traction architecture with low voltage battery and multi-input high gain DC-DC converter is introduced in this work. The proposed architecture with multiple 48 V battery packs and integrated, multi-input, high conversion ratio DC-DC converters, can reduce the maximum voltage in the vehicle during emergencies to 48 V, mitigate cell balancing issues in battery, and provide a wide variable DC link voltage. The implementation of high conversion ratio converter in multiple configurations for the proposed architecture has been discussed in detail and the proposed converter operation is validated experimentally through a scaled hardware prototype.