With the increasing interest in wide-band gap devices, their potential benefits in power applications have been studied and explored with numerous studies conducted for both SiC and GaN devices. This thesis investigates the use of wide-band gap devices as the switching element in a semiconductor DC breaker. It involves the design of an efficient semiconductor DC breaker, its simulation in SPICE, construction of a hardware prototype and the comparative study of SiC and Si versions of the aforementioned breaker. The results obtained from the experiments conducted in the process of concluding this thesis show that the SiC version of the breaker is a superior option for a semiconductor DC breaker.

This thesis presents the topic of using the Four-Switch Buck-Boost topology as a DC-DC converter for the Energy Harvesting from Elliptical Machines Project. The project works toward providing a modular synchronous power generation system. Due to the dynamic and sporadic output voltage and power characteristics of the Precor elliptical machine, the system requires a DC-DC converter as a voltage preregulator. The inherent wide input range, high efficiency, and low parts cost of this converter well suit the application. This paper further discusses other topologies and their shortcomings, as well as characterizes the Precor elliptical machine and Enphase Microinverter for interfacing. This report contains a detailed discussion on component selection and PCB layout. The converter averages 94% efficiency during a normal workout power range. This paper also derives a system level control scheme for a modular grid-tie energy harvesting power electronics unit. The Four-Switch Buck-Boost topology efficiently and effectively harvests energy from the Precor elliptical machine as a constant input impedance and wide input voltage regulator for a constant voltage grid-tie inverter.

Cal Poly’s Recreation Center expansion project provides an opportunity to implement Energy Harvesting From Exercise Machines (EHFEM). Part of this implementation is a system that reports the exercise machines’ energy production. Although products capable of reporting exercise machine energy harvesting statistics exist, they have limited capabilities. This thesis project defends a system capable of reporting exercise machine power statistics in near real-time. The system consists of display, database, and power measurement modules. The display module presents statistics in an interactive, graphical, and widely-accessible way. The database module provides an efficient way of organizing and accessing stored statistics. Multiple power measurement module types gather power and energy generation measurements from multiple exercise machine types and transmit those measurements to the database module over the computer network.

DC-DC converters can be separated into two main groups: switching converters and linear regulators. Linear regulators such as Low Dropout Regulators (LDOs) are straightforward to implement and have a very stable output with low voltage ripple. However, the efficiency of an LDO can fluctuate greatly, as the power dissipation is a function of the device’s input and output. On the other hand, a switching regulator uses a switch to regulate energy levels. These types of regulators are more versatile when a larger change of voltage is needed, as efficiency is relatively stable across larger steps of voltages. However, switching regulators tend to have a larger output voltage ripple, which can be an issue for sensitive systems. An approach to utilize both in cascaded configuration while providing a negative output voltage will be presented in this paper. The proposed two-stage conversion system consists of a switching pre-regulator that can track the negative output voltage of the second stage (LDO) such that the difference between input and output voltages is always kept small under varying output voltage while maintaining the high overall conversion efficiency. Computer simulation and hardware results demonstrate that the proposed system can track the negative output voltage well. Additionally, the results show that the proposed system can provide and maintain good overall efficiency, load regulation, and output voltage ripple across a wide range of outputs.

In this thesis, a method was developed to evaluate the impact of bidirectional electric vehicle (EV) charging on power system reliability using Synergi Electric software. Load profiles, EV availability, EV state-of-charge (SOC) were important factors considered in this study. The analysis in this study is based on local and interregional vehicle-to-grid (V2G) implementation at different load points in the system. In general, local V2G implementation is observed to be more effective in improving system reliability over interregional V2G power flow. System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) improvements were observed to increase with increasing SOC but are less progressive between higher SOCs. Based on the simulation results, the method proves to be sufficient in calculating SAIDI and SAIFI reliability indices as the simulation results are corroborated with theory.

With carbon dioxide levels in our atmosphere reaching record highs and 2020 quickly approaching, California is expected to pave the way for the United States in terms of replacing fossil fuel generation facilities with various renewable energy power plants. It is well documented that the inherent variability and limited duty cycle of renewables has hindered their growth. Energy storage technologies represent the bridge that can help us cross the divide from where we stand to where we must stand in the next 6 years. Utility companies value services such as peak shaving, voltage support, and frequency regulation, all of which energy storage technologies can provide. Commercial and residential clients’ of the utility will begin to install their own storage systems once properly incentivized. In this paper, a control algorithm for Sodium Sulfur batteries is proposed with hopes that the aforementioned services will be provided to the utilities while system owners realize profit.

The classical methodologies for synchronous machine modeling provide a solid estimation for synchronous machine behavior but are limited in terms of accuracy due to the assumptions made in the modeling process. The equivalent circuit model developed by the classical approach breaks down the entire machine into a singular impedance component. This allows the model to be generated more quickly but limits its accuracy. In the pursuit of developing a more realistic model, this thesis outlines the parameter measurement of a Hampden SM-100 synchronous machine. In determining the SM-100’s experimental parameters, this thesis executes and analyzes new experimental approaches to synchronous machine modeling. With the results of these approaches, a model for the Hampden SM-100 synchronous machine is developed that considers the rotor, stator, and core parameters of the synchronous machine separately.

As climate change continues to worsen, environmental effects are felt by many people around the world. In California, some of its most damaging wildfires have been found to be started by utilities. As the state continues to suffer from worsening wildfire conditions, the utilities need to implement a variety of wildfire mitigations to help reduce the risk of wildfires that can affect the state and its residents. This paper analyzes the effectiveness of four mitigations employed across three California utilities and suggests potential ways for the mitigations to be used together. The technologies evaluated are covered conductor, rapid earth fault current limiter, distribution fault analysis, and early fault detection. Each of these mitigate different failure drivers of utility lines, whether it is due to a contact from a foreign object, an equipment failure, or another driver. Because each mitigation is more effective against different drivers, a suggestion for multiple mitigations to use together is given. This also includes a path for utilities to evaluate mitigation effectiveness in a different way that may more accurately represent how many fires are stopped by the mitigations employed.

Cal Poly San Luis Obispo’s Electrical Engineering Department and the Power Energy Institute have developed a microgrid laboratory with various generators, loads, and protection systems over the past several years. To improve and make the Cal Poly microgrid laboratory more realistic, this thesis outlines the process of improving the EE microgrid setup by adding a wind energy generation system via an induction generator, excitation capacitors, and a protection relay. By adding an induction generator, the microgrid system becomes more resilient to sudden power fluctuations by maintaining a stable voltage and frequency when the microgrid is islanded. To test this, a disturbance was introduced to the islanded microgrid by turning on and off the pump motor load for one second and by adding a torque load. Without the wind energy system, the system frequency drops below 59.7Hz causing the microgrid to collapse. However, with the wind energy system, the microgrid frequency is kept above 59.7Hz and can remain operational even if the pump motor is loaded to 2 lb•in. This is due to the large inertia the induction generator contains. This is further investigated by creating a Simulink model that models a wind turbine system with wind fluctuations. The model shows that by having a large rotating mass, the inertia keeps the output power stable even if there are rapid wind speed fluctuations.

Nowadays islanding has become a big issue with the increasing use of distributed generators in power system. In order to effectively detect islanding after DG disconnects from main source, author first studied two passive islanding methods in this thesis: THD&VU method and wavelet-transform method. Compared with other passive methods, each of them has small non-detection zone, but both of them are based on the threshold limit, which is very hard to set. What’s more, when these two methods were applied to practical signals distorted with noise, they performed worse than anticipated. Thus, a new composite intelligent based method is presented in this thesis to solve the drawbacks above. The proposed method first uses wavelet-transform to detect the occurrence of events (including islanding and non-islanding) due to its sensitivity of sudden change. Then this approach utilizes artificial neural network (ANN) to classify islanding and non-islanding events. In this process, three features based on THD&VU are extracted as the input of ANN classifier. The performance of proposed method was tested on two typical distribution networks. The obtained results of two cases indicated the developed method can effectively detect islanding with low misclassification.