Electrical cabinet fires occur frequently in commercial and industrial facilities. However, the severity of these fire events varies widely, making it difficult to estimate the fire growth and size with certainty. This study aims to identify the significant parameters that affect electrical cabinet fires, which are quantified as the heat release rate (HRR), and properly categorize them. With this knowledge, optimal parameter-response relationships can be developed to predict the electrical cabinet fire behavior. Statistical analysis conducted in this study on historical fire incident data revealed that the fires in Nuclear Power Plants (NPP) were primarily associated with electrical cabinets. The database used in this research was an electronic version of the publicly available Updated Fire Event Database developed by Electric Power Research Institute, including 2,111 fire events. 540 of these events were labeled as being challenging fires with 74.2% of these challenging fire events being due to eleven selected fire types. Electrical cabinets were found to represent a majority (40.7%) of all the challenging fire events. Although historically conducted electrical cabinet fire experiments sought to explore the influence of parameters on HRR, the parameters were not systematically varied to statistically quantify which parameters were most important/relevant. Research in this study used statistical analysis on a series of simulation results on electrical cabinet fires from the computational fluid dynamics code Fire Dynamic Simulator (FDS). Simulation matrices were developed and evaluated using fractional factorial Design of Experiments (DOE) to screen the importance of different parameters on the electric cabinet HRR. Based on statistical analysis of the results, the combustible material surface area was found to be the most significant parameter followed by cabinet volume, combustible configuration, burning duration, and combustible material heat release rate per unit area. Material ignition temperature was found to not be statistically significant. The last phase of this research assessed the robustness of the electrical cabinet parameters on the predicted HRR with more detailed simulations. Two investigations were undertaken. To identify the nonlinear effects of parameters on the electrical cabinet fire HRR, a Response Surface Methodology (RSM) based Central Composite Design (CCD) was used to create a simulation matrix that would allow statistical analysis of important parameters as well as their effects on the fire heat release rate while keeping the combustible configuration inside the cabinet constant. A series of simulations were conducted to explore the impact of combustible configuration and ignition source location while keeping all other variables consistent. The analysis revealed that all variables had a statistically significant effect on peak HRR. For the average HRR, both the ventilation area into the cabinet and the ignition source HRR were found to be statistically insignificant. For both output variables, the cabinet volume, material heat release rate per unit area, and material surface area were the most significant parameters. Combustible configuration and ignition source location were also found to be statistically significant. / Master of Science / Electrical cabinet fires are a significant concern for industries, commercial electric plants, telecommunication buildings, and nuclear power plant (NPP) facilities. These cabinets typically represent a metallic enclosure of varying sizes. Additionally, several different electronic components of heterogenous composition and configuration are included within this cabinet. As a result, the fires within the cabinet can propagate to several other nearby components, resulting in large fires that are difficult to suppress. Thus, it becomes necessary to understand the fire behavior of electrical cabinets and the factors influencing fire propagation. Knowing the factors influencing the electrical cabinet fires will enable facilities to have better fire resilience and further prevent multiple components and structures from being damaged by these fires. Statistical analysis of historic fire events validated that the most frequently challenging fires in NPP involve electrical cabinets.Therefore, aA detailed study was conducted to investigate what parameters most significantly affect the size of the electrical cabinet fire, which is quantified as the heat release rate (HRR). The parameters in the study included cabinet volume, ventilation area, combustible fuel detail (ignition temperature, heat release rate per unit area (HRRPUA), burning duration), fuel configuration inside the cabinet, and size of the ignition source. To determine which of these factors significantly impacted the electrical cabinet HRR, a computational fluid dynamics code Fire Dynamic Simulator (FDS), was used to predict the fire growth of electrical cabinet fires. After employing a rigorous statistical analysis of the FDS results, the combustible material surface area was found to be the most significant parameter, followed by cabinet volume, combustible configuration, burning duration, and flammable material HRRPUA. The last phase of the research sought to explore the significance of the parameters while developing a nonlinear expression to predict the fire HRR based on cabinet parameters. Given the wide range of electrical cabinet parameters, especially combustible configuration, two studies were conducted where the configuration was fixed or varying with respect to other parameters. For fixed combustible configuration, simulations were conducted with FDS systematically varying the other parameters so their importance could be ranked. Simulations were also performed with all parameters fixed except the combustible configuration and ignition source location. The analysis revealed that all variables had a statistically significant impact on peak HRR. For the average HRR, both the ventilation area into the cabinet and the ignition source HRR were found to be statistically insignificant. For both output variables, the cabinet volume, material heat release rate per unit area, and material surface area were found to be the most significant parameters. Combustible configuration and ignition source location were also found to be statistically significant.