The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this dissertation. The DC-DC stage is based on a quad-active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A unified gyrator-based average model is developed for a general multi-active-bridge (MAB) converter controlled through phase-shift modulation (PSM). Expressions to determine the power rating of the MAB ports are also derived. The developed gyrator-based average model is applied to the QAB converter for faster simulations of the proposed SST during the control design process as well for deriving the state-space representation of the plant. Both linear quadratic regulator (LQR) and single-input-single-output (SISO) types of controllers are designed for the DC-DC stage. A novel technique that complements the SISO controller by taking into account the cross-coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models. The experimental results presented herein show that the transient responses for each control strategy match those from the simulations results thus validating them.

After a power system blackout, system restoration is the most important task for the operators. Most power systems rely on an off&ndashline; restoration plan and the experience of operators to select scenarios for the black start path. Using an off&ndashline; designed restoration plan based on past experience may not be the most reliable approach under changing network configurations and loading levels. Hence, an objective restoration path selection procedure, including the option to check constraints, may be more responsive in providing directed guidance to the operators to identify the optimal transmission path to deliver power to other power plants or to pick up load as needed. After the system is subjected to a blackout, parallel restoration is an efficient way to speed up the restoration process. For a large scale power system, this system sectionalizing problem is quite complicated when considering black&ndashstart; constraints, generation/load balance constraints and voltage constraints. This dissertation presents an ordered binary decision diagram (OBDD) &ndashbased; system sectionalizing method, by which the splitting points can be quickly found. The simulation results on the IEEE 39 and 118&ndashbus; system show that the method can successfully split the system into subsystems satisfying black&ndashstart; constraints, generation/load balance constraints and voltage constraints. A power transfer distribution factor (PTDF)&ndashbased; approach will be described in this dissertation to check constraints while restoring the system. Two types of restoration performance indices are utilized considering all possible restoration paths, which are then ranked according to their expected performance characteristics as reflected by the restoration performance index. PTDFs and weighting factors are used to determine the ordered list of restoration paths, which can enable the load to be picked up by lightly loaded lines or relieve stress on heavily loaded lines. A transmission path agent can then be formulated by performing the automatic path selection under different system operating conditions. The proposed restoration strategy is tested on the IEEE&ndash39; bus system and on the Western region of the Entergy system. The testing results reveal that the proposed strategy can be used in real time.