This paper presents the design of a Model Predictive Control (MPC) scheme to optimally manage the thermal and electrical subsystems of a small-size building ("smart house"), with the objective of minimizing the expense for buying energy from the grid, while keeping the room temperature within given time-varying bounds. The system, for which an experimental prototype has been built, includes PV panels, solar collectors, a battery pack, an electrical heater in a thermal storage tank, and two pumps on the solar collector and radiator hydraulic circuits. The presence of binary control inputs together with continuous ones naturally leads to using a hybrid dynamical model, and the MPC controller solves a mixed-integer linear program at each sampling instant, relying on weather forecast data for ambient temperature and solar irradiance. The procedure for controller design is reported with focus on the specific application, and the proposed method is successfully tested on the experimental site.

Flying capacitor based multilevel PWM converter may be an attractive choice due to the natural balancing property of the capacitor voltages. Previous research on natural balancing of a 4-level H-bridge Flying Capacitor Converter (FCC) with Phase Shifted (PS) PWM showed slow natural balancing rate for small modulation indices. Theoretical natural balancing time constant becomes infinitely large for zero output voltage (modulation index). This was probably the reason that many researchers became interested in active balancing methods for this and other capacitive multilevel voltage source inverters. The paper addresses natural voltage balancing improvement possibilities by employing modified PWM strategies different from classical PS PWM. Dramatic improvement in natural voltage balancing rate for small output voltages is achieved due to making a better use of switching states redundancy. Suggested is a family of improved PWM strategies and the best strategy is identified based on natural balancing damping rate considerations. Natural balancing mechanisms are analyzed using time domain averaging methodology that results in simple easy to interpret expressions regarding parameter influence.

No abstract

No abstract

No abstract