This thesis proposes a fault detection and isolation (FDI) method for switched linear systems. The
method builds on a class of FDI filters for linear time-invariant (LTI) systems based on reduced-order
observers. If certain conditions are satisfied, then it is possible to apply these filters to
switched linear systems. When system parameters change slowly, a system is considered to be in
a faulty mode. In the absence of faults, the system dynamics are described by a switched linear
state space model. In a faulty mode, the state space model is modified by adding disturbance
terms associated with parameter changes and component degradations. An FDI filter consists of a
bank of reduced-order observers with residual generators which have certain geometric properties
that allow detecting and isolating faults. In order to distinguish different faults, an FDI filter uses
different residual generators and observers. We demonstrate practical feasibility of our approach
by applying it to a photovoltaic (PV) system with differential power processing (DPP) converters.
Our simulation results confirmed the fact that an FDI filter can detect and pinpoint multiple faults
which can simultaneously affect a system. Finally, we experimentally demonstrate the feasibility
of our approach
