During this period, an electricity generation system will be investigated, which includes a wind turbine (WT) with a self-excited induction generator (SEIG) and a switched reluctance generator, a photovoltaic (PV) system, batteries, and supercapacitors as power sources. The proposed system will be investigated both in the standalone and grid-tied mode. Research can be divided into the following guidelines:

1. Research in the field of induction machine control and switched reluctance generator. The application of the model predictive control in an induction machine’s indirect field-oriented control system will be investigated. The part of the control system based on the model predictive control will consider machine magnetic saturation, machine iron losses, and additional losses. The individual justification of considering the mentioned effects and their influence on the machine efficiency, the average switching frequency of the converter, and the total harmonic distortion factor of the phase currents will be analyzed. A voltage control system of a switching reluctance generator will also be investigated, taking into account mutual inductances of adjacent windings, iron losses and remanent magnetic flux.
2. Research in the field of PV systems. The research will be conducted on a quasi-Z-source inverter powered by a PV system. The focus of the research will be on finding the maximum power point of the PV system at different temperatures and irradiances, as well as under partial shading conditions.
3. Research in the field of battery system modeling. An algorithm will be developed to track the state of charge (SOC) of the batteries during system operation. In addition, an algorithm for the estimation of the battery system state of health (SOH) will be developed.
4. Power flow management in the analyzed electricity generation system. Strategies for managing the power flow of the entire system will be developed taking into account the available power from the PV and WT systems, and also the SOC and SOH of the battery system. A new optimization algorithm with two distinct fuzzy-logic optimizations will be developed: one for maximizing the wind energy and the other for minimizing the SCIG losses. The measurement of wind speed, WT torque, SEIG electrical power, and PV current will be avoided by utilizing the same input signal (i.e., qZSI’s input power) for all the optimization algorithms, thus increasing the system’s reliability and reducing the cost of implementation.
5. Research in the field of the quasi-Z-source inverter and Split-Source inverter. A laboratory setup of the quasi-Z-source bidirectional inverter will be designed, and a laboratory setup of the Split-Source inverter will be designed.