Field-oriented control (FOC) is standardly used in high-performance induction machine (IM) applications. However, the performance of FOC algorithms strongly depends on the level of agreement between the actual IM and the respective model used for the algorithm design. IM parameters' values are known to vary with the operating conditions, so their online identification is preferable. In addition, it is often preferred to acquire the rotor speed by estimation because speed sensors increase the cost and size of the system while reducing its reliability. Model-reference-adaptive-system (MRAS) estimators are popular due to their simple implementation, but they are sensitive to IM parameters and, hence, usually include a parameter tuning mechanism. This paper proposes an indirect rotor-field-oriented controller with the parallel MRAS speed estimation and IM parameter tuning - implemented in an induction generator system. Both the control algorithm and the MRAS include the stray load and iron losses, magnetic saturation, and rotor resistance variation, whereas the MRAS additionally accounts for variations of the stator and stray-load resistances. The system performance is experimentally evaluated over wide ranges of the IM flux, speed, torque, and temperature.

This paper presents two novel algorithms for the calculation of semiconductor losses of a three-phase quasi-Z-source inverter (qZSI). The conduction and switching losses are calculated based on the output current-voltage characteristics and switching characteristics, respectively, which are provided by the semiconductor device manufacturer. The considered inverter has been operated in a stand-alone operation mode, whereby the sinusoidal pulse width modulation (SPWM) with injected 3rd harmonic has been implemented. The proposed algorithms calculate the losses of the insulated gate bipolar transistors (IGBTs) and the free-wheeling diodes in the inverter bridge, as well as the losses of the impedance network diode. The first considered algorithm requires the mean value of the inverter input voltage, the mean value of the impedance network inductor current, the peak value of the phase current, the modulation index, the duty cycle, and the phase angle between the fundamental output phase current and voltage. Its implementation is feasible only for the Z-source-related topologies with the SPWM. The second considered algorithm requires the instantaneous values of the inverter input voltage, the impedance network diode current, the impedance network inductor current, the phase current, and the duty cycle. However, it does not impose any limitations regarding the inverter topology or switching modulation strategy. The semiconductor losses calculated by the proposed algorithms were compared with the experimentally determined losses. Based on the comparison, the correction factor for the IGBT switching energies was determined so the errors of both the algorithms were reduced to less than 12%.

This paper presents a hybrid renewable energy system (RES) including wind and photovoltaic (PV) power sources. The wind energy subsystem (WES) consists of a squirrel-cage induction generator (SCIG) driven by a variable-speed wind turbine (WT) and corresponding power electronic converter, by means of which a speed-sensorless indirect-rotor-field-oriented control of the SCIG is implemented. The outputs of both the WES and PV power source rated 1.5 kW and 3.5 kW, respectively, are connected to the DC bus, with the quasi-Z-source inverter (qZSI) acting as an interlinking converter between the DC bus and the AC grid/load. An advanced pulse-width-modulation scheme is applied to reduce the qZSI switching losses. The considered RES can operate both in grid-tie and island operation, whereas the battery storage system—integrated within the qZSI impedance network—enables more efficient energy management. The proposed control scheme includes successively executed algorithms for the optimization of the WES and PV power outputs under varying atmospheric conditions. A perturb-and-observe PV optimization algorithm is executed first due to the significantly faster dynamics and higher-rated power of the PV source compared to the WES. The WES optimization algorithm includes two distinct fuzzy logic optimizations: one for extraction of the maximum wind power and the other for minimization of the SCIG losses. To reduce the number of the required sensors, all three MPPT algorithms utilize the same input variable—the qZSI’s input power—thus increasing the system’s reliability and reducing the cost of implementation. The performance of the proposed hybrid RES was experimentally evaluated over wide ranges of simulated atmospheric conditions in both the island and grid-tie operation.

Standard vector controllers for induction machines (IMs) are based on the conventional IM model. Consequently, they are unaware of the existence of stray load and iron losses. However, these losses may induce a considerable mismatch between the actual and reference flux vectors in vector controllers. This mismatch is greatly affected by the operating flux and load. In this paper, two different approaches are considered for compensation of this mismatch: the first involves investment in IMs with low stray load and iron losses, whereas the other involves development of a control algorithm in which both these losses are compensated. The performance of the developed vector controller is compared to the standard controller and tested on three small IMs of different efficiency class and rotor cage material (aluminum vs. copper). Simulation and experimental testing is carried over wide ranges of loads, speeds, and magnetization levels. The estimated rotor speed is used as a measure of the controller detuning. A standard estimator which utilizes rotor flux-linkage estimates within the model reference adaptive system is here for the first time modified to include stray load and iron losses. Finally, problems regarding numerical integration, sampling frequency, and temperature-induced variations of IM parameters are addressed.

This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.

The paper considers the model predictive current control (MPCC) of an induction motor (IM) drive and evaluates five IM models of different complexities—from conventional to magnetic saturation, iron losses, and stray-load losses—for the MPCC design. The validity of each considered IM model and the corresponding MPCC algorithm is evaluated by comparison of the following performance metrics: the total harmonic distortion of the stator current, the average switching frequency, the rotor flux magnitude error, the rotor flux angle error, and the product of the first two metrics. The metrics’ values are determined in wide ranges of the rotor speed (0.1–1 p.u.) and load torque (0–1 p.u.) through simulations performed in the MATLAB Simulink environment. The obtained results allow us to identify the IM model that offers the best tradeoff between the practicability and accuracy. Furthermore, a control effort penalization (CEP) is suggested to reduce the average switching frequency and, hence, the power converter losses. This involves constraining the simultaneous switching to a maximum of two branches of the three-phase power converter, as well as inclusion of the weighted switching penalization term in the cost function. Finally, the performance—both steady-state and dynamic—of the proposed MPCC system with CEP is compared with that of the analogous field-oriented controlled (FOC) IM drive. The inverter switching frequency is reduced more than twice by including the frequency-dependent iron-loss resistance in the MPCC. It is additionally reduced by implementing the proposed CEP strategy without sacrificing many other performance metrics, thus achieving a performance comparable to the FOC IM drive.