In this paper, an engineering approach to estimation of the fundamental stray load and iron losses in small induction machines (IMs) is proposed. This approach consists of experimental testing carried on several IMs of different efficiency class and rotor cage material, all rated 1.5 kW, followed by the derivation of simple empirical formulas for estimation of these two types of losses. The iron losses are evaluated as variable with respect to the operating frequency and flux, whereas the stray load losses are evaluated as variable with respect to the operating frequency, flux, and torque. Application of the proposed formulas does not require any additional tests other than those imposed by international standards, and, equally important, they do not include any constructional details of the IM. The validity of the formulas is verified by comparison of the estimated and measured losses in wide ranges of the operating frequency, flux, and torque. The proposed methods of estimation offer possibilities for application in the analysis of small IMs under various operating conditions with sinusoidal supply, as well as in developing advanced IM models and vector control algorithms.

This paper presents a search-based approach to efficiency optimization of a vector controlled self-excited induction generator (SEIG). The main parts of the control system, besides the SEIG, are a prime mover, a three-phase power converter, and a dc load. The optimization algorithm is executed online and it relies on fuzzy logic (FL) to minimize the internal losses of the SEIG. This is done by adjusting the rotor flux reference. As opposed to existing strategies, additional effort is made to ensure stable operation of the SEIG while the optimization is put on hold and also to avoid the optimization-induced detuning. The iron losses of the SEIG are accounted for in the proposed vector control algorithm as dependent on both the operating flux and frequency. The simulation model of the control system is developed in the MATLAB/Simulink for the purpose of FL controller design, followed by experimental validation. The control algorithm is experimentally implemented using dSpace DS1104 board.

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.