In this paper, a method for induction machine (IM) torque/speed tracking control derived from the 3-D non-holonomic integrator including drift terms is proposed. The proposition builds on a previous result derived in the form of a single loop non-linear state controller providing implicit rotor flux linkage vector tracking. This concept was appropriate only for piecewise constant references and assured minimal norm of the stator current vector during steady-states. The extended proposition introduces a second control loop for the rotor flux linkage vector magnitude that can be either constant, programmed, or optimized to achieve either maximum torque per amp ratio or high dynamic response. It should be emphasized that the same structure of the controller can be used either for torque control or for speed control. Additionally, it turns out that the proposed controller can be easily adapted to meet different objectives posed on the drive system. The introduced control concept assures stability of the closed loop system and significantly improves tracking performance for bounded but arbitrary torque/speed references. Moreover, the singularity problem near zero rotor flux linkage vector length is easily avoided. The presented analyses include nonlinear effects due to magnetic saturation. The overall IM control scheme includes cascaded high-gain current controllers based on measured electrical and mechanical quantities together with a rotor flux linkage vector estimator. Simulation and experimental results illustrate the main characteristics of the proposed control.
COBISS.SI-ID: 22899222
The availability of high-resolution LiDAR (Light Detection And Ranging) geospatial data has increased immensely, providing new opportunities to solve challenges in the field of spatial energy planning. This paper presents a new method for large-scale placement of photovoltaic arrays over buildings’ rooftops in an optimal manner by using the global optimisation approach. The position, aspect and slope are the ey geometrical parameters being optimised for each photovoltaic array. The predicted energy generation (i.e. photovoltaic potential) is simulated by using state-of-the-art hourly shadowing estimation from the surroundings, anisotropic diffuse, reflected, and direct irradiances that are based on a Typical Meteorological Year, and non-linear efficiency characteristics of a considered photovoltaic system configuration. The optimisation performs multiple simulation scenarios throughout an entire year for each photovoltaic array, in order to maximise its photovoltaic potential. The method was tested over three LiDAR datasets with different landscape topographies and urban densities. In comparison to the methods for photovoltaic arrays’ fixed optimal slope estimation, the proposed method is substantially more suitable for application in urban environments.
COBISS.SI-ID: 23002646
This paper deals with rotary and linear synchronous reluctance machines and synchronous permanent magnet machines. It proposes a general method appropriate for determining the two-axis dynamic models of these machines, where the effects of slotting, mutual interaction between the slots and permanent magnets, saturation, cross-saturation, and—in the case of linear machines—the end effects, are considered. The iron core is considered to be conservative, without any losses. The proposed method contains two steps. In the first step, the dynamic model state variables are selected. They are required to determine the model structure in an arbitrarily chosen reference frame. In the second step, the model parameters, described as state variable dependent functions, are determined. In this way, the magnetically nonlinear behavior of the machine is accounted for. The relations among the Fourier coefficients of flux linkages and electromagnetic torque/thrust are presented for the models written in dq reference frame. The paper presents some of the experimental methods appropriate for determining parameters of the discussed dynamic models, which is supported by experimental results
COBISS.SI-ID: 22575126