Position-sensorless control of permanent-magnet-assisted synchronous reluctance motor

The sensorless control of permanent-magnet-assisted synchronous reluctance (PMASR) motors is investigated, in order to conjugate the advantages of the sensorless control with full exploitation of the allowed operating area, for a given inverter. An additional pulsating flux is injected in the d-axis direction at low and zero speed, while it is dropped out, at large speed, to save voltage and additional loss. A flux-observer-based control scheme is used, which includes an accurate knowledge of the motor magnetic behavior. This leads, in general, to good robustness against load variations, by counteracting the magnetic cross saturation effect. Moreover, it allows an easy and effective correspondence between the wanted torque and flux and the set values of the chosen control variables, that is d-axis flux and q-axis current. Experimental verification of the proposed method is given, both steady-state and dynamic performance are outlined. A prototype PMASR motor will be used to this aim, as part of a purposely assembled prototype drive, for light traction application (electric scooter

Observability analysis of sensorless synchronous machine drives

This paper studies the local observability of synchronous machines using a unified approach. Recently, motion sensorless control of electrical drives has gained high interest. The main challenge for such a technology is the poor performance in some operation conditions. One interesting theory that helps understanding the origin of this problem is the observability analysis of nonlinear systems. In this paper, the observability of the wound-rotor synchronous machine is studied. The results are extended to other synchronous machines, adopting a unified analysis. Furthermore, a high-frequency injection-based technique is proposed to enhance the sensorless operation of the wound-rotor synchronous machine at standstill

Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions

An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work

Local weak observability conditions of sensorless AC drives

Alternating current (AC) electrical drive control without mechanical sensors is an active research topic. This paper studies the observability of both induction machine and synchronous machine sensorless drives. Observer-based sensorless techniques are known for their deteriorated performance in some operating conditions. An observability analysis of the machines helps understanding (and improving) the observer's behavior in the aforementioned conditions.Comment: arXiv admin note: text overlap with arXiv:1512.0366

Flux observer algorithms for direct torque control of brushless doubly-fed reluctance machines

Direct Torque Control (DTC) has been extensively researched and applied to most AC machines during the last two decades. Its first application to the Brushless Doubly-Fed Reluctance Machine (BDFRM), a promising cost-effective candidate for drive and generator systems with limited variable speed ranges (such as large pumps or wind turbines), has only been reported a few years ago. However, the original DTC scheme has experienced flux estimation problems and compromised performance under the maximum torque per inverter ampere (MTPIA) conditions. This deficiency at low current and torque levels may be overcome and much higher accuracy achieved by alternative estimation approaches discussed in this paper using Kalman Filter (KF) and/or Sliding Mode Observer (SMO). Computer simulations accounting for real-time constraints (e.g. measurement noise, transducer DC offset etc.) have produced realistic results similar to those one would expect from an experimental setup

Magnetic Modelling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems