318 research outputs found

    Identification of the Induction Motor Parameters at Standstill Including the Magnetic Saturation Characteristics

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    Identification of the induction motor parameters at standstill is studied in this thesis. The main goal of this work is to search for the feasible and applicable speed sensorless self-commissioning schemes. The magnetic saturation of the magnetizing inductance is taken into account. The magnetizing inductance is modeled as a function of the stator flux. Two different identification schemes are chosen based on the literature review. First method uses the single-axis sinusoidal excitation as the test signal. Second method uses the DC-decay test for the magnetizing inductance estimation and DC-biased sinusoidal excitation for the leakage inductance and rotor resistance identification. The DC-decay test is found to be a suitable method for identification of the magnetizing inductance. The single-axis sinusoidal excitation is found to be problematic in the saturation region. The source of the inaccuracy in the single-axis sinusoidal excitation is studied and the reasons are explained. The sensitivity of the schemes to the stator resistance and stator voltage errors is evaluated. Both methods show a very high sensitivity to the stator voltage errors in the case of the magnetizing inductance estimation. However, the DC-decay test shows a lower estimation error in the presence of the stator resistance errors. The estimation of the leakage inductance is robust against the stator voltage errors when the method based on the single-axis sinusoidal excitation is used. The estimation of the rotor resistance using the DC-biased excitation depends on the DC offset current in the presence of both the stator resistance and stator voltage errors

    Sensorless self-commissioning of synchronous reluctance motors at standstill without rotor locking

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    This paper proposes a standstill method for identification of the magnetic model of synchronous reluctance motors (SyRMs). The saturation and cross-saturation effects are properly taken into account. The motor is fed by an inverter with a short sequence of bipolar voltage pulses that are first applied on the rotor d- and q-axes separately and then simultaneously on both the axes. The stator flux linkages are computed by integrating the induced voltages. Using the current and flux samples, the parameters of an algebraic magnetic model are estimated by means of linear least squares. The proposed method is robust against errors in the stator resistance and inverter voltage, due to the high test voltages (of the order of the rated voltage). The fitted model matches very well with the reference saturation characteristics, measured using a constant-speed method, and enables extrapolation outside the sample range. The method was tested with a 2.2-kW SyRM, whose shaft was uncoupled from any mechanical load, which is the most demanding condition for this method. The proposed method can be used for automatic self-commissioning of sensorless SyRM drives at standstill

    High-frequency issues using rotating voltage injections intended for position self-sensing

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    The rotor position is required in many control schemes in electrical drives. Replacing position sensors by machine self-sensing estimators increases reliability and reduces cost. Solutions based on tracking magnetic anisotropies through the monitoring of the incremental inductance variations are efficient at low-speed and standstill operations. This inductance can be estimated by measuring the response to the injection of high-frequency signals. In general however, the selection of the optimal frequency is not addressed thoroughly. In this paper, we propose discrete-time operations based on a rotating voltage injection at frequencies up to one third of the sampling frequency used by the digital controller. The impact on the rotation-drive, the computational requirement, the robustness and the effect of the resistance on the position estimation are analyzed regarding the signal frequency

    Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method

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    This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs

    Non-intrusive efficiency estimation of inverter-fed induction machines

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    Motorised loads using induction machines use approximately 60% of the electricity globally. Most of these systems use three-phase induction motors due to their robustness and lower cost. They are often installed in continuously operating industrial plants/applications that require no operational interruptions. Whilst most of these induction machines are supplied from ideally sinusoidal supplies, applications are emerging where induction machines are fed from non-sinusoidal supplies. In particular, pulse width modulated inverters realize efficient control of induction machines in many automated industrial applications. From an energy management perspective, it is vital to continually assess the efficiency of induction machines in order to initiate replacement or economic repair. It is therefore of paramount importance that reliable and non-intrusive techniques for efficiency estimation of induction machines be investigated, that consider sinusoidal and non-sinusoidal supplies. This work proposes a non-intrusive efficiency estimation technique for inverter–fed induction motors that is based on harmonic regression analysis, harmonic equivalent circuit parameter estimation and harmonic loss analysis using limited measured data. Firstly, considerations for inverter-fed induction motor equivalent circuit modelling and parameter estimation techniques suitable for non-intrusive efficiency estimation are presented and the selection of one equivalent circuit for analysis is justified. Measured data is obtained from two different induction motors on a flexible 110kW test rig that utilises an HBM Gen 7i data acquisition system. By measuring voltage, current and input power at the supply terminals of the inverter-fed motor, the fundamental equivalent circuit parameters are estimated using population based incremental learning algorithm and compared with those obtained from the IEC 60034-2-1 Standard. The harmonic parameters are estimated using the bacterial foraging algorithm basing on the input impedance of the motor at each harmonic order. A finite harmonic loss analysis is carried out on the tested induction motors. The proposed techniques and harmonic loss analysis provide accurate efficiency estimates of within 1.5% error when compared to the direct method. Lastly, a related non-intrusive efficiency estimation technique is proposed that caters for a holistic loss contribution by all harmonics. The efficiency results from the proposed techniques are compared to those obtained from the IEC-TS 60034-2-3 Technical Specification and a direct method. The estimated efficiencies are comparable to those measured by the Technical Specification and a direct method within 2% error when tested on 37kW and 45kW PWM inverter-fed motors across the loading range. Furthermore, this work conducts a comprehensive non-intrusive rotor speed estimation comparative analysis in order to recommend the best technique(s), in terms of intrusiveness, accuracy and computational overhead. Errors of less than 1% have been reported in literature and experimental verification when using vibration analysis, Motor Current Signature Analysis (MCSA), Rotor Slot Harmonic (RSH) and Rotor Eccentricity Harmonic (REH) analysis techniques in inverter-fed IMs

    A Methodology for Solving the Equations Arising in Nonlinear Parameter Identification Problems: Application to Induction Machines

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    This dissertation presents a method that can be used to identify the parameters of a class of systems whose regressor models are nonlinear in the parameters. The technique is based on classical elimination theory, and it guarantees that the solution for the parameters which minimize a least-squares criterion can be found in a finite number of steps. The proposed methodology begins with an input-output linear overparameterized model whose parameters are rationally related. After making appropriate substitutions that account for the overparameterization, the problem is transformed into a nonlinear least-squares problem that is not overparameterized. The extrema equations are computed, and a nonlinear transformation is carried out to convert them to polynomial equations in the unknown parameters. It is then show how these polynomial equations can be solved using elimination theory using resultants. The optimization problem reduces to a numerical computation of the roots of a polynomial in a single variable. This nonlinear least-squares method is applied to the identification of the parameters of an induction motor. A major difficulty with the induction motor is that the rotor’s state variables are not available measurements so that the system identification model cannot be made linear in the parameters without overparameterizing the model. Previous work in the literature has avoided this issue by making simplifying assumptions such as a “slowly varying speed”. Here, no such simplifying assumptions are made. This method is implemented online to continuously update the parameter values. Experimental results are presented to verify this method. The application of this nonlinear least-squares method can be extended to many research areas such as the parameter identification for Hammerstein models. In principle, as long as the regressor model is such that the system parameters are rationally related, the proposed method is applicable

    Self-commissioning of interior permanent- magnet synchronous motor drives with high-frequency current injection

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    In this paper, a simple and robust method for parameter estimation at rotor standstill is presented for interior permanent magnet (IPM) synchronous machines. The estimated parameters are the stator resistance through dc test, the dq inductances using high-frequency injection, and the permanent magnet flux by means of a closed-loop speed control maintaining rotor stationary. The proposed method does not require either locking the rotor or additional/special power supplies. The validity of the suggested method has been verified by implementation on two IPM motor prototypes. Finally, the estimated parameters have been compared against results obtained through finite-element simulations and with machine magnetic characterization, separately performed, to validate the method's effectiveness. Saturation and cross-saturation effects are taken care of through amplitude modulation and cross-axis current application, respectively
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