Estimation Of Machine Parameters Research Articles

Estimation of Synchronous Machine Parameters by Stand Still Frequency Responses Testing

This paper presents a general method based on the standstill frequency response (SSFR) tests cited in IEEE Std 115-A. The used methodology consists of determining the frequency response of the measured values of the direct axis and quadrature operational inductances. Subsequently, we identify the different models of transfer functions, which are functions of time constants, using curve-fitting techniques. Then considering the time constants as inputs and the parameters of the equivalent circuits of the synchronous machine as outputs. We can identify all the parameters of different topologies of the models, using the numerical Newton Raphson method. The approach used is inexpensive, accurate, and reliable.

Data provenance - from experimental data to trustworthy simulation models and standards

FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general. When implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods and tools is outlined.We begin with a motivation on potential use cases for the application of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.In a second part, different options for structuring the data (including metadata schema) are discussed. The first one is the openBIS system, which is an open-source lab notebook and PostgreSQL based data management system. A second option is a semantic representation using RDF based on ontologies for the domain of interest.In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. A metadata schema for a rheological measurement setup is derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behavior, the automatic workflow to use the stored data for model parameter estimation is demonstrated.

A new inductance estimator based on Lagrange Form polynomials for real-time switched reluctance machine control

This paper presents a new methodology for modeling the inductance curves of switched-reluctance machines using cubic splines based on Lagrange form polynomials. The proposed methodology allows for the estimation of the instantaneous inductance and derivative of the inductance in each phase of the machine. All steps of the proposed methodology are presented and discussed, detailing the information necessary for its use. The results of an experimental and computational simulation, both as a motor and a generator, are presented and discussed, and the estimated curves are compared with the design curves of each result. The inductance estimator was embedded in a low-cost DSP, and the machine was driven using an experimental bench. The experimental results are presented and compared with the simulation results, allowing for the evaluation of the accuracy level obtained by the proposed estimator. The estimator combines precision with low computational cost, which makes this method a strong candidate for systems that require estimation of machine parameters in real time, such as torque and flux, permitting the implementation of new control techniques.

Novel Machine Parameter Estimation Scheme Toward Accurate Maximum Torque Production for Dual Three-Phase PMSMs

This article proposes a hybrid signal injection-based maximum torque per ampere (MTPA) control for dual three-phase permanent magnet synchronous machines (PMSMs). The proposed method consists of a real dc current injection in the harmonic subspace and a virtual ac current injection in the fundamental subspace. The real dc current injection in the harmonic subspace does not impact the average torque production, and induced losses are small enough to be neglected. In the meantime, to guarantee the accuracy of the proposed MTPA method, a novel machine parameter estimation scheme considering voltage source inverter nonlinearity effects is proposed in this article, which can track the variation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{1}$ </tex-math></inline-formula> -axis inductance and resistance accurately. Therefore, the proposed method only requires command voltages and currents in the torque model, and the variations of machine parameters are compensated by the proposed parameter estimation scheme. As the command voltages have correspondence with the variations of machine parameters, the magnetic saturation and temperature effects have been taken into account. The proposed method is validated by simulations and experimental results under various torque, speed, and temperature conditions. Additionally, the accuracy of the proposed method is validated, and its performance is proven to be superior through comparison of the derived results with those obtained from various existing methods.

Physical and mathematical modeling of transients in a synchronous generator utilizing synchronized phasor measurements

There have been quite a few attempts to compute synchronous generator parameters based on voltage and current synchrophasors taken under power system transients. However, we have not seen any publications with thorough analysis as to how soon the phasor measurement unit reacts to disturbance conditions, which components of the transient are filtered out and which are passed through, as well as what the total vector error is. The goal of this research is to determine all of these characteristics of a phasor measurement unit when playing back transient oscillograms for a stator short circuit obtained through mathematical modeling. The transient oscillograms have been derived via both a full Park-Gorev system of flux linkage equations as well as the MATLAB/Simulink Synchronous Machine block. Physical modeling was then conducted via a real-time digital simulator (RTDS) along with a dedicated phasor measurement unit ENIP-2 (PMU), and the stator current phasors were recorded. Our analysis has shown that both RTDS and ENIP-2 (PMU) almost entirely filter out the exponentially decaying DC component of the fault current while closely following the periodical signal envelope. The total vector error has been estimated to become below 1–2 % after around 0,02–0,03 s into the fault when selecting the “P” class filters according to IEEE C37.118. We have come to a conclusion that synchrophasor measurements under power system disturbances could be utilized for estimating the synchronous, transient, and subtransient generator parameters. The selected synchronous machine model in the form of flux linkage equations is correct, as the obtained transient oscillograms are exactly the same as those produced by Simulink. “P” class phasor measurements can be recommended for representing transients in the stator circuit of a synchronous generator. The results of this investigation are meant to be employed for synchronous machine parameter estimation based on phasors sourced from RTDS and, hopefully, from phasor measurement units installed at power plants.

SENSORLESS VECTOR CONTROLLED THREE-PHASE PWM INVERTER-FED INDUCTION MOTOR DRIVE SYSTEM WITH AUTO-TUNING ESTIMATION OF MACHINE PARAMETER APPROACH

This paper deal with a practical development technology on a highly accurate faster response adjustable speed drive implementation for the three-phase sinewave fed general purpose induction motor system which is based upon sensorless slip frequency type vector control scheme with an automatic auto-tuning machine parameter estimation strategy. The essential procedure and considerations to measure and estimate the exact stator and cage rotor circuit parameters of the induction motor treated here are discussed under its operating conditions. The speed regulation characteristics of the induction motor is illustrated and evaluated for the induction machine parameter variations under the actual operating conditions ranging from a low frequency to a high frequency for various specified load torque setting. The variable speed induction motor drive system employing sensorless slip frequency-based vector control scheme which incorporates the current controlled three-phase high frequency carrier PWM switching inverter with automatic auto-tuning estimation strategy on the temperature-dependent and -independent machine circuit parameters is practically implemented using DSP-based vector controller. The dynamic speed response performances under essentially changed load torque disturbances as well as steady state speed against torque characteristics of the proposed variable speed drive control and implementations are illustrated and discussed from an experimental point of view.

Adaptive Full-Order Displacement Observer for Sensorless Resonant Frequency Tracking Control of Linear Oscillatory Machines

Linear oscillatory machines are becoming more widely used in refrigeration compressors due to advantages of compact volume, little maintenance, and so on. In order to further increase the efficiency and reduce the cost of the whole system, both resonant frequency tracking control and sensorless control strategies should be implemented simultaneously. In this article, an adaptive full-order observer with simple structure and less computation is proposed, which can not only provide accurate displacement estimation signals for the system piston stroke control, but also achieve resonant frequency tracking control through the estimation of machine parameters. The system stability analysis proves the quick convergence of the proposed observer, and comprehensive simulation and experimental results demonstrate the effectiveness of the proposed algorithm.

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature.

Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation

This paper presents a method for selecting the sampling time for induction machine parameter estimation from the machine line start measurements. the metaheuristic optimization method is used to find the optimal Prony exponential series approxiamtion of the line start transient current. From the optimal approximation, poles of the linearized induction machine model are computed and used to determine the optimal sampling time. the results show that sampling frequencies needed for parameter estimation are much lower than 1–15 kHz commonly used today. This reduces the necessary amount of collected data and the computing power needed for the estimation. the optimal sampling time is computed for the simulated and for the measured data. Referenced parameter estimation technique is tested for the measured transient showing benefits of using the optimal sampling time.

Estimation Procedure Based on Less Filtering and Robust Tracking for a Self-Sensing Control of IPMSM

For the self-sensing control of interior permanent magnet synchronous motors (IPMSM), the rotor position and speed estimation based on high-frequency (HF) injection techniques generally employs two parts after the signal injection: signal processing/heterodyning process and tracking. This article proposes significant improvements for these two parts. First, the low-pass filters used in the classical heterodyning process of HF injection techniques are removed, which leads to the proposal of a novel heterodyning process in order to improve the estimation procedure in terms of delays, cost/complexity of implementation, and harmonics. Second, an independent machine parameters estimation based on step-by-step sliding mode observer using only the sign of the rotor position estimation error is developed. Thanks to this observer, the tuning of position, speed, and acceleration observer gains is achieved in a decoupled manner, which ensures states convergence separately in finite time. Third, the machine acceleration is estimated to improve the estimation procedure in transient modes. These contributions enhance significantly the estimation procedure in all operating ranges. A comparative study of the robustness/performance of the proposed strategy and classical tracking algorithms is carried out. Both simulations and experimental tests on an IPMSM rig are conducted in the framework of electric propulsion benchmark used in automotive applications, showing good agreement with the expected results.

Induction Machine Parameter Range Constraints in Genetic Algorithm Based Efficiency Estimation Techniques

Estimation of induction machine parameters, which are commonly used in efficiency evaluation and control methods, can be effectively achieved by utilizing genetic algorithms (GAs). One of the difficulties of using GAs for efficiency estimation is the determination of variable (parameter) constraints (ranges). This paper focuses on the range determination of the parameters (variables) for GA applications. A wide range of variables can cause unstable outcomes. Hence, it is essential to determine an acceptable range for each variable prior to a GA run to produce stable and repeatable results. In this paper, relationships based on the nameplate information and a large database of tested induction motors provided by Hydro-Quebec are proposed to determine reasonable induction motor parameter ranges. The proposed method is applied to three different cases. The results of the three cases are compared to each other and also against the corresponding experimental results, which validate the effectiveness and accuracy of the proposed method.

Torque ripple minimization in non-sinusoidal synchronous reluctance motors based on artificial neural networks

This paper proposes a new method based on artificial neural networks for reducing the torque ripple in a non-sinusoidal synchronous reluctance motor. The Lagrange optimization method is used to solve the problem of calculating optimal currents in the d–q frame. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents giving a constant electromagnetic torque and minimizing the ohmic losses. Thanks to the online learning capacity of neural networks, the optimal currents can be obtained online in real time. With this neural control, each machine's parameter estimation errors and current controller errors can be compensated. Simulation and experimental results are presented which confirm the validity of the proposed method.

High-Frequency Injection-Based Stator Flux Linkage and Torque Estimation for DB-DTFC Implementation on IPMSMs Considering Cross-Saturation Effects

Deadbeat-direct torque and flux control (DB-DTFC) is a direct torque control method that provides the fastest possible torque control, low ripple, easily integrated dynamic loss minimization control, and one control law that uses the inherent volt-second source properties of power electronics to enable operation over all operating conditions, including voltage limits. DB-DTFC is a model-based discrete time controller that relies on precise machine parameter, flux, and torque estimation to provide fast dynamic torque control. In this paper, a new method for high-frequency injection (HFI)-based self- and mutual incremental inductance estimation is proposed and evaluated in both simulations and experiments. In this new method, the estimated incremental inductances including cross-coupling inductance due to cross saturation are curve fitted offline and then used to calculate the apparent inductances. With the apparent inductances, the stator resistance and permanent-magnet flux linkage are estimated using recursive least squares method online. The stator flux linkage observer using the identified machine parameters yields improved stator flux linkage estimates that enable precise torque calculation. Experimental evaluation of the HFI-based flux estimates on torque estimation and control accuracy is performed at an interior permanent-magnet synchronous machine drive test bench with a torque transducer mounted.

Optimum DC Bus Voltage Analysis and Calculation Method for Inverters/Motors With Variable DC Bus Voltage

In this paper, an online method to calculate the optimum dc bus voltage for motors and inverters with variable dc bus voltages is proposed. The required dc bus voltage in the low-speed region is much lower than that for the rated-speed region. As a result, the machine drive system efficiency can be improved with lower dc bus voltage since the switching losses in the inverter are reduced. To achieve both desired machine output torque and minimized losses simultaneously, an optimum dc bus voltage is required. However, machine parameter estimation errors and spatial harmonics in the machine result in calculation errors for the required dc bus voltage. A voltage disturbance state filter (VDSF), which is formed by a Luenburger-style closed-loop stator current vector observer with voltage command feedforward, is used to mitigate the calculation errors. Voltage errors caused by the parameter deviations and machine spatial harmonics will inherently be estimated by the observer's state feedback controller. Therefore, the calculation voltage error can be corrected by the VDSF's disturbance voltage so that the optimum dc bus voltage can be obtained.

Hardware based Analysis and Process Control for Laser Brazing Applications

Abstract Laser brazing is widely used for joining metal sheets in industrial applications, in particular in the automotive sector, where the requirements on surface quality are extremely high. Therefore, quality control and process observation cannot be omitted. This paper presents the current works of a camera based process control system. Hardware-based algorithms for estimation of machine parameters during the process are implemented on FPGA technology. In particular the process velocity is measured in real time which makes the system suitable for controlling tasks to react instantaneously on changes of the velocity.First experimental results on a controlled laser brazing process are presented. Additionally an evaluation of the accuracy of the hardware-based velocity measurement is given.

Genetic Algorithm with Dynamic Selection Based on Quadratic Ranking Applied to Induction Machine Parameters Estimation

This article presents an efficient off-line identification method for induction machines by a real-coded genetic algorithm. Using only the starting current and the corresponding phase voltage, the electrical and mechanical parameters are estimated simultaneously. This is achieved by minimization of the quadratic output error between the current acquired experimentally from the induction machine and the computed one from the adopted model at the same instance. To improve real-coded genetic algorithm performance and avoid a risk of premature convergence, a dynamic selection based on quadratic ranking is proposed for varying the selection pressure across the generation evolution. A comparison of the different real-coded genetic algorithms—the proposed, linear ranking, Roulette wheel and Boltzmann real-coded genetic algorithms—is carried out on two motors' (1.5 and 0.4 kW) parameter estimation. The transient and steady-state computed current using the estimated parameters are best matched to the measured current, proving that the estimated parameters are well suited for these machines. The results obtained show the superiority of the proposed real-coded genetic algorithm versus the other algorithms in terms of computing time and speed convergence.

Influence of Nonideal Voltage Measurement on Parameter Estimation in Permanent-Magnet Synchronous Machines

This paper investigates the influence of nonideal voltage measurements on the parameter estimation of permanent-magnet synchronous machines (PMSMs). The influence of nonideal voltage measurements, such as the dc bus voltage drop, zero shift in the amplifier, and voltage source inverter nonlinearities, on the estimation of different machine parameters is investigated by theoretical and experimental analysis. For analysis, a model-reference-adaptive-system-based estimator is first described for the parameter estimation of the q-axis inductance, stator winding resistance, and rotor flux linkage. The estimator is then applied to a prototype surface-mounted PMSM to investigate the influence of nonideal voltage measurement on the estimation of various machine parameter values. It shows that, at low speed, the inverter nonlinearity compensation has significant influence on both the rotor flux linkage and winding resistance estimations while, at high speed, it has significant influence only on the winding resistance estimation and has negligible influence on the rotor flux linkage estimation. In addition, the inverter nonlinearity compensation will not influence the q-axis inductance estimation when it is under id = 0 control. However, the dc bus voltage drop due to the load variation and zero shift in the amplifier will significantly influence the q-axis inductance estimation.

Inrush current simulation of two‐winding power transformer using machine parameters estimated by winding structure design procedure

AbstractThis paper presents a technique for estimation of machine parameters of a two‐winding power transformer using a winding structure design procedure. In particular, it is very difficult to obtain machine parameters for transformers in customers' facilities. Using the estimation technique, the machine parameters can be calculated from only the nameplate data of these transformers. Subsequently, EMTP‐ATP simulation of the inrush current was carried out using the machine parameters estimated by the techniques developed in this study, and the simulation results were found to reproduce the measured waveforms. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 177(2): 1–9, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.20994

A cluster analysis applied to volumetric errors of five-axis machine tools obtained by probing an uncalibrated artefact

This paper presents a cluster approach to the analysis of volumetric error for five-axis machine tools. The probed artefact is uncalibrated so the analysed volumetric errors exclude isotropic scale errors. First, a simultaneous model based estimation of machine and artefact geometric parameters enhances the estimation of the artefact. Then, a point-to-point least squares adjustment is performed between the measured and the predicted ball centre clusters. Information about the volumetric impact of both the linear and rotary axes as separate groups is obtained by analysing the position measurement error of the artefact as a whole and of each ball individually. Simulation is conducted for the validation of the method supported by experiments.

Estimation of parameters of interior permanent magnet synchronous motors

This paper presents a magnetic circuit model to the estimation of machine parameters of an interior permanent magnet synchronous machine. It extends the earlier work of Hwang and Cho that focused mainly on the magnetic aspects of motor design. The proposed model used to calculate EMF, d- and q-axis reactances. These calculations are compared to those from finite element analysis and measurement with good agreement.