scholarly journals An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8138
Author(s):  
Faa-Jeng Lin ◽  
Syuan-Yi Chen ◽  
Wei-Ting Lin ◽  
Chih-Wei Liu
Keyword(s):  
Parameter Estimation ◽  
Learning Algorithm ◽  
Current Injection ◽  
Operating Conditions ◽  
Least Square ◽  
Voltage Source ◽  
Current Loop ◽  
Constant Torque ◽  
Neural Network Controller ◽  
Rotor Flux

An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives in the constant torque region. First, a d-axis current injection-based parameter estimation methodology considering the nonlinearity of a CCVSI is proposed. Then, during current injection, a simple linear model is developed to model the cross- and self-saturation of the dq-axis inductances. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, to improve the bandwidth of the current loop, an intelligent proportional-integral-derivative (PID) neural network controller with improved online learning algorithm is adopted to replace the traditional PI controller. The estimated the dq-axis inductances and the rotor flux are adopted in the decoupled control of the current loops. Finally, the experimental results at various operating conditions of the IPMSM in the constant torque region are given.

Machine learning-based secure data analyzing for FPGA-based solar PV FED speed control of brushless DC motor using versatile threshold optimization technique

2019 ◽  
Vol 18 (01) ◽  
pp. 1941022
Author(s):  
G. Saravanan ◽  
C. Karthikeyan
Keyword(s):  
Control Method ◽  
Optimization Technique ◽  
Speed Control ◽  
Current Control ◽  
Operating Conditions ◽  
Least Square ◽  
Voltage Source ◽  
Solar Pv ◽  
Automotive Applications ◽  
Threshold Optimization

The predicted global energy emergency expected shortly is due to the fast exhaustion of conventional fossil non-renewable energy source assets and reliably decreasing expenses of solar photovoltaic (SPV) modules. This photovoltaic (PV) power conversion stage prompts an expanded cost, size, unpredictability and diminished effectiveness for automotive applications. As an advanced system, the solar PV fed brushless direct current motor (BLDCM) is being used for most of the automotive applications. A primary control method fit for working the solar PV array at its peak power utilizes a conventional voltage source inverter (VSI) to control proposed fractional order BLDCM chaos control utilizing versatile threshold optimization (VTO) algorithm. The SPV is used as primary sources while the battery as reinforcement. The proposed control takes out the BLDCM phase using current control techniques. No extra power is related to the speed control of BLDCM and its fine start. The suitability of proposed framework is shown through its execution assessment utilizing MATLAB2017a programming in light of the simulated results and experimental approval on a created model, under practical operating conditions. Execution of the proposed FPGA-based control drive of BLDC motor is likewise researched through experimental test setup utilizing SPARTAN-6 FPGA, the run-time data are analyzed through IOT network using Adaptive Least Square Regression. The proposed system simulation result is justified by the hardware result.

Analysing integration issues of the microgrid system with utility grid network

2021 ◽  
Vol 22 (1) ◽  
pp. 113-127
Author(s):  
Mulualem Tesfaye ◽  
Baseem Khan ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Neeraj Gupta ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Control Mode ◽  
Voltage Source ◽  
Modulation Scheme ◽  
Main Challenge ◽  
Utility Grid

Abstract Generation of renewable energy sources and their interfacing to the main system has turn out to be most fascinating challenge. Renewable energy generation requires stable and reliable incorporation of energy to the low or medium voltage networks. This paper presents the microgrid modeling as an alternative and feasible power supply for Institute of Technology, Hawassa University, Ethiopia. This microgrid consists of a 60 kW photo voltaic (PV) and a 20 kW wind turbine (WT) system; that is linked to the electrical distribution system of the campus by a 3-phase pulse width modulation scheme based voltage source inverters (VSI) and supplying power to the university buildings. The main challenge in this work is related to the interconnection of microgrid with utility grid, using 3-phase VSI controller. The PV and WT of the microgrid are controlled in active and reactive power (PQ) control mode during grid connected operation and in voltage/frequency (V/F) control mode, when the microgrid is switched to the stand-alone operation. To demonstrate the feasibility of proposed microgrid model, MATLAB/Simulink software has been employed. The performance of fully functioning microgrid is analyzed and simulated for a number of operating conditions. Simulation results supported the usefulness of developed microgrid in both mode of operation.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

Identification of Bearing Coefficients of Flexible Rotor-Bearing Systems

2000 ◽  
Author(s):  
Tachung Yang ◽  
Wei-Ching Chaung
Keyword(s):  
Industrial Applications ◽  
Operating Conditions ◽  
Least Square ◽  
Flexible Rotor ◽  
Fem Modeling ◽  
Identification Method ◽  
Reaction Forces ◽  
Manufacturing Assembly ◽  
Stiffness And Damping

The accuracy of stiffness and damping coefficients of bearings is critical for the rotordynamic analysis of rotating machinery. However, the influence of bearings depends on the design, manufacturing, assembly, and operating conditions of the bearings. Uncertainties occur quite often in manufacturing and assembly, which causes the inaccuracy of bearing predictions. An accurate and reliable in-situ identification method for the bearing coefficients is valuable to both analyses and industrial applications. The identification method developed in this research used the receptance matrices of flexible shafts from FEM modeling and the unbalance forces of trial masses to derive the displacements and reaction forces at bearing locations. Eight bearing coefficients are identified through a Total Least Square (TLS) procedure, which can handle noise effectively. A special feature of this method is that it can identify bearing coefficients at a specific operating speed, which make it suitable for the measurement of speed-dependent bearings, like hydrodynamic bearings. Numerical validation of this method is presented. The configurations of unbalance mass arrangements are discussed.

scholarly journals Third harmonic current injection into highly saturated multi-phase machines

2017 ◽  
Vol 66 (1) ◽  
pp. 179-187
Author(s):  
Felix Klute ◽  
Torben Jonsky
Keyword(s):  
Current Injection ◽  
Saturation Level ◽  
Permanent Magnet Synchronous Machines ◽  
Flux Linkage ◽  
Harmonic Current ◽  
Fem Model ◽  
Third Harmonic ◽  
The Third ◽  
Rotor Flux ◽  
Multi Phase

Abstract One advantage of multi-phase machines is the possibility to use the third harmonic of the rotor flux for additional torque generation. This effect can be maximised for Permanent Magnet Synchronous Machines (PMSM) with a high third harmonic content in the magnet flux. This paper discusses the effects of third harmonic current injection (THCI) on a five-phase PMSM with a conventional magnet shape depending on saturation. The effects of THCI in five-phase machines are shown in a 2D FEM model in Ansys Maxwell verified by measurement results. The results of the FEM model are analytically analysed using the Park model. It is shown in simulation and measurement that the torque improvement by THCI increases significantly with the saturation level, as the amplitude of the third harmonic flux linkage increases with the saturation level but the phase shift of the rotor flux linkage has to be considered. This paper gives a detailed analysis of saturation mechanisms of PMSM, which can be used for optimizing the efficiency in operating points of high saturations, without using special magnet shapes.

Adaptive SRF-PLL Based Voltage and Frequency Control of Hybrid Standalone WECS with PMSG-BESS

2018 ◽  
Vol 19 (6) ◽  
Author(s):  
Anjana Jain ◽  
R. Saravanakumar ◽  
S. Shankar ◽  
V. Vanitha
Keyword(s):  
Reactive Power ◽  
Frequency Control ◽  
Storage System ◽  
Synchronous Generator ◽  
Energy Storage System ◽  
Voltage Regulation ◽  
Operating Conditions ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Control Scheme

Abstract The variable-speed Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion System (WECS) attracts the maximum power from wind, but voltage-regulation and frequency-control of the system in standalone operation is a challenging task A modern-control-based-tracking of power from wind for its best utilization is proposed in this paper for standalone PMSG based hybrid-WECS comprising Battery Energy Storage System (BESS). An Adaptive Synchronous Reference Frame Phase-Locked-Loop (SRF-PLL) based control scheme for load side bi-directional voltage source converter (VSC) is presented for the system. MATLAB/Simulink model is developed for simulation study for the proposed system and the effectiveness of the controller for bi-directional-converter is discussed under different operating conditions: like variable wind-velocity, sudden load variation, and load unbalancing. Converter control scheme enhances the power smoothening, supply-load power-matching. Also it is able to regulate the active & reactive power from PMSG-BESS hybrid system with control of fluctuations in voltage & frequency with respect to varying operating conditions. Proposed controller successfully offers reactive-power-compensation, harmonics-reduction, and power-balancing. The proposed scheme is based on proportional & integral (PI) controller. Also system is experimentally validated in the laboratory-environment and results are presented here.

scholarly journals Active and Reactive Power Compensation Control Strategy for VSC-HVDC Systems under Unbalanced Grid Conditions

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3140 ◽  
Author(s):  
Weiming Liu ◽  
Tingting Zheng ◽  
Ziwen Liu ◽  
Zhihua Fan ◽  
Yilong Kang ◽  
...  
Keyword(s):  
Control Strategy ◽  
Disturbance Observer ◽  
Reactive Power ◽  
Operating Conditions ◽  
Voltage Source ◽  
Double Frequency ◽  
Compensation Control ◽  
Active And Reactive Power ◽  
Passivity Based Control ◽  
Unbalanced Grid

This paper presents a power compensation strategy to suppress the double frequency power ripples of Voltage source converter high-voltage direct current (VSC-HVDC) systems under unbalanced grid voltage conditions. The mathematical control equations of the double frequency ripple power of VSC under unbalanced operating conditions are firstly derived and established, where the dynamic behaviors of the double frequency ripples in active and reactive power are regarded as being driven by current-relevant components and voltage-relevant components, respectively. Based on the equations, a power compensation control strategy of VSC-HVDC is proposed via the passivity-based control with disturbance observer to suppress both the current-relevant and voltage-relevant components in the power ripples. With this control strategy, the double frequency ripples in active and reactive power are suppressed simultaneously and system performance is significantly enhanced with the implementation of the disturbance observer in the passivity-based control. Theoretical stability analysis and simulation cases show the effectiveness and superiority of the proposed strategy.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

Least Square Regression with lp-Coefficient Regularization

2010 ◽  
Vol 22 (12) ◽  
pp. 3221-3235 ◽  
Author(s):  
Hongzhi Tong ◽  
Di-Rong Chen ◽  
Fenghong Yang
Keyword(s):  
Error Analysis ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Least Square ◽  
Explicit Learning ◽  
Regularization Term ◽  
Least Square Regression ◽  
Primary Focus ◽  
Selection Of

The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on lp-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.

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