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

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

Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability

Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability

Model Predictive Current Control with Fixed Switching Frequency and Dead-Time Compensation for Single-Phase PWM Rectifier

The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD

Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network

Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network (LSTM-RNN). In order to learn the mapping function without employing battery models and filtering techniques, the LSTM-RNN is initially fed into the health indicators (HIs) extracted from the charging process and trained to encode the dependencies of the related data sequence. Subsequently, the trained LSTM-RNN can properly estimate online SOHs of LIBs using extracted HIs. We experiment on two public datasets for model construction, validation, and comparison. Conclusively, the trained LSTM-RNN achieves an overall root mean square error (RMSE) lower than 1% on the cases with the same discharging current rate and an RMSE of 1.1198% above 80% SOH on another testing case that underwent a different discharging current rate

Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network

Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network (LSTM-RNN). In order to learn the mapping function without employing battery models and filtering techniques, the LSTM-RNN is initially fed into the health indicators (HIs) extracted from the charging process and trained to encode the dependencies of the related data sequence. Subsequently, the trained LSTM-RNN can properly estimate online SOHs of LIBs using extracted HIs. We experiment on two public datasets for model construction, validation, and comparison. Conclusively, the trained LSTM-RNN achieves an overall root mean square error (RMSE) lower than 1% on the cases with the same discharging current rate and an RMSE of 1.1198% above 80% SOH on another testing case that underwent a different discharging current rate

The Virtual Harmonic Power Droop Strategy to Mitigate the Output Harmonic Voltage of the Inverter

The harmonic voltage issue becomes a challenge for a distributed generation system. Considering that droop control is the most common control algorithm used in the distributed system, a virtual harmonic power droop strategy which aims to mitigate the harmonic voltage is proposed in this paper. First, the conventional droop control is analyzed. Based on that concept, the virtual power algorithm is introduced. Second, the output harmonic voltage issue and the mathematical model of the inverter are presented. In addition, the second-order generalized integrator is briefly discussed. Third, taking into consideration the algorithms and models presented, a virtual harmonic power droop strategy is proposed to implement the harmonic voltage mitigation. In this algorithm, signals in fundamental frequency and harmonic frequency are separated with the help of second-order generalized integrators. Unlike the conventional voltage–current dual loop structure which is used to mitigate system harmonics, this method only needs the virtual power feedback to mitigate the harmonic voltage. Based on these features, the system’s control structure is simplified. Simulation and experimental results verified the harmonic voltage mitigation ability of the proposed strategy

A Novel Power Distribution Strategy and Its Online Implementation for Hybrid Energy Storage Systems of Electric Vehicles

Hybrid energy storage systems (HESS) composed of a battery and ultracapacitor (UC) provide a feasible solution to the economy of electric vehicles (EVs). To fully exploit the potential of HESSs, a power distribution strategy that can split power between the battery and UC in HESSs plays an important role. Therefore, a novel power distribution strategy and its online application are proposed in this paper. First, a new and simple power distribution model of HESSs is proposed, and the model parameters are optimized offline through particle swarm optimization (PSO). Then, a driving condition recognizer based on a neural network is introduced, and the online application of the strategy is realized by combining offline global optimization and online recognition. Compared with the traditional rule-based strategy, the strategy proposed reduces the average fluctuation of the battery current by 52.53% and the average amplitude of the battery current by 11.51%. Meanwhile, it can be seen from the results that the strategy proposed is very close to the offline PSO-based strategy proposed and exhibits good performance under all driving cycles

Wind-Solar-Biogas Renewable Energy Distributed Power system

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