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In the application of electric railway traction, induction machines may suffer momentary power interruptions. Restarting control while machine running is a critical part of such applications, but residual flux in the free-running rotor deteriorates conventional controller performances, leading to inrush current and torque impact. Thus, this article proposes an improved feed-forward current controller that compensates voltage variations in the restart process. The residual rotor flux, as well as the induced back electromotive force, is estimated only with the measured speed in the free-running stage. The exact electromotive force is compensated at the restart moment, and the following current re-establishing stage. Based on the feed-forward compensation, the restart impact is eliminated and the restart current is controlled accurately in the predefined trajectories. Verified by experiments, the restart controller provides flexible restart manipulations. [ABSTRACT FROM AUTHOR]

This letter proposes an optimization-free technique to estimate the parameters of voltage dependent loads through the measurements of only the load active and reactive power consumption and frequency deviations at the load bus. The technique is shown to be a relevant consequence of the dependency of power flow equations on the bus voltage phase angles. The WSCC 9-bus and the IEEE 14-bus systems serve to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

The Kalman filter (KF) algorithms based on traditional models, which, applied in real-time detection of grid voltage, have the margin to improve tracking accuracy. Their tracking models do not specify the covariance matrix of state noise in theoretical derivation. They can only be taken as a unit matrix. In this paper, a dynamic tracking model (DTM) is proposed. Further, a linear KF algorithm based on DTM model (DTM-KF) is presented. The proposed DTM-KF algorithm gives the covariance matrix of state noise and overcomes the defects of the traditional models based KF algorithms. It is compared with two traditional models–based KF algorithms by simulation and experimentation. The tracking accuracy of the fundamental component and the estimation accuracy of the harmonic components are analyzed and compared. The results show that the proposed DTM-KF algorithm has high tracking accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a systematic model-based approach for the state estimation of permanent magnet synchronous motors to build up sensorless drives is presented. A moving horizon estimation algorithm, an optimization-based scheme that yields excellent performance, is applied for the speed and position estimation. Under mild assumptions, an optimal problem of equality constrained quadratic programing type has been solved each iteration. The steady state and transient performance of the proposal are evaluated considering different horizon lengths. The influence of the latter is analyzed in terms of estimation error and computational burden. Besides, the behavior of the scheme is analyzed with different current control loops, i.e., with conventional proportional integral regulators and with model predictive control, highlighting its adaptability and good response in control schemes with completely different features. The performance is compared with that of an extended Kalman filter and the results prove the effectiveness of the proposed control scheme in terms of the estimation accuracy and robustness even at low speed operation. The algorithm has been efficiently implemented showing the real-time feasibility of the proposed approach up to 10-kHz sampling rate. [ABSTRACT FROM AUTHOR]

A global parameter estimation method for a permanent magnet synchronous machines (PMSM) drive system is proposed, where the electrical parameters, mechanical parameters, and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters, and VSI of PMSM drive by using dynamic self-learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multiscale based interactive learning operator is introduced for accelerating the convergence speed of the $Pbest$ particles; meanwhile a dynamic opposition-based learning strategy is designed to facilitate the $gBest$ particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously. [ABSTRACT FROM AUTHOR]

This paper presents a new powerful density estimator and the application of the generalized cross-entropy method. A framework for probability density function estimation of the probabilistic optimal power flow problem results is presented. Large-scale probabilistic problems have a lot of uncertain parameters, some of which are correlated. Most of the existing methods for probabilistic power system analysis have a large computational burden or rely on asymptotic approximation to mitigate the computational burden, which reduces the precision level. Others that depend on the uncertainties are not applicable in large-scale problems and do not provide precise results in small cases. Compared to conventional methods, the proposed method has several advantages in probabilistic analysis. Reducing the computational burden while maintaining high level of accuracy is one of them. The proposed method can deal with correlated problems. This method is tested on some valid and conventional networks containing renewable energy resources, nonstationary loads, and their correlations. The results are compared against more accurate results obtained from Monte Carlo simulation, which demonstrate the high level of accuracy according to the error indices. Results confirm the high capacity of the proposed method in accuracy and speed response compared to other methods. [ABSTRACT FROM AUTHOR]

In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is presented for use with battery state estimation (BSE) algorithm development; particular focus is given to state-of-power (SOP) or peak power availability reporting. A practical optimization-based method is presented for model parameterization fitting. Two novel model-based SOP algorithms are proposed to improve voltage-limit-based power output accuracy in larger time intervals. The first approach considers first-order extrapolation of resistor values and open-circuit voltage (OCV) based on the instantaneous equivalent circuit model parameters of the cell. The second proposed approach, which is referred to as multistep model predictive iterative (MMPI) method, incorporates the cell model in a model predictive fashion. Finally, a SOP verification methodology is presented that incorporates drive cycle data to realistically excite the battery model. Simulation results compare the proposed SOP algorithms to conventional approaches, where it is shown that higher accuracy can be achieved for larger time horizons. [ABSTRACT FROM PUBLISHER]

The characterization of peripheral nerve fiber distributions, in terms of diameter or velocity, is of clinical significance because information associated with these distributions can be utilized in the differential diagnosis of peripheral neuropathies. Electro-diagnostic techniques can be applied to the investigation of peripheral neuropathies and can yield valuable diagnostic information while being minimally invasive. Nerve conduction velocity studies are single parameter tests that yield no detailed information regarding the characteristics of the population of nerve fibers that contribute to the compound-evoked potential. Decomposition of the compound-evoked potential, such that the velocity or diameter distribution of the contributing nerve fibers may be determined, is necessary if information regarding the population of contributing nerve fibers is to be ascertained from the electro-diagnostic study. In this work, a perturbation-based decomposition of compound-evoked potentials is proposed that facilitates determination of the fiber diameter distribution associated with the compound-evoked potential. The decomposition is based on representing the single fiber-evoked potential, associated with each diameter class, as being perturbed by contributions, of varying degree, from all the other diameter class single fiber-evoked potentials. The resultant estimator of the contributing nerve fiber diameter distribution is valid for relatively large separations in diameter classes. It is also useful in situations where the separation between diameter classes is small and the concomitant single fiber-evoked potentials are not orthogonal. [ABSTRACT FROM AUTHOR]

This paper presents a load torque estimation method for self-sensing brushless dc drives. Torque ripples in brushless dc machines can be reduced using load torque information. This method uses the terminal voltage, the virtual neutral point voltage, and the dc bus current of the machine. The algorithm uses the variation of successive back electromotive force (back EMF) samples to estimate the rotor speed. The rotor position is estimated by defining an intermediate function of estimated speed and back EMF samples. An estimate of acceleration is used to estimate load torque. The load torque information is used for increasing the dynamic stiffness of the drive. The mathematical background is given and discussed, and the simulations and the experimental results prove the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

A modified particle swarm optimization algorithm for conducting near-real-time parameter estimation of an electrical model for lithium batteries is presented. The model comprises a dynamic capacitance for characterizing the nonlinear relationship between the battery electromotive force and the state-of-charge, and a resistor-capacitor network for characterizing the static and transient responses. The algorithm is confirmed by successfully determining all parameters in a predefined simulation model. It is also evaluated on a hardware test bed with two samples of 3.3-V, 40-Ah, Lithium Iron Phosphate (LiFePO4) battery driven under six different loading patterns. The intrinsic parameters are estimated by first processing 15-min samples of the battery terminal voltage and current. The whole process takes 2 min. Then, the voltage-current characteristics in the following 15 min are predicted. Results show that the extracted parameters can fit the first 15-min voltage samples with a maximum error of 16 mV and an average error of 3.8 mV. With the extracted parameters, the electrical model can predict voltage-current characteristics in the following 15 min with a maximum error of 31 mV and an average error of 15 mV. The algorithm is further verified by successfully determining the emulated variation of the output resistance. [ABSTRACT FROM AUTHOR]