Virtual Signal Injection-Based Direct Flux Vector Control of IPMSM Drives

This paper describes a novel virtual signal injection-based direct flux vector control for the maximum torque per ampere (MTPA) operation of the interior permanent magnet synchronous motor (IPMSM) in the constant torque region. The proposed method virtually injects a small high-frequency current angle signal for tracking the optimal flux amplitude of the MTPA operation. This control scheme is not affected by the accuracy of the flux observer and is independent of machine parameters in tracking the MTPA points and will not cause additional iron loss, copper loss, and torque ripple as a result of real signal injection. Moreover, by employing a bandpass filter with a narrow frequency range the proposed control scheme is also robust to current and voltage harmonics, and load torque disturbances. The proposed method is verified by simulations and experiments under various operating conditions on a prototype IPMSM drive system

Error analysis for the numerical evaluation of the diagonal forms of the scalar spherical addition theorem

The numerical solution of wave scattering from large objects or from a large cluster of scatterers requires excessive computational resources and it becomes necessary to use approximate -but fast - methods such as the fast multipole method; however, since these methods are only approximate, it is important to have an estimate for the error introduced in such calculations. An analysis of the error for the fast multipole method is presented and estimates for truncation and numerical integration errors are obtained. The error caused by polynomial interpolation in a multilevel fast multipole algorithm is also analyzed. The total error introduced in a multilevel implementation is also investigated numerically.published_or_final_versio

On Accuracy of Virtual Signal Injection based MTPA Operation of Interior Permanent Magnet Synchronous Machine Drives

This correspondence analyzes the accuracy of maximum torque per ampere (MTPA) operations of interior permanent magnet machines based on the technique described in [T. Sun, J. Wang, and X. Chen, “Maximum Torque Per Ampere (MTPA) Control for Interior Permanent Magnet Synchronous Machine Drives Based on Virtual Signal Injection,’’ IEEE Trans. Power Electron., vol. 30, no. 9, pp. 5036-5045, Sep. 2015] in responses to a few inquiries made by the readers. It is shown that due to parameter variations with stator currents, any technique for MTPA tracking based on piecewise constant parameter assumption, i.e., the machine parameters are assumed as constants during the calculation of ∂Te/∂β, would result in tracking error even though the machine parameters are obtained from lookup table or online machine parameter estimations. The error is dependent on machine nonlinear characteristics and operating conditions. It is also shown that for the prototype interior permanent magnet synchronous machine the virtual signal injection control technique described in the paper mentioned above yields a better tracking accuracy

Performance Improvement of Direct Torque Controlled IPM Drives by Employing a Linear Combination of Current and Voltage Based Flux Observers

Flux observers in direct torque controlled (DTC) motor drives are of paramount importance as the drives rely on estimated variables for feedback control. It is well known that current based (CB) estimations are advantageous at low speeds, whereas voltage based (VB) estimations are more accurate at high speeds. Hence, a large number of state-of-the-art DTC drives utilize closed loop flux observers in which the CB and VB estimations become dominant at low and high speeds, respectively. However, it has been discovered that the performance and current waveforms with the closed loop observers significantly deteriorate at low speeds since the residual error of the VB estimation causes current distortions. In addition, these observers have nonlinear flux transition trajectory resulting in reduced accuracy during transitions. To improve the low speed performance and achieve the linear transition, an alternative combination of the two estimations is proposed. Experimental results on a 10kW interior mounted permanent magnet (IPM) machine drive designed for electric vehicle traction applications validate significant improvements on the drive performanc

Self-learning Direct Flux Vector Control of Interior Permanent Magnet Machine Drives

This paper proposes a novel self-learning control scheme for interior permanent magnet synchronous machine (IPMSM) drives to achieve maximum torque per ampere (MTPA) operation in constant torque region and voltage constraint maximum torque per ampere (VCMTPA) operation in field weakening region. The proposed self-learning control scheme (SLC) is based on the newly reported virtual signal injection aided direct flux vector control. However, other searching based optimal control schemes in the flux-torque (f-t) reference frame are also possible. Initially the reference flux amplitudes for MTPA operations are tracked by virtual signal injection and the data are used by the proposed self-learning control scheme to train the reference flux map online. After training, the proposed control scheme generates the optimal reference flux amplitude with fast dynamic response. The proposed control scheme can achieve MTPA or VCMTPA control fast and accurately without accurate prior knowledge of machine parameters and can adapt to machine parameter changes during operation. The proposed control scheme is verified by experiments under various operation conditions on a prototype 10 kW IPMSM drive

Estimating Forage Biomass using Unmanned Ground and Aerial Vehicles

The assessment of the amount of biomass in the field is one of the critical factors that helps to manage and optimize numerous operations associated with forage management in the livestock industry. Pasture management decisions about stocking rate, grazing duration, and fertilizer application rate depend on accurate forage availability measurements. The objective of this study was to develop different nondestructive methods of forage biomass estimation using unmanned vehicles based on the relationship between crop height (CH) and the measured above-ground biomass. The unmanned vehicle-based methods were developed and tested on Alfalfa (Medicago Sativa) and Tall Fescue (Schedonorus phoenix (Scop.) Holub) fields. The real-time compressed crop height was measured using the ultrasound proximal sensor and a compression ski installed on the unmanned ground vehicle (UGV) and orthomosaic from aerial images was used for plot identification for site-specific analysis. The experiment was carried out before and after harvest to calculate the harvested CH to generate its regression relation with wet and dry biomass yield of forage. The results show that these systems produce promising results with R-square values of 0.8 and 0.5 for biomass estimation in Alfalfa and Tall Fescue respectively. These methods will significantly reduce the on-field destructive forage sampling for biomass estimation and aid in predicting the available biomass along with reducing the human efforts and resources for performing biomass sampling tasks, resulting in reduction of time and cost

Self-Learning MTPA Control of Interior Permanent-Magnet Synchronous Machine Drives Based on Virtual Signal Injection

This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control (SLC) scheme generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system

Social media actions and interactions: The role of the Facebook and Twitter during the 2014 European Parliament elections in the 28 EU nations.

Most political parties across the democratic sphere have created their own spaces within social media. While ostensibly studies show that social media is being utilized by political parties to further their electoral goals, the uses of their social media profiles by visitors is largely beyond official control without devoting significant resources to moderation. This study will be the first to gather data that allows us to detect patterns of participation within and potentially across nations, but in particular within nations and across parties to determine the extent that visitors use social media to promote parties (through liking and sharing) or for entering comments on party posts or for entering into discussions with other visitors. We specifically seek to understand whether we can detect evidence of a political ecosystem in which visitors visit multiple party profiles, enter debates across differing profiles and so contribute to something that might resemble an informed and engaged public sphere

Analytical Solutions of Klein-Gordon Equation with Position-Dependent Mass for q-Parameter Poschl-Teller potential

The energy eigenvalues and the corresponding eigenfunctions of the one-dimensional Klein-Gordon equation with q-parameter Poschl-Teller potential are analytically obtained within the position-dependent mass formalism. The parametric generalization of the Nikiforov-Uvarov method is used in the calculations by choosing a mass distribution.Comment: 10 page