Sensorless Control of Induction Motors by the MSA based MUSIC Technique

This paper proposes a speed sensorless technique for induction motor drives based on the retrieval and tracking of the rotor slot harmonics (RSH). The RSH related to the rotor speed is first extracted from the stator phase current signature by the adoption of two cascaded ADALINEs (ADAptive Linear Element), whose output is the estimated slot harmonic. Then, the frequency of this slot harmonic as well as the speed is estimated by using minor space analysis (MSA) EXIN neural networks, which work on-line to iteratively compute the frequency of the slot harmonics based on MUSIC spectrum estimation theory. Thanks to its sample-based learning and the reduced mean square frequency estimation error, the speed estimation is fast and accurate. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2-kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3 % of it

Contrôle de la machine asynchrone sans capteur de vitesse avec un modèle harmonique plus élevée

The thesis first studies the relation between the rotor slot harmonics (RSHs) and the instan-taneous rotor speed. To directly track the RSH, the requirements of the system are fully ad-dressed.Second, the thesis presents a sensorless scheme based on phase-locked loop (PLL): The centre bandwidth is tuned on-line on the basis of the reference values of the supply and slip frequencies provided to the PWM converter, the PLL is tuned to track the machine rotor slot-ting harmonic without the need of any high frequency signal injection, neither rotating nor pulsating. This speed estimation scheme, which is suitable for the scalar controller, had been integrated with the scalar drive, leading to a simple, computationally not demanding, low cost sensorless IM drives. The experiment results show that the system is able to track the machine speed in a very wide speed range.Finally, an improved sensorless scheme based on minor component analysis (MCA) neu-rons is described. According to the Pisarenko’s theory, it has been verified that the MC which lies in the noise subspace is orthogonal to the signal subspace, thus, the signal frequencies contained in the input can be computed from a polynomial formed by the MC. Conventional-ly, this will require the bulky eigen-decomposition, nevertheless, the neural method proposed in this thesis can retrieve the MC recursively with less computation and improved error per-formance (the solution is of total least square meaning). Moreover, the speed estimator is ap-plied to the scalar drive with experimental verification, the overall system is well behaved, and the MCA method enhanced by neural networks has provided a good potential in the ap-plication of harmonics retrieve.La thèse étudie tout d’abord la relation entre les harmoniques à fentes du rotor (RSHs) et la vitesse du rotor instantanée. Pour suivre directement l'RSH, les exigences du système sont pleinement prises en compte.Dans un deuxième temps, les travaux de thèse ont permis de développer un système sans capteur en fonction de boucle à verrouillage de phase (PLL): La largeur de bande centrale est réglée en ligne sur la base des valeurs de référence, des fréquences d'alimentation et de glissement prévues au convertisseur PWM, la PLL est réglée pour suivre le rotor de la machine à RSH sans la nécessité de toute injection de signal à haute fréquence, ni en rotation, ni de pulsation. Ce système d'estimation de vitesse, qui est approprié pour le contrôleur scalaire, avait été intégré avec le lecteur scalaire, conduisant à un simple calcul peu exigeant, à faible coût de l’entraînement de la machine à induction sans capteur à faible coût. Les résultats expérimentaux montrent que le système est en mesure de suivre la vitesse de la machine dans une plage de vitesse très étendue.Enfin, un système sans capteur amélioré basé sur l'analyse de composant mineur (MCA) neurones est décrit. Selon la théorie de Pisarenko, il a été vérifié que le MC qui se trouve dans le sous-espace de bruit est orthogonale au sous-espace de signal, par conséquent, les fré-quences de signal contenues dans l'entrée peuvent être calculées à partir d'un polynôme formé par la MC. Classiquement, ce qui nécessitera la décomposition propre encombrants, néan-moins, la méthode de neurones proposée dans cette thèse peut récupérer le MC de façon ré-cursive avec moins de calculs et des performances améliorées d'erreur (la solution est sur un total de moins sens carré). En outre, l'estimateur de vitesse est appliquée à l'entraînement scalaire avec vérification expérimentale, l'ensemble du système se comporte bien, et la méthode MCA renforcée par réseaux neuronaux a fourni un bon potentiel dans l'application des harmoniques récupérer

Speed Sensorless Control Of Induction Motors Based On MCA Exin Pisarenko Method

This paper proposes a speed sensorless technique for high performance induction motor drives based on the retrieval and tracking of the rotor slot harmonic. First, two cascaded ADALINEs (Adaptive Linear Elements) are used to extract the rotor slot harmonic (RSH) from the stator phase current signature, acting as adaptive filters, respectively in configuration band and notch, whose output consists of the RSH. Second, the MCA EXIN neurons are used to extract the eigenvector corresponding to the minimum eigenvalue of the autocorrelation matrix, which is formed by the ADALINEs' output sequence. Then, the slot frequency is estimated by using Pisarenko's theory with this retrieved minimum eigenvector, and subsequently the speed of the motor is estimated. Compared to the original Pisarenko's method however, not only the proposed algorithm can work recursively sample by sample, but the computational complexity and mean square frequency estimation error are largely reduced. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2-kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3 % of it

Loci and natural alleles underlying robust roots and adaptive domestication of upland ecotype rice in aerobic conditions.

A robust (long and thick) root system is characteristic of upland japonica rice adapted to drought conditions. Using deep sequencing and large scale phenotyping data of 795 rice accessions and an integrated strategy combining results from high resolution mapping by GWAS and linkage mapping, comprehensive analyses of genomic, transcriptomic and haplotype data, we identified large numbers of QTLs affecting rice root length and thickness (RL and RT) and shortlisted relatively few candidate genes for many of the identified small-effect QTLs. Forty four and 97 QTL candidate genes for RL and RT were identified, and five of the RL QTL candidates were validated by T-DNA insertional mutation; all have diverse functions and are involved in root development. This work demonstrated a powerful strategy for highly efficient cloning of moderate- and small-effect QTLs that is difficult using the classical map-based cloning approach. Population analyses of the 795 accessions, 202 additional upland landraces, and 446 wild rice accessions based on random SNPs and SNPs within robust loci suggested that there could be much less diversity in robust-root candidate genes among upland japonica accessions than in other ecotypes. Further analysis of nucleotide diversity and allele frequency in the robust loci among different ecotypes and wild rice accessions showed that almost all alleles could be detected in wild rice, and pyramiding of robust-root alleles could be an important genetic characteristic of upland japonica. Given that geographical distribution of upland landraces, we suggest that during domestication of upland japonica, the strongest pyramiding of robust-root alleles makes it a unique ecotype adapted to aerobic conditions