35,562 research outputs found
Magnetic Modelling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks
The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems
Bio-inspired Dynamic Formant Tracking for Phonetic Labelling
It is a known fact that phonetic labeling may be relevant in helping current Automatic Speech Recognition (ASR) when combined with classical parsing systems as HMM's by reducing the search space. Through the present paper a method for Phonetic Broad-Class Labeling (PCL) based on speech perception in the high auditory centers is described. The methodology is based in the operation of CF (Characteristic Frequency) and FM (Frequency Modulation) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus in the automatic detection of formants and formant dynamics on speech. Results obtained informant detection and dynamic formant tracking are given and the applicability of the method to Speech Processing is discussed
Real-Time Local Volt/VAR Control Under External Disturbances with High PV Penetration
Volt/var control (VVC) of smart PV inverter is becoming one of the most
popular solutions to address the voltage challenges associated with high PV
penetration. This work focuses on the local droop VVC recommended by the grid
integration standards IEEE1547, rule21 and addresses their major challenges
i.e. appropriate parameters selection under changing conditions, and the
control being vulnerable to instability (or voltage oscillations) and
significant steady state error (SSE). This is achieved by proposing a two-layer
local real-time adaptive VVC that has two major features i.e. a) it is able to
ensure both low SSE and control stability simultaneously without compromising
either, and b) it dynamically adapts its parameters to ensure good performance
in a wide range of external disturbances such as sudden cloud cover, cloud
intermittency, and substation voltage changes. A theoretical analysis and
convergence proof of the proposed control is also discussed. The proposed
control is implementation friendly as it fits well within the integration
standard framework and depends only on the local bus information. The
performance is compared with the existing droop VVC methods in several
scenarios on a large unbalanced 3-phase feeder with detailed secondary side
modeling.Comment: IEEE Transactions on Smart Grid, 201
Kuhn-Tucker-based stability conditions for systems with saturation
This paper presents a new approach to deriving stability conditions for continuous-time linear systems interconnected with a saturation. The method presented can be extended to handle a dead-zone, or in general, nonlinearities in the form of piecewise linear functions. By representing the saturation as a constrained optimization problem, the necessary (Kuhn-Tucker) conditions for optimality are used to derive linear and quadratic constraints which characterize the saturation. After selecting a candidate Lyapunov function, we pose the question of whether the Lyapunov function is decreasing along trajectories of the system as an implication between the necessary conditions derived from the saturation optimization, and the time derivative of the Lyapunov function. This leads to stability conditions in terms of linear matrix inequalities, which are obtained by an application of the S-procedure to the implication. An example is provided where the proposed technique is compared and contrasted with previous analysis methods
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