Permanent Magnet Synchronous Machines (PMSMs) are increasingly used in many industrial fields for their efficiency, robustness, reliability, and low torque inertia. Despite their widespread use, they can operate in severe conditions when faults appear in PMSM drive components such as inverters, stator windings, sensors, etc. Fault diagnosis and fault-tolerant methods are then considered to improve the stability and robustness of PMSMs. Furthermore, it is much more important to be able to identify how many faults are present in a faulty system. This task is not obvious since one fault can hide another. Consequently, it is crucial to address design phase of the system. In this paper, we show the applicability of our fault localization approach based on sequence mining to unveil PMSMs’ multiple faults presence in the models considered for their design phase.
An analytical model is developed to determine the thermal performance of a nanofluid-filled copper loop heat pipe for battery thermal management in electric vehicles. Modeling the heat transfer in the evaporator is particularly considered, and the heat transfer coefficient of evaporation is determined from a dimensionless correlation that is developed based on experimental data. The working nanofluid is composed of de-ionized water and copper nanoparticles. The thermal performances of the LHP are predicted for different concentrations, and it is demonstrated that the heat transport capacity of the LHP is enhanced and the evaporator temperature is deceased by augmenting the nanoparticle concentration.
This paper presents a complete 2D analytical model for calculating the magnetic field, eddy current and mutual inductance in a conductive rod of finite length wound by a cylindrical coil. The proposed model combines the truncated region eigenfunction expansion (TREEM) method with the separation of variables technique to solve Maxwell’s equations in terms of the magnetic vector potential in the axisymmetric problem. This developed analytical model allows considering the finite length of the cylindrical coil and the conductive rod at the same time, also solves the nonlinear equation that gives the complex eigenvalues. The analytical results are compared to the numerical ones obtained by FEM (COMSOL).
This paper investigates the topology optimization of the rotor of a 3-phase flux switching machine with 12 permanent magnets located within the stator. The objective is to find the steel distribution within the rotor, maximizing the average torque for a given stator, permanent magnets, and electrical currents. The optimization algorithm relies on a density method based on gradient descent. The adjoint variable method is used to compute the sensitivities efficiently. Since the rotor topology depends on the current feedings, this approach is tested on several electrical periods. The obtained structures are then analyzed and classified.
The Taïba plant is a wind power plant interconnected to the SENELEC network, and its production fills a gap in electricity consumption. Indeed, it represents 157 MW of installed capacity. Like all intermittent power plants, production depends on the environmental parameters of the area where it is located. Bad weather can cause instability in the electricity grid. It is necessary to use methods to forecast its production. This will facilitate the decision making on the amount of energy to be produced to meet the demand. In this sense, this paper aims to predict wind generation by dividing the prediction data into 80% for training our model and 20% for testing the prediction efficiency of the model in order to quantify the energy produced and to allow an optimal transition between intermittent and fossil energy sources. The proposed neural network model was combined with the Levenberg-Marquardt algorithm while varying the number of hidden layers to evaluate the impact of these layers on the prediction efficiency. The rapid convergence of this algorithm and the increase in hidden layers enhanced the prediction performances. The results obtained show that varying the number of hidden layers increases the performance of neural models applied to intermittent energy prediction. The proposed approach gives its best prediction accuracy of 94.57% for the 100 hidden layer network.
