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.
This study presents an optimal design of a low-speed aero generator based on the approach of associating geometric parameterization with electromagnetic performance evaluation. The optimal topology of the hybrid excited flux switching (HEFS) generator is dedicated to low-wind speed ranges. The machine is analyzed by the 2-D finite element method (FEM) and the influence of different excitation currents on the saturation state of the magnetic circuit is investigated. Sensitivity analyses of the Form Factor (FF), as well as the Flux Excursion Factor (FE), are investigated using the Non-Sorting Genetic Algorithm (NSGA II) to assess the speed/power limitations of the proposed topology. The two-dimensional FEM of the HEFS generator is used to perform the sensitivity analyses and to establish a multi-objective optimization of the generator for a rated power of 3kW. The multi-objective design optimization leads to trade-off solutions between conflicting objectives (maximizing the generated power and minimizing the basic speed). Three study cases, based on FEA simulations and conventional sequential design strategies, are presented for performance comparison in order to minimize the size of the generator and maximize its performance. By evaluating the criterion of minimizing the generator’s weight, relevant machine candidates of the Pareto front solutions are compared to the initial machine as well as other existing prototypes for small wind turbine generators.
Low inertia systems with high penetration of Renewable Energy sources need sophisticated control to ensure frequency stability. Virtual inertia control-based storage systems is used to improve the inertia of the microgrid. However, the selection of the virtual inertia constant will have a crucial contribution in the performance of frequency regulation, more precisely in terms of Rate of change of frequency ROCOF and nadir deviation and even frequency stability of faulty microgrid. To overcome such a problem, this paper proposes a method for determining critical inertia. A limit value of the inertia which makes it possible to operate the microgrid according to the grid code requirements and to avoid the destabilization of the system. For this purpose, a stability analysis, in steady-state and in transient mode according to the variation of inertia, makes it possible to identify the limit values. To verify the efficiency of the proposed algorithm, Simulation under MATLAB environment of the experimental platform Pla-NeTE integrating a BESS system is carried out.
Open-circuit fault under different load and variable working conditions is the most severe issue that affects the robustness of fault diagnosis algorithms. Considering this, this research work proposes a high-frequency fault diagnosis approach that is capable to detect and isolate a single open-switch fault in a single-phase five-level Packed U-Cell (PUC5) inverter, not only under stable conditions but also under different load and variable working conditions. Firstly, a high frequency model of the PUC5 inverter is proposed. Then, the conducted emissions, measured across the electromagnetic interferences filter resistor, are employed as a fault signature. Finally, simulation results are considered to verify the effectiveness of the proposed fault diagnosis method.
Power transformer’s insulation is an integral part of the health and performance of this power component. This paper uses Physics-Informed Neural Networks (PINNs) for predicting the lifetime and health indicator of the power transformer’s insulation material, which is expressed as the Degree of Polymerization (DP) of the polymeric material (in this case kraft paper). PINNs are a promising deep learning technique for solving scientific computing problems and are designed to incorporate prior knowledge of physical or chemical systems and to respect any symmetries, invariances, and conservation laws. The dynamics of the degradation process is modeled using ordinary differential equations. One major challenge in analyzing kraft paper degradation is estimating the unknown model parameters (e.g. rate constants) and thus predicting model dynamics. For this work, we aim to solve the data-driven discovery of the degradation process, infer the hidden kinetic parameters and predict the degree of polymerization. The final discussion also addresses the advantages and limitations of PINNs for solving this type of problems.
Electric cars (EVs) on the road have been plagued by range anxiety. Additionally, the inconvenience of EVs having to spend a lot of time charging while on the highway. Future automated and electrified highways could be powered by inductive power transmission, according to a solution. In terms of providing EVs with convenient service and easing range anxiety, wireless charging has promise. It is essential to control the EVs correctly in order to avoid congestion and to provide high-quality service because the capacity for charging EVs on the highway is constrained (QoS).
In this paper the issue of H ∞ control for a class of one-sided Lipschitz (OSL) nonlinear systems is investigated. At first, the model of a single-link flexible joint manipulator (SLFJM) is presented. then, a decoupling between Lyapunov and system matrices is used to formulate a less-conservative analysis condition. Thereafter, the control gain which guarantees asymptotic stability and H ∞ disturbance attenuation performances is determined through LMI formulation. At last, the proposed approach is proved via the simulated behaviour of the controlled SLFJM system.
This paper presents an efficient Fault Tolerant Control (FTC) strategy for the double-fed induction generator subject to faults using a projection- based approach. This research aims to construct an algorithm that can diagnose the presence of a fault in the closed loop system and switch itself between a nominal control (vector control) approach and a robust control (sliding mode control) designed for faulty conditions. The vector control can’t deal with the fault effect, which can achieve gradual system degradation, so we propose a sliding mode control when the faults occur to ensure a ripple-free operation. Moreover, the MRAS (model reference adaptive system) is used to analyse the dynamics of the residual vector (estimation error). This will serve as an indication of which control law should be used for such fault. The obtained results confirm that the suggested FTC has better robustness against the faults where the DFIG operates with acceptable performance in both active and reactive power.
The photovoltaic system (PVS) studied in this article consists of a photovoltaic generator (PVG) supplying a battery through a step-down chopper. The objective of the work is to speed up the convergence of the particle swarm algorithm in order to find the maximum power point of the photovoltaic generator. For this, a modified version of the Particle Swarm Optimization (PSO) algorithm suitable for our PVS is applied to the control of the DC-DC converter in order to accelerate de maximum power point of the generator. The developed algorithm is simulated in Matlab-Simulink for uniform dynamic irradiation and temperature on the PVG. The obtained results show that for an estimated MPPT control efficiency of at least 99.7 %, the improved PSO algorithm converges to near MPP with a smaller number of iterations than the PSO without improvement in all test cases.
Photovoltaic (PV) self-consumption installations have increased by 101.84% in Spain, from 2020 to 2021. In this context, developing PV generation forecasting tools can contribute to increase the PV self-consumption ratio, boosting the use of renewable energies. On this matter, this paper presents two PV generation forecasting models for the next 24 hours. These are an analytical model developed in OpenModelica software and a model based on artificial intelligence (AI), specifically a feedforward neural network (FFNN). Both models use measured meteorological data obtained from a weather station 3km from the PV installation. This work analyses how the use of different data as input information affects the prediction of the FFNN. It was found that adding a time vector as an input of the FFNN improves the prediction, thus compensating the fact that it is not a recurrent network. Furthermore, the behaviour of both models has been compared. Both the analytical model and the FFNN obtain a correlation coefficient r of 0.941 and 0.94, respectively. Even so, the MAE and RMSE metrics highlight how the analytical model has a higher error and level of dispersion.
In this paper, a Sliding Mode Observer (SMO) for flux and then speed-sensorless of three-phase Induction Motor (IM) as robust Wind Turbine Emulator (WTE) design is investigated. A Soft-Voltage Source Inverter (VSI) structure, which is controlled by a specific Space Vector Modulation (SVM), is applied to drive the IM. Hence, appropriate three vectors which synthesize the desired output voltage are selected to allow the proposed system to react as a real turbine considering the wind velocity, static and dynamic behaviors, and parametric variations. Mathematical models of each system part are described to highlight electrical, mechanical, and electromagnetic relations. Simulation results confirm that the presented control method provides good flux and speed estimations despite rotor resistance and load torque variations in terms of trajectory tracking.
The purpose of this study is to propose an energy management strategy (EMS) based on a load following approach for a hybrid power system. The adopted hybrid power system is made up of a lithium-ion battery and supercapacitor, along with two bidirectional DC-DC converters. The proposed EMS algorithm aims not only to provide the necessary power references to the power system but also to extend the power elements lives. The effectiveness of the suggested energy management strategy under load profile process conditions is proven by processor in the loop (PIL) co-simulation results.
The power generated by the photovoltaic generator varies continuously with the solar illumination and the temperature, allowing to continue the maximum power point by interposing one or more controlled static converters between the generator and the receiver.This article presents the modeling and hardware implementation of the MPPT (Maximum Power Point Tracking) algorithm of extremal research by control in slip mode in the conversion chain of a photovoltaic system. To optimize conversion efficiency, the control algorithm is applied directly to the output of the module and connected to the DC-DC conversion step. The selected converter is a DC-DC amplifier converter. We present the control method in sliding mode. An optimal choice of control parameters enables system-wide performance. The simulation results show the efficiency and performance of this proposed system.
Energy harvesting systems for low power consumption devices are increasingly required for different applications, such as autonomous sensors and Internet of Things. This work is about the evaluation of the electrical energy recovered by a ceramic piezoelectric disc, under different mechanical and electrical conditions. The vibration excitation of the disk is of different amplitudes and frequencies, the disk is fixed in different ways, while the recovery electric circuit is of different configurations.
This study aims to analyze the techno-economic and environmental performance of the hybrid energy system (HES) to meet the electricity demand of an off-grid community and the dump load in the Indalek village located in the southern of Algeria. Different combinations of HES, such as PV/FC/DG/battery (BESS) and PV/FC/DG/Pumped hydro storage (PHS), are modeled, analyzed and compared using HOMER software. The techno-economic environmental performance analysis has evaluated the net present cost (NPC), the cost of energy (COE), excess electricity (EE), a fraction of renewable energy (RF) and CO2 emissions of the different combinations of HES. The simulation results show that the BESS hybrid energy system has the best feasibility technoeconomic performance with the least NPC, COE and the higher EE of $438335.21, $0.1423/KWh, 36222 KW/year, respectively. On the contrary, the HES with PHS has the highest fraction of renewable energy of 87.4% and the most environmentally friendly with 96.43% reduction in CO2 emissions compared to the HES with BESS. Finally, the sensitivity analysis is performed on the hybrid energy system with BESS shows that the improvement of the derating factor with the increase load leads to a lower the COE.
This paper deals with the investigation of the influence of different parameters such as end effect and power cable length on the machine performances. This influence is highlighted for the extra low voltage applications (< 60 V DC) using six-phase permanent magnet assisted synchronous-reluctance machines. The investigation presented in this work combines different methods such as 3D finite elements, experimental tests, and information from technical data sheets. Firstly, the machine is designed using 2D finite element analysis, and then tested using two test benches, the first one with long cables and the second with short cables. The experimental power- speed characteristics obtained by these tests are compared to those obtained with simulations. The impact of the real modulation index, end effect and power cable length on the power speed characteristics is evaluated.
