Improved Performance of a Photovoltaic Panel by MPPT Algorithms

This work is devoted to the presentation and realization of a digital control card (maximum power point tracking) which serves to improve the performance of a photovoltaic generator (GPV). This makes it possible to increase the profitability of the latter, on the one hand, and the stability of electrical networks, on the other hand. The command card has been developed using simple circuits, and tested on a system that includes a photovoltaic panel powering a resistive load under changing weather conditions. The aim of this paper is to implement three well-known MPPT algorithms (Hill-Climbing, Pertube & Observe and Incremental Conductance), using a PIC microcontroller type 16F877A

Improvement the DTC system for electric vehicles induction motors

A three-phase squirrel-cage induction motor is used as a propulsion system of an electric vehicle (EV). Two different control methods have been designed. The first is based on the conventional DTC Scheme adapted for three level inverter. The second is based on the application of fuzzy logic controller to the DTC scheme. The motor is controlled at different operating conditions using a FLC based DTC technique. In the simulation the novel proposed technique reduces the torque and current ripples. The EV dynamics are taken into account

Enhancing PV Systems with Intelligent MPPT and Improved control strategy of Z-Source Inverter

The Improved Z-Source Inverter (IZSI) has gained attention in the photovoltaic industry for its ability to boost PV voltage with a single-stage topology, simplifying system design and reducing costs. However, research on integrating IZSI into PV systems, particularly regarding the Maximum Power Point Tracker (MPPT) and IZSI control strategy, is limited. This study proposes an Intelligent Improved Particle Swarm Optimization (IPSO) algorithm as an MPPT method for PV systems under constant and varying irradiance conditions. The IPSO algorithm is compared to the FPA, CSA, and traditional MPPT algorithm (PSO), and the results demonstrate that IPSO outperforms all algorithms in terms of speed, efficiency, and convergence in finding the Maximum Power Point (MPP). Two methods, Simple Boost Control (SBC) and Maximum Constant Boost Control with Third Harmonic Injection (THIMCBC), are employed to control IZSI. Simulation results using MATLAB-Simulink show that both strategies successfully find and track the MPP, but THIMCBC exhibits superior voltage-boosting performance compared to SBC. Overall, the proposed IZSI topology with the IPSO MPPT method and THIMCBC IZSI control strategy offers several advantages, including improved voltage boost ability, reduced z-source capacitor voltage stress, inherent inrush current limitation, and cost-effectiveness. These advantages make the proposed system a promising solution for photovoltaic systems

Implementation of MRAC controller of a DFIG based variable speed grid connected wind turbine

International audienceThis paper presents the design and the implementation of a model reference adaptive control of the active and reactive power regulation of a grid connected wind turbine based on a doubly fed induction generator. This regulation is achieved below the synchronous speed, by means of a maximum power-point tracking algorithm. The experiment was conducted on a 1 kW didactic wound rotor induction machine in association with a wind turbine emulator. This implementation is realized using a dSPACE 1104 single-board control and acquisition interface. The obtained results show a permanent track of the available maximum wind power, under a chosen wind speed profile. Furthermore the proposed controller exhibits a smooth regulation of the stator active and reactive power amounts exchanged between the machine and the grid

Neural network power management for hybrid electric elevator application

International audienceThe present paper addresses the control and the power management of a hybrid system dedicated to an elevator application. In fact, the multi-source includes a photovoltaic generator as a main source supported by a battery-bank and a stack of super capacitors (SC). On the traction part, a permanent magnet synchronous motor (PMSM) is used to carry the elevator box. The power supervising mission is performed via a neural network (NN) routine trained with a frequency based strategy (FBS). The main objective of the applied control routines is to manage effectively the splits of the load demand. Therefore, they can provide the required power amounts in both steady-state and transient state, respecting the dynamic behavior of each source. Obviously, a fuzzy logic MPPT method has been applied to the PV side to permanently track the maximum power point through an adequate tuning of a boost converter regardless of the solar irradiance variations. Whereas, the controller of the DC–DC bidirectional converters of the battery and SC stack is based on the direct Lyapunov theory. To test the effectiveness of the proposed techniques, intensive numerical tests are done using MATLAB/Simulink Package. The obtained results prove the feasibility of the proposed approach, where the system switches smoothly between the operating modes