Sensorless MPPT technique for PMSG micro wind turbines based on state-flow

Maximum power point tracking (MPPT) techniques had evolved rapidly in the last decade with the evolution of power electronics. This paper presents a control technique for a variable speed, grid connected direct driven permanent magnet synchronous generator (PMSG). The PMSG is connected to the grid through three-phase diode rectifier followed by a boost converter controlled with the MPPT technique to maintain a constant DC bus voltage for different wind velocities. The paper compares between two MPPT techniques, HCS and fuzzy logic. The control depends upon sensorless rotor speed estimator through measuring the input dc voltage and current for the boost converter. The system performance is investigated using a MATLAB/ SIMULINK model for 2 MW PMSG wind turbine.

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

Aeronautic instruments

The present paper aims to design shunt active power filter (APF) for harmonic cancelation of grid-connected wind turbine (WT). The wind energy conversion system (WECS) consists of permanent magnet synchronous generator (PMSG) driven by a variable-speed WT. The output of the PMSG is connected to a direct three phase AC/AC matrix converter (MC) to interface the system with the distribution grid. The MC controls the wind maximum power point tracking (MPPT) via perturbation and observation (P&O) technique. The MC output current harmonics and harmonics of non-linear load are canceled by the proposed shunt APF based on d-q theory. The system under study is simulated using MATLAB/SIMULINK platform. The digital results prove that, the suggested system can submit the generated wind power to the grid without currents/voltages harmonic distortion at grid side, it can also satisfactory eliminate the non-linear load harmonic currents. The system dynamic performance is investigated under different wind speed and loading conditions. The system responses prove that the system works significantly irrespective of wind speed values and demanded load.

The direct-drive permanent-magnet synchronous generator (PMSG) with full power converter can achieve wind maximum power point tracking (MPPT) in wide range variation of wind-speed. In this paper an operating characteristic of PMSG is investigated, a MPPT strategy based on hill climbing method for PMSG are present. Simulation and experimental results show that the controllers can extract maximum wind power in good dynamic performance.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

This paper proposes a novel application of a runner-root (RR) algorithm for maximum power point tracking (MPPT) of a wind energy conversion system (WECS) direct-driven by a permanent magnet synchronous generator (PMSG). The WECS is required to operate at the MPP under varying wind conditions. The RR algorithm is the meta-heuristic algorithm, inspired by the function of runners and roots of some plants in nature. The plants are propagated through runners. They look for water resources and minerals by developing runners and roots. The RR algorithm is proposed to define maximum power points (MPP) corresponding to the optimal output voltage of the DC/DC boost converter of the PMSG-based WECS. The numerical results show that the RR algorithm based MPPT approach efficiently defines and tracks the MPPs of the PMSG-based WECS. A comparison with the hill-climb search (HCS) and particle swarm optimization (PSO) algorithms demonstrates the superiority of the RR algorithm and confirms its potential in the MPPT strategy of the PMSG-based WECS.

Sensorless wind energy conversion system maximum power point tracking using Takagi–Sugeno fuzzy cerebellar model articulation control

A new sensor-less maximum power point tracking (MPPT) technique is introduced for wind power conversion applications. This new technique is founded upon a modified variable step hill climb search (HCS) method. The modifications are made, based on a simple and novel idea. The step sizes of the hill climbing technique are varied throughout the MPPT operation, entirely independent of the wind speed and the rotor speed, without requirement for any prior information of the wind power conversion system's parameters and configurations. These features make this new technique applicable on different kinds of wind power conversion systems. The novel method introduced in this study, exhibits very fast performance and high accuracy in sensing wind speed variations as well as maintaining its performance under extreme conditions, such as high wind disturbances. Computer simulations are performed in order to evaluate the new technique and the results support the feasibility of the presented MPPT technique.

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

In the last decade, there has been a substantial surge in the advancement of research into the maximum power point tracking (MPPT) controller. The MPPT approaches, on the other hand, continue to be in high demand due to the ease and simplicity with which tracking techniques can be implemented on the maximum power point (MPP). Diverse MPPT approaches and their modifications from various literature are categorized and thoroughly explored in this work, which is divided into two sections. The discussions are centered on the primary goal of attaining the most extraordinary feasible MPPT technique that produces the best results at the lowest possible expense. In order to determine which MPPT approaches to use, evaluations from earlier literature are used to guide the decision. In this section, we will examine the evaluation of the MPPT technique in two sections. Previously, in Part I, we explored the MPPT techniques based on constant parameters and trial-and- error. Part II of this article will examine the MPPT technique, which is based on mathematical computation, measurement, and comparison, and the algorithm development that has occurred in recent years. Furthermore, this section’s assessment for selecting MPPT approaches is based on previous literature reviews. To aid with this selection, the following criteria for the MPPT approach are proposed: sensors and analog/digital requirements, costeffectiveness, simplicity, stability, efficiency, and tracking speed. This enables the reader to select the MPPT technique that is most appropriate for their application.

A novel MPPT method for photovoltaic application under partial shaded conditions

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

Hybrid maximum power point tracking techniques: A comparative survey, suggested classification and uninvestigated combinations

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.