High Performance Maximum Power Point Research Articles

High-performance MPPT control of DFIG with optimized flux reference in presence of nonlinear magnetic characteristic

In recent years, an expansive research exertion has been devoted to the control for aeorogenerators conforming of a Doubly Fed Induction Generator (DFIG) coupled to a wind turbine. Most preceding controllers based on standard models (whose magnetic induction characteristic is considered linear and iron losses have not been taken into account). Since such a magnetic characteristic is nonlinear in practice, this work develops a variable speed control strategy on the basis of a DFIG model in presence of nonlinear magnetic characteristic and iron losses. The main contribution of this paper is to present a high-performance maximum power point (MPPT) control of DFIG based on rotor flux optimization, designed to reach an optimal operational conditions. Of the foregoing, the control goal is twofold: i) minimization of the stator and rotor currents circulating by controlling the rotor flux for a maximum electromagnetic torque; ii) captured the maximum wind power to insure the attainment of MPPT. The performances of the proposed command has been studied theoretically and validated by simulation studies with Matlab/Simulink.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

An Advanced High Performance Maximum Power Point Tracking Method with Ant Colony and Particle Swarm Optimization Method Using Interleaved Boost Converter

To obtain efficient maximum power point tracking operation under varying and steady state environmental conditions which is based on Ant Colony Optimization (ACO) combined with Particle Swarm Optimization (PSO) that controls an interleaved DC-DC converter connected at the output of PV array and maintains a constant input-power load. The operation of maximum power point tracking is to adjust the power interfaces so that the operating characteristics of the load and the photovoltaic array match at the maximum power points. The simulation is based on the computed algorithms which show very good perspectives. The searching routine is done in less than 15 steps and takes less than 50ms. The tracking accuracy is better than those found in conventional methods. DOI: http://dx.doi.org/10.11591/telkomnika.v14i3.7900

A high performance maximum power point tracker for PV systems

The solar cell characteristics are non-linear and largely influenced by solar radiation, temperature and load condition. The power output of a PV array changes with varying temperature and irradiation. A maximum power point algorithm is investigated to obtain maximum power from a PV array on varying operating conditions. So far various methods have been proposed to achieve the maximum power from PV module. The incremental conductance (INC) and perturb and observe (P&O) algorithm are more noteworthy. In this paper two high performance and simple maximum power point tracker (MPPT) are proposed. These algorithms are modified P&O and INC algorithms. These modified algorithms are capable to track maximum power under rapidly changing atmospheric conditions with higher accuracy than their conventional methods. They increase the harvested power from PV array and thus improve the efficiency of MPPT algorithm. The algorithms using a high step-up-DC/DC converter are implemented on MATLAB/SIMULINK tool. The results demonstrate a good performance and accurate tracking under rapidly changing atmospheric conditions.

Single Phase Transformerless Grid-Tied Inverter with Maximum Power Point Tracking for Solar Photovoltaic Roof-Top Systems

The key requirements for the control of the solar photovoltaic (SPV) energy conversion systems are to achieve very fast yet quite accurate tracking of the maximum power point under rapidly changing environmental conditions and to obtain efficient unperturbed tracking operation under steady environmental conditions. In this work, a high-performance Maximum Power Point Tracking (MPPT) technique, based on one cycle control (OCC), is proposed to meet these challenging requirements. This paper proposes single phase z-source inverter with maximum power point tracking using OCC for solar photovoltaic power generation. OCC is based on output current adjustment according to the voltage of photovoltaic array so as to extract the maximum power from it. A 1 kW grid-tied roof-top system is designed using the OCC method for MPP. Analysis & simulation using MATLAB tool, is carried out to validate the proposed technique

Optimized Maximum Power Point Tracker for Fast-Changing Environmental Conditions

This paper presents a high-performance maximum power point tracker (MPPT) optimized for fast cloudy conditions, e.g., rapidly changing irradiation on the photovoltaic panels. The rapidly changing conditions are tracked by an optimized hill-climbing MPPT method called dP-P&O. This algorithm separates the effects of the irradiation change from the effect of the tracker's perturbation and uses this information to optimize the tracking according to the irradiation change. The knowledge of the direction of the irradiation change enables the MPPT to use different optimized tracking schemes for the different cases of increasing, decreasing, or steady irradiance. When the irradiance is changing rapidly this strategy leads to faster and better tracking, while in steady-state conditions it leads to lower oscillations around the MPP. The simulations and experimental results show that the proposed dP-P&O MPPT provides a quick and accurate tracking even in very fast changing environmental conditions.