A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel

The electrical energy produced by photovoltaic (PV) process is inexhaustible, developable everywhere and clean. Whatever the conditions, it is desirable to extract the biggest amount of power from the solar panel. This is achieved with the use of a Maximum Power Point Tracking (MPPT) algorithm. Fluctuations in weather conditions (irradiation and temperature) strongly degrade the performance of the photovoltaic module's energy conversion and therefore all the power cannot be transferred to the load. The objective is to study and compare different approaches of MPPT algorithms to evaluate the power extracted under the standard test conditions and varying weather conditions. Results of the performance with all these algorithms are compared under different operating conditions. The results show that the Fuzzy Logic Controller (FLC) is the fastest in terms of stabilization and is followed respectively by Fractional Short-Circuit Current (FSCC), Fractional Open-Circuit Voltage (FOCV), Perturb and Observe (P&O), Incremental Conductance (INC) and Hill Climbing (HC) algorithms. The FLC also gives the best results in extracting, followed by P&O INC, HC, FSCC and FOCV algorithms

A comparative study of MPPT algorithms to optimal power extraction of a photovoltaic panel

The demand for electrical energy has been increasing in recent years as well as the constraints related to its production. Thus, more electrical energy will be produced by photovoltaic process which is the conversion of sunlight into electricity because it is inexhaustible energy, developable everywhere, clean and requires little maintenance. The drawbacks of this source of energy are the intermittency of the photovoltaic source and the power supplied by the photovoltaic generator depends on unpredictable meteorological conditions. Among the solutions to remedy this, it is possible to consider the storage of energy in batteries and the implementation of a strategy of the maximum power point tracking (MPPT) to extract at any time the maximum power. Indeed, the improvement of the efficiency of the photovoltaic generator requires optimal operation of the DC-DC converters used as an interface between the photovoltaic generator and the load to be powered. The principle of this control is based on the automatic variation of the duty cycle of the converters by bringing it to the optimal value so as to maximize the power delivered by the photovoltaic module. This work studies and compares the most popular MPPT algorithms. The Perturb and Observe (P&O) algorithm is based on a periodic disturbance of the voltage at the terminals of the photovoltaic module and the comparison of the output power of the photovoltaic module with that of the previous disturbance cycle. The INcremental of Conductance (INC) algorithm uses the knowledge of the conductance value and the increment of conductance to derive the position of the operating point from the point of maximum power. The Hill Climbing (HC) algorithm is based on the duty cycle in each sampling period that is determined by comparing the current power to the previous one. The algorithm for measuring a fraction of the open-circuit voltage (FCO) is based on the linear relationship between the open circuit voltage and the voltage at the peak power point. The algorithm for measuring a short circuit current fraction (FCC) is a technique based on the linear relationship between the short-circuit current and the current at the point of maximum power. Finally, the fuzzy logic control algorithm (FLC) works with inaccurate inputs that do not require a precise mathematical model. All of these techniques have been implemented under the MatLab/Simulink environment to manage the duty cycle. The comparison of the results obtained under different operating conditions shows that the FLC algorithm is the fastest in terms of stabilization time with a response time of 0.005 seconds. It shows good oscillation behavior around the operating point. The latter is followed by the P&O algorithm with a response time of 0.06 seconds, by the INC algorithm with a time of 0.07 seconds, by the Hill Climbing (HC) algorithm with a response time of 0.08 seconds, by the FCC and FCO algorithms with a response time of 0.10 and 0.11 seconds, respectively. The power of the different MPPT algorithms is evaluated at the maximum power point with a 40 W photovoltaic module. The fuzzy logic control algorithm (FLC) gives the best results by extracting 39.8 W, followed by P&O algorithms (38 W), INC (37.5W), HC (36W), FCC (35W) and FCO (34W)

Real-Time Experimental Assessment of Hill Climbing MPPT Algorithm Enhanced by Estimating a Duty Cycle for PV System

Better functioning of maximum power point tracking (MPPT) can significantly increase the energy efficiency of photovoltaic systems. This process is provided by MPPT algorithms. Such as fractional open-circuit voltage, perturb and observe, fractional short-circuit current, hill climbing, incremental conductance, fuzzy logic controller, neural network controller, just to name a few. The hill climbing algorithm uses the duty cycle of the boot converter as a retraction parameter when the MPPT task is performed. However, this technique has disadvantages in terms of the stability of the system during periods of constant radiation. To overcome this disadvantage, A MPPT technique based on the estimation of the boost converter duty cycle associated with the conventional hill climbing, fractional open-circuit voltage and fractional short-circuit current algorithm is proposed. A comprehensive description of the experimental implementation hardware and software platforms is presented. On the basis of the measured data, the enhanced algorithm was compared to the conventional hill climbing MPPT technique according to various criteria, showing the disadvantages and advantages of each. Experimental results show advantage of the enhanced algorithm compared to the conventional hill climbing MPPT technique in time response attenuation (0.25s versus 0.6s), little oscillations (0.5 W versus 2.5 W), power loss reductions and better maximum power point tracking accuracy (98.45 W versus 92.75 W) of the enhanced algorithm compared to the conventional hill climbing MPPT technique