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In this paper, a fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is proposed. The power variation and output voltage variation are chosen as inputs of the proposed FLC, which simplifies the calculation. Compared with the conventional perturb and observe (P&O) method, the proposed FLC-based MPPT can simultaneously improve the dynamic and steady state performance of the PV system. To further improve the performance of the proposed method, an asymmetrical membership function (MF) concept is also proposed. Two design procedures are proposed to determine the universe of discourse (UOD) of the input MF. Comparing with the proposed symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by 42.8% and 0.06%, respectively.

In recent years, the concerns over greenhouse gas emission and the ever rising fuel prices have stimulated urgent demands for alternative energy. Government incentives and the soaring cost of fossil fuels have significantly promoted the development of renewable energies. Among them, solar energy is one of the most important green energy resources due to its environmental sustainability and inexhaustibility [

In terms of the input variable selection, most FLC-based MPPT techniques take the error [_{PV}/d_{PV} or d_{PV}/d_{PV}] and the error variation [Δ_{pv}) and voltage or current variation (Δ_{pv} or Δ_{pv}) as inputs, which avoids the precision loss and overflow problem when dealing with fixed-point division, thus simplifies the calculation. The inputs in [_{PV}/d_{PV} and _{MPP} − _{PV}), while the inputs in [_{MPP} − _{PV}) and error variation [Δ

Regarding the design of input/output membership functions (MFs), it is known that the input/output MFs design has a great impact on FLCs' performance. In order to deal with this issue, genetic algorithm (GA), particle swarm optimization (PSO) and Hopfield artificial neural network (ANN) are proposed in the literatures to optimize the FLC MFs [

The aim of this paper is to design a low cost, high efficiency MPPT algorithm. In this paper, power variation (Δ_{pv}) and output voltage variation (Δ_{pv}) are chosen as the inputs of the proposed FLC. The design of the FLC scheme and rule table will be introduced in detail first. To further improve the performance of the proposed MPPT method, an asymmetrical MF concept is proposed. A systematic design procedure verified by grid-search method will be proposed to determine the universe of discourse (UOD) of the input MF. Finally, a low cost digital signal controller (DSC) will be used to realize the proposed method. Experiments are then conducted to validate the correctness and effectiveness of the proposed system. According to the experimental results, the proposed asymmetrical FLC-based MPPT method can simultaneously shorten the tracking time and increase the tracking accuracy comparing with the traditional P&O and symmetrical FLC-based MPPT algorithm.

The block diagram of the proposed system is shown in

Energy conversion unit: the energy conversion unit is used to supply the power to the load. The topology of the energy conversion unit is illustrated in

The relationship between output voltage and input voltage in boost converter can be expressed as:

From

Main control unit: As shown in _{i}

After the filtered PV voltage and current are obtained, needed gating signals are then determined using the developed MPPT controller. In this paper, three different MPPT schemes will be implemented for performance comparison, detailed description of these MPPT methods will be provided in Section 3.

Data logging system: In order to validate the effectiveness of the proposed method, long term recording of the operating condition is required. Solar array simulator (SAS) TerraSAS DCS80-15 from AMETEK Corp. (Berwyn, PA, USA) is adopted as the input power source in this paper. This simulator features long-term recording function with the recording time interval as short as 0.05s. The recorded data will be stored to a spreadsheet file for further analysis.

_{K}_{S}_{SH}_{SC}_{PV}_{O}

^{2} irradiation level is given in _{pv}_{pv}

To achieve MPPT, a fuzzy controller is used to determine the required perturbation step size. The proposed fuzzy controller is carried out every 20 ms. The block diagram of the implemented FLC-based MPPT controller is shown in

In this paper, the inputs of the proposed MPPT controller are the power variation (Δ_{pv}) and the voltage variation (Δ_{pv}). The MFs of the utilized input and output variables for the proposed controller are illustrated in _{pv} and Δ_{pv} MFs are in triangular form. _{pv} and Δ_{pv} is mapped into five different linguistic values. Therefore, the proposed FLC will contain 25 different rules. The complete set of the fuzzy control rules for the proposed system will be explained in Section 3b. The defuzzification method used in this paper is the commonly used center of gravity method as shown in _{i}_{i}_{COG}

It is known that the FLC performance will be affected by the design of MFs. Generally, the shape of MFs in FLC can be in triangular, trapezoidal, symmetric Gaussian function, generalized Bell curve and sigmoidal function forms. Since a low cost DSC will be used in this paper to realize the proposed FLC-based MPPT controller, triangular MF is adopted to reduce the computation complexity. Next, the UOD of the MFs will be determined by the following procedures:

Designing the UOD of the output MF: In this paper, duty cycle variation is chosen to be the output variable. The advantage of using duty cycle as the control variable is its simplicity. For direct duty control, no close-loop control is needed to achieve voltage control for MPPT realization [_{max}, is set as 5%;

Designing the UOD of input MFs: In this context, voltage variation Δ_{pv} and power variation Δ_{pv} are chosen to be input variables. The UOD of Δ_{pv} and Δ_{pv} can be determined by the following procedures:

With a 1% step size, a profile can be plotted by increasing duty cycle from 0% to 100% with ^{2}, 25 °C). From _{pv} (denoted as d_{max}). Likewise, the UOD of Δ_{pv} (denoted as d_{max}) can be determined in similar ways. In this paper, d_{max} is 1.5 V and d_{max} is 8.2 W.

For control rule design, since Δ_{pv} and Δ_{pv} are taken as the inputs and Δ_{pv} and Δ_{pv} is mapped into five different linguistic values. Therefore, the rule base of the proposed FLC will contain 25 different rules. The basic principle of designing the rules is explained as follows:

As mentioned in Section 2, boost converter is utilized in the paper. As shown in

In Section 3, the design procedures of the proposed symmetrical FLC have been explained. To further improve the performance of the proposed FLC, the derivation of asymmetrical input MF technique will be explained. _{pv}, it is essential to take this phenomenon into consideration. To deal with this issue, a variety of artificial intelligent (AI) methods have been proposed in the literatures to obtain the optimal configuration of the MFs. These AI methods include GA, PSO and Hopfield ANN [_{pv} has relatively larger impact on the proposed FLC-based MPPT controller, which implies that the problem complexity can be reduced if only the Δ_{pv} optimization is taken into account. Therefore, grid search technique is employed in this paper to obtain the optimal design of the UOD of Δ_{pv} _{max}/100 W (referring to _{max} = 2 × d_{max} in this paper), and the d_{max}/100 W to NEG_{max} W (NEG_{max} = −1 × POS_{max}) through a fixed increment of NEG_{max}/100, yielding a total of 100 points. Subsequently, the d_{max}/100 W through a fixed increment of POS_{max}/100; with this new value, the previous procedure, in which the d_{max} W. This test method produced 10,000 (100 × 100) combinations of simulation test points. Then, according to these possible UOD combinations, simulation is conducted under a step change in solar irradiation from 0 to 1000 W/m^{2} for the proposed system. The steady-state tracking time can then be observed in a 3D plot in which the z-axis represents the numbers of tracking steps. Consequently, it can be concluded that the point with the lowest _{pv} MF remains as triangle shape and the ratio of large value to small value remains as 2 to 1 (that is, d

Because grid search method is capable of searching every possible region and its generated 3D surface is a smooth surface; therefore, the proposed grid search method can yield optimal results. However, grid search method requires large amount of simulations (20,000 simulations in this case), thus increases the complexity of design. Consequently, a simple design method which can be used to determine the UOD of the MF of Δ_{pv} is also proposed in this paper. It can also be observed from _{pv} remains unchanged, the asymmetrical UOD problem can be adequately addressed by setting the ratio d_{pv} with quite satisfactory performance. Experimental results for these two methods will be provided in Section 5 for comparison.

To verify the correctness of the proposed FLC-based MPPT controller, a 300 W prototyping circuit is implemented from which experiments are carried out accordingly. The proposed algorithm is validated using an AMETEK Solar Array Simulator TerraSAS DCS80-15 in SAS mode. The parameters of the utilized PV panel are listed in ^{2} solar irradiance and 25 °C PV panel temperature. From

The performances of the tested methods are summarized in _{pv} MF; therefore, the implementation complexity of these two methods is the same. That is, asymmetrical FLC-based MPPT method can enhance the tracking speed and tracking accuracy over symmetrical FLC-based MPPT without increasing the calculation burden. To further validate the performance improvement of the proposed methods, simulations on the full day energy production are also provided. The obtained data is also provided in

In this paper, a FLC-based MPPT method is proposed for the first time. The design and implementation of the proposed method is discussed in detail in this paper. By using the power variation and voltage variation as the inputs, the calculation is simplified. Comparing with the conventional P&O method, the proposed MPPT method can satisfactorily address the tradeoff between the tracking speed and steady state oscillations. To further improve the performance of the proposed MPPT method, an asymmetrical membership function concept is proposed. Two design procedures are presented and experiments are carried out to validate the effectiveness of the proposed method. Comparing with the symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by about 42.8% and 0.06%, respectively.

The authors are indebted to utility-scaled energy storage system and interconnection technology development project from the Bureau of Energy, Ministry of Economic Affairs for supporting this study.

The authors declare no conflict of interest.

The block diagram of the proposed system.

The topology of the energy conversion unit.

Equivalent circuit of the photovoltaic (PV) cell.

(

Block diagram of the implemented FLC-based maximum power point tracking (MPPT) controller.

Membership functions of the input and output variables (_{pv} and Δ_{pv}; (

Concept of determining the universe of discourse (UOD) of input MFs (_{pv}; (_{pv}.

Typical

Concept for determining the UOD of the input variables Δ_{pv}.

The implementation concept of the grid search method (different d

The obtained 3D simulation result for different UOD configuration.

Measured starting waveform of five different algorithms. (

Complete rule base for the proposed FLC.

_{pv} |
Δ_{pv} | ||||
---|---|---|---|---|---|

| |||||

NB | NS | ZE | PS | PB | |

NB | NS |
NB |
PB |
PB |
PS |

NS | ZE |
NS |
PS |
PS |
ZE |

ZE | ZE |
ZE |
ZE |
ZE |
ZE |

PS | ZE |
PS |
NS |
NS |
ZE |

PB | PS |
PB |
NB |
NB |
NS |

Parameters of the utilized PV panel.

Maximum Power (P_{max}) |
220 W | Short Circuit Current (I_{sc}) |
5.65 A |

Open Circuit Voltage (V_{oc}) |
52.3 V | Maximum Power Current (I_{mp}) |
5.17 A |

Maximum Power Voltage (V_{pm}) |
42.7 V | Temperature Coefficient (α_{v}) |
−0.336%/°C |

Specification of the utilized boost converter.

Input Voltage (_{in}) |
40–70 V |

Rated Output Voltage (_{o}) |
160 V |

Rated Ouput Current (_{o}) |
2.5 A |

Rated Output Power (_{o}) |
400 W |

Switching Frequency (_{s}) |
50 kHz |

Output Voltage Ripple (Δ_{o}/_{o}) |
<1% |

Parameters of the implemented algorithms.

1 | P&O (0.5%) | Fixed Duty Cycle 0.5% | denoted as method 1 |

2 | P&O (5%) | Fixed Duty Cycle 5% | denoted as method 2 |

3 | Symmetrical FLC | d |
denoted as method 3 |

d | |||

4 | Asymmetrical FLC #1 | d |
denoted as method 4 |

d | |||

5 | Asymmetrical FLC #2 | d |
denoted as method 5 |

d |

Summarized performance of methods.

^{1} | ||||
---|---|---|---|---|

P&O (0.5%) | 206.89 W | 92.84% | 0.25 s | 1466.07 Wh |

P&O (5%) | 222.45 W | 99.82% | 3.13 s | 1561.15 Wh |

Symmetrical FLC | 222.58 W | 99.87% | 1.28 s | 1571.66 Wh |

Asymmetrical FLC #1 | 222.65 W | 99.91% | 0.97 s | 1573.92 Wh |

Asymmetrical FLC #2 | 222.69 W | 99.93% | 0.91 s | 1574.56 Wh |

The full day energy production is 1580.45 Wh for ideal condition.