^{1}

^{2}

^{3}

^{*}

This paper presents an intelligent controller employing Adaptive Neuro-Fuzzy Inference System (ANFIS) for extracting maximum power from the wind energy conversion system even during the change in the wind speed conditions with improved quality of power. The proposed induction generator with multilevel inverter along with intelligent controller based Maximum Power Point Tracking (MPPT) technique aims at integrating winds system with improved maximum power injection and minimum harmonic issues. The proposed method will improve the power quality which is delivered to the grid in terms of harmonic, and inject the maximum power to the grid. To validate the effectiveness of the proposed control strategy, ANFIS controller, Fuzzy Inference System (FIS) and without MPPT controller have been presented and tested using MATLAB/Simulink environment.

In the last decade, great increase has been witnessed by the use of renewable energy due to the exhaustion of fossil fuels and different policies of industrial countries with the aim of reducing air pollution [

The algorithms without sensors track the maximum power point by monitoring the power variation. These algorithms are Perturbation and Observation (P&O) and Incremental Conductance [

Most common methods to achieve MPPT in wind turbines are Tip-Speed Ratio (TSR) algorithm, Hill-Climb Searching (HCS) algorithm and the Optimal Torque Control (OTC) [

In this work, ANFIS based approach for controlling the rectifier output and current controller for deriving gating signals for multilevel inverter are presented. The proposed scheme provides a better sinusoidal injection of current into the grid and extracts maximum power from the wind energy conversion system. It is verified with the comparison of other techniques such as fuzzy inference system and without a controller. The paper is organized as follows: mathematical modeling of wind energy conversion with the grid connected system is presented in Section 2. Section 3 explains the FIS control strategy. ANFIS based control strategy is discussed in Section 4. Results and discussions are outlined in Section 6.

The electrical behavior of the wind turbine is examined by a simplified aerodynamic model. In the proposed grid connected wind power generation system, the Induction Generator [IG] is used because, the advantages of simplicity and absence of separate field circuit can operate with constant and variable speed operational modes and naturally, it protects against short circuit. The structure of the proposed control technique of wind energy system is illustrated in

ployed to enhance the operating performance of the system. The output of variable voltage variable frequency from IG is fed to the controlled rectifier. ANFIS based control scheme has been employed to improve the output performance of the rectifier. The multilevel inverter gate drive pulse is generated by the PI current controller. PI controller is one of the control theory based control techniques and the performance of the controller depends on the controller gain. The inputs of PI controller are the error signal of DC bus voltage and inverter output current. The controlled PWM signal is obtained by a PI current controller, which is applied to the multilevel inverter gate terminals for extracting maximum power and to inject pure sinusoidal current into the grid. So, the conversion performance of the multilevel inverter gets improved.

The detailed description of ANFIS based control is described in Section 4. The DC link voltage is fed to the grid through multilevel inverter and thereby, total harmonic distortion can be reduced.

The mechanical power generated by the wind energy system is derived [

where

The available power of the wind energy system is illustrated as,

where ^{3}); ^{2});

In IG model, the output power extracting equation is needed to analyze the energy conversion of wind system. The real and reactive power flow of IG is derived in terms of voltage, flux, synchronous speed and stator resistance of the system. The real and reactive power flow equations of IG are given below,

Real power,

Reactive power,

where

The Total Harmonic Distortion (THD) of the system is expressed as follows,

Voltage THD,

Current THD,

where

The real and reactive power flow of the wind energy system to

where

where P is the absolute pressure, M is the molar mass, R is the gas constant (8.314472 J∙K^{−1}∙mol^{−1}), and T is the absolute temperature. If the pressure increases by 10%, the temperature decreases by 15% and the air density will increase about 30%. The power co-efficient and the efficiency of wind turbine are the function of the tip- speed ratio. In general, wind turbine must be operating at the maximum value ofpower co-efficient at all wind speeds. The above said problem has been eliminated by FIS and ANFIS based MPPT control strategy.

Fuzzy Inference System is a multidisciplinary computing technique based on the concepts of fuzzy set theory, fuzzy if then rules and fuzzy reasoning. The applications of FIS in a wide variety of areas like automatic control, decision analysis and time series prediction. With crisp input and outputs, a fuzzy inference system implements a non linear mapping from its input space to output space. This mapping is accomplished by a number of fuzzy if then rules, each of which describes the behavior of the mapping.

In this paper, FLC has been employed for extraction of maximum power at different wind speed to operate the wind turbine at maximum torque condition. The inputs of the FLC are error and change in error. The error value has been derived from the electrical parameters of the induction generator (voltage and current).

The result of the defuzzification has to be a numeric value which determines the change of duty cycle of the PWM signal used to drive the Switch.

Cluster 1: When induction generator voltage and current are negative big or negative small, the firing angle of the rectifier should be negative big or negative small in order to maintain the DC link voltage at the rated value.

Cluster 2: When induction generator voltage and current are zero, the firing angle of the rectifier should be zero in order to maintain the DC link voltage at the rated value.

Cluster 3: When induction generator voltage and current are positive small or positive big, the firing angle of the rectifier should be positive big or small in order to maintain DC link voltage at the rated value.

There are many methods to calculate the crisp output of the system. Centre of Gravity (CoG) method is used in the proposed system because it gives better results. In the present work, the CoG is expressed mathematically as

where Y[i] is the ith member of the output vector and F[i] are the multiplying coefficients of the output mem-

E ΔE | NB | NS | ZE | PS | PB | |
---|---|---|---|---|---|---|

NB | −1 | −0.5 | −0.5 | 0 | 0 | Cluster 1 |

NS | −0.5 | −0.5 | −0.5 | 0 | 0.5 | |

ZE | −0.5 | −0.5 | 0 | 0.5 | 0.5 | Cluster 2 |

PS | −0.5 | 0 | 0.5 | 0.5 | 1 | |

PB | 0 | 0 | 0.5 | 0.5 | 1 | Cluster 3 |

bership function as shown in

Fuzzy system output presented in surface plot which is shown

ANFIS holds the benefits of both neural network and fuzzy logic controller. In ANFIS, the fuzzy inference system is implied through the structure and neurons of the feed forward adaptive neural network. In the proposed control, the data set is developed by neural network in terms of voltage

The structure of ANFIS system consists of five layers which are categorized as the input layer, input membership function layer, rule layer, output membership function layer and output layer. The first order two input Sugeno fuzzy model is employed and it is given in

The typical fuzzy if-then rule set for the first order Sugeno fuzzy inference model can be stated as follows,

where

The circular nodes represent nodes that are fixed; whereas the square nodes are nodes that have parameters to be learnt. The structure of the ANFIS and each layer are explained below.

Layer 1: In this layer, the adaptive nodes with node functions are included; the node function is defined and given in

In

where the parameter set

Layer 2: Every node in this layer is fixed. This is where the t-norm is used to “AND”, the membership grades (for example―the product). The firing strength of the rule is calculated as

Layer 3: The

Layer 4: The nodes in this layer are adaptive and perform the consequent of the rules,

where

Layer 5: By taking the summation of all the inputs by the single node that exists in this layer indicated as

Overall output,

The overall output

In the proposed control system, the ANFIS is trained by giving the voltage vector and the current vector of IG as inputs which determine the desired PWM control pulses for rectifier. Thereby, maximum power can be extracted from IG.

After generating the initial input membership function and fuzzy rules based on the training data, fuzzy inference system is trained by the hybrid learning algorithm of neural network. One hundred epochs have been considered for training and

The structure consists of five layers. First layer is the input layer and the inputs are error and the rate of change of error. Next layer is the input membership function layer where inputs are distributed with five fuzzy sets. Third layer is the rule layer where the inputs and outputs are linked with AND operator. Fourth layer is the output membership function layer where the output is distributed with ten constant values. Last layer is the output layer which sums up all the inputs coming from the previous layer and transforms the fuzzy classification results in to a crisp value.

The proposed MPPT based control technique has been implemented in MATLAB environment and the performances are evaluated. The proposed MPPT controller performance is tested with an Induction Generator model with rating 480 V, 275 KW. IG current and voltage are applied to ANFIS and the control output is obtained. Based on the control output, the PWM pulse is generated to control the operation of the rectifier. The PI current controller provides switching signals for a multilevel inverter to reduce the harmonic distortion in the output. The operation of the PI current controller is based on the DC link voltage, grid current, and grid voltage.

The voltage from the IG, rectifier voltage, inverter output voltage and current injected to the grid and current harmonics are evaluated at different wind speed. The output voltage of the Induction Generator and the current injected by the inverter to the grid system are shown in

The harmonics are evaluated at different wind speed such as 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s, respectively. The performance of the proposed (ANFIS and PI) control strategy is compared with the existing control strategy. The maximum power extraction with the change in wind speed of 6 m/s and 14 m/s has been tested for different control strategies. It is shown in

The measurement of harmonics is used to analyze the performance of the proposed control strategy [

Wind speed (m/sec) | Maximum power (KW) | ||
---|---|---|---|

Without MPPT strategy | Fuzzy control strategy | Proposed ANFIS based control strategy | |

6 | 78.45 | 80.75 | 82.5 |

8 | 128.8 | 132.54 | 135.7 |

10 | 177.59 | 182.34 | 189.6 |

12 | 215.49 | 220.65 | 229.8 |

14 | 260.89 | 265.25 | 274.5 |

Wind speed (m/sec) | Current THD in % | ||
---|---|---|---|

Without MPPT strategy | Fuzzy control strategy | Proposed ANFIS based control strategy | |

6 | 10.94 | 9.65 | 4.62 |

8 | 10.93 | 9.16 | 4.60 |

10 | 9.63 | 9.10 | 4.50 |

12 | 8.89 | 8.39 | 3.69 |

14 | 8.31 | 8.20 | 3.17 |

The bar chart is used to analyze the deviation of the proposed control strategy which is represented in

and without an MPPT control strategy has provided 10.94% at a wind speed of 6 m/s. Hence, the power quality parameters of current injected into the grid connected system have drastically improved. Also, the proposed control strategy performs well at different wind speed with reduced current harmonics compared to the existing control strategy.

The performance of the multilevel inverter based grid-connected wind energy system has been evaluated by using the proposed ANFIS-based power extraction controlled strategy. The output current of multilevel inverter has been injected to the grid, and maximum power extracted from the wind and current harmonics have been investigated for different wind speeds. The proposed control strategy has extracted the maximum output power of 274.5 KW with reduced current harmonics of 3.17% at a wind speed of 14 m/s. The current THD deviation of the proposed strategy has been compared with the existing control strategy. The comparative analysis highlights that the proposed control strategy has less harmonics and extracts maximum power compared to the existing control strategy.

B. Meenakshi Sundaram,B. V. Manikandan,H. Habeebullah Sait, (2016) Intelligent-Based Maximum Power Extraction on Grid-Integrated Multilevel Inverter-Fed Wind-Driven Induction Generators. Circuits and Systems,07,2551-2567. doi: 10.4236/cs.2016.79221