Comparative analysis of intelligent controller based microgrid integration of hybrid PV/wind power system

Abstract

In present years the consumer power demand has been increased day by day due to pollution growth and new industry establishments. The fossil fuel based power generation system unable to meet alone the consumer demand and environmental issues. In this paper, we have been investigating different renewable energy sources for generate the electricity and environmental safety. The hybrid renewable energy sources are very effective to generate the power even though the absence of any one source. The simulation model of hybrid 60 kW PV and Wind power system developed using Matlab Simulink. The renewable energy system is operated with MPPT techniques based ANFIS controller for improving the system efficiency. The hybrid renewable energy system is presented as interconnected Microgrid system by using an intelligent controller. The proposed intelligent controller based phase loop locker show improvement in the system stability during Microgrid integration simulation. The proposed system analysed with the different intelligent controller such as fuzzy, ANN and ANFIS using Matlab Simulink environment. The simulation results are evaluated and compared with the existing system. Finally recommends the proposed system for Microgrid integration of hybrid renewable energy system.

1 Introduction

There unit many places among the ecosphere wherever the depleted zones unit established isolated from well-built humanities because of lack of convenience of power. In last decades, increasing worries for global warning and unpredictable fossil fuel prices have made renewable energy sources as an alternative source and environment friendly. The one achievable because of provide electricity for a faraway space by victimization renewable energy based entirely microgrid system. The planned topology throughout this paper consists of hybrid PV—wind energy and battery management system. a substantial literature is accessible for various topologies, hold precious power physical science for PV and Wind energy conversion systems, Intelligent controller, MPPT techniques, Grid-connected converter topologies etc demand (Indumathi et al. 2012; Zhang et al. 2013). The PV and wind energy systems unit perpetually operated with MPPT techniques employing a raise DC–DC convertor to generating most power at varied climate. throughout this paper, the extraordinarily regarded formula ANFIS MPPT techniques unit used for each PV and Wind Energy system for obtaining most power at very unsteady climate. associate intelligent controller-based voltage provide convertor is employed to integrate hybrid PV/Wind into microgrid with biface power flow capability and power quality improvement. The microgrid could also be a heap of reliable to supply its preparing to shopper with tons of smart by victimization eco-friendly resources, less transmission power losses and rising power quality compared with commonplace power plants. Thus, the planned system and management ways in which within which provides a well-designed integration of hybrid PV/Wind into a microgrid. it’s the following benefits.

• 1. The MPPT techniques applied to each PV and energy system to induce most power at very unsteady climate.

• 2. Intelligent Controller based totally Battery management system provide} responsibility of power offer to the consumer.

• 3. The planned ANFIS controller will operate in dissimilar approaches of microgrid connected topologies, ensuring correct operational conditions and sleek power transition between hybrid PV/Wind facility into Microgrid.

The paper is organized as follows. Section 2 presents the ANFIS based MPPT operation for PV system and analyze system performance. Sections 3 and 4 presents the ANFIS controller based wind / PV system power and analyze system performance. The hybrid renewable energy with grid integration simulation models and results are presented in Sect. 5. Finally, evaluation of simulation results and pronouncement of proposed research work is presented in Sect. 6.

2 MPPT controller

The renewable energy sources play a significant role to meet consumer power demand due to their ample obtainability and a smaller amount impact of environment. The main difficulty in PV energy expansion is the investment cost of the PV power system implementation (Vigneysh et al. 2013). PV energy generation is not constant throughout the day due to the change of weather. The efficiency of power generation is very low (the range of efficiency is only 9–17% in low irradiation regions). Therefore, MPPT technologies have an important role in PV power generation to operate in the maximum power generation at various weather conditions. The various MPPT methods are established with respect to usage of equipment and cost.

2.1 ANFIS controller modelling

This section presents the simulation and control of an ANFIS inference approach for MPPT of PV and Wind Power system. The MPPT controller has major rule to generate maximum power due to nonlinear weather condition. ANFIS is a graphical network representation and Sugeno type- fuzzy system, capable by neural learning capabilities. To illustrate the representational strength of ANFIS, the neural fuzzy control system consider here is based on Tagaki—Sugeno—kang (TSK) fuzzy rules whose consequent part are linear combinations of the preconditions (Shiva et al. 2017; Gupta et al. 2016).

2.1.1 PV ANFIS design

TSK fuzzy rules in the following forms for PV MPPT design, Two inputs X₁ (PV Voltage) and X₂ (PV Current) and one output Y (Duty Cycle).

R¹: IF PV Voltage is A₁¹ AND PV current is A₂¹ THEN Y = a₀¹ + a₁¹ PV Voltage + a₂¹ PV current,

R²: IF PV Voltage is A₂¹ AND PV current is A₂² THEN Y = a₀² + a₁² PV Voltage + a₂² PV current

$$Y=~~\frac{{\mu 1f1~+~\mu 2f2~}}{{\mu 1~+~\mu 2~}}$$

(1)

Where µ_j are firing strength of R^j (j = 1, 2).

2.1.2 Wind ANFIS Design

TSK fuzzy rules in the following forms for Wind MPPT design, Two inputs X₁ (Wind Voltage) and X₂ (Wind Current) and one output Y (Duty Cycle).

R¹: IF Wind Voltage is A₁¹ AND Wind current is A₂¹ THEN Y = a₀¹ + a₁¹ Wind Voltage + a₂¹ Wind current.

R²: IF Wind Voltage is A₂¹ AND Wind current is A₂² THEN Y = a₀² + a₁² Wind Voltage + a₂² Wind current

$$Y=~~\frac{{\mu 1f1~+~\mu 2f2~}}{{\mu 1~+~\mu 2~}}$$

(2)

Where µ_j are firing strength of R^j (j = 1, 2).

3 Simulation of ANFIS based MPPT controller

In this method, the ANFIS logic controller is designed for MPPT control of PV system (Vide Fig. 1). This controller has two major parts such as ANN controller design and the input and output membership function of fuzzy logic controller and then forming the fuzzy IF-then rules. Finally, the fuzzy controller generates the duty cycle based on input data.

Fig. 1

ANFIS controller based MPPT for PV system

Step 1: The ANFIS controller, network has been training using input data, such as PV parameters under various weather conditions and target data (duty Cycle) based on changing inputs. The above process has been developed using Neuro-Fuzzy training tools in MATLAB environment (Vide Figs. 2, 3, 4).

Fig. 2

MATLAB simulation model for ANFIS controller based MPPT for PV system

Fig. 3

ANFIS controller network for MPPT of PV system

Fig. 4

ANFIS training for MPPT of PV system

Step 2: The trained data are validated and tested to provide best solution. The ANN controller network is developing fuzzy input and output membership functions and then fuzzy IF-Then rules after completing the processes of training, testing and validation (Venkateshkumar 2017).

Step 4: Finally, ANFIS controller is implemented for MPPT control of PV system. The ANN controller generates the duty cycle based on input data and then the signal is fed into a PWM generator to generate the pulse for DC–DC converter.

3.1 Results

The ANFIS controller is implemented in MPPT simulation model and simulation at various weather conditions and rules are presented in Table 1. The simulation results of PV power generation waveform are analyzed with different solar irradiation conditions such as 250, 500, 750 and 1000 W/M² as shown in Fig. 5.

Table 1 Fuzzy rules – PV MPPT

Fig. 5

a ANFIS based 10 kW PV system output power waveform at various irradiance. b ANFIS based 10 kW PV system output voltage waveform at various irradiance

4 PMSG design with and without MPPT controller

A control method to control wind turbine rotor speed by controlling the generating controlling torque which is called MPPT (maximum Power Point Tracking) controller. The blade pitching drive produces a delay in response time with response to act accordingly with change in wind conditions, such as turbulent and gusty winds, which plays an influence role on the energy yield and causes subsequent mechanical stress on the wind turbine. Conversely, to maximize the electric power production, the generator rotor speed can be controlled electrically. MPPT based control techniques are been developed with a motive of achieving the maximum power coefficient. The generator output power, in a variable speed wind energy system is efficiently controlled with the help of a power electronics based converters. In this paper, the author analyzed the ANFIS based MPPT of Wind power system (Mokryani et al. 2013).

The proposed PMSG design parameter as follows

• 1. Mechanical output power = 10,000 W

• 2. Maximum voltage Vmax = 230 V

• 3. Maximum current Imax = 43 amp

• 4. Base electrical generator power = 9999.77 VA

• 5. Wind speed—base = 12 m/s

• 6. Rotational speed—base = 1 p.u

• 7. At base wind speed, maximum power = 0.8 p.u

• 8. Pitch angle = 0 deg

The proposed system has been simulated MATLAB Simulink environment at various weather conditions as shown in Fig. 6. The turbine mechanical power vs generation speed Characteristics are presented in Fig. 7.

Fig. 6

simulation model of PMSG based WES without MPPT controller

Fig. 7

Wind turbine characteristics

4.1 Modeling an ANFIS based MPPT

The estimation of the wind speed using the ANFIS concepts is presented here. The ANFIS is trained routinely by least-square inference and the back-propagation algorithm. Figure 8 shows the structure of ANFIS with two inputs and one output. According to the designed model, the ANFIS network could make decision to estimate maximal wind speed to be achieved based on the inputs/outputs data used for training.

Fig. 8

Structure of ANFIS controller

The ANFIS controller has been designed for MPPT controller for Wind Energy system as presented in Fig. 8. The proposed ANFIS MPPT controller has two input triangle membership function such as input one WES voltage and second input current. The rules are presented in Table 2. The output of fuzzy control is triangle membership function such as duty cycle. ANFIS based MPPT controller reference model is developed using ANFIS editor of MATLAB/Simulink software package (Figs. 9, 10, 11, 12, 13, 14, 15, 16).

Table 2 Fuzzy rules—wind MPPT

Fig. 9

ANFIS based MPPT controller for WES with buck converter

Fig. 10

ANFIS control system training

Fig. 11

ANFIS control system output

Fig. 12

ANFIS control system rules

Fig. 13

WECS voltage and current RMS waveform

Fig. 14

WECS output power and wind speed waveform

Fig. 15

WECS generator RPM and duty cycle waveform

Fig. 16

a WECS power output waveform at various wind speed (ANFIS). b WECS power output waveform at various wind speed (ANFIS)

5 Microgrid integration of hybrid PV/wind power system

The proposed Microgrid power system model is presented in Fig. 17. The proposed model has been simulated in Matlab environment. The simulation has been developed hybrid PV/Wind power and design intelligent controller for voltage source converter (Qi et al. 2017; Liu et al. 2013) (Fig. 18).

Fig. 17

Proposed microgrid integration of hybrid PV/Wind power system

Fig. 18

Hybrid PV/wind power system

The control of voltage source converter has phased locked loop, Voltage regulator and current regulator as shown in Fig. 19. The current regulator has been operated with intelligent controller and analysis their operation with different intelligent controller such as Fuzzy and ANN (Mokryani et al. 2013; Qiao et al. 2011). Finally compare the simulation results and its performance are evaluated then choose best controller for Microgrid (Zhao Dongmei et al. 2012; Samir et al. 2018).

Fig. 19

Voltage source converter (VSC) controller

5.1 Fuzzy controller for VSC

The fuzzy logic controller has been designed for current regulator for VSC. The fuzzy controller has two input signals and two output signals such as input (direct axis and quadrature axis current) output (regulated direct axis and quadrature axis current) shown in Fig. 20 (Ferraro et al. 2017; Rana et al. 2017). The direct axis current value is positive converter deliver the active power to Microgrid and quadrature axis current value is negative converter observe the reactive power from Microgrid (Pathak et al. 2014; Zhao et al. 2017). The proposed fuzzy control has been applied hybrid PV /Wind with Microgrid and its voltage source converter as shown in Fig. 21. The Matlab simulation results are presented such three-phase voltage wave form and three phase THD value as shown in Figs. 22, 23, 24 and 25 respectively.

Fig. 20

Fuzzy controller membership function and its rules for current regulator of VSC

Fig. 21

Fuzzy based current regulator

Fig. 22

Microgrid integration of hybrid PV and wind power system voltage waveform

Fig. 23

Voltage at phase R—THD level

Fig. 24

Voltage at phase Y—THD level

Fig. 25

Voltage at phase B—THD level

5.2 Fuzzy based results

Figures 22, 23, 24 and 25.

5.3 ANN controller for VSC

The ANN controller has been developed for current regulator of VSC as shown in Fig. 26 (Farrokhabadi et al. 2018). The ANN controller has been trained by back propagation method and provides the 70% data for training, 15% data for testing and 15% data for validation as shown in Fig. 27. The error of ANN training data as shown in Fig. 28. After ANN training the network has been analysed overall results as shown in Fig. 29 and finally ANN network has been developed after successful training for Current regulation for VSC as shown in Fig. 30. The proposed ANN controller is applied for VSC of hybrid PV/Wind based Microgrid system (Milczarek 2017; Sedghi and Fakharian 2017). The simulation results are presented such as three phase voltage waveforms and three phase voltage THD as shown in Figs. 31, 32, 33 and 34 respectively.

Fig. 26

ANN based current regulator

Fig. 27

ANN training, testing, validation and over all performance

Fig. 28

ANN training performane

Fig. 29

Over all performance of ANN network

Fig. 30

proposed ANN controller for VSC

Fig. 31

Microgrid integration of hybrid PV and wind power system voltage waveform

Fig. 32

Voltage at Phase R—THD level

Fig. 33

Voltage at Phase Y—THD level

Fig. 34

Voltage at Phase B—THD level

Finally, the different intelligent controller results are compared as shown in Table 1 and the results are evaluated with IEEE 1547.

5.4 ANN based micro grid system

Figures 31, 32, 33 and 34.

5.5 ANFIS controller for VSC

The ANFIS controller has been developed in MATLAB Simulink environment for current regulator. This controller has been designed with two inputs and two outputs namely, error value of I_d and I_q and regulated I_d and I_q respectively (Vide Fig. 35).

The error value of I_d and I_q can be calculated by the difference between I_d, I_q reference generated by voltage regulator and I_d, I_q measured. Based on training of ANN network fuzzy input, output membership function and fuzzy rules are generated by controller (Vide Figs. 36, 37).

Fig. 35

ANFIS Controller model for current regulator

Fig. 36

ANFIS Controller training for current regulator

The ANFIS output signal is fed into feed forward current regulator of converter. Figure 38 presents voltage and current waveform of ANFIS controller based grid integration of Hybrid PV–FC power system. Its total harmonics distortions values are presented in Figs. 39, 40 and 41 respectively.

Fig. 37

ANFIS controller rules for current regulator

Fig. 38

Voltage and current waveform ANFIS controller based grid integration hybrid PV–FC power system

Fig. 39

Voltage at phase R—THD level

Fig. 40

Voltage at phase Y—THD level

Fig. 41

Voltage at phase B—THD level

6 Conclusion

The renewable energy based Microgrid power system has been modeled and simulated in Matlab Simulink software. The PV and Wind system has been simulated and controlled by ANFIS controller then analyzed simulation results under weather condition. The hybrid PV/ Wind power system is modeled and integrated with Microgrid power system by using ANFIS controller. The proposed system analyzed with various intelligent control such as Fuzzy, ANN and ANFIS. The simulation results are evaluated and compared with dissimilar intelligent controller performance. The Table 3 presented simulation results of ANFIS controller based VSC has minimum level THD observed compared with fuzzy and ANN based VSC and improve the power system stability as well as quality in all three phases. Finally based on simulation results performance the ANFIS controller has been recommended to hybrid PV/Wind based Microgrid power system.

Table 3 Comparison THD of proposed system for fuzzy and ANN