Wiley

Microgrid dynamic responses enhancement using artificial neural
network-genetic algorithm for photovoltaic system and fuzzy
controller for high wind speeds
Alireza Rezvani1,
*,†
, Maziar Izadbakhsh1
and Majid Gandomkar2
1
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran
2
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
ABSTRACT
The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage
devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking
(MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind
speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the
photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized
by GA, and then these optimum values are used in ANN. The simulation results show that the ANN-GA in compar-
ison with the conventional algorithms with high accuracy can track the peak power point under different insolation
conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to
achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that
have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The
models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.
Received 28 July 2014; Revised 19 March 2015; Accepted 12 May 2015
KEY WORDS: microgrid; photovoltaic; permanent magnet synchronous generation (PMSG); neural network;
genetic algorithm
1. INTRODUCTION
The application of distributed energy resources (DER) is proposed to provide efficient and reliable
power to electricity customers closer to the point of use. They are usually clean, renewable, small,
flexible and have become important elements in a diversified set of alternative generation sources.
Interconnection networks of distributed energy resources, energy storage systems and loads define a
MG that can operate in stand-alone or in grid-connected mode [1, 2]. The MG is disconnected automat-
ically from the main distribution system and change to islanded operation when a fault occurs in the
main grid or the power quality of the grid falls below a required standard. MGs are capable to improve
the reliability of electrical energy supply if appropriate control techniques are implemented. It can rep-
resent a complementary infrastructure to the utility grid due to the rapid change of the load demand. In
grid- connected mode, the grid dominates most of the system dynamics and no significant issues need
to be addressed except the power flow control, whereas in the islanding mode, once the isolating switch
disconnects the utility from the MG. The MG concept enables high penetration of distributed genera-
tion (DG) without requiring re-design or re-engineering of the distribution system itself [3, 4].
Developing photovoltaic energy sources can reduce fossil fuel dependency. PV panels are low-
energy conversion efficient; therefore, using the MPPT system is recommended. In other words, the
*Correspondence to: A. Rezvani, Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran.
†
E-mail: alireza.rezvani.saveh@gmail.com
INTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS
Int. J. Numer. Model. (2015)
Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/jnm.2078
Copyright © 2015 John Wiley & Sons, Ltd.
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output power of a PV module varies as a function of the voltage, and also the MPP point is changed by
variation of temperature and sun irradiance [5].
The most prevalent techniques are perturbation and observation (P&O) algorithm [5], incremental
conductance (IC) [6, 7], fuzzy logic [8, 9] and ANN [10–12]. P&O and IC can track the MPP all
the time, regardless of the atmospheric conditions, type of PV panel, by processing real values of
PV voltage and current. Due to the aforementioned inquiries, the profits of P&O and IC methods are
low cost execution and elementary method. One of the drawbacks of these techniques is vast variation
of output power around the MPP even under steady state; therefore, it caused the loss of available
energy more than the other methods [13, 14]. Nevertheless, rapid changing of weather condition affects
the output power, and these methods cannot track easily the MPP.
Using fuzzy logic can solve the two mentioned problems dramatically. In fact, fuzzy logic controller
can reduce oscillations of output power around the MPP and losses. Furthermore, in this way, convergence
speed is higher than the other two ways mentioned. A weakness of fuzzy logic in comparison with ANN
refers to oscillations of output power around the MPP [15, 16].
Nowadays, artificial intelligence (AI) methods have numerous applications in determining the size of
PV systems, MPPT control and optimal structure of PV systems. In most cases, multilayer perceptron
(MLP) neural networks or radial basis function network (RBFN) are employed for modeling PV module
and MPPT controller in PV systems [17, 18]. ANN-based controllers have been applied to estimate
voltages and currents corresponding to the MPP of PV module for irradiances and variable temperatures.
A review on AI techniques applications in renewable energy production systems has been presented in
these literatures [10, 19].
In [20–22], GA is used for data optimization, and then, the optimum values are utilized for training
neural networks, and the results show that, the GA technique has less fluctuation in comparison with
the conventional methods. However, one of the major drawbacks in mentioned papers is that they
are not practically connected to the grid in order to ensure the analysis of PV system performance.
As one of the eminent DG sources, wind power generation system (WPGS) is presented [23, 24]. Also,
amongst the synchronous and asynchronous generators, permanent magnet synchronous generator (PMSG)
is more favorable due to self-excitation, lower weight, smaller size, less maintenance cost and the elimination
of gearbox have high efficiency and high power factor comparing to Wound Rotor Synchronous Generator
(WRSG), Squirrel Cage Induction Generator (SCIG), Doubly Fed Induction Generator (DFIG) and so on.
The PMSG does not require a supplementary supply for magnetic field excitation or slip rings and brushes.
Moreover, they can operate in a relatively vast range of wind speeds [24, 25]. The main advantage of
variable wind turbines is the capability of the MPPT from wind energy sources [26].
The major disadvantage of the PMSG is the risk of demagnetization caused by too high temperatures
or high currents. However, in order to obtain the maximum power of wind energy, using a MPPT system
is too indispensable. Variable speed wind turbines operate in two primary regions as below rated power
and above rated power. When power production is below the rated power for the machine, the
turbine operates at variable rotor speeds to capture the maximum amount of energy available in
the wind [27, 28]. Generator torque provides the control input to vary the rotor speed, and the blade
pitch angle is held constant. In above-rated power conditions, the primary objective is to maintain a
constant power output. This is generally achieved by holding the generator torque constant and
varying the blade pitch angle. MPPT controller somehow changes the rotor speed according to
variations of wind speed that the tip speed ratio (TSR) is maintained in optimum value.
One of the approaches to reach the MPPT is pitch angle control (B) which in small turbines with low
power delivery is not possible due to mechanical difficulties in production [29]. In high speed wind the extra
production of active power via wind turbine leads to increased consumption of reactive power in generator,
and in which case, we should utilize the reactive power compensator for injecting reactive power that has
extra cost, too. Moreover, in above rated wind speed operation, mechanical erosion and damages will make
us to have more maintenance cost, and this leads us to use controller with fast and suitable response.
The PIDs are used mostly in controllers design, but by the introduction of fuzzy logic instead of PID
created a better performance such that it was the best preventative way to eliminate the profound
mathematical understanding of the system. In comparing PIDs and fuzzy logic systems, fuzzy logic has
more stability, faster and smoother response, smaller overshoot and does not need a fast processor; also it
is more powerful than other non-linear controllers [30, 31]. The pitch angle based on fuzzy logic controller
A. REZVANI, M. IZADBAKHSH AND M. GANDOMKAR
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Numer. Model. (2015)
DOI: 10.1002/jnm

is reported in [32–34]. In [34], active power and in [32, 33], both reactive power and rotor speed are imple-
mented as input signals and because in mentioned items wind speed is neglected, the controller has not fast
response which causes mechanical fatigues to the PMSG. Moreover, another drawback in these papers is
that it is not practically connected to grid to investigate the system performance [33–35].
Microturbine generation (MTG) in recent decades because of their small size, relatively low cost,
low pollution, fuel diversity, low maintenance cost, relatively simple control and ability to operate in
both grid-connected and stand-alone modes has also received a lot of attention [36, 37], and this model
taking control, speed, temperature, acceleration and fuel is developed.
Flywheel energy storage system (FESS) is an energy storage technology which can transform
electrical energy into mechanical energy. It has fast response, high dynamics, long life, good efficiency
and characteristics of infinite times of charge and discharge. However it has small storage capacity and
high initial cost. The flywheel can be used alone to supply loads in the short-term failure of system,
which can increase electric reliability, and stabilize the power fluctuations of DGs and loads [38].
In [39], the dynamic characteristics of a grid connected MG associated with power conditioning
system (PCS) to regulate its power have been investigated. Also, four-quadrant operation of PCS
and utilization of PCS to control the power of MG are reported. The MG during grid connected and
islanding modes is presented in [40]. In [41], the MG’s grid connected operation during and subse-
quent to the islanding mode was investigated; however, the dynamic model of distributed generations
(DGs) is not considered, which has a tremendous effect on dynamic responses of the MG subsequent to
islanding occurrence. Moreover, DGs (wind, PV, MTG, FC and etc.) are not included in their model.
Virtually, in previous references the grid connected process has not reported the influence of wind
speed deviations in dynamic responses of the MG, especially the islanding occurrence. In [42], a typ-
ical configuration of an MG including three DGs was presented but it has not been analyzed the DG
structure, controllers of each micro source and fault occurrence. The P&O method in PV and wind sys-
tem in the MG is addressed in [43], while the P&O method has enormous deviation of output power
around the MPP. Also, in the aforementioned paper, there is not any controller (pitch angle control)
in order to control the output power of WT in high speed which can lead to the damages to PMSG;
besides, the P/Q control technique for wind system was not utilized in inverter.
The main objectives of the present study to overcome the disadvantages of the aforementioned
references are as follows: (i) it is worth to mention that the major part of ANN is the desired data
for training process should be achieved for each PV system and each particular position. First PV
system is simulated, then GA-based offline trained ANN is applied to provide the reference voltage
corresponding to the maximum power by using Matlab software. Temperature and irradiance as input
data are given to GA, and optimal voltages (Vmpp) corresponding to MPP are obtained, and then these
optimum values are used in neural network training. (ii) The FLC (for pitch angle) is proposed to
smooth the output power fluctuations of WT in above rated speed and a comparison of the
performances of the FLC with the conventional PI and GA controller.
The paper is organized as follows: In section 2 the structure of photovoltaic module has been
described. In section 3 the steps of implementing genetic algorithm and neural network are discussed.
In section 4 PMSG generator and pitch angle controller based on fuzzy logic are discussed. In section 5
the MTG system is explained. In section 6 FESS is investigated. In section 7 P–Q, droop and backup
controllers are described. In section 8 the results are presented based on case studies. Finally, the
conclusion is presented in section 9.
2. PHOTOVOLTAIC CELL MODEL
A PV module is a collection of PV panels. A PV cell can be represented by an equivalent circuit, as
illustrated in Figure 1. The characteristics of the PV cell can be represented by the following equations
[5, 10, 12]:
IPV ¼ Id þ IRP þ I (1)
I ¼ IPV À I0 exp
V þ RSI
Vthn

À 1

À
V þ RsI
RP
(2)
MICROGRID; PHOTOVOLTAIC; PMSG; NEURAL NETWORK; GENETIC ALGORITHM
Copyright © 2015 John Wiley Sons, Ltd. Int. J. Numer. Model. (2015)
DOI: 10.1002/jnm

Vth ¼
NskT
q
(3)
I0 ¼ I0;n
Tn
T
3
exp
q*Eg
n*k
1
Tn
À
1
T

(4)
where, I is the output current, V is the output voltage, Ipv is the photocurrent of the PV cell (A), Id is the
diode current, IRP is the shunt leakage current, I0 is the diode reverse saturation current and n is the
ideality factor (1.36) for a p–n junction. Vth is known as the thermal voltage. q is the electron charge
(1.60217646× 10À19
C), k is the Boltzmann constant (1.3806503 × 10À23
J/K) and T (in Kelvin) is the
temperature of the p–n junction. Eg is the band gap energy of the semiconductor (Eg ≈ 1.1 eV for the
polycrystalline Si at 25 °C), and I0,n is the nominal saturation current. T is the cell temperature, and
Tn is cell temperature at reference conditions. Under normal circumstances, the Rp has a large value,
and Rs has a small value. In order to simplify the analysis, Rp and Rs can be neglected [10, 12, 39].
Hence, we could assume that series resistance Rs is close to zero and shunt resistance Rp is close to
infinite. This model is simulated by Matlab Simulink. Red sun 90 w is taken as the reference module
for simulation as well as comparison of parameters of the adjusted model and red sun data sheet values
at reference conditions is presented in Table I. The arrays of PV modules are established by connecting
11 panels in series, and 6 panels in parallel to obtain the power output of 6kW.
3. MPPT—NEURAL NETWORK AND GENETIC ALGORITHM TECHNIQUE
3.1. The steps of implementing genetic algorithm
The GA-based offline trained network is employed to provide the reference voltage corresponding to
the maximum power. Alongside, GA is utilized for optimum values and then, optimum values are used
for training network [20–22, 44]. The procedure for exerting GA can be presented as follows [20–22]:
(i). assigning the objective function and recognizing the design parameters, (ii). determining the initial
production population, (iii). evaluating the population using the objective function and (iv). conducting
convergence test stop if convergence is provided.
The objective function of GA is applied for its optimization by the following: finding the optimum
X =(X1, X2, X3,…, Xn) to put the F(X) in the maximum value, where the number of design variables is
considered as 1. X is the design variable equal to array current (Ix) and also, F(X) is the array output
Figure 1. Equivalent circuit of one PV array.
Table I. Comparison of parameters of the adjusted model and red sun data sheet values at reference conditions.
Parameters Model Datasheet
IMP (current at maximum power) 4.84 A 4.94 A
VMP (voltage at maximum power) 18.45 V 18.65 V
PMAX (maximum power) 89.3 W 90 W
VOC (open circuit voltage) 22.12 V 22.32 V
ISC (short circuit current) 5.04 A 5.24 A
NP (total number of parallel cells) 1 1
NP (total number of parallel cells) 36 36
Series resistance (Rs) .1 Ω Not specified
Shunt resistance (Rp) 161.34 Ω
DOI: 10.1002/jnm

power which should be maximized [21]. The GA parameters are given in Table II. The relationship
between voltage and current of the array is demonstrated by the following equations:
F Xð Þ ¼ VX*IX (5)
VX ¼ ns v0 À
RS
np
IX þ nk T þ 273ð Þ=qð ÞLn*
IPV À IX
nP
þ I0
I0
!!
(6)
To determine the objective function, the power should be arranged based on the current of array (IX):
F Xð Þ ¼ ns v0 À
RS
np
IX þ nk T þ 273ð Þ=qð ÞLn*
IPV À IX
np
þ I0
I0
!!
*IX (7)
0 IX ISC (8)
The current constraint should be noted too. By maximizing this function, the optimum values for
Vmpp and MPP will result in any particular temperature and irradiance intensity.
3.2. MPPT improvement by combination of proposed neural network with genetic algorithm
ANN is the most suitable method for the forecasting of nonlinear systems. Non-linear systems can be
approximated by multi-layer neural networks, and these multi-layer networks have better outcome in
comparison to other methods [16]. In this paper, feed forward neural network for MPPT process
control is implemented. The major part of ANN is that, the desired data for training process should
be achieved for each PV system and each particular position [20, 21]. Based on the PV characteristics
which depend on PV model and climate changes, neural network should be trained periodically.
Three layers can be considered for the proposed ANN. The input variables are temperature and solar
irradiance, and Vmpp corresponding to MPP is the output variable of ANN as depicted in Figure 2.
Furthermore, a block diagram of the proposed MPPT scheme is displayed in the Figure 3.
The output characteristic of arrays have changed over time and environmental conditions. Thus,
periodic training of the neural network in order to increase precision is essential. Training of the
ANN is a set of 390 data as demonstrated in Figure 4 (irradiance between 0.05Watts per square meter
(W/m2
) to 1 W/m2
and temperatures between À5 °C and 55 °C), and also, a set of 390 Vmpp
corresponding to MPP is obtained by GA that is depicted in Figure 5.
To perform of the ANN for MPPT, the number of layers, number of neurons in each layer,
transmission function in each layer and kind of training network should be assigned. The proposed
ANN in this paper has three layers which ﬁrst and second layers have 15 and 12 neurons, respectively
and third layer has 1 neurons. The ﬁrst and second layers of the transfer functions are Tansig and third
layer is Purelin. The training function is Trainlm. The admissible sum of squares for the ANN is
assigned to be 10À9
. Training ANN is carried out in 800 iterations that it will converge to a required
Table II. The genetic algorithm parameters.
Number of design variable 1
Population size 20
Crossover constant 80%
Mutation rate 10%
Maximum generations 20
Figure 2. Feed forward neural network for MPPT.
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target. After training, the output of training network should be close to optimized output from GA. The
ANN training with the target data is illustrated in Figure 6. A set of 80 data are applied for the ANN
test. The ANN test with the target data show trifling training error percentage about 0.4% as depicted
in Figure 7.
4. WIND TURBINE SYSTEM CONFIGURATION
The major configuration of a wind turbine based PMSG is displayed in Figure 8. Turbine output is rec-
tified by implementing uncontrolled rectifier. Then DC link voltage is adjusted by PI controller until it
reaches to the constant value, and then, the constant voltage is inverted to AC voltage using sinusoidal
PWM inverter. Inverter regulates the DC link voltage and injected active power by d-axis and injected
reactive power by q-axis using P/Q control method. Furthermore, turbine output is regulated via pitch
angle based on FLC in high wind speeds.
Figure 3. Proposed MPPT scheme.
Figure 4. Inputs data of irradiance and temperature.
Figure 5. Output of Vmpp–MPP optimized by GA.
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4.1. Wind turbine and PMSG modeling
The value of electricity that turbine is capable to produce depends on the rotor speed and wind speed
[45, 46]. The WT mechanical power can be expressed using equation (17):
P ¼ 0:5ρACp λ; βð ÞV3
w (9)
λ ¼
WmR
Vw
(10)
where P, ρ, A, Vw, Wm and R are power, air density, rotor swept area of the wind turbine, wind speed
in m/s, rotor speed in rad/s and radius of turbine, respectively. Also, Cp is the aerodynamic efficiency
of rotor. PMSG voltage equations and other equations of wind turbine are presented in [35, 46].
4.2. Pitch angle based on fuzzy controller
Fuzzy logic controller is made of three parts which is demonstrated in Figure 9. The first part is
fuzzification which is the process of changing a real scalar value into a fuzzy set. The second part is
Figure 6. Output of the neural network: (a) the output of the neural network with the amount of target data; (b) the out-
put of the neural network of Vmpp with the amount of target data and (c) total error percentage of the Vmpp training data.
DOI: 10.1002/jnm

fuzzy inference motor that combines IF–THEN statements based on fuzzy principle and, ﬁnally, it has
defuzziﬁcation which is the process that changes a fuzzy set into a real value in output [38].
Figure 7. Output of the neural network test: (a) the output of the neural network test with the amount of target data;
(b) the output of the neural network test Vmpp with the amount of target data and (c) total error percentage of the
Vmpp test data.
Figure 8. Block diagram of the system.
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The proposed fuzzy logic controller comprises of two input and one output signals. The ﬁrst input sig-
nal is based on the difference between measurement active power and the nominal value in (P.U.) that is
called as error signal. Therefore, the positive value shows turbine’s normal operation, and the
negative value depicts the extra power generation during the above nominal speed. Also, the fuzzy
controller must change the pitch angle degree by increasing the rated value. The pitch angle degree
is adjusted on zero in a normal condition. The total wind energy can be transformed to mechanical
energy. The pitch angle starts to increment from the zero value which wind attach angle to the blades
will be incremented, thereby leading to decrement of aerodynamic power and decreasing the turbine
output power. Alongside, the second signal is derived from anemometer nacelle [45, 46].
Figure 9. Structure of the fuzzy system.
Figure 10. Membership function of fuzzy logic: (a) membership functions of active power (error signal), (b) mem-
bership functions of wind speed and (c) membership functions of output (β).
DOI: 10.1002/jnm

The responses of fuzzy controller will be faster, smoother and more stable while wind speed is applied
as an input signal comparing to the rotor speed and reactive power in wind turbines [32–34]. Although,
mechanical fatigues in wind turbines will be alleviated by regulating the fuzzy logic controller. Sketching
a pitch angle based on fuzzy logic controller for wind turbine power regulation in high wind speeds is be-
ing presented in this paper. Three gaussian membership functions are applied in this paper. Moreover, the
Min–Max method is applied as a defuzzification reference mechanism for centroid. The given member-
ship functions are depicted in Figure 10. The three-dimensional curve in fuzzy logic is shown in Figure 11.
In addition, the rules applied to get the needed pitch angle (β) are depicted in Table III. The linguis-
tic variables are shown by VG (very great), SG (small great), OP (optimum), SL (small low) and VL
(very low) for error signal and VL (very low), SL (small low), OP (optimum), SH (small high) and VH
(very high) for wind speed signal and NL (negative large), NS (negative small), Z (zero), PS (positive
small) and PL (positive large) for output signal, respectively.
5. MTG SYSTEM CONFIGURATION
In Figure 12, the simulation of a single shaft MTG is illustrated. The model includes the speed governor,
acceleration control block, temperature control and fuel system control. In [36, 37], MTG details are
reported. The power generator is a PMSG, which has two poles and a salient pole rotor. The nominal
output power is generated by MTG is 25 kW. The rated design speed of the generator is 66 000 rpm.
6. FLYWHEEL ENERGY STORAGE SYSTEM (FESS)
Flywheel has partly fast responses in comparison to other kinds of storage device. Also, flywheel is totally
effective while there is an imbalance between supply and demand. In MG, the flywheel can handle the power
demands of the peak load and save the energy at the low load period. The flywheel can chip in to the stability
of MG voltage amplitude and frequency. For providing instantaneous power desired, the flywheel is con-
nected at the DC bus by droop controller. In this article, the storage device utilized is a FESS connected
to the voltage source inverter (VSI). The details of the model of flywheel are mentioned in [38].
Figure 11. Three-dimensional curve in fuzzy logic.
Table III. Fuzzy rules.
Pitch command Active power (error)
Wind speed VG SG OP SL VL
VL PL PS Z Z Z
SL PL PS Z Z Z
OP PL PS Z Z Z
SH PL PS PS PS PS
VH PL PL PL PL PL
DOI: 10.1002/jnm

7. CONTROL STRATEGY
7.1. P–Q control strategy
Inverter control model has been illustrated in Figure 13. The goal of controlling the grid side is keeping
the dc link voltage in a constant value regardless of production power magnitude. In grid-connected
mode MG must supply local needs to decrease power from the main grid.
One of the main aspects of P–Q control loop is applied in grid connected and stand-alone mode.
Higher power reliability and higher power quality are the advantages of this operation mode [47]. id
and iq, are active and reactive components of the injected current, respectively. iq current reference
is generally set to zero in order to obtain zero phase angle between voltage and current and so unity
power factor can be attained. For the autonomous controls of both id and iq, the decoupling terms
are employed. To synchronize the converter with grid, a three-phase lock loop (PLL) is used. PLL re-
duces the difference between grid phase angle and the inverter phase angle to zero using PI controller,
thereby synchronizing the line side inverter with the grid.
7.2. Droop control strategy
The VSI is to be coupled with a storage device (Flywheel) to balance load and generation during
islanded operation. Its control is performed using droop concepts [40]. The proposed control strategy
Figure 12. Simulink implementation of microturbine model.
Figure 13. The P–Q control model.
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of V/F employs the voltage and current based on conventional V/F droop control which is shown in
Figure 14. During islanded operation, when the unbalance of active power and reactive power occur,
the frequency and voltage will ﬂuctuate. As a result, the MG will experience a blackout without any
effective controller. If the system is transferred to the islanded mode when importing power from
the grid, then the generation needs to increase power to balance power in the islanded mode. The
new operating point (B) will be set at a frequency (f1) lower than the nominal value (f0). If the system
is transferred to the islanded mode when exporting power to the grid, then the new frequency (f2) will
be higher [42]. Also, the reactive power is injected when voltage (V1) falls from the nominal value (V0)
and absorbs the reactive power if the voltage (V2) rises above its nominal value.
7.3. Back up controller
FESS as one of the storage devices has high capacities for injecting power during islanding mode; however,
one of the disadvantages is a limited storage capacity. Consequently, it is required the complementary source
with appropriate controller to decrement the frequency ﬂuctuation [42]. The structure of back up controller is
depicted in Figure 15. In this paper, MTG is applied for compensating the frequency deviations.
8. SIMULATION RESULTS
In this section, simulation results under different terms of operation in MG are presented using
Matlab/Simulink. System block diagram is shown in Figure 16. The grid voltage and frequency are
220V and 60Hz, respectively. Detailed model descriptions are given in Appendix A. In Figures 17, 18
and 19, PV, wind system and MTG connected to grid by applying P–Q controller can be seen.
Figure 20 also shows FESS connected to grid by applying droop controller.
8.1. Case study 1
In this section, the variation of wind speeds, irradiance, temperature and load fault analysis of MG
connected to the grid is investigated. It is noted that the Sensitive Loads (SLs) are not connected in
MG, and DG sources feed only the Non Sensitive Load (NSL). The amount of NSL is 75 kW.
(a)
(b)
Figure 14. Droop control: (a) frequency-droop characteristic; (b) voltage-droop characteristic.
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The MG includes 6-kW photovoltaic system, 88-kW wind turbine system, 25-kW MTG and 25-kW
FESS. The P/Q and droop control technique are implemented in MG. The results obtained from PV
system are illustrated in Figure 21. The main objective of PV system is the comparative study of MPPT
Figure 15. Back up controller.
Figure 16. Case study system.
Figure 17. PV system connected to grid by applying P–Q controller.
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Figure 19. MTG system connected to grid by applying P–Q and backup controller.
Figure 18. Wind system connected to grid by applying P–Q controller.
Figure 20. Flywheel system connected to grid by applying droop controller.
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algorithms under variations of irradiance. Different irradiance levels, according to Figure 21(a)
evaluate the PV’s performance. The output voltage and the current of PV are depicted in Figures 21
(b) and 21(c). Figure 21(c) illustrates the current of PV system. When irradiance is decreased at
Figure 21. Simulated results for PV in case 1: (a) irradiance; (b) output voltage of PV (after ﬁlter); (c) output cur-
rent of PV (after ﬁlter) and (d) out power of PV system.
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Figure 22. Simulated results for wind system in case 1: (a) wind speed; (b) variation of pitch angle with
presence of fuzzy controller; (c) turbine output power with absence of controller; (d) turbine output power
with presence of fuzzy controller; (e) inverter output current with absence of controller; (f) inverter output
current with presence of controller; (g) THD(%); (h) DC link voltage; (i) inverter output voltage and (j)
reactive power.
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t= 1 s and t= 3 s, it causes to decrease the output current of PV. It is worth to mention that the
evaluation of the proposed controller is compared and analyzed with the PO, IC and fuzzy logic
algorithms. It can be derived that the proposed ANN-GA method has smoother power signal line,
Figure 22. Continued.
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better stable operating point, better performance and more accuracy for operating at MPP than of PO,
IC and fuzzy logic methods as demonstrated in Figure 21(d).
Figure 23. Simulated results for grid in case 1: (a) grid voltage;(b) grid current with absence of fuzzy controller;
(c) grid current with presence of fuzzy controller; (d) active powers with absence of fuzzy controller and (e) active
powers with presence of fuzzy controller.
DOI: 10.1002/jnm

Moreover, in this case, during 0 t 1 s, the load power is 75 kW, and at t= 1 s, it has 40% step
increase in load. Wind speed during 0 t 1 s is 11 m/s and at t =2.3 s it is declined to 9 m/s. After-
ward, during 1 t 2.3 s, wind speed is 9 m/s, and at t =3.8 s, it is sorely increment to 16 m/s. Using
fuzzy logic controllers, when wind speed is more than rated value (12 m/s), turbine output power is
incremented by sorely incrementing wind speed; although, without fuzzy controller, the power is kept
in high level and by using fuzzy logic controller, it is declined to rated power and it was made
smoother, which leads to the prevention of mechanical damages to PMSG.
Figure 22(a) illustrates variations of the wind speed. Figure 22(b) depicts the variations of pitch an-
gle in the presence of fuzzy logic controller. In normal condition, pitch angle is adjusted as zero.
Figures 22(c) and 22(d) depict the turbine output power in the absence and presence of fuzzy controller
according to wind speed. It is clear that, by using fuzzy logic controller, the power curves are smoother.
By incrementing the pitch angle degree using fuzzy logic controller, the extra power of wind turbine is
limited and reached the rated value. Figures 22(e) and 22(f) show inverter output current in the absence
and presence of fuzzy logic controller, respectively. It depicts the effectiveness of fuzzy logic controller
by incrementing the pitch angle degree. The extra power of wind turbine is more limited and also, the
inverter output current is more declined in comparison to PI controller. According to IEEE
Figure 24. Wind system: (a) pitch angle (deg); (b) active power and (c) voltage at terminal (Bus 3).
DOI: 10.1002/jnm

Std.1547.2003, total harmonic distortion (THD) should be around 5%. In THD curve, it is around 1.5%
to 2.5% as illustrated Figure 22(g). DC link voltage remains at a constant value (1050V), which proves
the effectiveness of the established fuzzy controller as displayed in Figure 22(h). Inverter output volt-
age as demonstrated in Figure 22(i) is constant. The reactive power produced by wind turbine is ad-
justed at zero to keep the power factor as unity as depicted in Figure 22(j). Figures 23(a) and 23(b)
illustrate the grid current in the absence and presence of fuzzy controller, respectively. It can be derived
from Figures 23(c) and 23(d), that pitch angle based on fuzzy logic controller can limit the extra output
power of turbine. Then, by the decreasing of the injected output power of wind turbine, the injection of
extra total active power of MG to grid is declined. It is clear that the grid, with the cooperation of wind,
PV, MTG systems and FESS, can easily meet the load demand.
8.2. Case study 2
A three-line-to-ground (3LG) fault as network disturbance occurs at the grid. The main objective of
this section is investigating the MG from grid connected state to the islanding mode. It is supposed that
NSL is not connected in MG and the DG sources feed only SLs. The MG is importing around 15 kW
and 11kvar from the upstream MV network, with a local generation of 93 kW and 5kvar and an MG
load of 108kW and 16kvar. Depending on the load, real and reactive power is defined. The VSI is used
to interface the flywheel (storage device) to the MG during and subsequent to islanding occurrence.
The fault events to the system at t= 5s, which leads to islanding of MG. Moreover, wind power has
high fluctuations, which causes high deviations in frequency, active and reactive power injected by the
VSI, and the voltages of the MG buses. It worth to mention that, FLC operates when wind speed is
more than rated value. The variations of pitch angle (using PI and FLC) are depicted in Figure 24(a)
which FLC causes to have more smooth output power and bus voltage of wind generation as illustrated
in Figures 24(b) and 24(c), respectively. Smoothing the output power of wind system leads to smooth
active and reactive power injected by the VSI, frequency which shown in Figures 25(a), 25(b) and 26.
Figure 25. Flywheel: (a) active power and (b) reactive power.
DOI: 10.1002/jnm

By implementing FLC, the output power of FESS and MTG have higher numerical value and less
oscillation than PI controller. By incrementing the pitch angle of WT using FLC, it leads to smoothing
the output power of FESS and MTG as demonstrated in Figures 25 and 27(a), respectively.
For realistic conditions, it is needed to analyze the noises in a PV system. The performance of the
ANN controller in PV is compared and analyzed with the conventional techniques such as PO, IC
and fuzzy logic when operating during a cloudy day with rapid irradiance changes. Irradiance of the
Figure 26. Frequency variation.
Figure 27. Generated active power: (a) MTG; (b) irradiance of PV system and (c) output power of PV system.
DOI: 10.1002/jnm

PV system is illustrated in Figure 27(b). According to Figure 27(c) the proposed MPPT algorithm can
track accurately the MPP when the irradiance changes continuously; also, this method has well-
regulated PV output power, and it produces extra power rather than aforementioned method. It is worth
to mention that the evaluation of the proposed controller shows the better performance in severe
conditions than the aforementioned methods. Performance of systems by using proposed method is
desirable even with limited changes in system parameters. However, using conventional methods leads
to wide error in these conditions.
9. CONCLUSION
Detailed dynamic modeling of MG, including WT, PV, MTG, FESS and grid level control strategies,
was investigated. Control strategy and precise modeling of DC/AC grid connected converter were
presented. Inverter adjusted the DC link voltage and active power was fed by d-axis and reactive power
was fed by q-axis using P–Q control method in grid connected mode. When MG operated as an
islanding mode, droop control via FESS had to regulate the voltage value at the PCC and also the
frequency of the whole grid.
Based on the PV characteristics which depend on PV model and climate changes, neural network
should be trained periodically, but one of the main features of ANN-GA controller can dramatically de-
crease the weaknesses of the conventional methods. Actually, the proposed method shows smoother
power, less oscillation and better stable operating point than PO, IC and fuzzy logic methods. It
produces exceeding power, and it has faster dynamic response rather than mentioned techniques. More-
over, the proposed fuzzy logic controller in the wind turbine, by adding wind speed as an input signal
of fuzzy logic, could have faster and smoother response. The advantage of fuzzy logic controller is that
it remains the turbine output power in an acceptable value and can prevent more mechanical damages,
and, also, the dynamic performance of PMSG can be enhanced. In other words, by increasing pitch angle
degree by fuzzy logic controller, the extra power of wind turbine is limited, reaching the rated value and
decreasing inverter output current. Also, by the decreasing of injected output power of wind turbine, the
injection of extra total active power of MG to grid is declined. Finally, by implementing the suitable con-
troller, the MG in grid connected and islanding modes could meet the load demand certainly.
APPENDIX
Description of the detailed model
Photovoltaic parameters: output power = 6 kW, carrier frequency in VMPPT PWM generator: 4 kHz and
in grid-side controller: 5.5 kHz, boost converter parameters: L=7 mH, C =1100 μF, PI coefﬁcients in
grid-side controller: KpVdc = 2, KiVdc = 9, KpId =8 KiId = 300, KpIq = 8, KiIq = 300.
PMSG parameters: output power: 88 kW, stator resistance per phase =2.7 Ω, inertia: 0.9eÀ3
kg-m2
,
torque constant 12 N-M/A, pole pairs =8, nominal speed =12 m/s, Ld =La = 8.9 mH. Grid parameters:
X/R = 7, and other parameters, DC link capacitor = 5300μF, DC link voltage = 1050V. PI coefﬁcients
in grid-side controller: KpVdc = 8, KiVdc = 400, KpId = 0.83, KiId = 5, KpIq = 0.83, KiIq = 5.
MTG parameters: MTG ratings: 25 kW, Rotor speed: 66 000 rpm, T1 = 0.4, T2 = 1, K= 25. FESS
parameters: output power = 25 kW, J= 0.07kg m2
, L= 8mH.
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