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International Journal of Advances in Applied Sciences (IJAAS)
Vol. 13, No. 3, September 2024, pp. 467~477
ISSN: 2252-8814, DOI: 10.11591/ijaas.v13.i3.pp467-477 467
Journal homepage: http://ijaas.iaescore.com
Advanced controller design for D-FACTS device in grid-
connected photovoltaic system controller
Ali Jaber AlQattan, Fadhel Albasri, Sayed Ali AL-Mosawi
Department of Electrical Engineering, Faculty of Engineering, University of Bahrain, Zallaq, Bahrain
Article Info
ABSTRACT
Article history:
Received Feb 4, 2024
Revised Mar 28, 2024
Accepted Apr 24, 2024
Photovoltaic (PV) solar energy is considered one of the highest renewable
energy (RE) resources worldwide. Hence, PV system installation capacity is
increasing, triggering new power quality problems in grid systems. Some
examples of these problems include unbalanced voltages, fluctuating power
levels, harmonic distortions, or reverse power flow. To mitigate the adverse
impacts of PV integration on voltage regulation and harmonic distortion in
electrical distribution systems, a distribution static synchronous
compensation (D-STATCOM) is considered a solution. A simulation study
is performed by modeling a power system model with an integrated PV
system and D-STATCOM. Two control schemes, proportional-integral (PI)
and artificial neural network (ANN), were applied within the internal control
of D-STATCOM to enhance the power quality of the power system. Two
different inverter configurations were adapted, a sinusoidal pulse width
modulation (SPWM) and a hysteresis current controller (HCC). Results are
obtained as voltage profiles for all the considered control schemes with
different inverter types under different contingency conditions. The
performance is also evaluated by control characteristics evaluation for
different controllers. The controller ANN has better performance than the PI
controller and it can mitigate power quality problems and the impact of the
PV integration on voltage regulation and harmonic distortion.
Keywords:
Artificial neural network
D-STATCOM
Hysteresis current controller
Power quality
Proportional integral
Sinusoidal pulse width
modulation
Total harmonic distortion
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali Jaber AlQattan
Department of Electrical Engineering, Faculty of Engineering, University of Bahrain
1017 Road 5418, Zallaq 1054, Bahrain
Email: aliqwert30@hotmail.com
1. INTRODUCTION
The massive increase in power demand resulting from residential, industrial, and agricultural sector
loads causes a significant demand increase in power consumption [1]. Power quality affects both producers
and consumers of electricity and grid operators. Power quality primarily concerns voltage and current
deviations from their ideal waveforms. Examples of the power quality concerns entail long-duration voltage
variations (sustained voltage oscillation and under voltage); oscillatory and impulsive transients, voltage
flicker, short-duration voltage variations (dip or sag, interruption, and swell); waveform distortion (direct
current (DC) offset, notching, and harmonics), and voltage imbalance, natural disasters, distribution or
transmission system failures, and power users are all factors that contribute to these issues in most cases [1].
Voltage sag is a decrease in supply voltage root mean square (RMS) value at the fundamental frequency fall
occurring for a short time. Depending on the voltage sag duration, it can last anywhere from five cycles to
one minute as shown in Figure 1.
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468
Renewable energy (RE) sources such as solar photovoltaic (PV) power are increasingly popularly
used in different countries. PV is considered among the fastest-developing RE resources globally. Hence, it is
rapidly integrated into power systems, and the capacity of PV system installations is growing. As a result, it
has triggered new power quality concerns for grid systems, such as low inertia, unbalanced voltage or
fluctuating power levels, harmonic distortion, and reverse power flow [2].
Figure 1. Classification of voltage sag and swell [3]
Integration of PV power into the grid presents severe power quality challenges such as voltage and
frequency variation, voltage sag, voltage surge, harmonics, reverse power flow, synchronization issues, and
intermittent power flow [4]. Voltage fluctuation on the grid is caused by the intermittent nature of PV energy
sources, which can worsen as the penetration of PV rises [5]. Voltage fluctuation occurs when the voltage
profile does not follow the defined limit by Institute of Electrical and Electronics Engineers (IEEE) when the
amplitude of a voltage fluctuation is consistent and does not exceed the range stipulated in the ANSI C84.1
and IEEE std 1250-1995. Generally, the deviations from the nominal value do not exceed 0.9 to 1.1%.
Similarly, as per IEEE standard 519-1992, the total harmonic distortion (THD) rate must be less
than 5% of the fundamental frequency, and each particular harmonic distortion should not be more than 3%
[6]. If the THD exceeds the abovementioned limit, it will be considered a severe power quality concern.
According to international standards, such systems are not recognized [6]. As a result, the lifespan of
electronic equipment is shortened because of the harm done to its delicate components [7].
In addition, voltage regulation devices, capacitor bank devices, and overcurrent devices malfunction
will generate power quality issues. As a result, the electric power system will eventually suffer from a lack of
stability, dependability, efficiency, and security [8]. Flexible alternating current transmission system
(FACTS) devices and reactive power assistance to improve power system performance have grown in
popularity in recent years [9]. Several FACTS devices can help improve the security of the power system by
redistributing power flow and regulating bus voltages [10], [11]. Distribution static synchronous
compensation (D-STATCOM) is one of the shunt-connected FACTS controllers.
D-STATCOM can reduce energy flutter and tiny voltage instability, improving the quality of the
power supplied to end-users. Staggered energies in distributed energy systems can ensure effectiveness using
FACTS devices [12]. D-STATCOM is now being used to compensate for the power at the Sullivan Power
station and Inuyama in Japan [13]. The control of active power injection/absorption is the best solution for
the power oscillation damping and can improve the transient stability. A D-STATCOM with an energy
storage system can control both the reactive and the active power injection/absorption, thus providing a more
flexible power system operation [12].
The distribution static synchronous compensator (D-STATCOM) is implemented dynamically to
address power quality (PQ) issues on the distribution side. Conventional distribution techniques have
declined in performance because of the rapid increase in the use of electrical power. Thus, better-
compensated strategies were developed by using D-STATCOM [14]. A voltage source converter (VSC) and
the controller are the primary components of D-STATCOM. Using a voltage source inverter and a DC
capacitor as the input to a D-STATCOM, a power system can generate a variable alternating current (AC)
voltage source. A reactive and active power transfer occurs when a voltage differential across this reactance
creates the D-STATCOM. Different algorithms have been presented in the literature, including carrier-based
algorithms such as synchronous reference frame (SRF) [15], [16], carrier-fewer algorithms [17], and Power
Balanced theory (PBT) [18], fuzzy-proportional-integral (PI)-based channel state information (CSI) control
algorithm [19].
Int J Adv Appl Sci ISSN: 2252-8814
Advanced controller design for D-FACTS device in grid-connected photovoltaic … (Ali Jaber AlQattan)
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The use of artificial intelligence (AI) for RE sources has increased, especially for solar. The
application of AI for renewable and energy sources offers interesting characteristics, such as independent
learning, and large-scale decision-making [20]. However, there is no such study that has a detailed investigation
between two controllers as well as different inverter types with further performance evaluation of the control
characteristics. These goals will be achieved by investigating different controllers for D-STATCOM of PI and
artificial neural network (ANN), as well as different inverter types of sinusoidal pulse width modulation
(SPWM) controller and hysteresis current controller (HCC). The results of this investigation would reveal the
best kind of controller that will be proposed. This investigation will be carried out by developing the proposed
controllers in MATLAB/Simulink.
This thesis aims to mitigate power quality problems related to PV integration by designing an
advanced control system for D-STATCOM to reduce the adverse impact of PV integration on voltage
regulation and harmonic distortion in electrical distribution systems. The control schemes of PI, ANN, and
adaptive neuro fuzzy inference system (ANFIS) are applied with the SPWM controller and HCCs in D-
STATCOM installed in the study test system. The results are obtained as voltage profiles for all control schemes
under different operating and fault scenarios. Further performance evaluation was performed by evaluating the
control characteristics of different controllers in MATLAB/Simulink with different scenarios. In addition,
analyzing all control schemes using THD. The following steps are put into consideration to fulfill the goal of the
thesis: i) The study was created via designing a network combining PV and DSTATCOM and ii) Assessing
the system's overall flexibility with different conditions via voltage measurement and scoop monitoring on
points of the system. The results obtained are as voltage profiles for all control schemes under different
scenarios. Further performance evaluation was done by applying the control characteristics evaluated by
calculating the root mean square error (RMSE), steady state error, settling time, settling average value, rise
time, and peak value to explore an in-depth analysis of each controller type.
2. SYSTEM CONFIGURATION
A typical distribution system with 11 kV, 50 Hz voltage rating is used, and a step-down transformer
is employed to feed the different load types. The distribution line voltage to the consumer end is 415 V,
which is a standard rating in Bahrain. Voltage source inverter (VSI)-based D-STATCOM switching is used
for voltage compensation under different load situations, and the controller obtains the fundamental reference
control signals for switching. Study work done in MATLAB/Simulink, the D-STATCOM Simulink model
was used to build the suggested approach and run simulations as shown in Figure 2. Creating the
compensating reference current is critical to D-STATCOM's performance and quality. Where the key factor
in the DC-link is the voltage regulation controller. There are two ways to compensate for compensatory
current: decreasing or increasing the DC-link voltage ( ). A particular reference value on the inverter's DC
side is required to ensure proper VSI operation.
Figure 2. Distribution static compensator (D-STATCOM) configuration
ISSN: 2252-8814
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470
3. D-STATCOM REGULATION CONTROLLER IN MATLAB/SIMULINK
VSC with six inductively coupled bipolar transistors (IGBTs), a DC energy storage, and three
inductors that are shunt-linked. It is possible to think of D-STATCOM as a synchronous compensator (or
condenser) that can supply variable voltage regulation and reactive power of the bus to which it is connected.
D-STATCOM can provide a more rapid energy response than the synchronous compensator. The voltages
generated from D-STATCOM are connected to the utility grid. Correction of the magnitude and phase of the
D-STATCOM output voltage enables enhanced regulation of the reactive and real power flow between the
D-STATCOM output voltage and the utility grid, resulting in increased grid stability. Figure 3 shows the D-
STATCOM regulation controller.
First by regulating the DC-link voltage to create the compensating reference current as shown in
Figure 3(a). Where it is critical to D-STATCOM's performance and quality. There are two ways to
compensate for compensatory current: decreasing or increasing the DC-link voltage (). A particular
reference value on the inverter's DC side is required to ensure proper VSI operation. Maintaining a steady
DC-link voltage reduces the VSI switching. As a result of the rotating frame theory, the mean active current
factor ( ) must be added to the dc-link voltage to keep it stable.
Second by regulating the voltage of the power control center (PCC) bus and keeping its level of the
reference voltage . A PI controller is employed in this scenario as shown in Figure 3(b), where PCC
bus voltage magnitudes are
. To determine the error, where the and
are compared. The PI
controller then uses the erroneous value. The quadrature axis component's reference to the D-STATCOM
current , which is applied to the inner current loop, is derived from this error signal. When
exceeds
, D-STATCOM attempts to inject reactive power to boost
. Reactive power is absorbed by
D-STATCOM when the value of is smaller than
to reduce the value of
. Maintaining a
steady DC-link voltage reduces the VSI switching. As a result of the rotating frame theory, the following
controller will be compared to conventional PI control with ANN control. The proposed ANN controller is
selected based on the ability to integrate learn and adapt to new conditions having intelligent Neuro
techniques used for modeling.
(a)
(b)
Figure 3. D-STATCOM regulation controller in (a) DC-link voltage controller and (b) AC voltage controller
3.1. PI control
Figure 4 shows the PI controller. The block diagram of the PI controller is depicted in Figure 4(a).
To supply losses in the D-STATCOM and filter, the source must deliver both the active reference current
component () and the loss reference. Current losses () are determined by comparing and
at the sampling instants (nth sampling instants) to determine which one is greater. The PI controller
is employed and is shown in Figure 4(b). To determine the error, the and
voltages of PCC bus
magnitudes are compared. The reference quadrature axis component of the D-STATCOM current ) is
provided by this error signal, which is applied to the D-STATCOM's inner current loop through the D-
STATCOM's error signal.
(a)
(b)
Figure 4. PI Controller in (a) block diagram of Id PI control and (b) block diagram of Iq PI control
Int J Adv Appl Sci ISSN: 2252-8814
Advanced controller design for D-FACTS device in grid-connected photovoltaic … (Ali Jaber AlQattan)
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3.2. Artificial neural network
ANNs, which are adaptive techniques with incredible information-processing capabilities, comprise
layers of neural nodes that communicate with one another. The ability to integrate nonlinearities that are not
readily visible in the inputs allows them to learn and adapt to new conditions as well as learn and adapt to
varied surroundings when presented with nonlinearities that are not readily obvious in the inputs [21]. To
derive the necessary control signal, the new controller (ANN) will be developed based on the input-output
training data [22]. The necessary training data set has been prepared from the system and consists of two
inputs (error and integration of error) and one output of each PI controller. training data was collected by
simulation. But by using only load-1 and load-2 and by connecting D-SATCOM from 1 s to 12 s. the PI
controller parameter is calculated from following TLE7242 and TLE8242 application and the result is
, , and 00 where , and by considering
[23], [24].
4. SIMULATION ANALYSIS
An electric distribution network system with an integrated RE system is modeled and simulated in
MATLAB/Simulink. Figure 2 illustrates the simulated electrical system, which consists of a voltage source
representing the grid with a 90-kW solar PV array, a distribution transformer, a D-STATCOM, and six
different loads. The adopted power supply in the simulation model is a three-phase AC voltage with a short-
circuit capacity of 31 MVA, and the actual line voltage value is 415 V/50 Hz [25]. Six different electrical
loads are active in the different time slots, and their details are presented in Table 1. The PV irradiation is
changed with different irradiation values to simulate changes in irradiation and their effect on the grid
network as shown in Figure 5. The solar irradiance of 1000 W/m2 is used from time 0-6 s, and then reduced
to 500 W/m2 for t=6-10 s and then increased again to 800 W/m2.
Figure 5. Solar PV irradiance graph
Table 1. Different load types and parameters used in the simulation
Load number
Active time
Load type
Parameters
Load 1
0-12 s active
Fixed impedance load
Phase A: (15 Ω+j 11 Ω)
Phase B: (30 Ω+j 15 Ω)
Phase C: (60 Ω+j 65 Ω)
Load 2
0-12 s active
Constant active and inductive power load
Active power 0.21 MW+inductive reactive power 2 k VAR
Load 3
2-8 s active
Constant active and inductive power load
Active power 20 kW+induction reactive power 3 k VAR
Load 4
2-8 s active
Non-linear load
200-ohm, 1×10-3 H, 600×10-6 F
Load 5
0-12 s active
Variable load
The mean load value is 7 Ω with a standard deviation of ±1
Load 6
0-12 s active
Non-linear (harmonic) load
150 Ω, 500×10-6 F
5. RESULTS AND DISCUSSION
The next sections will go into each voltage regulation controller. By evaluating the settling time,
settling average value, rise time, and peak value, together with the RMSE, steady state error (SSError), and
other voltage profile control parameters, this result is further analyzed quantitatively. The eight simulation
scenarios are presented briefly in Table 2. The six types of loads have been already presented in Table 1.
Figure 6 shows the performance of the four control schemes (SPWM-PI, SPWM-ANN, hysteresis-PI, and
hysteresis-ANN) in the controlling load voltage profile under eight operating scenarios of the power system.
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The PV irradiation is shown in Figure 6(a). The overall RMS voltage profile of the load is illustrated in
Figure 6(b). Figure 6(c) shows the timing and the sequence of the operating conditions for the fault, loading
condition, D-STATCOM operation, and PV system integration.
(a)
(b)
(c)
Figure. 6. Simulation result for 12 s of simulation describes (a) PV irradiation, (b) RMS voltage, and
(c) the eight different operating scenarios
Table 2. The eight different scenarios of simulation
Sr.
Scenarios
Seconds
1
D-STATCOM system connected
1
2
Load-3 and load-4 active
2
3
PV connected to the grid with 100% capacity
2.5
4
ABG fault active
5
5
PV drops to 50% capacity
6
6
Load-3 and load-4 dis-active
8
7
PV increase to 80% capacity
10
8
PV dis-connected from the grid
11
5.1. Scenario 1: D-STATCOM on
It can be seen from Figure 6 that the voltage profile of the SPWM D-STATCOM using ANN
smoothly reaches the desired reference point as compared with other control schemes. Table 3 reveals that
the ANN controller employing SPWM has the lowest RMSE, settling time, rise time, and the best settling
average value and SSError. This demonstrates that the performance of the ANN controller with the SPWM
inverter is better than the hysteresis inverter.
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Table 3. Performance evaluation during 1-2 s when D-STATCOM is active
D-STATCOM on
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
6.356%
5.483%
5.299%
5.023%
SSError
0.9099
0.5403
0.603
0.1020
Settling time (ms)
167.388
66.563
114.377
66.664
Settling average value
413.047
413.649
414.009
414.332
Rise time (ms)
448.736
176.536
242.715
142.554
Peak
414.154
414.981
415.397
415.765
5.2. Scenario 2: active, reactive, and non-linear load on
It can be seen from the voltage profile in Figure 6 that the fastest response is achieved by using the
controller of ANN modulated by SPWM. Similarly, it can be seen from Table 4 that controller ANN with
SPWM has the lowest RMSE, SSError, and rise time. This shows that the performance of the ANN controller
with the SPWM inverter is better than the hysteresis inverter.
Table 4. Simulation results for 2-2.5 s when additional load is active
Additional load on
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
5.517%
3.467%
3.610%
2.413%
SSError
3.2424
0.5379
0.5676
0.2761
Settling time (ms)
363.485
295.507
358.228
393.773
Settling average value
409.956
410.908
410.798
411.625
Rise time (ms)
1514.347
544.447
704.784
607.911
Peak
414.210
414.478
415.056
415.472
5.3. Scenario 3: photovoltaic on with 1000 W/m2 irradiation
It can be seen from the voltage profile in Figure 6 that the fastest response reached to the reference
point is achieved by using the controller of ANN modulated by SPWM. Table 5 shows that the ANN
controller using SPWM has the lowest RMSE, settling time, and rise time. This shows that the performance
of the ANN controller with the SPWM inverter is better than the hysteresis inverter.
Table 5. Performance evaluation of results during 2.5-4.5 s when PV is active
PV on 100%
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
11.213%
7.803%
8.641%
6.338%
SSError
1.0292
0.0418
0.0951
0.0832
Settling Time (ms)
893.602
361.889
475.931
257.025
Settling average value
429.718
430.947
430.744
430.466
Rise Time (ms)
947.992
812.672
858.923
565.054
Peak
443.608
447.177
446.758
446.400
5.4. Scenario 4: two-phase to ground fault on the system
The voltage profile in Figure 6 shows the fastest response reached to the reference point by using
the controller of PI modulated by the hysteresis current source inverter. Table 6 shows the PI controller using
hysteresis achieved the lowest RMSE and SSError. In this scenario, the PI controller has better performance
using the hysteresis inverter due to the slow response the controller has compared to ANN which gets
affected faster due to the fault generated in the grid.
Table 6. Performance evaluation of results during 5-5.5 s while the fault is active
Two-phase to ground fault
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
2.130%
2.403%
2.248%
2.128%
SSError
0.0962
0.1565
0.2212
0.2168
Settling time (ms)
90.834
267.457
471.603
273.025
Settling average value
415.175
415.493
412.031
415.572
Rise time (ms)
0.363
0.445
0.372
0.386
Peak
415.513
416.248
415.906
416.612
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5.5. Scenario 5: photovoltaic drops to 50%
It can be noticed from the voltage profile in Figure 6 that the fastest response reached to the
reference point is achieved by using the ANN controller with SPWM. It can be also seen from Table 7 that
the ANN controller using SPWM has the lowest RMSE, settling time, rise time, SSError value, and the best
settling average value than the other controllers. This shows that the performance of the ANN controller with
the SPWM inverter is better than the hysteresis inverter.
Table 7. Performance evaluation of results when PV power drops to 50%
PV on 40%
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
16.532%
9.985%
11.568%
8.101%
SSError
4.3148
0.8114
0.2677
0.0509
Settling time (ms)
776.749
445.170
525.932
330.660
Settling average value
396.451
400.313
414.671
415.050
Rise time (ms)
719.182
664.967
706.406
626.384
Peak
414.745
414.882
414.867
415.506
5.6. Scenario 6: the additional load disconnected
The load voltage profile illustrated in Figure 6 shows that the fastest response is achieved when
employing the ANN controller with SPWM. Also, it can be noted in Table 8 that the ANN controller with
SPWM achieved the lowest RMSE, settling time, and rise time. This shows that the performance of the ANN
controller with the SPWM inverter is better than the hysteresis inverter.
Table 8. Performance evaluation of results of disconnection of additional load
Additional load off
Hisit_PI
Hisit_ ANN
SPWM_PI
SPWM_ ANN
RMSE
3.284%
3.124%
2.694%
1.980%
SSError
0.7337
0.1751
0.2467
0.1751
Settling time (ms)
626.083
452.464
436.496
255.502
Settling average value
419.353
419.464
419.342
419.093
Rise time (ms)
1514.347
544.447
704.784
607.911
Peak
423.534
424.238
424.167
423.808
5.7. Scenario 7: photovoltaic increases to 80%
It can be noticed from the voltage profile in Figure 6 that the fastest response reached to the
reference point is achieved by using the ANN controller with SPWM. It can be also seen from Table 9 that
the ANN controller using SPWM has the lowest RMSE, settling time, rise time, SSError value, and the best
settling average value than the other controllers. This shows that the performance of the ANN controller with
the SPWM inverter is better than the hysteresis inverter.
Table 9. Performance evaluation of results while PV power increased to 80%
PV on to 80%
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
6.964%
5.061%
5.079%
3.596%
SSError
1.4215
0.0824
0.0818
0.0618
Settling time (ms)
774.759
282.371
531.192
271.763
Settling average value
422.886
422.464
422.050
421.614
Rise time (ms)
1097.086
826.993
836.779
850.257
Peak
429.515
430.355
429.479
428.922
5.8. Scenario 8: photovoltaic off
The load voltage profile illustrated in Figure 6 shows that the fastest response reached to the
reference point is achieved by using the ANN controller modulated by SPWM. Furthermore, Table 10
illustrates that the ANN controller using SPWM has the lowest RMSE, SSError, settling time, and rise time.
This shows that the performance of the ANN controller with the SPWM inverter is better than the hysteresis
inverter.
Int J Adv Appl Sci ISSN: 2252-8814
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Table 10. Performance evaluation of results during 11-12 s while the PV system is disconnected
PV system disconnected
Hisit_PI
Hisit_ANN
SPWM_PI
SPWM_ANN
RMSE
4.849%
3.813%
3.818%
2.783%
SSError
1.0885
0.3099
0.1060
0.0643
Settling time (ms)
654.519
274.732
441.472
263.822
Settling average value
409.130
408.804
414.889
409.322
Rise time (ms)
1124.434
897.449
701.865
534.085
Peak
416.554
415.341
415.263
415.533
5.9. Total harmonic distortion
The THD in the voltage for the SPWM-(PI and ANN) and hysteresis-(PI and ANN) are listed in
Table 11. The lowest THD percentage was observed with SPWM with ANN control strategy, with only
1.83% THD. However, both controllers with different inverter types have almost similar values and whiten
accepted levels which are below the 5%THD [6].
Table 11. Comparison of THD for different controllers
PI
ANN
Without D-STATCOM
Hysteresis
2.11%
1.94%
3.57%
SPWM
1.94%
1.83%
3.57%
6. CONCLUSION
This study investigated the use of artificial intelligence for RE sources, the application of AI for
renewable and energy sources offers interesting characteristics, such as independent learning, and large-scale
decision-making. However, there is no such study that has a detailed investigation between two controllers as
well as different inverter types with further performance evaluation of the control characteristics. The
simulation was carried out by comparing the performance of two controllers (PI, ANN) to control of D-
STATCOM.
We found that the controller of ANN-based D-STATCOM can mitigate power quality problems
related to PV integration to reduce the adverse impact of the PV integration on voltage regulation and harmonic
distortion. The PV system and D-STATCOM were integrated into the power system, and simulations were
conducted for 12 seconds. Simulations encounter eight different simulation scenarios briefly described in
section 4. The key outcomes of the analysis of the simulation are highlighted as follows: i) It demonstrates
that the smoothness of the load voltage profile, the minimum RMSE, and the fastest control response is
achieved with ANN SPWM D-STATCOM. This indicates the powerful control of ANN, which has an
intelligent Neuro technique used to model and control ill-defined and uncertain systems; ii) The hysteresis PI
control scheme shows the lowest settling time and rise time in scenario 4 (fault condition), and this is the
most critical scenario in all cases as an external disturbance is applied to the system. That could be attributed
to the low response of the controller following a fault in the system, so it sustains the system's voltage profile
without having a high reaction response that could distort the system; and iii) The THD was calculated in the
voltage profile, and it reveals that the THD in voltage for SPWM-(PI, ANN) and hysteresis- (PI, ANN)
indicates that the controller ANN employing SPWM has the lowest value of THD in the system.
The performance of SPWM with ANN was better than other control schemes in most scenarios.
Hence, it was found that the SPWM inverter strategy adapts the control input faster than the hysteresis
scheme. The above conclusion signifies that the performance of advanced controllers, especially ANN, which
has an intelligent Neuro technique used for modeling and controlling ill-defined and uncertain systems, was
better controller to other controllers. It can effectively enhance the power quality of the power system using
D-STATCOM devices.
The ANN controller can integrate nonlinearities that are not readily visible in the inputs allowing
them to learn and adapt to new conditions. As well as learn and adapt to varied surroundings when presented
with nonlinearities that are not readily obvious in the inputs. It is desired for ANN models to have better
controllers because better data fit is made possible by nonlinearity, in the presence of unclear data and
measurement errors and noise insensitivity leads to accurate forecasting, learning, and adaptability making it
possible for the system to respond to changes.
This research motivates further investigation of new and modern algorithms such as adaptive neuro-
fuzzy Takagi-Sugeno-Kang (ANFTSK), adaptive neuro-fuzzy wavelet (ANFW), and Typ-2 ANF controllers.
Further research will focus on the performance of these advanced control techniques when integrated with
FACTS devices to enhance voltage profile, improve power quality, and reduce THD in voltage and current
profiles. Similarly, finding the optimal parameters of a control technique at which distributed flexible
ISSN: 2252-8814
Int J Adv Appl Sci, Vol. 13, No. 3, September 2024: 467-477
476
alternating current transmission system (D-FACTS) devices perform at their best is a crucial aspect of future
research. Furthermore, the integration of Wind power systems can also bring its limitations into the power
system, and the research can be extended by incorporating wind and PV-integrated power systems.
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Int J Adv Appl Sci ISSN: 2252-8814
Advanced controller design for D-FACTS device in grid-connected photovoltaic … (Ali Jaber AlQattan)
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BIOGRAPHIES OF AUTHORS
Ali Jaber AlQattan is an Electrical Engineer at KC and a Student at the
University of Bahrain. He demonstrated experience and a history of working as an electrical
Engineer in different industrial sectors. He received an M. Sc degree in renewable energy and
a B.Sc. degree in Electronics Engineering from the University of Bahrain. His research
interests include solar energy, photovoltaic application systems, and FACTS Devices. He can
be contacted at email: aliqwert30@hotmail.com.
Fadhel Albasri is an Assistant Professor in the Department of Electrical
Engineering at the University of Bahrain. He received the B.Sc. and M.Sc. degrees in
Electrical Engineering from the University of Bahrain, Bahrain, and a Ph.D. degree in the
same field from the University of Western Ontario, Canada, in 1992, 1997, and 2007,
respectively. He worked in the Ministry of Electricity and Water, Bahrain, as an electrical
engineer from 1993 to 1994. In 1994, he joined the University of Bahrain as a teaching and
research assistant and is currently an assistant professor in the Department of Electrical and
Electronics Engineering. He published more than 22 journal and conference papers. He is a
Fellow of The Higher Education Academy, UK, and a member of IEEE, USA. He has
supervised M. Sc. students. His research interests are power systems protection, power
systems analysis, and FACTS devices. He can be contacted at email: falbasri@uob.edu.bh.
Sayed Ali AL-Mosawi is an Associate Professor in the Department of Electrical
Engineering, at the University of Bahrain. He received PhD in Power Electronics Engineering
from Imperial College, University of London, 1995, UK He Received his B.Sc. degree (F.
Hons) in Electrical and Electronics Engineering from the University of Bahrain in 1988, and
his M.Sc. degree in Power Electronics Engineering from the University of Bradford in 1990.
Dr Al-Mosawi published more than 35 papers in highly reputed refereed international journals
and conferences on Power Electronics Applications and FACTS devices. He has supervised
several Master's Thesis and currently, he is supervising one PhD and two Master Thesis. His
research interests include Power Electronics, FACTS Devices, and Control Applications. He
can be contacted at email: aalmossawi@uob.edu.bh.
