comparison between pi, fuzzy & predictive ... filters or the custom power devices (cpd) like...
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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 8, August 2017, pp. 819–829, Article ID: IJMET_08_08_089
Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
COMPARISON BETWEEN PI, FUZZY &
PREDICTIVE TECHNIQUES FOR STATCOM TO
IMPROVE THE TRANSIENT STABILITY OF
MICROGRID
Prajith Prabhakar
Research Scholar, Noorul Islam Centre for Higher Education,
Kumaracoil, Tamilnadu, India
H Vennila
Assistant Professor, Noorul Islam Centre for Higher Education,
Kumaracoil, Tamilnadu, India
ABSTRACT
In present day power systems, the transient stability problem and damping
oscillations is alleviated using Static Compensator (STATCOM). This paper discusses
the different STATCOM control schemes like Fuzzy logic controller (FLC),
Proportional –Integral controller (PI) and Model Predictive controller (MPC) for the
distributed resources connected to the Microgrid system to improve the transient
stability via MATLAB/SIMULINK. The required reactive power between the
STATCOM and the power grid is controlled and exchanged using PI, FLC and MPC
signals. A study has been done on the behavior of the proposed Microgrid system with
different voltage fluctuations. A comparison has been done on the simulated results of
PI, FLC and MPC. The case of both Grid connected and Islanded mode and the
efficiency of different controllers in reducing the oscillations of the same have been
discussed.
Keywords: Distribution Static Compensator (DSTATCOM), Microgrid, PI Controller,
Fuzzy Logic Controller (FLC), Model Predictive Controller
Cite this Article: Rajesh Prabha N and Edwin Raja Dhas J, Design Optimization of
Surface Roughness by Turning Process Using Response Surface Methodology and
Grey Relational Analysis, International Journal of Mechanical Engineering and
Technology 8(8), 2017, pp. 819–829.
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1. INTRODUCTION
Most of the power and emissions are generated by large centralized power plants in
conventional power systems. The electric power generated is transferred over long
transmission lines towards large load centers. To establish the quality of the power (the
frequency and the voltage) the system control centre, monitor and control the system
Comparison between PI, Fuzzy & Predictive Techniques for STATCOM to Improve the Transient
Stability of Microgrid
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incessantly. There is a rise in the demand for new generation capacities, efficient energy
production and utilization due to the escalated global energy consumption and diminishing
fossil fuels. The issue of energy shortage and global climatic change can be solved by
utilizing renewable energy, distributed generation and large scale storage of energy [1].
The Microgrid model is an example of an apt structure that can be used to interconnect the
distributed energy resources. A Microgrid is a low-voltage distribution system which is
subjected to lot of disturbances when nonlinear such as electric arc welders, adjustable speed
drives, electric arc furnaces and switch mode power supplies are connected [3]. These loads
may generate harmonics and increase the demand of reactive power flow from the renewable
energy resources. An isolated Microgrid has to be operated from the main Grid in case of
faults controlled by the Microgrid central controller. Elimination of these power quality issues
in the connection with the distributed energy resources can be done by the usage of Series
active filters or the Custom power devices (CPD) like DSTACOM ,Dynamic voltage restorer
(DVR) and Unified power conditioner (UPQC).
The conventional shunt compensators are replaced by static VAR compensators (SVC) for the
power system voltage stability improvement. In Microgrid these compensators are used to
damp out power swings thereby reducing the transmission loss by reactive power control and
enhances the transient stability. For the improvement of reactive power control in the
Microgrid quick acting static synchronous compensators are used as aggressive shunt
compensator.
VSC based DSTACOM have been developed to control power system dynamics during
fault condition. It has been reported that many researches are going on with the stability of the
Microgrid based DSTACOM. For the improvement of transient stability of the Microgrid,
many advanced technologies have been proposed by the researchers in the field of power
system.
A typical Microgrid has been proposed to implement the comparison of the three
controllers and they are checked with load disturbances in the Microgrid load side. The
Proposed Microgrid works with both modes of operation in Simpower system tool boxes.
Proportional Integral, Fuzzy logic and Model Predictive controllers are compared and the best
method is discussed.
2. PROPOSED METHOD
In this section a model of a typical Microgrid is explained that was used to carry out the
analysis of the power quality issues. A typical Microgrid is shown in Figure 1.
Figure 1 Proposed Microgrid
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In the proposed Microgrid diagram, three distributed energy resources blocks like Solar
Energy, Wind Energy and Micro turbine are modeled with the help of different control
techniques. It is then connected to two linear and non-linear loads via a static switch. Static
switch is a device which is initiated by the disturbances and isolates the faulty part from the
healthy. Linear loads like Incandescent lamps, Heaters etc and the Non- linear loads are
SMPS, refrigerators, Television etc. The static switch is closed in grid connected and opened
in islanded mode. A controller block is attached to the DSTATCOM to give control signals on
the basis of feedback to the type of method used. Three types of controllers are used for the
modeling of each distributed energy resources and they are Proportional and Integral (PI),
Fuzzy logic (FLC) and Model Predictive (MPC) controllers. Each controllers are been
checked with two modes of operation.
a) Grid Connected Mode:
In this mode of operation, Microgrid is connected to the Maingrid through a static switch. The
references for the DS controller can be obtained from the Grid. Reactive power is
compensated via different controllers.
b) Islanded Mode:
This mode of operation takes place when the Microgrid is isolated from the main grid by
accidental events like faults or by intended actions. In this mode also three DGs are modeled
using PI, FLC and MPC controllers and connected to the PCC. The Microgrid has to work
autonomously feeding the loads. Controller generates references for the function of the
Microgrid. After 0.2s the Grid is added to the Microgrid. In the isolated mode of operation all
DG’s have to compensate the loads in the absence of the Grid as there are nonlinear loads
present. The present network has a dominant impact in the distribution of quality power to
loads in the case of Islanded mode while considering the non-linear load. For the
improvement of reliability of the present system, a modification in the Microgrid is proposed.
DSTATCOM was proposed to be added to the existing system. The need for the CPD in the
distribution side of the Microgrid was for the smooth function whose performance was very
sensitive to the quality of power delivered to the loads.
3. DYNAMIC MODEL WITH DSTATCOM
A DSTACOM is a custom power device which eliminates and balances the harmonics from
the source current by providing reactive power to enhance the power factor or regulate the
load bus voltage. With the shunt connected controllers (DSTATCOM), it is possible to
control the power flow in critical lines[4]. The active Microgrid model with single inverter
connected to the load with DSTATCOM is shown in the Figure 2.
Figure 2 Dynamic Model of DSTATCOM
The active losses of the transformer and transmission line, inverter switching losses and
power losses in the capacitor are neglected in this single inverter Microgrid model. The three
important stages of STATCOM are power stage of the converter, the control system and the
Comparison between PI, Fuzzy & Predictive Techniques for STATCOM to Improve the Transient
Stability of Microgrid
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passive components [2]. The STATCOM dynamic model comprises of a generating source
voltage (UT) after a leakage reactance of the transformer (XS ) and a dc capacitor (UDC ) is
coupled with a voltage source converter (VSC). The STATCOM V-I characteristics are
displayed in Figure 3.
Figure 3 V-I Characteristics of STATCOM
Both capacitive and inductive compensation is provided by the controller and is capable of
controlling the output the output current value over the rated maximum inductive and
capacitive range in the ac system voltage. The capacitor voltage UDC is effectively controlled
by monitoring difference in phase angle between the line voltage of AC system and the
voltage source converter voltage. If the firing angle is advanced then the dc voltage is
increased and reactive power flow into the STATCOM. On the other hand if the firing angle
is delayed then increase in the dc voltage occurs and the STATCOM will supply reactive
power into ac system. Hence by the control of the firing angle of the STATCOM, absorbing
or pumping of reactive power can be initiated in the PCC of the Microgrid.
4. RESULTS & DISCUSSIONS
1. A Microgrid Model Simulation without Using DSTATCOM
To study the transient stability phenomenon the proposed Microgrid model in the figure 2.1,
is analyzed by the utilization of Simpower System toolbox presented in the
MATLAB/Simulink software environment. This arrangement is first studied without
Distributed STATCOM using a generating source with 230kV voltage which simulates a
synchronous generator of 500MVA capacity with terminal voltage 11kV and its associated
step up transformer with 500MVA, 11/230kV rating. The active and reactive power flow in
this Microgrid and the other parameters have been observed. The Simulink Power system
blocks, the reactive and real power blocks are available for measuring the power flow both
grid side and load side. A Microgrid model in Simulink platform is shown in the Figure 4.
Figure 4 .Microgrid Model- Simulink Diagram
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The RL load value is assumed to have a real power value of 500 MW and the reactive
power of 100 MVAR. The length of the transmission line is assumed to be 300Km. Three
distributed energy resources are modeled and added to point of common coupling (PCC).
Two linear loads and one non-linear load is also added to the PCC. The readings of the both
Grid side and load side power and voltages values are observed and it is tabulated in table 1.
Table 1 Microgrid without DSTATCOM- Simulation Results
Parameters Grid side Load side
Real power kW 294 MW 271 MW
Reactive Power MVAR 238 MVAR 245 MVAR
Voltage kV 230 kV 210 kV
Under this loading condition, the real power at the load side is lesser than the real power
in grid and reactive power in load side is also lesser than the Grid reactive power.
2. A Microgrid Model Simulation with DSTATCOM
The same proposed Microgrid model shown in Figure 1 is analyzed with incorporation of
DSTATCOM controller Simulink model has been developed with the controller connected to
the DSTATCOM to PCC. Voltage source converter based DSTATCOM arrangement is
implemented which has six arm bridges IGBT coupled with shunt transformer is connected to
the PCC. Here the working of static compensator is to inject the reactive power into the PCC
on the basis of the type of controller connected. When the bus voltage is lesser than inverter
output voltage reactive power is injected and from system bus reactive power is absorbed
when bus voltage is greater than inverter output voltage. Figure 5 shows the Simulink
diagram of the Microgrid model with DSTATCOM.
Figure 5 A Microgrid Model with DSTATCOM-Simulink Diagram
To compensate the power in Islanded mode of operation a battery source is connected to
the PCC. Voltages and powers are measured from all the parts of the system with voltage
measurement devices and it is converted to real and reactive power for the comparison
process.
Table 2 Microgrid with DSTATCOM Simulation Results
Parameters Grid side Load side
Real Power kW 480 MW 475 MW
Reactive Power MVAR 401 MVAR 404 MVAR
Voltage kV 230 kV 229 kV
Comparison between PI, Fuzzy & Predictive Techniques for STATCOM to Improve the Transient
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3. A Microgrid Model Simulation with DSTATCOM Controlled by PI Controller
Figure 6 represents the PI controller based DSTATCOM device which compensates the
voltage control & reactive power at load side[9]. First to simulate the Microgrid with
DSTATCOM is controlled by PI controller and read the response when load disturbance will
occur in simulated system [9].
Figure 6.Microgrid Model using DSTATCOM- PI Controller
The coupling transformer output is taken as feedback and with the help of a reference
value is supplied to PI auto-tuned controller and to the PWM generator and that pulse is fed to
the gate sources of the IGBTs of the DSTATCOM device. Compensation voltages are
injected or absorbed from the point of common coupling.
4. A Microgrid Model simulation with DSTATCOM controlled by Fuzzy
Logic Controller (FLC)
The controller with Fuzzy logic is an operative and more precious than other classical
controllers like PI controller, PID controller etc. It took less storage and it is suitable for non-
linear systems. Here it is used in the control loop of the static compensator. From PCC the
voltage Vpcc and a reference value Vpccref and the change in error value is calculated and fed as
input values to Controller. Figure 7 denotes the simulation diagram of the Fuzzy logic
controller with DSTATCOM.
Figure 7 Microgrid Model using DSTATCOM - Fuzzy Controller
a) Mamdani Method: Mamdani method is used in this work and it is computationally
proficient and more compact[6]. The two inputs and the one output is available in two rule
system. Here the inputs are X1 and X2 then the output represented by Y. In this system, error
and the change in error are represented as X1 and X2. The output Y is denoted as alpha [10].
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b) Fuzzification: Five linguistic sets of fuzzy using triangular membership function is
represented in Figure 8a & b. and the five sets of fuzzy variables used are PVB (positive very
big), Z (zero), NB( Negative big), NVB (Negative very big).
Figure 8a: Error (w) &Change is Error (dw)- Input
Functions
Figure 4.5b: Alpha (Y) – Output Membership
Functions
c) Defuzzification: This is the reverse of Fuzzification. Defuzzification using the weighted
average method is used in this work. The Pulse duration is obtained as the defuzzified output.
d) Rule base: “If- Then” format is used in forming fuzzy rules. The fuzzy controller increases
the pulse duration during positive error condition and decreases the duration during negative
error condition.
5. A Microgrid Model Simulation with DSTATCOM Controlled by
Predictive Controller (MPC)
Model Predictive Controller (MPC) is the most widely used of all modern advanced control
technique in many control application [13]. MPC has four tuning parameters: the weight
matrix Λ, the output weight matrix Γ, Prediction horizon P and control horizon M. The
control horizon M is the number of MV moves that MPC calculates at each sampling time to
remove the current prediction error. Prediction horizon P represents the number of samples in
to the future over which MPC computes the predicted process variable profile and reduces the
prediction error [11]. Weighting matrix Γ is used for scaling in the multivariable case; it
permits the assignment of more or less weight for the objective of reducing the predicted error
for the output variable. A dynamic system model is used in order to forecast the controlled
variables. The regulator variables variation to predict the response of system at each time
horizon is allowed by linear vector function. In MPC, receding horizon concept is represented
as shown in Figure 9.
Figure 9 Receding Horizon Concept
From this graph, MPC can be expressed as equation, when normal model is predicted by
control horizon and prediction horizon method and shows the predicted output. The MPC
Comparison between PI, Fuzzy & Predictive Techniques for STATCOM to Improve the Transient
Stability of Microgrid
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based DSTATCOM is developed & the performance is analyzed by using MPC toolbox in
Simpower system tool box. Simulation diagram is shown in Figure 10.
Figure 10 Microgrid Model using DSTATCOM- MPC Controller
6. Discussion of Results and Experimental Analysis
I. Analysis of PI Controller Results
a) Without Load Disturbances
Figure 11 Active and Reactive power Waveform of DSTATCOM Using PI Controller
Figure 11 gives active and reactive power waveform of STATCOM device with PI
controller. The system settles down depending upon the gain values of PI controller. Due to
the higher values of gain in PI controller, it causes peak overshoot in waveform at initial
condition. This waveform is captured by using three phase active and reactive power link
block in Simulink model. Here the system is settled at 0.06 sec for real and reactive power.
The peak overshoot value for real and reactive power is 260 MW and 151 MVAR
respectively.
b) With Load Disturbances
Figure 12 Load Voltage Waveform
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Figure 12 represents the waveform of load voltage and STATCOM current when load
disturbance is occurred. In SMIB system, two RL series load is connected as parallel in
receiving end and the three phase circuit breaker is connected in between two RL load. The
response of load voltage and STATCOM current is getting disturbed. Here the overshoot level
of load voltage is 260 kV and response is settled at 0.12 sec, at mean time the overshoot value
of STATCOM current is 870 A and settled time is 0.16sec.
II. Analysis of Fuzzy Logic Controller Results
a) Without Load Disturbance
Figure 13 Real and Reactive Power Waveform of STATCOM with Fuzzy Logic Controller
Now Fuzzy logic Controller replacing PI controller. Figure 13 displays real and reactive
power response of STATCOM with Fuzzy logic Controller. In that response, peak overshoot
is reduced and fastest settling time when compared to PI controller output. The values of peak
overshoot of Real and reactive power is 138 MW and 222 MVAR, the settling time is 0.04
sec respectively.
b) With Load Disturbance
Figure 14 Load Voltage waveform
Here the overshoot level of load voltage is 240 kV and response is settled at 0.10 sec, at
the mean time the overshoot value of STATCOM current is 485A and settled time is 0.14 sec
respectively. When compared to PI controller response, the overshoot value of load voltage is
reduced from 260 kV to 150 kV and it reaches the steady state from 0.12 sec to 0.10 sec
respectively.
III. Analysis of Model Predictive Controller Results
a) Without & With Load Disturbance
Comparison between PI, Fuzzy & Predictive Techniques for STATCOM to Improve the Transient
Stability of Microgrid
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Figure 15 Real & Reactive Waveform and with Load Disturbance
In that response, peak overshoot is reduced and the settling time is faster when compared
to both Fuzzy and PI controller output response. Here the overshoot level of load voltage is
232 kV and response is settled at 0.09 sec, at the mean time the overshoot value of
STATCOM current is 418 A and settled time is 0.11 sec respectively. When compared to both
PI controller and Fuzzy logic controller response, the overshoot value of load voltage is
reduced. Table 3, Table 4 gives the comparison of PI, Fuzzy Logic and Model Predictive
Controllers of Peak overshoot values measured for Real & Reactive power, load current and
STATCOM respectively.
Table 3 Comparison of Real & Reactive Power without Load Disturbance
Sl. No. Real Power in MW and Reactive Power in MVAR
Controllers Peak Overshoot Settling Time
1. PI Controller 151 MW
260 MVAR 0.06 sec
2. Fuzzy Logic Controller 138 MW
222 MVAR 0.04 sec
3. Model Predictive Controller 125 MW
210 MVAR 0.03 sec
Table 4 Comparison of Real & Reactive Power with Load Disturbance
Sl. No. Load Voltage kV
Controllers Peak Overshoot Settling Time
1. PI Controller 260 kV 0.06 sec
2. Fuzzy Logic Controller 240 kV 0.04 sec
3. Model Predictive controller 232 kV 0.03 sec
5. CONCLUTION
Proposed Microgrid system is simulated using MATLAB/Simulink to improve the transient
stability. The Simulation models of PI, FLC and MPC were developed. The Performance of
different controllers is analyzed for a load disturbance. When comparing the results,
performance of PI controller with STATCOM, gives high peak overshoot and more settling
time. Performance of fuzzy logic controller with STATCOM, gives low peak overshoot and
quick settling time when comparing the results with PI controller. The Response of Model
Predictive controller with STATCOM, the values of peak overshoot and settling time is found
to be lower than the results of FLC with STATCOM. Thus MPC provide better control in
transient stability improvement of the power quality in Distributed generation based
Microgrid.
Prajith Prabhakar and H Vennila
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