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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.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

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


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

Prajith Prabhakar and H Vennila


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




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



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




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].



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




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



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




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.



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