Analysis of Distribution Static Compensator Control Strategies for

Mitigating Voltage Dip Impact on Distribution Network

ABOYEDE ABAYOMI, AGHA F. NNACHI

Department of Electrical Engineering,

Tshwane University of Technology, eMalahleni,

SOUTH AFRICA

Abstract: - Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the

key characteristics of poor power quality (PQ) issues on the power distribution network with a significant

negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM),

in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is

investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage

source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this

study implements and compares the effectiveness of the conventional PI controller with an intelligent

optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance

of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results

demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy

reducing the current THD from 11.68% to 3.74%.

Key-Words: - D-STATCOM, Dynamic Gravitational Search Algorithm, voltage dip correction, voltage

regulation, grid optimization, stability.

Received: October 15, 2022. Revised: October 6, 2023. Accepted: November 13, 2023. Published: December 14, 2023.

1 Introduction

There has been an ongoing rise in global electricity

usage with industrial and household loads

consuming the largest portion of the globally

generated power, [1]. Contemporary industrial

processes heavily rely on large numbers of

electronic devices, such as programmable logic

controllers (PLCs) and adjustable-speed drives.

Consequently, these industrial systems exhibit

decreased tolerance to disruptions in power supply,

such as brief interruptions, voltage fluctuations like

dips and swells, flickers, and the presence of

harmonics, [1]. Among these disturbances, voltage

dips stand out as the most detrimental to industrial

equipment, [2]. For instance, a mere 10% voltage

drop lasting 100 milliseconds can significantly

impact the operations of an electronic machine, [3].

Moreover, a voltage dip of 75% from the nominal

voltage, enduring for less than 100 milliseconds, can

incur substantial financial losses amounting to

thousands of U.S. Dollars within the semiconductor

industry, [4]. Nonlinear power electronic loads, such

as converter-driven equipment, have distorted

electrical power systems with undesired fluctuations

in the voltage and current output, which leads to

poor power quality (PQ), [2]. The opinions of

utilities, equipment makers, and electric power end-

users entirely differ when it comes to describing

electric power quality. PQ is viewed by utilities in

terms of system reliability. equipment

manufacturers view PQ as a level that permits

equipment to operate appropriately while the end-

users view excellent PQ as the continued operation

of equipment, activities, and enterprises. Any power

issue that manifests as voltage, current, or frequency

distortion that causes failures or malfunctions in

customer equipment is referred to be a PQ issue, [3],

[4], [5]. Due to their dispersed characteristics,

renewable energy resources are widely installed to

produce electricity on a modest scale. These power

generators generally range in capacity from a few

thousand kilowatts (kW) to several tens of

megawatts (MW). The types of grid integration

equipment utilized with photovoltaic (PV) and wind

energies are power electronics converters and

induction generators. Severe power quality issues

including flicker, voltage dips, and other PQ issues

may result from the integration of these devices into

the distribution network, [6]. A few of the solutions

presented by several studies that have investigated a

range of power quality problems are highlighted

below:

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1.1 Voltage Regulation

Since conventional distribution networks were

designed to handle load demand from the generation

side, integrating distributed generation (DG) into

existing power networks potentially results in

several PQ issues. Being that these network

designers anticipated unidirectional power flow,

however, with DG penetration of the distribution

network, power flow becomes bidirectional because

the DG produces additional power that is fed back

to the generation side. The distribution system's

operation, voltage regulation, and system protection

are all significantly affected by bidirectional power

flow. As a result, two different approaches to

controlling voltage in the distribution networks have

been studied and presented. These strategies are

classified into two categories: conventional and

contemporary, [7]. The conventional method

involves active network management. Setting up

appropriate management policies that the

distribution network operators (DNO) could

implement including real-time monitoring,

communication, and network control. Step voltage

regulators, also known as Static Voltage Regulators

(SVR), On-Load Tap Changer (OLTC), and

distribution static compensators (D-STATCOM) are

the common equipment used in the conventional

method for voltage regulation, [8]. Another method

to maintain the distribution network voltage at

desirable levels is intelligent distributed control,

which involves machine learning techniques, [9],

[10], [11]. A three-phase D-STATCOM with three-

phase unbalanced grid current compensation, total

harmonic distortion (THD) reduction of the grid

current, and power factor (PF) correction was

suggested in, [12] as a way to enhance power

quality. A new online trained wavelet Takagi-

Sugeno-Kang fuzzy neural network (WTSKFNN)

controller was utilized in place of the conventional

proportional-integral (PI) controller to improve the

D-STATCOM performance and transient responses

of the grid currents and DC-link voltage control.

The experimental findings confirmed the viability

and efficiency of intelligently controlled D-

STATCOM for improving power quality and

controlling the DC-link voltage under load

variation. The Lyapunov stability theory was

proposed in, [13], to demonstrate the stability of the

enhanced first-order linear active disturbance

rejection controller (LADRC), which corrects the

error of the total disturbance and improves track

anti-interference performance. To enhance the

system's anti-interference capabilities and the linear

extended state observer's (LESO) ability to perceive

interference in the presence of high-frequency noise,

the output of the complete interference channel is

rectified. According to the experimental findings,

the enhanced LADRC controller has better tracking

and anti-interference performance than the

proportional-integral (PI) controller.

1.2 Voltage Dip

Research studies have established voltage dip as one

of the most frequent occurrences in power systems.

Industrial machines are becoming increasingly

automated. The fundamental equipment –

microelectronics, power electronics, and high-tech

precision tools become susceptible to voltage dip,

[14], [15]. Therefore, reducing the number of

voltage dips and their impact needs coordinated

efforts from the power supply side, client side, and

equipment manufacturer side, [16], [17].

Consequently, measures to mitigate power supply

side voltage dips include prevention and control,

which limit the frequency and duration of short-

circuit faults as well as the damage caused by

voltage dips. Installation of custom power devices

(CPD) such as distribution static compensators (D-

STATCOM), active voltage conditioners (AVC),

and dynamic voltage restorers (DVR) on the client

side mitigates voltage dip at the point of common

coupling (PCC). In addition, the CPD also controls

three-phase unbalance, compensates reactive power,

and generally enhances power quality. The

introduction of D-STATCOM, a cost-effective and

viable solution to enhance the performance of the

distribution network has been studied in, [13]. D-

STATCOM, a dynamic reactive power

compensation device, mitigates voltage fluctuations

and power loss, increases the system power factor,

and efficiently stabilizes the voltage. It is a crucial

device for enhancing power supply reliability and

power quality regulation, [17]. The tracking control

of the compensating current is connected to reactive

current compensation, which is the main

technological feature of the D-STATCOM

compensatory current. Hence, research on the D-

STATCOM AC side current control method has

gotten a lot of attention and the implementation of

D-STATCOM for improved power quality during

voltage dip on the client side is the emphasis of this

study. The conventional linear control strategy,

which primarily linearizes the system of the

nonlinear mathematical model of D-STATCOM, is

the major technique used in current loop control.

The study in, [18], decouples the system into the

dq0 synchronous coordinate system using a PI

controller since the control structure of the PI

controller is uncomplicated, however, its

performance drops if the actual operating conditions

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are different from the assumptions, particularly

when there are significant short-circuit faults or

sharp disturbances from heavy load changes. As a

result, other intelligent control optimization

strategies have been developed in the context of the

limitations of PI controllers. A Dynamic

Gravitational Search Algorithm (DGSA) controlled

D-STATCOM was implemented in this study to

mitigate the impact of voltage dips and smooth the

grid voltage profile. The benefit of this control

strategy for reactive power compensation, voltage

dip mitigation, and current total harmonic distortion

(THD) reduction is demonstrated by simulation

results and compared to the results of the

conventional PI controller.

2 The D-STATCOM Circuit Design

To increase the dynamic performance of the power

distribution network when the grid voltage dips, this

study advances the idea of intelligently regulating a

D-STATCOM device for an efficient distribution

network. Figure 1 depicts the equivalent circuit of a

D-STATCOM, which is designed with a coupling

transformer's reactance, linked with the AC system,

a DC bus, and a power inverter built using power

electronic components.

Fig. 1: D-STATCOM equivalent circuit.

The control strategy for power transfer between

the converter and the distribution network, which

also depends on the converter's alternative output

voltage, is subject to the basic operating principle of

D-STATCOM. The following statement

summarizes the operating principle:

• If the distribution network voltage amplitude

is lower than the D-STATCOM output

voltage, the D-STATCOM injects reactive

power then the current flows through the

reactance to the network.

• If the amplitude of the D-STATCOM output

voltage is lower than that of the network, the

D-STATCOM absorbs reactive power from

the network for current to flow to the

network.

• If the converter's output voltage equals that of

the network, the D-STATCOM remains at an

equilibrium state and the reactive power

exchange value will be zero.

The D-STATCOM main circuit depicted in

Figure 1, has the following structural components:

the voltage support capacitor, which is utilized to

support the device's voltage; the voltage source

converter (VSC) is made up of power electronic

switching components controlled by space vector

pulse width modulation (SVPWM). The filter is

used to remove high-order harmonics from the

inverter output voltage, converting the capacitor's

DC voltage into an AC voltage with a specific

amplitude and frequency that is approximate to a

sine wave. The grid-connected inverter's ability to

provide effective control has a significant impact on

the output power quality. The overall control

structure of the voltage-type D-STATCOM is

depicted in Figure 2, where the three-phase grid

voltage is denoted with usa, usb, and usc; ica, icb, and

icc for the compensator's output current; uca, ucb, and

ucc for the D-STATCOM's output voltage; udc for the

voltage of the DC side capacitor; and R, L for the

filter's equivalent resistance and inductance is also

connected with the D-STATCOM in parallel to

smoothen the compensation currents.

Fig. 2: D-STATCOM control structure.

3 The D-STATCOM Control Algorithm

The control strategy used to generate the VSC

switch signals affects the performance of D-

STATCOM at the PCC. The fundamental objective

of the D-STATCOM control model is to reduce the

impact of voltage dip and mitigate THD using two

different control methods. The D-STATCOM

operation is activated by obtaining the source

voltage amplitude, reference current, and error

signal from the actual current and the generated

current. The device VSC switching is controlled by

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the derived error to manage the D-STATCOM bi-

directional flow of active power. The

MATLAB/Simulink program was used to simulate

the proposed DGSA methodology, and the results

were contrasted to those of a conventional PI

controller.

3.1 PI Controll]er

PI controllers have been utilized to enhance steady-

state and transient performance as well as for the

elimination of sudden disturbances brought on by

operational events, [19], [20]. The controller

includes both proportionate and integral actions. By

utilizing the proportion of system error to regulate

the system, the proportional controller reduces

system error. It however introduces into the system

an offset error. The output of the integral controller

is proportional to how long an error has been

present in the system. The offset introduced by the

proportional control is removed by the integral

action. PID tuning with actuator restriction has been

used in this study to optimize the KP and KI tuning

parameters for the PI controller. Figure 3 illustrates

the block diagram used to determine the PI

controller parameters.

Fig. 3: Simulink model of the system control.

Where R(s), E(s) and Y(s) denote the input signal, the

output signal and the error respectively. Using a PI

controller, the tuning parameters are controlled to

ensure that the D-STATCOM DC capacitor voltage

does not deviate from the reference value. The

control output of the PI controller is given by

Equations (1) and (2). The integral performance

criteria have been defined by using the integral

squared error (ISE) expressed in Equation (3) to

choose the appropriate controller parameters based

on the error in the control system.

󰇛󰇜 󰇛󰇜󰇛󰇜

󰇛󰇜 󰇛󰇜

 󰇛󰇜





(1)

    

(2)

   󰇛󰇜





(3)

The optimized Simulink block diagram in

Figure 3 is run to determine the controller

parameters. Table 1 presents the PI controller

parameters for the designed system using integral

performance criteria.

Table 1. PI Controller Gain

Parameter

Gain with actuator

3.675

-0.4672

The error in the dc link voltage serves as the PI

controller's input, and its output is the amount of

power exchanged by the D-STATCOM at the PCC.

The value of the power is influenced by KP, KI, and

dc-link voltage error values. Therefore, the gains

must be correctly tuned. However, it is challenging

to adjust the controller's gains because of the

system's intrinsic nonlinearity and complexity.

Usually, it involves a lot of trial and error.

Therefore, the DGSA-optimized PI controller has

been used in place of the conventional PI controller

because it is simpler, easier to implement, and more

resilient to system uncertainties and disturbances.

3.2 DGSA-based PI Controller

An intelligent algorithm, the dynamic gravitational

search algorithm (DGSA) based on Newtonian laws

and mass interactions is implemented to optimally

tune the D-STATCOM PI controller due to its rapid

convergence property of errors in finite time. The

population in this algorithm is referred to as masses,

and performance is assessed by the masses' position.

Position, inertial mass, active gravitational mass,

and passive gravitational mass are the four

characteristics of each mass. While the mass's

gravitational and inertial masses related to the

fitness function, the mass's position represented a

solution. Newtonian laws state that gravity will

cause all of these particulars to gravitate toward one

another. Due to this force, heavier weight masses

which are equivalent to excellent solutions, move

more slowly than lighter masses, which are

equivalent to bad solutions. The global solution and

the problem's overall fitness at the final recorded

iterations are determined by the finest fitness and

position of the relevant agent within the search

space. The algorithm's exploitation stage in the

system is modeled and depicted in Figure 4.

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Fig. 4: D-STATCOM control structure with DGSA.

In a system comprising n masses, Equation (4)

provides the position Wi for the i-th mass.

   

(4)

Where is the ith mass position in the dth

dimension. At a specific time t, Equation (5)

describes the force exerted by mass j on mass i.



󰇛󰇜  󰇛󰇜󰇛󰇜󰇛󰇜

󰇛󰇜 󰇛󰇜󰇛󰇜

(5)

In Equation (8), 󰇛󰇜󰇛󰇜󰇛󰇜

correspond to the active gravitational mass

associated with agent j with gravitational constant at

time t, passive gravitational mass is linked to agent i

and  is the Euclidian distance between two mass i

and j respectively.

Within a d-dimensional space, the cumulative

force acting on mass i can be determined with

Equation (6).

󰇛󰇜   

󰇛󰇜





(6)

Therefore, the acceleration of mass i at time t

within the dth dimension, as defined by the

principles of motion, is represented in Equation (7).



󰇛󰇜  󰇛󰇜

󰇛󰇜

(7)

where Dii represents the inertia of the ith mass. The

modification of a mass's velocity depends on both

its current velocity and acceleration. Equations (8)

and (9) outline the velocity and position of the mass.

󰇛󰇜 󰇛󰇜

󰇛󰇜

(8)

󰇛󰇜 󰇛󰇜󰇛󰇜

(9)

To commence, a randomized attribute is employed

in the search process, where a random number is

utilized to calculate the gravitational constant, thus

regulating the accuracy of the DGSA. The

evaluation of inertia masses and the gravitational

search relies on the fitness function, which is

associated with the more heavier masses in

Equations (10) and (11).

󰇛󰇜  󰇛󰇜󰇛󰇜

󰇛󰇜󰇛󰇜

(8)

󰇛󰇜  󰇛󰇜

󰇛󰇜





(9)

Where stable i (t) denote ith mass fitness value at

time t and best (t) for a minimization cost defined by

Equation (10).

󰇛󰇜 󰇛󰇜

(10)

󰇛󰇜 󰇛󰇜

Equations (11) and (12) define the algorithmic

processes to optimize the gain parameter of the PI

controller. While Equation (13) calculates the

voltage error.’

󰇛󰇜 󰇛 󰇜󰇛󰇜

(11)

󰇛󰇜 󰇛 󰇜󰇛󰇜

(12)

󰇛󰇜 󰇛󰇜󰇛󰇜

(13)

When carefully chosen, an objective function is a

crucial component of any optimization process and

enhances system performance while fulfilling the

requirements of the control design. Applying D-

STATCOM to the grid-tied PV system is intended

to reduce voltage deviation by adjusting the error

input from the current controllers to zero steady-

state. The D-STATCOM may not adjust for the

power quality concerns or may function at a lesser

efficiency if the dc-link voltage is not kept at its

reference value or the PI controller gain parameters

are not properly calibrated. The Proportional (P)

phase of the PI controller gives a quick response,

while the Integral (I) phase assures there is no

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steady-state inaccuracy. Together with each other,

these two phases work to regulate the dc-link

voltage. The optimization objective function was the

Integral Square Error (ISE) performance index 'J,'

denoted in Equation (13).

(13)

A minimizing cost function was selected for better

compensation and to reduce the input error. The

optimization's objective was to track the best values

for the parameters that were to be optimized so that

the error input of the D-STATCOM current

controllers could be maintained at zero steady-state.

The maximum sensitivity function that measures the

robustness of a PI controller tuning algorithm is

defined by Equation (14).

 



jSMSmax

(14)

where: MS is the maximum sensitivity function,

max|S(jω)| is the impact of feedback on the output.

The value of MS ranges from 1.1 to 2 and

provides reasonable robustness of the closed-loop

when max|S(jω)| < 1, the disturbances are mitigated

and amplified when max|S(jω)| > 1. The robustness

of the closed-loop increases with the decrease in MS.

4 Simulation Result and Discussion

The simulation of the proposed DGSA-controlled PI

controller methodology has been carried out by

using the MATLAB/Simulink software and

compared the results with a conventional regulated

PI controller. A voltage dip fault in the electrical

distribution network as shown in Figure 5 was used

to simulate a capacitive operation mode in the

electrical distribution network to evaluate the

dynamic behaviour of the system analyzed with

these two types of controllers. Keeping the PCC

reference voltage constant at 1 pu, a voltage dip is

introduced to the PCC at time t = 0.05 second.

Figure 5 presents the simulation results for the

voltage at PCC without D-STATCOM

compensation, and Figure 6 illustrates the voltage at

PCC with the control configuration using a

conventional PI D-STATCOM compensation.

Fig. 5: Voltage at PCC without compensation from

D-STATCOM.

Fig. 6: Voltage at PCC with D-STATCOM

compensation.

Figure 7 depicts the spontaneous damping of the

induced voltage faults by the D-STAMCOM. Figure

8 presents in comparison to a control loop based on

the conventional PI controller, the voltage dip

induced at the instant t=0.5 second is automatically

compensated by the DGSA-controlled D-

STATCOM with less oscillation and minimized

error. The differences in the thickness of the voltage

response achieved by the conventional PI controller

and that obtained by the proposed DGSA controller

indicate the better performance of the proposed

method. Since the voltage feeds the current loop,

which is characterized as the inner loop, these

oscillations affect the responses of the current. The

proposed controller compensates for the high-

frequency oscillations that make up the signal

thickness.

Fig. 7: Voltage profile at PCC with conventional PI

D-STATCOM controller.

deeISEJ  

   

)( qrefqdrefdr iiiieError 

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Fig. 8: Voltage at PCC with D-STATCOM

compensation.

Figure 9a and Figure 9b indicate that the

reactive current is positive by 0.5 to 1 second,

indicating that the D-STATCOM is operating in

capacitive mode (producing reactive power to

counteract the voltage dip) at the PCC. In addition,

compared to a D-STATCOM running through a

conventional PI controller, the static error in the D-

STATCOM's response to the DGSA is decreased.

Fig. 9a: D-STATCOM conventional PI controller

dynamic reactive currents

Fig. 9b: D-STATCOM with a DGSA controller

dynamic reactive currents

The dynamic responses of the D-STATCOM's

active and reactive powers are depicted in Figure

10. The figures show how the D-STATCOM

operates in an inductive mode for 0.5 to 1 second,

supplying the reactive power required to keep the

voltage steady in a power distribution network.

Fig. 10: D-STATCOM active and reactive powers

dynamic response.

In addition, by comparing the reactive powers

provided using the proposed control method in

Figure 11, an error reduction is achieved with less

oscillation compared to the system using the

conventional PI controller.

Fig. 11: DGSA controlled D-STATCOM active and

reactive powers dynamic response.

Table 2 presents the spectrum analysis of

current harmonics injected by the D-STATCOM. It

is noted that with the use of the DGSA-controlled

method, the harmonics in these injected currents are

reduced compared to the conventional PI controller-

based D-STATCOM. The current total harmonic

distortion (THD) for the D-STATCOM with the

conventional PI controller is 11.68%, while the

THD spectrum analysis for DGSA-controlled D-

STATCOM is 3.74%. Hence, the proposed

optimized controller demonstrates better

performance in terms of current harmonics

reduction.

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Table 2. Current Harmonics Analysis

Description

Result

Conventional PI controller

11.68%

DGSA-controlled device

3.74%

5 Conclusion

The simulation of the proposed Dynamic

Gravitational Search Algorithm (DGSA) controlled

D-STATCOM strategy was implemented in this

study to mitigate the impact of voltage dips and

smooth the grid voltage profile. The benefit of this

control strategy for reactive power compensation,

voltage dip mitigation, and current total harmonic

distortion (THD) reduction is demonstrated by the

simulation results and compared to the results of the

conventional PI controller. The simulation results

show that the proposed control method for D-

STATCOM not only achieves sinusoidal and

symmetrical grid current with fewer harmonics, in

addition efficiently eliminates the oscillation

produced in active and reactive power. The DGSA-

optimized PI control strategy reduced the current

THD from 11.68% to 3.74% below the maximum

standard limit of 5% helps mitigate the effect of

voltage dip and improves the overall performance of

the distribution network.

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Policy)

The authors made equal and substantial

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conclusion.

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Scientific Article or Scientific Article Itself

I want to use this [opportunity to appreciate

Tshwane University of Technology for the success

of this research work.

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

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(Attribution 4.0 International, CC BY 4.0)

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WSEAS TRANSACTIONS on POWER SYSTEMS

DOI: 10.37394/232016.2023.18.31

Aboyede Abayomi, Agha F. Nnachi

E-ISSN: 2224-350X

309

Volume 18, 2023