Power System Security Enhancement by NARX ANN Model Based

Interline Power Flow Controller (IPFC)

Harsh R. Mankar

and Vinod K. Chandrakar

Dept. of Electrical Engineering, G H Raisoni College of Engineering Nagpur, India

Keywords: Power System Security, Transient Stability, IPFC, NARX ANN Model, Damping of Oscillation.

Abstract: Ensuring stability and reducing oscillations are critical for the reliable operation of modern power systems.

One effective approach to achieve these goals is through the use of an Interline Power Flow Controller (IPFC).

This paper focuses on the design of a PI-based controller for two Static Synchronous Series Compensators

(SSSCs), employing a unified and simultaneous control method across multiple transmission lines. The main

objective is to enhance the system’s initial first peak stability, shorten the settling time, and mitigate

oscillations in large and complex power networks. The control mechanism for the voltage-source converter-

based SSSC aims to improve transient performance under various operating scenarios. The study compares

the performance of the PI-based IPFC with an NARX ANN Model based IPFC, demonstrating the enhanced

stability provided by the ANN approach during large and sudden disturbances. The effectiveness of both

control strategies is validated in the MATLAB environment.

1 INTRODUCTION

Power systems worldwide are increasingly facing

stability challenges due to the integration of

renewable energy sources, rising demand, and more

complex interconnections. Oscillations, a natural

aspect of these dynamic systems, can cause

significant disruptions if not properly managed.

Traditional methods for damping these oscillations

often fall short in addressing the needs of modern,

complex, and variable power grids (Hingorani and

Gyugyi, 2000), (Padiyar, 2007), (Zhang, Rehtanz, et

al. , 2006), (Kundur, 1994). The Interline Power Flow

Controller (IPFC), a component of Flexible AC

Transmission System (FACTS) technology, offers a

versatile and efficient solution to these challenges. As

a multifunctional FACTS device, the IPFC is capable

of controlling power flow and enhancing system

stability. It uses multiple Voltage Source Converters

(VSCs) connected through a common DC link,

allowing it to manage power flow across multiple

transmission lines simultaneously. This capability

makes the IPFC particularly effective at reducing

https://orcid.org/0009-0008-9030-5989

https://orcid.org/0000-0002-0912-7281

oscillations and improving overall system stability

(Dhurvey, Chandrakar, et al. , 2011), (More,

Chandrakar, et al. , 2016), (Dhurvey, Chandrakar, et

al. , 2016), (Dhurvey, Chandrakar, et al. , 2016),

(Dhurvey, Chandrakar, et al. , 2016), (Dhurvey,

Chandrakar, et al. , 2019).

The IPFC's ability to suppress oscillations comes

from its dynamic power flow management across

several transmission lines. By adjusting the voltages

in the series compensators, it can influence power

transfers and phase angles, effectively mitigating

oscillations. Its rapid response and adaptability give

the IPFC a significant advantage over traditional

damping methods, particularly in complex and

variable power networks (Belwanshi, Chandrakar, et

al. , 2011), (Bhande, Chandrakar, et al. , 2022),

(Dhurvey, Chandrakar, et al. , 2019), (Dhurvey,

Chandrakar, et al. , ). Additionally, incorporating a

NARX (Nonlinear Autoregressive with Exogenous

Inputs) Artificial Neural Network (ANN) enhances

the IPFC’s performance. The NARX ANN provides

an advanced control mechanism by accurately

predicting system behaviour and improving the

IPFC's ability to handle large disturbances and

656

Mankar, H. R. and Chandrakar, V. K.

Power System Security Enhancement by NARX ANN Model Based Interline Power Flow Controller (IPFC).

DOI: 10.5220/0013599600004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 656-661

ISBN: 978-989-758-763-4

varying operating conditions (Chandrakar, Dhurvey,

et al. , 2018).

The effective damping of oscillations with

Interline Power Flow Controllers (IPFCs) depends on

advanced control systems that continuously monitor

key parameters such as voltage, current, and power

flow. IPFCs can quickly adjust the outputs of Voltage

Source Converters (VSCs) to provide the necessary

compensating voltages. By optimizing power flow,

IPFCs improve the efficiency of existing transmission

infrastructure, thereby increasing system capacity

without the need for new transmission lines. Despite

their benefits, IPFCs face challenges such as complex

control requirements, integration difficulties, and

high maintenance needs (Bhande, Chandrakar, et al. ,

2022), (Bhande, Chandrakar, et al., 2023).

(Chandrakar, Kothari, et al., 2004), (Tadurwar,

Chandrakar, et al. , 2021).

The firing control of VSCs is critical to achieving

the IPFC's goal of efficient power flow management.

PI-based controllers are commonly used in the

industry to manage power flow in large power

systems during transient conditions. However, their

performance can degrade under significant system

changes, requiring precise tuning of the PI parameters

to maintain optimal performance. The performance of

PI based controllers deteriorates under large and

sudden variation of operating condition in large

power system. The PI constants Kp, Ki are fixed for

one operating condition therefore under varying

operating condition PI performance not satisfactory

hence there is need of replacement by ANN Based

controllers.

The Multilayer feedforward network is commonly

used algorithm in place of PI controller. The large

power system with IPFC is non-linear system

therefore NARX ANN Model deals better as

compared to multilayer feedforward network.

This paper presents the design of a PI-controlled

IPFC and compares it with an NARX ANN Model

based IPFC, both aimed at managing two Static

Synchronous Series Compensators (SSSCs) on

different transmission lines. The proposed controllers

are designed to improve the initial peak deviation of

generator rotor speed during sudden and large

disturbances and to reduce oscillations under

transient conditions. The performance of these

controllers is evaluated under various transient

conditions and load scenarios in complex network

configurations using MATLAB simulations. The

results show that the ANN-based IPFC significantly

enhances damping effectiveness and successfully

meets its objectives.

1.1 IPFC Modeling

The IPFC is made up of several Static Synchronous

Series Compensators (SSSCs) that are interconnected

through a shared DC link, as illustrated in Figure 1.

Each SSSC helps manage reactive power

compensation for its specific transmission line.

Furthermore, the system can facilitate the transfer of

real power between lines, enabling power to be

shifted from a less loaded line to a more heavily

loaded one using the common DC link.

Figure 1: Interline Power Flow Controller with Sampled

Power System.

Figure 2 depicts the maximum output voltages of

the two inverters as represented by the circle. The

central voltage compensation line, aligned with V1

and passing through the circle's center, represents the

voltages that neither supply nor absorb active power

from the transmission lines. On the right side of this

central line, the voltage compensation lines

correspond to voltages that deliver active power to the

transmission line. On the left side, these lines indicate

voltages that draw active power from the

transmission line.

Figure 2: Vector diagram of IPFC.

The mathematical model is developed with the

assumption that no power is exchanged through the

DC link. By replacing Vsein with Isein in parallel

with the transmission line and neglecting the

resistances of the transmission line and series

Power System Security Enhancement by NARX ANN Model Based Interline Power Flow Controller (IPFC)

657

coupling transformers, the current source can be

expressed as follows (Belwanshi, Chandrakar, et al. ,

2011)].

ISeinjbSein (1)

Complex power injected at 1st bus is

Sinj1

∑

𝑉

,

1 (-ISein)* (2)

Sinj,1

∑

𝑉

,

1 ( jbseinVsein)* (3)

Active & reactive power injection at 1st bus are

Pinj,1 = Re (Sinj,1) =

∑

𝑉

,

1VSein b Sein Sin

(𝜃1 – 𝜃Sein)) (4)

Qinj,1 = Im = -

∑

𝑉

,

1 VSein bSein cos (𝜃1 –

𝜃Sein))

Similarly, complex power, active power &

reactive power injection at nth bus ( n=2,3) is

Sinj,n = Vn (ISein)* = Vn (-jbSein VSein)*

Pinj,n = Re (Sinj,n) = -Vn VSein bSein sin (𝜃n –

𝜃Sein)

Qinj,n = Im ( Sinj,n) = Vn VSein bSein cos(𝜃n –

𝜃Sein) (5)

2 CONTROL SCHEME FOR IPFC

Figure 3: Scheme for IPFC Control.

In the control configuration shown in Figure 3,

converter 1 is the primary converter, and converter 2

operates as the secondary converter, supporting the

operation of converter 1. Converter 2 defines the

active power limit for converter 1. Each converter

generates its own phase angles using a phase-locked

loop, which are then compared with the phase angle

of the injected voltage. This comparison results in the

generation of firing pulses for both converters.

2.1 System Model

In the control configuration illustrated in Figure 1,

SSSC 1 acts as the primary converter, and SSSC 2

supports it as the secondary converter. SSSC 2

establishes the active power limit for SSSC 1. Both

converters utilize individual phase-locked loops to

generate their respective phase angles, which are then

compared with the phase angle of the injected

voltage. This comparison leads to the creation of

firing pulses for both converters.

3 ANN BASED IPFC

Figure 4 illustrates the architecture of the multilayer

feedforward network. This network is designed with

two input neurons, which receive the measured

voltage and reference voltage at the IPFC location.

The hidden layers process these input signals, and at

the output layer, a single neuron generates the firing

signal for the pair of SSSCs.

Additionally, a Nonlinear Autoregressive with

Exogenous Inputs (NARX) Artificial Neural

Network (ANN) is incorporated into the system. The

NARX ANN enhances the network's ability to predict

and adapt to dynamic changes in the power system,

improving the overall control and performance of the

IPFC by capturing nonlinear relationships and time-

dependent behavior in the system's operation. The

defining equation for the NARX model is,

y(t) = f (y(t-1), y(t-2),…, y(t-n

),u(t-2),…, u(t-

)) (6)

Figure 4: NARX Neural Network.

Figure 5 illustrates the design of a PI controller

combined with a NARX ANN controller,

implemented in the MATLAB environment.

INCOFT 2025 - International Conference on Futuristic Technology

658

Figure 5: NARX ANN Model.

4 SIMULATION RESULT

The test system response under three phase fault at

bus number 4 for the 0.11 second. Simulation

response shown in fig. 6.

Figure 6.1: Rotor Speed Deviation dw (pu).

Figure 6.2: Voltage (pu).

Figure 6.3: Power (pu).

Figure 6.4: Reactive Power Variation (pu).

The figures 6.1, 6.2, 6.3, 6.4 shows the simulation

response under disturb condition of the sampled

power system indicates that the first peak

significantly reduced by ANN Model and the

oscillations are also reduced.

5 CONCLUSION

The study clearly highlights the advantages of using

NARX Model based Interline Power Flow

Controllers (IPFCs) over traditional PI-based

controllers in improving power system stability. The

ANN-based IPFC not only enhances the damping of

power system oscillations but also reduces the first

peak deviation and settling time during transient

disturbances.

Power System Security Enhancement by NARX ANN Model Based Interline Power Flow Controller (IPFC)

659

Simulation results show that the ANN-based

control strategy provides a more flexible and dynamic

response to system disturbances, enabling efficient

power flow management across multiple

transmission lines.

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