Transient Stability Enhancement of Power Grid

by Neural Network Controlled BFCL

Considering Cyber-Attacks
Mohammad Ashraf Hossain Sadi1 Mohd. Hasan Ali3
Huaxi Zheng2 Department of Electrical and Computer Engineering3
School of Technology1 University of Memphis3
University of Central Missouri1 Memphis, Tennessee, USA3
Warrensburg, Missouri, USA1 mhali@memphis.edu3
Power Systems Consulting 2
ABB Inc.2
Raleigh, North Carolina, USA 2
sadi@ucmo.edu1
huaxi.zheng@us.abb.com2 reasonable installation characteristics, it is gaining increasing
acceptance to the power engineers.
Abstract—In this paper, a neural network predictive controlled
bridge type fault current limiter (FCL) is proposed to enhance the Neural networks have extensive applications and have been
transient stability of power systems. Cyber security issues on the implemented in various scientific fields for proper modeling and
performance of neural network controller is also investigated. It is controlling purpose [9]. Neural networks can be easily
noteworthy that simulations have been conducted by the implemented for transient stability enhancement of power
Matlab/Simulink software. Both symmetrical and unsymmetrical systems, as it offers the inherent quality of storing and learning
types of permanent and temporary faults have been considered at information about the nonlinearities of the system [9]. Moreover,
different locations of a multi-machine power system. Based on the neural network can provide the stored information whenever
simulation results, it can be concluded that the bridge type FCL
required. It can optimize the systems performance based on few
based on neural network predictive controller can enhance the
layers. Further, neural networks are non-parametric models that
transient stability of the system well. Moreover, the cyber-attack has
profound effect on the controller performance and the system
represent similar characteristics to human decision making based
becomes fully unstable even with the presence of the neural network on its ability to function from vast data and explore exact
predictive controlled bridge type FCL. solutions [10].
A neural network predictive controlled BFCL has been
Index Terms— Bridge type FCL, cyber-attack, neural network, proposed in this work to enhance the transient stability of multi-
power system stability.
machine power system. As per the literature, no application of
I. INTRODUCTION neural network predictive controlled BFCL has ever been
reported to augment the stability of multi-machine power system.
The ability of a power system to regain its stability following And here lies the originality of this work.
unexpected and severe faults in the network is defined as the
transient stability [1]. Several methods have been proposed by Wide Area Measurement Systems (WAMS) represents the
the power engineers to augment the transient stability of power real-time measurements that could be used to improve and
systems. The dynamic braking resistor (BR) [2], Flexible AC maintain the stability of the power systems. Traditional point to
Transmission Systems (FACTS) devices [3], Superconducting point communications are unable to meet the communication
Fault Current Limiter (SFCL) [4], Static VAR Compensator requirements, as the power grid is becoming more complex,
(SVC) [5], Superconducting Magnetic Energy Storage (SMES) increased, and interconnected [11]. The WAMS overcomes the
[6], etc., are the most popular transient stability enhancing traditional measurement systems drawbacks by using modern
methods and they are getting more applications day by day. Wide Area Network (WAN) or Local Area Network (LAN)
technology [11]. Communication/Information infrastructure as
Fault current limiters decrease the magnitude of fault current well as WAMS are the backbone of the power grid where
by introducing fixed impedances during fault [7, 8], and thus different grid components are connected with each other through
enhance the transient stability of the power system. Due to its these structures and managed by the Supervisory Control and
ability to effectively control high fault current, the bridge type Data Acquisition (SCADA) system for supervisory control [12].
fault current limiter (BFCL) has recently been known as a very The vulnerabilities of the communication architecture are high
popular and effective means to augment the transient stability [7]. and thus the communication network in the smart grid can be
Moreover, due to its simple construction, lower price value and exploited by the cyber intruders to damage different layers
because of poor and insufficient network security. As power grid

978-1-5386-1539-3/17/$31.00 ©2017 IEEE

is a complex cyber physical interconnected network, cyber the Point of Common Coupling (PCC) points of generator 1,
intrusion in the cyber network consequently will have devastating generator 2, generator 4 and generator 6 based on the coherency
impact on the physical network too. analysis [14, 15]. Details about the positioning of BFCL are
described in [13].
The proposed neural network predictive controlled BFCL will
use the Total Kinetic Energy (TKE) of the generators as the Fig. 1 also represents a SCADA system based on the
control input. The calculation of TKE depends on the generators NASPInet architecture [16, 17]. The field devices like IEDs or
speed responses and they are collected from field devices like RTUs are used to collect state responses from the generators
Intelligent Electronic Devices (IEDs) or Remote Terminal Units situated in diverse locations of the IEEE 39 bus power system.
(RTUs). But in the process of collection and processing, the Then the state responses from the generators are transferred to
TKE/Total Kinetic Energy Deviation (TKED) signal can be the SCADA system through IP based communication networks
hacked or lost by cyber-attacks for further processing. Real time synchronization for the
collected data is achieved by timing signals from the Global
Positioning System (GPS).

Fig. 1. IEEE 39 bus system with BFCL and connected to SCADA

In this paper, the cyber security issues have been explored on

the performance of the BFCL against transient stability of power
grid. Moreover, for cyber security testing purpose, the Denial of
Service (DoS) and tampering the communication signal cyber-
attacks have been considered in the event of TKE/TKED data III. BRIDGE TYPE FAULT CURRENT LIMITER (BFCL)
transfer from the SCADA to the neural network controller of the The BFCL is an auxiliary device and it introduces resistance
BFCL. So far, based on the literature review carried out by the and inductance in the event of fault. Moreover, for its proper
authors, no work is available on the effect of cyber-attacks on the operation, no superconductivity is needed [7, 18, 19]. Thus, its
controller signal accumulation process for fault current limiters, installation cost is much lower than that of other FCLs [7, 18,
which is another novelty of this work. 19]. There are two main parts of BFCL as shown in Fig. 2. The
The IEEE 39 bus large power system model [13] has been diode rectifier bridge, a small dc limiting reactor (Ldc) along with
used to demonstrate the effectiveness of the neural network a very small resistor (Rdc), a IGBT/GTO based semiconductor
predictive controlled BFCL. Both permanent and temporary switch and a freewheeling diode (Df) combinedly create the
types of balanced and unbalanced faults are considered in the bridge part. Additionally, the shunt path consists of a variable
system. All simulations have been conducted in the resistor and an inductor (Rsh+ jωLsh), which is considered as the
Matlab/Simulink software platform. main part [7, 18, 19]. When the IGBT switch remains turned on,
the line current passes through D1, Ldc, semiconductor switch and
II. MODEL SYSTEM DESIGN D4 during positive half cycle of the normal operating conditions.
To analyze the transient stability, the IEEE 39 bus power Also, the line current passes through D3, Ldc, semiconductor
system consisting of ten generating units as shown in Fig. 1 is switch and D2 during negative cycle of the normal operating
used. The generators are interconnected with 19 loads, 36 conditions. During normal operating condition, the Ldc is charged
transmission lines and 12 transformers. The system base is 100 to the peak of the line current, and behaves like a short circuit,
MVA and 60 Hz. Three buses are rated as 135 KV, whereas the and consequently almost negligible voltage drop appears.
other buses as 33 KV. As shown in Fig. 1, the BFCL is placed at
 The performance of the BFCL switch is controlled by the less significant disturbance at fault, the BFCL is expected to
TKE variation of all the generators in the system. During initial dissipate most of the power. The currents passes through the
stage of the fault, the TKE of the generators varies abruptly and shunt impedance during fault current limiting mode. Therefore,
the line current increases excessively, but the dc reactor Ldc the improved active power given in (3) was calculated to
reduces its swelling rate and protects the IGBT/GTO switch from approximate the value of ZBFCL= Rsh + jωLsh for each BFCL in
severe di/dt change. As soon as the TKE change becomes very the system.
acute, the IGBT switch turns off and the shunt impedance
appears in series with the disturbed line. Thus the series From the parametric equations, the optimal values of each
impedance reduces the fault current to an acceptable limit. The shunt path are approximated to be within 1 pu. The shunt
free path of dc reactor current as soon as the IGBT switch turns resistance values of the BFCL are varied within 1 pu for different
off is provided by the freewheeling diode Df [7, 18, 19]. The fault conditions depending on the systems operation. The
IGBT switch turns on again after the removal of fault and the calculated parameters of BFCL are given in Table I.
BFCL resumes its usual operation. The BFCL control scheme is
described in detail below.
A. Design of the Bridge Type Fault Current Limiter
Since power system is inherently nonlinear and always there
are load changes, determination of the suitable values of the
current reducing impedance is needed. As mentioned in section
II, four BFCLs are connected in four coherent groups having two
generators in the group. Therefore, for determining the proper
impedance value of the BFCLs, the equivalent network for each
coherent group having two areas as represented in Fig. 2 are
considered.
Fig.2. Equivalent two area network of the coherent group with the BFCL.
The power transmitted in the coherent network without BFCL
TABLE I
can be expressed as: BFCL PARAMETERS
Parameters Values
∗ Bridge Type Fault Ldc=0.01H, Rdc= 0.05Ω,
( ) = sin (1) Current Limiter Lsh= 0.012H, Rsh ≤4.8Ω.

IV. NEURAL NETWORK PREDICTIVE CONTROL

Where ZL = ZL1+ZL2 and = − .
Artificial neural networks have been widely used in different
research fields because of their ability to represent the
mathematical modeling through human neural networks. A
Similarly, power transmitted with the presence of BFCL can
neural network consists of single or multiple layers of neurons
be expressed as:
connected through weights which are adjusted to achieve the
desired control [9, 10, 20, 21]. Fig. 3 represents a neural network
( ) =
∗
sin (2) predictive control system.

Where ZBFCL= Rsh + jωLsh is the BFCL shunt path impedance.

The BFCL is active power compensating auxiliary device and
the active power improved by it can be expressed as:

∗ ( ∗ )
PBFCL = P(t) – P(e) = sin (3)
( )

Fig.3. Neural network predictive controller.

Equal amount of current is carried by each line during normal
operations. For resuming the normal operation and to ensure the
A. BFCL Closed Loop Control Scheme Repeat the previous three steps for each time step.
The closed loop control scheme of the proposed neural C. Neural Network Predictive Control Design
network predictive controlled BFCL is given in Fig. 4. The TKE
of the generators in the system is used as the input signal for the A neural network with an input layer, output layer and two
controller switching signal. The neural network predictive hidden layer neurons are used in this work. Sigmoidal hidden
controller is designed to produce varied limiting resistance Rsh as layers are used to ensure the nonlinear and linear relation
its output. Because of the variation in the TKE, the output Rsh between the input and outputs of the neural network [9, 10]. The
from the controller is varying in nature. To keep the shunt hidden layers consist ten neurons each and the output layer
resistance value Rsh within the limit as shown in Table I, it is consists of one neurons. The neural plant identifier uses the value
passed through a limiter. Then it’s passed through a logic circuit of total kinetic energy at two previous instances to predict the
to generate the actual variable shunt resistance of the BFCL. value of total kinetic energy. Therefore, two-layer feedforward
network with 2 inputs, 2 hidden layers with 10 sigmoidal neurons
each and a linear output neuron as an output layer is used. The
hidden layers enable the learning of linear as well as nonlinear
relationships between inputs and outputs of the plant. The neural
network identifier used in this work is presented in Fig. 5.

Fig.4. Neural network predictive controller connection scheme with BFCL.

The IGBT switch is controlled by the TKED variation

through a logic circuit. The logic is set in such a way that the
IGBT switch remains turned on if TKED becomes less than
0.0002 pu, otherwise the switch is turned off.
B. Neural Network Predictive Controller Algorithm
The purpose of the Neural Network Predictive Control
Fig.5. Neural network identifier structure.
(NNPC) is to generate a signal u(t) so that the output of plant y(t)
is made as close to the reference signal r(t). The input signal r(t)
The output of each input neurons are given as
is converted into yr(t) and then fed into the Cost Function
Minimization (CFM) block as represented in Fig. 3. For each
sampling time instances, the control input is calculated in such a a02*1 = [TKE (t-1) TKE (t-2)] T (4)
way to minimize the cost function [10, 20]. Here, N1 is the
prediction horizon. The predicted control inputs for future Therefore, the net input to the neuron i in the first hidden
horizons are fed to the neural plant identifier to enable the cost layer is
function minimization. Then the best optimized control input 2
signal calculated by CFM is fed to plant. The neural identifier is n 1 (i ) =  w (i , j ) a 0
( j ) + b 1 (i ) (5)
used to predict the performance of the plant based on previous j =1

control input [10, 20]. The control input is calculated until the Here, b(i) is the bias and w(i, j) is the weight matrix between
cost function is minimized. the neurons.
The Neural Network Predictive Control algorithm is The output of the first hidden layer is given in the following
described below. matrix form.
i) Generate a reference signal.
ii) Predict the performance of the system depending on a110*1 = tansig([W] 110*2 a02*1 + b110*1) (6)
the previously calculated control input vector and neural
identifier. Similarly, the net input of the second hidden layer neuron is
iii) Calculate the new control input which will optimize the
CFM. 10 (7)
iv) Repeat the first two steps until optimization of CFM is n 2
(i) = 
j =1
w (i, j )a 1 ( j) + b 2
(i)

achieved.
v) Provide the control input vector to the plant. Similarly, the output from the second hidden layer is
 a210*1 = tansig([W] 210*10 a010*1 + b210*1) (8) SCADA system state estimation scheme or data sent from the
RTUs to the SCADA system can provoke negative
The net output from the hidden layers to output layer will be consequences. There are possibilities of the false data not
10
detectable by the state estimation scheme or even the false data
n 3 (i ) =  w (i , j ) a
j =1
2
( j ) + b 2 (i ) (9) attack can be detectable without remedy.
There are mainly three types of attack that can happen in the
Finally the output from output layer will be smart grid system. Packet drop attacks can be caused at choke
points in the communication path, Distributed Denial of Service
TKE= purelin(W31*10 a210*1 + b3) (10) (DDoS) type attacks are mainly caused by disrupting, blocking or
jamming the flow of information through control and
To train the neural network, the Backpropagation algorithm communication networks, tampering communication data/signal
utilizing the Levenberg-Marquardt algorithm is used [10, 20]. type attacks not only delays communication but also
contaminates the data in the communication. In the literature,
In backpropagation algorithm, the gradient of the error several other cyber-attack scenarios are considered, for example,
function is determined to demonstrate the convergence of the intentionally data injection into the power system through
neural network under consideration. The error function of the telemetered data [24], malicious modification of network stored
considered network is data [25], coordinated data attack staged without being exposed
by the state estimation algorithm [26], and undetected
N 2 manipulation and the incorrect database parameters by cyber-
E ( x) = e
j =1 j
( x) (11)
attacks [27].
The National Electric Sector Cybersecurity Organization
Here, x is the weight of the neurons. Resource (NESCOR) recently defined many cybersecurity
realistic events that create negative impact on the power systems
The weights of the network are modified during the training
operation [28]. In this work, two NESCOR recommended
process until a desired performance is achieved. Therefore, the
realistic events have been considered to see the effectiveness of
update in x can be expressed as
cyber-attack on the performance of neural network controlled
bridge type FCL. Fig. 6 represents the cyber-attack scenarios
Δ x = −[Δ
2
E ( x )] − 1
Δ E (x) (12) considered in this work. Firstly, false data injection in the TKE
calculation scheme of the SCADA system has been used as input
The update in the x is further modified using the Levenberg- for the neural network predictive controller. This is considered as
Marquardt algorithm. Thus, the Levenberg-Marquardt algorithm Distributed Denial of Service (DDoS) type cyber-attack.
is used for convergence of the backpropagation algorithm [10, Secondly, tampering the signal or data attack can happen either
20]. in the RTU level or even in the communication channel between
the RTUs and SCADA system. In this work, a cyber-attack has
The cost function minimization (CMF) block works on the been considered, where data to be sent from the RTUs to the
receding horizon technique. The CMF block minimizes the SCADA are altered to values specified by the attacker.
difference between the instantaneous value of the total kinetic
energy and its steady state values over some operating time
horizon.
V. CYBER-ATTACKS IN POWER GRID
In power systems, numerous critical equipment and field
devices are used at the remote locations to effectively and
efficiently collect the customer operating conditions and thus
represents the wide area measurement and control technique. The
field devices like PLCs, IEDs, RTUs, PMUs, and smart meters
have algorithms which can be manipulated by either customers or
Fig.6. Cyber-attack scenarios.
cyber intruders [12]. By hampering and tampering the data
collection process, the utilities can get misleading feedbacks and
thus can cause interruptions which may ultimately result in a VI. SIMULATION RESULTS AND DISCUSSION
blackout. All simulations have been conducted in the Matlab/Simulink
The communication network in the power systems can be software platform. Both temporary and permanent balanced
exploited by the intruders to damage different layers present in (3LG: three-phase-to-ground) and unbalanced (1LG: single-
the grid. The RTUs forward the state estimation data to the phase-to-ground) faults are considered in the simulations. Also,
SCADA center [22, 23]. The false data injection either in the twenty-two fault points (A to V) as shown in the model system of
Fig. 1 have been considered. For the simulation, 20s and 50µs are ( )
Wc (sec) = / (13)
considered as simulation time and time step, respectively. The
temporary fault is considered to persist for 0.5 sec, while the
permanent fault persists for a substantial time.
Where, T is the simulation time and Wtotal is the total kinetic
A. Transient Stability Analysis for Permanent and Temporary energy of the system which can be evaluated from the rotor speed
Fault Without Considering Cyber-Attacks of each generator. Lower value of Wc, indicates better system’s
It is considered that for permanent fault, the circuit breakers performance. Table II and Table III represent the stability index
open at 0.0833 sec after the fault occurrence, reclose according to values for 3LG permanent and temporary faults, respectively, at
the optimal reclosing time (ORCT) [4, 5],[13] and reopen at three different fault positions in the IEEE 39 bus power system.
0.0833 sec after the reclosing. On the other hand, for temporary The values of the stability indexes prove the efficacy of the
fault, the circuit breakers open at 0.0833 sec after the fault neural network predictive controlled BFCL for improving the
occurrence and recloses according to the ORCT time [4, 5],[13]. transient stability.
The generators TKE responses for 3LG permanent fault at TABLE II
position B in the IEEE 39 bus power system is presented in Fig. VALUES OF WC WITH NEURAL NETWORK PREDICTIVE CONTROLLED BFCL FOR
7. Similarly, the generators total kinetic energy responses for PERMANENT FAULT
1LG temporary fault at position B in the IEEE 39 bus power Fault Wc values (with Wc values
system is represented in Fig. 8. It is explicit from the TKE Fault point neural network (without
responses that the neural network predictive controller based Type predictive controlled controller)
BFCL makes the system stable. BFCL) (sec) (sec)
A 0.501 1.521
3LG B 0.505 1.735
C 0.524 1.441

TABLE III
VALUES OF WC WITH NEURAL NETWORK PREDICTIVE CONTROLLED BFCL FOR
TEMPORARY FAULT
Fault Wc values (with Wc values
Fault Point neural network (without
Type predictive controlled controller)
BFCL) (sec) (sec)
A 0.503 1.462
3LG B 0.498 1.688
C 0.508 1.379

Fig.7. Total kinetic energy of the generators for 3LG permanent fault at position
B and without any cyber-attack.
B. Transient Stability Analysis in the Event of Distributed Denial
of Service (DDoS) type Attack.
For the neural network predictive controller, the TKE/TKED
is used as control input in this work. We collected speed response
from all the remotely located generators and calculated the
TKE/TKED in a simply designed SCADA system as represented
in Fig. 1, and Fig. 4. For initiating the DDoS attack, we
considered total loss of TKE/TKED by signal failure to the
considered controllers of the BFCL. Fig. 9 and Fig. 10 represent
the generators total kinetic energy responses considering DDoS
cyber-attack for three phase to ground and single phase to ground
permanent fault, respectively, at point B with the presence of
neural network predictive controlled BFCL. From the responses,
it is clear that for DDoS cyber-attack, the total kinetic energy
Fig.8. Total kinetic energy of the generators for 1LG temporary fault at position B goes out of step and control even with the presence of neural
and without any cyber-attack. network predictive controlled BFCL.
C. Transient Stability Analysis in the Event of Tampering the
To assess the transient stability, in this work the TKE based Communication Data/Signal type Attack
stability index, Wc [4, 5] has been used, which is given by
For initiating tampering the communication data/signal
attack, a malicious data was inserted in the TKE/TKED
calculation process in the SCADA system. Therefore, an abruptly
high value of TKE/TKED was passed to the controllers in
operation. Generators total kinetic energy responses considering
tampering the communication signal cyber-attack for 3LG and
1LG permanent faults at point B with the presence of neural
network predictive controlled BFCL are represented in Fig. 11
and Fig. 12 respectively. From the response, it is clear that for
tampering the communication signal cyber-attack, the total
kinetic energy goes out of step and control even with the
presence of neural network predictive controlled BFCL. Table IV
and Table V represent the Wc stability index values for 3LG
permanent fault considering DDoS and tampering the
communication signal cyber-attack.

Fig.11. Total kinetic energy of the generators considering tampering the

communication signal cyber-attack and 3LG permanent fault at position B.

Fig.9. Total kinetic energy of the generators considering DDoS cyber-attack and
3LG permanent fault at position B.

Fig.12. Total kinetic energy of the generators considering tampering the

communication signal cyber-attack and 1LG permanent fault at position B.

TABLE V
VALUES OF WC WITH NEURAL NETWORK PREDICTIVE CONTROLLED BFCL FOR
TAMPERING THE COMMUNICATION SIGNAL CYBER ATTACK
Wc values (with Wc values (with Wc values
Fault Fault controller and controller and (without
Type point cyber-attack) without cyber- controller
(sec) attack) (sec) and cyber-
attack) (sec)
A 1481 0.501 1.521
Fig.10. Total kinetic energy of the generators considering DDoS cyber-attack and 3LG B 1509 0.505 1.735
1LG permanent fault at position B. C 1492 0.524 1.441

TABLE IV VII. CONCLUSION

VALUES OF WC WITH NEURAL NETWORK PREDICTIVE CONTROLLED BFCL FOR
DDOS CYBER ATTACK This paper presents the implementation of neural network
Wc values (with Wc values (with Wc values predictive controlled BFCL for enhancing the transient stability
Fault Fault controller and controller and (without of multi-machine power system. The cyber security issues on the
Type point cyber-attack) without cyber- controller controller performance have been investigated. From the
(sec) attack) (sec) and cyber-
attack) (sec) simulation results, the following conclusions can be drawn.
A 1233 0.501 1.521 a) The proposed neural network predictive controlled BFCL
3LG B 1331 0.505 1.735
C 1189 0.524 1.441
can enhance the transient stability of the large power system.
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