Improved technique for under voltage load shedding using Genetic Algorithm and Particle Swarm Optimization

Introduction
ACADEMICS
B.E (Electrical Engineering), NED University of Engineering& Technology
Karachi, Sindh-Pakistan. 1998.
M.Engg (Electrical Engineering, CGPA 3.45/4.00), NED University of
Engineering& Technology Karachi, Sindh-Pakistan. 2006
Doctor of Philosophy (Electrical Engineering) from Universiti Teknology
Malaysia (UTM) Johor Bharu Skudai. 81310. 2019.
JOB PROFILE
Assistant Plant Manager at Manipur Sewerage Treatment Plant from China
Petroleum Engineering Construction Corporation (CPECC) 1998-2000
Laboratory Engineer at NED University of Engineering& Technology Karachi,
Sindh-Pakistan 2000-2003
Assistant Executive Engineer at K-Electric 2003-2005
Lecturer at SSUET (Electronic Engineering), Karachi. 2005-2006
Assistant Professor at NED University of Engineering& Technology Karachi.
2
Engr. Dr. Raja Masood Larik

Research Activities
He has total of 20 Publications as Main and Co Author
He was Ex-President of Post Graduate Student Society (School of Electrical
Engineering) UTM Johor Bharu Malaysia. 2016-2017
He served IEEE society as Active Senior Member since November 2018
He is Active Life time Member Pakistan Engineering Council since 1998
He served as Potential Reviewer for many ISI journals which includes
• Renewable and Sustainable Energy Review (RSER) (ISSN No 1364-0321) IF =9.05
• IET Journal Generation Transmission and Distribution (ISSN 1751-8687) IF= 2.213
• IEEE Systems Journal (ISJ) (ISSN# 1932-8184) IF =4.337
He has total 73 no's of citations having h index 4 and i-10 index 1
He served an Active Program Coordinator for different National Events at UTM JB
Malaysia with International Students Society Pakistan since 2014-2019.
3

Presenter
Engr. Dr. Raja Masood Larik
Senior Member IEEE
Faculty of Engineering
School of Electrical Engineering
Universiti Teknologi Malaysia
An Improved Technique based on Genetic
Algorithm and Particle Swarm Optimization for
Under Voltage Load Shedding Scheme
Supervisor
Prof. Ir. Dr. Mohd Wazir bin Mustafa
2019®

Outline
Introduction
Problem Statement
Objectives
Scope and Significance of Research
Literature Review
Research Methodology
Results and Conclusion
5

➢ Modern power systems operates near to their stability boundaries due to
strict economic limitations. Due to rapid increased loading the chance of
voltage collapse is certain. The driving force for voltage instability is
usually the loads.
➢ Disconnect some load from power system under heavy stress condition is
known as load shedding. A small amount of load shed can make the
system from collapse to survival
Introduction
Demand
Generation
6

Introduction.. Cont’d
➢ Voltage collapse in power system is the main concern for the system
operators which is complex and confined in nature. It is typically
associated with reactive power demand of load not being met due to
shortage of reactive power and transmission system limitation.
➢ Load shedding scheme requires coordination between protection
engineers and system planners, who together can determine the amount
of load and time delay required for the shedding
7

Real Power (MW) vs. Voltage (P-V) Curve -- Nose Curve

Problem statement
This Research
Focused on
Conventional
Load Shedding
Under Voltage
Load Shedding
Under
Frequency
Load Shedding
Frequency
drops pre set
threshold
value
Voltage
drops pre set
threshold
value
12

Problem statement…Cont’d
Conventional
Techniques
Computational
Techniques
Optimal
Load
Shed
13

Under Shed
Voltage
Collapse
Blackout
Over Shed
Over
Frequency
Unnecessary
Interruption
Sub-optimal
Load Shed
14

1. Load shedding scheme should be designed in coordination with protective
devices and control schemes for momentary voltage dips, sustained faults, low
voltages caused by stalled air conditioners, etc.
2. Time delay to initiate load dropping should be in seconds, not in cycles. A
typical time delay varies between 3 to 10 seconds.
3. UVLS relays must be on PTs that are connected above the automatic LTCs.
4. Voltage pick-up points for the tripping signals should be set reasonably
higher than the “nose point” of the critical P-V or Q-V curve.
5. Voltage pick-up points and the time delays of the local neighboring systems
should be checked and coordinated.
6. Redundancy and enough intelligence should be built into the scheme to
ensure reliable operation and to prevent false tripping.
7. Enough loads should be shed to bring voltages to minimum operating
voltage levels or higher while Maintaining VAR margins according to WSCC’s
Voltage Stability Criteria.
Design criteria for UVLS
North American Electric Reliability Corporation (NERC)Planning standards
Western Electricity Coordinating Council (WECC) Reliability Criteria

Research Methodology..cont
Genetic Algorithm Steps
Selection of
Parents
Crossover
between
Parents
Mutation
New
Children
16

Methodology…cont
18
Working Steps of Genetic Algorithm

19
Genetic Algorithm Flow Chart

Particle Swarm Optimization Steps
Returned
Global Best
20

Particle Swarm Optimization
21

Particle Swarm Optimization Flow Chart
22

Computational Techniques
Genetic
Algorithm
Particle
Swarm
Optimization
Hybrid
GAPSO
Slow
Complex
nature of
cost
functions
Fast due to
calculation
of position
and velocity
Slow and
sub-optimal
load shed
23
Accurate
Sub-
Optimal
solution
Absence of
Voltage
stability as
constraint

Limitations of
GAPSO
Slow due to
crossover
operator of GA
No criteria for
weak bus
selection
Voltage Stability
not considered
as constraint
Sub optimal
load shed
1.The pervious research considers
only one loading factor.
2.No criteria being set for weak
bus selection
3.Suboptimal load shed
24

1
• To develop a disaster circumstances by contingency
through various loading factors.
2
• To develop a ranking base load shedding scheme for
weak bus identification by using customized FVSI index.
3
• To develop an improved algorithm based on hybrid of
GA and PSO for optimal amount of load shed.
Objectives
25

➢ The data used is standard IEEE Bus System.
➢ Transmission network is considered (IEEE 30 and 57 Bus Test System)
➢ Distributed generations are not considered.
➢ The disturbance is created by increasing the power demand in this
study i.e. large contingency such as major generator outages or
important power transmission line outages.
➢ Minimization of losses due to increased demand are not considered
➢ Frequency decay due to increased demand ignored while consider
the voltage decay only.
➢ Composite load demand is considered.
Scope
26

➢ The improved algorithm is simple and could be easily integrated into
practical existing test systems.
➢ The developed technique is useful where generation and transmission
facilities are limited.
➢ The system planners and operators have deep insight the condition of power
failure with the help of developed technique.
➢ The developed technique is fast enough which is suitable for real time
applications.
➢ The developed technique has the ability to bring back the power system
from collapse to restoration.
Significance of Research
27

Literature review
S.NO Reference Technique/Test
System
Other
Techniques for
comparison
Achievements Limitations
1 Luan, Irving
[113]
2002
GA /
Practical UK system
None A technique for supply restoration in
distribution networks
optimal load shedding
Applicable to the particular
distribution system only.
2 Al-Hasawi and
El Naggar [114]
2002
GA /
IEEE 14 Bus test
system and IEEE 30
Bus test system
The load flow
equations
Optimal load shed for abnormal
conditions
Long Convergence time.
3 Amraee,
Mozafari and
Ranjbar [110]
2006
PSO /
IEEE 14 Bus test
system and IEEE 118
Bus test system
GA Identification of collapse point
Minimum service interruption cost
Consideration of technical and
economic aspects of each static load
Dynamics nature loads were
not considered.
4 Rad and Abedi
[112]
2008
GA and PSO /
IEEE 30 Bus test
system
PSO Minimizes the amount of load shed
using GA
Faster convergence time achieved
through PSO
Not scalable to large and
complex power systems.
Voltage stability not
achieved.
5 Sadati, Amraee
and Ranjbar
[108] 2009
HPSO-SA /
IEEE 14 bus test
system and IEEE 118
bus test system
PSO based
Simulated
Annealing
(PSO-SA)
Optimal load shed using PSO-SA
Static voltage stability margin and its
sensitivity at maximum loading point
Not suitable for transient
conditions due to slow
convergence rate.
28

Literature review.. Cont’d
S.NO Reference Technique/Test
System
Other
Techniques for
comparison
6 Jalilzadeh, hadi
Hosseini [55]
2010
Hybrid Modal Analysis
and PSO /
Gharb and Bakhtar
areas of Iranian
transmission network
PSO and Modal
Analysis
Achieves best transformer tap
setting and voltage stability
margin
Optimal amount of load shed at
best location
Designed for a particular
transmission network and
unable to identify critical areas
or maximum loading point
7 Hagh and
Galvani [24]
2011
HPSO-LP
Linear Programming/
IEEE 14 bus test system
LP and PSO Fast convergence
Elimination of transmission line
overloading
Unable to solve non-linear and
large power systems.
8 Guichon, Melo
[16]
2012
GA /
500kV power system
Uruguay
None Achieve optimal load shed
through an automatic process
Limited to DC load flow only.
9 Hosseini-Bioki,
Rashidinejad
[30]
2013
PSO /
3-Bus and modified IEEE
30Bus test system
Locational
marginal price
(LMP)
Greater voltage stability margin
achieved through social welfare
Technique is unable to apply on
large and complex power
systems.
10 Ahmadi and
Alinejad-
Beromi [7]
Hybrid Discrete
PSO/IEEE 14 and 30 Bus
test systems
PSO A new method for voltage
stability using integer-value
modelling
Sub-optimal load shed
29

Literature review.. Cont’d
S.NO Reference Technique/Test System Other
Techniques
for
comparison
11 M.Ojaghi
[35]
2014
HGAPSO/IEEE 57 Bus test
system
PSO Minimum customer interruption
cost
Minimum active power loss
Elimination of Transmission line
under over loading
Unable to shed optimum load.
Voltage stability not achieved
12 Sonar and Mehta
[56]
2015
Firefly Algorithm and
PSO/ IEEE 30 bus test
system
Fire fly and
PSO
Firefly converged faster than PSO Sub optimal load shed.
13 Estebsari, Pons [34]
2015
Techno-economic
impacts/European
transmission systems
Manual and
Automatic
UVLS
Comparison of automatic and
manual UVLS schemes showed
that automatic UVLS is superior
Short term voltage stability ignored
while has slow convergence.
Designed and tested on a
particular Austrian grid.
14 Kaffashan and
Amraee [33]
2015
Probabilistic UVLS point
estimate method/IEEE 14
and 118 Bus test system
Monte carlo
simulations
An accurate UVLS scheme using
the point estimate method with
less computational complexity
Long convergence time, not
suitable for real time applications.
15 Tamilselvan and
Jayabarathi [57]
2016
Hybrid GA and Neural
Network/
IEEE 6 bus test system
and IEEE 14 bus test
system
GA and NN Minimum load shed with less
deviations in voltage
Slow convergence rate. Unsuitable
for large power systems
30

Summary Literature Review
Reference Minimum load shed Stabilized voltage Profile Fast convergence Scalable to large and
complex power
systems
Ref.15 use GA
Ref.29
Used individual GA
and PSO
Ref.23 Hybrid PSO
and SA
Ref.24 Hybrid PSO
and LP
Ref.35 Hybrid PSO
and GA
Ref.56 Hybrid FA
and PSO
Ref.57 Hybrid GA
and NN
Proposed Technique
Improved GAPSO
31

Features
Features GAPSO IGAPSO
Voltage stability as constraint NO YES
Consideration of FVSI NO YES
Weak bus selection criteria NO YES
Faster Speed NO YES
Multiple loading factors NO YES
32

Research Methodology
Stability Indices
➢Line Stability Index (LSI)
➢Line Stability Factor (LSF)
➢Line Voltage Stability Index (LVSI)
➢Stability Index (SI)
➢Power Transfer Stability Index (PTSI)
➢New Voltage Stability Index (NVSI)
➢Fast Voltage Stability Index (FVSI)
Focused on
33

Research Methodology.. Cont’d
GAPSO Improved
GAPSO
GA PSO
34

GAPSO
Main frame
work PSO
Utilization of
GA Global
search ability
Absence of FVSI
inside
Algorithm
Voltage Stability
not considered
as Constraint
Limitations
1. No criteria for weak bus
selection
2.Slower speed
2.Suboptimal load shed
3.Unable to stabilize the voltage
magnitude
35

Start
Inputs
Initialize
Iterations
Selection
Population
P1 crossover with
P2
Mutate C1 and C2
Populati
on
Assign global best
Update current
position
Returned best
solution
C1 + C2 =Children
Replace with new
population
Random moment
Update velocity and
position
Evaluate Fitness
Cost
Function
personn
el
Assign personnel
best
Cost
Function
global
Returned
optimal solution
GA

Start
Inputs
Initialize
Iterations
Assign pop
Random moment
Population
Update velocity and
position
Evaluate Fitness
Cost
Function
personnel
Assign personnel
best
Cost
Function
global
Assign global best
Update current
position
Returned
optimal solution
VoltageStability
Constraint
PSO

Parents Selected by
PSO
Parents
belongs to
P1 and P2
P1 crossover with P2
C1 + C2 =Children
Replace with new
population
Returned Best
solution
Mutate C1 and C2
FVSI Threshold
values

Improved GAPSO
Main frame
work GA
Utilization of
PSO Global
search ability
Integrate FVSI
inside
Algorithm
Voltage Stability
as Constraint
Objective Functions
1.FLS
2.FVD
OF = min  1 2( )f OF OF+
0.7 ( ) 0.3 ( )
sel bus
b a b a
i i i i
i B i N
Q Q V V
 
− + − 
𝑄𝑖
𝑏 Reactive power demand at bus i before load shed
(post-contingency)
𝑄𝑖
𝑎
Reactive power demand at bus i after load shed (pre-
contingency)
𝑉𝑖
𝑏 Voltage magnitude at bus i before load shed (post-
contingency)
𝑉𝑖
𝑎 Voltage magnitude at bus i after load shed (pre-
contingency)
𝑁𝑏𝑢𝑠
Set of all buses
𝐵𝑠𝑒𝑙
Set of selected weak buses for load shedding
b
iQa
iQb
iVa
iV busNselB
39

( cos sin ),
bus
i i j ij ij ij ij bus
j N
P V V G B i N 

= + 
( sin cos ),
bus
i i j ij ij ij ij bus
j N
Q V V G B i N 

= − 
( , ) cos( )
bus
i i j ij i j ij
j N
P V V V Y

   = − −
( , ) sin( )
bus
i i j ij i j ij
j N
Q V V V Y

   = − −
Equality Constraints
𝑃𝑖 Active power at bus i
𝑄𝑖 Reactive power at bus i
𝑉𝑖 Voltage magnitude at bus i
𝑉𝑗 Voltage magnitude at bus j
𝐺𝑖𝑗 Conductance of (ij) th element in the bus
admittance matrix
𝜃𝑖𝑗 Admittance angle of line (ij)th
𝐵𝑖𝑗 Susceptance of (ij)th element in the bus
admittance matrix
𝑌𝑖𝑗 Line Admittance of (ij)th (1/Ω)
𝛿𝑖 Voltage angle ith at bus
𝛿𝑗 Voltage angle at jth bus
40

Inequality Constraints
min max
,i i i GenP P P i B  
min max
,i i i GenQ Q Q i B  
min max
,i i i busesV V V i N  
𝑃𝑖
min Minimum active power limit at bus i
𝑃𝑖
max
Maximum active power limit at bus i
𝐵 𝐺𝑒𝑛 Set of generation buses
𝑄𝑖
min Minimum reactive power limit at bus i
𝑄𝑖
max
Maximum reactive power limit at bus i
𝑉𝑖
min Minimum voltage magnitude at bus I
𝑉𝑖
max
Maximum voltage magnitude at bus I
𝑁𝑏𝑢𝑠 Set of all buses
41

Results
FVSI values against increased loading
Figure 1
43

Results…Cont’d
Plot of Fast Voltage Stability Index versus Reactive loading
Figure 2
44
Near to
Collapse

Results…Cont’d
Voltage profile against increased loading
Figure 3
45

Results…Cont’d
0.8
0.85
0.9
0.95
1
1.05
1.1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
VoltageMagnitude(pu)
No. of Buses
Voltage Profile
Overloading GA PSO GAPSO IGAPSO
Voltage magnitude profile 30 bus
Figure 4
46

Results…Cont’d
Voltage magnitude profile of bus 30
Figure 5
47

Results…Cont’d
Voltage Profile of Bus 30 after load shedding
Figure 6
48

Results…Cont’d
Plot of FVSI index against increased loading
Figure 7
49

Results…Cont’d
Reactive load shed at weak buses
Figure 8
50

Results…Cont’d
Total load shedding versus increasing loading factor
Figure 9
51

Results…Cont’d
Figure 10
Voltage magnitude of weak buses on increased loading
52

Results…Cont’d
Voltage magnitude improvement at all buses
Figure 11
53

Results…Cont’d
Convergence time
Figure 12
PSO
9 iterations
3.60 seconds
GA
23 iterations
28.56seconds
IGAPSO
13 iterations
13.30 seconds
GAPSO
20 iterations
22.25 seconds
IGAPSO is 40% Faster
than GAPSO
54

Results…Cont’d
56
FVSI values against increased loading
Figure 13

Results…Cont’d
Figure 14
Voltage profile 57 bus
57

Results…Cont’d
0
0.2
0.4
0.6
0.8
1
1.2
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657
VoltageMagnitude(pu)
No. of Buses
Voltage Profile
Overloading GA PSO GAPSO IGAPSO
Voltage magnitude profile 57 bus
Figure 15

Results…Cont’d
Plot of Fast Voltage Stability Index versus Reactive loading
Figure 16
59

Results…Cont’d
Figure 17
Plot of FVSI index against increased loading
60

Results…Cont’d
Figure 18
Voltage Profile of Bus 31 after load shedding
61

Results…Cont’d
Total load shedding against increased loading
Figure 19
62

Results…Cont’d
Figure 20
63
Voltage magnitude improvement at all buses

Results…Cont’d
64
Amount of load shed on weak buses
Figure 21

Results…cont
65
Voltage magnitude of bus 31 against increased loading
Figure 22

Results…Cont’d
66
Voltage magnitude on 5 weak buses against increased loading
Figure 23

Results…Cont’d
Figure 24
PSO
13 iterations
5.25 seconds
GA
34 iterations
64.90 seconds
IGAPSO
15 iterations
22.63 seconds
GAPSO
22 iterations
34.44 seconds
IGAPSO is 36% Faster
than GAPSO
67
Convergence Time

Conclusion
The developed algorithm IGAPSO is better than hybrid GAPSO in terms of
68
Optimal Load Shed
Voltage Stability
Fast Convergence

Contributions
➢ Devised a new improved algorithm based on hybrid of GA and
PSO termed as IGAPSO for optimal load shed.
➢ Design a new criteria for weak bus selection criteria by
introducing a threshold value on FVSI index.
➢ A new constraint handling of voltage stability is developed
inside IGAPSO algorithm.
➢ The IGAPSO is fast enough to recover the power system from
collapse to restoration.
69

Future Recommendations
➢ Frequency decay may also be considered due to overloading.
➢ Minimization of losses may be considered due to increased
overloading.
➢ The developed technique can be implemented on some
practical system.
➢ The developed technique may be useful for islanded power
systems.
70

Publications
• R. M. Larik, M. W. Mustafa, M. N. Aman, "An Improved Algorithm for Optimal Load
Shedding in Power Systems," Energies, MDPI, vol. 11, no. 7, pp. 1-16, 2018. (ISI Indexed,
IF 2.76)
• R. M. Larik, M. W. Mustafa, M. N. Aman, “A Critical Review of The State-of-Art Schemes
For Under Voltage Load Shedding,” International Transactions on Electrical Energy
Systems (ITEES) Willey (ISI Indexed, IF 1.69)
• R. M. Larik, M. W. Mustafa "A New Technique of Load Shedding to Stabilize Voltage
Magnitude and Fast Voltage Stability Index by using Hybrid Optimization," 2006. ARPN
Journal of Engineering and Applied Sciences 13.8.April 2018 p.p 2734-2745. (Scopus
Indexed)
• R. M. Larik, M. W. Mustafa "Optimal Load Shedding Under Contingency Conditions Using
Voltage Stability Index For Real-Time Applications In Power Systems," 2006. ARPN
Journal of Engineering and Applied Sciences 13.22.November 2018 p.p 8693-8704.
(Scopus Indexed)
• R. M. Larik and M. W. Mustafa, "Smart Grid Technologies in Power Systems: An
overview," 2015. Research Journal of Applied Sciences, Engineering and Technology 11.6.
Oct.25, (2015) pp 633-638. ISSN: 2040-7459 Maxwell Scientific Organization. (Scopus
Indexed)
71
Published Journals

Publications
• R. M. Larik and M. W. Mustafa, "Technologies used in Smart Grid to Implement
Power Distribution System," TELKOMNIKA Indonesian Journal of Electrical
Engineering, vol. 16, no. 2, pp. 232-237, 2015. (Scopus Indexed)
• R. M. Larik and M. W. Mustafa “A Statistical Jacobian Application for Power System
Optimization of Voltage Stability” in Indonesian Journal of Electrical Engineering and
Computer Science, (IJEECS) vol.13 No.1 January 2019. pp 331-338 (Scopus Indexed)
• R. M. Larik, M. W. Mustafa S. H. Qazi, 179. “Under Voltage Load Shedding Scheme to
Provide Voltage Stability," 2016. Fourth International Conference on Energy,
Environment and Sustainable Development 2016 (EESD 2016) November 1-3 2016.
• R. M. Larik and M. W. Mustafa, "Meta-heuristic optimization Methods for Under
Voltage Load Shedding Scheme” (IEEC 2016). First International Electrical
Engineering Congress (IEEC 2016) May. 13-14, 2016 in IEP Centre, Karachi, Pakistan.
72
Published Journals
Published Conferences

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