Minimizing harmonic distortion impact cause by CS using meta heuristic technique

TELKOMNIKA, Vol.17, No.4, August 2019, pp.1992~2000
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v17i4.12768 ◼ 1992
Received August 29, 2018; Revised February 19, 2019; Accepted March 21, 2019
Minimizing harmonic distortion impact cause by CS
using meta heuristic technique
S. N. Syed Nasir*, J. J. Jamian, M. W. Mustafa
School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia,
81310, Johor Bahru, Johor, Malaysia, tel/fax: +607-553 3333/ +607-555 7236
*Corresponding author, e-mail: syedazizul@gmail.com
Abstract
Non-linear load in the distribution system has caused negative impact to its power quality
especially on harmonic distortion. Charging Station (CS) is a non-linear load that widely promoted with
the aim to support the continuous usage of Electric Vehicle (EV). This research is focusing on optimal
placement and sizing of multiple passive filter to mitigate harmonic distortion due to CS usage at
distribution system. There are 6 units of CS which being placed in low voltage buses which indirectly will
inject harmonic to the system during charging. Power system harmonic flow, passive filter, CS, battery and
the analysis will be model in MATLAB. Multi-objective function which are weight summation approach
(WSA) and Pareto Front are used to assist meta heuristic technique which is Modified Lightning Search
Algorithm (MLSA) to identify optimum location and sizing of passive filter based on improvement on
propose five parameters. From the result, the optimal placements and sizing of passive filter able to reduce
the maximum Total Harmonic Distortion (THD) for voltage, current and apparent losses respectively.
Therefore, the propose method is suitable to reduce harmonic distortion as well as apparent losses at
distribution system with present of CS.
Keywords: apparent losses, harmonic distortion, impact of charging station, meta heuristic, passive filter
Copyright © 2019 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Unstable price for crude oil and vision to reduce carbon dioxide emission has caused
many countries start to change their direction to EV which more environmental friendly and
more stable in price. This indirectly has increase the number of CS installation in the distribution
network [1]. Based on [2, 3], the charging behaviour from a large number of customers will
cause bad effects to the distribution system especially on harmonic distortion issues. Other than
that, various existing studies reported that the CS increase THD voltage and current especially
when the number connected to the grid simultaneously is increased [4]. Moreover, the sudden
increase of load due to the EV usage may create chaotic situation especially on power losses in
distribution network.
Generally, when many CS are installed in the distribution system, total load will be
increased, causing the distribution transformer to transfer extra amount of power to EV
customer which indirectly causes overheat on distribution transformers [5]. Next, the unplanned
CS installation may cause high power losses especially when all EV are operated
simultaneously and causing utility loss in profit. Other than that, some researchers’ works have
shown that the uncoordinated EV charging can introduce higher peak demand which is a
drawback to the overall power losses of the grid [5, 6]. Since the CS involve converting AC
source to DC source, this indirectly create power quality issue such as harmonic distortion [7].
There are three categories of CS, which are CS Level 1, Level 2 and Level 3. The Level 1 and 2
CSs are considered as having a slow charging characteristic which are normally installed at low
voltage distribution system. CS Level 3 has a fast charging characteristic which has higher
power consumption and normally installed at medium voltage network. There are many
approaches introduced to overcome these problems in improving the distribution system
performance. In the case of power losses problem, the most popular approaches nowadays are
by placing a capacitor bank [8], filter placement [9] and coordinating EV charging schedule [10].
Next, filter placement [9] and improving CS topology [11] are the examples of approaches which
can be used in minimizing harmonic distortion impact in distribution system caused by CS.

TELKOMNIKA ISSN: 1693-6930 ◼
Minimizing harmonic distortion impact cause by… (S.N. Syed Nasir)
1993
Next, due to the complexity of the distribution system nowadays especially after rapid
development of EV and presence of distribution generation, the method to solve the problems
has become critical and unique for every problem [12]. Although passive filter is able to sink the
harmonic based on design, it should not be randomly located at any place in the system
because it also can cause losses and harmonic distortion to become more severe. Proper
research needs to be conducted to identify the best placement and sizing to maximize benefit to
the distribution system.
2. Research Method
In general, there will be two types of harmonic that need to be mitigated in distribution
network, which are voltage and current harmonic. Voltage harmonic is measured at the bus
while current harmonic is measured at the lines and cables. In order to model the value of
harmonic in the distribution system, it is important to identify the source of harmonic distortion
connected to the network. In this research, CS is the main source of harmonic distortion
injection to the network. Harmonic load flow method used in this research will be presented.
Next, CS and battery modelling will be discussed in detail. Then, design of passive filter, which
is single tuned filter will be derived. Constraint for this research will identify in detail. Finally,
meta heuristic technique, MLSA with Weight Summation Approach (WSA) assistance and
MLSA with Pareto assistance will be presented in finding optimal placement and sizing of
passive filters.
2.1. Harmonic Load Flow
Many techniques have been used to perform harmonic load flow analysis [13, 14].
Generally, there are two methods used to run harmonic load flow, which are time domain
analysis and frequency domain analysis [14]. In this research, frequency domain analysis was
selected due to the capability of the method to save computational time when dealing with large
scale power system analysis [14]. In addition, the current injection analysis method which is also
known as current penetration method was used to determine voltage and current harmonic.
Basic principle of this method is by injecting ideal current sources based on related frequency to
represent harmonic current spectrum. The injected current will flow through system impedance
which indirectly causes voltage distortion and current distortion [15].
Therefore, parameters affecting the forming of harmonic admittance bus are the line
impedances, load impedances and filter impedances values. In (1) and (2) represent the line
impedance formulation and the impedance for single tuned filter at harmonic h respectively
which indirectly cater for resonance impact.
𝑍𝑖,ℎ = 𝑅𝑖 + 𝑗ℎ𝜔𝐿𝑖 (1)
𝑍 𝐹𝑖𝑙𝑡𝑒𝑟,ℎ = 𝑅 𝐹𝑖𝑙𝑡𝑒𝑟 + 𝑗ℎ𝜔𝐿 𝐹𝑖𝑙𝑡𝑒𝑟 − 𝑗
1
ℎ𝜔𝐶 𝐹𝑖𝑙𝑡𝑒𝑟
(2)
where:
𝑍𝑖,ℎ - Line impedance for ith line at hth harmonic order
𝐿𝑖 - Line inductance for ith line
𝑅 𝐹𝑖𝑙𝑡𝑒𝑟 - Filter resistance
𝐶 𝐹𝑖𝑙𝑡𝑒𝑟 - Filter capacitance
𝑅𝑖 - Line resistance for ith line
𝑍 𝐹𝑖𝑙𝑡𝑒𝑟,ℎ - Filter impedance at hth harmonic order
𝐿 𝐹𝑖𝑙𝑡𝑒𝑟 - Filter inductance
The impact of harmonic can be seen when all orders are added up together with
fundamental order. In (3), (4) and (5) show the impedance formulation for the load used in
setting up the harmonic admittance matrix. Based on the equations, the load will be changed to
impedance form which will later be translated in harmonic impedance order. The harmonic flow
is calculated using in (6), which consists of harmonic admittance matrix and harmonic current
injection as (7) and (8).
𝑍 𝐿,ℎ = 𝑅 𝐿 + 𝑗𝑋𝐿,ℎ (3)

◼ ISSN: 1693-6930
TELKOMNIKA Vol. 17, No. 4, August 2019: 1992-2000
1994
𝑅 𝐿 =
𝑉𝐿
2
𝑃 𝐿
(4)
𝑋𝐿,ℎ =
𝑉𝐿
2
ℎ𝑄 𝐿
(5)
where:
𝑍 𝐿,ℎ - Load impedance at hth harmonic order
𝑅 𝐿 - Load resistance
𝑋𝐿,ℎ - Load inductance at hth harmonic order
𝑉𝐿 - Load Voltage
𝑃𝐿 - Load Active Power
𝑄 𝐿 - Load Reactive Power
𝐼ℎ =
𝑉ℎ
𝑍ℎ
= 𝑌ℎ. 𝑉ℎ (6)
(7)
(8)
2.2. 10-Bus Radial Distribution System and CS Location
A typical 10-Bus Radial Distribution System was used to investigate the optimal
placement and sizing of variable passive filter. The harmonic distortion pattern for individual EV
charger was modelled based on actual single phase charger impact as measured by
Bass et. al [16]. Figure 1 show a single line diagram for IEEE 10-bus radial distribution system
with low voltage 415 V buses residential feeder that consist of several CS respectively
connected at bus number 10. Total numbers of buses in the network (medium and low voltages)
were 28-buses. There were 6 unit of CS installed at low voltage area in this distribution system.
Line data for 28-Bus radial distribution system is shown at Table 1.
Figure 1. 28-Bus radial distribution system with EV load












=
hNNhN
hh
hNhh
h
yy
yy
yyy
Y
,,1
,22,21
,1,12,11
....
:..::
:..
..

1995
Table 1. Line Data for 28-Bus Radial Distribution System
Line No Start Bus End Bus R (ohm) X (ohm) P (kW) Q (kVAr) CS
1 1 2 0.1233 0.4127 736 184
2 2 3 0.014 0.6057 392 392
3 3 4 0.7463 1.2060 716 178.4
4 4 5 0.6984 0.6084 639.2 736
5 5 6 1.9831 1.7276 644 240
6 6 7 0.9053 0.7886 312 44
7 7 8 2.0552 1.1640 460 24
8 8 9 4.7953 2.7160 392 52
9 9 10 5.3434 3.0264 656 80
10 10 11 0.0000 0.0654 2 0.4
11 11 12 0.0415 0.0145 2 0.4 1 unit
12 11 26 0.0424 0.0189 2 0.4
13 11 29 0.0444 0.0198 2 0.4
14 12 13 0.0369 0.0165 2 0.4
15 12 25 0.0520 0.0232 2 0.4 1 unit
16 13 14 0.0524 0.0234 2 0.4
17 13 24 0.0005 0.0002 2 0.4
18 14 15 0.2002 0.0199 2 0.4
19 14 23 1.7340 0.1729 2 0.4
20 15 16 0.2607 0.0260 2 0.4 1 unit
21 16 17 1.3605 0.1357 2 0.4
22 16 21 0.1400 0.0140 2 0.4
23 16 22 0.7763 0.0774 2 0.4
24 17 18 0.5977 0.0596 2 0.4 1 unit
25 17 19 0.1423 0.0496 2 0.4
26 17 20 0.0837 0.0292 2 0.4
27 26 27 0.3123 0.0311 2 0.4 1 unit
28 27 28 0.0163 0.0062 2 0.4 1 unit
2.3. CS and Battery Modelling
CS is typically divided into three categories which are level 1, level 2 and level 3 as
per SAE Standard [17]. The level of the CS is based on the power consumed by the charging
station and types of specification connection. In addition, CS modelling in this research has
considered the harmonic distortion value achievable by individual CS based on the battery
characteristic and SOC. EV coordination requires accurate modelling of battery and CS
characteristic. At lower SOC, higher current is required to charge the battery while voltage
increases proportionally with SOC. Starting at 80% of SOC, voltage level reaches its maximum
value and the current starts to reduce inversely proportional with SOC until the battery is fully
charged. In this research, 6 types of battery that have different capacity and specification will be
used to show variety and practicallity of EV in the distribution system. All battery will be put at
different SOC level as per Table 2.
Table 2. EV Battery Specification
Type Battery Capacity (kW) Charging Rate (A) Power usage (kW) CS Efficiency SOC (%)
1 10 0.052 0.52 0.93 47.92
2 15 0.125 1.875 0.93 54.18
3 10 0.235 2.35 0.93 46.24
4 15 0.15 2.25 0.93 47.37
5 10 0.215 2.15 0.93 40.38
6 20 0.102 2.04 0.93 55.99
2.4. Passive Filter Modelling
The main function of a passive filter is to sink the harmonic current that flow in
the system based on a selected frequency. The filter impedance will become very low to allow
the harmonic to sink. Among several passive filter types, single tuned filter is the most popular
type of filter which is used widely in dealing with harmonic pollution especially in the industrial
area [18]. Figure 2 shows a typical design of a single tuned filter. In this paper, 4 units of single
tuned filters is considered as 1 set of filter which can eliminate four frequencies. In [19] is used
to calculate capacitor, inductance and resistance components respectively as in (9), (10)
and (11). Capacitor is calculated based on injected reactive power (Q) and voltage (V) at that

◼ ISSN: 1693-6930
1996
bus, meanwhile inductance and resistance is based on the chosen harmonics (n) that need to
be reduced. Three sets of filter will be used to eliminate 3rd, 5th, 7th and 9th harmonic order in
the network.
𝐶 𝐹𝑖𝑙𝑡𝑒𝑟 =
𝑄
2𝜋𝑓𝑉2
(9)
𝐿 𝐹𝑖𝑙𝑡𝑒𝑟 =
𝑉2
2𝑛2 𝑄2 𝜋𝑓
(10)
𝑅 𝐹𝑖𝑙𝑡𝑒𝑟 =
𝑉2
𝑛. 𝑄 𝑢. 𝑄
(11)
Figure 2. 1 Set of passive filter
2.5. Modified Lightning Search Algorithm, WSA, Pareto and Fuzzy
Modified Lightning Search Algorithm (MLSA), which is developed based on
the improvements made to the existing LSA [20, 21], is proposed in this research. The important
equations used in MLSA are approximately similar to LSA. There are four improvements that
made at existing LSA to produce MLSA which detail out at [9, 12]. Load flow technique that use
in this research presented in detailed at [9,12, 22, 23]. Multi-objective technique which are WSA
and Pareto also been presented in detail at [9, 12]. The parameters that use to assist MLSA are
presented at (17) to (21) at [12]. Fuzzy technique that use in this research use same method as
per mention at [9, 12, 24, 25].
2.6. Assumption and Constraint
Since 3 passive filters will be placed in the low voltage system, the total variables will
become 6, which are 3 locations and 3 optimal sizes. All these parameters will have its own
constraints that needs to fulfill. In general, the parameters can be fraction into 2 categories
which are:
a. Filter location:
The filter will be place in low voltage bus to minimize any harmonic injection to upper feeder.
There are 3 locations in 28-bus radial distribution system. Furthermore, only 3 passive filter
will be placed at low voltage 415 buses system. The constraints are as follows:
𝑎_𝑖 ≤ 𝐹𝑖𝑙𝑡𝑒𝑟_𝑖 ≤ 𝑠_𝑖, 𝑖 = 11 … 29 (12)
b. Filter reactive value:
Each individual variable filter reactive value is limited to:
0𝑘𝑉𝐴𝑟 ≤ 𝑄_𝑖 ≤ 30𝑘𝑉𝐴𝑟, 𝑖 = 1 … 3 (13)

1997
3. Results and Analysis
From the existing system without passive filter, maximum 𝑇𝐻𝐷 𝑉 is recorded at bus 18
with 13.546% and maximum 𝑇𝐻𝐷𝑖 is recorded at lines between busses 10 and 11 with
the value 137.53%. Apparent losses 𝑆𝑙𝑜𝑠𝑠 recorded at 0.1941MVA. MLSA with WSA assistance
technique is presented at [9, 12] which 5 parameters to be considered. Coefficient for all
5 parameters will be determined based on trial and error for all possible combination. Passive
filter placement using MLSA with different coefficient of WSA as shown at Table 3. Based on 37
simulations with different coefficient, the best coefficient determines using fuzzy method as per
simulation number 35. The result achieve from MLSA with WSA able to reduce maximum 𝑇𝐻𝐷 𝑉
to 9.72175% while maximum 𝑇𝐻𝐷𝑖 also improve to 39.94711%. However, apparent losses
increases to 0.21452MVA which cause the objective to improve all 5 parameters not achieve
using this method.
Next, simulation using MLSA with Pareto and Fuzzy technique, there are 4337 sets of
“non-dominated” solutions. For this research, the best solution was chosen based on
the improvement for all 5 parameters which is able to achieve better 𝑇𝐻𝐷𝐼 for all lines in
the distribution system. From 4337 sets of solution, there are only 2005 sets of solution that
show improvement for all five parameters which were then used in the fuzzy stage. Table 4
shows 10 best result using MLSA with Pareto and Fuzzy assistance while Table 5 shows
the location and sizing of passive filter for that 10 best result. Based on the result, all parameter
are improve with maximum 𝑇𝐻𝐷 𝑉 improve to 11.25281%, maximum 𝑇𝐻𝐷𝐼 reduce to 58.62805%
and apparent losses 𝑆𝑙𝑜𝑠𝑠 able to minimize to 0.192308%.
Next, Table 6 shows the best solution gather for both techniques. Although using WSA
method able to reduce 𝑇𝐻𝐷 𝑉 and 𝑇𝐻𝐷𝐼 significantly compare to Pareto and Fuzzy method, it is
important to make sure all parameter improve to give stability on overall system. Figure 3 shows
the 𝑇𝐻𝐷 𝑉 for medium voltage busses which directly shows significant reduction of 𝑇𝐻𝐷 𝑉.
Moreover, 𝑇𝐻𝐷𝐼 for all lines at medium voltage system also improve significantly. Based on
the result at Figure 3 and Figure 4, shows that the appropriate placement and sizing of passive
filter able to divert most of harmonic distortion from injected to medium distribution system.
Figure 3. 𝑇𝐻𝐷 𝑉 before and after using MLSA with pareto and Fuzzy asisstance
Figure 4. 𝑇𝐻𝐷𝐼 before and after using MLSA with pareto and Fuzzy asisstance

◼ ISSN: 1693-6930
1998
Table 3. Result using Different Coefficient of WSA with Fuzzy
Table 4. 10 best result using MLSA with
Pareto and Fuzzy Assistance
Table 5. Passive Filter Location and Sizing using MLSA with
Pareto and Fuzzy Assistance

1999
Table 6. Passive Filter Location and Sizing with Best Solution for Both Techniques
Parameter Existing MLSA with WSA MLSA with Pareto
Bus MVAr Bus MVAr Bus MVAr
Passive Filter 1 - - 22 0.029995 28 0.020682
Passive Filter 2 - - 27 0.029999 18 0.027732
Passive Filter 3 - - 28 0.029998 11 0.029792
Passive Filter 4 - - 20 0.029994 16 0.021177
Max THDV 13.54593 9.72175 11.25281
Max THDI 137.53168 39.94711 58.62805
THDI mean 5.68835 2.03143 2.80798
THDI sum 159.27392 56.88017 78.62344
Sloss 0.194110 0.21452 0.192308
4. Conclusion
Preliminary results have shown that increasing the number of CS in the system will
cause higher harmonics in the distribution system. In order to eliminate these harmonics,
appropriate passive filters are needed to be installed in parallel with all the CSs. However, it is
not practical to install filters at all buses. Therefore, MLSA with WSA and Pareto was used to
propose a solution for obtaining the appropriate passive filter placement and size to cater for
require voltage harmonics, current harmonic and apparent power losses. Fuzzy techniques also
implemented to assist in determine the best solution among all solution. Three sets - four unit
single tuned filters connected in parallel to selected buses were used to reduce the four
harmonic order. The design adopted in this paper was based on the minimum number of filters
used to cater for a big distribution system. From the final results, it has been proven that
the proposed strategy using MLSA with Pareto and Fuzzy asistance is able to give the best
placement and size of passive filters in radial distribution system with improvement on overall
𝑇𝐻𝐷 𝑉, 𝑇𝐻𝐷𝐼 and 𝑆𝑙𝑜𝑠𝑠.
References
[1] Lucas A, Bonavitacola F, Kotsakis E, Fulli G. Grid harmonic impact of multiple electric vehicle fast
charging. Electric Power Systems Research. 2015; 127: 13-21.
[2] Jiang C, Torquato R, Salles D, Xu W. Method to assess the power-quality impact of plug-in electric
vehicles. IEEE Transactions on Power Delivery. 2014; 29(2): 958-65.
[3] Shafad KH, Jamian JJ, Nasir SA. Harmonic distortion mitigation for multiple modes charging station
via optimum passive filter design. In Systems, Process and Control (ICSPC). 2016 IEEE Conference
on 2016; 219-223.
[4] Koch AS, Myrzik JM, Wiesner T, Jendernalik L. Harmonics and resonances in the low voltage grid
caused by compact fluorescent lamps. In Harmonics and Quality of Power (ICHQP), 2010 14th
International Conference on 2010 Sep 26. IEEE. 2010; 1-6.
[5] Dubey A, Santoso S. Electric vehicle charging on residential distribution systems: Impacts and
mitigations. IEEE Access. 2015; 3: 1871-93.
[6] Muhamad NA, Samah MF, Bashir N, Nasir SN, Kadir Z. Potential energy saving for student
dormitories: A case study at Institut Latihan Perindustrian (ILP) Pasir Gudang, Jabatan Tenaga
Manusia, Malaysia. In Power and Energy (PECon), 2014 IEEE International Conference. 2014;
382-387.
[7] Pazouki S, Mohsenzadeh A, Haghifam MR, Ardalan S. Simultaneous allocation of charging stations
and capacitors in distribution networks improving voltage and power loss. Canadian Journal of
Electrical and Computer Engineering. 2015; 38(2): 100-5.
[8] de Araujo LR, Penido DR, Carneiro Jr S, Pereira JL. Optimal unbalanced capacitor placement in
distribution systems for voltage control and energy losses minimization. Electric Power Systems
Research. 2018; 154: 110-21.
[9] Syed Nasir SN, Jamian JJ, Mustafa MW. Minimization of harmonic distortion impact due to
large‐scale fast charging station using Modified Lightning Search Algorithm and Pareto‐Fuzzy
synergistic approach. IEEJ Transactions on Electrical and Electronic Engineering. 2018; 13(6):
815-22.
[10] Bharati GR, Paudyal S. Coordinated control of distribution grid and electric vehicle loads. Electric
Power Systems Research. 2016; 140: 761-68.
[11] Tareen WU, Mekhielf S. Three-Phase Transformerless Shunt Active Power Filter with Reduced
Switch Count for Harmonic Compensation in Grid-Connected Applications. IEEE Transactions on
Power Electronics. 2018; 33(6): 4868-4881.

◼ ISSN: 1693-6930
2000
[12] Syed Nasir SN, Jamian JJ, Mustafa MW. Minimizing Harmonic Distortion Impact at Distribution
System with Considering Large-Scale EV Load Behaviour Using Modified Lightning Search Algorithm
and Pareto-Fuzzy Approach. Complexity. 2018; 19: 1-14.
[13] Fuchs E, Masoum MA. Power Quality in Power Systems and Electrical Machines. Academic
press; 2011.
[14] Bonner A, Grebe T, Gunther E, Hopkins L, Marz MB, Mahseredjian J, Miller NW, Ortmeyer TH,
Rajagopalan V, Ranade SJ, Ribeiro PF. Modeling and simulation of the propagation of harmonics in
electric power networks. Concepts, models, and simulation techniques. IEEE Transactions on Power
Delivery. 1996; 11(1): 452-65.
[15] Yifei W, Chen S, Choi SS. An overview of various approaches to power system harmonic analysis. In
Power Engineering Conference, 2007. IPEC 2007. 2007; 338-343.
[16] Zimmerman N, Bass R. Impacts of electric vehicle charging on electric power distribution
systems. 2013.
[17] Katić VA, Pecelj M, Todorović I. Effects of Individual Battery Charger Station on Power Quality. In
10th International Symposium on Industrial Electronics–INDEL 2014. 2014; 6-8.
[18] Awadalla M, Omer M, Mohamed A. Single-tuned filter design for harmonic mitigation and optimization
with capacitor banks. In Computing, Control, Networking, Electronics and Embedded Systems
Engineering (ICCNEEE), 2015 International Conference on 2015 Sep 7. IEEE. 2015;
242-247.
[19] Halpin SM, Ribeiro PF, Dai JJ. Frequency-domain harmonic analysis methods. IEEE Power
Engineering Society: Tutorial in Harmonics Modeling and Simulation. 1998:49-54.
[20] Shareef H, Ibrahim AA, Mutlag AH. Lightning search algorithm. Applied Soft Computing. 2015; 36:
315-33.
[21] Shareef H, Mutlag AH, Mohamed A. A novel approach for fuzzy logic PV inverter controller
optimization using lightning search algorithm. Neurocomputing. 2015; 168: 435-53.
[22] Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GA. Optimal power flow solutions using
differential evolution algorithm integrated with effective constraint handling techniques. Engineering
Applications of Artificial Intelligence. 2018; 68: 81-100.
[23] Crow ML. Electric Vehicle Scheduling Considering Co-optimized Customer and System Objectives.
IEEE Transactions on Sustainable Energy. 2018; 9(1): 410-9.
[24] Mukhtaruddin RN, Rahman HA, Hassan MY, Jamian JJ. Optimal hybrid renewable energy design in
autonomous system using Iterative-Pareto-Fuzzy technique. International Journal of Electrical Power
& Energy Systems. 2015; 64: 242-9.
[25] Tabrizi N, Babaei E, Mehdinejad M. An interactive fuzzy satisfying method based on particle swarm
optimization for multi-objective function in reactive power market. Iranian Journal of Electrical and
Electronic Engineering. 2016; 12(1): 65-72.

Minimizing harmonic distortion impact cause by CS using meta heuristic technique

More Related Content

What's hot (15)

Similar to Minimizing harmonic distortion impact cause by CS using meta heuristic technique (20)

More from TELKOMNIKA JOURNAL (20)

Recently uploaded (20)

Minimizing harmonic distortion impact cause by CS using meta heuristic technique