CNISOGI_and_VSSMCLMS_Filters_for_Solving_Grid_and_Load_Abnormalities_for_a_Microgrid_With_Seamless_Mode_Transfer_Capability.pdf

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 59, NO. 4, JULY/AUGUST 2023 4225
CNISOGI and VSSMCLMS Filters for Solving Grid
and Load Abnormalities for a Microgrid With
Seamless Mode Transfer Capability
Vivek Narayanan , Member, IEEE, and Bhim Singh , Fellow, IEEE
Abstract—A cascaded non-identical second-order generalized
integrator (CNISOGI) and a variable step size modified clipped
least mean square (VSSMCLMS) adaptive filter are proposed for a
microgrid to solve the grid and load abnormalities. The CNISOGI
filter estimates the positive sequence components (PSCs) from pol-
luted (unbalanced/distorted) grid voltages. Hence improved quality
grid currents are realized even though the grid voltages are pol-
luted. Moreover, the CNISOGI filter estimates the accurate phase
angle and frequency of voltages; thus, seamless transition of the
microgrid is achieved between different modes. The comparative
performance of the CNISOGI filter is studied over different control
techniques and it demonstrates the better filtering capability of
the proposed control. The microgrid is connected to the grid or a
diesel generator (DG) set in subject to the availability of energy
sources and the load demand to supply uninterrupted electricity
to loads. Therefore, it provides an attractive solution for powering
critical loads. The load compensation is achieved with the help of a
VSSMCLMS filter. It estimates the active fundamental component
of the load currents with faster response and lesser weight oscil-
lations. The comparative performance of the VSSMCLMS filter is
studied over conventional filters in MATLAB/Simulink and under
laboratory conditions to ascertain its superiority in the filtering
process.
Index Terms—Adaptive filter, grid pollution, load compensation,
microgrid, solar PV generation, synchronization.
NOMENCLATURE
PV, MPP, BES, BDC Photovoltaic, maximum power
point, battery energy storage,
bidirectional buck-boost converter.
Manuscript received 13 August 2022; revised 8 November 2022 and 20
January 2023; accepted 20 February 2023. Date of publication 20 March
2023; date of current version 19 July 2023. Paper 2022-IPCC-0902.R2,
presented at the 2021 IEEE Energy Conversion Congress and Exposition,
Vancouver, BC, Canada, Oct. 10–14, and approved for publication in the
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Industrial Power
Converter Committee of the IEEE Industry Applications Society [DOI:
10.1109/ECCE47101.2021.9595078]. This work was supported in part by UI-
ASSIST project under Grant RP03443, in part by FIST project under Grant
RP03195, and in part by SERB National Science Chair Fellowship. (Corre-
sponding author: Vivek Narayanan.)
Vivek Narayanan is with the Electrical Engineering, Indian Institute of
Technology Delhi, Hauz Khas 110016, India (e-mail: viveksw.narayanan7@
gmail.com).
Bhim Singh is with the Department of Electrical Engineering, Indian Institute
of Technology Delhi, New Delhi 110016, India (e-mail: bsingh@ee.iitd.ac.in).
Color versions of one or more figures in this article are available at
https://doi.org/10.1109/TIA.2023.3259387.
Digital Object Identifier 10.1109/TIA.2023.3259387
DG, SG, DE, AVR Diesel generator, synchronous gen-
erator, diesel engine, automatic volt-
age regulator.
GCM, SAM, DGM Grid connected mode, standalone
mode, diesel generator mode.
VSC, PCI, PQ Voltage source converter, point of
common interface, power quality.
STS Solid transfer switch.
SRFT Synchronous reference frame the-
ory.
IRPT Instantaneous reactive power theory.
LMS Least mean square.
LMF Least mean fourth.
VSSLMS Variable step size LMS.
RLS Recursive least square.
VSSMCLMS Variable step size modified clipped
least mean square.
MSE Mean square error.
PLL Phase-locked loop.
SOGI Second order generalized integrator.
SOSOGI Second order-second order general-
ized integrator.
CSOGI Cascaded second order generalized
integrator.
DDSRF Decoupled double synchronous ref-
erence frame.
CNISOGI Cascaded non-identical second-
order generalized integrator.
PSC Positive sequence component.
DSTATCOM Distribution static compensator.
THD Total harmonics distortion.
QSG Quadrature signal generator.
Vdc, V ∗
dc, Vde Sensed and reference dc-link voltage
and their error.
Vpv, Ipv, Ppv PV array voltage, current and power.
Ib, I∗
b , Ibe Sensed and reference battery current
and their error.
vgab, vgbc Sensed grid line voltages.
vga, vgb, vgc Grid phase voltages.
vα
g , vβ
g Grid voltages in αβ frame.
vα+
g , vβ+
g PSCs of grid voltages in αβ frame.
v+
ga, v+
gb, v+
gc PSCs of grid voltages in abc frame.
uga, ugb, ugc, Vtg In-phase unit templates of grid phase
voltages, the amplitude of grid volt-
ages.
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4226 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 59, NO. 4, JULY/AUGUST 2023
iga, igb, igc, i∗
ga, i∗
gb, i∗
gc, Sensed and reference grid currents.
vLab, vLbc Sensed load line voltages.
v∗
La, v∗
Lb, v∗
Lc Reference load voltages.
V∗
tL, ωL Amplitude and frequency of refer-
ence load voltages.
iLa, iLb, iLc Sensed load currents.
vdga, vdgb, vdgc, Vtdg DG voltages and their amplitude.
udga, udgb, udgc Unit templates of DG set voltages.
idga, idgb, idgc, Sensed and reference DG
i∗
dga, i∗
dgb, i∗
dgc currents.
θg, θdg, θL Phase angles of grid, DG, and load
voltages.
fg, fdg Frequency of grid and DG voltages.
Ψpv Feed-forward weight of PV power.
Ψa, Ψb, Ψc Fundamental active weights of load
currents for phase ‘a’, ‘b’, and ‘c’.
ΨLeq Average fundamental active weight
of load currents.
Ψg, Ψdg Amplitude of grid and DG currents.
Kpd, Kid, Kpb, Kib Proportional and integral gains of
outer and inner PI controllers of
BDC.
I. INTRODUCTION
THE electrification and efficiency are the key drivers of
the energy transition, enabled by renewables, hydrogen,
and sustainable biomass. This approach, which would need a
considerable change in how societies generate and consume
energy, would result in a decrease of roughly 37 gigatonnes
of annual CO2 emissions per year by 2050 [1]. This goal
can be fulfilled by 1) increasing the generation of electricity
using renewable energy sources; 2) significant improvements
in energy efficiency; 3) electrification of end-use sectors (e.g.,
electric automobiles and heat pumps); 4) clean hydrogen and
its derivatives; 5) bioenergy with carbon capture and storage.
The worldwide weighted-average cost of electricity from newly
commissioned utility-scale solar PV plants has fallen by 85%
between 2010 and 2020. Like ways, concentrated solar power,
onshore and offshore wind have experienced a cost reduction of
68%, 56%, and 48%. As a consequence, in almost all countries,
renewables have already become a default choice for expanding
their generation capacities in the power sector [2].
Hospitals, military bases, fire stations, and grocery store
chains have frequently installed microgrids to reduce their vul-
nerability to power outages. While fossil fuels powered 80% of
microgrids in 2020, that figure is likely to fall as more organi-
zations embrace renewable energy. Aiming to become carbon
neutral, the Kaiser Permanente medical center in Richmond,
California, has replaced its diesel-fueled backup power system
with a microgrid powered by renewable energy in 2020 [3]. The
U.S. Department of Energy’s Idaho National Laboratory has
initiated a net-zero microgrid program in 2021 to incorporate
renewable energy sources into current and newly developed
microgrids [3].
The primary causes of PQ issues in a microgrid are: 1)
transient conditions, such as an islanding event caused by a grid
problem; 2) renewable generation caused by transient changes in
weather; 3) increased nonlinear or rectifier loads; 4) increased
use of highly reactive loads; 5) increased use of unbalanced
loads.Theintermittencyofrenewablegenerationismitigatedus-
ing BES in the microgrid [4]. The linear loads draw a reasonable
amount of reactive power from the ac distribution, resulting in
a lower power factor. The nonlinear and unbalanced loads draw
harmonicsandunbalancedcurrentsfromthedistributionsystem.
All of these lead to the deterioration of the quality of supply
voltages and currents. Hence improvements in PQ play a critical
role in a microgrid that feeds power to reactive, harmonics,
and unbalanced loads. These issues have been mitigated using
DSTATCOM [5]. Many time-domain controls are reported in the
literature for controlling the DSTATCOM, and two commonly
used controls are IRPT [6] and SRFT [7]. The IRPT utilizes
a three-phase to two-phase transformation and the SRFT is
based on a conversion from a stationary frame to a rotating
frame. These techniques use low pass filters (LPFs) and complex
conversion blocks. As a result, the dynamic response of IRPT
and SRFT controls diminishes. Moreover, the reference currents
estimatedusingthesemethodsdeterioratewhenthegridvoltages
experience distortions or unbalance. Hence these controls give
poor load compensation under the abnormalities in the grid
voltages.
Adaptive theory-based control utilizes a feedback mecha-
nism and self-adjusts the internal parameters [8]. Some of the
adaptive controls include LMS [9], VSSLMS [10], and RLS
[11]. The LMS-based adaptive controllers have been commonly
employed for the shunt compensator due to the simplicity of
implementation.However,theconventionalLMSleadstoslower
weight convergence and larger steady-state inaccuracy. As a
result, the performance of the LMS is just satisfactory, and
better algorithms may be necessary. This paper proposes a
VSSMCLMS control. This algorithm is realized to minimize
an appropriate function of error known as MSE and extracts
the weight corresponding to the fundamental active component
of load currents. It provides a fast and accurate estimation
of sinusoidal reference currents from distorted load currents
by minimizing the error between sensed and estimated cur-
rents. The application of this filter for shunt compensation is
innovative and has not been reported in the literature [12]. Multi-
pleobjectivesareachievedfromtheproposedVSSMCLMScon-
trol, viz., harmonics mitigation, reactive power compensation,
power factor improvement, and balancing of supply currents.
Nowadays, utility grid outage is a common scenario. Hence,
a normal microgrid without mode transfer capability leads to
complete supply failure to local loads. Therefore, the seamless
transfer capability is essential for a microgrid to ensure unin-
terrupted power to loads. Different seamless transition methods
are reported in the literature [13], [14], [15], [16]. In grid-tied
applications, a PLL provides information about the amplitude,
frequency, and phase angle of utility grid voltages for obtain-
ing synchronization and reference currents generation [17]. An
SRF-based PLL (SRF-PLL) is a commonly used PLL structure
in grid-tied systems [12]. The fundamental positive-sequence
voltage of the utility appears as a dc component in the dq
frame under non-ideal grid voltage circumstances, whereas
anomalies like unbalance, harmonics, and dc-offset show as ac
components. Therefore, the load compensation and seamless
transfer capabilities deteriorate using SRF-PLL. The fundamen-
tal positive-sequence voltage is the information of interest for
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NARAYANAN AND SINGH: CNISOGI AND VSSMCLMS FILTERS FOR SOLVING GRID AND LOAD ABNORMALITIES 4227
synchronization and controls. Hence the need for a filter arise
in the microgrid that can estimate the PSCs from the polluted
voltages. PLLs based on a DDSRF is given in the literature
[18], [19]. A dual QSG followed by PSC removes the negative
sequence components caused by unbalanced voltages. As a re-
sult, the THD of the voltage components (v+
α and v+
β) depends
solely on the QSGs harmonics attenuation and dc-offset removal
capacity. Fourth-order QSGs (FO-QSGs) [20], [21] provide
complete dc-offset rejection and higher harmonics attenuation
than second [22], third [23], and mixed (second and third)
[24] QSGs. A SOSOGI [20] and a CSOGI [21] are two such
FO-QSGs. The SOSOGI parameters are determined depending
on the system’s settling time. In [20], the fourth-order system is
approximated to a lower-order system for parameter selection.
As a consequence, for various combinations of damping factors
andsettlingtime,thereisasignificanterrorbetweentheexpected
and actual settling times. In [21], two SOGIs having similar
parameters are cascaded to form CSOGI. In CSOGI, the first
SOGI eliminates dc-offset from sensed grid voltages, while the
two cascaded SOGIs offer fourth-order harmonics attenuation.
If the single SOGI-QSG damping factor isn’t appropriately
set, the cascaded SOGIs can get over-damped, resulting in a
more delayed response than a single SOGI. This work pro-
poses a CNISOGI filter to estimate the PSCs from the polluted
grid voltages to obtain improved compensation and seamless
transition.
The technical contributions involved in this microgrid are
enumerated as follows.
r A CNISOGI filter is applied for the first time for the
microgrid to compensate for unbalanced and distorted grid
voltages. It estimates the PSCs from the polluted grid
voltages and they are utilized to generate the grid voltages
unit templates. Therefore, the grid currents follow smooth
sinusoidal and balanced even though the grid voltages
experience abnormalities. Moreover, it estimates the phase
angle and the frequency of the grid voltages. Thereby,
seamless synchronization of the microgrid with the utility
grid is achieved.
r The load compensation is achieved using a VSSMCLMS
adaptive filter, which calculates the fundamental active
weight of load currents with a faster convergence rate and
minimal weight oscillations. Thereby, the supply currents
quality complies with the IEEE-519 standard [25].
The paper is organized as follows. The literature survey and
proposed work are described in Section I. The schematic of the
microgrid is discussed in Section II. The impact of unbalanced
and distorted grid voltages in the microgrid is discussed in
Section III. The impact of unbalanced and harmonics loads
on microgrids is discussed in Section IV. The adopted control
methodology for the proposed microgrid is addressed in Sec-
tion V. Section VI presents simulation and experimental results.
Section VII concludes this paper. Appendix gives the parameters
of the microgrid.
II. SCHEMATIC OF MICROGRID
Fig. 1 presents the structure of the proposed microgrid. It
comprises a solar PV array, BES, utility grid and a DG set as
energy sources. The PV array is connected at the dc-link of the
microgrid using a dc-dc converter and it is controlled to harvest
Fig. 1. Structure of microgrid.
the maximum power. The BES is interfaced at the dc-link using
a BDC, which protects the battery from second harmonic and
ripple currents. The dc energy sources are interfaced at the PCI
using a three-leg insulated gate bipolar transistor (IGBT) based
VSC through interfacing inductors (Lf). The Lf removes the
high-frequency ripples in the VSC currents. The nonlinear loads
of the uncontrolled diode bridge rectifier (DBR) and resistive-
inductive load are connected at the PCI. The 3φ grid and DG set
are integrated at the PCI using STS1 and STS2. The switches
are controlled to realize smooth connection or disconnection of
the microgrid into the grid or the DG set. Ripple filters (Rf and
Cf) eliminate the high-frequency ripples in the voltages.
III. IMPACT OF UNBALANCED AND DISTORTED UTILITY GRID
VOLTAGES ON MICROGRID
The impact of unbalanced and distorted grid voltages is dis-
cussed in this section. Considering the grid voltages experience
unbalanced and distorted and can be expressed as,
vg = v+
g
F undamental
+ve seq.
+ v−
g
F undamental
−ve seq.
+

h=5,7,11,13,..
vgh (1)
It shows that the vg has fundamental negative sequence (v−
g )
and harmonics (vgh) components along with the fundamental
positive sequence component (v+
g ).
The unit templates of the grid voltages are estimated from the
grid voltages as follows.
ug(a, b, c) =
vg(a, b, c)
Vt
(2)
Therefore, the unit templates experience the same abnormal-
ities that the voltages have. The reference grid currents are
generated by multiplying the amplitude of grid currents (Wg)
with the unit templates, as follows.
i∗
g(a, b, c) = Wg ∗ ug(a, b, c) (3)
Hence, reference currents experience unbalanced/harmonics
components. The objective of controlling the VSC is to follow
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the actual grid currents to reference grid currents. Therefore, the
quality of grid currents deteriorates.
IV. IMPACT OF UNBALANCED AND HARMONICS LOADS ON
MICROGRID
The impact of unbalanced and harmonics loads is discussed
in this section. Considering the microgrid is feeding unbalanced
and distorted loads and there is no compensation scheme is
applied. Then the source current (grid/DG) is expressed as (4),
is = i+
s
F undamental
+ve seq.
+ i−
s
F undamental
−ve seq.
+

h=5,7,11,13,..
ish (4)
It shows that the is has fundamental negative sequence (i−
s )
and harmonics (ish) components along with the fundamental
positive sequence component (i+
s ).
The PCI voltages can be expressed as (5),
vP CI = vs − vdrop = vs − isRs − Ls (dis/dt) (5)
Where vdrop is the voltage drop across the impedance (Rs
and Ls) of the grid/DG set.
Substituting (4) into (5), the PCI voltages can express as (6),
vP CI =
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩

vs − i+
s Rs − Ls(di+
s

dt)
F undamental+ve seq.P CI voltage
−

i−
s Rs + Ls(di−
s

dt)

Unbalanced P CI voltage drop
−

h=5,7,11,13...
{ishRs + Ls(dish/dt)}
Harmonics P CI voltage drop
(6)
ItshowsthePCIvoltagesincludefundamentalpositive,unbal-
anced, and harmonic components due to uncompensated loads.
Hence the performance of all other loads connected at the PCI
deteriorates. This is a significant issue when the microgrid is
associated with the DG set than a utility grid. This is due to the
higher impedance of the DG set than the grid.
V. CONTROL TECHNIQUES
Details of the control strategies applied in the analyzed mi-
crogrid are presented here.
A. Boost Converter Control
It needs to operate the solar PV array at its MPP to achieve
optimum use of solar energy. Different controls developed for
capturing the maximum PV power are reported in [26], [27].
Here, the optimum usage of the PV array is accomplished by
governing the boost converter according to the incremental
conductance (INC)-MPP algorithm. The dc-dc converter duty
is formulated as,
D = 1 −

V ∗
pv/Vdc

(7)
V ∗
pv represents the INC controller output and corresponds to
PV array MPP voltage and Vdc is the dc-link voltage.
B. BDC Control
Fig. 2 exhibits the BDC control. It regulates the Vdc to refer-
ence voltage (V ∗
dc) and controls the battery’s charging and dis-
charging current. To produce the reference battery current (I∗
b ),
Fig. 2. BDC control.
the discrepancy between Vdc and V ∗
dc is fed to a proportional-
integral (PI) controller. Its output is formed as,
I∗
b (k + 1) = I∗
b (k) + Kpd(Vde(k + 1)
− Vde(k)) + KidVde(k + 1) (8)
Where, Kpd and Kid represent the PI controller gains and Vde
represents the error voltage and is expressed as,
Vde(k) = V ∗
dc(k) − Vdc(k) (9)
The I∗
b is correlated with the battery current (Ib) and the error
is passedtoaPI controller togeneratetheBDCcontrollingsignal
(δ) and is formulated as,
δ(k + 1)=δ(k)+Kpb(Ibe(k + 1) − Ibe(k))+KibIbe(k + 1)
(10)
Where,
Ibe(k) = I∗
b (k) − Ib(k) (11)
C. VSC Switching Strategy
The VSC switching strategy is categorized into GCM, SAM,
and DGM controls. The control structure for providing the
switching pulses to the VSC is depicted in Fig. 3. In GCM and
DGM, the VSSMCLMS filter is used to estimate the load current
active weight constituents for achieving the load compensation.
The control steps are explained here.
1) Estimation of Reference Grid Currents and Switching
Pulses Generation in GCM: The control steps in the GCM are
explained here.
a) Estimation of PSCs of grid voltages: The phase volt-
ages are estimated from the sensed grid line voltages (vgab and
vgbc) as follows.
vga =
2vgab +vgbc
3
, vgb =
−vgab + vgbc
3
, vgc =
−vgab − 2vgbc
3
(12)
The grid voltages are processed through the CNISOGI filter
to estimate their PSCs. The structure of the CNISOGI filter is
shown in Fig. 4. The transfer functions of the CNISOGI filter
in-phase component (vl
) and quadrature-phase component (qvl
)
to the input (v) are given in (13) and (14).
G(s)=
vl
(s)
v(s)
=

K1ω0S
S2 +K1ω0S + ω2
0

K2ω0S
S2 + K2ω0S + ω2
0

(13)
H(s)=
qvl
(s)
v(s)
=

K1ω0S
S2 +K1ω0S+ω2
0

K2ω2
0
S2 +K2ω0S + ω2
0

(14)
Fig. 5 shows the pole-zero map of the CNISOGI filter. It
shows that the system is stable because all the poles are on the
left-hand side of the imaginary axis. The filtering ability of the
CNISOGI filter is compared with SOSOGI and CSOGI filters
using a Bode plot. The transfer functions of the SOSOGI filter

Fig. 3. Structure of VSC control.
Fig. 4. Structure of CNISOGI filter.
in-phase component (vl
)
to the input (v) are given in (15) and (16), respectively.
G(s)=
vl
(s)
v(s)
=
K1K2ω2
0S2

S2 +ω2
0

S2 +K2ω0S+ω2
0

+K1K2ω2
0S2
(15)
H(s)=
qvl
(s)
v(s)
=
K1K2ω3
0S

S2 +ω2
0

S2 + K2ω0S+ω2
0

+K1K2ω2
0S2
(16)
The transfer functions of the CSOGI filter in-phase compo-
nent (vl
) to the input (v)
are given in (17) and (18), respectively.
G(s)=
vl
(s)
v(s)
=

Kω0S
S2 +Kω0S+ω2
0

Kω0S
S2 +Kω0S + ω2
0

(17)
Fig. 5. Pole-zero map of G(s) of CNISOGI filter.
Fig. 6. (a)–(b) Bode plot comparison of in-phase and quadrature-phase com-
ponents of CNISOGI, SOSOGI, and CSOGI filters.
H(s)=
qvl
(s)
v(s)
=

Kω0S
S2 +Kω0S+ω2
0

Kω2
0
S2 +Kω0S+ω2
0

(18)
Fig. 6(a) and (b) show the Bode plots of the in-phase and
quadrature-phase components of the CNISOGI, SOSOGI, and
CSOGI filters. It shows that the CNISOGI filter offers higher
harmonics attenuation than SOSOGI and CSOGI filters.
The selection criterion involved in choosing the values of
constants of SOSOGI and CSOGI filters for comparison is
illustrated here. The Bode plot and the pole-zero map of G(s) of
the SOSOGI filter for different values of K2 at constant K1 are
shown in Figs. 7 and 8.

Fig. 7. Bode plot of G(s) of SOSOGI filter at different values of K2.
Fig. 8. Pole-zero map of G(s) of SOSOGI filter at different values of K2.
From the Bode diagram, it is clear that the controller is a tuned
band pass filter (BPF) with large attenuation below and above
the fundamental frequency with a tuning factor K2. Moreover, it
can be observed that the filtering capability is getting reduced as
K2 increases. From the pole-zero map, it is clear that the system
is stable because all the poles are on the left-hand side of the
imaginary axis and the poles move further left and reach the real
axis while increasing K2. Hence, the stability of the system is
improved with K2. The Bode diagram and the pole-zero map
of G(s) of the CSOGI filter for different values of K are shown
in Figs. 9 and 10. The Bode diagram shows that the controller
filtering capability is getting reduced as K increases. The pole-
zero map shows that the system is stable because all the poles are
on the left-hand side of the imaginary axis and the poles move
further left and reach the real axis while increasing K. Hence,
the stability of the system is improved with K. Considering the
trade-off between better filtering, response time and stability, an
optimum value for the parameters are selected and are presented
in Table I.
The first step in PSCs estimation is to generate the grid
voltages in the αβ frame using the following expression.

vα
g vβ
g
T
=

2/3 − 1/3 − 1/3
0 1/
√
3 −1/
√
3

[vga vgb vgc]T
(19)
Fig. 9. Bode plot of G(s) of CSOGI filter at different values of K.
Fig. 10. Pole-zero map of G(s) of CSOGI filter at different values of K.
TABLE I
CONTROLLER PARAMETERS
The vα
g and vβ
g are processed through the CNISOGI filter to
eliminate the harmonics and dc-offset. It provides filtered values
of αβ components (vα
g and vβ
g ) and their 900
delayed signals
(qvα
g and qvβ
g ).
These signals are used to estimate the PSCs of the grid
voltages in the αβ frame (vα
g
+
and vβ+
g ). Further, the conversion
from αβ to abc yields the PSCs of the grid voltages in abc frame,
as shown in Fig. 3. This process eliminates any imbalance in grid
voltages (vga, vgb, and vgc).
vα
g
CNISOGI
=
=
=
=
⇒
filter
vα
g and qvα
g ; vβ
g
CNISOGI
=
=
=
=
⇒
filter
vβ
g and qvβ
g (20)
vα+
g = 0.5

vα
g − qvβ
g

; vβ+
g = 0.5

qvα
g + vβ
g

(21)

v+
ga v+
gb v+
gc
T
=
1
2
⎡
⎣
2 0
−1
√
3
−1 −
√
3
⎤
⎦

vα+
g vβ+
g
T
(22)

b) Generation of unit templates of grid voltages: The unit
templates (uga, ugb and ugc) are generated from the estimated
PSCs of the grid voltages as follows.
uga =
v+
ga
Vtg
, ugb =
v+
gb
Vtg
, ugc =
v+
gc
Vtg
(23)
The amplitude of PSCs of the grid voltages (Vtg) is estimated
as,
Vtg =

2
3

v+
ga
2
+ v+
gb
2
+ v+
gc
2

(24)
c) Generation of PV feed-forward weight component:
The feed-forward weight of the PV power is given as,
ψpv(k) =
2Ppv(k)
3Vtg
(25)
d)Evaluationofactivepowercomponentofloadcurrents:
The assessment of the weight function of the VSSMCLMS
algorithm for the load current of phase ‘a’ is as follows:
ψa(k + 1) = ψa(k) + μa(k)ea(k)xa(k) (26)
Where,
ea(k) = iLa(k) − uga(k)ψa(k) (27)
μa(k + 1) = μa(k) − e2
a(k) ∗ α (28)
This algorithm reduces the complexity of the conventional
LMS algorithm. Here, the clipped input data, {+1, −1} is fed
into the weight update procedure as follows.
xa(k) =
⎧
⎨
⎩
+1, β ≤ uga(k)
0 , −β uga(k) β
−1 , −β ≥ uga(k)
(29)
It shows that when the uga(k) is lesser than the threshold β,
then xa(k) is equal to zero and no coefficient adaptation for the
corresponding weight needs to be performed.
i) Principle Behind Selection of VSSMCLMS Filter Parame-
ters: The proposed VSSMCLMS filter involves the terms
α, β, and step size μ. The α is a fixed constant and
should be small. In (28), the subtraction process is used
to make the next step size µa(k+1) always smaller than
the current step size µa(k). Initially, in the beginning,
a large step size is chosen to increase the convergence
rate and then the step size is to be decreased gradually
to make the misadjustment lower at steady-state. Thus,
the VSSMCLMS filter provides a better convergence rate
and lesser steady-state weight error. The computational
complexity of the VSSMCLMS algorithm is proportional
to the chosen threshold parameter β. A large β results in
lower computational complexity, but in this case, the filter
needs more information to track the system, so the conver-
gence speed decreases. The maximum value of parameter
β can go 1. Here, the value for β is selected as 0.8, where
the algorithm gives a faster convergence rate with lower
computational complexity.
The other phases load current fundamental active weight
constituents are expressed as,
ψb(k + 1) = ψb(k) + μb(k)eb(k)xb(k) (30)
Where,
eb(k) = iLb(k) − ugb(k)ψb(k) (31)
Fig. 11. Hysteresis operation (a) switching occurs at each sampling instance,
(b) switching occurs at more than one sampling intervals.
μb(k + 1) = μb(k) − e2
b(k) ∗ α (32)
xb(k) =
⎧
⎨
⎩
+1, β ≤ ugb(k)
0 , −β ugb(k) β
−1 , −β ≥ ugb(k)
(33)
And,
ψc(k + 1) = ψc(k) + μc(k)ec(k)xc(k) (34)
ec(k) = iLc(k) − ugc(k)ψc(k) (35)
μc(k + 1) = μc(k) − e2
c(k) ∗ α (36)
xc(k) =
⎧
⎨
⎩
+1, β ≤ ugc(k)
0 , −β ugc(k) β
−1 , −β ≥ ugc(k)
(37)
The equivalent load weight constituent is formulated as,
ψLeq =
(ψa + ψb + ψc)
3
(38)
e) Estimation of amplitude and reference grid currents:
The amplitude of the grid currents is calculated as,
ψg = ψLeq − ψpv (39)
The reference grid currents are evaluated as,
i∗
ga = ψguga, i∗
gb = ψgugb, i∗
gc = ψgugc (40)
f)GenerationofVSCswitchingpulses: Thereferencegrid
currents are compared with sensed grid currents using hysteresis
current controllers (HCC) to yield the switching pulses for
the VSC in GCM. The operation of the HCC at two different
sampling instances is shown in Fig. 11(a) and (b).
Fig. 11(a) shows at each sample, the current crosses the
hysteresis band; hence, the switching happens in every sampling
instance. Therefore, in this case, the switches are being triggered
at the maximum frequency and is given as,
fsw =
1
2Ts
=
1
2 ∗ 50 ∗ 10−6
= 10 kHz (41)

Where, sampling time, Ts = 50 μs.
In Fig. 11(b), the switching is not happening at each sampling
instance. Rather, in the first sampling instance, the actual current
doesn’t cross the upper hysteresis band; hence the switching
won’t occur. At the second sampling instance, the current has
crossed the upper hysteresis band; thus, the switching takes
place, and the current begins to fall. In this case, the switching
frequency can be calculated as,
fsw =
1
4Ts
=
1
4 ∗ 50 ∗ 10−6
= 5 kHz (42)
2) Estimation of Reference DG Set Currents and Switching
Pulses Generation in DGM: The steps of operation in the DGM
are discussed here.
a) Generation of unit templates of DG set voltages: The
DG set voltage unit templates (udga, udgb and udgc) are produced
as follows:
udga =
vdga
Vtdg
, udgb =
vdgb
Vtdg
, udgc =
vdgc
Vtdg
(43)
Vtdg is the amplitude of the DG set voltages.
Vtdg =

2
3

v2
dga + v2
dgb + v2
dgc

(44)
b) Active power component of load currents evaluation:
The VSSMCLMS adaptive filter is used to get the load filtered
current components (ψa, ψb, and ψc). The load currents equiv-
alent weight constituent is formed as,
ψLeq =
(ψa + ψb + ψc)
3
(45)
c) Estimation of amplitude of DG set currents and ref-
erence DG set currents: The DG currents amplitude (ψdg) for
maximum fuel efficiency is obtained by passing the ψLeq to a
limiter.
ψdg =

ψLeq ; ψl ψLeq ψu
ψl or ψu ; otherwise
(46)
Where,
ψl =
2Pmin
3Vtdg
and ψu =
2Pmax
3Vtdg
(47)
And,
Pmin = 0.8Prated and Pmax = Prated (48)
The DG set reference currents are evaluated as follows.
i∗
dga = ψdgudga, i∗
dgb = ψdgudgb, i∗
dgc = ψdgudgc (49)
d) Generation of VSC switching pulses: The reference
and sensed DG currents are compared using HCC to yield the
switching pulses for the VSC in DGM.
3) Estimation of Reference Load Voltages and Switching
Pulses Generation in SAM: The steps of operation in the SAM
are discussed here.
a) Generation of reference load voltages: The voltages of
desired magnitude (V ∗
tL =
√
2(VLL/
√
3)) and frequency (ωL =
314rad/s) are produced as,
⎡
⎣
v∗
La
v∗
Lb
v∗
Lc
⎤
⎦ = V ∗
tL
⎡
⎣
sin(ωLt)
sin

ωLt − 2π
3

sin

ωLt + 2π
3

⎤
⎦ (50)
Fig. 12. Experimental setup of microgrid.
b) Generation of reference VSC currents: The phase volt-
ages are estimated from the sensed load (or PCI) line voltages
(vLab and vLbc) as follows.
vLa =
2vLab +vLbc
3
, vLb =
−vLab +vLbc
3
, vLc =
−vLab − 2vLbc
3
(51)
To produce reference VSC currents, the estimated reference
load voltages are compared with the sensed load voltages and
are given to the PI controllers.
c) Generation of VSC switching pulses: The reference
and the sensed VSC currents are compared using HCC to yield
switching pulses for the VSC in SAM, as shown in Fig. 3.
D. Control for Solid Transfer Switches
The switches STS1 and STS2 are controlled to realize seam-
less connection/disconnection of the microgrid into the grid or
the DG set. When the utility grid is available to exchange power,
the microgrid is synchronized with the grid and operates in
GCM. When the grid goes down, it switches to the remaining
modes subject to the PV array power generation and the load
demand. If the PV array output is adequate to accommodate the
load demand or the battery sustains the load for overlong, then
the microgrid works in SAM; otherwise, it works in DGM. The
DG set is thus only incorporated in the microgrid in the worst
operating conditions. Hence, the minimum usage of such energy
sources ensures continuous supply to critical loads.
VI. RESULTS AND DISCUSSION
The application of CNISOGI and VSSMCLMS filters in the
microgrid is discussed in MATLAB/Simulink and in the exper-
imental prototype developed in the laboratory. Different case
studies are intended to validate the performance effectiveness of
the proposed controls. The photograph of the experimental setup
of the microgrid is shown in Fig. 12. It includes (1) utility grid,
(2) solar PV simulator, (3) BES, (4) dc motor running as a prime
mover, (5) SG-based DG, (6) dSPACE-1202 MicroLabBox,
(7) STS, (8) Boost converter interfacing PV array simulator to
dc-link,(9)dc-dcbuckconverterusedasanAVR,(10)Controller
PC, (11) VSC, (12) PQ analyzer, (13-14-15-16-17) Hall-effect
based sensors used for sensing Ipv-Ib-Vdc-vL-iL-vg-ig-vdg-idg,
(18) DBR, (19) Inductive load, (20) Resistive load, (21) Interfac-
ing inductors, (22) dc regulated power supply for optocouplers

Fig. 13. (a)–(f) Performance of CNISOGI filter at distorted grid voltages.
and converter switches, (23) 6N136 optocoupler giving pulses
to VSC from dSPACE.
A. Performance of CNISOGI Filter at Abnormal Grid Voltages
Performance of the CNISOGI filter at abnormal grid voltages
is shown in Fig. 13. Fig. 13(a) shows the grid voltages are
distorted and have a THD of 10.3%, as shown in Fig. 13(e).
Therefore, the αβ components of the grid voltages are distorted,
as shown in Fig. 13(b). The CNISOGI filter estimates the filtered
values of αβ components, as shown in Fig. 13(b). The PSCs of
the grid voltages in abc frame estimated using the CNISOGI
filter are shown in Fig. 13(c). Fig. 13(d) shows vga, v+
ga, uga,
and iga. It demonstrates that the CNISOGI filter estimates the
PSC of vga (v+
ga) and the unit template (uga) is generated from
it. Therefore, sinusoidal and distortion-free grid current (iga) is
realized with a THD of 1.9%, as shown in Fig. 13(f).
B. Dynamic Performance Under Varying Solar Irradiation
and Unbalanced Loads in GCM
Performance at varying solar irradiations and unbalanced
loads is shown in Figs. 14 and 15. Fig. 14(a) shows vga, iga, iLa,
and Ipv. It shows that increased solar irradiation injects more
current into the grid. Moreover, the current injected into the grid
is reduced at decreased irradiation. Fig. 14(b) shows iga, iva,
iLa, and Ipv. The VSC supplies the load harmonics; hence grid
currents are free from harmonics. Fig. 14(c) and (d) demonstrate
the operation of PV array at MPP at different irradiations. These
results show the percentage of MPP achieved at 1000W/m2
and
700W/m2
are 99.91% and 99.82%.
Fig. 14. (a)–(d) Performance at varying solar irradiation.
Fig. 15. (a)–(b) Performance at unbalanced loads.
Fig. 15(a) and (b) show the response at sudden load removal
in phase ‘a’. Fig. 15(a) shows iga, iLa, iLb, and iLc. Fig. 15(b)
shows iga, igb, igc, and iLa. These results indicate that even
thoughthemicrogridfeeds power tounbalancednonlinear loads,
the grid currents are maintained sinusoidal and balanced.

Fig. 16. (a)–(e) Performance at varying solar irradiation.
Fig. 17. (a)–(e) Performance of microgrid in DGM.
C. Operation of Microgrid in SAM
The response to solar irradiation variation is shown in Fig. 16.
Fig. 16(a) shows vLa, iLa, Vpv, and Ipv. It shows that distortion-
less sinusoidal voltages are produced at the load end by con-
trolling the VSC. Fig. 16(b) depicts Vb, iLa, Ib, and Ipv. It
illustrates that during the decreased solar irradiation, the PV
array generation reduces; hence power fed to the battery reduces.
Fig. 16(c)–(e) present the waveforms of vL, iLa and their
harmonics spectra. It shows vL and iLa have THDs of 1.1% and
25.1%. Thus, the THD of vL is retained within the acceptable
limit [28].
D. Operation of Microgrid in DGM
The response of DG set feeding nonlinear loads is illustrated
in Fig. 17. Fig. 17(a) shows Ipv, Ib, idga, and iLa. It shows that
the PV array power is insufficient to feed the load demand; hence
DG set develops the power to meet the load demand. Fig. 17(b)
shows vdga, idga, iva, and iLa. It depicts that the VSC supplies
the nonlinear components to the loads. Therefore, the DG set
Fig. 18. (a)–(b) Seamless mode shift of the microgrid.
draws sinusoidal currents. The waveforms of vdg and idga and
their harmonics spectra are depicted in Fig. 17(c)–(e). These
show the THDs of vdg and idga are 2% and 2.1%.
E. Seamless Mode Shift of Microgrid
An automatic and uninterrupted mode switching of the mi-
crogrid is presented in Fig. 18(a) and (b). Fig. 18(a) presents
vgab, iga, iLa, and vLab. It illustrates that in the grid failure, the
microgrid is safely disconnected from the grid and operates in
SAM. When the grid voltages have appeared, the synchroniza-
tion of the microgrid into the grid begins. The microgrid gets
synchronized to the grid when all the synchronization conditions
have matched. Moreover, it shows that uninterrupted power is
being dispatched to the loads. Fig. 18(b) shows the phase angle
matching of load with the grid once the grid voltages have been
restored.
F. Comparative Analysis Between Control Techniques
The comparative analysis of different controls in MAT-
LAB/Simulink is shown in Figs. 19–26. Fig. 19 illustrates the
response of VSSMCLMS, MCLMS, LMS and LMF filters at
load perturbations. It indicates that the oscillations in the derived

Fig. 19. Comparison of control techniques in MATLAB/Simulink.
phase ‘a’ load current weight constituent (Ψa) and the equivalent
weights of load currents (ΨLeq) with the proposed VSSMCLMS
filter are minimum compared to the LMS and LMF filters. The
steady-stateresponseof theMCLMSfilter is similar tothat of the
VSSMCLMS filter. The dynamic behavior of the MCLMS filter
is poor compared to the VSSMCLMS filter. The VSSMCLMS
filter settles quicker to the steady-state value as compared to the
MCLMS filter.
The comparative analysis of the CNISOGI filter with SOGI,
SOSOGI, CSOGI, and DDSRF filters under distorted and un-
balanced grid voltages is given in Fig. 20. The waveform and
the harmonics spectrum of grid voltage are shown in Fig. 21.
It depicts that the grid voltage is having THD of 36.05%. It
consists of significant 5th and 7th harmonics. Figs. 22–26 show
the waveforms and the harmonics spectra of generated PSCs
of grid voltages using CNISOGI, SOGI, SOSOGI, CSOGI, and
DDSRF filtering techniques. The CNISOGI filter estimates the
grid voltages PSCs with minimum THD compared to SOGI,
SOSOGI, CSOGI, and DDSRF filters, as shown in Fig. 22.
This indicates the maximum harmonics attenuation capability
of the CNISOGI filter compared to other filtering techniques.
Moreover, From Fig. 20, it is clear that the CNISOGI filter
Fig. 20. Performance comparison of filters at distorted and unbalanced grid
voltages.
Fig. 21. Waveform and harmonics spectra of vg.
Fig. 22. Waveform and harmonics spectra of v+
g using CNISOGI filter.
estimates the proper balanced PSCs of the grid voltages faster
at unbalanced grid voltages compared to the other controllers.
The experimental validation of the superior performance of
the VSSMCLMS filter over the LMS filter is shown in Fig. 27.

g using SOGI filter.
g using SOSOGI filter.
g using CSOGI filter.
g using DDSRF filter.
Fig. 27(a) and (b) show the estimated Ψa and ΨLeq with VSSM-
CLMS and LMS filters. These show the VSSMCLMS filter
estimates the load currents weight component with minimum
weight oscillations and steady-state error. Fig. 27(c)–(d) present
the grid current (iga) with VSSMCLMS and LMS filters at load
perturbations.
These results show that iga has significant harmonics with
the LMS filter. The VSSMCLMS filter generates smooth, si-
nusoidal, and harmonics-free grid currents. Fig. 27(e) and (f)
show the waveform and harmonics spectrum of iga, with LMS
filter. It shows the THDs of iga with LMS is 6%. Fig. 27(g) and
(h) show the waveform and harmonics spectrum of iga, with
VSSMCLMS filter. It shows the THD of iga with VSSMCLMS
filter is 1.8%. These show the role of the VSSMCLMS filter in
obtaining enhanced PQ features.
Fig. 27. (a)–(h) Experimental comparison between VSSMCLMS and LMS
filters.
VII. CONCLUSION
An application of a CNISOGI and a VSSMCLMS adap-
tive filter in a microgrid has been demonstrated in this work.
The CNISOGI filter effectively mitigates the abnormalities

in the utility grid voltages by estimating their PSCs. More-
over, it realizes seamless transition between different operating
modes without causing oscillation and overshoot in load volt-
ages and currents by estimating the accurate phase angle and
frequency. Thereby, clean and uninterrupted power has been
delivered to the critical loads. Performance of the CNISOGI
filter is compared with different filters and it is found that
the CNISOGI filter provides higher harmonics attenuation as
compared to other filtering methods. The load compensations,
such as harmonics mitigation, reactive power compensation
and balancing of supply currents, are achieved with a VSSM-
CLMS adaptive filter. Performance of the VSSMCLMS fil-
ter is compared with different adaptive filters and it is found
that the proposed controller gives better filtering capability.
Therefore, improved PQ features are targeted in the micro-
grid and satisfy the requirements of IEEE-519 and IEEE-1547
standards.
APPENDIX
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