Advance in Electronic and Electric Engineering.
ISSN 2231-1297, Volume 4, Number 3 (2014), pp. 247-252
© Research India Publications http://www.ripublication.com/aeee.htm
D. Chinna Kullay Reddy
1
and J. Praveen
2
1
Asst .Professor, EEE Dept., MITS, Madanapalle.
2
Professor, EE Dept. GRIET, Hyderabad.
Abstract
The Electrical power system contains a wide range of electrical and power electronics components in industrial and commercial applications. The power quality will be affected by various factors like ripples contamination, voltage sag and voltage swells due to the increase in loads of non-linear type like large power electronics converters such as IGBT and GTO, rectifiers, voltage and current glittering and switching of loads respectively which also affects the perceptive loads to be fed from the system. In this paper the compensation of harmonics and reduction of voltage sag/swells by using Kalman filter based UPQC is introduced.
The aim of this paper is to regulate voltage of the source side of distribution line against any power quality issues like balanced and unbalanced voltage sag and reducing harmonics by using Realization of Multilevel Inverter using Intelligent based UPQC. The promising results are presented using MATLAB/Simulink.
Nowadays the ever growing usage of nonlinear loads in many companies [1] leads to harmonics generation. This causes great trepidation for both the customers and utilities
[2]. The customer faces voltage sags and voltage swells in the voltage supply and less power factor at the supply end and also include the numerous power quality problems
[3]. The fulfillment with power quality standard, i.e., IEEE Standard 519, is followed by installing the compensating devices. Compensating devices mainly passive filters are used. Passive filters are easy in configuration and can causes unnecessary resonance and magnify the harmonic currents.

248 D. Chinna Kullay Reddy & J. Praveen
To overcome the above drawback of passive power filter are replaced by the active power filters . Active power filters are broadly classified into two types like series active filters and shunt active filters based on functionality of system. The grouping of series and shunt active power filter is called the unified power-quality compensator
(UPQC). In order to controls the voltage sag and offers cost effective compensation
UPQC employs a quadrature insertion method. The energy required to compensate the reactive power can be reduced by injecting a voltage that has an phase difference with the source current. The series active filter and shunt active filter are used in the UPQC for power conditioning in distribution systems.
The Disturbances like harmonics, voltage dips, which tends deteriorate the local load operation in the source voltage was eliminated by using series Active power filter.
In addition to that the shunt Active power filter controls the dc-bus voltage in order to confirm the compensation capability of the UPQC. These functionalities can be carried out by applying inverse control strategies which can operate in the time domain, in the frequency domain or both. Time domain methods, such as PQ or DQ based methods, allow the fast compensation of time-variant disturbances but make more complex their selective compensation. In this sense, frequency domain methods are more flexible but their dynamical response is slower.
In this paper the output has been regulated by using controller and Kalman filters for the best functioning of the UPQC. To produce control signals of ORC for the active filters of the UPQC in a synchronized mode. As the ORC ensure the overall stability, it as well helps for periodic reference tracking and disturbance rejection .The UPQC also operate on self-charging circuits not depending on extra external dc source. The exogenous Kalman filter is a part of the control design, also function as a ripple extractor. This exogenous Kalman filter also assist selective filter the harmonic components [9], and is very much reactive to sudden variations in the working conditions, i.e., voltage and current ripples or load demand variations. As a state observer also relaxes the requirement of numerous state measurements by sensors using the plant Kalman filter.
In this controller was modeled to construct the plant output y = (v
L i s
)
T to trace a reference r = (v
L
∗ i s
∗ )
T
, under a cyclic disturbance (v s i
L
)
T
. The reference signals v
L
∗ and i s
∗ alternating waves of 50 Hz through no unwanted distortions.
This paper applies a Linear Quadratic Regulator based on which an interruption signals will be generated and reference signal of the model is used in the controller design. This will results the reduction of steady state error to zero. This method treats the UPQC as a Multi-input and Multi-outputs directly. Hence, this will give a more effective coordination of two control variables u
1 and u
2
.

Kalman Filter Based Unified Power Quality Conditioner for Output Regulation 249
2.1 Output Regulation-Based Controller (ORC)
Here all the exogenous signals V s
, I
L
, V
L
*
, I s
∗ are periodic, they can be represented by a state-space model. The exogenous system state space model represented in general form as
  A 
(V
s
I
L
)
T
= Ĉ w
ξ +w r=(V
L
*
I s
*
)
T
= Ĉ d
ξ state model of the UPQC model can be
.
x  Ax  B
0
  B
2 u
(1)
(2)
(3)
(4)
Y u
 Cx 
 U  
D
0

F ( x 
 D
2 u
X  )
(5)
(6) where u is signal control parameter for the UPQC. x and ξ are the outputs of the exogenous and plant Kalman filters. U and X are the matrix gains, these are computed from a pair of matrix linear equations called the regulator equations
X Ã  AX  B
0
 B
2
U
(7)
Ĉ = CX +D
0
+ D
2
U (8) transform the overall closed-loop system by multiply (7) and (8) with ξ , subtracting them from (4) and (5) and then applying (1), (3) and (6) become to
– X ξ = (A + B
2
F) – X ξ (9) e=(C + D
2
F) – X ξ (10)
Where e = Y − r is the error of tracking . In practice, e is suitably small but non zero due to the nonlinear effect of the Pulse width modulation. x composes of physical quantities which can be measured with sensor circuitries. However, this will result in higher implementation cost. Therefore, this paper adopts another Kalman filter called the Plant Kalman filter to estimate the x . Exogenous system can be decomposed into two components, one for reference signal r with the state variable vector ξ d.
and the other for the ( V s
I
L
)
T with the state variable vector ξ w.
If ( V s
I
L )
T is measured and then applied to Kalman filter to the first component. From state variable vector ξ w
( V s
I
L
)
T can be estimated, which actually consists of the elementary harmonics of v s and i
L
. The second component of the exogenous system may be written as

 w
 A w
 w
(11)
 V s


I
L



 C w
  w (12)

250 D. Chinna Kullay Reddy & J. Praveen
The Kalman filter estimates the states of output equation ξ d and deduced ξ w
from the estimate of the state. To be more precise, the Kalman filter first measures ( v s i
L
)
T to estimate ξ w from the equations(10), (11). Then the corresponding ξ d is computed based
ξ w
. The computed ξ d has been passed through the second Kalman filter to obtain a filtered version ξ d on the basis of equation (12) . By combining ξ w and ξ d
, we estimate the total exogenous state ξ , which is used as a state feedback control law (6). Both the
Kalman filters mentioned above together called as exogenous Kalman filter. The first set of diagonal matrices, the covariances of the measurement noise properly matching the noise levels of the measuring signal of respective sensors .
The process noise adjustment of the covariance for the plant Kalman filter is based on trial-and-errors to make sure that the poles locations of A − LC are as well satisfactory. In the end, the damping factors of all A – LC poles are at least ten times of the fundamental frequency, and the damping ratios are at least 0.87.
Fig. 1 : Feedback control ORC with
Kalman filters.
Fig. 2 : Self charging circuit of LQR.
Fig. 3 : Multilevel Inverter using Intelligent based UPQC with Kalman filter.

Kalman Filter Based Unified Power Quality Conditioner for Output Regulation 251
In ORC with Kalman filter has only four measurement parameters such as v s
, i s
, v
L
, and i
L
, are essential. certainly, these are the four variable parameters that one would physically choose to measure from the power system point of view. Fig.1 shows feedback control ORC with Kalman filters.
This paper proposes that the UPQC operates without depending on an external dc supply. For controlling the Dc link voltage the supply end has to send extra real power through the shunt active filter. Fig. 2 shows self charging circuit of LQR control strategy were proposed to meet up this objective.
Fig. 4 : Waveforms of voltage unclear supply with nonlinear load.
Fig. 5 : Waveform of current under nonlinear load
Fig. 6 : Waveform of voltage sag and voltage swells.
Fig. 7 : Current waveforms of load side Fig. 8 : Load Current THD waveform.

252 D. Chinna Kullay Reddy & J. Praveen
In this paper a Kalman Filter with UPQC has been modeled. This model circuit consists of design consists of the ORC, multilevel inverter and linear quadratic regulator charging circuit .A LQR circuit is included into the design, the UPQC can run exclusive of depending on outside dc supply. The Kalman based filter with UPQC eliminates the harmonics on load side as well as supply side current and voltage with improvement in power factor .An state observer for UPQC and Kalman filter to produce control signals for extraction harmonics has been implemented effectively for
UPQC control scheme . The controller design for improving the power quality is very accurate, more cost effective for practical implementation.
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