Model predictive-based shunt active power filter with a new reference current estimation strategy

ELECTRICAL PROJECTS USING MATLAB/SIMULINK
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Model predictive-based shunt active power filter with
a new reference current estimation strategy
ABSTRACT:
This study presents a new reference current estimation method using proposed robust extended
complex Kalman filter (RECKF) together with model predictive current (MPC) control strategy
in the development of a three-phase shunt active power filter (SAPF). A new exponential
function embedded into the RECKF algorithm helps in the estimation of in phase fundamental
component of voltage (vh) at the point of common coupling considering grid perturbations such
as distorted voltage, measurement noise and phase angle jump and also for the estimation of
fundamental amplitude of the load current (ih). The estimation of these two variables (vh, ih) is
used to generate reference signals for MPC. The proposed RECKF-MPC needs less number of
voltage sensors and resolves the difficulty of gain tuning of proportional–integral (PI) controller.
The proposed RECKF-MPC approach is implemented using MATLAB/SIMULINK and also
Opal-RT was used to obtain the real-time results. The results obtained using the proposed
RECKF together with different variants of Kalman filters (Kalman filter (KF), extended KF
(EKF) and extended complex KF (ECKF)) and PI controller are analysed both in the steady state
as well as transient state conditions. From the above experimentation, it was observed that the
proposed RECKF-MPC control strategy outperforms over PI controller and other variants of
Kalman filtering approaches in terms of reference tracking error, power factor distortion and
percentage total harmonic distortion in the SAPF system.
SOFTWARE: MATLAB/SIMULINK

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CIRCUIT DIAGRAM:
Fig.1a Proposed RECKF-MPC-based SAPF

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EXPERIMENTAL RESULTS:
Fig.2 Capacitor voltage response in SAPF in steady state for KF, EKF, ECKF, RECKF and PI with
a MATLAB b Real-time Opal-RT (voltage scale: 100 V/div, time scale: 10 ms/div), compensating current response
in SAPF in steady state for KF, EKF, ECKF, RECKF and PI with c MATLAB d Real-time Opal-RT (current scale:
15 A/div, time scale: 10 ms/div)

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Fig. 3 Continued

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Fig. 4 Load current response of SAPF in steady state with a MATLAB b Opal-RT (current scale: 12.5 A/div, time
scale: 10 ms/div)

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Fig. 5 Actual and reference source current response in SAPF in steady state for KF, EKF, ECKF, RECKF and PI
with a MATLAB b Real-time Opal-RT (current scale: 12.5 A/div, time scale: 10 ms/div), source voltage and source
current after compensation in SAPF in steady state for KF, EKF, ECKF, RECKF and PI with c MATLAB d Real-
time Opal-RT (current scale: 25 A/div, time scale: 10 ms/div)

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Fig. 6 Continued

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Fig. 7 Transient state response in SAPF system for PI and RECKF with MATLAB
a Load current
b Capacitor voltage
c Compensating current
d Source voltage and source current

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Fig. 8 Transient state response in SAPF system for PI and RECKF with real-time Opal-RT
a Load current b Capacitor voltage c Compensating current d Source voltage and source current (for (a), (c) and (d),
current scale: 25 A/div and for (b), voltage scale: 125 V/div, time scale: 20 ms/div)

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CONCLUSION:
In this paper, a model predictive-based SAPF with a new reference current estimation scheme
has been presented. This scheme exploits the estimation of in phase fundamental component of
distorted PCC voltage along with the estimation of fundamental amplitude of load current using
KF, EKF, ECKF and proposed RECKF algorithms. The proposed RECKF algorithm is based on
applying a new weighted exponential function as a factor to limit the variation of innovation
vector, to restrain the unusual measured value and to enhance the estimated accuracy with
consideration of grid perturbations such as voltage distortion, measurement noise and phase
angle jump. MPC strategy presented in this paper is very simple and powerful and
advantageously considers the discrete nature of power converters. In addition, it is not necessary
to include any type of modulator and the drive signals for the IGBTs are generated directly by
this control. The proposed RECKF-MPC control strategy avoids the use of external linear and
non-linear controllers; hence a cheaper control strategy can be implemented while high
performance is maintained. The performances of the proposed RECKF-MPC-based SAPF have
been verified both in steady state and transient state conditions. The proposed RECKF approach
overcomes difficulties encountered with the fixed-gain PI controller, such as flexibility and
robustness over stabilisation of capacitor voltage when changing loads.
Determination of current reference and current controller for SAPF is one of the most important
issues in improvement of power quality. From the real-time and simulation results, it is observed
that RECKF-MPC exhibits excellent tracking performance thus is a better control approach to
SAPF design in steady state as well as transient state condition which improves power quality
more effectively in terms of efficient harmonics mitigation, power factor improvement and
tracking error reduction in presence of above all grid perturbations.

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REFERENCES:
1 Grady,W.M., Samotyj, M.J., Noyola, A.H.: ‘Survey of active power line conditioning
methodologies’, IEEE Trans. Power Deliv., 1990, 5, pp. 1536–1542
2 Heydt, G.T.: ‘Electric power quality’ (Stars in a Circle, West Lafayette, IN, 1991)
3 Clark, J.W.: ‘AC power conditioners – design, applications’ (Academic, San Diego, CA, 1990)
4 Rastogi, M., Mohan, N., Edris, A.A.: ‘Hybrid-active filtering of harmonic currents in power
systems’, IEEE Trans. Power Deliv., 1995, 10, pp. 1994–2000
5 Akagi, H., Kanazawa, Y., Nabae, A.: ‘Instantaneous reactive power compensators comprising
switching devices without energy storage components’, IEEE Trans. Ind. Appl., 1984, IA-20, pp.
625–630

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