An MPC Based Algorithm for a Multipurpose Grid Integrated Solar PV System With Enhanced Power Quality and PCC Voltage Assist

ABSTRACT:

The continuously fluctuating energy output and varying power demands in the renewable energy systems have led to the degradation of power quality. This work presents a model predictive based control for a solar PV system integrated to the grid for optimal management and control of the power transfer. The double stage three-phase configuration is controlled using model predictive control (MPC) strategy, which considers the power converters’ switching states to predict the next control variable. The control uses a modified-dual second-order generalized-integrator for estimation of the power requirements based on the continuously varying system parameters. The PCC voltages assist and the ride through operation are performed based on the drops in voltage levels and optimum switching state is selected based on the minimization of the cost function to deliver the required active and reactive powers to the grid. The performance of the controller is validated through simulation and is also shown using hardware implementation. The IEEE-519 standard is followed throughout and a comparative analysis shows the remarkable performance of the presented grid controller.

KEYWORDS:

1.      MDSOGI

2.      Model predictive control

3.      PCC voltage assist

4.      Ride through, solar photovoltaic

5.      Voltage source converter

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

 

Fig. 1. Circuit diagram of system.

EXPECTED SIMULATION RESULTS:

Fig. 2. Steady State UPF operation. (a) vg_ab-iinv_a (b) Inverter Power (c) THD of inverter current (d) vg_ab-ig_a (e) Grid Power (f) Grid voltage THD (g) Grid Current THD (h) Load Power (i) Load Current THD.

Fig. 3. Load current unbalance (a)-(c): (a) Phase ‘a’ PCC line voltage, Grid current, Load Current and VSC current (b) Phase ‘b’ PCC line voltage, Grid current, Load Current and VSC current (c) Internal Components Φloss, Φpvg, Φload and Φnet. (d) Grid current THD in steady state, (e) Load Current THD in steady state. (f) Solar Irradiation variation: PV current (IPV ), PV Voltage (V PV ) and DC link Voltage (V dc).

Fig. 4. Waveforms during grid voltage variations (a) Overvoltage: iL_a, V dc, vg_ab, ig_a (b)Undervoltage: iL_a, V dc, vg_ab, ig_a. (c) Grid current THD after overvoltage, (d) Load current THD after overvoltage, (e) Grid current THD after undervoltage, (f) Load current THD after undervoltage.

Fig. 5. High grid distortion (a) extracted fundamental voltage, highly distorted grid voltage, load current, current in the grid, (b) THD in voltage in the grid, (c) grid currentTHDfor Damped SOGI control based on [24] (d)LCS-MPC [25] (e) Presented MDSOGI-MPC control.

CONCLUSION: 

A modified dual second order generalized integrator based model predictive control (MDSOGI-MPC) is presented in this work for the control of two stage three phase grid tied solar PV system. Various adverse grid variations are performed to highlight the performance of the control technique. The robustness and simple configuration as well as the implementation of the control make its performance superior to present control methods based on MPC. Themodified-dual second order generalized integrator has estimated the power requirements based on system parameters. The performance during the sag in the voltage is shown while the controller demonstrates the PCC voltage assist operation as well as the ride through performance. Optimum switching states are predicted based on the minimization of the cost function. The performance is tested on simulation as well as hardware setup and the results show that the implementation of this control is advantageous. The harmonic spectrum of the current in the grid network is maintained within the prescribed limits of IEEE-519 std. limits. A generic comparison is made with the current modern control strategies, which shows that it works well as compared to other techniques.

REFERENCES:

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