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:

[1] Y. Chen et al., “From laboratory to production: Learning models of efficiency and manufacturing cost of industrial crystalline silicon and thin-film photovoltaic technologies,” IEEE J. Photovoltaic, vol. 8, no. 6, pp. 1531–1538, Nov. 2018.

[2] V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi, “A rapid circle centre-line concept-based MPPT algorithm for solar photovoltaic energy conversion systems,” IEEE Trans. Circuits Syst. I: Regular Papers, vol. 68, no. 2, pp. 940–949, Feb. 2021.

[3] A. Tazay and Z. Miao, “Control of a three-phase hybrid converter for a PV charging station,” IEEE Trans. Energy Convers., vol. 33, no. 3, pp. 1002–1014, Sep. 2018.

[4] IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems - Amendment 1. IEEE Standard 1547a-2014 (Amendment to IEEE Standard 1547-2003), pp. 4–16, May 21, 2014, doi: 10.1109/IEEESTD.2014.6818982.

[5] V. L. Srinivas, B. Singh, and S. Mishra, “Fault ride-through strategy for two-stage grid-connected photovoltaic system enabling load compensation capabilities,” IEEE Trans. Ind. Electron., vol. 66, no. 11, pp. 8913–8924, Nov. 2019.

 

 

An Improved Deadbeat Control Strategy Based on Repetitive Prediction Against Grid Frequency Fluctuation for Active Power Filter

ABSTRACT:

In order to improve the harmonic compensation performance of active power filter (APF) in distribution network, based on deadbeat control theory, the command current prediction algorithm and current tracking control strategy are optimized in this article. Firstly, the command current repetitive prediction in abc coordinate system is transferred to dq for improving its accuracy in lead compensation, and the equivalent for fractional delay beat is achieved by Lagrange Interpolation Polynomial to solve the problem of inaccurate prediction caused by grid frequency fluctuation. Then, considering the inherent half-sampling-period delay of sinusoidal PWM (SPWM), an improved deadbeat control strategy for current tracking is proposed by estimating the output current of next sampling period. Because the output current in next sampling period is replaced by that in current sampling period with traditional deadbeat control strategy, this estimation could make up for the defect of low control precision caused by that replacement. After that, adding error repetitive correction into the improved deadbeat control channel to reduce the periodic tracking error of output current. Finally, the stability and accuracy of the improved control system are analyzed theoretically, and its feasibility and effectiveness are verified by the simulation and hardware-in-the-loop (HIL) experiments.

KEYWORDS:

1.      Deadbeat control

2.      Frequency fluctuation

3.      Harmonic compensation

4.      Lagrange interpolation polynomial

5.      Error repetitive correction

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Three-Phase Apf Topology And Overall Control Structure.

 EXPECTED SIMULATION REUSLTS:

Figure 2. Predicted Command Current With Traditional Or Proposed Prediction Algorithm At Different Grid Frequency. (A) Grid Frequency of 50hz. (B) Grid Frequency Of 50.5hz. (C) Grid Frequency Of 49.5hz.

 

Figure 3. Simulation Results For The Improved Deadbeat Control Strategy With Traditional Or Proposed Repetitive Prediction. (A) With Traditional Prediction At Grid Frequency Of 50.5hz. (B) With Proposed Prediction At Grid Frequency Of 50.5hz (C) With Traditional Prediction At Grid Frequency Of 49.5hz. (D) With Proposed Prediction At Grid Frequency Of 49.5hz.

 

Figure 4. Power Grid Voltage And Nonlinear Load Current. (A) Power Grid

Voltage. (B) Nonlinear Load Current.

 

Figure 5. Grid-Side Current And Output Current Of Phase A With Traditional Or Improved Deadbeat Control Strategy. (A) Traditional Deadbeat. (B) Improved Deadbeat (Krc D 0).

 

Figure 6. Grid-Side Current And Output Current Of Phase A With Different Value Of Krc. (A) Krc D 0:15. (B) Krc D 0:30. (C) Krc D 0:45.

 

CONCLUSION:

 In order to improve the harmonic compensation performance of APF, command current prediction algorithm and dead-beat control strategy for current tracking are optimized in this article. The feasibility and effectiveness of the proposed method are verified by theoretical analysis, simulation, and experiment. The conclusions are as follows:

(1) The accuracy of command current prediction is the pre- requisite for optimizing the current tracking control strategy. Compared with the traditional command current repetitive prediction algorithm, the proposed one exhibits higher prediction accuracy and stronger adaptability to the fluctuation of grid frequency.

(2) Compared with the traditional deadbeat control, because the APF output current in the next sampling period has been estimated, the effective controlled frequency band of the control system is enlarged on the premise of ensuring system stability.

(3) When current tracking error repetitive correction is added into the improved deadbeat control channel, the periodic tracking error could be reduced to some extent, and the control accuracy is increased as well.

(4) The simulation and experiment results demonstrate that the proposed control method has a fine steady-state performance to grid frequency fluctuation and a satisfactory dynamic response to the sudden change of load current.

 REFERENCES:

[1] Y. Fang, J. Fei, and T. Wang, ``Adaptive backstepping fuzzy neural controller based on fuzzy sliding mode of active power filter,'' IEEE Access, vol. 8, pp. 96027_96035, Jun. 2020.

[2] J. Chen, H. Shao, Y. Cheng, X. Wang, G. Li, and C. Sun, ``Harmonic circulation and DC voltage instability mechanism of parallel-SVG system,'' IET Renew. Power Gener., vol. 14, no. 5, pp. 793_802, Apr. 2020.

[3] J. Fei and Y. Chu, ``Double hidden layer output feedback neural adaptive global sliding mode control of active power filter,'' IEEE Trans. Power Electron., vol. 35, no. 3, pp. 3069_3084, Mar. 2020.

[4] W. U. K. Tareen and S. Mekhielf, ``Three-phase transformerless shunt active power filter with reduced switch count for harmonic compensation in grid-connected applications,'' IEEE Trans. Power Electron., vol. 33, no. 6, pp. 4868_4881, Jun. 2018.

[5] Z.-X. Zou, K. Zhou, Z. Wang, and M. Cheng, ``Frequency-adaptive fractional-order repetitive control of shunt active power filters,'' IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1659_1668, Mar. 2015.

An Enhanced EPP-MPPT Algorithm With Modified Control Technique in Solar-Based Inverter Applications: Analysis and Experimentation

ABSTRACT:

In this paper, an optimized adaptive perturb-perturb (PP) based algorithm is presented. The modified algorithm has a predictive variable step size calculated through the Newton-Raphson procedure, making its programming effort simple. This combination merits fewer calculations, faster response time and can simply be applied effectively in both bright and shady conditions. The algorithm is developed as a C language code linked to the PSIM simulation representing a typical photovoltaic module system. The proposed algorithm's simulation results proved faster tracking time response with a reduced error than the standard system. The tracking time is ten times faster than the MPPT method and reduced by 10 seconds in a 100 kHz converter. The measured error is less than 0.03% at steady state. A modified control modulation scheme is blended with the algorithm as well. Experimental results are provided using a 10Wprototype for

telecom applications and another 300W practical micro inverter as a proof of concept, and in agreement with both modelling and simulation results. In addition, the results validate the viability of the proposed algorithm in the cases of linear (resistor) and non-linear (brushless motor) loads. The PSIM and experimental setups are provided to prove the concept of the proposed methodology, which is critical for universal solar-inverter applications.

KEYWORDS:

1.      DC-DC converters

2.      MPPT improved algorithms

3.      Rural water pump applications

4.      Solar energy

5.      Standalone rural inverters

6.      Telecom distribution

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

 Figure 1. Block Diagram For The Complete System.

EXPECTED SIMULATION RESULTS:

Figure 2. The Output Power With The Conventional P&O Method.

 

Figure 3. (A) Power Tracking For A Resistive Data Chip Load. (B) The Optimum Power Versus The Output Tracking Power For Load And Light Intensity Of 1000 W/M2 And Varying The Temperature From 20 _C To 30 _C.

Figure 4. The Optimum Power Versus The Output Tracking Power At Resistive Inductive (Motor) Load And Varying Light Intensity From 800 W/M2 To 1000 W/M2 And Temperature Of 25 _C.

Figure 5. (A) The Traditional Mppt Algorithm Has A Slow Tracking Time. (B) The Epp-Mppt Tracking Time For The Proposed Algorithm. (C) Curve Translating The Voltage Across The Dc-Link In A Pv-Ev-Grid System For A Variable Irradiation.

 CONCLUSION:

 This paper provided a (i) novel adaptive numerical EEP-MPPT algorithm with a new EPP modified algorithm and a predictive variable step size calculated using Newton-Raphson method, (ii) This combination gives outstanding results; the steady-state error has been reduced from 8% in MPPT and 1.2% in incremental conductance to 0.063 % with a tracking time of 1 _s instead of 10 _s, (iii) The system proves to have the ability to adjust itself in a very short period of time to track the new operating point for maximum power, within acceptable error, (iv) The new control proves excellent results under normal and shaded conditions as well. This will optimize the overall output power and add to the reliability, which is paramount for this industry, (v) PSIM simulation and experimental measurements are presented using different linear/non-linear loads; pure resistive load, and a brushless DC motor, (vi) Experimental results have verified the proof of concept, ensuring that the proposed numerical and control algorithms are working efficiently and precisely under motor loading conditions, (vii) In addition, the controller's ability to recover the output voltage waveform under faulty conditions, proves compliant to the IEEE 519 standard. These advantages prove a reliable solution for this research problem.

 REFERENCES:

[1] M. A. A. M. Zainuri, M. A. M. Radzi, A. C. Soh, and N. A. Rahim, ``Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost DC_DC converter,'' IET Renew. Power Gener., vol. 8, no. 2, pp. 183_194, Mar. 2014.

[2] C. R. Sullivan and M. J. Powers, ``A high-ef_ciency maximum power point tracker for photovoltaic arrays in a solar-powered race vehicle,'' in Proc. IEEE Power Electron. Spec. Conf., Jun. 1993, pp. 574_580.

[3] K. Hussein, I. Muta, T. Hoshino, and M. Osakada, ``Maximum photovoltaic power tracking: An algorithm for rapidly changing atmospheric conditions,'' IEE Proc. Gener., Transmiss. Distrib., vol. 142, no. 1, pp. 59_64, 1995.

[4] S. H. Hosseini, A. Farakhor, and S. K. Haghighian, ``Novel algorithm of MPPT for PV array based on variable step Newton-Raphson method through model predictive control,'' in Proc. 13th Int. Conf. Control, Autom. Syst. (ICCAS). Gwangju, South Korea: Kimdaejung Convention Center, Oct. 2013, pp. 1577_1582.

[5] Y. Chen, Y. Kang, S. Nie, and X. Pei, ``The multiple-output DC_DC converter with shared ZCS lagging leg,'' IEEE Trans. Power Electron., vol. 26, no. 8, pp. 2278_2294, Aug. 2011.

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

 ABSTRACT:

Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units. In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV. In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation. For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power. To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions. The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed. The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method. The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT. The proposed method is verified and tested through the simulation of a grid-connected PV system model. The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions. Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions.

KEYWORDS:

1.      Maximum power point tracking

2.      Fuzzy logic

3.       Incremental conductance

4.      PV system

5.      Dynamic responses

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. An Overview Of The Grid-Connected Pv Array With The

Proposed Flc Based Variable Step Inc Mppt Method.

EXPECTED SIMULATION RESULTS:

Figure 2. Testing The Flc Based Algorithm Through The Step Variations

Of (A) The Solar Irradiance (G) (B) The Cell Temperature (Tc ).

Figure 3. Comparisons Of Flc Based And Fixed Duty Cycle Of The Inc

Mppt Method (Fixed Step=0.0003 S) For Step Variations Of G And Tc :

(a)     For The Step Change At 0.8 S; (B) For The Step Change At 1 S.

Figure 4. The Output Dc Power Comparison When Applying The

Conventional Fixed Step Inc Method, The Fixed Step P&O Method And The

(a)     Flc Based Variable Step Inc Method For Mppt.

 

Figure 5. The Difference Between The Output Dc Power When Applying

The Flc Based Algorithm And These Of The Conventional Fixed Step Inc And

(a)     P&O Methods For Mppt.

 

Figure 6. Proximate Views Of The Output Dc Power Comparison When

Applying The Flc Based Algorithm And These Of The Conventional Fixed

Step Inc And P&O Methods For Mppt: (A) From 0.2 To 0.5 S; (B) From

(a)     0.7 To 0.95 S; (C) From 1.2 To 1.4 S; (D) From 1.4 To 1.5 S.

 

Figure 7. Testing The Flc Based Algorithm Through The Ramp Variations

(a)    Of: (A) The Solar Irradiance (G); (B) The Cell Temperature (Tc ).

 

CONCLUSION:

The PV system efficiency is a crucial index to evaluate the performance of grid-connected PV systems where the MPPT performance is a keynote. The conventional fixed step INC method for MPPT is widely used but it lacks some accuracy and speed of convergence. To tackle this issue, the proposed improvement of the INC method is introduced to employ a fuzzy logic algorithm to generate a variable step voltage increment or decrement, which is executed through decrement or increment of the duty cycle of the dc-dc boost converter. The voltage (duty cycle) step has five different sizes according to proposed five regions of the fuzzy inputs. The simulation results demonstrate that the proposed FLC based variable step INC method for MPPT enhances the output dc power and reduce the time of convergence to reach the steady state when switching of the environmental conditions. To illustrate the efficacy of the proposed MPPT method, it is compared to two conventional methods. The first one is the INC method with fixed step sizes of 0.0003 s and 0.001 s. The second method is the conventional P&O method with fixed step of 0.0003 s. In future work, the experimental application of the proposed FLC variable step method will be studied in a grid-connected PV systems.

REFERENCES:

[1] N. Priyadarshi, F. Azam, A. K. Bhoi, and A. K. Sharma, ``Dynamic operation of grid-connected photovoltaic power system,'' in Advances in Greener Energy Technologies. Singapore: Springer, 2020, pp. 211_218.

[2] H. Rezk, M. Aly, M. Al-Dhaifallah, and M. Shoyama, ``Design and hardware implementation of new adaptive fuzzy logic-based MPPT control method for photovoltaic applications,'' IEEE Access, vol. 7, pp. 106427_106438, 2019.

[3] A. S. Bayoumi, R. A. El-Sehiemy, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, ``Assessment of an improved three-diode against modified two-diode patterns of MCS solar cells associated with soft parameter estimation paradigms,'' Appl. Sci., vol. 11, no. 3, p. 1055, Jan. 2021, doi: 10.3390/app11031055.

[4] B. Subudhi and R. Pradhan, ``A comparative study on maximum power point tracking techniques for photovoltaic power systems,'' IEEE Trans. Sustain. Energy, vol. 4, no. 1, pp. 89_98, Jan. 2013.

[5] D. Sera, L. Mathe, T. Kerekes, S. V. Spataru, and R. Teodorescu, ``On the Perturb-and-Observe and incremental conductance MPPT methods for PV systems,'' IEEE J. Photovolt., vol. 3, no. 3, pp. 1070_1078, Jul. 2013.