Single Stage PV Array Fed Speed Sensorless Vector Control of Induction Motor Drive for Water Pumping

ABSTRACT:  

This paper deals with a single stage solar powered speed sensorless vector controlled induction motor drive for water pumping system, which is superior to conventional motor drive. The speed is estimated through estimated stator flux. The proposed system includes solar photovoltaic (PV) array, a three-phase voltage source inverter (VSI) and a motor-pump assembly. An incremental conductance (InC) based MPPT (Maximum Power Point Tracking) algorithm is used to harness maximum power from a PV array. The smooth starting of the motor is attained by vector control of an induction motor. The desired configuration is designed and simulated in MATLAB/Simulink platform and the design, modeling and control of the system, are validated on an experimental prototype developed in the laboratory.

KEYWORDS:

  1. Speed Sensorless Control
  2. Stator Field-Oriented Vector Control
  3. Photovoltaic (PV)
  4. InC MPPT Algorithm
  5. Induction Motor Drive (IMD)
  6. Water Pump

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. PV fed induction motor drive configuration

 EXPECTED SIMULATION RESULTS:

Fig. 2. Starting and MPPT of PV array at 1000 W/m2

Fig. 3. Intermediate signals during starting at 1000 W/m2

 

(a)

(b)

Fig. 4. Simulation results during starting at 1000 W/m2 (a) Proposed drive (b) Waveforms showing sensed speed and estimated speed

Fig. 5. SPV array performance during decrease in insolation from 1000 W/m2 to 500 W/m2

(a)

 (b)

Fig. 6. Dynamic performance during irradiance decrement from 1000 W/m2 to 500 W/m2 (a) Proposed drive (b) Waveforms showing sensed speed and estimated speed

Fig. 7. PV array performance on increasing insolation from 500 W/m2 to 1000 W/m2

(a)

(b)

Fig. 8. Dynamic performance during irradiance decrement from 500 W/m2 to 1000 W/m2 (a) Proposed drive (b) Waveforms showing sensed speed and estimated speed

CONCLUSION:

 A single stage solar PV array fed speed sensorless vector-controlled induction motor drive has been operated subjected to different conditions and the steady state and dynamic behaviors have been found quite satisfactory and suitable for water pumping. The torque and stator flux, have been controlled independently. The motor is started smoothly. The reference speed is generated by DC link voltage controller controlling the voltage at DC link along with the speed estimated by the feed-forward term incorporating the pump affinity law. The power of PV array is maintained at maximum power point at the time of change in irradiance. This is achieved by using incremental-conductance based MPPT algorithm. The speed PI controller has been used to control the q-axis current of the motor. Smooth operation of IMD is achieved with desired torque profile for wide range of speed control. Simulation results have displayed that the controller behavior is found satisfactory under steady state and dynamic conditions of insolation change. The suitability of the drive is also verified by experimental results under various conditions and has been found quite apt for water pumping.

REFERENCES:

[1] R. Foster, M. Ghassemi and M. Cota, Solar energy: Renewable energy and the environment, CRC Press, Taylor and francis Group, Inc. 2010.

[2] M. Kolhe, J. C. Joshi and D. P. Kothari, “Performance analysis of a directly coupled photovoltaic water-pumping system”, IEEE Trans. on Energy Convers., vol. 19, no. 3, pp. 613-618, Sept. 2004.

[3] J. V. M. Caracas, G. D. C. Farias, L. F. M. Teixeira and L. A. D. S. Ribeiro, “Implementation of a high-efficiency, high-lifetime, and low-cost converter for an autonomous photovoltaic water pumping system”, IEEE Trans. Ind. Appl., vol. 50, no. 1, pp. 631-641, Jan.-Feb. 2014.

[4] R. Kumar and B. Singh, “ Buck-boost converter fed BLDC motor for solar PV array based water pumping, ” IEEE Int. Conf. Power Electron. Drives and Energy Sys. (PEDES), 2014.

[5] Zhang Songbai, Zheng Xu, Youchun Li and Yixin Ni, “Optimization of MPPT step size in stand-alone solar pumping systems,” IEEE Power Eng. Society Gen. Meeting, June 2006.

 

Model Predictive Control of PV Sources in A Smart DC Distribution System Maximum Power Point Tracking and Droop Control

 

ABSTRACT:

In a dc distribution system, where multiple power sources supply a common bus, current sharing is an important issue. When renewable energy resources are considered, such as photovoltaic (PV), dc/dc converters are needed to decouple the source voltage, which can vary due to operating conditions and maximum power point tracking (MPPT), from the dc bus voltage. Since different sources may have different power delivery capacities that may vary with time, coordination of the interface to the bus is of paramount importance to ensure reliable system operation. Further, since these sources are most likely distributed throughout\ the system, distributed controls are needed to ensure a robust and fault tolerant control system. This paper presents a model predictive control-based MPPT and model predictive control-based droop current regulator to interface PV in smart dc distribution systems. Back-to-back dc/dc converters control both the input current from the PV module and the droop characteristic of the output current injected into the distribution bus. The predictive controller speeds up both of the control loops, since it predicts and corrects error before the switching signal is applied to the respective converter.

KEYWORDS:

  1. DC microgrid
  2. Droop control
  3. Maximum power point tracking (MPPT)
  4. Model predictive control (MPC)
  5. Photovoltaic (PV)
  6. Photovoltaic systems

 SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

image001

Fig. 1. Multiple-sourced dc distribution system with central storage.

EXPECTED SIMULATION RESULTS:

image002

Fig. 2. Ideal bus voltage and load power as system impedance increases and loads are interrupted to prevent voltage collapse. (a) Bus voltage decreases in response to increased system impedance at t1 to reach the operating point on the new P–V curve at t2 . The new bus voltage is below the UVP limit, so control action cause load to be shed, moving to a new operating point on the same P–V curve at t3 with a higher bus voltage. (b) Load power in the system changes as point-of-load converters are turned OFF to reduce total system load when the bus voltage drops below the UVP.

image003

Fig. 3. Response of dc bus voltage to step changes in the power drained by load.

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Fig. 4. Response of dc bus voltage and output power to imbalanced input PV sources

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Fig. 5. Response validation of dc bus voltage to step changes in the power drained by load.

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Fig. 6. Response validation of dc bus voltage and output power to imbalanced input PV sources.

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Fig. 7. Response of dc bus voltage and output power to the input PV sources of Fig. 7.

CONCLUSION:

 High efficiency and easy interconnection of renewable energy sources increase interests in dc distribution systems. This paper examined autonomous local controllers in a single-bus dc microgrid system for MPP tracked PV sources. An improved MPPT technique for dc distribution system is introduced by predicting the error at next sampling time using MPC. The proposed predictive MPPT technique is compared to commonly used P&O method to show the benefits and improvements in the speed and efficiency of the MPPT. The results show that the MPP is tracked much faster by using the MPC technique than P&O method.

In a smart dc distribution system for microgrid community, parallel dc/dc converters are used to interconnect the sources, load, and storage systems. Equal current sharing between the parallel dc/dc converters and low voltage regulation is required. The proposed droop MPC can achieve these two objectives. The proposed droop control improved the efficiency of the dc distribution system because of the nature of MPC, which predicts the error one step in horizon before applying the switching signal. The effectiveness of the proposed MPPT-MPC and droop MPC is verified through detailed simulation of case studies. Implementation of the MPPT-MPC and droop MPC using dSPACE DS1103 validates the simulation results.

REFERENCES:

[1] Z. Peng, W. Yang, X. Weidong, and L. Wenyuan, “Reliability evaluation of grid-connected photovoltaic power systems,” IEEE Trans. Sustain. Energy, vol. 3, no. 3, pp. 379–389, Jun. 2012.

[2] W. Baochao, M. Sechilariu, and F. Locment, “Intelligent DC microgrid with smart grid communications: Control strategy consideration and design,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2148–2156, Dec. 2012.

[3] R. Majumder, “A hybrid microgrid with DC connection at back to back converters,” IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 251–259, Jun. 2013.

[4] R. Lasseter, A. Akhil, C. Marnay, J. Stephens, J. Dagle, R. Guttromson, A. S. Meliopoulous , R. Yinger, and J. Eto, “Integration of distributed energy resources. The CERTS microgrid concept,” U.S. Dept. Energy, Tech. Rep. LBNL-50829, 2002.

[5] T. Esram and P. L.Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 439–449, Jun. 2007.

 

 

Performance Analysis of P&O and Incremental Conductance MPPT Algorithms Under Rapidly Changing Weather Conditions

 

ABSTRACT:

In this paper, the comparative analysis of two maximum power point tracking (MPPT) algorithms namely Perturb and Observe (P&O) and Incremental conductance (InC) is presented for the Photo-Voltaic (PV) power generation system. The mathematical model of the PV array is developed and transformed into MATLAB/Simulink environment. This model is used throughout the paper to simulate the PV source characteristics identical to that of a 20 Wp PV panel. The MPPT algorithms generate proper duty ratio for interfacing dc-dc boost converter driving resistive load. The performances of these algorithms are evaluated at gradual and rapidly changing weather conditions where it is observed that InC method tracks the rapidly changing insolation level at a faster rate as compared to P&O. Depending upon the prevailing environmental conditions the MPPT algorithms finds a unique operating point to track the maximum available power. The algorithms find a fixed duty ratio by comparing the previous power, voltage and current thereby optimizing the power output of the panel. The main objective is to compare the tracking capability and stability of the algorithms under different environmental situations on par with other real world tests.

KEYWORDS:

  1. Maximum Power Point Tracking (MPPT)
  2. Photovoltaic (PV)
  3. DC-DC Boost Converter
  4. Perturb & Observe (P&O)
  5. Incremental Conduction (InC)

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

 image001

Fig. 1. PV Panel Interfaced with Boost Converter for MPP Tracking

 EXPECTED SIMULATION RESULTS:

 image002

 Fig. 2. Experimental Measured PV Characteristics

 image003

 Fig. 3. Experimental Results showing Source Voltage, Load Voltage and Duty Ratio

image004

Fig. 4. Performances of P&O and InC under slowly changing climatic conditions (a) Irradiations Levels (b), (c) & (d) Duty ratio (e) Panel Voltage (f) Panel Power (g) Oscillations in Duty by the algorithms

image005

Fig. 5. Performances of P&O and InC under rapidly changing climatic conditions (a) Insolations (b)& (c) Duty ratio (d)&(e) Panel Voltage (f) Panel Power

 CONCLUSION:

The presented studies in this paper were the comparative analysis of two MPPT algorithms, Perturb & Observe and Incremental Conductance and conducted through boost converter. The simulation results prove positively that the P&O and the Incremental Conductance MPPTs reach the intended maximum power point. In the slowly changing whether both algorithms perform without significantly changes. It has observed that the Incremental Conductance reaches at the MPP three times faster than P&O in all cases and shows better performance for rapid changes and a better stability when the MPP is achieved. It has observed that P&O shows oscillations around the MPP when it reaches in steady state position which results in some power loss. But in case of InC there are no additional oscillations at steady state condition. However the P&O MPPT are mostly used in practice due to their simplicity. The originality and the specificity of the presented results obtain during this research reside in the fact that external parameters as irradiation and fixed temperature were introduced, at first as linear functions (ramp input) and, at second as random (step input) ones describing more closely the actual applicative conditions. The effect of the changing weather on the voltage and power of the PV panel according to change in MPP has shown in the results section.

REFERENCES:

[1] Tariq, J. Asghar, “Development of an Analog Maximum Power Point Tracker for Photovoltaic Panel”, PEDS. International Conference on, 2005, vol. 1, no., pp. 251, 255.

[2] H. Al-Bahadili, H. Al-Saadi, R. Al-Sayed, M.A.-S. Hasan, “Simulation of maximum power point tracking for photovoltaic systems”, Applications of Information Technology to Renewable Energy Processes and Systems (IT-DREPS), 1st International Conference & Exhibition on the , 2013, vol., no., pp. 79,84.

[3] Lu Yuan, Cui Xingxing, “Study on maximum power point tracking for photovoltaic power generation system”, Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference on, 2010, vol. 9, pp. 180,183.

[4] G. Walker, “Evaluating MPPT converter topologies using a MATLAB PV model”, Journal of Electrical & Electronics Engineering, 2001, Australia, IEAust, vol. 21, No. 1, pp. 49-56.

[5] Beriber, D.; Talha, A, “MPPT techniques for PV systems,” Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on, vol., no., pp.1437, 1442, 13-17 May 2013.

Integrating Hybrid Power Source Into an Islanded MV Microgrid Using CHB Multilevel Inverter Under Unbalanced and Nonlinear Load Conditions

 

ABSTRACT:

This paper presents a control strategy for an islanded medium voltage microgrid to coordinate hybrid power source (HPS) units and to control interfaced multilevel inverters under unbalanced and nonlinear load conditions. The proposed HPS systems are connected to the loads through a cascaded H-bridge (CHB) multilevel inverter. The CHB multilevel inverters increase the output voltage level and enhance power quality. The HPS employs fuel cell (FC) and photovoltaic sources as the main and supercapacitors as the complementary power sources. Fast transient response, high performance, high power density, and low FC fuel consumption are the main advantages of the proposed HPS system. The proposed control strategy consists of a power management unit for the HPS system and a voltage controller for the CHB multilevel inverter. Each distributed generation unit employs a multiproportional resonant controller to regulate the buses voltages even when the loads are unbalanced and/or nonlinear. Digital time-domain simulation studies are carried out in the PSCAD/EMTDC environment to verify the performance of the overall proposed control system.

KEYWORDS:

  1. Cascaded H-bridge (CHB) multilevel inverter
  2. Fuel cell (FC)
  3. Hybrid power source (HPS)
  4. Multiproportional resonant (multi-PR)
  5. Photovoltaic (PV)
  6. Supercapacitor (SC)

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

image001

Fig. 1. Single-line diagram of MV microgrid consisting of two DG units.

image002

Fig. 2. Proposed structure of the hybrid FC/PV/SC power source.

EXPECTED SIMULATION RESULTS:

image003

Fig. 3. Microgrid response to unbalanced and nonlinear load changes in feeder F1 . (a) and (b) Instantaneous real and reactive powers of feeders.

image004

Fig. 4. Microgrid response to the unbalanced and nonlinear load changes applied to feeder F1 ; positive-sequence, negative-sequence, and harmonic components of loads currents at (a) feeder F1 and (b) feeder F2 .

 image005

Fig. 5. Dynamic response of DG units to unbalanced and nonlinear load changes applied to feeder F1 . (a) and (b) Real and reactive power components of DG units.

image006

Fig. 6. Microgrid response to the unbalanced and nonlinear load changes applied to feeder F1 ; positive-sequence, negative-sequence, and harmonic currents of (a) DG1 and (b) DG2 .

image007

Fig. 7. (a) Instantaneous current waveforms, (b) five-level-inverter output voltage, and (c) voltage waveforms of each phase of DG1 ’s CHB inverter due to the nonlinear load connection to feeder F1 .

image008

Fig. 8. (a) Instantaneous current waveforms, (b) five-level-inverter output voltage, and (c) voltage waveforms of each phase of DG1 ’s CHB inverter due to the single-phase load disconnection from feeder F1 .

image009

Fig. 9. (a) Voltage THD and (b) VUF at DG1 ’s terminal.

image010

Fig. 10. Voltages of dc links for DG1 ’s units.

image011

Fig. 11. Dynamic response of DG1 to load changes; currents of FC stacks and PV units for each HPS. (a) Phase a, (b) phase b, and (c) phase c.

image012

Fig. 12. Dynamic response of DG1 to load changes; average current of SC module of each HPS. (a) Phase a, (b) phase b, and (c) phase c.

 

CONCLUSION:

This paper presents an effective control strategy for an islanded microgrid including the HPS and CHB multilevel inverter under unbalanced and nonlinear load conditions. The proposed strategy includes power management of the hybrid FC/PV/SC power source and a voltage control strategy for the CHB multilevel inverter. The main features of the proposed HPS include high performance, high power density, and fast transient response. Furthermore, a multi-PR controller is presented to regulate the voltage of the CHB multilevel inverter in the presence of unbalanced and nonlinear loads. The performance of the proposed control strategy is investigated using PSCAD/EMTDC software. The results show that the proposed strategy:

1) regulates the voltage of the microgrid under unbalanced and nonlinear load conditions,

2) reduces THD and improves power quality by using CHB multilevel inverters,

3) enhances the dynamic response of the microgrid under fast transient conditions,

4) accurately balances the dc-link voltage of multilevel inverter modules, and

5) effectively manages the powers among the power sources in the HPS system.

 REFERENCES:

[1] H.Zhou,T. Bhattacharya,D.Tran,T. S. T. Siew, and A. M. Khambadkone, “Composite energy storage system involving battery and ultracapacitor with dynamic energymanagement in microgrid applications,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 923–930, Mar. 2011.

[2] W. S. Liu, J. F. Chen, T. J. Liang, and R. L. Lin, “Multicascoded sources for a high-efficiency fuel-cell hybrid power system in high-voltage application,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 931–942, Mar. 2011.

[3] A. Ghazanfari, M. Hamzeh, and H. Mokhtari, “A control method for integrating hybrid  power source into an islanded microgrid through CHB multilevel inverter,” in Proc. IEEE Power Electron., Drive Syst. Technol. Conf., Feb. 2013, pp. 495–500.

[4] IEEE Recommended Practice for Electric Power Distribution for Industrial Plants. ANSI/IEEE Standard 141, 1993.

[5] IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power System. IEEE Standard 519, 1992.