MPPT With Single DC–DC Converter and Inverter for Grid-Connected Hybrid Wind-Driven PMSG–PV System

ABSTRACT:

A new topology of a hybrid distributed generator based on photovoltaic and wind-driven permanent magnet synchronous generator is proposed. In this generator, the sources are connected together to the grid with the help of only a single boost converter followed by an inverter. Thus, compared to earlier schemes, the proposed scheme has fewer power converters. A model of the proposed scheme in the d − q-axis reference frame is developed. Two low-cost controllers are also proposed for the new hybrid scheme to separately trigger the dc–dc converter and the inverter for tracking the maximum power from both sources. The integrated operations of both proposed controllers for different conditions are demonstrated through simulation and experimentation. The steady-state performance of the system and the transient response of the controllers are also presented to demonstrate the successful operation of the new hybrid system. Comparisons of experimental and simulation results are given to validate the simulation model.

KEYWORDS:

  1. Grid-connected hybrid system
  2. Hybrid distributed generators (DGs)
  3. Smart grid
  4. Wind-driven PMSG–PV

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

 

Fig. 1. Proposed DG system based on PMSG–PV sources.

 EXPECTED SIMULATION RESULTS:


Fig. 2. DC link steady-state waveforms. (a) Experimental (voltage—50 V/div, current—10 A/div, and time—500 ms/div). (b) Simulated (voltage—20 V/div, current—5 A/div, and time—500 ms/div.

Fig. 3. Steady-state grid voltage and current waveforms. (a) Experimental (voltage—50 V/div, current—10 A/div, and time—20 ms/div). (b) Simulated (voltage—50 V/div, current—5 A/div, and time— 20 ms/div).

Fig.4. Transient response for a step change in PMSG shaft speed. (a) Changes in rectifier output voltage and duty cycle of the boost converter. (b) Changes in dc-link voltage and current. (c) Changes in grid current.

 

CONCLUSION:

A new reliable hybrid DG system based on PV and wind driven PMSG as sources, with only a boost converter followed by an inverter stage, has been successfully implemented. The mathematical model developed for the proposed DG scheme has been used to study the system performance in MATLAB. The investigations carried out in a laboratory prototype for different irradiations and PMSG shaft speeds amply confirm the utility of the proposed hybrid generator in zero-net-energy buildings. In addition, it has been established through experimentation and simulation that the two controllers, digital MPPT controller and hysteresis current controller, which are designed specifically for the proposed system, have exactly tracked the maximum powers from both sources. Maintenance-free operation, reliability, and low cost are the features required for the DG employed in secondary distribution systems. It is for this reason that the developed controllers employ very low cost microcontrollers and analog circuitry. Furthermore, the results of the experimental investigations are found to be matching closely with the simulation results, thereby validating the developed model. The steady state waveforms captured at the grid side show that the power generated by the DG system is fed to the grid at unity power factor. The voltage THD and the current THD of the generator meet the required power quality norms recommended by IEEE. The proposed scheme easily finds application for erection at domestic consumer sites in a smart grid scenario.

REFERENCES:

[1] J. Byun, S. Park, B. Kang, I. Hong, and S. Park, “Design and implementation of an intelligent energy saving system based on standby power reduction for a future zero-energy home environment,” IEEE Trans. Consum. Electron., vol. 59, no. 3, pp. 507–514, Oct. 2013.

[2] J. He, Y. W. Li, and F. Blaabjerg, “Flexible microgrid power quality enhancement using adaptive hybrid voltage and current controller,” IEEE Trans. Ind. Electron., vol. 61, no. 6, pp. 2784–2794, Jun. 2014.

[3] W. Li, X. Ruan, C. Bao, D. Pan, and X. Wang, “Grid synchronization systems of three-phase grid-connected power converters: A complexvector- filter perspective,” IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1855–1870, Apr. 2014.

[4] C. Liu, K. T. Chau, and X. Zhang, “An efficient wind-photovoltaic hybrid generation system using doubly excited permanent-magnet brushless machine,” IEEE Trans. Ind. Electron, vol. 57, no. 3, pp. 831–839, Mar. 2010.

[5] S. A. Daniel and N. A. Gounden, “A novel hybrid isolated generating system based on PV fed inverter-assisted wind-driven induction generators,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 416–422, Jun. 2004.

A Study on Anti-Islanding Detection Algorithms for Grid-Tied Photovoltaic Systems

 

ABSTRACT

This study analyzes various anti-islanding (AI) protection relays when the islanding condition of Grid-Tied PV (photovoltaic) System appears at the Point of Common Coupling (PCC) between the PV Solar Power System and the power grid. The main purpose of the study is to determine the performance of several AI prevention schemes in detecting the presence of an island, by monitoring the detection time of the islanding condition through different methods. The devices used to implement the methods include over-current and under-current (OI/UI) relays, over-voltage and under-voltage (OV/UV) relays, over-frequency and under-frequency (OF/UF) relays, rate of change of frequency (ROCOF) and Vector Shift relays. The protection was tested in case of complete disconnection of the PV system from the electric power grid and also in case of various grid faults.

 

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

 

Fig. 1. Simulink model of the 100kW Grid-Connected PV Array

EXPECTED SIMULATION RESULTS

   

Fig.1: Output results of boost converter

Fig. 2. The output result of dc link voltage (V DC) in VSC

Fig. 3. Id and Iq currents (pu) of VSC Control

Fig. 4. The Voltage between phase A and phase B of VSC

Fig. 5. Simulation result in 20kV measurement point of utility grid.

Fig. 6. The RMS value of voltage in PCC.

Fig. 7. The RMS value of current in PCC.

Fig. 8. The output result of frequency in PCC.

      

CONCLUSION :

This paper studies and compares different AI detection techniques such as passive AI prevention by standard protection schemes: OI/UI, OV/UV, OF/UF, as well as ROCOF and Vector Shift in case of a 100kW Grid-Connected PV Array. The PV System is completely disconnected from EPS and continues to energize a 20kV utility grid at 50Hz, and respectively various grid faults occurs at 5km away from the PCC of the PV System. The effectiveness of different AI detection algorithms is tested and the impact on network fault conditions and relays behavior during islanding is studied. From the results provided by the performed Matlab/Simulink simulations, it was observed that using traditional relays for islanding detection such as the OC or UV resulted

in significantly better performance in respect to detection time of islanding conditions. The ROCOF and Vector Shift relays have a detection time comparable with frequency relays. However, if the ROCOF threshold is exceeded, the formation of an island is quickly detected. The terminal voltage of PV inverter needs to exceed a certain threshold when the frequency is not stabilized by VSC. The UC relay failed entirely to detect the islanding in both analyzed cases. The effects of unintentional islanding were observed from simulation of transient grid faults on a power distribution network. The protection equipment needs to distinguish between islanding event and grid faults. The Grid-Tied PV System protections should detect the fault and trip before islanding occurs as a result of the opening of the circuit breaker in response to a downstream fault. In order to minimize these effects and to perform according to the. international standards, the AI relays have to be inserted at the points where islanding conditions may occur. The theoretical simulation results are useful to select these points and design the AI protection devices for Grid-Tied PV Systems.

 

REFERENCES

[1] D. Rekioua and E. Matagne, Optimization of Photovoltaic Power Systems, Modelization, Simulation and Control. Springer, 2012.

[2] IEEE Std 1547-2003, Standard for Interconnecting Distributed Resources with Electric Power Systems, IEEE, 2003.

[3] R. Teodorescu, M. Liserre and P. Rodríguez, Grid Converters for Photovoltaic and Wind Power Systems. John Wiley & Sons, Ltd., 2011.

[4] CIGRE Working Group B5.34, “The Impact of Renewable Energy Sources and Distributed Generation on Substation Protection and Automation,” CIGRE, 2010.

[5] IEEE Std 1547.2-2008, IEEE Application Guide for IEEE Std 1547™, IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems, IEEE, 2008

A Function Based Maximum Power Point Tracking Method for Photovoltaic Systems

ABSTRACT:

In this paper a novel maximum power point tracking (MPPT) algorithm based on introducing a complex function for photovoltaic systems is proposed. This function is used for determination of the duty cycle of the DC-DC converter in PV systems to track the maximum power point (MPP) in any environment and load condition. It has been suggested based on analyzing the expected behavior of converter controller. The function is formed by a two-dimensional Gaussian function and an Arctangent function. It has been shown that contrary to many algorithms which produce wrong duty-cycles in abrupt irradiance changes, the proposed algorithm is able to behave correctly in these situations. In order to evaluate the performance of method, various simulations and experimental tests have been carried out. The method has been compared with some major MPPT techniques with regard to start-up, steady state and dynamic performance. The results reveal that the proposed method can effectively improve the dynamic performance and steady state performance simultaneously.

 

KEYWORDS:

  1. Gaussian-Arctangent Function Based MPPT
  2. Maximum Power Point Tracking
  3. Photovoltaic Systems
  4. Variable Perturbation Frequency

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Electrical scheme of the system under test.

EXPECTED SIMULATION RESULTS:

 

Fig. 2. Output power of PV for battery load in startup test.

(a)

(b)

(c)

(d)

Fig. 3. The output power and duty cycle in step irradiance change for: (a) VSSINC, (b) LCASF method (c) Fuzzy method and (d) Proposed method.

Fig. 4. Response of algorithms to load change.

(a)

(b)

(c)

(d)

Fig. 5. Response of GAF-VPF algorithm to changes in (a) , (b) , (c) and (d) k.

CONCLUSION:

In this paper a new MPPT algorithm named Gaussian-Arctangent Function-Based (GAF) method was proposed. The method is based on introducing a complex function formed by multiplying a two-dimensional Gaussian function with an Arctangent function. This function is used for generating an adaptive perturbation size. In addition, variable perturbation frequency has been utilized for computing the time of applying the next duty cycle. Simulation results and experimental measurements confirm the attractiveness and superiority of the proposed method with respect to some well-known MPPT methods such as variable step-size Incremental Conductance, load-current adaptive step-size and perturbation frequency (LCASF) and Fuzzy method. The algorithm behaves robustly in case of load variation and measurement noise. The other advantage of proposed method is its simplicity of design. It does not require exact tuning of so many parameters. The only system-dependent constants required for controller setup are open-circuit voltage and short-circuit current and standard condition. Although, the computational cost of proposed method is higher than methods like P&O and Incremental Conductance, it can be easily implemented in low cost micro-controllers. All in all, these features make it well-suited for tracking uncommonly fast irradiance variations like mobile solar applications.

REFERENCES:

[1] Moacyr Aureliano Gomes de Brito, Luigi Galotto, Jr., Leonardo Poltronieri Sampaio, Guilherme de Azevedo e Melo, and Carlos Alberto Canesin, „Evaluation of the Main MPPT Techniques for Photovoltaic Applications”, IEEE Trans. Ind. Electron., vol. 60, no. 3, pp. 1156-1167, March 2013.

[2] C. Hua, J. Lin, and C. Shen, “Implementation of a DSP-controlled photovoltaic system with peak power tracking,” IEEE Trans. Ind. Electron., vol. 45, no. 1, pp. 99–107, Feb. 1998.

[3] A.R Reisi, M.H.Moradi, S.Jamasb, “Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review”, Renewable & Sustainable Energy Reviews, vol. 19, pp. 433-443, March 2013.

[4] Qiang Mei, Mingwei Shan, Liying Liu, and Josep M. Guerrero, “A Novel Improved Variable Step Size Incremental-Resistance MPPT Method for PV Systems”, IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2427-2434, June 2011.

[5] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Trans. Power Electron., vol. 20, no. 4, pp. 963–973, July 2005.