A Buck & Boost based Grid Connected PV Inverter Maximizing Power Yield from Two PV Arrays in Mismatched Environmental Conditions

ABSTRACT:

A single phase grid connected transformerless photo voltaic (PV) inverter which can operate either in buck or in boost mode, and can extract maximum power simultaneously from two serially connected subarrays while each of the subarray is facing different environmental conditions, is presented in this paper. As the inverter can operate in buck as well as in boost mode depending on the requirement, the constraint on the minimum number of serially connected solar PV modules that is required to form a subarray is greatly reduced. As a result power yield from each of the subarray increases when they are exposed to different environmental conditions. The topological configuration of the inverter and its control strategy are designed so that the high frequency components are not present in the common mode voltage thereby restricting the magnitude of the leakage current associated with the PV arrays within the specified limit. Further, high operating efficiency is achieved throughout its operating range. A detailed analysis of the system leading to the development of its mathematical model is carried out. The viability of the scheme is confirmed by performing detailed simulation studies. A 1.5 kW laboratory prototype is developed, and detailed experimental studies are carried out to corroborate the validity of the scheme.

KEYWORDS:

  1. Grid connection
  2. Single phase
  3. Transformerless
  4. Buck & Boost based PV inverter
  5. Maximum power point
  6. Mismatched environmental condition
  7. Series connected module

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1. Dual Buck & Boost based Inverter (DBBI)

 EXPECTED SIMULATION RESULTS

 

Fig. 2. Simulated waveform: Variation in (a) ppv1 and ppv2, (b) vpv1 and

vpv2, (c) ipv1 and ipv2 during entire range of operation

Fig.3. Simulated waveform: vg and ig and their magnified views

Fig. 4. Simulated waveform: iL1 and iL2 and their magnified views

Fig. 5. Simulated waveform: vco1 and vco2 and their magnified views

CONCLUSION:

A single phase grid connected transformerless buck and boost based PV inverter which can operate two subarrays at their respective MPP was proposed in this paper. The attractive features of this inverter were i) effect of mismatched environmental conditions on the PV array could be dealt with A single phase grid connected  transformerless buck and boost based PV inverter which can operate two subarrays at their respective MPP was proposed in this paper. The attractive features of this inverter were i) effect of mismatched environmental conditions on the PV array could be dealt with in an effective way, ii) operating efficiency achieved, _euro = 97.02% was high, iii) decoupled control of component converters was possible, iv) simple MPPT algorithm was employed to ensure MPP operation for the component converters, v) leakage current associated with the PV arrays was within the limit mentioned in VDE 0126-1-1. Mathematical analysis of the proposed inverter leading to the development of its small signal model was carried out. The criterion to select the values of the output filter components was presented. The scheme was validated by carrying out detailed simulation studies and subsequently the viability of the scheme was ascertained by carrying out thorough experimental studies on a 1.5 kW prototype of the inverter fabricated for the purpose.

REFERENCES:

[1] T. Shimizu, O. Hashimoto, and G. Kimura, “A novel high-performance utility-interactive photovoltaic inverter system,” IEEE Trans. Power Electron., vol. 18, no. 2, pp. 704-711, Mar. 2003.

[2] S. V. Araujo, P. Zacharias, and R. Mallwitz, “Highly efficient singlephase transformerless inverters for grid-connected photovoltaic systems,” IEEE Trans. Ind. Electron., vol. 57, no. 9, pp. 3118-3128, Sep. 2010.

[3] B. Ji, J. Wang, and J. Zhao, “High-efficiency single-phase transformerless PV H6 inverter with hybrid modulation method,” IEEE Trans. Ind.Electron., vol. 60, no. 5, pp. 2104-2115, May 2013.

[4] R. Gonzalez, E. Gubia, J. Lopez, and L. Marroyo, “Transformerless single phase multilevel-based photovoltaic inverter,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2694-2702, Jul. 2008.

[5] H. Xiao and S. Xie, “Transformerless split-inductor neutral point clamped three-level PV grid-connected inverter,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 1799-1808, Apr. 2012.

Microgrid connected PV-Based Sources

ABSTRACT

Microgrid connected This article studies the control configuration of a microgrid-connected photovoltaic (MCPV) source. In the control of an MCPV, maximum power point (MPP) tracking, droop control, and dc bus voltage regulation are the main required functions. To increase their penetration in the microgrid, MCPV sources have to participate in the microgrid’s frequency regulation. Consequently, MCPVs may be forced to depart from MPP for short periods of time. In this article, a control method is proposed to operate the MCPV in the MPP at all times except when there is a need to stabilize the frequency. The method achieves this objective autonomously without the need to change the control configuration. This method is explained, and its superiority over other controllers to achieve the same objective is investigated. The suggested control configurations are validated through simulation studies and experiments.

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

microgrid connected

(a)

(b)

Fig 1.The hybrid control configuration of the MCPVs: (a) the MPP control configuration and (b) the droop control configuration.

 EXPERIMENTAL RESULTS:

Fig 2.The responses of the dc bus voltage and the reactive  power of the hybrid MCPVs: (a) the dc bus voltage of the  MCPVs and (b) the reactive power of source1, source2, and the hybrid MCPVs.

Fig 3.The frequencies of source1, source2, and the hybrid MCPVs

Fig4.The responses of the dc bus voltage and the reactive power of the universal MCPVs: (a) the dc bus voltage of the MCPV  and (b) the reactive power of source1, source2, and the hybrid MCPV.

 

Fig5.The responses of the power and dp-dv for the universal MCPVs: (a) the power provided by source2 and the universal MCPVs and (b) the value of dp/dv of the universal MCPVs.

Fig6.The frequencies of source1, source2, and the universal MCPVs.

Fig7.The power-voltage characteristics of the simulated PV.

Fig 8.The hybrid MCPV experimental results: (a) the power of source2 and (b) the value of dp/dv of the hybrid MCPVs.

 CONCLUSION

In this article, the control strategies for the MCPVs were investigated. The considered MCPVs comprised the PV source: dc/dc and dc/ac converters. The need for a new control configuration for MCPV sources to participate in the frequency and voltage regulation in addition to the MPPT controller was justified. One way to control the MCPVs was to switch between two controllers, one for MPPT and the other to perform droop control. The combination of the two controllers is called a hybrid controller. The hybrid controller suf In this article, the control strategies for the MCPVs were investigated. The considered MCPVs comprised the PV source: dc/dc and dc/ac converters. The need for a new control configuration for MCPV sources to participate in the frequency and voltage regulation in addition to the MPPT controller was justified. One way to control the MCPVs was to switch between two controllers, one for MPPT and the other to perform droop control. The combination of the two controllers is called a hybrid controller. The hybrid controller suffered from two problems. The first was the need for an external switching signal to switch from one controller to the other, indicating a lack of plug-and-play capability. The second problem was the poor transient in the dynamics whenever there was a change in the controller or the load. A new controller was then proposed that achieved the MPPT, droop control, and dc bus voltage regulation without the need to switch between different configurations. The proposed controller was denoted as the universal controller. In this controller, a dc bus regulator controls the dc bus voltage by adjusting the duty ratio of the dc/dc converter, while both the droop controller and the MPPT controller drive the dc/ ac inverter phase. The controllers were tuned in such a way that, whenever there is a significant change in the load, the droop controller response is dominant to stabilize the frequency of the microgrid. Later, the MPPT moves the operating point to the MPP automatically but smoothly to avoid any disruption in the frequency. The proposed controllers were tested by simulations and experiments, where the validity of the method was verified in terms of stabilizing the frequency, maximizing the power production, regulating the dc bus voltage, and operating autonomously without the need for an external switching decision.

REFERENCES

[1] M. Amin, “Toward self-healing energy infrastructure systems,” IEEE Comput. Appl. Power, vol. 14, no. 1, pp. 20–28, 2001.

[2] G. Venkataramanan and C. Marnay, “A larger role for microgrids,” IEEE Power Energy Mag., vol. 6, no. 3, pp. 78–82, 2008.

[3] M. Prodanovic and T. Green, “High-quality power generation through distributed control of a power park microgrid,” IEEE Trans. Ind. Electron., vol. 53, no. 5, pp. 1471–1482, 2006.

[4] S.-J. Ahn, J.-W. Park, Il-Y. Chung, S.-Il Moon, S.-H. Kang, and S. Nam, “Power-sharing method of multiple distributed generators considering control modes and configurations of a microgrid,” IEEE Trans. Power Delivery, vol. 25, no. 3, pp. 2007–2016, 2010.

[5] F. A. Farret and M. G. Simoes, Integration of Alternative Sources of Energy. Hoboken, NJ: Wiley, 2006.

Maximum Power Point Tracking Using Fuzzy Logic Controller under Partial Conditions

Scientific Research Publishing, Smart Grid and Renewable Energy, 2015.

Maximum Power Point  ABSTRACT: This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar panel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink. A simulation model consists of PV panel, boost converter, and maximum power point tack MPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation; 2) Sudden changing; 3) Partial shading. Results showed that fuzzy controller successfully find MPP for all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01 second when PV is subjected to sudden changes and partial shading in irradiation.

KEYWORDS:

  • Fuzzy Logic Controller
  • Maximum Power Point
  • Photovoltaic System
  • Partial Shading

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Figure 1. Schematic diagram of PV system with MPPT.

EXPECTED SIMULATION RESULTS:

 

Figure 2. P-V characteristics at different irradiations.

Figure 3. P-V characteristics when partial shading from 1000 to 600 Watt/m2.

Figure 4. Output of fuzzy at1000 Watt/m2.

Figure 5. Output of fuzzy controller. (a) Full shading from 600 to 300 Watt/m2; (b) Full shading from 700 to 400 Watt/m2; (c) Full shading from 900 to 400 Watt/m2; (d) Increasing shading from 300 to 800 Watt/m2.

Figure 6. Comparison between fuzzy and P & O partial shading (partial shading 1000 to 800 Watt/m2).

CONCLUSION:

 In this study, FLC has been developed to track the maximum power point of PV system. PV panel, boost converter with FLC connected to a resistive load has been simulated using Matlab/Simulink. Simulation results have been compared to nominal power values. The proposed system showed its ability to reach MMP under uniform irradiation, sudden changes of irradiation, and partial shading. Simulation results have shown that using FLC has great advantages over conventional methods. It is found that Fuzzy controller always finds the global MPP. It is found that fuzzy logic systems are easily implemented with minimal oscillations with fast convergence around the desired MP

 REFERENCES:

 [1] Devabhaktuni, V., Alam, M., Reddy Depuru, S.S.S., Green II, R.C., Nims, D. and Near, C. (2013) Solar Energy: Trends and Enabling Technologies. Renewable and Sustainable Energy Reviews, 19, 555-556. http://dx.doi.org/10.1016/j.rser.2012.11.024

[2] Bataineh, K.M. and Dalalah, D. (2012) Optimal Configuration for Design of Stand-Alone PV System. Smart Grid and Renewable Energy, 3, 139-147. http://dx.doi.org/10.4236/sgre.2012.32020

[3] Bataineh, K. and Dalalah, D. (2013) Assessment of Wind Energy Potential for Selected Areas in Jordan. Journal of Renewable Energy, 59, 75-81.

[4] Bataineh, K.M. and Hamzeh, A. (2014) Efficient Maximum Power Point Tracking Algorithm for PV Application under Rapid Changing Weather Condition. ISRN Renewable Energy, 2014, Article ID: 673840. http://dx.doi.org/10.1155/2014/673840

[5] International Energy Agency (2010) Trends in Photovoltaic Applications. Survey Report of Selected IEA Countries between 1992 and 2009. http://www.ieapvps.org/products/download/Trends-in Photovoltaic_2010.pdf

Maximum Power Point

Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV Panel

IEEE Transactions on Sustainable Energy, 2015

ABSTRACT: This paper proposes an adaptive and optimal control strategy for a solar photovoltaic (PV) system. The control strategy ensures that the solar PV panel is always perpendicular to sunlight and simultaneously operated at its maximum power point (MPP) for continuously harvesting maximum power. The proposed control strategy is the control combination between the solar tracker (ST) and MPP tracker that can greatly improve the generated electricity from solar PV systems. Regarding the ST system, the paper presents two drive approaches including open- and closed-loop drives. Additionally, the paper also proposes an improved incremental conductance algorithm for enhancing the speed of the MPP tracking of a solar PV panel under various atmospheric conditions as well as guaranteeing that the operating point always moves toward the MPP using this proposed algorithm. The simulation and experimental results obtained validate the effectiveness of the proposal under various atmospheric conditions.

KEYWORDS:

  1. Maximum power point tracker (MPPT)
  2. Solar tracker (ST)
  3. Solar PV panel

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of the experimental setup.

EXPECTED SIMULATION RESULTS:

Fig. 2. Description of the variations of the solar irradiation and temperature.

Fig. 3. Obtained maximum output power with the P&O and improved InC algorithms under the variation of the solar irradiation.

Fig. 4. Obtained maximum output power with the InC and improved InC algorithms under the variation of the solar irradiation.

Fig. 5. Obtained maximum output power with the P&O and improved InC algorithms under both the variations of the solar irradiation and temperature.

Fig. 6. Obtained maximum output power with the InC and improved InC algorithms under both the variations of the solar irradiation and temperature.

Fig. 7. MPPs of the solar PV panel under the variation of the solar irradiation

Fig. 8. MPPs of the solar PV panel under both the variations of the solar irradiation and temperature.

Fig. 9. Experimental result of obtained maximum output power with the improved InC algorithm under the variation of the solar irradiation.

CONCLUSION:

It is obvious that the adaptive and optimal control strategy plays an important role in the development of solar PV systems. This strategy is based on the combination between the ST and MPPT in order to ensure that the solar PV panel is capable of harnessing the maximum solar energy following the sun’s trajectory from dawn until dusk and is always operated at the MPPs with the improved InC algorithm. The proposed InC algorithm improves the conventional InC algorithm with an approximation which reduces the computational burden as well as the application of the CV algorithm to limit the search space and increase the convergence speed of the InC algorithm. This improvement overcomes the existing drawbacks of the InC algorithm. The simulation and experimental results confirm the validity of the proposed adaptive and optimal control strategy in the solar PV panel through the comparisons with other strategies.

REFERENCES:

[1] R. Faranda and S. Leva, “Energy comparison of MPPT techniques for PV systems,” WSES Trans. Power Syst., vol. 3, no. 6, pp. 446–455, 2008.

[2] X. Jun-Ming, J. Ling-Yun, Z. Hai-Ming, and Z. Rui, “Design of track control system in PV,” in Proc. IEEE Int. Conf. Softw. Eng. Service Sci., 2010, pp. 547–550.

[3] Z. Bao-Jian, G. Guo-Hong, and Z. Yan-Li, “Designment of automatic tracking system of solar energy system,” in Proc. 2nd Int. Conf. Ind. Mechatronics Autom., 2010, pp. 689–691.

[4] W. Luo, “A solar panels automatic tracking system based on OMRON PLC,” in Proc. 7th Asian Control Conf., 2009, pp. 1611–1614.

[5] W. Chun-Sheng,W. Yi-Bo, L. Si-Yang, P. Yan-Chang, and X. Hong-Hua, “Study on automatic sun-tracking technology in PV generation,” in Proc. 3rd Int. Conf. Elect. Utility Deregulation Restruct. Power Technol., 2008, pp. 2586–2591.