A Neural Network Based Control System for Single Phase Grid Connected Photovoltaic Energy Conversion

Bambang Sujanarko, Jember University /Graduate School of Institute Technology Sepuluh Nopember Indonesia Mochamad Ashari and Mauridhi Hery Purnomo, Lecture of Institute Technology Sepuluh Nopember Indonesia

email : bbsujanarko@yahoo.co.id

Abstract- The direct conversion of sunlight into electricity is a very elegant process to generate in environmentally friendly, renewable energy, known as Photovoltaic. This modular can contribute substantially to future energy needs. One important part of integrating them with Grid system is its control. In this research, a Neural Network based controlled for power electric single phase designed to improve an optimum, simple and flexibility of Photovoltaic energy conversion connected on Grid. The control inputs are solar insolation, inverter current and Grid voltage. They used to determine phase angle and magnitude of inverter voltage. The phase angle controls the real power injected to Grid, while the magnitude controls reactive power. Laboratory experiment by Matlab Simulink, defined this system has good performance. Index Terms-photovoltaic, single phase Grid connected, maximum power point tracker, synchronization, neural network, voltage source inverter, real power, reactive power, voltage phase angle and magnitude.

I. INTRODUCTION There is no denying the fact that energy is an essential element for the industrial and socio-economic development. However, the link between energy and environmental pollution is one of the biggest challenges the world is facing today. In this context utilization of solar energy, which is environment friendly, is important for sustainable development. The immense power of the solar energy can be harness for use by humans in two basic ways. Thermal, in which infrared radiation generate heat and Photovoltaic, in which solar radiation generates electricity. Thermal radiation can also be used to generate electricity. Mirrors focus solar radiation onto a steam boiler and the steam is used to generate electricity, using conventional technology. Theoretically, solar power could provide all the energy needed by the industrial world, but this would require an immense capital investment and a considerable length of time to build. It would, however, reduce dependence on imported oil and gas, reduce pollution and nearly put an end to the human contribution to Global Warming. In Grid connected Photovoltaic (PV) systems, as seen in the figure 1, the Grid connected system is consist by some part for example PV, MPPT and DC-DC converter, Inverter, Grid and Load. Some control needed in this system, that is the sun direction controller, MPPT [1],[2], and also synchronization and adjustment phase/magnitude of voltage inverter [3][4].

Figure 1. Grid connected Photovoltaic

In the previous researches, the controls generally studied separately, so will generate problems when system have to be

integrated. To overcome it, hence in this research, control system integrated. Integrating control system build by NN, so connected PV and Grid can be conducted easier, more flexible and reachable of optimum operational circumstance.

II. DESCRIPTION OF CONTROLLER DESIGN In this research, the system to be build have block diagram as Figure 2. An integrated system control will function to control the DC-DC converter based on MPPT, and the inverter control based on capacities of PV and requirement of power flow.

P Vm o d u le D C - D C IN V E R

T E R

N NC O N T R O L L E R

G R ID

L O A D

P W M P W M

Ira dT

U s e rA n g le a n dM a g n itu d e

C o n tro l

Figure 2. Block Diagram of PV connected Grid with NN based controller DC-DC controller get the input from the PV insolation and PV temperature, which in conventional system through MPPT, and then will generate the gate signal for the DC-DC converter and the MPPT power value. From DC-DC converter will be get maximal power DC for the certain insolation and temperature condition. Scheme of conventional DC-DC control is like Figure 3.

PWM DC-DC

Figure 3. DC-DC controller

Maximal energy from DC-DC converter then will be converted to become the AC by inverter. This Inverter is control by the power of MPPT as well as by consumer to consideration of optimum power flow on connected PV and Grid system. Basically, the controlling of power flow conducted by making sinusoidal control signal of Pulse Wave Modulation (PWM) result different phase angle and magnitude at Grid system [5][6]. When phase angle of sinusoidal signal control result the voltage inverter leading by voltage Grid, hence the real power (P) will be flow, from PV to Grid. On the contrary when the lagging condition will be happened, P will

be flow to PV. While when control signal magnitude make the voltage inverter become higher than voltage Grid, hence reactive power (Q), will be flow to the Grid system, on the contrary when result the lower voltage inverter, hence Q will be flow to the PV. Figure 4 shows the scheme of conventional control system for the inverter.

xPIDcontrollerV grid

AngleAdjust

(P)

MagnitudeAdjust (Q)

PWMINVERTER

Figure 4. Conventional controller of connected inverter and Grid

III. RESULT AND DISCUSSION

NN control system use 7-10-30-2 configuration, with the activation functions are tansig-tansig-tansig, and training method use the Back Propagation. Data training will be taken from graph of PV characteristic, insolation, temperature and current of inverter and grid. The data then kept in Matlab Workspace, and after normalization and reduce the data through sampling method by certain interval, hence process the training done. Normalization and sampling conduct to get more precise of process training on system NN and to reduce require time training [7]. Figure 5 shows the control configuration system based NN and Figure 6 shows some result of using the control based NN system.

20[tansig]

10[tansig]

2[tansig]

Control signalDC-DC PWM

Control signalInverter PWM

Insolation

Temperature

Angle

Magnitude

MPPT

VGrid

IInverter

Figure 5. NN controller of Photovoltaic connected Grid

By this Figure, the system can provable that if increasing phase angle, hence real power of inverter (Pinv) will be increase, and the real power of Grid (Pgrid) will decrease. In another condition, when amplitude of voltage inverter increased, hence inverter reactive power (Qinv) will be increase, whereas Qgrid will decrease. This occurence shows the power flow as explained previously.

In half time of graph in the Figure 6, insolation increasing will be result PV power increase. The other Parameter changing, reality also will give the result consistency such as which expected.

Figure 6. Temperature, Insolation and PQ

IV. CONCLUSION

The result of this research indicate that the system control of the PV connected Grid can be made integrated by using NN. The result expected can push to easier implementation for using PV as sustainable and environment friendly source energy, although still need continuation research so this controller applicable to all of PV module type and product and in the compact form as Integrated Circuit (IC).

V. REFERENCE [1] T. Hiyama, and K. Kitabayashi, “Neural network based estimation of

maximum power generation from PV module using environmental information,” IEEE Trans. Energy Conversion, vol.12, pp.241-247, Sept. 1997.

[2] Il-Song Kim, Myung-Bok Kim, and Myung-Joong Youn, “New Maximum Power Point Tracker Using Sliding-Mode Observer for Estimation of Solar Array Current in the Grid-Connected Photovoltaic System,” IEEE Transactions On Industrial Electronics, Vol. 53, August 2006, pp 1027-1035.

[3] Frede Blaabjerg, Remus Teodorescu, Marco Liserre, and Adrian V. Timbus, “Overview of Control and Grid Synchronization for Distributed Power Generation Systems,” IEEE Transactions On Industrial Electronics, Vol. 53, October 2006, pp 1398-1409.

[4] Sung-Hun Ko, Seong R. Lee, Hooman Dehbonei, and Chemmangot V. Nayar, “Application of Voltage- and Current-Controlled Voltage Source Inverters for Distributed Generation Systems,” IEEE Transactions On Energy Conversion, Vol. 21, September 2006, pp 782-792.

[5] T.I. Marisa, St. Kourtesib, L. Ekonomouc, G.P. Fotisd, “Modeling of a single-phase photovoltaic inverter,” Solar Energy Materials & Solar Cells 91 (2007), pp 1713–1725.

[6] Mochamad Ashari,W.W. L. Keerthipala,and Chernmangot V. Nayar, “A Single Phase Parallely Connected Uninterruptible Power Supply/Demand Side Management System,” ,IEEE Transactions On Energy Conversion, Vol. 15, No. 1, March 2000, pp 97-102.

[7] Mauridhi Hery Purnomo, Agus Kurniawan, “Supervised Neural Networks dan Aplikasinya,” Graha Ilmu, 2006.