E Engineering project proposal

Jiayu2016-Modelignofphotovolotaicgrid.pdf

Home >Engineering homework help >Electrical Engineering homework help >E Engineering project proposal

Modeling of photovoltaic grid connected generation system based on parameter identification method

Ren Jiayu1, Li Chunlai2,Teng Yun 1, Yang Xia1,Yao Shengpeng1

1 Shenyang University of Technology;No.111, Shenliao West Road, Economic & Technological Development Zone, Shenyang, 110870, P.R.China.

2 Qinghai Electric Power Research Institute, Xining 810008, China. [email protected]

Abstract—In order to ensure the accuracy of the output of the photovoltaic power generation system, the identification of the photovoltaic parameters is carried out. In photovoltaic power generation system, the analysis is made from two aspects: photovoltaic array and grid connected inverter. The key parameters affecting PV model output are found. Using the recursive least squares method to develop grid-connected PV system parameter identification, and use of photovoltaic measured data parameter identification photovoltaic cells and photovoltaic inverter. Then, a more accurate PV system parameters could be obtained. Finally get a set of more accurate parameters of photovoltaic power generation system. By comparing the analytical calculation, system identification simulation output and photovoltaic measured output, to verify the feasibility of photovoltaic power generation system parameters method and results, lay the foundation for further study on distribution network distributed PV connected to the distribution network problems.

Key words: PV Power Generation System; Simulation modeling ;parameters identification; least square method

INTRODUCTION According to the forecast of solar photovoltaic power

generation in twenty-first Century will occupy an important place in the world's energy consumption, and will become the main body of the world's energy supply. With the relaxation of electric power policy, the photovoltaic power generation access distribution network has become an inevitable trend. In order to study the influence of large scale access to the distribution network, the digital simulation of power system is an important method in the research institutions at home and abroad[1].

The key of digital simulation technology of photovoltaic power generation is to ensure the output characteristic of the photovoltaic power generation model could in line with the measured characteristics of the system. For this purpose it needs more accurate and reasonable parameters of the photovoltaic power generation system. In control parameters setting of photovoltaic power generation system, commonly used methods are theoretical analysis and system identification method. The theoretical analysis method can maximize the reproduction of the internal process of photovoltaic power generation system. So it is widely used by researchers. But because of the complexity of photovoltaic system, the theoretical analysis method in solving control parameters encountered nonlinear differential equations, and some unmeasured variables will affect the

calculation results of the parameters, the parameters can not be obtained directly for photovoltaic control, often require multiple manual adjustment; system identification method is used to model the measured input and output for unknown parameters. A high fitting degree so the output of the model and the measured system, the economic system and in the aerospace, motor control and other fields have been successfully applied "but in the field of photovoltaic power generation, photovoltaic array is currently only for themselves to carry out the identification work, there is no Research on the overall results on photovoltaic power generation system[2-3].

In this paper, the photovoltaic array and grid inverter has carried on the systematic parameter identification. Firstly, the model of photovoltaic generation system is analyzed, and get the key parameters that influence the output of the model of grid connected PV system. Then the recursive least squares method is used to determine the photovoltaic grid connected power system parameter identification method, and the use of photovoltaic measurement data and grid connected photovoltaic power generation system model for multiple identification, a specific set of control parameters are obtained. Finally, the system identification and theoretical analysis parameters of the photovoltaic operation characteristics are compared to verify the feasibility of photovoltaic power control parameters identification, and enhance the credibility of the photovoltaic power generation system simulation.

STRUCTURE OF PHOTOVOLTAIC POWER GENERATION SYSTEM

Photovoltaic power generation system is composed of photovoltaic array can convert light energy into direct current, and through the inverter dc can transform into ac power, connected to the grid. The typical photovoltaic structure as shown in Figure.1.

PV Array DC/DC Converter

Voltage Source Converter

Line Filter Grid

C1 C2

PgQg

Xs VDC Vgrid

IDCIPV PPV

VPV

Fig ure.1 Photovoltaic (PV) power grid structure

2016 International Conference on Smart City and Systems Engineering

378

2016 International Conference on Smart City and Systems Engineering

378

In Figure.1, the grid connected photovoltaic systems mainly include two parts: photovoltaic cells and photovoltaic inverters. The ideal equivalent circuit of the photovoltaic cell is shown in Figure.2, which is obtained by a light source and a diode in parallel.

Iph

Ipv IshId

vpv

Figure.2 Equivalent circuit model of PV cells Its volt-ampere relation is:

sAkT IRVq

sph R IRVeIII

s � � � �

�

� �

�

��

�

1 )(

Type: V:the output voltage of the photovoltaic cell; I:the output current of the photovoltaic battery; phI : photo generated current source; sI : the diode saturation current; Q:

electronic power constant, the value is 1.602×10 19- C; K:the Boltzmann constant, 1.381 ×10J/K. For this equivalent circuit, when its open circuit, the output voltage V corresponding to the open circuit voltage is refocV , ; its short circuit, the output current I corresponding to the short circuit current is refscI , .

In order to maximize the utilization of solar energy, photovoltaic cells need to use maximum power point tracking control (MTTP), every time increase or reduce the output voltage of the photovoltaic array, and the observation of the changes of PV array output power, in order to determine the adjustment direction of the next step. At this time equivalent circuit of output voltage V and output current I corresponding to the maximum power voltage

refmpV , and maximum power point current refmpI , . In summary, for the photovoltaic power generation system, we need to identify and control of four parameters refocV , , refscI , and refmpV , , refmpI , [4-6] .

Photovoltaic system inverter using voltage power outer loop control and current inner loop control. The power voltage ring photovoltaic array by MTTP control of DC voltage and inverter output reactive power respectively with the reference signal were compared, and the error of PI control, so as to obtain the reference signals of the inner loop controller drefI and qrefI .

The inner current loop controller adopt dq rotating coordinate system control, namely the use of orthogonal Park transformation, the three-phase voltage and current to the rotating frequency of W transform to dq0 coordinate system, three-phase symmetrical balance at this time will be the direct current component. Park coordinate transformation formula:

abcdq xTx � 0 (2)

Where:

� � � � � �

�

� � � � � �

�

��

�

2 1

) 3

2sin() 3

2sin(sin

) 3

2cos() 3

2cos(cos

3 2 � �

� �

ttt

In the inverter control of key parameters of the system outer loop PI parameters active proportional coefficient PK , active integral coefficient iPK , reactive power proportional coefficient QK , reactive integral coefficient iQK and inner loop PI parameters direct axis proportional coefficient dK and direct axis time constant dT , axis ratio coefficient qK , quadrature axis time constant qT .

PARAMETER IDENTIFICATION OF PHOTOVOLTAIC GRID CONNECTED SIMULATION

MODEL Parameter identification is based on the input and output

information of the system, in a certain criterion, estimated the unknown parameters of the model, the basic principle is shown in Figure 3.H(k) and Z(k) is the system input and output variables, � for unknown model parameters.

Figure 3. Schematic diagram of parameter identification

Commonly used parameter identification method is least square method, using system input and output data with minimum variance as the goal to parameter estimation values are constantly revised, in order to obtain more accurate parameter estimates. For from 1t to nt time input and output observation ix y and parameter identification number i� value to construct m linear equations

)()()()( 2211 ixixixiy nn�� ),,2,1( mi � (3)

According to the error of the system �� Xy � , the corresponding variance J is:

)()( 1

2 �� XyXyJ TT m

i i ��

(4)

At this time there is a set of parameters �

� to meet

379379

yXXX TT 1- � � (5)

�

� is called the least squares estimate of � , that is, the best parameters of the model identification.

In order to reduce the complexity of the least square method in the case of large matrix dimension, it is needed to be improved and improved. The basic idea is: new estimate value = old estimate value and correction, that means the estimated value is using observational data of the predicted value is corrected, with the introduction of new observational data of successive, step by step to estimate the parameters until the estimated value reached satisfactory accuracy so far[9-11].

According to the relevant regulations, the single point access capacity of the distribution network is not more than 6MW, this paper studies the selection of 1MW photovoltaic power generation system, and the use of digital simulation software Dig SILENT before and after parameter identification. PV inverter and the output of the transformer capacity to take 1MW, inverter AC side voltage to take 0.328kV, inverter control using 0dcU control, the control power factor of 0.995, through the 10kV to access distribution network.

The flow chart of control parameter identification of photovoltaic power generation is shown in Figure 4:

Figure 4. Flow chart of PV parameter identification

According to the key control parameters of photovoltaic power generation system refoc,V refscI , refmp,V

refmp,I , as well as the outer loop control parameter

iQQiPP TKTK ,,, and the inner loop control parameter

qqd TKTK ,,,d , the objective function is the difference between the measured data and the simulation results.

� � �

�

��

�

i i

SMG

SMF

2 22

1 11

])[(

�

(6)

Type: ii

IV 11 , photovoltaic battery in the measurement

and Simulation of voltage and current, respectively; i� is the need to photovoltaic array requires identification of the parameter T,,,, I,VIV refocrefmprefscrefoc ; ii IV 22 , respectively for the measured and simulated output of the inverter output voltage and current; i for the PV inverter need to identify the loop control parameters and inner loop control parameters T),,,,,,,( qqddiQQiPP TKTKTKTK .

PV SYSTEM IDENTIFICATION RESULTS AND VERIFICATION

First by analytical method of the photovoltaic system all the control parameters were calculated, and then set the photovoltaic system inverter control parameters remain unchanged, and the control parameters of photovoltaic cell identification, the parameter analysis and identification of system parameters as shown in Table 1 below.

Table 1. Parameter identification of PV array

According to the existing model of photovoltaic cells were set the control parameters of the parameters obtained from analytical parameters and identification method, and set the illumination variations in the 1000-600W/m2 within the scope of mutation, at this time, PV output voltage and output current waveforms recorded, and photovoltaic measured data of ratio, and the simulation results are as shown in Figure 5 and Figure 6. Can be seen, when the PV system illumination disturbance, photovoltaic and network voltage and current will fluctuate, then resume the steady state, which after the application of system identification parameters of the PV model output and the real situation in steady state operation and fluctuation trend fitting degree were higher than analytical method. The simulation results.

Figure 5. Results of PV voltage with different parameters

380380

Figure 6. Results of PV current with different parameters

After the identification of the photovoltaic array control parameters, parameters of the two loop control parameters of the grid connected inverter are identified, and the parameters of the system are identified and the parameters of the system are shown in Table 2.

Table 2. Results of parameters identification of PV inverter

Respectively set control parameters for the parameters obtained from analytical parameters and identification method, and set the voltage on DC side of the inverter in 0.5 seconds occurred disturbance reference value of 0.95, power output waveform of the record of photovoltaic power generation system, also with photovoltaic measured data were compared. Simulation results as shown in Figure 7. It can be seen that the parameter identification characteristics of photovoltaic inverter power compared with the power characteristics of the analytical method to recover faster from the steady state, and actual measurement system variation trend of closer, can better reflect the actual situation of photovoltaic power generation system.

Figure 7. Output power waveforms of PV inverter with different parameters

CONCLUSIONS (1) The structure and principle of photovoltaic power

generation system are analyzed, and the key parameters of the array and inverter which affect the output of PV system model are obtained.

(2) Using the recursive least square method to study the parameter identification method of distributed photovoltaic system, and the control parameters of the PV model are obtained.

(3) Compared with the measured data, the simulation data of the PV system in the analytical parameters, identification parameters, and the accuracy of the identification method and the simulation results are verified.

It is worth pointing out that the initial value of the parameter identification before setting and the results of the success of the identification of certain constraints, to be further improved. But the results of this study will be helpful to grasp the operation characteristics of photovoltaic power generation system, and provide the necessary technical support for the study of the impact of photovoltaic power distribution network.

ACKNOWLEDGMENTS Project Supported by QingHai Province Key

Laboratory of Photovoltaic grid connected power generation technology( NO. 2014-Z-Y34A).

REFERENCES [1] Wang Fei, Yu Shijie, Su Su, et al. Study on the grid connected

photovoltaic system [J]. Chinese Journal of electrical engineering, 2005,20 (5): 72-74,91.

[2] Zhu Zhu. Multi variable system identification for process control [M]. Changsha: National University of Defense Technology press, 2005

[3] Zhou Jianliang, Wang Bing, et al. Parameter identification and output power prediction of photovoltaic array based on measured data [J]. renewable energy, 2012,30 (7): 1-4.

[4] Liu Bangyin, Duan Shanxu, Liu Fei, et al. Photovoltaic array maximum power point tracking based on improved perturbation observation method [J]. Journal of electrical engineering, 2009,24 (6): 91-94.

[5] Cui Yan, Cai Binghuang, Li Dayong, et al. A comparative study on the MPPT control algorithm of the solar photovoltaic system [J]. Journal of solar energy, 2006,27 (6): 535-539.

[6] Wang Ling, Li Peiqiang, Li Xinran, et al. Modeling of micro power supply and its application in micro grid simulation [J]. electric power system and its automation, 2010,22 (3): 32-38.

[7] Liu Bangyin, Duan Shanxu, Kang Yong. Performance evaluation of high energy efficiency DC modular photovoltaic power generation system [J]. Journal of solar energy, 2008,29 (9): 1107-1111.

[8] Li Guangkai, Li Gengyin, Liang Haifeng, et al. Study on transient characteristics of photovoltaic grid connected system based on voltage source converter [J]. Journal of solar energy, 2007,28 (7): 715-720.

[9] Wang Haining, Su Jianhui, Ding Ming, et al. Photovoltaic grid connected power regulating system [J]. proceedings of the Chinese society of electrical engineering, 2007,27 (2): 75-79.

[10] Zhang Yangfei, Yuan Yue, Hao Sipeng, et al. Parameter identification and experimental verification of digital PI controller [J]. electric power automation equipment, 2010,30 (11): 40-43.

381381