Paper Study the Operation of Wind/Photovoltaic

8/14/2019 Paper Study the Operation of Wind/Photovoltaic

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Operation and Control Strategy ofPV/WTG/EU Hybrid Electric Power System

Using Neural Networks

Faculty of Engineering, Elminia University,

Elminia, Egypt


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This paper introduces an application of anartificial neural network on the operationcontrol of the PV/WTG/EU to improvesystem efficiency and reliability.

Object of this paper


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This paper focus on a hybrid system consists o

PV/WTG interconnected with utility grid taking

into account the variation of solar radiation, Windspeed and load demand during the day. Different

feed forward neural network architectures are trained

and tested with data containing a variety of operation patterns. A simulation is carried out over one year

using the hourly data of the load demand, wind

speed, insolation and temperature at El'Zafranna

site, Egypt as a case study.


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2- System Model

2-1 Modeling of PV/WTGThe design of PV/WTG HEPS interconnected to EU

depends on dividing the load into two parts between

photovoltaic (PV) and wind turbine generator (WTG).A typical modeling of PV/WTG HEPS, in a grid-

connected situation, is shown in the following Figure

.


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S 3

S 2

S 1

E U

L o a d

B u s b a r

B u s b a r

~

S t e p - d o w n

T r a n s f o r m e r

I n p u t O u t p u t

N N f o r P V / W T G / E U

D C / D C D C / A C F i l t e rRadiatio

S t e p - u p


F i l t e r

S t e p - u p


G . B . I . G .

WindSpee

d

D C / A CA C / D C

S 4

S 5

S t e p - d o w n


Fig. 1 Layout of PV/WTG interconnected with EU and control strategy

App. And Res

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Pgtotal

=0, Ppv

(t)=0, PWTG

(t)=0

WTG DC voltage out of limitsPV DC voltage out of limits

OFFOFFOFFONOFF4

Pgtotal

< PL, P

pv(t)>0, P

WTG(t)>0

PV DC voltage within limits

WTG DC voltage within limits

ONONOFFONON

Pgtotal

> PL, P

pv(t)>0, P

WTG(t)>0



ONONONOFFON

3

Pgtotal

< PL, P

pv(t)=0, P

WTG(t)>0

PV DC voltage out of limits


ONOFFOFFONON

Pgtotal

> PL, P

pv(t)=0, P

WTG(t)>0

PV DC voltage out of limits


ONOFFONOFFON

2

Pgtotal < PL, Ppv(t)>0, PWTG(t)=0PV DC voltage within limits

WTG DC voltage out of limits

OFFONOFFONON

Pgtotal

> PL, P

pv(t)>0, P

WTG(t)=0


WTG DC voltage out of limits

OFFONONOFFON

1

Generated power vs. Load demandS5S4S3S2S1Mode


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The ANN will send an ON-trip signal to switch S4

only if the following condition is realized:

550430 dcpv

V


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Fig. 2. The daily load curves for January, April, July and

October [6].

It is assumed here that the

load demand variesmonthly. This means that

each month has daily load

curve different from other

months. Therefore, thereare twelve daily load

curves through the year.

Fig. 2 shows the daily load

curves for January, April,

July and October [6].

2-2 Load Characteristics


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X1, X2, X3, X4 and t are the Five-input training matrix which

represent DC output voltage from PV system, DC output

voltage from WTG system, AC voltage of electric utility

power, load demand, and time respectively. W(1)

and W(2)

represents the weight matrices. The network consists of five

input layers, ten nodes in hidden layers and five nodes in

output layer which sigmoid transfer function. The network has

been found after a series of tests and modifications.


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/1230This Figure shows the DC voltages from WTG

Fig. 4 DC output voltage from WTG during March, June, September

and December


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This Figure shows the DC voltage from PV system.

Fig. 5 DC output voltage from PV array during March, June,

September and December


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This Figure shows the evaluation of the 5+10+5 ANN errors.


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Fig. 7 Optimal Operation of the PV/Wind HEPS interconnected to EUto feed the load demand during December

This Figure sows the optimal Operation of the PV/Wind HEPS

interconnected to EU to feed the load demand during December

From this Fig. 7 it canbe seen that the deficit

energy has been taken

from EU and surplus

energy has beeninjected to EU

through the day,

which represents the

month of December.

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Figure 8 shows the difference between output from ANN and the


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Figure 8 shows the difference between output from ANN and the

desired output for the test data of 120 examples (Five months). These

differences are displayed for switches S1, S2, S3, S4 and S5. From

this Figure, it can be seen that the ANN of 5+10+5 operates with a

high accuracy.

Fig. 8 Relation between outputs and target for five months


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Figure 9 displays the output of the proposed ANN of 5+10+5 for month

of December using test data. This output may be 1 or 0 for each switch.

Fig. 9 Outputs of Neural Network for month of December

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From Figures 7 and 9 (December) it can be noticed that the trip signal

which produced from ANN sent to switch S1 at hours 1, 2, 3, 4, 5, 6, 7,

8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 21, 22 and 23. This means that

the PV/WTG feed the load demand at these hours. On the other hand,

switch S2 (for example) equal to 1 at hours 4, 5, 6, 7, 8, 9, 10, 11, 12, 14,15, 16, 17, 18, 19, 22, 23 and 24 This means that the EU should supply

the load demand at these hours. On the other hand, the power injected to

EU through switch S3 at hours 1, 2, 3, 13, 20 and 21. From switch S1

and S2 it can be noticed that the hybrid PV/WTG with EU feed the loaddemand at hours 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 19, 22 and 23.

The electric utility feed the load demand without PV/WTG HEPS at hour

24. From switch S4 it can be seen that the PV system feed the load

demand at hours 8, 9, 10,11, 12, 13, 14, 15, and 16 which there is no

radiation at hours 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20, 21, 22, 23 and 24. On

the other hand, the WTG feed the load demand at hours 1, 2, 4, 5, 6, 7, 9,

10, 13, 19, 20, 21, 22 and 23. Which there is no wind speed or the DC

output voltages not lay within acceptable limits of PCU at hours 8, 11,

12, 14, 15, 16, 17, 18 and 24 as shown in switch S5.


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This paper presents one possible application of intelligent

system. The ANN proposed shows the importance ofestablishing an optimized control, both in terms of the

selection of the optimal strategy, and of the relationship

between the power generated by the PV system, wind system,

EU and load profile. From the results obtained above thefollowing conclusions can be drawn from this paper:

1. A novel technique based on ANN is proposed to achievethe optimal operation control strategy of PV/WTG

HEPS. This ANN operates the PV/WTG HEPS to feed

the load demand.

Conclusions


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2. The 5+10+5 ANN is the suitable neural network for

optimal operation and control of PV/WTG HEPS at

El'Zafarana site.

3. The ANN has a very high accuracy and achieve the optimal

hour by hour operation for PV/WTG HEPS as shown in

Figures 8 and 9.

4. Using this strategy minimizes the lost time of switching

ON and switching OFF. Then, the reliability of the whole

system will be improved.


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