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.2012 China Inteational Conference on E[ectricit Distribution (CCED 2012) Shanghai, 5- 6 Sep. 202

D-STATCOM Control based on Self-tuning PI with NeuralNetworks

YU LeiNCEP universit-China

ylei@1 6 3.com

ZHANG JiahuaNCEP universit-China

E-mail address

JANG ChengNCEP universit-China

jc_2002@ 63.com

Abstract-Powe diiuion yem inlled wih D

STTCOM Diiuion Si Synhonou Compeno)i lgele nonline, mulivile yem. The

diionl PI onolle i no onen o moden onol

equiemen eue of i xion pmee. In hi ppe,

node volge onol egy of DSTTCOM ed on elf

dpive PI onolle wih neul newok i popoed, ndhe onolle uue i deigned. In dynmi poe, PI

pmee of he node volge onolle e online

djued wih neul newok. Thu, hi onolle i good

oune nd dpiliy. The imulion model of he

onol yem i uil in he MTLB dynmi imulion

plfom, ed on whih he eguling poe of noe

volge i imuled. The eul veied he dpiliy nd

feiiliy of he popoed onol egy

KrsDSTTCOM; Selfuning PI onol; Neul

Newok; Noe volge onol

NN

As a compensator device installed in parallel to thetransmission line, D-ST ATCOM was introduced toreactive power compensation eld with faster responseand higher control precision compared to the traditional

reactive power compensation device ].

Node voltage control is one of the main nctions of DSTA TCOM, and choosing a good control method is

pivotal to show its effect. P control is wildly applied toD-STATCOM because of its perfecting theor, clearconcept, convenient adjustment and easy realization 2].However the power distribution system installed with D

STA TCOM is a large-scale nonlinear, indeterminationand multivariable systems; the structure and parameters ofsystem will change especially when systems failure andload disturbance happens accidentally. Therefore the

traditional P controller has a limited application in somecases because of its non-adaptive parameters and linearit.

n order to solve this problem, a self-tuning P controlstrategy with zz logic is proposed 3], due to the P

parameters are regulated on line using zz logic, thecontroller with the proposed strategy is adaptive and

robust. But the zz variable grade and zz rule aredifcult to con in some cases. Besides, choosing the

membership nction is devoid of academic guidance. npaper 4], the indirect voltage control method forSTATCOM based on self-tuning P with neural networkis proposed. This control method has a limited applicationin nonlinear systems, even if it could regulate P

parameters on line, because its PI parameters tuning isunattached and linear.

By completely considering the characteristic of thepower distribution system and the control method, thispaper propose a node voltage control strategy for DSTATCOM based on self-adaptive PI control with neural

network. Due to the PI parameters could be regulated online in the dynamic process the node voltage of the

power distribution system is exibly, real-timely andquickly controlled using the proposed controller in this

paper.

SMLL OWER SBN SYSTEM WTHD-STTCOM

D-STATCOM is a device installed in paallel to thedistribution system. Its principle can be described as: itinject the node Bus a current whose amplitude is

changeable independent of the node Bus voltage, andphase is perpendicular to the node Bus voltage's phase.Changing the cuent's amplitude is equivalent to change

node Bus voltage, with which it can control the Busvoltage. However in practice the current is not strictlyperpendicular to the node Bus voltage, the difference isused for compensating the loss of D-STATCOM.

Fig. illustrates a power distribution system installedwith D-STATCOM 5].

y

Figure 1. A small power distribution system with D

STATCOM

Where U represents a voltage source, D-STATCOM is

installed in parallel to the distribution system Bus B.

Fig.2 illustrates the control block diagram of DSTATCOM in power distribution systems. Wherevoltage calculate' is employed to give the current voltageamplitude by the metrical voltage of Bus B. Thedifference between the current and given voltageamplitude is compensated by PI controller whose output

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7/27/2019 D-STATCOM Control based on Self-tuning PI with Neural Networks

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012 Cha nteatna[ Conference on E[ectrct Dstrbutn (CCED 2012) Shanghai, 5- 6 Sep. 202

is applied to drive the SHPWM as a modulation ratio.

p sm

-H Ii-D

bc

Figure Control block diagram of D-STATCOM

The control rule of PI regulator is shown in (1).

M = Kpl + l )(Ure! - U) (ITaking into account the voltage margin of the main

circuit, especially the switching devices can not be toolarge. f the DC lik voltage is too high, it will affect thesafet of the main circuit, especially the switching devices.So the DC capacitor voltage control is needed in order toensure that the switching devices are in the range ofsecurit in a variety conditions. From the literature 6],changing the phase angle of voltage emitted by DSTA TCOM could change the DC bus voltage. Therefore

in this paper is selected as control variable, regulating

which the DC bus voltage could be limited to a desiredrange. The control rule of Be is shown as:

c = Kp2 + 2 )(V;c-re! -VdJ - ; 9 (2)Where Kp and Ki is the proporion ratio and integral

ratio of PI controller, respectively. Ure! and U is thegiven and current values of Bus B voltage amplitude,

respectively. Vde-rj and Vdc is the given and current

values of DC lik voltage, respectively.

the modulation ratio and is initial phase of PWM.

SELF-NNG CONTROLLER WTH NERL

NETWOKSArticial Neural Networks because of its strong non

linear tting and relatively simple leaing algoritm hasa growing number of applications in industrial controls.

n this paper, two neural networks are employed toachieve the adaptive PI control. The one is applied to

model the power system with a D-STATCOM, and theother to adjust the parameters of PI controller for DSTATCOM on line. The control system structure isshown in Fig.3.

u

Figure 3 Control block diagram of neural networkself-tuning P

where NNI is the identication network and NNT is thetuning network.

Nua Ntwok ntfaton NNWhen applying neural network in system identication,

the rst thing we conceed is the architecture of theneural network, and the pivotal step is to decide the inputvector of the neural network. Aer that, rst option is thefeed forward neural network (FNN) and the back

propagation training algoritm to solve the systemidentication problem.

In this paper, a neural network is employed to modelthe power system with D-STATCOM. The neuralnetwork is of the feed forward tpe and has tree layersconsisting of an input layer with twelve inputs, a single

hidden layer with sigmoid activation nctions consistingof twent neurons and an output layer with two outputs.

The inputs of the NNI are Be , U and Vde attimes and and outputs U and Vdc at time +I .For training NNI, inputs of plant were perturbed usingPseudorandom Binary Signals (PRBS). The amplitudes of

PRS signals applied to U , Vde , and Be arelimited to 10% of their main signal. The network strctureis shown in Fig. 4.

ow U dbony

U(t + 1)

A

V 1)

Figure Structure of identification network

Where DEL represents delay'.

In this paper, the objective nction including a

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012 Cha Inteatnal Conference on Electrct Dstrbutn (CICED 2012) Shanghai, 5- 6 Sep. 20I2

discount factor forNI is shown as:

={A[U(t) - U(t)]+( - A)[Vdc(t) -Vdc(t)]}2 (3)Where (05 < A < 1) is a discount factor

Based on the Steepest Descent Algoritm, the adjustingrule ofNI is shown as follows 7 ]:

awIC+l=w1C-7I (4)

8wICaw2(+l)=w2() -7I (5)

8w2()Where WI is the 2 x 2 weight matrix belonging to hiddenand output layer, W2 is the 20 x 12 weight matrix

belonging to input and hidden layer, (0 < 1 < 1) is the

leaing rate.

When the training is complete, the plant outputs

U(t+) andVdc(t+) , and the Jacobian parameters i.e.au I aM and avd/ can be obtained which ae

applied in the PI parameters tuning network.

Nua Nok Tunng CNNTIn this paper a neural network is employed to adjust

the PI parameters on line. The neural network is of thefeed forwad tpe and has tree layers consisting of aninput layer with six inputs, a single hidden layer with

sigmoid activation nctions consisting of twenty neuronsand an output layer with four outputs.

The inputs of the NT are U and Vdc at times t t-Iand t- and outputs Kp' Kp' K' K at time + 1

TheNT is training on line using the differences betweenthe reference values and prediction values at time t+Ioutputting by NI, and the Jacobian parameters i.e.

au I aM and avd/ at time t indirectly given by NI.The network structure is shown in Fig. 5.

Figure 5 Structure ofP parameters tuning network

In this paper, the objective nction including adiscount factor forNT is given as:

={[(Urej -(t+)]+(-)[(dcrej - c(t+)]}2 (6)Where r ( 0 5 < < I) is a discount factor

Based on the Steepest Descent Algoritm and PIalgorit, the adjusting rule ofNT is shown as follows8]:

w3C+=w3C -

(aJr aU(t+l)

+aJr aVdc(t+l)

) (8)72aU(t+l) aw4c aJt+l) 8w4C

Where W3 is the 4 x 20 weight matrix belonging tohidden and output layer,

W4is the 20x 6 weight matrix

belonging to input and hidden layer, ( 0 < < 1) is

the leaing rate.

SML ON SLTS

In order to veri the adaptabilit and feasibilit of theproposed controller, the simulation model shown in Fig.is built in MATLAB. The simulation paraeters areshown in Table 1.

Table 1. Parameters of the simulation model

ParametersBs UB (pu)

DC ink otage(pu)eqUiaent indctance L (pu)eqUiaent resistance (pu)

synchronization angar eocityearning rate earning rate

discontfactor Adiscontfactor

vluO

0.5

0.0058

0.06

314

0.1

0.3

0.7

0.7

Fig.6 and Fig.7 are the regulation process of node Busvoltage when the power distribution system suffer smalldisturbance. Evidently, the self-tuning PI controller issuperior to the traditional ones in the response speed.

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