45

CHAPTER-IV

NN-FL TECHNIQUE TO SELF TUNE THE

PARAMETERS

46

CHAPTER-IV

NN-FL TECHNIQUE TO SELF TUNE CONTROLLER

PARAMETERS

4.0. INTRODUCTION

HVDC finds a major application in long transmission system.

Current in the plant increases rapidly whenever an irregularity

occurs. Abrupt increase in current could both damage the system and

also affect its efficiency. To avoid this damage and to maintain the

current in normal values PI (Proportional – Integral) controllers are

used. Alteration of the controller constraints is the preliminary option

to retain systems consistency. PK and IK are the variable parameters.

An expert hybrid technique incorporating FL and NN is adapted to

control these parameters.

The deviation in current value and degree at which this error

changes are acquired from the system current values. By using these

error and rate values, fuzzy logic is used to calculate the fuzzy gain

and by giving fuzzy gain as input to the neural network, proportional

and integral gain are obtained as its output. For the operation of FL, a

fuzzy rule base is formulated. Then the fuzzy gain is given as input to

the neural network and corresponding proportional and integral gain

values are obtained as its output. By using this proportional and

integral gain the PI controller makes the system to remain stable. The

47

detailed process of self tuning the PI controller parameters using this

hybrid technique is described in the following section.

4.1. HVDC TEST SYSTEM

Figure 4.1 HVDC Test System Ref [27]

Figure 4.2 Details of ac System Representation on Either Side

Test system in Fig4.1 is adapted from Ref[27] with slight

modifications The sending end of the system consists of a constant

voltage and constant frequency source behind a reactance and ac

filters of fifth, seventh, eleventh and thirteenth harmonics are

included to produce a pure signal. SCR at the sending end is

maintained quite large. Then AC to DC converted power is exchanged

over the dc connector which consists of a large smoothing reactor and

a twelfth harmonic filter which reduces the ripple content. The

48

receiving end consists of a similar ac source as that at the input side

but with a very low SCR. Fault may occur at any point of the

transmission system during transfer of power from sending terminal

to the receiving station. Faults may affect terminal stations. Here, the

faults that occur both in the rectifier side as well as the inverter side

are considered.

4.2. FAULTS IN A HVDC SYSTEM

Faults arise as a result of unexpected variations in voltage and

current due to some interruptions along the dc line. Apart from

interrupting the system, these faults also influence functioning of the

system by and large. The following faults are more frequent with a

HVDC system.

(i).Single Line to Ground Fault

This is a recurrent fault in power transfer system. This fault is a

short circuit between line and ground.

(ii). Line to Line Fault

This fault arises between transmission lines. A short among any

two lines is treated as a fault.

4.3. ERROR AND RATE CALCULATION

On reducing the sending end network of the HVDC system in to a

thevenin‟s voltage behind a thevenin‟s impedance the current

supplied to the dc system under normal condition is equal to 1 KA. So

a reference current value ( rI ) of 1 KA is considered in the problem. If

the current is above 1 KA or below 1 KA then it can be inferred that a

49

fault has occurred in the system. Once fault initiates, the immediate

step is to make a note of the variation in current. The deviation of

fault current with respect to reference is termed as error. The error in

the value of Current and degree at which current varies are

calculated using these equations.

nr III ----------------------------(4.1)

T

II p

----------------------------(4.2)

).( IGE ----------------------------(4.3)

)( . 1

GR ----------------------------(4.4)

where, rI ,the reference current , nI - measured current,

pI - previous

value of error, T , sampling rate, G and 1G are the gains for

normalization, E is the error and R is the rate. If the system current

and the reference value are the same, then the E is zero and, E varies

as per the variation in current. After calculating the error and rate

values the next step is to apply these values to fuzzy logic and

generate fuzzy rules to obtain the fuzzy gain.

4.3.1. Generating Dataset to Train Fuzzy Logic system.

The fuzzy rules are generated using error in current and rate at

which it deviates from the reference. For deriving required set of data,

50

initially a current dataset maxminminmin ,,2,, IIIIIII qq is acquired

within the range [ maxI ,minI ]. Here, the Imax is 2.5 KA and Imin is 0 KA.

For generating training dataset different current values are taken

between 0 to 2.5 KA. After generating current dataset, the deviations

in current and rate of deviation are found out for each current value.

The error dataset is termed as nEEE ,,, 21 and the rate dataset is

termed as nRRR ,,, 21 . After calculating the error and rate values, by

applying fuzzy logic we get fuzzy gain values as the output. Then by

using the training dataset the fuzzy rules are devised.

4.3.2. Generation of Fuzzy Rules

After generating training dataset next step is to devise a fuzzy rule

base. Input variables are fuzzified into three sets namely, large,

medium and small and the output variables are fuzzified into five sets

namely, very large, large, medium, small and very small. By using

these sets fuzzy rule are engendered. The fuzzy rules developed are

put in Table-4.1.

In the formulated technique a triangular symmetrical MF is used.

Fuzzy rules are generated by considering both normal and abnormal

conditions. Under non contingencies no function is carried by FL.

Under abnormal conditions based on the error and rate values a

corresponding fuzzy gain is obtained as the output.

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Table 4.1 FUZZY RULES

S.No Fuzzy rules

1

2

3 4

5

6 7

8

9

if, E=large and R=large, then G=very

large

if, E=large and R=medium, then G=large

if, E=large and R=small, then G=small

if, E=medium and R=large, then G=large

if, E=medium and R=medium, then

G=medium

if, E=medium and R=small, then G=large

if, E=small and R=large, then G=small

if, E=small and R=medium, then G=large

if, E=small and R=small, then G=very

small

Fuzzy Logic is trained by adapting the above devised rules.

During an abnormality the error may be either large or small. Based

on the error value, adjusting appropriate parameters by the PI

controller is essential to make the system remain stable.

4.3.3. Obtaining the Fuzzy Gain

As a first step, fuzzy logic is trained with the generated dataset, by

employing the devised fuzzy rules. After training, if any error and rate

52

value as given as input, the fuzzy gives the corresponding fuzzy gain

as output.

4.4. OBTAINING CONTROLLER PARAMETERS USING NN

The role of Neural Network is used to calculate the proportional and

integral gains. The input to NN is fuzzy gain and its outputs are

and . A Back propagation algorithm is applied to train the NN.

Training and testing procedures are elaborated in the next few

sections. For practical applications, neural network is trained once

and testing can be done any number of times.

4.5. Structure of Neural Network Used in NN-FL Technique

Back propagation algorithm is utilised to train the neural network.

In our method the role of this intelligent network is to calculate

and gains of the controller. A manipulation over these values

improves the system stability.

The network used in our method consists of one node in the input

layer, n nodes in the hidden layer and two nodes in the output layer.

The Fuzzy gain is transferred to the input layer and the outputs from

the Neural Network are the numerical values of proportional and the

integral gains. Configuration of Neural Network applied in the present

problem is shown below.

53

Figure 4.3: Single input - two output neural network employed for

obtaining Kp and KI

4.5.1. Training the Neural Network

For training, initially an input dataset is chosen and a common

weight is assigned for the hidden layers. A similarity check is made

between the obtained and target outputs. Then, by error applying

Back Propagation, weights from the output level to the hidden layers

are first modified. Then adjustment of weights from invisible level to

the input level is done to achieve the required output. This process of

comparison with the necessary output and modifications of the

weights are repeated until the network reaches the requisite output.

The Neural Network obtained by applying these initial weights is

shown in Figure 4.3.

w11n

y1

w2n2

w2n1

w212

w221

F2n

F21

F22

F31

F32

w211

w222

F1

y2

x

w111

w112

Input layer Hidden layer Output layer

w11n

y1

w2n2

w2n1

w212

w221

F2n

F21

F22

F31

F32

w211

w222

F1

y2

x

w111

w112

Input layer Hidden layer Output layer

54

Figure 4.4 Neural Network after the Application of Initial Weights

Various stages in training NN are:

Step 1: Initially assign weights to the neurons of the input layers.

Step 2: Apply the obtained training dataset to NN. Here x is the input

and 1y and 2y are outputs of NN.

)(1

1

121 ryWyn

r

r

------- (4.5)

)(2

1

222 ryWyn

r

r

------- (4.6)

)exp(1

1)(

11 xwry

r ------- ( 7.4 )

0.5

y1

0.9

1

0.8

0.6

F2n

F21

F22

F31

F32

0.2

0.1

F1

y2

x

0.3

0.7

Input layer Hidden layer Output layer

55

Equations 4.5 to 4.7 show the activation functions performed in the

input and output layers respectively.

Step 3: Determine the value of and using equations 4.5 and 4.6.

Step 5: Find out the error of the network after one pass and propagate

that error backwards.

Step 6: This process is carried out until the error reaches a minimum

value.

After training is completed, the network becomes fit for practical use.

The process involved in the above steps is drawn in the form of flow

chart as follows:

56

Figure 4.5 Flow Chart to train a neural network

57

4.6. FAULT CLEARANCE

During normal condition the system current equals the reference

value 1 KA. When a fault occurs in the system, the current increases

rapidly and reaches a value in between 2 and 2.5 KA and is calculated

using equation 4.3 and 4.4.To reduce this current PI controllers are

used.

4.6.1. PI Controller

Figure 4.6 PI Controller

The PI controller shown in Figure 4.6 substitutes the values

obtained from the proposed technique and outputs a current to the

HVDC test system of Figure 4.1. at every time instant considered.

Variations in the values of pK and iK results in a change of controller

output. Iout can be attained from eq‟n 4.8 as.

T

Ipout dtEKEKI0

(4.8)

Where , outI is the current,pK and IK ,the proportional and integral

gains and E is the error.

If the current calculated from equation 4.8 deviates from the

value under normal system conditions, the process is repeated and

another set of proportional and integral gains are found from the

_

+

+

+

Ir

In

E

EKpP

dtEKI I

∑ Process ∑

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proposed technique obtained and substituted in eq‟n 4.8. This

procedure is iteratively done till current returns to normal value.

In the presented work, the faults mentioned in section [4.2] on

either ac sides are considered. In inverter side, the maximum fault

current that occurs is 2 KA and for section 4.2 maximum current that

occurs is 2.5 KA. In rectifier side, the same highest fault currents of

1.5 KA occur for both the type of faults mentioned in 4.2.

4.7. SIMULATION ANALYSIS

Up to the preceding sections implementation of the proposed

technique‟ s carried out. Now the controller with the proposed features

is ready for practical use. So, the developed model has been tested by

subjecting the system to the faults mentioned in section 4.1 and the

analysis of Voltage and current waveforms is presented. The following

cases have been studied.

4.7.1. DC Line to Line Fault at the Inverter

This kind of fault is the severest fault when the connected AC

system is weak .The nature of the fault is balanced but most critical

due to low SCR of the inverter bus as the total power injection

becomes zero. The dc power oscillations may give rise to uncontrolled

dv/dt and di/dt stresses on the converter thyristors. Also oscillations

in the inverter ac bus voltage may be detrimental to the loads at the

inverter end .As seen from the figures the conventional PI controller

makes the system oscillate even after the fault is recovered .These

59

oscillations were minimized with the adapted technique. A 5.2 cycle dc

line to line fault at the inverter is simulated at the inverter bus at 0.5

cycles. From the graph of the inverter dc voltage we can observe that

the number of commutation failures are reduced a lot compared to

that of the conventional and fuzzy logic controller. Figures 4.10(a, b,

&c), 4.11(a,b and c) and 4.12(a, b& c) represent the currents and

voltages for this case.

4.7.2. Single Line to Ground Fault at the Inverter

A single line to Ground fault has been simulated at the inverter

end ac system for about 2.5 cycles. The inverter end ac system being

weaker this kind of fault results in sudden voltage collapses on all the

phases leading to commutation failures and other difficulties in

converter operation. This also leads to unbalanced operation of the

converter even after the fault is cleared. The inverter dc voltage

showed the resultant commutation failures. The firing instants are

now uncertain and inverter extinction angle loses control over the

HVDC link current recovery. The converter current regulator mostly

influences the transient performance under these conditions.

Therefore the comparative study of various controllers shows a

substantial difference in their performance. The proposed technique

exhibited best performance in terms of transient recovery, as the

damping is much faster. Figures 4.7(a, b, &c), 4.8(a ,b and c) and

4.9(a, b& c) represent the currents and voltages for this case.

60

4.7.3. Single Line to Ground Fault at the Rectifier

A 2.5 cycle Single Line to Ground Fault is created at the rectifier ac

bus. Oscillations in current are reduced.AC side SCR is very high .So

the waveforms are least affected by the controller actions. There isn‟t

any reversal of power. Figures 4.13(a, b, &c), 4.14(a, b and c)

represent the currents and voltages for this case

4.7.4. Line to Line Fault at the Rectifier

Variation of the dc link current , rectifier side dc voltage are shown

in figures 4.15(a, b &c) and 4.16 (a, b &c) for a 2.5 cycle fault at the

rectifier bus after the inductor at o.5 sec. The dc bus Voltage

completely collapses and may lead to commutation failure. During

fault the dc link current drops to zero and this condition leads to

complete de-energisation of the dc link .and current wave forms are

not much affected by the controller. Response resulting from the

proposed technique result in fewer oscillations as compared to

conventional and fuzzy controllers.

61

Figure 4.7(a) Dc Link Current for Single Line to Ground Fault

at the Inverter for Conventional Controller

Figure 4.7(b) Dc Link Current for Single Line to Ground Fault at

the Inverter for Fuzzy Controller

62

Figure4.7 (c) Dc Link Current for Single Line to Ground

fault at the Inverter for FL-NN Technique.

Figure 4.8(a) Rectifier end Dc Voltage for Single Line to Ground

Fault at the Inverter for Conventional Controller

63

Figure 4.8(b) Rectifier end Dc Voltage for Single Line to

Ground Fault at the Inverter for Fuzzy Controller

Figure 4.8 (c) Rectifier end Dc Voltage for Single Line to

Ground Fault at the Inverter for FL-NN Technique .

64

Figure 4.9(a) Inverter end Dc Voltage for Single Line to Ground

Fault at the Inverter for Conventional Controller

Figure 4.9(b) Inverter end Dc Voltage for Single Line to Ground

Fault at the Inverter for Fuzzy Controller

65

Figure 4.9 ( c) Inverter end Dc Voltage for Single Line to Ground

Fault at the Inverter for FL-NN Technique

Figure 4.10(a) Dc Link Current for Line to Line Fault at the

Inverter for Conventional Controller

66

Figure 4.10 (b) Dc Link Current for Line to Line Fault at the

Inverter for Fuzzy Controller

Figure 4.10( c ) Dc Link Current for Line to Line Fault at the

Inverter for FL-NN Technique

67

Figure 4.11(a) Rectifier end Dc Voltage for Line to Line Fault

at the Inverter for Conventional Controller.

Figure 4.11(b) Rectifier end Dc Voltage for Line to Line Fault

at the Inverter for Fuzzy Controller.

68

Figure 4.11 (c) Rectifier end Dc Voltage for Line to Line Fault

at the Inverter for FL-NN Technique.

Figures 4.11 (a,b and c) represent the rectifier side dc voltage during

the fault clearing process when the test system is subjected to a line

to line fault at the inverter side.

69

Figure 4.12(a) Inverter end Dc Voltage for Line to Line Fault at

the Inverter for Conventional Controller.

Figure 4.12(b) Inverter end Dc Voltage for Line to Line Fault

at the Inverter for Fuzzy Controller.

70

Figure 4.12(c) Inverter end Dc Voltage for Line to Line Fault at

the Inverter for FL-NN Technique .

Figures 4.12 (a, b and c) shows the performance comparison between

(1) conventional, (2) the fuzzy-based and (3) the hybrid PI controller

self tuning technique in clearing line-to-line fault at inverter.

Figure 4.13(a) DC link Current for Single Line to Ground Fault

at the Rectifier for Conventional Controller.

71

Figure 4.13(b) DC link Current for Single Line to Ground Fault

At the Rectifier for Fuzzy Controller.

Figure 4.13(c DC link Current for Single Line to Ground Fault

at the Rectifier for FL-NN Technique .

72

Figure 4.14(a) Rectifier end Dc Voltage for Single Line to Ground

fault at the Rectifier for Conventional Controller

Figure 4.14(b) Rectifier end Dc Voltage for Single Line to

Ground fault at the Rectifier for fuzzy Controller.

73

Figure 4.14(c ) Rectifier end Dc Voltage for Single Line to

Ground fault at the Rectifier for FL-NN Technique

Figures 4.14(a,b and c)epresent rectifier side dc voltage when the test

system is subjected to a single line to ground fault at the rectifier end

for the tree controllers.

Figure 4.15(a) DC link Current for Line to Line fault at the

Rectifier for Conventional Controller

74

Figure 4.15 (b) DC link Current for Line to Line fault at the

Rectifier Controller

Figure 4.15 (c) DC link Current for Line to Line fault at the

Rectifier for FL-NN Technique

75

Figure 4.16(a) Rectifier end Dc Voltage for Line to Line fault

at the Rectifier for Conventional Controller

Figure 4.16(b) Rectifier end Dc Voltage for Line to Line fault at

the Rectifier for Fuzzy Controller

76

Figure 4.16 (c) Rectifier end Dc Voltage for Line to Line fault at

the Rectifier for FL-NN Technique.

Figures 4.16(a,b,c) is an assesment of (1) conventional, (2) the fuzzy-

based and (3) the hybrid PI controller self tuning technique in

overcoming dc line-to-line fault at rectifier.

77

Table 4.2 Comparison of Fault Clearance times for Conventional,

Fuzzy & Fuzzy-NN Controllers

4.8. Discussion and Conclusions

The discussed methodology was programmed in MATLAB 7.10 and

its operation, simulated. A comparison of the traditional self tuning

technique with that of a fuzzy based technique and a neural network-

fuzzy logic based technique is shown in the above graphs by making a

Type of the Fault

Parameters

Fault Clearance Time for

Adopted Controller in Sec

Conventional Fuzzy Fuzzy-NN

SLG Fault at the

Inverter

IDCr

VDCr

VDCi

o.4

0.54

0.54

0.35

0.44

0.42

0.09

0.1

0.11

LL Fault at Inverter IDCr

VDCr

VDCi

0.5

0.49

0.54

0.4

0.44

0.49

0.12

0.1

0.1

SLG Fault at the

Rectifer

IDCr

VDCr

0.49

0.49

0.44

0.44

0.06

0.11

LL Fault at Rectifer IDCr

VDCr

o.47

0.49

0.44

0.44

0.12

0.11

78

comparison of the currents and voltages at the inverter and rectifier

for the considered faults.

A NN-FL technique to self tune the variables of the PI Controller in

a HVDC system, is implemented. When a fault occurs in the system

the current and voltage increases and by using this hybrid technique,

the system voltage and current can be made to return to their stable

values within a fraction of a second. The functioning of the system can

be assessed from the obtained results. The implementation results

showed that the fault clearance time of the hybrid technique is very

low compared to conventional methods and fuzzy based self tuning

methods. Thus it has been proved that the implemented methodology

improved controlling of HVDC systems effectively than traditional self

tuning methods. The same conclusions are mathematically verified

from the fault clearance times tabulated in table 4.2 for the

considered faults at the inverter and rectifier sides for various

currents and voltages.