2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

Overcurrent Relay Curve Modeling Using Adaptive

Neuro Fuzzy Inference System

1,2 Anang Tjahjono IDepartment of Electrical Engineering

Institut Teknologi Sepuluh Nopember 2Politeknik Elektronika Negeri Surabaya

Indonesia anang. tj@pens.ac.id

Abstract-In this paper, modeling of overcurrent relay (OCR)

curves using adaptive neuro fuzzy inference system (ANFIS) are

proposed. The accurate models of OCR curve with inverse time

relay characteristics have an important role for protection

coordination of power system. Models of OCR curve are

appropriate with IEC standard. This model implements of

microcontroller AT mega 128 as digital relay and personal

computer as facility to design of OCR curve. ANFIS is developed

to OCR curve modeling with different types of membership

function and each membership function is trained for 10

iterations. Input for training to OCR curve using the load

current and current setting or IJIs. Time to opening the circuit

breaker or TCB is used as output for training of OCR curve.

ANFIS is developed using visual basic. The simulation results are

compared with different types of membership function to obtain

the optimal design of OCR curve. Moreover, the testing results

are compared with OCR curve modeling to check validation and

accuracy of the proposed model.

Keywords- Overcurrent relay, protection, ANFIS, digital relay

I. INTRODUCTION

Study of the protection requires analysis of load flow and short circuit to determine setting of the protection relay. Commonly, OCR is the most used to protection than other relays in the power systems. The OCR Operation occurs when magnitude of detected currents exceed the pickup current level. OCR is used for generator protection, transformer protection, motor protection and the other important equipment protections. In the power systems, OCR uses 2 protection devices are the primary and backup OCR to reduce damage in the equipment. OCR has the time-curve characteristic for the overload condition. The time-curve characteristic for overload condition is the inverse OCR. Based on IEC standard, there are 4 OCR curves to protection such as normal inverse, very inverse, extremely inverse and long time inverse [1-3].

Recently, the modem power system uses digital relay as protection device. Modeling of OCR is initially started with analogue relay such as electromechanical relays and static relays. Digital relay implements digital signal processor (DSP) to the protection process. DSP will increase reliability and flexibility of protection because DSP has the high speed device. In the digital relay, modeling of OCR curve use digital computer to plotting the curve and calculating time delay for

978-1-4799-6726-1I14/$3l.00 © 2014 IEEE 103

lArdyono Priyadi, lMauridhi Hery Purnomo, and lMargo Pujiantara

priyadi@ee.its.ac.id, hery@ee.its.ac.id, margo@ee.its.ac.id

different setting. Modeling OCR curve uses two methods are the direct data storage and software model. Refers to IEEE std. C3 7.112 does not require mathematical equation to OCR modeling. These methods have the problem is requires a large amount of data in the computer memory for different settings and need for storing [4-14].

Modeling based on conventional mathematical tools is not well appropriate for dealing with ill-defined and uncertain systems. The fuzzy logic (FL) can be used to solve complex and uncertainty problems such as classification, database management, automatic control, modeling, time-series prediction, signal processing. Moreover, Neural network (ANN) is applied to modeling of OCR curve but it requires computational effort to modeling. Therefore, the integration FL with ANN is possible to modeling OCR curve. Integrating the AI methods between FL and ANN, is known as ANFIS. Membership functions and rules base are learned by neural network to obtain modeling with accurate and good [15-18].

To overcome the aforementioned weaknesses, in this paper, we work focus on modeling of OCR curve using ANFIS. Modeling of OCR curves implements microprocessor as digital relay. Microprocessor uses microcontroller ATmega 128. This feature is suitable to implement of digital relay modeling.

II. CONVENTIONAL OCR MODELING

A. System Configuration of Digital Relay

The implementation of the digital relay is described in this section. This system to OCR curve modeling consists of microcontroller ATmega 128, the function generator, current sensor, contactor magnetic driver, keypad and USART RS-232C. The function generator has function as device for supply of currents. The currents sensor has function as device to detect over current. The currents sensor uses ACS712. MicrocontrolIer ATmega 128 has function as a device to opening the circuit breaker. The contactor magnetic driver has function as circuit breaker. Moreover, interfacing between the user and prototype uses keypad to entry data. Whereas, USART RS-232C is used to downloading the learning process that is displayed in the PC and LCD graph. Microprocessor is equipped with ADC to convert analog inputs into digital before is processed. in the Fig. 1 describes configuration system of prototype and Fig. 2, prototype of circuit breaker.

ISBN: 978-1-4799-6726-1

2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

Fig. I. System configuration of digital relay

Fig. 2. Prototype of digital relay

B. ANFIS Architecture

ANFIS is incorporation of the fuzzy inference system (FIS) mechanism is described in the neural network architecture. ANFIS architecture is assumed with two inputs x and y and one output z. ANFIS uses Takagi-Sugeno model for learning algorithm so that it obtains membership functions. The ANFIS structure consists of 5 layers with different function for each layer. Visual basic model of ANFIS is described in fig. 3. A rule set with two fuzzy if-then rules can be obtained as follows:

Rule 1: if x is Al and y is BI then fl=Plx+qly+rl

Rule 2: if x is A2 and y is B2 then f2=P2x+q2y+r2

Mechanism of sugeno model is illustrated at the fig. 3. The ANFIS architecture is shown in fig. 4, where each node in the same layer has similar function. Each node is expressed as node i in the layer I is 0/,1.

104

(1)

A1 81

x X y y

Fig. 3. Sugeno model mechanism

Layer 4

x

f

y

Fig. 4. ANFIS structure of a two inputs Takagi-Sugeno model

Layer 1: every node i in the layer is an adaptive node with node output equation as follow:

01,1 = JLA,(X), 0l,i = JLBJ-2 (Y)

for i = 1,2 or

for i=3,4 (2)

where, x or y is the input to node i. IlA and IlB is a fuzzy set associated with this node function. Outputs of this first layer are membership function values of the premise part.

Layer 2: The function of node is multiplied with incoming signals. Every node in this layer is a fixed node. The output layer declares degree every fuzzy rule. Equation in the second layer is shown as follow:

i = 1,2 (3)

The every node output represents the firing strength of rule.

Layer 3: every node in this layer is fixed node. The i - th node summing of all rules firing strength. Equation in the third layer is shown as follow:

ISBN: 978-1-4799-6726-1

2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

i= 1,2 (4)

The every node output in this layer represents normalized firing strengths.

In the fourth layer, every node is an adaptive node. Every node is multiplied with p, q, r parameter. Equation in the fourth layer is shown as follow:

(5)

where w; is the normalized activation degree from layer 3 and (p;,q;,r;) are the parameter sets of this node, which are referred as consequent parameters.

Layer 5: the single node in this layer is a fixed node. The single node computes the overall output as summing all of inputs. Equation in the fifth layer is shown as follow:

(6)

Thus, the first to fifth layers can construct adaptive network that has precisely the same function as takagi sugeno model.

C. Conventional Overcurrent Relay Characteristic Curve

Modeling Using ANFIS

Modeling of OCR curve with mathematical model can be obtained with lLiIS and time dial setting (TDS). lLiIS is the load current divided by the current setting. The conventional OCR curves have some different characteristics such as nonnal inverse, very inverse, extremely inverse and long time inverse. T CB is the time to opening the circuit breaker that can be obtained with different characteristic equations, respectively as folIows:

(7)

(8)

105

(9)

(10)

ANFIS can be used to solve complex and uncertainty problems such as classification, database management, automatic control, modeling, time-series prediction, signal processing. In this paper, ANFIS is used to OCR curve modeling. ANFIS has some stages for modeling. The first stage is modeling of OCR curve with data training input. Data training can be made into a table with Idls as inputs and TCB as output.

The second stage is the initialization process of ANFIS. The initialization process comprises the third process are in the premise parameter uses fuzzy clustering mean (FCM), the improvement process of consequent parameter uses least square estimator (LSE) and the improvement process of the premise parameter uses gradient descent back propagation. The improvement of premise parameter and consequent parameter is conducted until iteration exceeds maximum iteration or error is smaller than the determined error. ANFIS with various membership functions can be shown, respectively in the Fig. 5, Fig. 6, Fig. 7 and fig. 8.

lUIS

W2

lUiS

W2 W2Q

Fig. 5. ANFIS with I input, 2 membership function and 1 output

IlllS

IlllS �==:::;j

W3

Fig. 6. ANFlS with 1 input, 3 membership function and 1 output

T cb

Tcb

ISBN: 978-1-4799-6726-1

2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

lUIS

Teb

Fig. 7. ANFIS with I input, 4 membership function and I output

lUIS

Fig. 8. ANFTS with 1 input, 5 membership function and 1 output

The third stage, the learning result of ANFIS is programmed to microcontrolIer ATmega128. Downloading to microcontroller is conducted repeat to appropriate vanous membership functions and types of IEC standard.

III. RESULT AND ANALYSIS

Inputs for training of ANFIS to OCR curve modeling can be shown in the Fig. 9. Input uses IdIs and TeB as data to be trained. ANFIS is developed with different types of membership function and each membership function is trained for 10 iterations. ANFIS is developed by using visual basic. Sample data for training ANFIS can be shown in the Table 1. In the Table 1, sample data comprises of normal inverse, very inverse, extremely inverse and long time inverse to OCR curves modeling.

Input T raining Data by UreI t cb ($)

108

80

50

30

10

o

��

•

1.1 1.3

I

I

I

� I C4p\ure 1

• 4. � 4 '

15 1.7 2 ILllS

Fig. 9. Graphic of OCR normal inverse curve for training of ANFIS

106

A. Membership Function Types of ANFIS

ANFIS uses various types of membership function to check influence membership function of the ANFIS capability. Types of membership function consist of ANFIS with 2 membership functions, ANFIS with 3 membership functions, ANFIS with 4 membership functions and ANFIS with 5 membership functions. The detail simulation with different types of membership function and types of IEC standard to evaluate the proposed method can be shown respectively in the Fig. [10-13] and table 2. The OCR curve modeling can be inferred that error of membership function decreases when membership function increases. In the OCR normal inverse curve, membership function gives very minimum error is 1.31e-22. In the OCR very inverse curve, membership function gives very minimum error is 3.9ge-21. In the OCR extremely inverse curve, membership function gives very minimum error is 6.41e-21. Moreover, in the OCR long time inverse curve, membership function gives very minimum error is 4.18e-19. In the normal inverse, OCR curve modeling gives very minimum for all OCR curves modeling are based on IEC standard.

Input I ralnl� Uala by User

t cb (s]

100

t:lU

50

30

10

o

�

�

1.1 I.J

�ar J CaDture j

• . � �

1.5 1.7 2 illiG

Fig. 10. The graphic comparison of ANFIS output with training target (2 membership function) for OCR normal inverse curve modeling

Input Training Data by User

t cb Is]

100

80

50

30

10

o

I

�

1.1 1.3

�, • •• It

1.5 1.7 2 ILIIS

Fig. I I. The graphic comparison of ANFIS output with training target (3 membership function) for OCR normal inverse curve modeling

ISBN: 978-1-4799-6726-1

2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

Input Training Data b� User

t cb (s)

100

80

50

30

10

o

u

11

i)

U

1.3

��

1.5

. � � . jlt

1.7 2 ILIIS

Input Training Data b� User

t cb (sJ

100

80

50

30

10

o

�

��

11 1.3

�� . � � .

1.5 1.7 2 ILIIS

Fig. 12. The graphic comparison of ANFIS output with training target (4 membership function) for OCR normal inverse curve modeling

Fig. 13. The graphic comparison of ANFIS output with training target (5 membership function) for OCR normal inverse curve modeling

Table 1. Sample data from characteristic unconventional OCR curve for training of ANFIS

Normal inverse Very inverse Extremely inverse Long time inverse no

Idls (input) T CB (output) Idls (input) T CB (output) Idls (input) T CB (output) Idls (input) TCB (output)

I 1.11 67.0055 1.11 122.7273 1.11 344.679 1.11 1090.909

2 1.2 38.3237 1.2 67.5 1.12 314.4654 1.2 600

3 1.3 26.6105 1.3 45 1.3 115.942 1.3 400

4 1.4 20.7342 1.4 33.75 1.4 83.33333 1.4 300

5 1.5 17.1942 1.5 27 1.5 64 1.5 240

6 1.6 14.8236 1.6 22.5 1.6 51.28205 1.6 200

7 1.7 13.122 1.7 19.28571 1.7 42.32804 1.7 171.4286

8 1.8 11.8392 1.8 16.875 1.8 35.71429 1.8 150

9 1.9 10.8361 1.9 15 1.9 30.65134 1.9 133.3333

10 2 10.029 2 13.5 2 26.66667 2 120

Table 2. The result summary of OCR curve modeling using ANFIS with various types membership function

OCR curve 2 membership functions 3 membership functions 4 membership functions 5 membership functions error error error error

Normal Inverse 225.21994882051 56.5205410379413 36.5923796869051 1.31E-22

Very In verse 839.27027439267 211.445718062396 136.428096151581 3.99E-21

Extremely Inverse 8702.3625236488 3412.07547083326 3026.94668592747 6.41E-21

Long Time Inverse 94253.269160893 73957.6695089537 19933.9237066595 4.18E-19

B. Testing of OCR normal inverse curve in the Prototype

Testing is conducted to check validation and accuracy of OCR nonnal inverse curve in the digital relay. The test is done by giving current corresponding to IdIs .. In the figure 14, the result of testing shows that the digital relay has the OCR nonnal inverse curve matching with the output of ANFIS.

107

Moreover, in the Fig. 15, the test is conducted by glvmg current which is smalIer than the first test. In the Fig. 15, the result of the test shows that the digital relay having the OCR normal inverse curve is not as accurate as the first test because the sample data of training to ANFIS are just 10. From the test of OCR in normal inverse curve for the digital relay, ANFIS gives accurate result to OCR curve modeling.

ISBN: 978-1-4799-6726-1

2014 Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI) Makassar Golden Hotel, Makassar, South Sulawesi, Indonesia 26-30 November 2014

80

70

60

50

� u 40 .. I

30

20

10

0

T CB in PC amI T CB inlVIicl'ocontl'ollel'

0 0.5

•

•

• •

•• ••••

1 1.5 2

ILIIS

• TCB-1.0

.l\1CU

2.5

Fig. 14. The graphic comparison of ANFIS output with OCR normal inverse

curve of prototype by giving current corresponding to IJIs

70

60

50

� 40

u

.. 130

20

10

0

T CB in PC and T CB in l\ficl'ocon frollel'

0 0.5 1 1.5

ILIIS

-TCB-1

- t-l\1CU

2 2.5

Fig. IS. The graphic comparison of ANFTS output with OCR normal inverse curve of prototype by giving smaller current.

IV. CONCLUSION

In this paper, ANFIS has function for modeling OCR curve based on IEC standard. ANFIS is developed with different types of membership function and each membership function is trained for 10 iterations. ANFIS is developed by using visual basic. The simulation results can be inferred that that error of membership function decreases when membership function increases. In the normal inverse, OCR curve modeling gives very minimum error for all OCR curves modeling based on IEC standard. Moreover, the test result of OCR normal inverse curve in the digital relay shows that when the test is done by giving current corresponding to IJIs in the input of training ANFIS, the digital relay has the OCR nonnal inverse curve matching with the output of ANFIS. Whereas, when the test is done by giving smaller current, the prototype is not as accurate as the test that is given current corresponding to IJIs. This happens because the sample data

108

of training to ANFIS are just 10. From the test of OCR normal inverse curve in the digital relay, ANFIS gives accurate result for OCR curve modeling.

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[16] Goh, Y.L., Ranasany, A.K., Nagi, F.H., Abidin, A.A.Z., "DSP based Overcurrent Relay Using Fuzzy Bang - bang Controller", Microelectronics Reliabillity pp. 2366-2373, 2011

[17] Sengul, M., pzturk, S., Yoriikeren, N., "Obtaining Characteristic Curves and modeling in Neural Network of An Under / OvervoItage Relay", IEEE Bologna Power Tech Conference, June 23-26 2003

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ISBN: 978-1-4799-6726-1