Digital Overcurrent Relay with Conventional Curve ... - IEEE Xplore

2015 International Seminar on Intelligent Technology and Its Applications

Digital Overcurrent Relay with Conventional Curve Modeling Using Levenberg-Marquardt Backpropagation 1,2

Anang Tjahjono

1

Dimas Okky Anggriawan, 1Ardyono Priyadi, 1Margo Pujiantara and 1Mauridhi Hery Purnomo

1

Department of Electrical Engineering Institut Teknologi Sepuluh Nopember 2 Politeknik Elektronika Negeri Surabaya Surabaya, Indonesia [email protected]

1

Department of Electrical Engineering Institut Teknologi Sepuluh Nopember [email protected], [email protected]

Abstract— Overcurrent relays (OCRs) play an important role in the protection component that requires high reliability to maintain high security for power systems. Modeling of the OCRcurve using methods like the direct data storage and curve fitting gave only approximate models. Therefore, in this paper proposes modeling of OCRs using Levenberg-Marquardt backpropagation (LMBP). An implementation of OCR in the digital OCR used ARM microcontroller STM32F407VGT6 is to improve performance of the relay significantly. LMBP is developed using different numbers of neurons. The current and opening time of the circuit breaker are used as input and output in the LMBP training. LMBP developed in the OCR curve model using sample data from protection coordination is implemented as real time in Hess Indonesia Corporation. The weights obtained by the LMBP are used to run the LMBP program in the digital OCR. The well known digital OCR product is used for comparison. The results show that this proposed method is accurate and encouraging with percentage error is 0.24% and very promising to be applied in the digital OCR. Keywords—overcurrent relay curve; protection; overcurrent; levernberg-Marquardt Backpropagation

digital

I. INTRODUCTION Protection is designed to ensure continuity of electric power supply. Protection requires analysis of load flow and short circuit for protective relay setting. OCRs are more commonly used for power system protection than other relay types. Digital OCRs have function to detect power system conditions of an abnormal and faults such as short circuit and overload. Digital OCR sends information to the circuit breaker (CB) to disconnect the affected area of a fault. In industrial power systems, OCRs use two protective device types, namely, primary and backup protective devices, to minimize equipment damage. The International Electrotechnical Commission (IEC) standard has adopted four OCR curves for protection coordination, such as normal inverse, very inverse, extremely inverse, and long-time inverse curves [1],[2],[3]. Many researchers using microprocessor, digital signal processor (DSP) or field programmable gate array (FPGA) to implement the digital OCR to increase reliability and flexibility of protection because a high-speed device. Early

researcher in the modeling of the OCR using mathematical model to obtain approximate the time-current characteristic curves for OCR. Modeling based on mathematical model is not very appropriate to deal with ill-defined and uncertain systems. Furthermore, this method requires large memory space to memory data under different settings. The IEEE std. C37.112 does not require mathematical representation from equations obtained by standard OCR modeling [4],[5],[6],[7],[8],[9],[10],[11],[12]. The neural network can be used to solve complex and uncertainty problems such as classification, database management, automatic control, modeling, time-series prediction and signal processing. Therefore, in this paper proposes modeling of OCRs using Levenberg-Marquardt backpropagation (LMBP) neural network to give accurate models. The focus of this study is to model the OCR curves using LMBP as applied in the digital OCR. The proposed OCR is implemented on ARM microcontroller STM32F407VGT6. Protection coordination is performed using the longest line of a single-line diagram in the Hess Indonesia Corporation. Thus, five types of OCRs (R-ACB-02, R-VCB-13, R-VCB-11, R-VCB-09, and R-VCB-03) are obtained. LMBP is developed from this model using the sample data (R-ACB-02, R-VCB-13, R-VCB-1, R-VCB-09, and R-VCB-03). The results under different numbers of neurons are compared to obtain the optimal OCR curve design [13],[14],[15]. II. CONVENTIONAL OCR MODELING A. Digital Overcurrent Relay Implementation The implementation of the digital OCR is described in this section. OCR curve modeling is applied in the digital OCR consists of ARM microcontroller STM32F407VGT6 as processor device for protection algorithm of digital relay, personal computer as program installing and emulation of the currents data, serial cable as interfacing between digital OCR and personal computer. ARM microcontroller STM32F407VGT6 is selected to implement relay because ARM microcontroller STM32F407VGT6 is a high performance microcontroller. It is microcontroller which operates at 167 MHz so that provides

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high performance solutions for control applications which require high accuracy and speed. ARM microcontroller STM32F407VGT6 has large memory that sufficient to store the protection algorithm code of the LMBP neural network. ARM microcontroller STM32F407VGT6 is equipped with three ADC to convert analog inputs into digital before processed by the processor. The 12 bit ADC module has a fast conversion rate of 140 ns. All these features are suitable for digital OCR implementation. The experimental algorithm of the digital OCR is shown in fig. 1. In the experimental algorithm, data sample from protection coordination in Hess Indonesia Corporation is used to training of LMBP. The load current and time of opening of the circuit breaker are used as input and output in the LMBP training. In the personal computer, the LMBP optimizes the weights with the iteration of the learning process reduces the error until the desired goal is reached. The weight values are used to run the program BPLM in the digital OCR. The result of operating time values from the LMBP process in the digital OCR is evaluated.

Refers to IEC std. 255-3 [19], characteristics of OCRs are represented as in eq. (1). Different types of inverse characteristics can be obtained by varying α and C values as described in table 1.

t=

C α

⎛ I ⎞ ⎜ ⎟ −1 ⎝ IS ⎠

xTDS

(1)

Information t is the relay operation time, C is the constant for relay characteristics, TDS is the time dial setting, Is is the current set-point, I is the current detected by relay, I>IS, α is the constant representing inverse time type, α >0. Table 1. Parameters for different inverse OCR characteristics Inverse OCR characteristics

Α

C

Standard inverse

0.02

0.14

Very Inverse

1

13.5

Extremely Inverse

2

80

Long Inverse

1

120

C. Lavernberg Marquart Backpropagation Architecture LMBP is used to determine the operation time for the OCR by using neural network approach than using inverse time equation for conventional OCR. LMBP requires the relevant data to build the inputs and outputs for training. The load current and time of opening of the circuit breaker are used as input and output in the LMBP training. Protection coordination is implemented using the longest line of the single-line diagram in Hess Indonesia Corporation to obtain the relevant data to the LMBP training.

G

In the LMBP method, the change (Δ ) in the weights (ω ) are obtained by solving

1 2

α Δ = − ∇E

(2)

Information E is the mean-squared network error

E=

1 N

N

G

G

∑[ y( x ) − d k

]2

k

(3)

k =1

Information N is the number of examples,

G y( xk )

is the

G network output appropriate to the example xk and d k is the

desired output for that example. Fig. 1. Flow chart of the experimental algorithm

B. Characteristics of Overcurrent Relay OCRs are used to detect power system conditions of an abnormal and faults such as short circuit and overload. OCR sends information to the circuit breaker (CB) to disconnect the affected area of a fault. The IEC standard has adopted four OCR curves for protection coordination, such as normal inverse, very inverse, extremely inverse, and long-time inverse curves. OCRs are widely used to protect power systems.

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The elements of the P

α

matrix are given by

⎡ ∂yr ( xk ) ∂yr ( xk ) ⎤ ⎥ ∂ω r ⎦ k =1 ⎣ ∂ωi N

α ij = (1 + λδ ij )∑∑ ⎢ r =1

(4)

Information p is the number of outputs of the network. Starting from initial random weights, both α and ∇E are evaluated and solving (2), the modification for the values of

G

G

the weights is obtained (ω = ω + Δ ) . This is known as the BPLM learning. Each the iteration of this learning reduces the error until the desired goal is reached or a minimum is found. The λ variable in (4) is a parameter that is adapted at each the learning, according to the error evolution. If it is very small the α matrix becomes an approximation to the hessian and the method is the inverse-Hessian method. If λ  1 , the method becomes analogous to steepest descent.

implementation results of the operating time using the five OCR types corresponding to the load currents and TDS under different neuron numbers are listed in Tables 2 to 6.

D. Case Study Application in the industrial power system is performed to evaluate the OCR curve modeling with digital OCR using LMBP. Protection coordination is implemented using the longest line of the single-line diagram in Hess Indonesia Corporation. Thus, five OCR types (R-ACB-02, R-VCB-13, R-VCB-11, R-VCB-09, and R-VCB-03) are obtained. The longest line of the single-line diagram in Hess Indonesia Corporation is shown in Fig. 2. III. SIMULATION RESULT AND ANALYSIS LMBP is implemented using different numbers of neurons, and each case is trained for 100 iterations. The protection coordination in Hess Indonesia Corporation yields five normal-inverse OCR types using Eq. (1). To ensure the capability and reliability of LMBP for OCR curve modeling, Time Dial Setting (TDS) is varied relative to the first TDS values for R-ACB-02, R-VCB-13, R-VCB-11, R-VCB-09, and R-VCB-03, which are 0.2, 0.3, 0.25, 0.34, and 0.49, respectively, and the second TDS values for R-ACB-02, R-VCB-13, R-VCB-11, R-VCB-09, and R-VCB-03, which are 0.25, 0.35, 0.4, 0.4, and 0.55, respectively. The detailed

Fig.2. Industrial power system in PT. Hess Indonesia

Table 2. Implementation results of the LMBP model of R-ACB-02 relay as applied in the digital OCR with the first and the second TDSs IL 5481 5552 5694 5765 6049 6120 6546 6617 7185 7256 Average

T_CB_TDS1 1.77 1.74 1.69 1.66 1.55 1.53 1.44 1.42 1.31 1.30

LMBP_N10 1.79 1.75 1.69 1.67 1.56 1.53 1.45 1.44 1.31 1.30

Err(%) 0.88 0.48 0.46 0.76 0.84 0.03 0.63 1.21 0.20 0.36 0.58

LMBP_N5 1.81 1.78 1.72 1.70 1.59 1.57 1.48 1.47 1.32 1.31

Err(%) 2.07 2.08 2.18 2.27 2.81 2.91 2.97 2.85 0.66 0.41 2.12

T_CB_TDS2 2.21 2.18 2.11 2.08 1.94 1.91 1.80 1.78 1.64 1.62

LMBP_N10 2.22 2.17 2.09 2.07 1.94 1.90 1.80 1.79 1.63 1.62

Err(%) 0.17 0.31 0.66 0.43 0.04 0.51 0.14 0.66 0.51 0.10 0.35

LMBP_N5 2.21 2.17 2.09 2.06 1.91 1.89 1.78 1.76 1.63 1.62

Err(%) 0.29 0.44 0.72 0.84 1.31 1.36 1.41 1.37 0.56 0.40 0.87

IL, load current; T_CB_TDS1, actual operating time in seconds with the first TDS; LMBP_N10, operating time of the LMBP model with 10 number of neurons; Err(%), error value; LMBP_N5, operating time of the LMBP model with 5 number of neurons; T_CB_TDS2, actual operating time in seconds with the second TDS; Table 3. Implementation results of the LMBP model of R-VCB-13 relay as applied in the digital OCR with the first and the second TDSs IL 296 316 335 355 569 589 608 628 784 803 average

T_CB_TDS1 3.06 2.80 2.59 2.42 1.55 1.51 1.48 1.45 1.25 1.23

LMBP_N10 3.04 2.82 2.63 2.42 1.56 1.52 1.48 1.44 1.23 1.22

Err(%) 0.54 0.69 1.46 0.06 0.26 0.07 0.27 0.45 1.22 1.15 0.62

LMBP_N5 3.00 2.73 2.52 2.36 1.58 1.54 1.50 1.46 1.23 1.21

Err(%) 2.12 2.52 2.83 2.39 1.52 1.46 1.22 1.06 1.33 1.65 1.81

T_CB_TDS2 3.57 3.26 3.02 2.82 1.81 1.77 1.73 1.69 1.46 1.44

LMBP_N10 3.59 3.26 3.00 2.81 1.81 1.77 1.73 1.69 1.45 1.42

Err(%) 0.41 0.03 0.63 0.48 0.10 0.30 0.30 0.35 0.81 0.98 0.44

LMBP_N5 3.68 3.30 2.98 2.74 1.75 1.72 1.70 1.67 1.52 1.51

Err(%) 2.94 1.07 1.33 3.00 3.35 2.46 1.65 0.77 4.58 5.00 2.61

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Table 4. Implementation results of the LMBP model of R-VCB-11 relay as applied in the digital OCR with the first and the second TDSs IL 870 924 979 997 1522 1540 1666 1685 1703 1721 average

T_CB_TDS1 29.55 14.60 9.88 8.95 2.81 2.76 2.45 2.41 2.38 2.34

LMBP_N10 29.70 14.70 9.87 8.90 2.81 2.76 2.45 2.42 2.38 2.34

Err(%) 0.51 0.68 0.13 0.51 0.14 0.08 0.06 0.13 0.03 0.30 0.26

LMBP_N5 29.07 14.29 9.79 8.89 2.83 2.77 2.44 2.40 2.36 2.33

Err(%) 1.62 2.13 0.90 0.67 0.55 0.46 0.42 0.53 0.64 0.74 0.87

T_CB_TDS2 47.28 23.36 15.81 14.32 4.50 4.42 3.92 3.86 3.80 3.75

LMBP_N10 47.24 23.44 15.86 14.32 4.49 4.42 3.90 3.86 3.82 3.78

Err(%) 0.08 0.34 0.31 0.02 0.26 0.19 0.54 0.02 0.52 0.79 0.31

LMBP_N5 46.86 23.11 15.86 14.38 4.47 4.39 3.85 3.79 3.73 3.68

Err(%) 0.90 1.04 0.36 0.44 0.58 0.70 1.69 1.80 1.90 1.99 1.14

Table 5. Implementation result of the LMBP model of R-VCB-09 relay as applied in the digital OCR with the first and the second TDSs IL 630 649 688 1303 1312 1322 1351 1361 1371 1381 average

T_CB_TDS1 38.23 25.65 15.74 2.99 2.97 2.94 2.86 2.84 2.81 2.79

LMBP_N10 38.20 25.87 15.82 2.99 2.96 2.92 2.84 2.81 2.79 2.78

Err(%) 0.08 0.82 0.51 0.18 0.32 0.54 0.87 0.79 0.57 0.36 0.50

LMBP_N5 38.16 25.97 15.94 3.01 2.98 2.96 2.90 2.88 2.86 2.84

Err(%) 0.18 1.24 1.28 0.37 0.50 0.65 1.26 1.47 1.75 1.99 1.07

T_CB_TDS2 44.98 30.18 18.52 3.52 3.49 3.46 3.36 3.34 3.31 3.28

LMBP_N10 44.93 30.31 18.61 3.52 3.50 3.47 3.37 3.33 3.30 3.27

Err(%) 0.11 0.43 0.52 0.05 0.24 0.42 0.21 0.08 0.16 0.13 0.24

LMBP_N5 45.27 30.49 18.63 3.55 3.51 3.48 3.40 3.37 3.35 3.32

Err(%) 0.65 1.03 0.59 0.66 0.68 0.71 0.95 1.02 1.18 1.27 0.87

Table 6. Implementation result of the LMBP model of R-VCB-03 relay as applied in the digital OCR with the first and the second TDSs IL 275 310 345 392 544 591 603 615 896 907 average

T_CB_TDS1 35.95 15.88 10.59 7.59 4.37 3.95 3.86 3.78 2.65 2.63

LMBP_N10 35.98 15.99 10.64 7.60 4.39 3.96 3.88 3.80 2.66 2.64

Err(%) 0.08 0.72 0.49 0.15 0.43 0.33 0.50 0.50 0.34 0.31 0.39

LMBP_N5 35.78 15.74 10.66 7.52 4.41 3.99 3.91 3.82 2.61 2.59

Err(%) 0.50 0.91 0.69 0.85 0.84 1.09 1.21 1.11 1.53 1.55 1.03

Tables 2 to 6, show the average percentage error of the OCR curve modeling using BPLM under different numbers of neurons as applied in the digital OCR. For R-ACB-02, modeling with 10 neurons BPLM yield more accurate results compared with 5 neurons BPLM yield because the 10 neurons BPLM provide very minimal average percentage errors in the first and second TDSs, which are 0.58% and 0.35%, respectively. For R-ACB-13, the 10 neurons BPLM yield more accurate results with average percentage errors of 0.62% and 0.44% in the first and second TDSs, respectively. For RACB-11, the 10 neurons BPLM yield more accurate results with average percentage errors of 0.26% and 0.31% in the first and second TDSs, respectively. For R-ACB-09, the 10 neurons BPLM yield more accurate results with average

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T_CB_TDS2 40.36 17.83 11.89 8.52 4.91 4.44 4.34 4.24 2.98 2.95

LMBP_N10 40.45 17.90 11.97 8.47 4.90 4.40 4.30 4.20 3.00 2.97

Err(%) 0.23 0.44 0.72 0.58 0.25 0.82 0.77 0.90 0.58 0.79 0.61

LMBP_N5 39.91 17.63 11.61 8.73 4.97 4.38 4.27 4.15 2.90 2.88

Err(%) 1.11 1.08 2.32 2.45 1.31 1.21 1.60 2.19 2.60 2.24 1.81

percentage errors of 0.50% and 0.24% in the first and second TDSs, respectively. For R-ACB-08, the 10 neurons BPLM yield more accurate results with average percentage errors of 0.39% and 0.61% in the first and the second TDSs, respectively. In all cases, the R-ACB-09 modeling using 10 neurons BPLM yields a very minimum average percentage error of 0.24%. Tables 2 to 6 demonstrate that the average percentage error of modeling using BPLM decreases when the number of neurons increases. The modeling of OCR curves using BPLM under different numbers of neurons as applied in the digital OCR mostly matches with the desired operating time to the load current and TDS. BPLM provides the accurate result because during the learning process, BPLM optimizes the

weights with the iteration of the learning process reduces the error until the desired goal is reached. Therefore, with BPLM method, the desired operating time is significantly close to the actually calculated operating time of the OCR. IV. CONCLUSION In this study, modeling of the OCR curve using BPLM as applied in the digital OCR. The OCR curve model is implemented using the sample data from the protection coordination of the power system in Hess Indonesia Corporation. BPLM is implemented under different numbers of neurons. The protection coordination obtains five types of OCR, namely, R-ACB-02, R-VCB-13, R-VCB-11, R-VCB09, and R-VCB-03, and TDS is varied. Each case is trained for 100 iterations. The implementation result indicates that modeling using 10 neurons BPLM yields more accurate results with a very minimal average percentage error of 0.24%. Moreover, the implementation result demonstrates that the average percentage error of the modeling using BPLM decreases when the number of neurons increases. The modeling of OCR curves using BPLM under different numbers of neurons as applied in the digital OCR mostly matches with the desired operating time to the load current and TDS. BPLM provides the accurate result because during the learning process, BPLM optimizes the weights with the iteration of the learning process reduces the error until the desired goal is reached. Thus, the result of BPLM in the OCR curve modeling as applied in the digital OCR is accurate and encouraging. REFERENCES [1]

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