Filter Design of New Protective Relaying System for Power Line Carrier Dong Zhi,Tang Guilin,Huang Zhiping, Liu Chunwu, Zhang Yimeng

Mechatronitics and Automation School of National University of Defense Technology, Changsha 410073, China

Dongzhi1128@163.com

Abstract

This paper presents a new filter design method for protective relaying system for power line carrier according to the characteristic of noise. Making advantages of FPGA( Field Programmable Gate Array)、Matlab and DSP tools, this paper designs a good performance IIR filter which is implemented in FPGA. The results of simulation show that the method is effective. Keywords: FPGA; Matlab; DSP; Protective-Relaying; IIR Filter; Coefficient Quantization

1. Introduction

With the development of power line system, the protective relaying system should be updated. The transmitting reliability of command is crucial to the stabilization of electric power network[1]. As the noise weakened the stability in the protective relaying system, the pre-requisite is to design high efficient filter to eliminate or weaken the impact of the noise. High development of electronic technology、computer and communication technology help protective relaying system to inject new vitality. FPGA has the advantage of high speed、high capacity，and has multi-task parallel processing ability. DSP module is integrated within FPGA, and can do complex calculation. Using dedicated DSP designing software, we can design filter in simulink of Matlab, and download the filter coefficient into FPGA with cable, which makes filter design easier. System Generator is a kind of development tool of DSP, which belongs to Xilinx, it can be integrated into simulink of Matlab. The Matlab used to be used as modeling or mathematics simulation, and the model can not generate programming codes directly. The results is only based on mathematical structure of the algorithm, but the problems of hardware delay、 truncation errors and data width is so complex that the Matlab can not deal with it. For System Generator, the development tool is simulink, almost all the

jobs can be done in Matlab, including modeling of DSP system、system simulation、converting from the model to VHDL codes、RTL simulation、routing 、timing simulation and programming, which reveals the characteristic and advantage of EDA.

2. The noises of protective relaying system for PLC

There are two kinds of noise, one is man-made noise, the

other is non-human noise[2]. The non-human noise is natural phenomenon, which is divided into some kinds such as flows:

a. Background noise. The background noise is a typical discrete of gauss

Gaussian, which will be always exist on the power line, the noise magnitude is in proportional to the temperature of the communication medium. The noise is larger, the temperature is higher. To limit the noise, the communication medium should keep low temperature.

b. Gusty noise. The arc discharge when thundering、circuit failure、

switch operation happens and other transient may cause impulse noise, which are not periodic. Bad weather will enlarge the level of the noise, for example, the thunder may enlarge 20dB noise level[3].

The man-made noise is divided as follows: a. Noise caused by small electric apparatus. These

noises has smooth power spectrum, which is generated by the electric apparatus that has different frequency from the electrical source, such as electromotor、dust collector、blender、sartorius、electric drill and so on.

b. Harmonious power supply noise of rectifier and dimmer. Solid dimmer of familial apparatus can generate high frequency harmonious noise.

c. Impulse noise caused by switch of electric apparatus. The oscillograph also can generate very high frequency periodic impulse noise.

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d. Asynchronous periodic noise of working frequency. It has vertical spectrum, which does not has correlation to 50Hz working frequency. Its noise has the frequency from 0~100kHz. Noise of TV belongs to this kind, whose power is concentrate on 15.734kHz.

e. Other noise sources. Such as, blower、radiogram and interphone can generate all kinds of noise. The filter should be designed neatly, and coefficient can be changed expediently. The delay of filter also should be small, because for the protective system, real-time quality is as important as to reliability. Taking advantage of the multithreading processing ability of FPGA, and utilizing Matlab and system generator, the system designed can satisfy these requirements. The design flow will be introduced as follows.

3. The principle of digital filter

Digital filter is commonly used in signal processing system. There are two kings digital filter which are in common use, they are IIR filter and FIR filter. Actually, digital filter is a discrete liner time-invarying(LTI) system which is implemented by limited precision. First, confirm the performance target according to the necessity. Then approach the performance target by H(z) which designed. Last, implement the H(z) by limited precision. The pole of IIR filter can be distributed anywhere of the unit cycle. it can be implemented by lower order, but has high selectivity, and it uses less memory cell economically and efficiently[4]. As for the FIR filter, the poles of transmission function is fixed at the origin, high selectivity purpose can be implemented only using higher order to achieve. Therefore, when the filter is designed to have higher selectivity requirements but lower phase-sensitive occasions, IIR filters can be chose to Make full use of their economic and efficient characteristics.

Therefore, the protective relaying system should utilize IIR filter. Before using system generator to design filter, principles of digital filter should be known.

(1) ......

)( 110

110

MM

NN

zazaazbzbb

zH −−

−−

++++++

=

Formula (1) is the IIR digital filter transfer function, in which the Z is the transformation variable and make denominator and numerator coefficients of the H(Z) for all real. Making Z-1 transform of formula(1), direct form、parallel

form and cascade form of IIR filter can be got, which we will not repeat here. Among them, the form of direct form has least delay, and multiplication is relatively less. Therefore, this paper will use the System Generator to implement direct form IIR filter, the signal flow chart is shown in Figure 1[5], in which N means the order of the filter.

Fig.1 Flow chart of IIR filter

4. Design of the filter 4.1 getting coefficient with Matlab

It is easy to use filter design tools provided by Matlab to get the coefficients of filter. Filter parameters that needed are as follows: IIR low-pass filter; method: ellipse;order:8;sampling frequency(Fs):48kHz, for the Frequency center is 5kHz;Apass:1;Astop: 80;input bit widths: the 8-bit. Parameter settings is showed in Figure 2, After parameters have been set up, it is need to view the magnitude response of the filter to see if it met the design requirements. The magnitude response of the filter is shown in Figure 3.

Fig.2 Parameter settings

As can be seen from Figure 3, the magnitude response of the filter is better, the signal can be seen in the decay after 5kHz faster to meet the requirements of design parameters. As the system does not require phase responses’ characteristics, the IIR filter’s characteristics do not have impact on the results.

2

4.2 coefficient quantification To a large extent, performance of the filter is determined by

the coefficient. In theory, the coefficients of digital filter

Fig.3 Magnitude of the filter

can be used by infinite precision, but the actual realization of the digital filter coefficients can only be limited to that precision. When using a binary number, if the effective coefficient of the median (that is, the word length) is large enough, the filter coefficients may choose a lot of bits, from the quantitative point of view, there will be no problem. However, due to hardware conditions, it had to be shortened when the word length is limited, it is necessary to study coefficient quantization to keep the performance and stability of filter. In order to obtain the optimal filter coefficients, the following steps should be takes.

(1). Compute the absolute value of each coefficient; (2). Find out the maximum absolute value; (3). Find out the minimum integers larger than the absolute value; (4). Take negative integers of the result of (3); (5). Finding out the minimum bits to transform the negative integer with binary; (6). Find out the other bits to transform the fraction with binary.

After coefficient quantification, the performance of magnitude response and phase response will change, increase its frequency response, phase frequency response will change. Figure 4 shows the comparison of the magnitude response before and after the quantitative, which used 8-bit wide input and 20-bit output, the decimal part of the wide use 6 bits. It is seen from Figure 4, the magnitude response is basically in consistent before and after quantitative, which will not affect the performance of the filter. 4.3 Export filter coefficient

The post-quantitative coefficient are divided into 4 parts, corresponding to IIR filter’s 4 cascade parts, which is

composed of a second-order system, the coefficients is shown in table 1.

Fig.4 Magnitude response comparation

In table 1, M denotes the cascade of the filter, “a” denotes the denominator, “b” denotes the numerator. Comparing to the coefficients after quantification the coefficients before quantification are shown in table 2.

5. Simulation of the IIR filter with system generator

The top level of IIR filter is shown in Figure 5. Only the

modules between “Gateway Out” and “Gateway In” will be translated into HDL file. The input of the system are two sine wave signals, whose frequency are 40KHz and 5KHz, sine wave of 40KHz can be regards as noise signal, 5KHz sine wave is useful signal. Every level of second-order section can bee regards as a sub-system, they were “Subsystem”, “Subsystem1”, “Subsystem2”, “Subsystem3”. Structure of the each second-order system is shown in Figure 6. All input and output signals are connected to the “Scope” for display. Module of “System Generator” is used to generate HDL files, after compiled, it will generate VHDL or Verilog HDL files automatically, which can be called by ISE modules. Figure 7 shows the simulation results of various input and output signals. It is seen that 40kHz signal which is regards as noise has being filtered out.

. Fig.5 Top level of the filter

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Tab.1 The coefficient after quantitative Coef. a1M a2M a3M b1M b2M b3M

1 1.000000 -0.765625 1.000000 1.000000 -1.640625 0.828125 2 1.000000 -1.359375 1.000000 1.000000 -1.562500 0.968750 3 1.000000 1.046875 1.000000 1.000000 -1.703125 0.750000 3 1.000000 -1.234375 1.000000 1.000000 -1.578125 0.906250

Tab2 The coefficient before quantitative Coef. a1M a2M a3M b1M b2M b3M

1 1.000000 -0.76180534 1.000000 1.000000 -1.6419418 0.82649985 2 1.000000 -1.35805910 1.000000 1.000000 -1.5675439 0.97456528 3 1.000000 1.04417739 1.000000 1.000000 -1.7037477 0.74742848 4 1.000000 -1.23172349 1.000000 1.000000 -1.5837326 0.91267884

Fig.6 Second order subsystem

6. Conclusions

The filter designed of this paper is mainly for protective relaying system of power line carrier to filter the noise. Based on stability and convenience, the filter makes full use of matlab/simulink tools to make the design process at one go. Simulation results show that the method has a better filtering effect of filters in the design and present a new way to design filter.

7. References [1]Cao Lanjuan. Remote protection Equipment Based on Digital Signal Processing. Electronics Engineers, 2007, Vol.33 (5),72-80. [2]Geng Xuan. Characteristics and model of Low-voltage power line communication channel [J]. Power System Communications, 2004 (4): 19 -24.

Fig.7 Simulation results

[3]Sun Xiujuan,Luo Yunhu,Liu Zhihai,Wang Chuanji-ang Yuweiwei.Low-voltage powerline carrier communication channel with the characteristics of anti-jam-ming measures. Power automation equipment. 2007, Vol.27 (2) :43-46. [4] Shailesh B. Nerurkar, Khalid H. Abed, Low-Power Decimator Design Using Approximated Linear-Phase N-Band IIR Filter. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, VOL. 54 (4). [5] Zheng Junli, Ying Qihang, Yang Weili. Signals and Systems. Higher Education Press. 2000.

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