On-line and long-term monitoring and diagnosis system for insulation on switchgear devices by

wireless communication technique

Cheng-Chien Kuo Department of Electrical Engineering

Saint John’s University Taipei, Taiwan

cckuo@mail.sju.edu.tw

Hong-Chan Chang Department of Electrical Engineering

National Taiwan University of Science and Technology Taipei, Taiwan

hcchang@mail.ntust.edu.tw

Abstract—An on-line partial discharge detection instrument for SF6-insulated switchgear using wireless communication technology is proposed. Two major parts is divided for the developed instrument. One is the remote wireless monitoring with on-line measurement unit that could be placed permanently onto the SF6-insulated switchgear equipment so as to measure the partial discharge signal from an ultrasonic sensor. The other is the server analysis and calculation unit. It is mainly composed of an industry computer containing a wireless module. The function of this unit is to collect, analyze and store the ultrasound signal captured from each remote monitoring unit, and to perform software-based signal noise and interference reduction. Through feature extraction of the original ultrasound signal, a digitalized assessment and recognition system of the state of insulation could be created.

Keyword: switchgear devices; partial discharge; wireless communication; on-line detection; insulation diagnosis system.

I. INTRODUCTION

The convenience and widespread usage of electricity has made it the most important form of energy used in daily life. The power is generated from power plants, and transmitted to customer terminals through energy transmission systems. During the transmission, the power must pass through many switchgear devices. Due to the present difficulty in obtaining land, and the maturity in insulation technology, use of SF6-insulated switchgears has gradually replaced traditional switch factories to become one of the most important and vital device in a transmission system. Therefore, the safety requirements of SF6-insulated switchgears have to be more stringent following the improving reliability of power generation [1-2].

The rated lifespan of switchgear devices are typically more than 20 years. During this long term operation, the devices will unavoidably be faced with many natural and man-made influences. The devices’ long term exposure to a mix of temperature changes, lightning shocks and internal over-voltage, Gradually induces aging in the insulation, which eventually leads to a very small internal discharge (known as partial discharge). If no action is taken, the insulation will eventually punch through. Over these past few years, the rapid development of microprocessor and wireless communication

technology has enabled existing switchgear devices to utilize these technologies in on-line real time and long term monitoring. This has enabled the real-time discovery of insulation defects, using easily installable and deployable equipment. Whereby, these real-time and recorded measurements of the Insulation parameter of the monitored switchgear devices can provide a reliable guarantee to the safe usage of electrical power systems [3].

Based on this, this paper is aimed to self-develop an on-line insulator quality measurement instrument for SF6-insulated switchgear using wireless communication technology. Some insulation deteriorations are caused by immediate changes due to natural or man-made causes, such as lightning surge, switching surge, nonlinear resonance, etc.., while others are caused by cumulative effects of insulation aging due to long term operation. Therefore, it is necessary when obtaining measurements of the state of insulation deterioration, to take into account both the sudden changes and the long-term effects. This will more comprehensively prevent a collapse in the insulation system, which would affect the entire power generation system.

II. HARDWARE STRUCTURE

The instrument to be developed in this paper is divided into two major portions as shown in Figure 1. Firstly, a remote monitoring with real-time alerting unit is to be placed permanently onto the SF6-insulated switchgear device to be measured. This unit is mainly composed of an embedded computing system combined with a wireless communication module. The function of the unit is to capture real-time ultrasound signals online. The second portion of the instrument is the server analysis and calculation unit. This unit is mainly composed of a computer containing a wireless transmission module. The function of this unit is to collect, analyze and store the ultrasound signal captured from each remote monitoring unit, and to perform software-based signal noise and interference reduction. Through feature extraction of the original ultrasound signal, a digitalized assessment and recognition system of the state of insulation can be created.

The main purpose of proposed remote wireless monitoring with on-line measurement unit is to gather an acoustic signal

A-195 2012 IEEE International Conference on Condition Monitoring and Diagnosis23-27 September 2012, Bali, Indonesia

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on the discharging equipment. The function of the proposed device are gathering discharge acoustic signal, digitize, store captured discharge signal, and data transfer, as shown in Figure 2

Figure 1. Instrucment structure

Figure 2. System function and data process flow

This device utilizes ultrasonic sensors to sense the acoustic signal and digitized via the microcontroller analog to digital converter interface; Then, in order to comply with the requirements of high-speed sampling, the digitized data use the direct memory access (DMA) interface embedded in microcontroller to store the data in external SDRAM; Finally, using the wireless network, ZigBee, send the store data to the host, to do follow-up data analysis. The main technical specifications of this device to digital sampling rate of 1MHz, the detection time of 0.5 seconds.

A. Hardware tructure The hardware architecture is showed in Figure 3, which has

power module, microcontroller, SDRAM and communication module. The main circuit is described as follow respectively.

B. Power Module The main function of the power module is to transfer AC

110V to DC 5V and 3.3V in order to provide the operating power for all modules in the smart node. Its circuit structure is shown in Figure 4. AC 110V is transferred by switching the power module to DC 9V, and then transferring to DC 5V and 3.3V by linear regulator.

C. Microcontroller STM32F207 is a 32-bit high performance microcontroller

based on ARM Cotex-M3, which contains 1M bytes

programming memory, 128K bytes data memory, 12-bit high speed analog to digital converter (ADC), Universal Asynchronous Receiver/Transmitter (UART), Flexible static memory control (FSMC), DMA, and general purpose input and output (GPIO) that adequately meet the function demands of the proposed device. The highest operating frequency of STM32F207 is 120MHz. The highest sampling rate of STM32F207 built-in ADC can reach 2 MHz, and the conversion result can save in external memory directly through DMA in order to enhance the accessing rate. The 8M-bit SDRAM is adopted in the proposed system to save 0.5M half word. The 1MHz sampling rate can save data for 0.5 second with 8M-bit SDRAM.

Figure 3. Hardware architecture

Figure 4. DC pwer mole

D. Communication Module The proposed device transmits the acoustic wave data

which in external SDRAM using the wireless ZigBee module. ZigBee is a wireless network protocol and adapted IEEE 802.15.4 standard owned by ZigBee Alliance, which defines the media layer and objective layer and possesses low transmission speed at low cost and low power consumption, with high security, and supports a large number of web node operations. The 802.15.4 standard is primarily aiming at monitoring and control applications. Low power consumption is the most important feature that makes battery operated devices operate for a long time.

In the ZigBee, the effective transmission distance between nodes is determined by the transmission power designed for module. At present, the transmission distance of commercial module can reach about 100m under a barrier-free condition. Although the partition blocks of buildings may reduce the communication distance, using ZigBee can support the network structure with tree or mesh, and setting some nodes in the network to router function can effectively overcome the issues of transmission in the same horizontal floor and different vertical floor with long distance. Conceptually, ZigBee communication can be applied to buildings without restriction on the transmission distance. As for the noise interference issue, ZigBee uses direct sequence spread spectrum (DSSS) to reduce

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the environmental interference. In addition, it uses Carrier Sense Multiple Access-Collision Avoidance (CSMA/CA) channel access mechanism, dynamic frequency selection and transmission power control to avoid channel collision

The Xbee module is adopted in proposed device, which composed of a processor that executes the ZigBee protocol with 2.4G radio frequency (RF); its structure is shown in Figure 5.

Figure 5. ZigBee module

E. Measurement System Figure 4shows the block diagram of the PD experiment in

the laboratory. All measured signals were converted to digital data for storage in a computer.

Figure 6. Block diagram of PD measurement experiment.

III. AE TECHNOLOGY

AE refers to natural phenomenon in many situations, such as earthquake, rock burst, thunder, crack of tree branches, fracture of glass or pottery, etc. In ancient times, the quality of pottery ware was identified with AE principle; at present, the maximum load of automobile components is tested with the same principle. The ASTM standard E610-82 gave a detailed description as “AE is a transient elastic waves generated by the rapid release of energy form localized sources within a material.” PD detection is basically considered as a pulse phenomenon of energy release, and subsequent sound release is named AE from the perspective of supersonic wave. Therefore, an ideal way to online judge the performance of transformer is

based on computer-aided AE method for PD detection. The following are three highlights of AE methods in PD detection for transformer [4].

• Online and real-time supervision: AE can be detected online without switching off, or any special arrangements of equipments.

• High safety: AE can be detected from a contact-free or outside contact manner, thereby helping to improve greatly the safety of maintenance personnel compare with high voltage testing.

• Perfect trouble shooting: In the case of new or unknown troubles of electric equipments, only AE data shall be required for future identification and prevention.

Therefore, this paper intends to apply AE principle to analyze PD of transformers that is difficult to diagnosis online by other method.

A. Features extraction After AE waveform signal is obtained, it is also impossible

to identify if numerous data are not analyzed to obtain representatively feature value. Therefore, acquisition of feature values are the key for higher recognition rate. The feature values in this paper are proposed according to (a) feature value of the same nature with little difference, (b) feature value of different natures with identifiable difference. Therefore, five feature values are taken as the identification data in this paper. There are Rise Time, Count, Duration, Amplitude and Energy. These values are represented in a statistical way as shown in Fig. 3.

• Rise Time: The time from the AE waveform above the threshold to the peak value.

• Duration: The time from beginning to the end of a complete AE signal, such that the magnetite is bigger than threshold value and then fallen below it.

• Count: The extraction number of AE wave conforming to the threshold value.

• Amplitude: The maximum value of the AE signal that pass through threshold.

• Energy: The power energy of AE waveform that pass through threshold and within duration period.

Figure 7. The features extraction from an AE signal

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IV. EXPERIMENT RESULTS

This section consists of two main parts: the experiment defect models and the measurement results. The details of the two main parts can be describes as follows.

A. Defect Model In this research, the experimental objects were 15 kV GIS

defect models, which filled with SF6 gas where experiments were conducted. Four types of relevant models are well-designed, based on investigations of numerous power equipment failures. Figure 7 shows the possible defect models that may be caused by humans during GIS construction. The four testing models are described as follows:

T1: Porcelain bushing containing grease inside. T2: SF6 gas tank containing metal particles. T3: A welding protrusion in the bearing. T4: Metal ring with abrasion defect.

Figure 8. Experiment defect models.

B. Recognition Accuracy Rate This study measures 160 sets of GIS PD patterns, and

measures 40 sets of PD patterns for each experiment model. For PD recognition, 20 sets of patterns were randomly chosen as the training pattern by neural network [5], and the remaining 20 sets of patterns served as the testing data for each defect type. The back-propagation neural network (BPNN) is the most representative of the common learning mode in a neural network. This study chooses a structure of a three-layered BPNN because of its simplicity. In NN recognition, the set learning target was 10-10, and the maximum learning epoch was 500. In our test results, 15 neurons in hidden layer were suitable for our pattern recognition. The recognition accuracy rate can reach 100% when no extra noise is added.

The input to a PD recognition system unavoidably contains some noise, which may be generated from the detector or environmental. To verify the proposed method, this study simulates the random white noises and adds them into the real measured electrical signals. The magnitudes of white noise were set in accordance with 5%, 10% and 15% of the maximum acoustic signals in each experimental model. Table 1

shows the average recognition rate under various random white noise signals. When the signal includes 5% random white noise, the recognition rate continues to reach 92%. When the white noise is increased to 15%, the recognition rate still reaches 74%. Experimental results indicate that the method presented in this study provides good recognition results even when the signal includes noise.

TABLE 1. ACCURACY RATE OF PD RECOGNITION

Random White Noise Average Recognition Rate (%)0% 1005% 92

10% 8515% 74

V. CONCLUSION

This paper has shown the successful application of on-line and long-term monitoring and diagnosis system for insulation on switchgear devices by wireless communication technique. The proposed instrument provides an effective approach to detect the possible failure of a power apparatus. The result shows good tolerance when random white noise is added. Moreover, this detection method can be used following the construction of GIS to identify any defects, thereby improving quality management and employee education and training. To show the effectiveness of the proposed approach, several classification and identification simulations are used to evaluate. In our research, even on 15% noise-added situation, at least 74% recognition rate can be guaranteed. These encouraging results show that this paper can provide a feasible and effective way to early detect the possible failure of SF6 insulated switchgears and can also determine the types of failure to help utility for maintenance needed.

ACKNOWLEDGMENT

The support of this research by the National Science Council of the Republic of China under Grant No. NSC100-2218-E-129-001, NSC99-2221-E-011-149-MY3 and NSC99-2221-E-129-021-MY3 are gratefully acknowledged.

REFERENCES

[1] C. Chang, C. S. Chang, J. Jin, T. Hoshino, M. Hanai, N. Kobayashi, “Source classification of partial discharge for gas insulated substation using waveshape pattern recognition,” IEEE Trans. Dielectr. Electr. Insul., Vol. 12, pp. 374-386, Apr. 2005.

[2] G. V. Nagesh Kumar, B. P. Singh, J. Amarnath, K. D. Srivastava, “Electric field effect on metallic particle contamination in a common enclosure gas insulated busduct,” IEEE Trans. Dielectr. Electr. Insul., Vol. 14, pp. 334-340, Apr. 2007.

[3] N. G. Boulaxis, M. P. Papadopoulos, “Optimal feeder routing in distribution system planning using dynamic programming technique and GIS facilities,” IEEE Trans. Power Delivery, Vol. 17, pp. 242-247, Jan. 2002.

[4] D. J. Kweon, S. B. Chin, H. R. Kwak, J. C. Kim and K. B Song, "The analysis of ultrasonic signals by partial discharge and noise from the transformer," IEEE Trans. Power Delivery, Vol. 20, pp. 1976-1983, July. 2005.

[5] T. Boczar, S. Borucki, A. Cichon, and D. Zmarzly, “Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emission method,” IEEE Trans. Dielectr. Electr. Insul, Vol. 16, No. 1, pp. 214-223, Feb. 2009.

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