Power system equipments such as transformer, bushing, circuit breaker etc can cause electrical faults when its internal temperature reached the abnormality. The increase of temperature in electrical equipment can lead to subsequent failure of components potentially resulting in unplanned outages, possible injury and fire hazard. In addition, the efficiency of an electrical grid becomes low prior to failure, thus energy is spent generating heat, causing unnecessary loses. Early prevention is required to avoid future faults and increase the reliability of the equipments. As the heat is invisible for human eyes, the most suitable technique to detect the heat is using the infrared thermography (IRT). IRT camera can detect the abnormality of power equipments without interrupting the power system operation. This paper presents the review of IRT applications in monitoring the condition of electrical power system equipments. It also provides an overview of IRT theory, type of fault that is normally found in power equipments and the improvement of inspection using IRT. Recent advances applications of IRT for diagnosing electrical power equipments are also reviewed. Some limitation of previous research and recommendation for future development also discussed.
Introduction
Non-destructive testing (NDT) simply can be defined as a testing that does not destroy the test object. One of the applications of NDT is it used for predictive and preventive maintenance. There are five nondestructive techniques that are commonly used for predictive and preventive maintenance management i.e. vibration monitoring, process parameter monitoring, thermography, tribology, and visual inspection. Each technique has a unique data set that assists the maintenance manager in determining the actual need for maintenance [6]. One of the popular techniques to detect the thermal anomalies in a system is by using thermographic inspection. Since early 1960s, IRT has been successfully used in many fields of applications in military, industrial, civil engineering and medical as well as electrical engineering. The IRT is a kind of non-contact, nondestructive and visualizing technique, which is becoming an important means for production quality and in service inspection [2]. Due to its advantage in terms of noncontact, large inspection coverage, easy and fast interpretation, free from electromagnetic interference, safe and reliable, IRT has taken a very important role in predictive and preventive maintenance technique [3][4]. The inspection can be done without shutting down the operation of the system. Preventive maintenance tells if parts are still satisfactory for use, resulting in fewer repairs, less accidents and lower overall operating costs.
In electrical power system, IRT plays a vital role in inspecting and diagnosing the integrity of electrical power systems. It has the distinct advantages of being fast and noncontact. It has become the preferred method for assessing equipment condition online in many electrical transmission and distribution systems around the world [28]. IRT can be used to monitor the thermal behavior of the power equipments as well as the structures of a system. It senses the emission of infrared energy (i.e., temperature) to detect the thermal anomalies areas that are hotter or colder than they should be where the people who make the inspection can locate and identify the incipient problems within the system. While heat is not a perfect indicator of all problems in electrical systems, heat produced by abnormally high electrical resistance often precedes electrical failures [7].
Although the technique for inspecting electrical systems is quite straightforward, there are several things need to consider. Many factors such as environmental effect and equipment condition normally will affect the analysis result particularly for outdoor inspection such as for power substation. Direct inspection without considering these factors definitely will result inaccurate measurement. A good electrical system thermographer must contend with several problems related to the component, the infrared instrument, and the interpretation of the data. All of these aspects will be discussed more detail in the next section.
Nowadays, due to the increased demand in NDT applications, the more advance and fast inspection systems are needed to solve various types of problems. The trends show that now NDT has involves diverse fields of applied physics, expert systems, neural network, fuzzy logic, computer science, electronics and electrical engineering, materials science and engineering, mechanical engineering, and structural engineering [10]. For the automatic/automated interpretation of NDT data to become more widespread, a technique is needed, which requires less cost and effort in building, using, and maintaining the system; and which at the same time can reliably handle the large variety in data within a single inspection and between various inspections. Therefore, to meet the increased demand for robust and effectiveness of automatic inspections in complex NDT tasks, more intelligence system such as wavelet co-efficient, neural networks, fuzzy logic and other techniques have been recently deployed in many applications [10][17] [18][26][20].
This paper will discuss about the recent research of IRT applied in diagnosing of electrical power equipments. It will start with the fundamental concept of IRT and its applications in electrical power system, and then followed by the various type of fault that normally occurs in power components. There is also a discussion about the improvement of IRT inspection technique in order to get measurement that is more accurate by considering various factors. The next section will focus on the recent advance development in automatic fault detection. Some limitation of the current research and recommendations for future development are discussed in the next section.
Infrared Thermography
Infrared radiation was discovered in 1800 by William Hershel, who used a prism to refract the sunlight onto thermometers placed just pass the red end of the visible spectrum generated by the prism. He found that this area had the highest temperature of all, contained the most heat, and therefore contained a form of light beyond red light. Herschel's experiment was important not only because it led to the discovery of infrared light, but because it was the first experiment that showed there were forms of light not visible to the human eye [5][7].
Human eyes only can see in visible light spectrum ranging from about 400 nm to a little over 700 nm. The electromagnetic spectrum is a band of all electromagnetic waves arranged according to frequency and wavelength. As shown in Figure 1, the wavelength spectrum where the infrared wavelength ranges is from about 1 mm down to 750 nm. All objects radiate energy that is transported in the form of electromagnetic waves, which travel at speed of light. The quantity of energy leaving a surface as radiant heat is proportional to its emissivity and the fourth power of its absolute temperature given by
(1)
where q is the hemispherical total emissive power ( radiated energy per unit area, W/m2), σ the Stefan-Boltzmann constant (5.67051 x 10-8 W/m2K), ε the total hemispherical emissivity of the surface (0 < ε <1) and Т is the surface absolute temperature (K) [11].
Figure 1: Electromagnetic wavelength
As infrared energy functions outside the dynamic range of the human eyes, special equipment is needed to transform the infrared energy to another signal, which can be seen by human eyes. For this purpose, infrared imagers were developed to see and measure this heat. There are two general types of infrared instruments that can be used for condition monitoring: infrared thermometers and infrared focal plane area (FPA) cameras [5]. Infrared thermometers only provide a temperature reading at a single and relatively small point on a surface area. Another type of instrument that can provides a one-dimensional scan or line of comparative radiation is line scanner. This type of instrument provides a somewhat larger field of view in predictive maintenance applications compared to infrared thermometer [6]. The more advance of infrared thermography technology started when FPA was developed.
Based on FPA technology, nowadays peoples have developed various type of thermographic camera with more advances and sophisticated features. Concurrently, the use of computers and image processing software has grown tremendously. The basic concept of thermographic camera is that the captured data are transformed into digital data and processed into video images. The digital image of IRT is called as thermograms. Each pixel of a thermogram has a temperature value, and the image's contrast is derived from the differences in surface temperature. Nearly all systems commercially available today offer software programs that facilitate analysis and report writing. Some more advance software system can store the report digitally and can sent electronically over the internet [7].
Application of IRT in electrical power system
Generally, the electric industry understands that temperature is an excellent indicator to the operating condition and hence the reliability and longevity of an electrical component. Associations like IEEE, ANSI, IEC and manufacturers all publish standards and temperature ratings for electrical components [25] [22]. It is well understood that the life of electrical components and materials is drastically reduced as temperatures are increased. Infrared condition monitoring is the technique capable of revealing the presence of an anomaly by virtue of the thermal distribution profile, which the defect produces on the surface of the component. The defect will normally alter the thermal signature of the surface due to the change in the amount of heat generated and the heat transfer properties of the component [25].
This section will discuss more detail about the diagnosis of electrical power system components using IRT inspection. The first part will focus on type of faults that are always happened in power system components. The second section will look into the method of improving the quality of inspection.
Faults Diagnosis in Power Equipments
Electrical devices are usually rated for power, which indicates the amount of energy the device can conduct without being damaged. If the device is operated at a power above its specifications, the excess power causes the atoms present in the device's material to resonate and resist the flow of electricity. This resistance to the flow of electricity generates heat, which in turns overheats the device and reduces its life cycle and efficiency. Another major problem that affects utility equipment is the change in resistance due to loose connection. The loose connection causes electricity to use smaller area of the defective connection than required or intended for proper flow and therefore increases the resistance and temperature of the connection. Any problem, which accompanies a change in resistance of the equipment, causes it to consume more power than the intended load [22].
Thermal imager using direct observation and measurement can be found in these abnormal phenomena, have the potential failure of the location and extent of the problem. The common faults always occur in the equipments such as reactor, arrester, capacitor, circuit breaker, transformer and switches [4]. Faults can be divided into two kinds of attribute according to the location of the faults of electrical equipments, the external and internal faults. As far as infrared thermography diagnosis, the external faults show mainly the overheat of connectors, and they are easy to be discerned. However, the internal faults are difficult to be penetrated because internal faults are much more complex. To find out the internal faults, one must know the law of the internal faults attribute to the relation of their infrared thermography characteristics. The internal faults of the electrical equipments can be divided into loose connection or contact of internal conductors and inferiority in insulation and other faults [24]. Table 1 summarized the examples of internal faults and its characteristics that are normally exist in electrical power equipments.
Table 1: Example of internal faults and its characteristics [4] [24]
No
Type of internal faults
Examples
Thermography characteristic
Loose connection or contact of internal conductor
Loose contact of internal contacts of short oil circuit-breaker
The loose contact is at the upper contact or intermediate contact. Either of them will lead to overheat of the circuit breaker. If the upper contact is in loose contact, the temperature of its header is the highest, then the basal stump flange, and the intermediate porcelain bushing is the lowest.
Loose connection of primary internal connection of current transformer
The header of the current transformer is overheating while the porcelain body is almost normally.
Loose connection of the internal outlet terminal of high-voltage bushing header
The bushing shows the characteristic of the bushing header is the heating center while the body of the porcelain bushing is in normal.
Loose connection of internal conductors of cable splice
The cable splice will show the characteristic of local overheat, and the heat centre is in the forked of the phase.
Inferiority in internal insulation of electrical
equipments
Inferiority in internal insulation of potential transformer
The whole body overheats in comparison with the other phases. There is not a remarkable overheating centre on its body but the temperature of its header is a little higher than that of the porcelain body.
Inferiority in internal insulation of current transformer
The characteristic of whole body overheat in comparison with the other phases. There is not a remarkable overheating centre on its body but the temperature of the header is higher than that of its porcelain body.
Inferiority in internal insulation of coupling condenser
When a phase of the coupling condenser is inferior in internal insulation, its thermography will show the characteristic of whole body overheat in comparison with the other phases. The whole body is almost homogeneous heating.
Deviation of post insulator
The thermography of the post insulator shows the characteristic of whole body overheat.
Insulation deliquescence of the cable splice
When the cable splice is deliquesced because of local damage or poor sealed will show the characteristic of whole body overheat or local overheat. When whole insulation of the cable splice is deliquesced, the thermography of the cable splice shows characteristic of whole body overheats.
Other Faults
Lack of oil in coupling condenser
There is a clear temperature gradient in oil level.
Lack of oil in potential transformer
Thermographic camera will show the true oil level with brighter at below.
Lack of oil in bushing of transformer
Thermography will show the true oil level.
Dampness in internal components of arrester because of poor sealed or porcelain damage
When whole components of the arrester are damped, the whole body overheats by comparison with other phases.
Improvement of inspection technique
The new development of modern IRT equipment has improved the quality of measurement. Most of modern IR imagers resolve surface temperature differences of 0.1°C or less [12] [7]. An infrared thermographic system is essentially imaging IR radiometers that can provide IR images continuously and in real time just like the image provided by a normal video cameras. Complete thermographic systems also integrate an advanced image processing and display system. Despite the advantage of modern design of IRT camera, there are still several factors need to be considered when doing an inspection. Even if temperatures can be measured accurately, several other factors must be taken into account if the real influence of the abnormal temperature difference is to accurately accounted for [14]. This is a very critical aspect especially for outdoor or opened environment inspection. The inspection of electrical power system using IRT can be divided into three areas; substation, underground distribution and aerial distribution [22]. This section will discuss all the factors that directly or indirectly affect the result of the inspection. When accurate measurements are required, all the factors may need to be identified first at the time the image is captured.
Generally, the factors that will affect the accuracy of IRT measurement can be categorized as procedural, technical and environmental. This can be summarized in Figure 2 [13]. The procedural factor is about the thermographer itself. This factor can be minimized if the certified or qualified personnel is employed. For technical factors, most of the information need to know are the emissivity of the component under inspection, load current variation, distance of the object being inspected [13][7]. Without considering all these factors, results may be misinterpreted or incorrect.
Figure 2: Procedural, technical and environmental sources of influence [13]
For an outdoor or uncover inspection such as power substation, environmental effect is a very critical issue. The data regarding the environmental factors are very important to collect. Table 2 summarized all the factors regarding the environmental effect that need to take into consideration when doing IRT inspection [7] [11] [13] [23].
Table 2: Environmental factors that needs to consider during IRT inspection
Environmental factors
Effect on IRT measurement
Ambient air temperature
The exact impact of changing air temperatures is difficult to predict. An increase in air temperature will result in an increase in the measured temperature of a component.
Precipitation/humidity
It can be any form whether snow, rain or fog, will result in evaporative cooling. The temperature differences (either phase to phase or rise over ambient), therefore, can be dramatically reduced with precipitation, leading to a misinterpretation of the data. Problems that are only slightly warm may be cooled below a point where they can be detected.
Information about wind or other convection
Wind speeds as small as 1-5 mph can have a significant cooling effect on a high resistance fitting. Wind alone in this range can completely mask a fitting that has begun to deteriorate; the situation is much worse when the conductor is not carrying full load current. Wind speeds above 5 mph can reduce the temperature difference between the component and ambient to a few degrees above ambient.
Impact of the sun or solar radiation
Solar heating of component, especially those with a high absorptivity of the sun's energy (such as aged conductors), will mask over small thermal differences. Typically, bright metal components are not heated significantly, but many other components are. Late afternoon inspections during the summer months are particularly problematic and should be avoided if possible.
However, there are other factors that affecting the thermal measurement. A study should be made before start any IRT inspection. In this particular case, most of the thermographer needs to get some information about the target location. Sometimes, the history of the target location and electrical power components also needs to take into consideration. The loads variations, type of equipments as well as the material used in building the equipments are among other important data when doing an inspection. In order to get the best and accurate measurement, the right and suitable tool should be selected. It is recommended that for an extensive outdoor inspection especially during sunny periods, long-wave sensing of IRT systems will give superior results. Short-wave systems should be used only on a limited basis or, if loads and other conditions allow, on overcast days or at night [7]. As for summary, Table 3 shows all the factors that will effect the IRT measurement related to the target equipments and the inspection tools.
Table 3: Factors related to the target equipments and the inspection tools [7] [23]
Equipments factors
Characteristic
Convective cooling
Convective cooling by the wind or inside plants by any forced air currents will quickly reduce the temperature of a high resistance connection. During windy condition, hot electrical components can be insignificant thermal problem but become too significant when convective cooling is reduced.
Electrical loads
Load on a typical distribution line may vary from very little to maximum twice a day. If the load increases, the temperature of the connection will increase at a rate that is greater than linear and less than exponential.
Component emissivity
Many electrical components have extremely low emissivities [7], resulting in the inability to accurately measure temperatures, typically 0.1-.03 [23]. While weathered and aged conductors can have emissivity values as high as 0.9, it is often difficult to assess this visually in the field from a distance. Low emissivity also means that components must be very hot before they radiate enough energy to be detected. Even small mistakes in determining emissivity values for shiny metals typically result in large errors.
Thermal gradient
The heat of high resistance usually is being generated at some internal point to the surface. There exists a thermal gradient between the hottest spot inside the component and the surface being viewed. This gradient can be very large, approximately hundreds of degrees.
Limitations of the infrared system
While the target and its environment present a number of difficulties, the infrared system itself also has inherent limitations. The two primary factors that must be addressed are resolution, both spatial and measurement, as well as detected waveband.
Measurement & Analysis Method
There are two ways to take a temperature measurement. The first is known as quantitative, which is to take the exact temperature values of objects. The second type is qualitative, which takes the relative temperature values of a hotspot with respect to other parts of the equipment with similar conditions. Infrared thermography can be used as both a qualitative and a quantitative tool. Some applications do not require obtaining exact surface temperatures. In such cases, it is sufficient to acquire thermal signatures, characteristic patterns of relative temperatures of phenomena or objects [2]. This method of qualitative visual inspection is expedient for collecting a large number of detailed data and conveying them in a fashion that can be easily interpreted. In contrast, accurate quantitative thermography demands a more rigorous procedure to extract valid temperature maps from raw thermal images.
Qualitative
A widely used method of using thermography on electrical equipment is by employing the ∆T criteria [1] [26]. Qualitative measurement some called as comparative thermography [7]. When the comparative technique is used appropriately and correctly, the differences between the two (or more) samples will often be indicative of their condition. The qualitative method of estimating the maintenance priorities is by using tables of temperature ratings to assess the severity of overheating of the equipment. These tables are usually divided into three or four different categories to indicate the maintenance priority based on the equipments temperature rise with respect to a similar reference component [26]. Table 4 shows the maintenance testing specifications for electrical equipment published by the InterNational Electrical Testing Association (NETA) [21]. NETA provides guidelines for thermal inspections of electrical equipment. These guidelines are based on differences in temperature from one phase conductor or component to another. Recommended action is dependent on the difference in the temperatures.
Table 4: Maintenance testing specifications for electrical equipment
Priority
∆T between similar components under similar load (°C)
∆T over ambient temperature (°C)
Recommended Action
4
1 - 3
1 - 10
Possible deficiency, warrants investigation
3
4 - 15
11 - 20
Indicates probable deficiency; repair as time permits
2
---
21 - 40
Monitor until corrective measures can be accomplished
1
> 15
> 40
Major discrepancy; repair immediately
Figure 3 shows the example of hotspot and the reference point. Hot area is the suspected component and the reference must be another similar component with the same condition. It could be similar components in other phases. In the ∆T method, the temperature rise at phase L1 is calculated as:
(2)
where TL1 is the hot-spot temperature of the measured object and TL2 is the hot-spot temperature of the reference object [26]. The severity of the hot spot is then checked using Table 4 under the column '∆T between similar components under similar load (°C)'. Action should be taken according to the range of ∆T.
Figure 3: The hotspot and the reference point [1].
The advantage of this method is that it is a practical method to establish "failure" or "no failure" and the emissivity has only a minor impact on the result [1]. A drawback is that the temperature tables are usually only found in handbooks and guidelines and hence there is a lack of a recognized standard. Moreover, the ∆T criterion does not say anything about whether the equipment temperature limits are actually exceeded. Furthermore, using the ∆T criteria will not expose systematic failures affecting all three phases [26].
Quantitative
In quantitative measurement, the reference is the ambient temperature. The observation is established by measuring the absolute temperature of electrical equipment under the same ambient conditions. As the reference temperature has to be measured, it is requires an even greater understanding of the variables influencing radiometric measurement, as well as a grasp of its limitations. It is vital to determine what margin of error is acceptable before beginning an inspection, and to work carefully to stay within those bounds [7]. This will include all the variables that have been discussed in the section of improving the inspection technique. In this case, related data must be collected and adjustment should be made accordingly.
Since this measurement forms the basis for subsequent calculations, it is very important that it is as accurate as possible. The temperature rise is calculated as [26]:
(3)
where TL1 is the hot-spot temperature of the measured equipments and Tamb is the ambient temperature at the time of measuring. Again, Table 4 under the column '∆T over ambient temperature' is used for testing specification.
Automatic diagnostic system
Nowadays, peoples are looking for automatic diagnosis system in order to get fast and accurate for a low cost maintenance. Most of the IRT cameras that are available today come with analysis software and even can prepare the inspection report. Besides that, there are also standalone analysis software that can be used for any thermographic image. Digital images are imported into the computer directly from the PC card and may be displayed in grayscale or with a variety of color palettes. Various color palettes can be selected. Adjustments can be made to all radiometric parameters, such as emissivity, background temperature, span, and level. Analysis functions may include spot, area, isotherms, and line thermal measurement, as well as size measurements. Analysis can extend beyond the image by displaying the numerical data in a spreadsheet or in various standard graphical forms such as a histogram [7].
However, despite the powerful and very easy to use the software, the analysis process still needs qualified or experienced personnel to do the inspection. A part from that, most of the analysis will take long time for preparing the result. Therefore, applying an intelligence system in thermographic analysis is the right solution. In recent years, rapid development in computer vision based on image processing technique and integrate with artificial intelligent has contributed many advantages in monitoring and diagnosing of power system equipments. For automatic inspection using IRT, currently most of the systems were applied in medical imaging, food processing and crack detection in concrete structure [19] [20]. In electrical power system, the application of IRT for automatic diagnosis and intelligence system is still in the early stage. This is due to the complex analysis and various factors need to consider. Most of the research in automatic diagnosis of electrical power equipments and machine condition monitoring started in the early year 2000 [1] [8] [15] [16] [17] [27].
One of the systems was proposed by Ying-Chieh Chou and Leehter Yao [1]. The system applied an automatic diagnostic system for electrical equipments using the simple method of image processing. They have proposed an algorithm known as Infrared Thermography Anomaly Detection Algorithm (ITADA). The algorithm was developed based on the principle of Otsu's statistical threshold selection algorithm using grey level histogram. The analysis was done using quantitative and qualitative method. Table 5 and 6 show the condition measurement for both qualitative and quantitative respectively.
Table 5: Condition qualitative measurement [1]
Type of Conditions
∆T Variations (%)
∆T Variations (°C)
Normal
∆T < 9
∆T < 10
Warning
9 ≤ ∆T < 90
10 ≤ ∆T < 25
Abnormal
90 ≤ ∆T
25 ≤ ∆T
Table 6: Condition quantitative measurement [1]
Type of Conditions
∆T Variations (°C)
Normal
Thot < 60
Warning
60 ≤ Thot < 90
Abnormal
90 ≤ Thot
The qualitative and quantitative condition rules as shown in Table 7, are applied to classify the different conditions in relation to a thermal inspection according to the experiential value, as indicated in Table 5 and 6. From the experiment conducted in the research, a breakdown of the results shows that about 91% accuracy and 9% mistake including 8% over caution and 1% less caution. The diagnosis was performed over capacitors, transformers, and other power transmission equipment [1].
Table 7: Strategy for various situations [1]
Equipment Condition
Weighting
Strategy
Normal
0
None
Warning
1
Short term follow-up on repair and maintenance required
Abnormal
2
Highly dangerous, require immediate maintenance
Another research work [8] was carried out by Baoshu Li, Xiaohui Zhu, Shutao Zhao, and Wendong Niu. Instead of static image, this research proposed a real time and multi channel diagnosis of high voltage equipment based on infrared imaging analyzing. The integral structure of the system is shown in Figure 4. As illustrated in the figure, every CCD is installed at suitable locations besides the power equipments that would be monitored. It transmits the running state of equipments to optical signal, which is delivered to the computer by multichannel collector. As the integral system's center, the computer controls capturing of images, finishes images' preprocessing, recognizes and analyzes images. For detecting the anomaly, the system uses simple method by subtracting the former image from the current image to check any changing in equipment's temperature. If some changes happen about image, such as convex hull, intensive stochastic noise and false edges, the running state must be not in order. The software of system judged running state of equipments, analyzed probability that troubles happen and gave out sound or text alarming signal. By using support vector machine (SVM) for pattern recognition and analysis, they claimed that the system has achieved the accuracy up to 90%.
Figure 4: Integral structure of system [8].
Recently, an intelligent thermographic diagnostic was developed to enable the detection and diagnosis of faults in surge arrester. The image of surge arrester was segmented using Watershed Transform. In this research, they have proposed neuro-fuzzy automatically classifying the condition of surge arrester. Figure 5(a) shows the methodology used for intelligence surge arrester diagnostic. The only thing that distinguishes this research with others is the input variables. Most of the previous works only depend on the captured image without considering other variables. Besides include the environmental variables it also includes the identification variables of surge arrester such as pollution index, rated voltage, material and manufacturer of the equipments. This is shown in Figure 5(b). The output of the surge arrester diagnosis is classified as normal condition (NORMAL), surge arrester in leaving normal condition (LIGHT), surge arrester in suspicious condition (SUSPICIOUS) and surge arrester in faulty condition (FAULTY). This method has achieved up to 90% of the accuracy [15].
(a) (b)
Figure 5: (a). the methodology used for intelligence surge arrester
diagnostic. (b). input and output for neuro-fuzzy. [15]
In another attempt, Zbigniew Korendo and Marek Florkowski [16] used another method to diagnosis power equipment. The developed tool was verified with a case study of a high voltage (HV) disconnector inspection. Invariant coefficient method was implemented for classifying the condition of the power equipment. At all times, current load and ambient conditions were noted. In this research, the defect could be detected according to its thermal behavior. The disconnector heads should yield the same surface temperature distribution and any load-dependent internal heat production variations should result in similar thermal responses by taking the fact that the disconnector construction features are full symmetry. The same applies to varying ambient conditions as both parts are subjected to the very same ambient conditions. The resulting conclusions of the method provide one of three possible condition states whether it is OK, warning or 'alarm' for each defined region.
Another research recently was carried out by Ali Md. Younus and Bo-Suk Yang [17] for machine fault diagnosis using the thermal image analysis. This is a new method of machine fault diagnosis using different machine conditions data such as normal, misalignment, mass-unbalance and bearing-fault from IRT. Figure 6 and Figure 7 show the experimental setup used in the study and the real thermal image of machine condition. In this research, they have proposed to use desecrate wavelet decomposition, which gives some significant statistical feature that enable to get machine health condition. In this work, bio-orthogonal wavelet algorithm has been successfully implemented to obtain real machine's condition.
Figure 6: experimental setup [17]
Figure 7: thermal image of machine condition [17]
Wai Kit Wong et.al (2009), have proposed another method of machine condition monitoring. In this research, two simple and fast detection algorithms were used into a cost effective thermal imaging surveillance system. This surveillance system is not only used for monitoring the functioning condition of different machines in a factory site, but can also use for detecting the trespassers in a poor lighting condition. Experimental results show that the proposed surveillance system achieves high accuracy in monitoring machines conditions and detecting trespassers. Using a simple image processing technique, this research used pixels analysis in order to detect either the machine is overheated. As illustrated in equation (3), S is defined as the minimum overheat size of a machine, the machine in that particular section is said to be overheated if total overheated pixels in a section are more than S. Otherwise, the machine is considered as functioned in normal condition [27]. Figure 8(a) and (b) show machine for monitoring and its thermal image respectively.
(a) (b)
Figure 8: (a) machines for monitoring and its (b) thermal image [27]
Future Recommendations
There are many things need to be done in improving the quality of IRT inspection over electrical power equipments. This will include the technology of IRT equipment as well as the method of inspection. Of course, this will involve various fields of studies. Since the demand for preventive maintenance and condition monitoring of electrical equipments are increasing, a robust and fast analyzing tool is required to do inspection. This section will highlight some recommendations for the future research in order to improve the condition monitoring as well as preventive maintenance particularly in electrical power system.
Latest technology in infrared cameras
The main factor that mostly will affect the quality of inspection is the IRT equipment itself. Past problems with radiometric instability have been resolved and the biggest change has been the remarkable, continued, downward price pressure. Advances in manufacturing processes of detectors have dramatically increased both yields and quality while reducing production costs. However, the quality of inspection is regarded with the mage resolution. The poor resolution will produce bad interpretation of inspected image. Therefore, a right selection of IRT camera is very critical. A part from that, the sensitivity of the camera should be considered.
Continuous Monitoring
The adoption of continuous thermal imaging can deliver increased benefits over periodic thermal inspection, especially in respect of mission critical electrical equipment. The advantages that continuous thermal monitoring provides are both numerous and tangible. In addition, it is not operator dependent, nor is it dependent on the inspection being carried out at when equipment is running at load. Perhaps one of the most important advantages is the ability to integrate continuous monitoring into existing SCADA systems, enabling real time and remote monitoring without the need for separate systems or reports, something that cannot be achieved with periodic thermal inspections. Real-time imaging system are not just fast, compact and frequency agile but they also have greater resolution improved imaging analysis and recording capabilities [22].
Advanced methods of fault diagnosis
Thermography can often use a comparison between similar components to detect faults, but there may be a need to show how severe the fault is. Fault diagnosis in mechanical and electrical systems often depends on predicting how hot a component could get at full load and the life of the component at that temperature. If the survey is done with all the components at full load and time has been allowed for them to warm up they should be at the maximum temperature they will reach. However, some equipment is often not at full load but experienced thermographers need to predict the temperature that will be reached at full load [28].
Intelligence Diagnosis System
Due to the high demand in preventive maintenance in electrical power system, there is a need to have an intelligence system. This is due to the conventional method, most of the inspection only can be done by a certified or experienced personnel. The fact that many power installations require a large amount of workers to conduct inspections, most power companies usually assign outside contractors to do electrical inspections for them and this will take very long time for evaluation [1]. Therefore, an advanced intelligence system need be integrated in analyzing IRT image. The use of artificial intelligence techniques such as neural networks, fuzzy logic, neuro-fuzzy or any other methods is believed that it can solve many problems. This is due to the capability of intelligence system to handle a large amount of IRT data [9].
Better models and testing of models
Models must be developed that can accommodate the complex interactions of the various factors discussed above, especially wind, load, ambient air temperature, emissivity, and resistance. Perhaps the goal should be to simply determine the conditions under which inspections are feasible, rather than trying to provide for corrected temperatures [23].
Conclusion - under correction
Early prevention is required to avoid future faults and increase the reliability of the electrical power equipments. The use of IRT is the right solution to do the preventive maintenance and condition monitoring. Some improvement in analysis method should be done by considering various factors. Recent trends in IRT inspection on power system show that there is a need to applying the real-time monitoring and automatic analyzing in order to improve the detection technique of power system equipments abnormality. A new approach has to develop for faults detection. For more complex image analysis, more complex image processing technique must be applied. Further development should enhance automatic processing capabilities in the form of automatic recognition of the measured objects and its critical parts.
Acknowledgment
This work was supported by USM…….
References
[2]Ying-Chieh Chou and Leehter Yao, "Automatic Diagnostic System of Electrical Equipment Using Infrared Thermography," socpar, pp.155-160, 2009 International Conference of Soft Computing and Pattern Recognition, 2009.
[3]Junyan Liu, Jingmin Dai, Yang Wang, Hui Liu and Zijun Wang," An IR Lock-in Thermography Nondestructive Test System Based on the Image Sequence Processing", 17th World Conference on Nondestructive Testing, 25-28 Oct 2008, Shanghai, China.
[4]Heriansyah, Rudi, and S.A.R. Abu-Bakar. "Defect detection in thermal image for nondestructive evaluation of petrochemical equipments." NDT & E International 42, no. 8 (December 2009): 729-740.
[5]Cao Yang, Gu Xiao-ming and Jin Qi," Infrared technology in the fault diagnosis of substation equipment", China International Conference on Electricity Distribution (CICED 2008), China.
[7]Milenko Braunovic , Nikolai K. Myshkin and Valery V. Konchits, "Electrical Contacts Fundamentals, Applications and Technology, CRC Press 2007, Print ISBN: 978-1-57444-727-9, eBook ISBN: 978-0-8493-9108-8.
[8]R. Keith Mobley, "An Introduction to Predictive Maintenance", 2nd ed., Butterworth-Heinemann, 2002.
[9]Hellier, Chuck , "Handbook of Nondestructive Evaluation", 1st ed. McGraw-Hill Professional, 2001.
[10]Baoshu Li, Xiaohui Zhu, Shutao Zhao, and Wendong Niu. "HV Power Equipment Diagnosis Based on Infrared Imaging Analyzing." International Conference on Power System Technology, 2006( PowerCon 2006), 1-4, 2006.
[11]Xavier Maldague, "Applications Of Infrared Thermography In Nondestructive Evaluation." http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2908.
[12]Darabi, A. (2000). Detection and Estimation of Defect Depth in Infrared Thermography using Artificial Neural Networks and Fuzzy Logic. Université Laval, Canada: Ph.D Thesis.
[13]C.A. Balaras and A.A. Argiriou, "Infrared thermography for building diagnostics," Energy and Buildings, vol. 34, Feb. 2002, pp. 171-183.
[14]Brent Griffith, Daniel Türler and Howdy Goudey "Infrared Thermographic Systems: A Review of IR Imagers and Their Use", The Encyclopedia of Imaging Science and Technology, 2001.
[15]L. dos Santos, E.C. Bortoni, L.E. Souza, G.S. Bastos, and M.A.C. Craveiro, "Infrared thermography applied for outdoor power substations," Thermosense XXX, V.P. Vavilov and D.D. Burleigh, Eds., Orlando, FL, USA: SPIE, 2008, pp. 69390R-11.
[16]J. Snell and R.W. Spring, "The new approach to prioritizing anomalies found during thermographic electrical inspections," Thermosense XXV, K.E. Cramer and X.P. Maldague, Eds., Orlando, FL, USA: SPIE, 2003, pp. 222-230.
[17]C. Laurentys Almeida, A. Braga, S. Nascimento, V. Paiva, H. Martins, R. Torres, and W. Caminhas, "Intelligent Thermographic Diagnostic Applied to Surge Arresters: A New Approach," Power Delivery, IEEE Transactions on, vol. 24, 2009, pp. 751-757.
[18]Z. Korendo and M. Florkowski, "Thermography based diagnostics of power equipment," Power Engineering Journal, vol. 15, Feb. 2001, pp. 33-42.
[19]A.M. Younus and Bo-Suk Yang, "Wavelet co-efficient of thermal image analysis for machine fault diagnosis." Prognostics and Health Management Conference, 2010.12-14 Jan. 2010, pp. 1-6.
[20]M. Fidali, "An idea of continuous thermographic monitoring of machinery," 9th International Conference on Quantitative InfraRed Thermography (QIRT2008), July 2-5, 2008, Krakow - Poland.
[22]G. Schaefer, M. Závisek, and T. Nakashima, "Thermography based breast cancer analysis using statistical features and fuzzy classification," Pattern Recognition, vol. 42, Jun. 2009, pp. 1133-1137.
[23]C. Quek, W. Irawan, and E. Ng, "A novel brain-inspired neural cognitive approach to SARS thermal image analysis," Expert Systems with Applications, vol. 37, Apr. 2010, pp. 3040-3054.
[25]Standard for Infrared Inspection of Electrical Systems & Rotating Equipment Infraspection Institute, 2008.
[26]Z. Azmat, D.J. Turner, "Infrared thermography and its role in rural utility environment", Rural Electric Power Conference, 2005.
[29]J. Snell and J. Renowden, "Improving results of thermographic inspections of electrical transmission and distribution lines," Transmission and Distribution Construction, Operation and Live-Line Maintenance Proceedings. 2000 IEEE ESMO - 2000 IEEE 9th International Conference on, 2000, pp. 135-144.
[30]Niancang Hou, "The infrared thermography diagnostic technique of high-voltage electrical equipments with internal faults," POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151), Beijing, China, pp. 110-115.
[31]F. Lizak, and M. Kolcun, "Improving Reliability and Decreasing Losses of Electrical System with Infrared Thermography", Acta Electrotechnica et Informatica Vol. 8, No. 1, 2008, 60-63.
[34]T.M. Lindquist, L. Bertling, and R. Eriksson, "Estimation of disconnector contact condition for modelling the effect of maintenance and ageing," 2005 IEEE Russia Power Tech, St. Petersburg, Russia: 2005, pp. 1-7.
[35]W.K. Wong, P.N. Tan, C.K. Loo, and W.S. Lim, "An Effective Surveillance System Using Thermal Camera," 2009 International Conference on Signal Acquisition and Processing, Kuala Lumpur, Maylaysia: 2009, pp. 13-17.
[36] T. Lindquist and L. Bertling, "Hazard rate estimation for high-voltage contacts using infrared thermography," Reliability and Maintainability Symposium, 2008. RAMS 2008. Annual, 2008, pp. 231-237.
TOC
Abstract
Introduction
Infrared Thermography
Application in electrical power system
Faults Diagnosis in Power Equipments
Improvement of inspection technique
Method of Analysis & Measurement
Qualitative & Quantitative
Assessment criteria
Automatic diagnostic system
Limitations and future developments
Conclusion
Acknowledgment
