A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis

Renewable and Sustainable Energy Reviews 46 (2015) 201–209

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis Haroldo de Faria Jr.n, João Gabriel Spir Costa, Jose Luis Mejia Olivas UFABC – Federal University of ABC, CECS, Rua Santa Adélia, No. 166, Santo André, SP, 09.210-170 Brazil

art ic l e i nf o

a b s t r a c t

Article history: Received 19 September 2014 Received in revised form 18 January 2015 Accepted 24 February 2015 Available online 17 March 2015

Electric power transformers are the link between the generators of a power system and the transmission lines and between lines of different voltage levels. Power transformers undergo changes in their operational life expectancy and reliability over the years. Currently, several tools for diagnosis and assessment of their operational condition are available, including diagnostic techniques based on dissolved gas analysis in the insulating oil. Through monitoring of dissolved gases in oil, it is possible to perform detailed data analysis, seeking systemic failure prediction. The adoption of new technologies for maintenance of power transformers can induce substantial changes in the reliability of such equipment in view of the existence of a global trend to decrease operational costs, predict maintenances and control substations in a centralized way. This paper describes the main factors that lead to lifetime reduction in transformers and reviews the main methods used for predictive maintenance based on dissolved gas analysis. The advantages and disadvantages of each one are outlined and some future directions for research are proposed. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Power transformers Predictive maintenance Dissolved gas analysis

Contents 1. 2.

3.

4.

n

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Electric power transformers and maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 2.1. Constructive aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 2.2. Maintenance methods for electrical equipment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 2.2.1. Corrective maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 2.2.2. Preventive maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 2.2.3. Predictive maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 2.2.4. Proactive maintenance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Fault analysis in power transformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 3.1. Operational lifetime degradation factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 3.1.1. Failures caused by the network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 3.1.2. Failures caused by components, parts and pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 3.1.3. Failures due to degradation of the insulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Methods for diagnosis and assessment of the operational condition of power transformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 4.1. Dissolved Gas Analysis (DGA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 4.1.1. IEC 60599 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 4.1.2. Key Gas Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 4.1.3. Method of Duval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 4.1.4. Method of Doernenburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Corresponding author. Tel.: þ 55 11992558733. E-mail address: [email protected] (H. de Faria Jr.).

http://dx.doi.org/10.1016/j.rser.2015.02.052 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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4.1.5. Method of Rogers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 5. Analysis of methods and future directions of research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

1. Introduction Electric power transformers are robust and efficient electric equipment that play a fundamental role in supplying electric energy at adequate voltage levels to consumers. Nevertheless, power transformers undergo changes in their reliability and operational lifetime over the years. This is mainly due to the heavy loading of the equipment, driven by the need to achieve increased profits and the related reluctance to invest in new facilities by the power companies in a competitive market environment [1]. Due to improved monitoring and maintenance methods which emerged with technological advances, their lifespan have increased. Since power transformers have a high cost and are very important to the availability of electrical power systems, several tools for diagnosis and assessment of their operational condition are available. Some diagnostic techniques can be based on the analysis of dissolved gases in oil [2–6], on the monitoring of liquid and solid insulation from the physical–chemical analysis of the insulating oil and analysis of lifetime from the definition of the degree of polymerization of insulating paper, among others [7]. Studies in the past decades have proved that the dissolved gases in transformer oil are related closely to incipient faults [4]. If an incipient failure of a transformer is detected before it leads to a catastrophic failure, predictive maintenance can be deployed to minimize the risk of failures and further prevent loss of services [10]. Therefore, online monitoring and offline testing are vital for assessing power transformer conditions [11]. Methods of diagnosis of potential faults concealed inside power transformers have attracted much research interest [5]. Dissolved gas analysis (DGA) is a common practice for incipient fault diagnosis and preventive maintenance of power transformers. These methods test and sample the insulation oil of transformers periodically to obtain the constituent gases in the oil due to breakdown of the insulating materials inside the equipment [6–12]. When there is any kind of fault, such as overheating or discharge fault inside the transformer, it will produce a corresponding characteristic amount of gases in the transformer oil [9]. Through the analysis of the concentrations of dissolved gases, their gassing rates, and the ratios of certain gases, the DGA method can determine the fault type. As study results indicate, corona, overheating and arcing are the three main causes for insulation degradation in power transformers [6]. In DGA, the fault related gases commonly used are hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), and carbon dioxide (CO2). Therefore, if we forecast these dissolved gases content in power transformer oil according to the recent historical data, incipient failures of power transformer and its development trend will be found out early, minimizing the probability of a transformer loss [5]. It is possible to monitor various parameters of a transformer, enabling early identification of failures, so that they can be treated predictively. Various types of sensors can be installed on the transformer to measure variables such as the temperature of the oil and the windings, the dissolved gases and moisture content of the oil, the capacitances and power factor of the bushings and the contact wear on load tap changers. The data obtained by these

sensors can be analyzed and the results used to indicate if the equipment is under some kind of fault or close to one. A risk analysis can also be performed on the data to calculate important indicators such as the probability of failure of a transformer operating at a certain condition. 2. Electric power transformers and maintenance 2.1. Constructive aspects Transformers applications are diverse and they are widely used by the industry and distribution and transmission systems. In these installations the transformer is commonly used to lower or raise the system voltage level [13]. The lifetime of a transformer is mainly determined by its insulation system, such as the type of material used and how it was manufactured [14]. The insulation system is designed based on factors determined by the shape and characteristics of the active part (set comprised of core and windings of a transformer) and the gradients of temperature specified. Among the various materials that make up the cooling system we can highlight the radiators (the main component of this system), fans, pumps and insulating oil, used as a refrigerant. The heat load to which the internal components of transformers are exposed to is severe so that the cooling system is essential for its proper functioning, emphasizing the fact that these systems vary according to the operational context (conditions and environment) and the power capacity of the transformer. 2.2. Maintenance methods for electrical equipment Maintenance is considered a strategic activity that ensures operation reliability of equipments and industrial processes. Maintenance should seek the intervention in equipment through a strategy of reducing the intervention time, leaving the system unavailable for the shortest time possible [7]. Among the various forms of maintenance we can highlight four main types which are: – – – –

Corrective maintenance; preventive maintenance; predictive maintenance; and proactive maintenance.

2.2.1. Corrective maintenance Corrective maintenance fixes flaws and performance indexes through system restoration. This form of maintenance can be divided into two types, namely: – Unplanned corrective maintenance: failure is corrected randomly, without an intervention plan. – Planned corrective maintenance: correction of fault and/or performance occurs in a planned manner, due to flaw detection through preventive and/or predictive maintenance, ensuring reduced costs and implementation time.

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2.2.2. Preventive maintenance This type of maintenance seeks to increase the reliability and availability of equipment by reducing failures, avoiding the need for unplanned corrective maintenance. The objective of this type of maintenance is the prevention of problems and failures, seeking correction of faults before they occur. Due to preventive action, the replacement of equipment parts may occur prematurely. Nevertheless, equipment failures can occur, since preventive maintenance should be performed at specific time intervals [16]. The intervals between the interventions are based on the experience of the maintenance team, the managers of the company, statistical studies or according to equipment manuals recommendations, while always considering the needs of the company. Table 1 shows a comparison between the types of corrective and preventive maintenance.

2.2.3. Predictive maintenance Predictive maintenance means maintenance with a focus on failure prediction, occurring through follow-ups with a specific systematic on parameters and equipment conditions. This type of maintenance did not emerge as a replacement for corrective and preventive maintenance, but as an additional tool, which seeks to minimize, through the monitoring of specific parameters, maintenance costs and losses in equipment. Its main function is to collect data with the equipment under operation, generating minimal interference in its operation. Through the data collected, a diagnosis and trend analysis should be done, identifying potential problems through historical analysis of similar equipment and knowledge acquired over time. Several variables can be monitored, such as density, flow, pressure, vibration, temperature, voltage, current, electrical resistance, capacitance, inductance, among others.

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3. Fault analysis in power transformers Through the experience with the use of transformers, together with data acquired over the years, it was realized that failures in these equipment follow a pattern which is known as the “Bathtub Curve” [19]. The “bathtub curve” can be divided into three main periods, as shown in Fig. 1, which are as follows: – First period: represented by failures due to “infant mortality” of equipment originated from errors in design or assembly. This defines the period that includes all faults, before the stable behavior of the curve. – Second period: represents the rate of continuous failures over time, indicating failure rate between 1% and 2% per year. – Third Period: an increase in the failure rate occurs, caused by aging of the equipment, which leads to degradation of the insulation (solid and liquid).

The useful lifetime of a transformer is related to its use and to the faults to which the equipment is subjected through time, which accelerates aging, increasing the likelihood of failure and reducing the period of stability of the product.

3.1. Operational lifetime degradation factors It is possible to relate the main faults in power transformers with their main causes, which are: Failures caused by the network. Failures caused by components, parts and pieces. Failures due to degradation of the insulation.

2.2.4. Proactive maintenance Proactive maintenance focuses primarily on determining the root causes of machine failure and dealing with those issues before problems occur. Anticipation is key in proactive maintenance programs since it commissions corrective actions aimed at the sources of failure. Proactive maintenance is geared to detect contamination of fluids and lubricants used in machines. In the case of power transformers, strict monitoring of oil condition would prevent the occurrence of problems that would require component replacement (preventive maintenance) or monitoring of established conditions for potential breakdown like vibration and heat (predictive maintenance).

Fig. 1. Bathtub curve [19].

Table 1 Types of maintenance. Maintenance Preventive

Corrective

Routine

Inspection

Systemic

Selective

Planned

Performed with the equipment in operation.

Monitoring of the condition of the equipment using human senses.

Based on the duration of operation – time calendar.

Performed after completion Performed after flaw and/or failure of operational lifetime. of the equipment or component.

Nonplanned

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3.1.1. Failures caused by the network 3.1.1.1. Mechanical deformation of the windings due to shortcircuits. Short-circuits in transformers result in high intensity mechanical stresses in the active part of the equipment since the resultant forces of these efforts are related to the current squared. Two main types of mechanical deformations in the windings due to the action of short-circuits can be identified: – Elastic deformation: reversible deformation not involving structural changes in the active part. – Plastic deformation: irreversible deformation, which permanently changes the structure of the active part, generating the need for intervention through corrective maintenance. 3.1.1.2. Defects originated from overvoltage. The increase of the dielectric insulation request from the windings and/or temperature rise is related to damage caused by overvoltage. Overvoltages of short duration, which occur in periods of the order of microseconds, are difficult to detect and its damages are caused by internal electrical arcs. Long duration overvoltage result in temperature increases. The overvoltages in transformers can be divided into: – – – –

Temporary overvoltage. Switching overvoltage. Very fast transient overvoltage. Atmospheric discharge overvoltage [15].

3.1.2. Failures caused by components, parts and pieces 3.1.2.1. Accessories and components. Failures in accessories and components can cause damage only to these parts and pieces or may cause serious damage to the transformer itself. Among the failures that do not cause serious damage to the equipment are functional problems such as improper operation of protection systems, erroneous variable readings, small amounts of oil leakage, among other faults that can be corrected by replacing parts and pieces. Serious damage to the equipment can be caused due to the explosion of bushings, switches and other failures that result in substantial losses to the transformer. In this case, it is necessary to put the equipment out of service for repairs in factory or field [15]. 3.1.2.2. Tap changers. Defects at On Load Tap Changers (OLTCs) normally cause serious damage to transformers and its main causes are: – – – –

Mechanical problems. Wear of contacts. Inadequate maintenance. Deterioration of insulating oil of load keys.

Whereas defects at No Load Tap Changers (NLTCs) commonly occur due to the following factors: – Maneuvers on energized transformers, generating electric arcs. – Incorrect maneuver, causing incorrect closing of the contacts so that the transformer energization produces electrical arcs or overheating. – Pressure loss on the springs responsible for tightening the moving contacts, generating increased temperature which, over the time, causes degradation of the contacts and could generate electrical arcing [15]. 3.1.2.3. Bushings. Loss of dielectric properties, degradation of seals and aging of the insulating material are among the major causes of internal faults in bushings. Among the external causes of failures

are occurrence of vandalism, contamination of porcelain and damage by mechanical shock. As a result of these failures catastrophic damages occur such as explosions and fires. These events can permanently damage the transformer or even cause damage to equipment and people nearby, such as when shards of porcelain are launched due to explosions. 3.1.2.4. Connections. Among the components of transformers there are several pieces and parts that are interconnected through connections such as connectors and screws. When faults occur in these components there is an increase in electric current density, causing localized temperature increase, which through time cause a deterioration of these parts of the equipment and leads to failures. 3.1.3. Failures due to degradation of the insulation A reduction in the capacity of the insulation system of a transformer as the equipment is subjected to faults and its operational lifetime increases is normal. Depending on the severity of the flaws, the insulation capacity is reduced in a shorter time. Among the many factors that cause degradation of the insulating material, highlights go to the action of high temperatures, humidity in oil and insulating paper and the presence of oxygen in the internal environment of the transformer [18]. An important point to note is the fact that the insulation system of transformers has elements that end up collaborating with the degradation of one another, so that the insulation ends up destroying itself over time. This process is accelerated by failures and overload of the equipment. An example of this interaction is that of the insulating paper acting as a catalyst to the formation of acids in the oil, which reacts with the paper, causing degradation [7].

4. Methods for diagnosis and assessment of the operational condition of power transformers Utilities and maintenance companies have been using predictive maintenance techniques in order to identify any faults or defects evolving in equipment. These methods measure different intrinsic characteristics of the insulation system such as insulation degradation compounds and physical or chemical parameters. The main techniques available are listed below: Physical–chemical analysis: characteristics of insulating oil are evaluated such as dielectric strength, moisture content, color, acidity, interfacial tension, among others. Furfural: verification of furfuraldehyde (2FAL) content dissolved in the oil, with the aim of detecting the aging of insulating paper. Particle analysis: SUSPENDED particles in insulating oil are identified, compared and classified according to size. Vibration: sensors are attached to the transformer to measure vibration during operation. Thermographic inspection: checks the status of connections and internal overheating detection by comparing the temperature of the hot spots, room temperature and normal operating temperature. Method of acoustic emission: through a set of high-frequency acoustic sensors, one can locate and triangulate pulses to identify and locate faults in transformers [20].

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Dissolved Gas Analysis (DGA): this method can continuously evaluate the condition of the transformer insulation system, identifying possible failures. 4.1. Dissolved Gas Analysis (DGA) With the DGA method it is possible to evaluate continuously the operating condition of the transformer, identifying potential flaws and causes to the formation of gases through the use of various criteria. Among the gases dissolved in oil, combustible and noncombustible gases can be found, as described in Table 2. The distribution of gases takes place according to the transformer failure and the components involved therein. These gases can also be classified according to the type of failure which originated them and the material involved in the process [7] as indicated in Table 3. These gases can be identified by the gas chromatography technique, which performs a physical–chemical analysis through separation of chemical compounds [21]. There are several methods for failure diagnosis of transformers through identification of dissolved gases. The most used are listed below: – IEC 60599 Method; – Key Gas Method;

Table 2 Dissolved gases in insulating oil. Gases Combustile

Non-combustile

Carbon monoxide (CO) Oxygen (O2) Hidrogen (H2) Methane (CH4) Nitrogen (N2) Ethane (C2H6) Ethylene(C2H4) Carbon dioxide (CO2) Acetylene (C2H2)

Table 3 Classification of dissolved gases in accordance with the type of failure and material involved. Corona Oil H2 Cellulose H2, CO, CO2 Pyrolysis Oil Low temperatures CH4, C2H6 High temperatures C2H4 H2 (CH4, C2H6) Cellulose Low temperatures CO2 (CO) High temperatures Electric arc H2, C2H2 (CH4, C2H6, C2H4)

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– Method of Duval; – Method of Doernenburg; and – Method of Rogers. All methods are empirical and their results are based on correlations between the gases found in gas analysis. The ratios used in these methods are listed below. R1: R2: R3: R4: R5:

(CH4/H2); (C2H2/C2H4); (C2H2/CH4); (C2H6/C2H2); and (C2H4/C2H6).

4.1.1. IEC 60599 Method The IEC 60599 Method classifies and identifies transformer failures according to the relationships shown in Table 4. Two important remarks may be made about the method. There are variations in the ratios used in some countries and the C2H2/C2H6 ratio can be used to replace the CH4/H2 ratio. These ratios are to be observed only in cases where one of the dissolved gases has a high concentration value and/or a high growth rate [7]. 4.1.2. Key Gas Method In this method, the flaws are associated with the gas composition profile as shown in Table 5. Key gases are defined in the IEEE guide as “gases generated in oil-filled transformers that can be used for qualitative determination of fault types, based on which gases are typical or predominant at various temperatures.” If historical data of dissolved gases for diagnosis is not available, risks present in the equipment can be identified and classified according to the criteria depicted in Table 6. Condition 1 represents normal operation. Condition 2 indicates that the equipment may be operating under fault with total gases above normal values. Condition 3 indicates an elevated level of decomposition. Condition 4 indicates excessive degeneration and continuous operation can result in failure. 4.1.3. Method of Duval This method interprets dissolved gas data through the use of a triangle of relative percentages of CH4, C2H2 and C2H4, where each vertex considers that 100% of the analyzed gases are comprised of one of these compounds [17]. 4.1.4. Method of Doernenburg In this method, relationships R1–R4 are used and a significant amount of gas is needed to validate its use. According to the method, there are three main types of failures, which are: – Thermal decomposition; – low intensity partial discharges (corona); and – partial discharges of high intensity (electric arc).

Table 4 Interpretation of data obtained by DGA and classification of flaw types according to IEC 60599. Abreviation

Description

C2H2/C2H4

CH4/H2

C2H4/C2H6

PD D1 D2 T1 T2 T3

Partial discharges Low energy discharges High energy discharges Thermal flaw, T o 300 1C Thermal flaw, 300 1C o To 700 1C Thermal flaw, T o 700 1C

Non-significant value 41.0 0.6–2.5 Non-significant value o 0.1 o 0.2

o 0.1 0.1–0.5 0.1–1.0 Non-significant value 4 1.0 4 1.0

o 0.2 4 1.0 4 2.0 o 1.0 1.0–4.0 4 4.0

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Table 5 Gas composition profile for the Key Gas Method. Key Gas Method Key gas

Fault type

Typical proportions of generated combustile gases

H2 e C2H2

Electrical high energy (arcing) Electrical low energy partial discharge Thermal oil Thermal oil and cellulose Electrolysis

High quantities of H2 e C2H2 are produced. Smaller proportions of CH4 and C2H4. CO2 e CO can be formed if cellulose is involved. Oil may be carbonized. Mainly H2. Small quantities of CH4. Traces of C2H4 and ethylene.

H2 C2H4 CO H2

Mainly C2H4. Smaller proportions of C2H6, CH4 and H2. Traces of C2H2 at very high fault temperatures. Mainly CO. Hydrocarbon gases such as CH4 and C2H4 if fault involves structures in oil. Mainly H2.

Table 6 Risk diagnosis in transformers in accordance with the concentrations of dissolved gases in ppm (part per million). Conditions

H2

CH4

C2H2

C2H4

C2H6

CO

CO2

Total gas

Condition Condition Condition Condition

100 101–700 701–1800 4 1800

120 121–400 401–1000 4 1000

1 2–9 10–35 435

50 51–100 101–200 4200

65 66–100 101–150 4150

350 351–570 571–1400 41400

2500 2500–4000 4001–10000 4 10000

720 721–1920 1921–4630 44630

1 2 3 4

No Yes

Yes

H2CH4 C2H2 C2H4 > 2L1

Gas input

Normal condition

No

C2H6 CO > L1

No

Relation test OK?

Relations analysis not applicable Resample

Yes R1 = CH4 / H2 No

R2 = C2H2 / C2H4

Yes R1 < 0.1

Yes R3 < 0.3

Yes R4 > 0.4

R3 = C2H2 / CH4 R4 = C2H6 / C2H2 No

No

R3 > 0.3

R2 > 0.75

0.1 < R1 < 1

Non identified fault Resample

No Yes

Yes

Yes

Yes Electric arc discharge

R4 < 0.4

No No Yes

No Yes

R1 > 1

R2 < 0.75

Non identified fault Resample

No Yes

R3 < 0.3

Partial discharges Radio interference voltage (RIV)

Yes R4 > 0.4

Thermal fault

Fig. 2. Gas relations analysis according to the method of Doernenburg.

Table 7 Limit values for gases dissolved in oil. Key gas

Concentrations (ppm)

Hydrogen (H2) 100 Methane (CH4) 120 Carbon monoxide (CO) 350 Acetylene (C2H2) 35 Ethylene (C2H4) 50 Ethane (C2H6) 65

Fig. 2 shows the step by step analysis of relationships by the Method of Doernenburg. Initially, the values of gases obtained by DGA are compared to threshold values, thus identifying the existence of problems with the equipment and if the concentration of gases is sufficient for the application of the method [17]. Limit values for the ratio of dissolved gases in oil can be found in Table 7. The same method can be used to analyze free gases; however, the limits should follow a different correlation. In Table 8 one can identify the threshold values for the correlations for dissolved gases and free gases.

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Table 8 Relationships for dissolved gas in oil and free gas suggested by the method of Doernenburg. Suggested diagnosis for fault

Relation 1 (R1) CH4/H2 gas origin

Relation 2 (R2) C2H2/C2H4 gas origin

Relation 3 (R3) C2H2/CH4 gas Relation 4 (R4) C2H6/C2H2 origin gas origin

Dissolved in oil

Free gas

Dissolved in oil

Free gas

Dissolved in oil

Thermal decomposition

41.0

40.1

o 0.75

o 1.0

Corona (low intensity partial discharges)

o 0.1

o0.01

Non-significant

Electric arc (high intensity partial discharges) 40.1 o 1.0

40.01 o0.1

40.75

41.0

Yes Gas input

R2 < 1

No

Dissolved in oil

Free gas

o 0.3

o 0.1

4 0.4

40.2

o 0.3

o 0.1

4 0.4

40.2

4 0.3

40.1

o 0.4

o 0.2

Yes 0.1 < R1 < 1

Free gas

Yes R5 < 1

Normal condition

No

No

R1 = CH4 / H2

Yes

R2 = C2H2 / C2H4

Low temperature thermal fault Overload

1 < R5 < 3

R5 = C2H2 / C2H6 Yes

Yes R1 > 1

Thermal fault < 7000 C

1 < R5 < 3 No Yes

Thermal fault > 7000 C

R5 > 3

Yes

Yes

Yes R1 < 1

R2 < 1

Partial discharges Radio interference voltage (RIV)

R5 < 1

No Yes

Yes 1 < R2 < 3

Yes 0.1 < R1 < 1

R5 > 3

High energy electric arc

Fig. 3. Gas relations analysis according to the method of Rogers.

Table 9 Relationships for dissolved gas in oil and free gas and failure diagnosis suggested by the Method of Rogers. Case Relation 2 Relation 1 (R2) C2H2/C2H4 (R1) CH4/H2 0

o 0.1

1

Relation 5 Failure diagnosis (R5) C2H4/C2H6 suggested o1.0

Normal unit

o 0.1

40.1 o1.0 o0.1

o1.0

2

1.0–3.0

0.1–1.0

43.0

3

o 0.1

1.0–3.0

4

o 0.1

40.1 o1.0 41.0

1.0–3.0

5

o 0.1

41.0

43.0

Partial discharge – low energy density arc Electric arc – high energy discharge Low temperature thermal failure Thermal failure o 700 1C Thermal failure 4700 1C

4.1.5. Method of Rogers This method follows the same procedure as the Method of Doernenburg, however, only three ratios are used and there is no dependence on the gas concentration level for validation of the method [17]. Fig. 3 shows the step by step analysis of gas ratios by the Method of Rogers. The ratios for dissolved gases in the oil and free gases, together with suggested failure diagnosis according to the Method of Rogers, are shown in Table 9.

5. Analysis of methods and future directions of research The five DGA methods presented in Section 4 are used to analyze and interpret the significance of gases present in oilimmersed power transformers.

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Another class of methods can be used to forecast the future gas contents of a power transformer according to historical data. Neural network models have been extensively used, but these rely on a large amount of historical data while the data in practice may not be available. Back propagation neural network is widely used for its simplicity, but does not present a high correct diagnosis rate. On the other hand, when the training samples and input information dimension numbers are large, the training time is long [6]. In this regard, the work presented in [5] develops a rough set, fuzzy wavelet neural network and least squares weighted fusion algorithm which simplifies the input dimension and training sample number, so that the network structure is simplified, and the training time is improved. Under the circumstances of limited samples, several models have been proposed [4]. Many effective attempts have been developed such as fuzzy linear regression model, grey model (GM), grey-extension model and support vector machine (SVM) model. The least squares support vector machine (LS-SVM) is introduced as reformulations to standard SVM which simplifies the model of standard SVM in a great extent by applying linear least squares criteria to the loss function instead of the traditional quadratic programming method. LS-SVM used in conjunction with optimization methods such as genetic algorithms and particle swarm optimization to determine optimum free parameters of support vector machines has achieved promising results. Since forecasting methods are not the main focus of this work, this section presents a comparison between the five analysis methods used for predictive maintenance based on dissolved gas analysis and points out some future directions of research. Comparisons were based on a literature review [2,3,16,18,20–27], and aspects considered important for the analysis of trends in the electric power industry were highlighted. One point of agreement among different authors is the fact that the methods of dissolved gas analysis present serious limitations when used separately for the analysis of incipient failures in power transformers. The main limitations can be highlighted: – The criteria listed in the methods are not able to cover all situations and the implementation of more than one method is sometimes necessary. – The same concentration of gases analyzed with different methods may lead to different results. – Presentation of erroneous results, i.e. when the gas concentration is very small, the methods can indicate failures in equipment that are in normal condition. – These methods are easily implemented and present good results for analysis after the occurrence of faults, however, are less sensitive in detecting faults in a predictive manner. – A good fault analysis in transformers using a dissolved gas analysis depends a lot on the accuracy and reliability of the measured concentrations of gases. An important point is the fact that each method of gas analysis has its own advantages and limitations, not always leading to the same results [27]. The accuracy of the analysis depends intrinsically on the specialist responsible for reading the data and showing the results. During the analysis, it is necessary to take into account factors such as differences in transformers, maintenance conditions, operation, voltage levels, oil volume, among others, making it difficult to obtain a tool with 100% accuracy and reliability. A successful methodology was developed in [20] using a sequential combination of methods to initially determine if the equipment is in a state of normality or failure. If a failure was detected, more than one method was applied to identify the type of anomaly. This combination of methods enabled the suppression

of individual weaknesses of each methodology by the other methods, providing a very high level of accuracy. There is a trend in the adoption of computational methods for analysis of failures in transformers. They must be used together or based on standard methodologies in order to obtain the highest possible degree of reliability and accuracy. Risk analysis tools are currently being used in a wide range of industry applications. A monitoring system for predictive maintenance of power transformers can also be built to reap the benefits of risk analysis. Through the employment of sensors, the gases dissolved in transformer oil can be constantly analyzed using a gas chromatography system. An extension of the main tank can be used to sample the circulating oil. The state of operation of the equipment can be detected using a combination of methods, as described in [20]. The information obtained from the equipment current operating state can be compared with information from a reliable database, indicating the state in which the equipment is operating, with the risk of failure. This type of analysis would allow the removal of the equipment for maintenance before the occurrence of serious shortages, increasing the reliability of the power grid. However, as highlighted in [3], even the use of software for DGA analysis using a combination of methods may not have a high accuracy and sometimes fail to distinguish some types of faults. It is recommended that the fault diagnosis be validated by an expert, avoiding with this any mistake in the diagnosis, giving a better credibility to the results. The work presented in [2] points out that a diagnosis totally dependent on an expert may leave unnoticed some types of faults that are in development. A solution to that could be the use of different types of tests and diagnosis like DGA, frequency response and thermal analysis. However, this solution may be difficult to implement as an online monitoring tool.

6. Conclusions This paper presented a review of predictive maintenance methods and current methodologies for fault diagnosis, seeking to present the key concepts related to transformer maintenance with the use of dissolved gas analysis in insulating oil. There is a trend toward the adoption of computational techniques as a means of implementing and/or combining methods of gas analysis such as Rogers, Doernenburg, Duval and Key Gas. These implementations and combinations seek to increase the reliability of the analysis, since these methods alone are not 100% reliable. A sequential combination of methods is indicated to first analyze whether the equipment is in a state of normality or abnormality. Abnormal operation can be studied with more than one technique together to identify the type of anomaly that is occurring. The main challenges found in the literature review are related to the need for confidence in the data obtained from the gas analysis. All methods investigated also present difficulty in obtaining a high level of reliability and accuracy when subjected to analysis of low amounts of dissolved gases. A state of the art predictive maintenance tool should employ a real time DGA monitoring system that uses a set of DGA methods for the analysis of gases and compares measured data with historic database to perform risk analysis. A prediction failure indicator could use results from Monte Carlo simulation, allowing the shutdown of the equipment before it is committed to significant damage. References [1] Fu W, McCalley JD, Vittal V. Risk assessment for transformer loading. IEEE Trans Power Syst 2001;16:346–53.

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