AP
A.P. Purnomoadi
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
6 records found
1
Following deregulation in the energy sector during the 1990s, which was also triggered by the ageing of infrastructure and the increasing demands from regulators and customers, many network utilities adopted the Asset Management (AM) in the hope to earn more, have better credit ratings and gain from stock prices. In line with this fact, the emergence of the AM international standard, such as the ISO 55000 series in 2014, gained rapid acceptance among network utilities around the globe.
AM has its core in the asset decision-making process. This activity lies simultaneously at the strategic, tactical and operational level of AM, over the lifecycle of the asset. In such an environment, the asset managing department should not only focus on the reliability of the asset but also on balancing costs, risks and asset performance. Regarding maintenance, the money spent on every maintenance task should benefit the company’s business values.
This thesis focuses on the development of decision-making tools for maintenance of high voltage AC (HVAC) gas-insulated switchgear (GIS) operating under tropical conditions. GIS has been chosen because of its critical role in the transmission network. Any GIS breakdown is usually expensive and requires an extensive outage. Moreover, under tropical conditions, this study observed GIS failure rates over twice the value reported by CIGRE’s survey of 2007. The study was conducted in this research’s case study termed the Java Bali (JABA) case study. The latter consists of 631 CB-bays of 150 kV and 500 kV GISs located in Java and Bali of Indonesia.
Today’s AM decision-making tools for electrical power grids are generally based on Asset Health Index (AHI) and risk assessment (RA) models. These models assist the asset manager in answering the following questions:
1. What is the condition of each GIS in the network?
2. Which one is more likely to fail compared to the others?
3. Which one is more critical compared to the others in terms of making a possible impact on the company’s business such that the mitigating action is prioritised?
4. What optimal action(s) is/are needed to be taken?
Developing the above-mentioned models requires sufficient knowledge of the characteristics of GIS operating under tropical conditions. To that purpose, both statistical analysis and forensic investigations in the JABA case study have been undertaken to find the critical condition indicators for the AHI model. The results are as follows:
1. The tropical conditions have influenced both directly and indirectly the performance of GIS. Corrosions at the exposed GIS parts were seen to have a common direct influence of tropical conditions. They can trigger leakages, secondary, and lead to driving mechanism subsystems’ failures, which reduce the GIS’ performance. The intensive and frequent lightning in tropical conditions is a so-called Failure Susceptibility Indicator (FSI), indicating that a failure mode is expected to initiate more likely than for the same GIS in other environments, especially if the surge arrester fails to protect. Moreover, the GISs outdoor and from the older generation are more susceptible to breakdown under tropical conditions.
2. A high amount of humidity was found in the non-CB enclosures of GIS from lower voltage class (i.e. Class 2 GIS with a voltage level of 150 kV). The origin of this humidity mainly comes from the desorption of moisture from the spacer or internal GIS surfaces during operation.
3. The critical failure modes in GIS operating under tropical conditions are as follows: dielectric insulation breakdown, loss of mechanical integrity in the primary conductor and failing to perform the requested operation due to driving mechanism failure.
Following this study’s findings, laboratory tests in the HV Laboratory of TU Delft were conducted to investigate the influence of high humidity content on the spacer flashover in GIS. The results confirmed without condensation, humidity has no impact on the withstanding strength of the insulation system under AC, LI+/- and SI. Our model also showed that the breakdown voltage under LI+ due to condensation at the surface of a solid insulator is lower than that due to a 2 mm metallic particle attached on the identical solid insulator at 3000 ppmV.
We applied the findings from both field investigation and laboratory tests into our models in the following ways:
1. In the AHI model:
a. Statistical and JABA lab case studies were performed to assess the system’s vulnerabilities and normative levels, in particular, the humidity content in GIS the non-CB enclosure as long as the value was far from the possibility of condensation.
b. The likelihood of failure is determined by so-called condition scale codes reflecting the deterioration of the subsystems.
c. The failure susceptibility indicators (FSI) flag deviating circumstances, such as heavy environmental conditions, operation and maintenance records and the inherent/design factor of GIS. The FSI are just an expectation that is not based on evidence as in a condition indicator. Therefore, the FSI work as warning flags for the decision-maker.
2. In the RA model:
a. Risk is defined as the likelihood of failure times the consequences. The result of the AHI defines the likelihood of failure in the RA model.
b. On the other hand, the consequences consist of seven business values of a transmission utility from the JABA case study, namely, safety, extra fuel cost, energy not served, equipment cost, customer satisfaction, leadership and environment.
We have successfully implemented these models on a GIS example from the JABA case study. Evaluation of possible risk treatments was also done using multi-criteria analysis (MCA) to optimise three parameters: cost, time-to-finish treatment and residual risk.
In practice, transmission utilities face more complex situations with more types of equipment in the network. The methodology discussed in this thesis, however, can be the cornerstone for the development of decision-making tools for other assets at the tactical level of AM as well. ...
AM has its core in the asset decision-making process. This activity lies simultaneously at the strategic, tactical and operational level of AM, over the lifecycle of the asset. In such an environment, the asset managing department should not only focus on the reliability of the asset but also on balancing costs, risks and asset performance. Regarding maintenance, the money spent on every maintenance task should benefit the company’s business values.
This thesis focuses on the development of decision-making tools for maintenance of high voltage AC (HVAC) gas-insulated switchgear (GIS) operating under tropical conditions. GIS has been chosen because of its critical role in the transmission network. Any GIS breakdown is usually expensive and requires an extensive outage. Moreover, under tropical conditions, this study observed GIS failure rates over twice the value reported by CIGRE’s survey of 2007. The study was conducted in this research’s case study termed the Java Bali (JABA) case study. The latter consists of 631 CB-bays of 150 kV and 500 kV GISs located in Java and Bali of Indonesia.
Today’s AM decision-making tools for electrical power grids are generally based on Asset Health Index (AHI) and risk assessment (RA) models. These models assist the asset manager in answering the following questions:
1. What is the condition of each GIS in the network?
2. Which one is more likely to fail compared to the others?
3. Which one is more critical compared to the others in terms of making a possible impact on the company’s business such that the mitigating action is prioritised?
4. What optimal action(s) is/are needed to be taken?
Developing the above-mentioned models requires sufficient knowledge of the characteristics of GIS operating under tropical conditions. To that purpose, both statistical analysis and forensic investigations in the JABA case study have been undertaken to find the critical condition indicators for the AHI model. The results are as follows:
1. The tropical conditions have influenced both directly and indirectly the performance of GIS. Corrosions at the exposed GIS parts were seen to have a common direct influence of tropical conditions. They can trigger leakages, secondary, and lead to driving mechanism subsystems’ failures, which reduce the GIS’ performance. The intensive and frequent lightning in tropical conditions is a so-called Failure Susceptibility Indicator (FSI), indicating that a failure mode is expected to initiate more likely than for the same GIS in other environments, especially if the surge arrester fails to protect. Moreover, the GISs outdoor and from the older generation are more susceptible to breakdown under tropical conditions.
2. A high amount of humidity was found in the non-CB enclosures of GIS from lower voltage class (i.e. Class 2 GIS with a voltage level of 150 kV). The origin of this humidity mainly comes from the desorption of moisture from the spacer or internal GIS surfaces during operation.
3. The critical failure modes in GIS operating under tropical conditions are as follows: dielectric insulation breakdown, loss of mechanical integrity in the primary conductor and failing to perform the requested operation due to driving mechanism failure.
Following this study’s findings, laboratory tests in the HV Laboratory of TU Delft were conducted to investigate the influence of high humidity content on the spacer flashover in GIS. The results confirmed without condensation, humidity has no impact on the withstanding strength of the insulation system under AC, LI+/- and SI. Our model also showed that the breakdown voltage under LI+ due to condensation at the surface of a solid insulator is lower than that due to a 2 mm metallic particle attached on the identical solid insulator at 3000 ppmV.
We applied the findings from both field investigation and laboratory tests into our models in the following ways:
1. In the AHI model:
a. Statistical and JABA lab case studies were performed to assess the system’s vulnerabilities and normative levels, in particular, the humidity content in GIS the non-CB enclosure as long as the value was far from the possibility of condensation.
b. The likelihood of failure is determined by so-called condition scale codes reflecting the deterioration of the subsystems.
c. The failure susceptibility indicators (FSI) flag deviating circumstances, such as heavy environmental conditions, operation and maintenance records and the inherent/design factor of GIS. The FSI are just an expectation that is not based on evidence as in a condition indicator. Therefore, the FSI work as warning flags for the decision-maker.
2. In the RA model:
a. Risk is defined as the likelihood of failure times the consequences. The result of the AHI defines the likelihood of failure in the RA model.
b. On the other hand, the consequences consist of seven business values of a transmission utility from the JABA case study, namely, safety, extra fuel cost, energy not served, equipment cost, customer satisfaction, leadership and environment.
We have successfully implemented these models on a GIS example from the JABA case study. Evaluation of possible risk treatments was also done using multi-criteria analysis (MCA) to optimise three parameters: cost, time-to-finish treatment and residual risk.
In practice, transmission utilities face more complex situations with more types of equipment in the network. The methodology discussed in this thesis, however, can be the cornerstone for the development of decision-making tools for other assets at the tactical level of AM as well. ...
Following deregulation in the energy sector during the 1990s, which was also triggered by the ageing of infrastructure and the increasing demands from regulators and customers, many network utilities adopted the Asset Management (AM) in the hope to earn more, have better credit ratings and gain from stock prices. In line with this fact, the emergence of the AM international standard, such as the ISO 55000 series in 2014, gained rapid acceptance among network utilities around the globe.
AM has its core in the asset decision-making process. This activity lies simultaneously at the strategic, tactical and operational level of AM, over the lifecycle of the asset. In such an environment, the asset managing department should not only focus on the reliability of the asset but also on balancing costs, risks and asset performance. Regarding maintenance, the money spent on every maintenance task should benefit the company’s business values.
This thesis focuses on the development of decision-making tools for maintenance of high voltage AC (HVAC) gas-insulated switchgear (GIS) operating under tropical conditions. GIS has been chosen because of its critical role in the transmission network. Any GIS breakdown is usually expensive and requires an extensive outage. Moreover, under tropical conditions, this study observed GIS failure rates over twice the value reported by CIGRE’s survey of 2007. The study was conducted in this research’s case study termed the Java Bali (JABA) case study. The latter consists of 631 CB-bays of 150 kV and 500 kV GISs located in Java and Bali of Indonesia.
Today’s AM decision-making tools for electrical power grids are generally based on Asset Health Index (AHI) and risk assessment (RA) models. These models assist the asset manager in answering the following questions:
1. What is the condition of each GIS in the network?
2. Which one is more likely to fail compared to the others?
3. Which one is more critical compared to the others in terms of making a possible impact on the company’s business such that the mitigating action is prioritised?
4. What optimal action(s) is/are needed to be taken?
Developing the above-mentioned models requires sufficient knowledge of the characteristics of GIS operating under tropical conditions. To that purpose, both statistical analysis and forensic investigations in the JABA case study have been undertaken to find the critical condition indicators for the AHI model. The results are as follows:
1. The tropical conditions have influenced both directly and indirectly the performance of GIS. Corrosions at the exposed GIS parts were seen to have a common direct influence of tropical conditions. They can trigger leakages, secondary, and lead to driving mechanism subsystems’ failures, which reduce the GIS’ performance. The intensive and frequent lightning in tropical conditions is a so-called Failure Susceptibility Indicator (FSI), indicating that a failure mode is expected to initiate more likely than for the same GIS in other environments, especially if the surge arrester fails to protect. Moreover, the GISs outdoor and from the older generation are more susceptible to breakdown under tropical conditions.
2. A high amount of humidity was found in the non-CB enclosures of GIS from lower voltage class (i.e. Class 2 GIS with a voltage level of 150 kV). The origin of this humidity mainly comes from the desorption of moisture from the spacer or internal GIS surfaces during operation.
3. The critical failure modes in GIS operating under tropical conditions are as follows: dielectric insulation breakdown, loss of mechanical integrity in the primary conductor and failing to perform the requested operation due to driving mechanism failure.
Following this study’s findings, laboratory tests in the HV Laboratory of TU Delft were conducted to investigate the influence of high humidity content on the spacer flashover in GIS. The results confirmed without condensation, humidity has no impact on the withstanding strength of the insulation system under AC, LI+/- and SI. Our model also showed that the breakdown voltage under LI+ due to condensation at the surface of a solid insulator is lower than that due to a 2 mm metallic particle attached on the identical solid insulator at 3000 ppmV.
We applied the findings from both field investigation and laboratory tests into our models in the following ways:
1. In the AHI model:
a. Statistical and JABA lab case studies were performed to assess the system’s vulnerabilities and normative levels, in particular, the humidity content in GIS the non-CB enclosure as long as the value was far from the possibility of condensation.
b. The likelihood of failure is determined by so-called condition scale codes reflecting the deterioration of the subsystems.
c. The failure susceptibility indicators (FSI) flag deviating circumstances, such as heavy environmental conditions, operation and maintenance records and the inherent/design factor of GIS. The FSI are just an expectation that is not based on evidence as in a condition indicator. Therefore, the FSI work as warning flags for the decision-maker.
2. In the RA model:
a. Risk is defined as the likelihood of failure times the consequences. The result of the AHI defines the likelihood of failure in the RA model.
b. On the other hand, the consequences consist of seven business values of a transmission utility from the JABA case study, namely, safety, extra fuel cost, energy not served, equipment cost, customer satisfaction, leadership and environment.
We have successfully implemented these models on a GIS example from the JABA case study. Evaluation of possible risk treatments was also done using multi-criteria analysis (MCA) to optimise three parameters: cost, time-to-finish treatment and residual risk.
In practice, transmission utilities face more complex situations with more types of equipment in the network. The methodology discussed in this thesis, however, can be the cornerstone for the development of decision-making tools for other assets at the tactical level of AM as well.
AM has its core in the asset decision-making process. This activity lies simultaneously at the strategic, tactical and operational level of AM, over the lifecycle of the asset. In such an environment, the asset managing department should not only focus on the reliability of the asset but also on balancing costs, risks and asset performance. Regarding maintenance, the money spent on every maintenance task should benefit the company’s business values.
This thesis focuses on the development of decision-making tools for maintenance of high voltage AC (HVAC) gas-insulated switchgear (GIS) operating under tropical conditions. GIS has been chosen because of its critical role in the transmission network. Any GIS breakdown is usually expensive and requires an extensive outage. Moreover, under tropical conditions, this study observed GIS failure rates over twice the value reported by CIGRE’s survey of 2007. The study was conducted in this research’s case study termed the Java Bali (JABA) case study. The latter consists of 631 CB-bays of 150 kV and 500 kV GISs located in Java and Bali of Indonesia.
Today’s AM decision-making tools for electrical power grids are generally based on Asset Health Index (AHI) and risk assessment (RA) models. These models assist the asset manager in answering the following questions:
1. What is the condition of each GIS in the network?
2. Which one is more likely to fail compared to the others?
3. Which one is more critical compared to the others in terms of making a possible impact on the company’s business such that the mitigating action is prioritised?
4. What optimal action(s) is/are needed to be taken?
Developing the above-mentioned models requires sufficient knowledge of the characteristics of GIS operating under tropical conditions. To that purpose, both statistical analysis and forensic investigations in the JABA case study have been undertaken to find the critical condition indicators for the AHI model. The results are as follows:
1. The tropical conditions have influenced both directly and indirectly the performance of GIS. Corrosions at the exposed GIS parts were seen to have a common direct influence of tropical conditions. They can trigger leakages, secondary, and lead to driving mechanism subsystems’ failures, which reduce the GIS’ performance. The intensive and frequent lightning in tropical conditions is a so-called Failure Susceptibility Indicator (FSI), indicating that a failure mode is expected to initiate more likely than for the same GIS in other environments, especially if the surge arrester fails to protect. Moreover, the GISs outdoor and from the older generation are more susceptible to breakdown under tropical conditions.
2. A high amount of humidity was found in the non-CB enclosures of GIS from lower voltage class (i.e. Class 2 GIS with a voltage level of 150 kV). The origin of this humidity mainly comes from the desorption of moisture from the spacer or internal GIS surfaces during operation.
3. The critical failure modes in GIS operating under tropical conditions are as follows: dielectric insulation breakdown, loss of mechanical integrity in the primary conductor and failing to perform the requested operation due to driving mechanism failure.
Following this study’s findings, laboratory tests in the HV Laboratory of TU Delft were conducted to investigate the influence of high humidity content on the spacer flashover in GIS. The results confirmed without condensation, humidity has no impact on the withstanding strength of the insulation system under AC, LI+/- and SI. Our model also showed that the breakdown voltage under LI+ due to condensation at the surface of a solid insulator is lower than that due to a 2 mm metallic particle attached on the identical solid insulator at 3000 ppmV.
We applied the findings from both field investigation and laboratory tests into our models in the following ways:
1. In the AHI model:
a. Statistical and JABA lab case studies were performed to assess the system’s vulnerabilities and normative levels, in particular, the humidity content in GIS the non-CB enclosure as long as the value was far from the possibility of condensation.
b. The likelihood of failure is determined by so-called condition scale codes reflecting the deterioration of the subsystems.
c. The failure susceptibility indicators (FSI) flag deviating circumstances, such as heavy environmental conditions, operation and maintenance records and the inherent/design factor of GIS. The FSI are just an expectation that is not based on evidence as in a condition indicator. Therefore, the FSI work as warning flags for the decision-maker.
2. In the RA model:
a. Risk is defined as the likelihood of failure times the consequences. The result of the AHI defines the likelihood of failure in the RA model.
b. On the other hand, the consequences consist of seven business values of a transmission utility from the JABA case study, namely, safety, extra fuel cost, energy not served, equipment cost, customer satisfaction, leadership and environment.
We have successfully implemented these models on a GIS example from the JABA case study. Evaluation of possible risk treatments was also done using multi-criteria analysis (MCA) to optimise three parameters: cost, time-to-finish treatment and residual risk.
In practice, transmission utilities face more complex situations with more types of equipment in the network. The methodology discussed in this thesis, however, can be the cornerstone for the development of decision-making tools for other assets at the tactical level of AM as well.
This article contains the development of the Health Index (HI) and the Risk Assessment (RA) models for Gas Insulated Switchgear (GIS) operating under tropical conditions. A case study of 631 bays of GIS in tropics with service time from 1 up to 30 years is used during the development of the models. The basis for the HI model is the condition assessment of subsystems in GIS. This assessment translates the measured parameters from GIS subsystems into condition status based on a set of norms and rules. The condition status is presented in a score, where the non-linear scaling is chosen as it allows the poor parameters to really stand out. On the other hand, the RA model calculates the risk of a GIS major failure. The Total Failure Probability (TPF) unifies six risk indicators of GIS failures under tropical conditions. The risk may fall into one of the four categories, namely: Low, Medium, High, and Very High.
...
This article contains the development of the Health Index (HI) and the Risk Assessment (RA) models for Gas Insulated Switchgear (GIS) operating under tropical conditions. A case study of 631 bays of GIS in tropics with service time from 1 up to 30 years is used during the development of the models. The basis for the HI model is the condition assessment of subsystems in GIS. This assessment translates the measured parameters from GIS subsystems into condition status based on a set of norms and rules. The condition status is presented in a score, where the non-linear scaling is chosen as it allows the poor parameters to really stand out. On the other hand, the RA model calculates the risk of a GIS major failure. The Total Failure Probability (TPF) unifies six risk indicators of GIS failures under tropical conditions. The risk may fall into one of the four categories, namely: Low, Medium, High, and Very High.
Humid insulating gas (SF6) has been observed in a case study of 631 CB-bays of Gas Insulated Switchgear (GIS) operating under tropical conditions. The routine gas quality check in the case study reported that 20% of the non-Circuit Breaker enclosures have humidity above the value recommended by the IEEE and IEC standards. Therefore, an investigation into the flashover characteristics of a spacer in humid SF6 has been initiated in the High Voltage Laboratory of TU Delft, The Netherlands. The setup is a small model resembling the insulation system of a GIS with controlled parameters of humidity content and gas pressure. The electrical stresses in the test are AC, LI+, LI−, and SI with homogeneous, quasi-homogeneous-, and inhomogeneous field configurations. In general, the humidity does not influence the withstand strength of the spacer as long there is no condensation. When condensation occurred, the flashover voltage dropped by 28% during the test under AC at 2.6 bars, and by 38% during the test under LI+ at 2.5 bars; both with quasi-homogeneous field configuration. In the test with homogeneous field setup, the flashover voltage was dropped by 67% under LI+ at 3.4 bars. In our setup, it has also been observed that the flashover-drop due to condensation is higher than due to a 2-mm aluminum particle attached to the sample close to the high electric field region simulating the inhomogeneous field configuration.
...
Humid insulating gas (SF6) has been observed in a case study of 631 CB-bays of Gas Insulated Switchgear (GIS) operating under tropical conditions. The routine gas quality check in the case study reported that 20% of the non-Circuit Breaker enclosures have humidity above the value recommended by the IEEE and IEC standards. Therefore, an investigation into the flashover characteristics of a spacer in humid SF6 has been initiated in the High Voltage Laboratory of TU Delft, The Netherlands. The setup is a small model resembling the insulation system of a GIS with controlled parameters of humidity content and gas pressure. The electrical stresses in the test are AC, LI+, LI−, and SI with homogeneous, quasi-homogeneous-, and inhomogeneous field configurations. In general, the humidity does not influence the withstand strength of the spacer as long there is no condensation. When condensation occurred, the flashover voltage dropped by 28% during the test under AC at 2.6 bars, and by 38% during the test under LI+ at 2.5 bars; both with quasi-homogeneous field configuration. In the test with homogeneous field setup, the flashover voltage was dropped by 67% under LI+ at 3.4 bars. In our setup, it has also been observed that the flashover-drop due to condensation is higher than due to a 2-mm aluminum particle attached to the sample close to the high electric field region simulating the inhomogeneous field configuration.
Conference paper
(2019)
-
A. P. Purnomoadi, M. J. D. Gugun Bonar, A. Rodrigo Mor, J. J. Smit, B. S. Munir
A Health Index model (HI model) for Gas-Insulated Switchgear (GIS) operating in tropical environment has been proposed in this paper. The model has the foundation on the condition assessment of subsystems of components in GIS. The condition indicators (CI) were captured from the Routine Visual Inspection (RVI) and the Diagnostic Test and Measurements (DM) on GIS. After then, a set of "norms" translates the condition indicators into one of logarithmic-base 3 condition codes (CC), before finally converted into an index from 1 to 5. Failure Susceptibility Indicators (FSI) have also been introduced in the model. FSI might initiate an onset of a failure mode, but it is not part of the asset health index. Triggers for FSI can be due to three factors, namely, environmental, GIS’ operational experience, GIS’ design/ make. The final output of the model consists of an index and a worksheet of FSIs coded with colors.
...
A Health Index model (HI model) for Gas-Insulated Switchgear (GIS) operating in tropical environment has been proposed in this paper. The model has the foundation on the condition assessment of subsystems of components in GIS. The condition indicators (CI) were captured from the Routine Visual Inspection (RVI) and the Diagnostic Test and Measurements (DM) on GIS. After then, a set of "norms" translates the condition indicators into one of logarithmic-base 3 condition codes (CC), before finally converted into an index from 1 to 5. Failure Susceptibility Indicators (FSI) have also been introduced in the model. FSI might initiate an onset of a failure mode, but it is not part of the asset health index. Triggers for FSI can be due to three factors, namely, environmental, GIS’ operational experience, GIS’ design/ make. The final output of the model consists of an index and a worksheet of FSIs coded with colors.
This paper proposes a risk assessment model for Gas Insulated Switchgear (GIS) operating under the tropical conditions. The model is a part of decision support tools at the tactical level of Asset Management in electricity network business. The model used the Asset Health Index (AHI) to estimate the likelihood of failure, while the consequences were defined by the business of a transmission utility in a case study. Risk discussed in the model is risk if a failure occurs in GIS. The output classifies the risk into five levels, namely, Very Low, Low, Moderate, High, and Very High.
...
This paper proposes a risk assessment model for Gas Insulated Switchgear (GIS) operating under the tropical conditions. The model is a part of decision support tools at the tactical level of Asset Management in electricity network business. The model used the Asset Health Index (AHI) to estimate the likelihood of failure, while the consequences were defined by the business of a transmission utility in a case study. Risk discussed in the model is risk if a failure occurs in GIS. The output classifies the risk into five levels, namely, Very Low, Low, Moderate, High, and Very High.
Gas Insulated Switchgear (GIS) has been in use in transmission system for more than 30 years with good reliability. However, in our case study of 631 CB-bays of GIS operating under tropical conditions, we observed failure rates over twice the value reported in the 3rd CIGRE's survey of 2007. Following this observation, it becomes practically important to develop a condition assessment model for GIS operating under tropical conditions. As a part of this process, knowledge rules for humidity content in SF6 insulation were investigated through the experiment inside the HV Laboratory in TU Delft with a solidand- gas insulation setup with AC voltage stress. The controlled parameter used in the experiments were resembling the actual condition in tropics. In this paper, the condition assessment model for GIS is proposed. To support the model, different diagnostics to assess the critical components of GIS operating under tropical conditions are discussed. Following that, an example of knowledge rules to interpret the outcome from the diagnostics is also given.
...
Gas Insulated Switchgear (GIS) has been in use in transmission system for more than 30 years with good reliability. However, in our case study of 631 CB-bays of GIS operating under tropical conditions, we observed failure rates over twice the value reported in the 3rd CIGRE's survey of 2007. Following this observation, it becomes practically important to develop a condition assessment model for GIS operating under tropical conditions. As a part of this process, knowledge rules for humidity content in SF6 insulation were investigated through the experiment inside the HV Laboratory in TU Delft with a solidand- gas insulation setup with AC voltage stress. The controlled parameter used in the experiments were resembling the actual condition in tropics. In this paper, the condition assessment model for GIS is proposed. To support the model, different diagnostics to assess the critical components of GIS operating under tropical conditions are discussed. Following that, an example of knowledge rules to interpret the outcome from the diagnostics is also given.