Implementing Risk-Based Maintenance Strategies for Distributed Control System as Power Plant Asset Management

doi:10.1088/1757-899X/1096/1/012108 

Abstract. The electricity generated from the power plant is subject to several requirements for active power, voltage, and frequency according to the grid system, so the machine must be controlled to achieve requirement by a power plant control system known as Distributed Control System (DCS). DCS system in block 1 Muara Tawar power plant using Procontrol P13 for gas turbine and Procontrol P-14 to control steam turbine. These systems had operated since 1997 and had been nearly operating for 23 years until now, and several failures tend to increase from time to time. The failures of the DCS and the lack of control cards will result in the loss of production. DCS system assets must handle properly to maintain the overall reliability of the power plant system. A method and strategy to maintain DCS must be carried out and ensure reliability and risk always under controlled conditions. Implementing Risk-based maintenance by carrying out quantitative calculations through the reliability approach and the level of the consequence of failure to calculate equipment risk is one of the methods of the DCS system. The result of the Risk-based maintenance method shows the highest risk on the risk map of the DCS system was in cubicles 14CBA02, 14CBA03 with a high-risk level and gas turbine and HRSG cubicle in medium-high level. The interval preventive maintenance time calculated by reliability within a year showed that cubicles 14CBA02 and 14CBA03 suggested being maintained every 29 days to reduce possibilities of failure.

        The number of DCS failures both cards control and monitoring system (human-machine interface) based on record during 2016 to mid-2020 shows the Procontrol DCS system contribute failures and result in operations and production decreasing. Based on data, DCS Procontrol P13 and P14 manufacturers in 2015, show that the card control and the whole system are in the classic, limited, and absolute phases. The classic phase to absolute has a span of 10 years. This situation brought a major issue on the availability of spare cards, especially how to keep and maintain the reliability of the overall DCS system. 

        According to card assessment, the majority of cards are in the classic and limited phases. This happened in all GT blocks 1-2 and ST 14 that used P13 and P14 Procontrol systems. DCS card failures data according to the type of card control can be shown in figure 3 the Pareto diagram according to failure of each type of card. Maintenance of DCS systems is an asset management challenge, especially on how "card control" management takes part in the situation where the availability of spare cards is starting to be limited and it will greatly affect the operation, production and at the same time the safety factor of the plant. A maintenance pattern is needed to accommodate and maintain the reliability of the DCS system. The ability to maximize the capabilities and reliability of DCS is an important asset to improve the power plant performance and profitability. 

        This research seeks to formulate a method of maintenance based on DCS’s Risk-based Maintenance and is expected to answer some maintenance problems in the DCS system then classify the possible causes of the failure of the DCS system. This research also shows how much the consequences arising from interference are, how likely the occurrence of the interference is, how big is the risk of DCS failure, and how to plan a DCS system maintenance strategy.

        Data analysis is carried out following the steps in the preparation of equipment maintenance strategies related to the DCS in its cubicle system that uses Risk-based maintenance. The following steps are: 

Classification of DCS generating systems based on cubicle according to Pareto chart that reflected the number of failures. 

• Determine the scenario of failure by analyzing the possible causes of disruption of the DCS system.     
• Determine the evaluation of consequences in a semi-qualitative way by determining the risk criteria for ranking loss of performance of units, financial, ecological, and health and safety so that the total consequences result from a system failure. The four factors are then combined to produce a                 total assessment of the consequences of equipment failure on the system and formulated with Consequence = [0.25A2 + 0.25B2 + 0.25C2 + 0.25D2 ] ^0.5 
• Conduct probabilistic analysis with precise distribution in accordance with the characteristics of the failure interval data so that the reliability value can be found as a basis for calculating the failure probability of the DCS system. The reliability value can be calculated using the equation.
• A risk assessment by combining the consequences of risk and analyzing the probability of failure that produces an acceptable or rejected value. At this stage, a matrix of risk is drawn to describe the position of DCS system risk.
• Based on the risk level and duration of operation of the DCS system, the most appropriate type of maintenance interval can be determined to ensure the reliability of DCS system. Evaluation of maintenance by making comparisons the maintenance process before risk-based maintenance is carried out and after this method is implemented. The reference of reliability value must not be less than 80% or 0.80 [8]. The results of the comparison were then plotted into a graph to find out the relationship between DCS reliability values and operational time. 

       The risk mapping of DCS system based on each cubicle in Muara Tawar Block 1 power plant from likelihood risk identification and consequences based on the failure report then visualized in a risk matrix (figure 12) as part of DCS system risk mitigation process in case of failure prevention that has an uncontrollable impact. The DCS system that is classified as high risk is 14CBA02 and 14CBA03, which are the system that handles the control of the steam turbine system. Meanwhile, the medium-risk were 11CRC30, 12CRC30 cubicle that handles the control of gas turbine and 12CBA11 cubicle handles control of HRSG 12. Risk control is carried out by making recommendations for maintenance and modification based on the level of risk and the likelihood of damage to the DCS system in a cubicle.

    

    Maintenance planning is based on the level of risk and the time interval for DCS system failure so that the most appropriate type and maintenance intervals can be seen and the DCS can be maintained. Based on the calculation of the decline in reliability values obtained maintenance time intervals that have been recapitulated in Table

    Determination of the maintenance interval of the DCS system on the cubicle with a critical level is crucial if it were done in a period based on the failure rate from time to time. Spare part readiness can help maintain DCS assets as an important part of a power plant.

Implementing Risk-Based Maintenance Strategies for Distributed Control System as 

Power Plant Asset Management

D T Yulianto, R M Isman, S N Ihsan and H G Susanto 

Pembangkitan Jawa Bali UP Muara Tawar, PLTGU Muara Tawar 1 street, West Java, Indonesia Corresponding email: *danantriyulianto@ptpjb.com