Scholarlink

For Indonesia, ensuring stable baseload power generation is essential to ensuring energy security. An essential and sustainable part of accomplishing this goal is the utilization of geothermal power plants. Like all thermal power plants, Geothermal also undergoes planned outages which involve a thorough major inspection resulting in a longer shutdown time. This case study investigates the use of probabilistic statistical analysis, condition monitoring analysis and risk-based maintenance to optimize turnaround inspection schedule to increase power plant availability & productivity. Through the application of Monte-Carlo simulation, the plant was able to determine if it could safely postpone its scheduled inspection until the following year. This option was validated by using a Plan- Do-Check-Act (PDCA) cycle that incorporated risk-based maintenance. This required carrying out in-depth asset condition assessments, carefully identifying failure symptoms and proactively managing any risks associated with the delay, and putting corrective measures in place as needed. This technique was successfully implemented, yielding a gain of 24 GWh of electricity generation, or almost USD Two million in additional revenue. This demonstrates not only how well probabilistic analysis works to optimize maintenance schedules, but also how crucial proactive risk management and continuous improvement techniques are to maximizing the availability of vital infrastructure, such as geothermal power plants.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Risk Mapping for a Geothermal Power Plant Combines Statistical and Condition Monitoring Analysis to Justify Plant Maintenance Overhaul Strategy

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Fransisco T. P. Simamora,
  • Efrata Pratenta Meliala,
  • Muhammad Vito Hamza,
  • Muhammad Thasril,
  • N. Hanifah

摘要

For Indonesia, ensuring stable baseload power generation is essential to ensuring energy security. An essential and sustainable part of accomplishing this goal is the utilization of geothermal power plants. Like all thermal power plants, Geothermal also undergoes planned outages which involve a thorough major inspection resulting in a longer shutdown time. This case study investigates the use of probabilistic statistical analysis, condition monitoring analysis and risk-based maintenance to optimize turnaround inspection schedule to increase power plant availability & productivity. Through the application of Monte-Carlo simulation, the plant was able to determine if it could safely postpone its scheduled inspection until the following year. This option was validated by using a Plan- Do-Check-Act (PDCA) cycle that incorporated risk-based maintenance. This required carrying out in-depth asset condition assessments, carefully identifying failure symptoms and proactively managing any risks associated with the delay, and putting corrective measures in place as needed. This technique was successfully implemented, yielding a gain of 24 GWh of electricity generation, or almost USD Two million in additional revenue. This demonstrates not only how well probabilistic analysis works to optimize maintenance schedules, but also how crucial proactive risk management and continuous improvement techniques are to maximizing the availability of vital infrastructure, such as geothermal power plants.