Optimizing Grid Reliability with Predictive Maintenan
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By Eng. Sergio Rataus, VP of Business Development and Innovation, OFIL Systems
Introduction
The Transmission and Distribution (T&D) sector faces numerous challenges that must be addressed to ensure reliable, efficient, and sustainable energy delivery. Aging infrastructure, the growing demand for energy, the integration of renewable energy sources, and the need to improve grid resilience and reliability are just some of the critical issues requiring innovative solutions. In this context, the implementation of predictive maintenance emerges as a key tool to proactively address these challenges.
Current & Future Grid
According to DNV’s New Power Systems report, by 2050, global grid infrastructure must grow 2.5 times its current size to meet the doubling electricity demand. Electricity 1xpa rise from 20% to 37% of global final energy use, with wind and solar generating 70% of electricity. Nearly 90% of electricity 1xpa come from non-fossil sources.
The current global grid infrastructure is insufficient to meet this rising electricity demand and lacks the capability to effectively integrate renewable energy sources. Underinvestment in grid infrastructure is also a critical issue, with financial shortfalls impeding essential upgrades and expansions. The rapid adoption of electric vehicles increasing electricity demand and putting additional pressure on the T&D grid.
Aging infrastructure presents an additional barrier, necessitating extensive modernization efforts and the implementation of predictive maintenance plans to maintain the health of operating assets, optimize for 1xpansi efficiency, and achieve grid resilience and reliability. It is essential to address these challenges, as slowdowns in the development of new grid projects due resource constraints, that complicate 1xpansión efforts.
Predictive Maintenance
Maintenance can be classified in various ways, with one significant classification distinguishing four types of maintenance: reactive (corrective) maintenance, preventive maintenance, predictive maintenance, and proactive maintenance.
The Different Types of Maintenance
- Reactive (Corrective) Maintenance: Fixing equipment or systems after they fail or malfunction. It’s typically unplanned and can lead to downtime and higher repair costs.
- Preventive Maintenance: Scheduled inspections and tasks aimed at preventing equipment failures. It involves routine servicing, repairs, and replacements to keep systems operating smoothly and avoid breakdowns.
- Predictive Maintenance (PdM): Using data and analytics to predict when equipment is likely to fail. It involves monitoring equipment conditions in real-time to schedule maintenance just before issues occur, optimizing maintenance schedules and minimizing downtime. PdM, in particular, stands out for its ability to detect the very early stages of system degradation.
- Proactive Maintenance: Using PdM data to anticipate and address potential issues before they lead to equipment failures or downtime. It focuses on continuous improvement and monitoring of systems to enhance reliability and performance.
Benefits of Predictive Maintenance
- Increased operational life/availability of components
- Enables preventive corrective actions
- Decreased equipment or process downtime
- Reduced costs for spare parts and labour
- Improved product quality
- Enhanced worker and environmental safety
Requirements for Predictive Maintenance
- Investment in diagnostic equipment
- Increased investment in personnel training
- Management involvement
Independent surveys indicate the following average savings resulting from the implementation of a functional predictive maintenance program:
- Return on investment = 10 times
- Reduction in maintenance costs = 25% to 30%
- Elimination of breakdowns = 70% to 75%
- Reduction in downtime = 35% to 45%
- Increase in production = 20% to 25%
The P-F Curve and UV Technology Positioning
The P-F curve is a graph that visualizes an asset’s condition over its practical lifespan. On the P-F curve, the horizontal axis represents the asset’s service time, while the vertical axis indicates its condition. Initially, the asset operates at an optimal condition level. The point “P” on the curve marks the moment when a “potential failure”
first appears and can be detected, while point “F” represents the occurrence of functional failure, where the asset no longer meets performance standards.
A critical component of the curve is the P-F interval – the period between the onset of potential failure and the point of functional failure. This interval is crucial for implementing maintenance strategies that align with Asset Management Programs, ensuring that assets remain in optimal condition throughout their lifecycle.
Different technologies, based on their sensitivity and detection parameters, are positioned at varying points along the P-F curve.
Partial Discharges
In high- and medium-voltage systems, large electric fields are inherently “labile,” meaning they can be easily altered by factors such as contamination, cracks, deformations, fissures, poor connections, insulation defects, and sharp edges. When the electric field intensity around a point exceeds a critical threshold of 2.4–3.0 kV/mm, the surrounding air becomes ionized. This ionization, caused by the intense electric fields, leads to partial discharges (PD) and the emission of ultraviolet (UV) photons.
Partial discharges can result in various forms of damage, including premature aging, insulator degradation, corrosion of metal connections, erosion, loss of hydrophobicity, tracking, physical damage, arcing, flashover, and flashunder. It is estimated that over 85% of disruptive failures in high- and medium-voltage equipment are related to PD. Therefore, detecting PDs at an early stage in transmission and distribution (T&D) grid assets places us at the optimal position on the P-F curve.
UV cameras are highly effective in this context, as they possess the sensitivity to detect even minimal photon emissions and provide precise location accuracy, pinpointing the origin or source of the partial discharge. Consequently, the early detection and accurate localization of PDs significantly enhance predictive maintenance efforts.
Integration of UV, Visible and IR Technologies in Data Processing
To have a comprehensive understanding of the health of the T&D asset grid, it’s essential to use a range of technologies. Infrared technology (IR), for instance, helps identify hot spots that need immediate attention, preventing potential failures. Visual inspections offer a direct look at the condition of equipment, while UV technology supports predictive maintenance. By integrating these technologies, we obtain a holistic view of asset health.
However, this generates an extensive quantity of data, including pictures, videos, and other information. Fortunately, AI can streamline this process, analysing and processing the information efficiently to enhance our diagnostic capabilities. The advent of Machine Learning (ML), Artificial Intelligence (AI), and Image Recognition systems has significantly improved our ability to manage and interpret the vast amounts of data generated during inspections with these technologies, supporting informed decision-making.
It is important to note that the human factor remains essential in the learning process of these systems, ensuring that the data is enriched with experience and expertise. Drawing from the experiences and standards established by organizations such as the Institute of Electrical and Electronics Engineers (IEEE) and the Electric Power Research Institute (EPRI) can be effectively implemented for continuous improvement.
Conclusion
Predictive Maintenance, particularly using UV cameras, plays a crucial role in addressing the challenges faced by the T&D sector. By detecting stress factors at the earliest stage and providing precise location information, UV cameras enable proactive actions that prevent deterioration and extend the life of assets.
The integration of other diagnostic technologies, such as IR and visible cameras, along with advanced data processing technologies, provide a comprehensive information of the health of assets, enhancing the reliability, efficiency, and sustainability of the current T&D grid, creating a solid foundation for its expansion.
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