Maintaining electric network reliability and operation efficiency is one of the key focus areas of every utility company. Electric asset inspection and maintenance (I&M) is critical function to maintain network reliability and reduce outages. Utilities faced several challenges in Asset I&M, this includes high cost, safety risks, resource limitations, manpower challenges and changing regulatory requirements. This white paper discusses the two major challenges, high costs and safety risks. It would then delve into how AI technologies, such as machine learning and computer vision, can significantly improve these processes to address these challenges.
Key challenges:
Ø High cost: The average annual expense for electric asset inspection varies widely, but a significant portion of utilities' operating and maintenance (O&M) budget is allocated to this area. In 2023, U.S. investor-owned utilities spent roughly $100.35 billion* on O&M, which includes expenses for inspecting and maintaining their assets. Cost factors in electric asset field inspections can be categorized into several areas:
1. Manpower: Manual inspections require skilled personnel who need to be trained and deployed for the task. Their wages, benefits, and overhead costs contribute significantly to the overall inspection expenses.
2. Equipment and Tools: Depending on the scope of inspection, specialized equipment such as drones, thermal cameras, ground penetrating radar, or climbing gear may be required. The acquisition, maintenance, and calibration of these tools can be costly.
3. Travel and Accommodation: For widespread or remote assets, travel expenses can add up quickly. This includes transportation costs, lodging, and meals for the inspection team.
4. Safety Measures: Ensuring the safety of the inspection team often involves additional costs, such as personal protective equipment, safety training, and risk assessment procedures.
5. Data Management and Analysis: Collecting, storing, and analyzing inspection data can involve significant costs, particularly if specialized software or third-party services are required.
6. Regulatory Compliance: Adhering to regulatory standards and obtaining necessary permits or certifications can entail additional costs.
The high cost of these factors can significantly impact the inspection process in several ways:
1. Frequency of Inspections: High costs may lead to reduced inspection frequency, potentially increasing the risk of undetected issues and unplanned outages.
2. Coverage: Due to budget constraints, critical assets may receive more frequent inspections, while others might be under inspected, leading to a skewed risk assessment.
3. Resource Allocation: The inspection budget might not allow for optimal resource allocation, resulting in inefficiencies or suboptimal maintenance strategies.
4. Safety: Cost pressures could lead to corners being cut on safety measures, putting inspectors at risk.
5. Data Utilization: Limited budgets might restrict the ability to fully leverage collected data for analytics, optimization, or predictive maintenance, further reducing the return on investment.
Potential solution:
Addressing high-cost challenges in electric asset field inspections through AI-based solutions and techniques can yield substantial benefits:
1. Automated Inspections: AI-driven drones, robots, or ground-based autonomous vehicles can replace or supplement manual inspections, significantly reducing labor costs. These systems can operate 24/7, cover larger areas, and access hard-to-reach places safely, improving inspection efficiency and coverage.
2. Predictive Maintenance: AI algorithms can analyze historical and real-time data to predict asset failures, enabling proactive maintenance and reducing the need for reactive repairs. This leads to lower labor costs and extended asset lifespans.
3. Anomaly Detection: Machine learning models can swiftly identify anomalies in sensor data, enabling early detection of potential issues. This reduces the likelihood of catastrophic failures and minimizes the need for expensive emergency repairs.
4. Computer Vision and Image Analysis: AI-powered computer vision techniques can automatically analyze images and videos captured during inspections, reducing the need for manual data analysis. This accelerates the inspection process and minimizes human error.
5. Optimized Scheduling and Resource Allocation: AI can help utilities allocate resources more efficiently by predicting the likelihood and severity of outages and identifying the most effective response strategies. This ensures that maintenance crews and replacement parts are deployed where they're needed most, minimizing downtime and reducing overall operational costs.
6. Predictive Equipment Maintenance: AI can monitor the health and performance of inspection equipment, predicting when maintenance or replacement is required. This helps avoid unexpected breakdowns and extends the lifespan of costly equipment.
7. Data Management and Analytics: AI-driven data analysis tools can extract valuable insights from large volumes of inspection data, enabling better decision-making and strategic planning. This reduces the need for expensive third-party services and enhances the return on investment from data collection efforts.
Ø Safety challenges:
Manual asset inspection can present several safety challenges, this includes
1. Working with or near electrical equipment, especially in overhead power lines or underground utilities, carries a constant risk of electrocution.
2. Inspecting assets like utility poles, towers, or substations often involves working at heights, increasing the risk of falls and related injuries.
3. Exposure to extreme weather (heat, cold, rain, wind), uneven terrain, and other environmental conditions can pose safety risks.
4. Awkward postures, repetitive movements, and the use of heavy tools during manual inspections can lead to sprains, strains, and other musculoskeletal disorders.
5. Manual inspections rely on human observation, which can be inconsistent due to factors like fatigue, distractions, or individual differences in perception.
6. Manual inspections can be time-consuming, leading to delays in identifying and addressing critical issues, which can be particularly dangerous during emergencies.
7. Inconsistent or inaccurate inspection data can lead to non-compliance with safety regulations and standards.
8. Manual inspections can only cover a fraction of assets at any given time, potentially missing critical issues.
9. Manual inspections can be labor-intensive and time-consuming, leading to higher costs for utilities.
10. Manual data collection and management can be inefficient and prone to errors, making it difficult to track asset conditions and maintenance needs.
Potential solution:
AI can significantly enhance safety in electric asset field inspections through various means:
1. Remote Inspection Capabilities: AI-powered drones, robots, or ground-based autonomous vehicles enable inspectors to examine hard-to-reach areas or hazardous zones remotely. This reduces the need for human exposure to dangerous conditions, minimizing the risk of accidents and injuries.
2. Real-time Hazard Detection: AI algorithms can analyze data from sensors and cameras to detect potential hazards in real-time, such as gas leaks, high temperatures, or structural weaknesses. Immediate alerts can be sent to inspectors, allowing them to evacuate or take necessary precautions.
3. Wearable Technology: AI-enhanced wearable devices can monitor inspectors' vital signs, track their location, and detect potential fatigue or distress. This enables supervisors to provide timely assistance and intervene if necessary, preventing accidents caused by human error or exhaustion.
4. Predictive Maintenance: By identifying signs of wear or potential failures before they escalate, AI can help prevent catastrophic incidents that could harm inspectors or cause service disruptions.
5. Virtual and Augmented Reality (VR/AR): AI-powered VR/AR systems can simulate inspection scenarios, providing inspectors with immersive training experiences. This improves their skills and decision-making abilities, reducing the likelihood of accidents during actual inspections.
6. Incident Analysis and Learning: AI can analyze past incidents and near misses to identify patterns and contributing factors. This information can be used to develop safety protocols, training programs, and standard operating procedures, further reducing risks in future inspections.
7. Autonomous Emergency Response: In the event of an emergency, AI-driven systems can quickly assess the situation and dispatch the appropriate resources, such as fire trucks or medical teams, optimizing response times and minimizing potential injuries or damages.
8. Safety Improvements: AI-powered safety systems can monitor inspectors' vital signs, detect hazardous conditions, and alert workers to potential risks. This reduces the likelihood of accidents, minimizing both direct and indirect costs associated with workplace injuries.
Conclusion:
By implementing AI-based solutions and techniques, utilities can significantly reduce overall inspection costs, including labor and equipment expenses, while improving safety, efficiency, and asset reliability. However, it's essential to consider the upfront costs of implementing these advanced technologies and weigh them against long-term benefits and potential savings. Utilities should conduct thorough cost-benefit analyses, develop clear implementation roadmaps, and secure buy-in from stakeholders. It's also crucial to pilot AI solutions on a smaller scale before full-scale deployment and to remain flexible and adaptive in the face of evolving technologies and market conditions.