Are UV Intelligent Sensors needed to Transform the Electric Grid Industry?

With nearly 185 million electric grid poles in the United States, utilities face the challenge of maintaining a complex ecosystem of field equipment critical to reliable energy delivery.

The North American Electric Reliability Corporation (NERC) has warned in its latest 2024 Long-Term Reliability Assessment (LTRA) that well over half of North America faces a potential shortage of electricity supplies in the coming years. This is compounded by surging demand growth, accelerating generator retirements, and delays in resource development.

The following issues within the electric utility T&D industry are capturing the most attention and funding:

1. Decarbonization: T&D utilization is too low and insufficient to connect enough renewable energy[1]. Significantly more T&D capacity is needed at the least cost possible, or capital investment should be deferred entirely through non-wires alternatives.
2. Reliability and Resiliency Issues: T&D problems arise from extreme weather, cyber and physical attacks, and preventative maintenance. Grid owners and operators desperately need to predict problems before they happen and take corrective action to avoid significant financial losses.
3. Affordability: Power generators have low-capacity rates due to downtime regardless of whether they are coal, gas, nuclear, solar PV and wind turbines. If the grid is down, then these generators must be ramped down or shutdown entirely. Any opportunity to avoid unplanned outages represents significant cost savings.
4. Increased Consumption: Expanded T&D is required within a short time due to new consumers such as EVs, high-power computing, quantum computing, AI, data centers, crypto mining, and electric heating, cooling moving from thermal. These large electricity consumers need to come on board as part of the decarbonization process, cost lowering, and improved reliability that electricity offers.

While all four of the above items are connected, this article will focus on the reliability issues of the transmission high and medium voltage grid.

In the last network-infrastructure review, the US Department of Energy found that approximately 70% of the US grid’s transmission lines are over 25 years old, and the average age of large power transformers, which handle 90% of the nation’s electricity flow, is more than 40 years. Some of our country’s electricity networks are over a century old, and most distribution systems have been operating for 50 or more years, past their expected useful life.

Many factors affect the quality, cost, and availability of power, such as long-term exposure to humidity, extreme heat and cold weather, ice, snow, air pollution of all types, incorrectly specified equipment, low-quality equipment, incorrect installation of good quality equipment, and incorrect maintenance techniques. Over-washing, for example, may degrade powerlines, substations, transformers, and other critical power system equipment.

One way to identify problems before they become major issues is to detect “corona partial discharge” on the surface of power equipment. Simply put, corona discharge is a sign of a poorly operating power system. The more you have of it and the more severe it becomes, the more likely you are to experience problems such as unreliable power flows, planned and unplanned grid shutdowns, and expensive major repairs.

To help the electric utility industry address this problem, the Electric Power Research Institute (EPRI), one of the world’s leading research institutions focusing on electric grid systems, has developed comprehensive guidelines for dealing with partial discharge. These guidelines emphasize the use of UV sensors and highlight several key benefits that make UV intelligent sensors invaluable tools in the electricity industry:

1. Detection Accuracy: UV intelligent sensors excel at detecting corona discharge, a type of partial discharge that emits ultraviolet light. This allows for early-stage detection, which is crucial for preventing equipment failures. Unlike IR (infrared) or thermal cameras, which are less effective due to the minimal heat emitted by corona discharge, UV intelligent sensors provide a more accurate detection method.
2. Preventing Equipment Failures: Early detection of partial discharge can prevent costly equipment failures and power outages. UV cameras enable timely maintenance and repairs, identifying issues before they escalate.
3. Safety Improvements: UV intelligent sensors enhance safety by allowing inspections from a distance, reducing the need for personnel to access high-risk areas. This significantly lowers the risk of workplace injuries.
4. Comprehensive Inspections: UV intelligent sensors complement other technologies like IR cameras. While IR cameras are excellent for detecting thermal anomalies, UV cameras provide additional insights by detecting electrical discharges that IR cameras might miss.
5. Cost-Benefit Justification: Although UV intelligent sensors come with a significant initial cost, their long-term benefits in preventing outages, reducing maintenance costs, and improving safety more than justify the investment.

To quantify the value of UV intelligent sensors, it's essential to calculate the financial and operational benefits compared to other technologies. Here's a breakdown of how you can approach this:

1. Detection Accuracy: Metric: Number of early detections of corona discharge. Value: Calculate the cost savings from preventing equipment failures due to early detection. This includes avoided repair costs, downtime, and potential revenue loss.
2. Preventing Equipment Failures: Metric: Reduction in the number of equipment failures. Value: Estimate the cost of equipment replacement and repair and compare it with the cost of using UV cameras. Include the cost of unplanned outages and their impact on revenue.
3. Safety Improvements: Metric: Number of safety incidents before and after implementing UV cameras. Value: Calculate the reduction in costs associated with workplace injuries, including medical expenses, compensation, and lost productivity.
4. Comprehensive Inspections: Metric: Number of issues detected by UV cameras that were missed by IR cameras. Value: Assess the cost of undetected issues that could lead to failures or inefficiencies. Include the cost of additional inspections and repairs.
5. Cost-Benefit Justification: Metric: Total cost of ownership (TCO) of UV intelligent sensors versus IR cameras. Value: Compare the initial investment and ongoing maintenance costs of UV cameras with the savings from reduced failures, improved safety, and comprehensive inspections. Calculate the return on investment (ROI) over a specified period.

While this topic is quite complex, breaking it down into a cost-benefit model helps to concretize the relevance and serves as a guideline for more effective performance of our grid systems. Other standards to consider for this topic should include the IEEE’s 1366 reliability indices and various ANSI standards for power system equipment.

Other topics I plan to explore here include, “Who and what are the key drivers of Performance Incentive Mechanisms (PIM) within T&D grid organizations?” and “What PIM programs have done to transform operational efficiency in T&D grid companies?”

UVCameras #GridEnhancement #SafetyFirst #CostSavings #EPRI #TechnologyAdvancements #ROI #Maintenance #Innovation #gridreliabilityimageintelligence #GRII

Sources used for this article

[1] Transmission Optimization with Grid Enhancing Technologies: Grid Enhancing Technologies Modelling Methodology

[2] NERC's 2024 Long-Term Reliability Assessment and Predictive AI for T&D[4] US Electric Utilities Transmission and Distribution Costs NREL Report

[3]?Brattle Group on Electric Transmission

[4] Berkeley Labs RE Integration Grid Congestion study

[5] Five Critical Insights from NERC’s 2024 Long-Term Reliability Assessment

[6] Predictive AI for T&D: How automated inspections optimise electric power systems and contain costs?

[7] Partial Discharge Testing Using UV and Thermography by OFIL

[8] Asset Inspections by Multispectral Imagery - EPRI Electrical Industry