AI-Assisted Fault Detection
February 2026
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Overview
AI-assisted Fault Detection (AIFD) is a modern approach to fault protection that leverages artificial intelligence (AI) and machine learning (ML) techniques. The circumstances surrounding a fault and the type of fault that occurs create unique signatures in the voltage waveform, which can be identified using specialized techniques such as neural networks and anomaly detection algorithms[1]. These models can detect subtle changes in incoming data, potentially identifying equipment issues before complete failure. AI and ML are particularly suited for elucidating patterns in the large and noisy datasets that characterize fault events.
AI-assisted fault detection may be particularly well-suited for distribution systems, where it could offer faster deployment and response times compared to traditional relay upgrades. This approach has the potential to enhance reliability and situational awareness without the need for extensive hardware changes[2].
While this technology is still under active development, many protection system vendors have begun deploying AI-based systems to assist with fault detection. However, it is important to note that large portions of this functionality have already been implemented in modern digital relays. Therefore, AI-assisted fault detection should not be viewed as a cheaper alternative to relay upgrades, but rather as a complementary tool that can enhance existing protection schemes. This application is especially promising for fault classification, as many fault types can be distinguished by identifying characteristic waveform patterns. That said, without point-on-wave detection capabilities, the utility of AI in improving fault location accuracy may be limited[3].
Benefits
Outage Mitigation
AIFD facilitates faster and more accurate restoration of service following outages. By quickly diagnosing the cause of faults, utilities can deploy repair crews more efficiently, minimizing downtime and improving overall grid resilience[4]. While this may help classify the type of fault and suggest the equipment likely needed for repair, it may not significantly aid in pinpointing the fault location—an important factor for streamlining dispatch and reducing response times.
Accompanying Aging Infrastructure
As infrastructure ages, the likelihood of mechanical failures increases, making it essential to rapidly detect and respond to faults. AIFD enhances the ability to identify incipient faults, allowing utilities to address issues before they escalate into major outages[5]. This capability is particularly valuable for maintaining reliability in older systems where equipment failures are becoming increasingly common. In summary, emphasis of AIFD is better placed on early detection and classification.
Technology Readiness Level (TRL)
- Full-scale or prototypical system
- Demonstrated in a relevant environment
- Integrated system (not just components)
- Near real-world conditions
- Not yet proven across all operating conditions
AIFD technologies have been demonstrated in laboratory settings, pilot projects and deployed in proprietary utility in-house developed implementations.
Widespread commercial adoption is still limited. This implies that this technology has reached TRL 7.
Adoption Readiness Level (ARL)
Value Proposition
Delivered Cost
Medium Risk
The upfront costs associated with training AI models can be very large, depending on model complexity. Occasionally, this training must be updated, which is also costly. This could require substantial R&D investment if highly complex models are necessary. In many cases, AIFD systems are likely to be composed of multiple specialized models tailored to different fault types, rather than a single generalized model[6]. This approach helps avoid the low estimation accuracy often associated with generalized models but introduces additional complexity. It also necessitates a preprocessing step to identify the fault type before the appropriate model can be applied.
Moreover, the need for retraining can be highly sensitive to the level of application. For instance, if a trained model is deployed in an area without generation sources, the later addition of generations, such as distributed energy resources, may significantly alter system dynamics, triggering the need for model refinement or retraining. The more models in use, the greater the retraining burden becomes. To support the development and maintenance of these models, organizations like the North American Electric Reliability Corporation (NERC) may serve as valuable partners in curating standardized datasets for training, given their role in overseeing grid reliability across North America[7].
Primarily software + existing sensors (PMUs, DFRs, PQ meters). Often leverages current infrastructure; incremental costs can be supported by GRIP or utility CAPEX/OPEX.
Functionality Performance
Medium Risk
AIFD systems must demonstrate high accuracy in fault detection, minimal false positives and negatives, and robust performance under varying operating conditions. Integration with diverse grid topologies and equipment vendors presents additional challenges, as each deployment may require specific tuning. Peer-reviewed studies have shown that AIFD can achieve high accuracy in fault classification and localization; however, robustness can vary significantly depending on factors such as inverter-based resource (IBR) penetration, sensor mix, and overall data quality[8]. Explainability and performance in edge cases—such as IBR-dominated systems or high-impedance faults—remain areas of active research and development.
Accurate fault localization may also depend on advanced capabilities such as traveling wave functionality and synchro phasor measurements, which enable timestamped waveform comparisons across multiple locations for triangulating fault positions. These features may not be present in all installed relays assumed to interface with the AI models, potentially limiting localization accuracy. Additionally, limited monitoring on the secondary side of the system—often due to financial constraints—can further restrict the functionality and effectiveness of AIFD, particularly in distribution networks where visibility is already reduced[9].
Ease of Use/Complexity
Medium Risk
Training will be required to integrate these models into existing systems and processes. Adding AI changes how control rooms operate to some extent. This will likely require some equipment adjustment, but where that is the case, it’s expected to be relatively simple. However, integration with Energy Management Systems (EMS), Distribution Management Systems (DMS), data pipelines, and MLOps frameworks represent a non-trivial lift. While typical operators can adopt these tools after appropriate training, the broader system integration effort may be significant.
If AI models are kept in an “assistive” role, there is no need to modify existing protection settings. A key consideration is the level of control granted to the AI models. Operators may choose to deploy these systems as passive observers with no direct control authority. In this configuration, AIFD can still support the assessment and classification of grid events—such as winding breakdowns, weak phase-to-phase faults, or high-impedance faults—but the speed advantage of AI is limited by the continued need for human review and decision-making[10]. During the initial market phase and likely for the first few years of adoption, AIFD will primarily serve as a passive tool that enhances situational awareness without altering the core protection scheme, which will continue to operate independently for severe faults.
Market Acceptance
Demand Maturity/Market Openness
Medium Risk
Interest in AI is growing across all industries, and the power sector is no exception. Utilities and vendors are actively piloting AI-driven analytics and fault detection tools, recognizing their potential to enhance grid reliability and operational efficiency. For grid applications, traditional protection systems—while robust—have limitations that AIFD could help address, particularly in complex, data-rich environments. Utilities considering upgrades are typically evaluating advanced solid-state relays, many of which are already beginning to incorporate AI-like functionalities.
Despite the promise of AIFD, barriers to adoption—such as long procurement cycles, integration challenges, and the inertia of legacy protection practices—are likely to limit near-term market scale. That said, the path to broader deployment is increasingly well understood. As grids become more complex, events evolve more rapidly, and interoperability standards mature, the demand for AIFD is expected to grow steadily, especially as utilities seek more adaptive and intelligent solutions to manage evolving grid dynamics.
Market Size
Low Risk
While this solution could be applied across the entire transmission and distribution (T&D) market, the true market size depends on AIFD becoming both cost-effective and standardized enough to deliver consistent value across diverse grid environments. All T&D utilities can benefit from AIFD, with the value proposition scaling particularly well in systems with extensive feeder miles or elevated wildfire risk. In fact, wildfire mitigation is a regulatory requirement for many utilities, especially in high-risk regions, and high-impedance faults—often difficult to detect with conventional methods—are a common trigger for such events at the distribution level.
Programs like the Grid Resilience and Innovation Partnerships (GRIP), which are national in scope, further highlight the growing interest in advanced fault detection technologies[11]. However, it’s worth noting that many relay vendors are already developing and integrating similar functionalities into their solid-state devices. As a result, AIFD may find itself competing directly with relay-based solutions in some segments of the market, particularly where utilities are already planning relay upgrades as part of broader modernization efforts.
Downstream Value Chain
Low Risk
The ability of AI models to enhance fault detection provides clear value to grid operators by improving situational awareness and enabling earlier intervention. The primary incentives for utilities include increased reliability and reduced maintenance costs. However, the value of AIFD extends beyond utilities—it also touches relay OEMs, system integrators, and industrial users. Despite this broad value proposition, split incentives among stakeholders and the need for process changes can slow down adoption. These challenges can be mitigated through the development of integration playbooks and robust MLOps frameworks that streamline deployment and maintenance.
In addition to traditional utility applications, AIFD has potential value in industrial settings such as data centers, where it could support fault location, isolation, and service restoration (FLISR) schemes[12]. Similar benefits could be realized in meshed systems or facilities with substation-level communications, where rapid fault classification and response are critical. As these use cases expand, the versatility of AIFD will become increasingly important in justifying its integration across a range of grid and non-grid environments[13].
Resource Maturity
Capital Flow
Medium Risk
Venture capital and private equity investments in AI/ML and energy storage sectors have been growing steadily, and AIFD stands to benefit from this broader trend—particularly as investors seek specific, high-impact applications of AI in critical infrastructure. Public funding, grants, and other incentives also play a key role in supporting AIFD startups and scale-up efforts. Strong federal cost-share programs and increasing utility spending have helped enable first-of-a-kind deployments, which often still rely on grant support to offset early-stage risk.
Access to facilities for testing and validating AIFD products remains scarce, posing a challenge for both startups and established vendors. Despite this, the sector may be considered a medium-risk opportunity for investors, as the growing interest in AI for grid applications could generate meaningful momentum. With the right combination of technical validation, regulatory support, and market alignment, AIFD could attract significant capital investment as a targeted solution within the broader AI and energy innovation landscape.
Project Development, Integration, and Management
Medium Risk
Given that AIFD solutions are primarily software-based, project development, integration, and ongoing management are generally not considered major technical risks. However, the relative newness of this technology and the lack of widespread deployment suggest that additional derisking is still needed from an implementation standpoint. While repeatable pipelines for data governance and MLOps are beginning to emerge, their maturity and consistency vary significantly across utilities. This unevenness can introduce integration challenges, particularly in environments where data infrastructure is less developed or where operational practices are not yet aligned with AI-driven workflows.
Infrastructure
Medium Risk
AI infrastructures, such as data centers and cloud computing, could become a limiting factor for scaling AI solutions, but this infrastructure currently exists and is actively being expanded. On the grid side, however, the readiness to support AIFD varies. While many utilities already have phasor measurement units (PMUs) and power quality (PQ) sensors in place, others may require incremental investments in communications infrastructure, computing resources, and data management systems. Additionally, dated grid infrastructure may need further sensor deployment to reliably capture the high-resolution data required for effective AI model performance. These disparities can influence the feasibility, scalability, and effectiveness of AIFD across different utility environments.
Manufacturing and Supply Chain
Low Risk
AIFD solutions primarily rely on software and data processing, with minimal hardware requirements. While additional sensors may be needed to support data collection—particularly in older grid infrastructure—these components are typically off-the-shelf and readily available. The supporting infrastructure generally consists of standard IT hardware and software, with no exposure to critical minerals or specialized components. This reduces supply chain risk and simplifies procurement, making AIFD more accessible for utilities looking to enhance fault detection capabilities without major hardware overhauls.
Materials Sourcing
Low Risk
While materials associated with the broader AI industry can be exposed to some geopolitical risk, AIFD solutions themselves do not rely on critical minerals or scarce materials. They are built on general-purpose software and standard IT hardware, which significantly reduces supply chain vulnerabilities and geopolitical exposure.
Workforce
Medium Risk
The workforce needed to support AI solutions largely exists in other industries and can be sourced without significant retraining or large-scale workforce development. However, AIFD applications require a unique blend of protection engineering and data science expertise. While pipelines for training in analytics and MLOps are emerging, they still need to be scaled to meet the specific demands of the utility sector. Given the current level of expertise in the industry and the need for cross-disciplinary skills, workforce availability for AIFD can be considered a medium-risk factor. Focused training programs and partnerships with academic or industry institutions could help accelerate workforce readiness.
License to Operate
Regulatory Environment
Medium Risk
There are currently no regulatory barriers explicitly preventing the use of AI within power grid systems. When deployed in an assistive capacity—such as generating advisory alarms or supporting event triage—AIFD aligns with existing NERC PRC maintenance and coordination standards, as well as CIP-013 supply chain risk management controls. However, broader uncertainty remains within the evolving AI regulatory environment, particularly as it relates to autonomous decision-making. If AIFD were to be used for autonomous tripping or control actions, it would introduce a significantly higher evidentiary and testing burden to meet regulatory and operational assurance requirements.
AIFD is likely to fall under Critical Infrastructure Protection (CIP) regulations, meaning it would be subject to specific security requirements. Any AI models intended for use within a CIP-protected server room would need to undergo a formal vetting process. Additionally, the configuration of the server room and the cybersecurity measures in place will depend on the nature and classification of the entity managing the infrastructure.
In addition, the cybersecurity implications of AIFD warrant careful consideration. These systems are likely to consolidate large volumes of localized system event data for training purposes, raising questions about where and how this data is stored and processed. If training datasets are shared across utilities or made publicly accessible, this could introduce risks related to data privacy, system exposure, and model integrity. Even if the inference model itself poses lower risk, the training pipeline and data handling practices must be secured, especially if the system captures sensitive information such as user data, usage patterns, addresses, or billing details. If not properly segmented behind a CIP-compliant firewall, these systems could become attractive targets for cyber threats. Given these factors, the regulatory and cybersecurity risk level for AIFD should be considered moderate to high, depending on the deployment architecture and intended level of autonomy.
Policy Environment
Medium Risk
Federal, state, and local policies broadly support grid modernization, reliability, and resilience. However, this policy environment often translates into a cautious and skeptical approach to adopting new technologies—particularly those perceived as unproven or disruptive to existing reliability standards. This conservatism, while rooted in a desire to protect grid stability, can slow the adoption of innovative solutions like AI-assisted Fault Detection (AIFD).
One specific barrier is the regulatory treatment of software-as-a-service (SaaS) and cloud-based solutions. Many states still do not routinely allow utilities to rate-base these expenditures, which can limit investment in AIFD platforms. That said, momentum is building: the National Association of Regulatory Utility Commissioners (NARUC) issued a resolution in 2016 supporting cloud adoption, and initiatives like the Clean Power Institute’s “Financial Toolbox” are helping commissions explore new cost-recovery models. Some jurisdictions have already begun allowing capitalized implementation of cloud-based tools under FERC guidance. As these frameworks mature, they may help reduce financial and regulatory friction, enabling broader deployment of AIFD and similar technologies.
Permitting & Siting
Low Risk
Little to no permitting or sitting is required for the implementation of AIFD. In cases where improvements to the existing grid are necessary—such as the addition of sensors or communications infrastructure, these upgrades typically occur in areas already permitted for utility work. Sensor installations and communications enhancements are considered routine activities for most utilities and do not introduce significant regulatory or logistical hurdles.
However, there may be some additional equipment costs to ensure sufficient interfaces, data collection, and communication capabilities are in place to support the functionality of the AIFD system. Despite these considerations, the overall permitting and siting burden is minimal.
Environmental & Safety
Low Risk
There are very few direct environmental or safety risks associated with the use of AI by utilities. In fact, AIFD can offer net-positive environmental and safety benefits by enabling faster fault detection and localization, which supports wildfire risk mitigation and helps reduce the impact of large-scale grid events. These capabilities are particularly valuable in regions prone to wildfires, where early identification of high-impedance faults or failing equipment can prevent ignition events.
While the overall safety risk is minimal, one potential operational consideration is the risk of false positives—where the system may incorrectly flag a non-event, potentially triggering unnecessary dispatches. However, such occurrences can be managed through proper model tuning and human-in-the-loop review processes. Overall, the environmental and safety risk profile of AIFD is appropriately considered low, with the potential for meaningful positive impact.
Community Perception
Medium Risk
Communities are generally supportive of grid modernization efforts, particularly when framed around improving reliability and safety. Technologies like AIFD, which are non-intrusive and enhance system resilience, tend to receive positive public reception—especially when linked to wildfire mitigation strategies. In regions where wildfire risk is high, AIFD can be positioned as part of a broader mitigation plan, which may help justify its inclusion and reduce resistance to adoption.
However, increased costs passed on to ratepayers may still generate pushbacks, particularly in areas without immediate resilience or wildfire concerns. While bundling AIFD into larger reliability or safety programs can help offset this concern, cost sensitivity remains a factor. Given that resilience and wildfire-related requirements are only present in a subset of states, the overall community acceptance risk is best categorized as medium from a national perspective.
Case Studies & Implementation
Southern California Edison – AWARE
AIFD is part of SCE’s AWARE (Advanced Waveform Anomaly Recognition) model, designed to enhance fault detection in the electric grid. SCE’s AWARE system integrates machine learning with physics-based models to detect subtle waveform anomalies and predict equipment failures before they occur. By combining real-time data streams from substations, AMI, and SCADA, the solution enables proactive maintenance, reduces outage durations, and improves safety. This case study serves as a practical example of how utilities can leverage AI for predictive analytics and operational efficiency, aligning closely with modern smart grid initiatives.
References
- Team, SmartCitiesWorld News. Sense announces edge artifical intelligence fault detection. AI and Machine Learning. [Online] February 3, 2026. https://www.smartcitiesworld.net/ai-and-machine-learning/sense-announces-edge-artificial-intelligence-fault-detection.
- AI-DRIVEN FAULT DETECTION IN ELECTRICAL POWER. Sinha, Gita, et al. 9, s.l. : Neuroquantology, 2022, Vol. 20. Doi: 10.48047/NQ.2022.20.9.NQ44956.
- Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks. Rezapour H, Jamali S, Bahmanyar A. 12, s.l. : Energies, 2023, Vol. 16. https://doi.org/10.3390/en16124636.
- AI-Driven Outage Management with Exploratory Data Analysis,Predictive Modeling, and LLM-Based Interface Integration. Ansarinejad, Kian, Huang, Ying and Yodo, Nita. 19, Fargo : Energies, 2025, Vol. 18. DOI:10.3390/en18195244.
- AI-DRIVEN FAULT DETECTION AND PREDICTIVE MAINTENANCE IN ELECTRICAL POWER SYSTEMS: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPROACHES, DIGITAL TWINS, AND SELF-HEALING GRIDS. Rana, Sohel. 1, Beaumont : American Journal of Advanced Technology and Engineering Solutions, 2025, Vol. 1. DOI: 10.63125/4p25x993.
- Artificial Intelligence: A Powerful Paradigm for Scientific Research. Xu, Yongjun, et al. s.l. : The Innovation, 2021, Vol. 2.
- Choi, Seong Lok, et al. eGridGPT: Trustworthy AI in the Control Room. Golden : National Laboratory of the Rockies, 2024.
- Anwar, Tahir, et al. Robust fault detection and classification in power transmission lines via ensenble machine learning models. s.l. : Scientific Reports, 2025. https://doi.org/10.1038/s41598-025-86554-2.
- Kasztenny, Bogdan & Smelich, Greg. Locating Faults by Using Incremental Quantities: Introduction, Application Considerations, and Performance. 2025.
- GoogleCloud. What is Human-in-the-Loop (HITL) in AI & ML? Discover. [Online] GoogleCloud. [Cited: February 3, 2026.] https://cloud.google.com/discover/human-in-the-loop.
- Grid Deployment Office. Grid Resilience and Innovation Partnerships (GRIP) Program. Federal Financing Tools. [Online] U.S. Department of Energy. [Cited: February 3, 2026.] https://www.energy.gov/gdo/grid-resilience-and-innovation-partnerships-grip-program.
- Schweitzer Engineering Laboratories. Fault Location, Isolation, and Service Restoration (FLISR). Distribution Automation Applications. [Online] Schweitzer Engineering Laboratories. [Cited: February 3, 2026.] https://selinc.com/solutions/p/flisr/.
- C. Vellaithurai, N. DeBruno, T. Doshi. Enhancing Data Center Resiliency: Industrial FLISR for Improved System Fault Management and Availability. 2025.
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