Abstract: Disclosed are systems and methods directed to a frequency analysis approach to transformer status monitoring using distributed fiber optic sensing to monitor a phase delay of a 120 Hz vibrational signal and determining an angular difference between a designated point and a reference point. Operationally, systems and methods according to aspects of the present disclosure identify phase delay patterns of a single transformer from its vibrational humming and combined vibrational signals of multiple transformers.

Abstract: Disclosed is a deep learning and language model enhanced system and method for wind turbine monitoring using distributed fiber optic sensing (DL-LM-DFOS) which combines advantages of distributed fiber optic sensing with the power of deep learning and large language models. Our system and method automatically learns and extracts useful features from raw sensor data, detects complex patterns indicating potential issues, and incorporates and learns from a wide range of data, including textual data such as maintenance logs, operational notes, or alarm messages. As a result, our inventive system and method provide comprehensive, efficient, and predictive monitoring of wind turbines.

Abstract: Methods and systems for anomaly detection include measuring time-series data about a system using an optical sensing system. The time-series data is adapted to natural language data. One or more anomaly detection models are selected based on the natural language data and a task. An anomaly is detected in the system using the selected one or more anomaly detection models. A corrective action is performed responsive to the anomaly.

Abstract: Integrated DFOS systems and methods for 3D gunshot, localization, and tracking utilizing Artificial Intelligence enhanced (AI-enhanced) systems and methods for infrastructure security including electrical substations. Our systems and methods provide a comprehensive solution for substation security enhancement, integrating 3D gunshot localization, real-time tracking, and AI-driven analysis. Utilizing Distributed Acoustic Sensing (DAS) technology, our systems and methods precisely detect and triangulate the origin of gunshots in three-dimensional space. The trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. Al algorithms discern between various acoustic events and provide identification of genuine threats. Upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment.

Abstract: Disclosed are integrated systems and operating methods that provide an integrated security system for substation monitoring and detection that effectively combines the strengths of distributed acoustic sensing, drones, and security cameras for comprehensive protection. The integrated system comprises a DAS system configured to monitor vibrations and acoustic signals along the length of fiber optic cables, one or more drones equipped with advanced sensors for aerial surveillance, and a plurality of security cameras installed throughout the substation to capture real-time video feeds and provide visual confirmation of activities. A central control system integrates and analyzes data from the DAS system, drones, and security cameras, and utilizes a novel, advanced algorithm, named Substation Security Analytics (SSA), specifically designed for the unique challenges associated with substation security monitoring and detection.

Abstract: Systems and methods include collecting vibration data along an optical fiber cable using distributed acoustic sensing (DAS). The collected vibration data is preprocessed to separate the vibration data into at least two mixtures. The at least two mixtures are combined into a mixture of mixtures. The mixture of mixtures is separated into a plurality of estimated source signals using a separation model. The separation model is trained using an unsupervised loss computed between the estimated source signals and the at least two mixtures.

Abstract: Systems and methods for a risk mitigation system for electrical power grids. To mitigate risks such as natural destructive forces, collected risk data and EPG data can be fused to obtain fused data. The vulnerability metric and fragility metric of the EPG based on risk profiles generated from the fused data can be predicted with a physics-informed neural network (PINN) trained with the fused data. EPG threat metrics can be developed by integrating the vulnerability metric, fragility metric, and the risk profiles into an integrated score that determines the probability of failure of the EPG caused by natural destructive forces. The present embodiments can perform a corrective action with an automated helper to mitigate the risks to the EPG caused by the natural destructive forces determined from the EPG threat metrics.

Abstract: Disclosed are integrated systems and methods providing intelligent anomaly detection for DFOS systems and applications, the systems and methods utilizing a natural language processing model, such as ChatGPT, to generate real-time alerts with actionable recommendations and potential consequences based on detected anomalies. Our innovative solution—OptiSenseGPT—solves problems left uncured by traditional methods by delivering easily understandable alerts in natural language, enabling timely response by relevant personnel. Our integrated OptiSenseGPT systems and methods disclosed provide context-aware recommendations and consequences, enhancing decision-making and improving overall performance and safety of a monitored infrastructure or environment. Our OptiSenseGPT systems and methods advantageously provide integration of natural language processing; context-aware recommendations; presentation of potential consequences; adaptability and customization; and seamless integration.

Abstract: Disclosed are integrated systems and methods that utilize a network of DFOS sensors installed on power lines, communication networks, and transportation systems, which continuously monitor infrastructures and provide real-time data. This data is collected, processed, and analyzed, and used to identify any affected areas of infrastructure and assess severity of any damage. Prioritization and resource allocation is performed and uses this analysis to prioritize restoration efforts and allocate resources such as repair crews, equipment, and materials to areas with a highest priority.

Abstract: Disclosed are systems, methods, and structures that employ distributed fiber sensing that utilizes existing fiber cables for detecting tree events. Our inventive systems and methods can detect when a tree or other types of vibration occur on the telecom cable or poles, creating a high-resolution spatial profile of the cable. Systems and methods according to aspects of the present disclosure distinguish between tree events and other vibrations, such as hammer strikes on a utility pole, by employing Information Noise Contrastive Estimation (InfoNCE) and contrastive learning. Our inventive approach accurately differentiates between different types of vibrations and identifies patterns specific to tree events.

Abstract: Disclosed are systems, methods, and structures that provide superior DFOS rain intensity measurements and introduce a universal solution for rain intensity detection based on the data collected by distributed acoustic sensing (DAS) technology and a designed domain generalization method. As a result, systems and methods according to the present disclosure distinguish the rain intensity of a large area through which the fiber optic cables traverse and address the domain shift issue, by employing a domain generalization technique based on machine learning technology in which newly collected target domain inference data may be distributed differently from the previously captured training source domain data. To generalize the trained model to different target domains, source domain distributions are enriched by disturbing the distribution in the frequency domain.

Abstract: Disclosed are systems, methods, and structures that enhance advanced metering infrastructure (AMI) event classification with graph neural networks (GNNs) in which AMI data is represented as a graph, where each meter is a node and connections between nodes represent the physical or functional relationship between meters. As a result, our systems and methods capture dependency information between different meters and use this information to predict events and anomalies.

Abstract: Disclosed are twin-based systems and methods for predicting solar power generation and optimizing power generation and distribution processes. Employed are a digital twin model of a solar power plant, which includes detailed representations of various components, such as solar panels, inverters, and transformers, as well as real-time weather data and historical data. This advantageously allows for accurate simulations of plant performance under various weather conditions and operational scenarios. Our systems and methods Incorporate novel machine learning algorithms that are trained on historical and real-time data from the digital twin model, weather data, solar power generation data, and other relevant factors.

Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

Abstract: Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

Abstract: Systems and methods for reducing work conflicts is provided. The method includes receiving a vibrational signal from a utility pole; identifying a location and type of field work on the utility pole from one or more features of the vibrational signal utilizing a trained neural network; and communicating the location and type of field work to a third party.

Abstract: Systems, methods, and structures providing dynamic line rating (DLR) for overhead transmission lines based on distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to determine temperature of the electrical conductors. Environmental conditions such as wind speed, wind direction, and solar radiation data, are collected from environmental sensors and an acoustic modem that convert the digital data collected from the environmental sensors into generated vibration patterns that are subsequently used to vibrationally excite a DFOS optical sensor fiber. The DFOS system monitors the optical sensor fiber and detects, measures, and decodes the vibrational excitations. An Artificial Neural Network (ANN) determines a heat transfer correlation between the temperature of the optical sensor fiber and electrical conductor(s) (core temperature).

Abstract: A data-driven street flood warning system that employs distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) and machine learning (ML) technologies and techniques to provide a prediction of street flood status along a telecommunications fiber optic cable route using the DFOS/DAS data and ML models. Operationally, a DFOS/DAS interrogator collects and transmits vibrational data resulting from rain events while an online web server provides a user interface for end-users. Two machine learning models are built respectively for rain intensity prediction and flood level prediction. The machine learning models serve as predictive models for rain intensity and flood levels based on data provided to them, which includes rain intensity, rain duration, and historical data on flood levels.

Abstract: A solar powered, self-contained DFOS extender that includes a wide-angle camera and a microphone. The DFOS extender monitors the perimeter constantly or at predetermined time intervals both visually and acoustically. It analyses these signals and if an alarming event is detected (such as an animal, a human, a car, or a truck seen or heard), then the DFOS extender generates a coded vibration via its in-built acoustic modem. These coded vibrations are detected by the fiber and an event log is generated. Consequently, a trespasser or false alarms (animals) are detected before they are within detectable distance of the underground DFOS optical sensor fiber. Advantageously, our DFOS extender can be placed in critical locations along a border where an increased detection range is desired and may later be relocated/reinstalled at other locations as well.

Abstract: Systems and methods for utility pole localization employing a DFOS/DAS interrogator located at one end of an optical sensor fiber remotely capture dynamic strains on the optical sensor fiber induced by acoustic events. A captured two-dimensional spatiotemporal map in an ambient noisy environment is analyzed by a trained machine learning model which then automatically detects an area in which a pole is located without requiring domain knowledge. Original DFOS/DAS signals are separated into pole regions and non-pole region time series for machine learning model training. A contrastive loss function measures similarities between low-frequency and high-frequency features. A Gaussian distribution is applied to the original signals to generate weighted labels to eliminate effects of label noise. The machine learning model fuses low-frequency and high-frequency features in the frequency domain for pole region classification.