{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853690","patent":{"patent_number":"US-9853690","title":"Generating high resolution inferences in electrical networks","assignee":null,"inventors":[],"filing_date":"2016-06-13T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04B","G06N","H04L"],"num_claims":17,"abstract":"Methods, systems, and computer program products for generating high resolution inferences in electrical networks are provided herein. A computer-implemented method includes collecting multiple items of data from one or more data sources arising from a power distribution network, wherein the multiple items of data comprise (i) one or more levels of temporal resolution and (ii) one or more levels of spatial resolution; determining a network topology of the power distribution network based on identification of one or more connections between each of multiple components within the power distribution network; and generating a power flow estimate for one or more nodes within the power distribution network at a pre-determined level of spatio-temporal resolution, wherein said generating comprises applying a model to (i) the multiple items of collected data, (ii) the determined network topology, and (iii) one or more relations constraining the power flow estimate at the pre-determined level of spatio-temporal resolution."},"analysis":{"summary":"The patent **Generating High Resolution Inferences in Electrical Networks** (US-9853690) introduces a sophisticated computer-implemented method designed to significantly enhance the visibility and analytical capabilities within power distribution networks. At its core, the innovation addresses the pervasive challenge of disparate data resolutions and incomplete network understanding that plagues traditional grid management systems.\n\nThe system operates by first collecting a wide array of data from various sources across the power distribution network. Crucially, this collected data encompasses different levels of both temporal (time-based) and spatial (location-based) resolution. This means it can integrate everything from high-frequency sensor readings to less frequent smart meter data, creating a comprehensive and harmonized dataset.\n\nSimultaneously, the method precisely determines the network topology. This involves identifying all connections between the multiple components within the power distribution network, creating an accurate digital map of the grid's physical layout. This detailed topological understanding is vital for correctly interpreting power flows and interactions.\n\nWith the harmonized data and the accurate network topology in place, the system then applies an advanced model. This model leverages the collected data, the determined topology, and one or more pre-defined relations (physical constraints) to generate highly accurate power flow estimates. These estimates are produced for specific nodes within the network at a *pre-determined level of spatio-temporal resolution*. This granular output allows utilities to 'zoom in' on specific areas and timeframes, providing unprecedented detail.\n\nThe business value of this patent is immense. It enables utilities to achieve superior operational efficiency, significantly reduce outage durations by pinpointing faults faster, optimize resource allocation, and seamlessly integrate distributed energy resources like solar and wind. This technology forms the bedrock for a truly intelligent, resilient, and proactive smart grid, leading to more reliable and sustainable energy delivery for consumers.","layman_explanation":"### What Problem Does This Solve?\nImagine running a vast delivery network, but you only get updates on your trucks' locations every few hours, and only for trucks on major highways. You'd have huge blind spots, making it hard to deliver efficiently, fix problems quickly, or plan for new routes. This is similar to the challenge faced by today's electrical utilities. They manage immense power distribution networks, but often rely on data that's not frequent enough (low temporal resolution) or detailed enough about specific locations (low spatial resolution). This leads to inefficiencies, longer power outages, difficulty integrating new energy sources like solar panels (which fluctuate quickly), and reactive rather than proactive management. The existing systems are simply not granular enough to handle the complexity and speed of modern energy demands.\n\n### How Does It Work?\nThe patent **Generating High Resolution Inferences in Electrical Networks** offers a sophisticated solution by acting like a super-intelligent, real-time map and monitoring system for the entire electrical grid. Think of it in three key steps:\n\n1.  **Collecting All the Clues:** The system first gathers every piece of data it can find from the power network. This isn't just one type of data; it's a mix. Imagine getting high-speed sensor readings from a major substation (like a very fast, detailed traffic camera on a highway) alongside less frequent meter readings from individual homes (like a slower, less detailed update from a side street). The key is that this system is smart enough to collect and understand all these different types of 'clues,' regardless of how fast or how specific they are.\n\n2.  **Building the Perfect Map:** While collecting data, the system also creates an extremely precise map of the entire electrical network. It identifies every single connection between components—from the big power lines down to the smallest transformers that deliver electricity to your neighborhood. This isn't a static map; it understands the live 'connections' and how they interact, much like a GPS system understands real-time road closures or traffic diversions.\n\n3.  **Predicting Power Flow with Precision:** With all the clues collected and the perfect map in hand, the system then applies a powerful analytical 'brain.' This brain uses all the available information, combined with the fundamental laws of physics that govern electricity, to generate highly accurate predictions about how power is flowing throughout the network. Crucially, these predictions aren't just broad estimates. The system can 'zoom in' to specific locations (e.g., a single street block) and specific moments in time (e.g., second-by-second changes), providing an unprecedented level of detail. It's like having a real-time, high-definition video of all the energy moving through the grid, showing exactly what's happening, where, and when.\n\n### Why Does This Matter?\nThis innovation fundamentally transforms grid management from a reactive, guesswork-driven process to a proactive, data-driven one. For utilities, it means significantly improved operational efficiency, leading to lower costs. They can identify and fix problems much faster, reduce energy waste, and optimize the use of expensive equipment, potentially deferring costly infrastructure upgrades. For consumers, this translates directly to more reliable power, fewer outages, and a more stable energy supply, especially as more homes adopt solar panels or electric vehicles. It also enables the grid to seamlessly integrate more renewable energy sources, which is critical for meeting sustainability goals. This creates a significant competitive advantage for any utility or energy technology company that adopts this approach, allowing them to lead in grid modernization and deliver superior service.\n\n### What's Next?\nThe capabilities unlocked by this patent pave the way for a truly 'smart grid.' We can expect to see enhanced predictive maintenance, where potential equipment failures are identified before they occur. It will enable advanced demand-side management, allowing the grid to dynamically respond to energy needs in real-time. This technology is a cornerstone for creating self-healing grids that can automatically detect and isolate faults, minimizing human intervention. The market adoption timeline will depend on utility investment cycles and regulatory incentives, but the clear ROI and strategic benefits suggest a strong uptake, positioning this as a key enabler for the energy transition and a more resilient, sustainable future.","technical_analysis":"The patent **Generating High Resolution Inferences in Electrical Networks** (US-9853690) outlines a computer-implemented methodology for overcoming the inherent limitations of conventional power grid monitoring and control systems, specifically concerning data granularity and network state estimation. The technical architecture proposed hinges on a multi-stage data processing and modeling pipeline designed to yield spatio-temporally resolved power flow inferences.\n\n**1. Data Collection and Harmonization:**\nAt the foundational layer, the system initiates by collecting multiple items of data from diverse sources within a power distribution network. The critical technical challenge here is the heterogeneity of this data, which inherently possesses varying levels of temporal (e.g., sub-second PMU data, 15-minute smart meter readings, hourly SCADA updates) and spatial (e.g., substation-level, feeder-level, individual consumer) resolutions. The invention implicitly requires robust data ingestion pipelines capable of handling high-volume, high-velocity time-series data from disparate protocols and formats. A key implementation detail would involve a data lake or stream processing framework (e.g., Apache Kafka, Spark Streaming) to normalize, time-align, and potentially downsample or upsample data to a common, albeit flexible, internal representation, thereby 'harmonizing' the resolutions for subsequent processing.\n\n**2. Network Topology Determination:**\nConcurrently or in parallel, the system determines the network topology. This isn't merely ingesting a static CAD drawing. The patent implies an active process of identifying connections between multiple components, suggesting capabilities for dynamic topology updates (e.g., detecting switch operations, reconfigurations) or continuous validation against real-world observations. This module would likely employ graph databases or specialized network modeling software to represent the grid as a graph, where nodes are components (e.g., buses, transformers, loads) and edges are connections (e.g., lines, cables). Accurate and up-to-date topology is paramount, as even minor errors can propagate significantly through power flow calculations.\n\n**3. Inference Generation Model:**\nThis is the core algorithmic component. The patent specifies applying a 'model' to (i) the harmonized collected data, (ii) the determined network topology, and (iii) one or more relations constraining the power flow estimate. This 'model' could encompass a hybrid approach:\n    *   **Physics-informed Models:** Traditional AC power flow algorithms (e.g., Newton-Raphson, Gauss-Seidel) are essential for satisfying fundamental electrical laws (Kirchhoff's Voltage and Current Laws, Ohm's Law). These would be constrained by real-time measurements (from the collected data) and the network's physical configuration (topology).\n    *   **Machine Learning/Statistical Models:** To address measurement sparsity, noise, and computational complexity, especially for high-resolution inferences, data-driven models (e.g., deep neural networks, Gaussian processes, Kalman filters) could be employed. These could learn the complex, non-linear relationships between available measurements, topology, and actual power flows. They could also be used for state estimation, predicting unmeasured quantities based on available data.\n    *   **Constraining Relations:** These refer to the physical and operational limits of the network. Examples include voltage limits, thermal limits of lines and transformers, reactive power balance equations, and known load profiles. These constraints would be integrated into the optimization problem solved by the model, ensuring physically plausible and operationally viable power flow estimates.\n\n**4. Spatio-Temporal Resolution Output:**\nThe key performance characteristic is the generation of power flow estimates at a 'pre-determined level of spatio-temporal resolution'. This implies that the system can dynamically adjust its output granularity based on user requirements or specific analytical tasks. For instance, an operator might request power flow at 1-second intervals for a specific feeder experiencing an anomaly, while daily averages suffice for long-term planning. This necessitates efficient data storage (e.g., time-series databases) and visualization layers capable of rendering high-density data effectively.\n\n**Integration Patterns and Performance:**\nSuch a system would likely be deployed as a microservices architecture, with dedicated services for data ingestion, topology management, model execution, and API endpoints for data retrieval. Performance characteristics would demand low-latency processing for real-time applications (e.g., anomaly detection, fault location) and high-throughput for historical analysis. Scalability would be achieved through distributed computing and parallel processing frameworks. The implications for code-level development involve robust data validation, error handling for sensor failures, adaptive modeling techniques that can learn from new data, and sophisticated visualization tools to make high-resolution data actionable. This patent provides a foundational technical blueprint for truly intelligent and responsive grid operations.","business_analysis":"The patent **Generating High Resolution Inferences in Electrical Networks** (US-9853690) represents a significant leap forward in grid intelligence, with profound implications for the energy sector's business landscape. This innovation addresses critical operational inefficiencies and strategic challenges faced by utilities, energy service providers, and technology developers, opening substantial market opportunities and fostering competitive advantages.\n\n**Market Opportunity Size:** The global smart grid market is projected to reach hundreds of billions of dollars in the coming decade, driven by decarbonization goals, grid modernization efforts, and increasing demand for reliable power. This invention directly targets the core of smart grid functionality: granular data analytics and real-time operational intelligence. Its applications span across transmission, distribution, and even microgrid segments, making the addressable market vast. Every utility operating a power distribution network stands to benefit, representing a market of thousands of entities globally, each with substantial annual IT and operational technology budgets.\n\n**Competitive Advantages:** Companies adopting or licensing the principles of Generating High Resolution Inferences in Electrical Networks will gain a distinct edge by:\n1.  **Superior Operational Efficiency:** Unprecedented visibility into power flow enables optimized load balancing, reduced technical losses, and proactive maintenance, significantly cutting operational expenditures (OpEx).\n2.  **Enhanced Grid Reliability and Resilience:** Faster fault detection and isolation, coupled with predictive capabilities, lead to fewer and shorter outages, improving customer satisfaction and regulatory compliance.\n3.  **Accelerated Renewable Energy Integration:** The ability to precisely model and manage localized power flows at high resolution is crucial for seamlessly integrating intermittent distributed energy resources (DERs) like solar and wind, which often destabilize traditional grids.\n4.  **New Service Offerings:** The granular data can power new services such as highly accurate demand response programs, localized energy management solutions, and predictive capacity planning for industrial and commercial clients.\n\n**Revenue Potential and Business Models:** This technology can drive revenue through several business models:\n*   **Software-as-a-Service (SaaS):** Offering the inference generation platform as a cloud-based service to utilities, with tiered pricing based on network size, data volume, and resolution requirements.\n*   **Licensing:** Licensing the patent to established grid technology vendors (e.g., GE, Siemens, Schneider Electric) or niche analytics firms to integrate into their existing offerings.\n*   **Consulting and Implementation Services:** Providing expert services for integrating the system with existing utility infrastructure, data migration, and custom model development.\n*   **Value-Added Data Products:** Anonymized and aggregated high-resolution data insights could be valuable for market forecasting, policy analysis, and urban planning.\n\n**Strategic Positioning:** This patent positions its implementers at the forefront of grid modernization. It moves utilities from a reactive operational paradigm to a predictive and prescriptive one. It enables a data-driven approach to capital expenditure (CapEx) planning, allowing for more targeted investments in grid upgrades rather than blanket overhauls. Furthermore, it supports regulatory compliance for reliability standards and facilitates the transition to a cleaner energy future by de-risking DER integration.\n\n**ROI Projections:** While specific ROI will vary, key indicators suggest strong returns:\n*   **Reduced Outage Costs:** Each minute of outage costs utilities and their customers millions. Faster restoration translates directly to significant savings.\n*   **Energy Loss Reduction:** Even a 1-2% reduction in technical losses across a large network can save tens of millions annually.\n*   **Deferred CapEx:** Optimized asset utilization and predictive maintenance can extend the lifespan of existing infrastructure, deferring costly new builds.\n*   **Increased DER Hosting Capacity:** Enabling more renewables can unlock new revenue streams from green energy mandates and carbon credits.\n\nIn essence, Generating High Resolution Inferences in Electrical Networks is not just a technical improvement; it's an economic catalyst for the entire energy ecosystem, promising a more efficient, reliable, and sustainable power future.","faqs":[{"answer":"**Generating High Resolution Inferences in Electrical Networks** is a patent (US-9853690) that describes methods, systems, and computer program products designed to provide highly detailed and accurate insights into the functioning of electrical power distribution networks. It addresses the critical challenge of traditional grid management systems which often suffer from limited data granularity, making it difficult to precisely understand power flows and identify issues.\n\nAt its core, this invention enables the collection and synthesis of diverse data from various sources within the electrical network, regardless of their original temporal (time-based) or spatial (location-based) resolution. This means it can integrate everything from high-speed sensor readings to less frequent smart meter data into a cohesive dataset. This integrated data, combined with a precise understanding of the network's physical layout, allows for the generation of power flow estimates with unprecedented detail.\n\nThe system's ability to produce these estimates at a 'pre-determined level of spatio-temporal resolution' is key. It allows operators to 'zoom in' on specific areas of the grid, down to individual components, and analyze events over very short timeframes, providing a level of visibility previously unavailable. This transforms grid management from a reactive process to a proactive, data-driven one, fostering greater efficiency and reliability.\n\nIn essence, Generating High Resolution Inferences in Electrical Networks provides the 'X-ray vision' for electrical grids, offering crystal-clear, real-time understanding of energy distribution and behavior across the entire network. This foundational intelligence is crucial for modernizing our energy infrastructure and meeting future demands. Keywords: `electrical network inferences`, `high resolution grid data`, `power distribution analytics`, `smart grid patent`, `US-9853690`.","question":"What is Generating High Resolution Inferences in Electrical Networks?"},{"answer":"The **Generating High Resolution Inferences in Electrical Networks** patent works through a sophisticated, multi-step computer-implemented process that leverages advanced data collection, network mapping, and modeling techniques to achieve its high-resolution insights.\n\nFirst, the system initiates a comprehensive data collection phase. It gathers 'multiple items of data' from various sources across the power distribution network. Critically, this data is heterogeneous, meaning it comes with different levels of temporal resolution (how frequently it's updated, e.g., seconds vs. minutes) and spatial resolution (how specific its location is, e.g., substation vs. individual house). The system is designed to ingest and harmonize these diverse data streams, creating a unified and rich dataset.\n\nSecond, in parallel with data collection, the invention 'determines a network topology'. This involves meticulously identifying and mapping every connection between the multiple components within the power distribution network. This creates a precise digital model of the grid's physical and electrical layout, which is crucial for understanding how power flows and how different parts of the network interact. This topology is dynamic, meaning it can account for changes in the network configuration.\n\nThird, with the harmonized data and the accurate network topology in place, the system applies a sophisticated 'model'. This model uses the collected data as its inputs, the determined network topology as its structural framework, and one or more 'relations constraining the power flow estimate' (which are essentially the fundamental physical laws and operational limits of electricity). By processing these elements, the model generates highly accurate 'power flow estimates' for one or more nodes (specific points) within the network.\n\nFinally, the key output is that these power flow estimates are generated at a 'pre-determined level of spatio-temporal resolution'. This means the system can deliver insights that are incredibly specific in both location and time, allowing grid operators to 'zoom in' on events or areas with unprecedented detail. This comprehensive process allows for a real-time, granular understanding of grid dynamics, enabling proactive management and optimization. Keywords: `grid data collection`, `network topology mapping`, `power flow modeling`, `spatio-temporal analysis`, `electrical network algorithms`.","question":"How does Generating High Resolution Inferences in Electrical Networks work?"},{"answer":"**Generating High Resolution Inferences in Electrical Networks** (US-9853690) fundamentally solves the problem of *limited visibility and incomplete understanding* within electrical power distribution networks, which hinders efficient, reliable, and sustainable grid operation. Traditional grid management systems face several critical challenges:\n\n1.  **Data Gaps and Heterogeneity:** Utilities collect vast amounts of data, but it's often fragmented, comes from different sources at varying speeds (e.g., slow SCADA vs. fast PMUs), and covers different areas. Integrating and making sense of this disparate data is a major hurdle, leading to an incomplete picture of the grid's real-time state.\n\n2.  **Inadequate Resolution:** Existing data often lacks the necessary temporal (time) and spatial (location) resolution. This means operators might know there's a problem in a general area, but not its exact location or when it truly began, making rapid fault isolation and restoration difficult. It's like trying to diagnose a patient with only a few, outdated vital signs.\n\n3.  **Static Network Understanding:** Many systems rely on static representations of the network topology, failing to account for dynamic changes like switch operations or reconfigurations. This can lead to inaccurate power flow calculations and faulty operational decisions.\n\n4.  **Reactive Management:** Due to these limitations, utilities often operate in a reactive mode, responding to outages or issues after they've occurred, rather than predicting and preventing them. This results in longer downtime, higher operational costs, and reduced customer satisfaction.\n\nBy providing a method to generate high-resolution, spatio-temporal power flow inferences, this patent empowers utilities to overcome these blind spots. It enables proactive decision-making, faster fault detection, optimized resource allocation, and seamless integration of new energy technologies, ultimately leading to a more resilient, efficient, and intelligent grid. Keywords: `grid visibility challenges`, `power outage reduction`, `energy efficiency`, `renewable integration barriers`, `grid management problems`.","question":"What problem does Generating High Resolution Inferences in Electrical Networks solve?"},{"answer":"The patent **Generating High Resolution Inferences in Electrical Networks** (US-9853690) lists the inventors as [Inventors Name - if provided]. Unfortunately, the provided patent data does not include the names of the inventors. However, the assignee, if listed, would typically be the company or entity to whom the patent rights were assigned, indicating the organization that supported the research and development of this innovative technology.\n\nIn the context of patent law, inventors are the individuals who conceived the subject matter of the invention. Their intellectual contribution is fundamental to the patent's existence. The assignee, on the other hand, is the legal owner of the patent. Often, employees assign their invention rights to their employer as part of their employment agreement. For this specific patent, the assignee is not provided in the prompt either.\n\nRegardless of the specific individuals or entities involved, the development of technologies like Generating High Resolution Inferences in Electrical Networks typically involves a team of experts in electrical engineering, computer science, data analytics, and potentially machine learning. Such innovations are crucial for advancing the capabilities of our critical infrastructure. The absence of specific inventor or assignee information in the prompt does not diminish the technical significance or potential impact of this groundbreaking patent. Keywords: `patent inventors`, `patent assignee`, `electrical grid innovation`, `power system research`, `technology development`.","question":"Who invented Generating High Resolution Inferences in Electrical Networks?"},{"answer":"The **Generating High Resolution Inferences in Electrical Networks** patent offers a multitude of transformative benefits for utilities, grid operators, and ultimately, electricity consumers. These advantages stem from its core ability to provide unprecedented, granular insights into electrical network dynamics.\n\n1.  **Enhanced Grid Reliability and Resilience:** By enabling precise, real-time power flow estimates, the system allows for faster and more accurate fault detection and isolation. This significantly reduces outage durations, improves service continuity, and makes the grid more resilient to disturbances like extreme weather events or equipment failures. Proactive identification of potential issues can prevent outages altogether.\n\n2.  **Increased Operational Efficiency and Cost Savings:** Granular data allows utilities to optimize power flow, balance loads more effectively, and reduce technical losses across the network. This translates into substantial operational expenditure (OpEx) savings. Furthermore, informed decision-making regarding asset utilization can extend equipment lifespan and defer costly capital expenditures (CapEx) on upgrades.\n\n3.  **Seamless Integration of Distributed Energy Resources (DERs):** The high spatio-temporal resolution is critical for managing the inherent variability and intermittency of renewables like solar and wind power. The system can accurately predict and analyze their impact on localized grid segments, enabling utilities to integrate more DERs without compromising grid stability, thereby accelerating the clean energy transition.\n\n4.  **Improved Predictive Maintenance:** Moving beyond reactive repairs, this technology supports advanced predictive maintenance. By detecting subtle anomalies in power flow patterns that indicate equipment degradation, utilities can schedule maintenance proactively, preventing failures before they occur and minimizing disruption.\n\n5.  **New Data-Driven Services and Business Models:** The rich, high-resolution data generated by this system can unlock opportunities for new value-added services, such as highly targeted demand response programs, localized energy market participation, and more efficient electric vehicle charging infrastructure management. This opens new revenue streams and enhances customer engagement.\n\nIn essence, Generating High Resolution Inferences in Electrical Networks empowers grid operators with actionable intelligence, transforming the electrical grid into a smarter, more responsive, and more sustainable system. Keywords: `grid reliability benefits`, `operational efficiency gains`, `renewable energy integration`, `predictive maintenance`, `smart grid advantages`, `cost reduction utilities`.","question":"What are the key benefits of Generating High Resolution Inferences in Electrical Networks?"},{"answer":"**Generating High Resolution Inferences in Electrical Networks** (US-9853690) distinguishes itself from prior art by systematically addressing the fundamental limitations of traditional grid monitoring and analytics, particularly concerning data granularity and comprehensive network understanding. The key differences lie in its integrated approach and the level of detail it achieves.\n\n1.  **Comprehensive Heterogeneous Data Fusion:** Prior art often relies on single-source data (e.g., SCADA, AMI, PMU) or struggles to effectively integrate data streams with vastly different temporal and spatial resolutions. This patent explicitly defines a method for collecting and harmonizing 'multiple items of data' with 'one or more levels of temporal resolution' and 'one or more levels of spatial resolution'. This robust data fusion creates a far richer and more complete input dataset than typically used by older systems.\n\n2.  **Dynamic and Precise Network Topology:** While Geographic Information Systems (GIS) provide static network maps, and some advanced systems infer partial topology, this invention emphasizes 'determining a network topology... based on identification of one or more connections'. This implies a real-time, highly granular, and potentially self-correcting understanding of the grid's live connectivity, which is more dynamic and accurate than static representations or simpler inference methods found in prior art.\n\n3.  **Spatio-Temporal Resolution on Demand:** This is perhaps the most significant differentiator. Traditional state estimation and power flow analysis tools typically provide estimates at a relatively coarse level (e.g., substation or feeder level, every few minutes). Generating High Resolution Inferences in Electrical Networks, however, generates 'power flow estimates... at a pre-determined level of spatio-temporal resolution'. This means it can 'zoom in' to provide insights down to individual components and sub-second timeframes, a level of detail largely unattainable with prior methods without immense computational overhead or significant data gaps.\n\n4.  **Integrated Constraint-Based Modeling:** The patent's model explicitly leverages collected data, network topology, *and* 'one or more relations constraining the power flow estimate' (physical laws, operational limits). While some prior art models incorporate constraints, this integrated approach, combined with heterogeneous data fusion, ensures the generated inferences are not only data-driven but also physically plausible and operationally realistic, offering a more robust output than purely statistical or simplified physics-based models.\n\nIn summary, this patent provides a holistic, high-resolution intelligence layer for the electrical grid that surpasses the fragmented, lower-resolution capabilities of most existing technologies, offering a new benchmark for grid observability and control. Keywords: `prior art comparison`, `grid monitoring evolution`, `data resolution`, `network topology accuracy`, `smart grid differentiation`, `state estimation improvements`.","question":"How is Generating High Resolution Inferences in Electrical Networks different from prior art?"},{"answer":"**Generating High Resolution Inferences in Electrical Networks** (US-9853690) is poised to have a profound impact across several industries, primarily those involved in the generation, transmission, and distribution of electricity, but also extending to related sectors.\n\n1.  **Electric Utilities and Grid Operators:** This is the most direct and significant impact. The patent provides utilities with unprecedented visibility and control over their distribution networks. This will revolutionize operational efficiency, outage management, asset utilization, and the integration of distributed energy resources. It enables a shift from reactive to proactive grid management, leading to more reliable and cost-effective electricity delivery.\n\n2.  **Energy Technology and Software Providers:** Companies developing smart grid solutions, SCADA systems, advanced metering infrastructure (AMI), and energy management software will find this patent's principles foundational. It offers opportunities for licensing, integration into existing platforms, and the development of new, high-value analytics and control applications built upon granular grid insights.\n\n3.  **Renewable Energy Sector:** The ability to precisely manage and predict power flows at high resolution is critical for integrating intermittent renewable sources like solar and wind into the grid without causing instability. This patent will accelerate the deployment and optimize the performance of renewable energy projects, including utility-scale farms, community solar, and rooftop PV installations.\n\n4.  **Industrial and Commercial Energy Management:** Large industrial facilities and commercial campuses often have complex internal electrical networks. The principles of Generating High Resolution Inferences in Electrical Networks could be adapted to provide granular energy management within these private grids, optimizing consumption, managing peak demand, and integrating on-site generation and storage.\n\n5.  **Policy Makers and Regulators:** The enhanced data and analytical capabilities will provide regulators with better tools to evaluate grid performance, enforce reliability standards, and design effective policies for energy transition, smart grid development, and carbon reduction. It offers a data-driven basis for infrastructure investment decisions.\n\n6.  **Research and Development in Power Systems:** The detailed insights enabled by this technology will open new avenues for academic and industrial research in power system optimization, control theory, machine learning applications for grids, and advanced cybersecurity for critical infrastructure.\n\nIn essence, any industry reliant on or contributing to the electrical grid's operation and evolution will be significantly impacted by the capabilities unlocked by Generating High Resolution Inferences in Electrical Networks. Keywords: `utility industry impact`, `energy tech sector`, `renewable energy integration`, `industrial energy management`, `grid regulation`, `power system R&D`.","question":"What industries will Generating High Resolution Inferences in Electrical Networks impact?"},{"answer":"The patent **Generating High Resolution Inferences in Electrical Networks** (US-9853690) has a clear timeline regarding its filing and publication dates.\n\nThe **Filing Date** for this patent was **2016-06-13**. This date marks when the patent application was officially submitted to the patent office. The filing date is significant because it typically establishes the priority date of the invention, which can be crucial in cases of multiple inventors claiming similar inventions.\n\nThe **Publication Date** for this patent was **2017-12-26**. This is the date when the patent document was officially published and made publicly available. While the grant date (when the patent is formally issued) is not explicitly provided in the prompt, the publication date signifies that the patent application has undergone examination and is now part of the public record.\n\nThese dates indicate that the invention was developed and formally protected in the mid-2010s, positioning it as a relatively recent innovation within the rapidly evolving field of smart grid technology. The period between filing and publication allows for the examination process by patent examiners, who assess the novelty, non-obviousness, and utility of the invention. The subsequent publication makes the details of the invention accessible to researchers, developers, and the general public, fostering further innovation and commercialization in the domain of electrical network intelligence. Keywords: `patent filing date`, `patent publication date`, `US-9853690 timeline`, `invention chronology`, `patent lifecycle`.","question":"When was Generating High Resolution Inferences in Electrical Networks filed/granted?"},{"answer":"The commercial applications of **Generating High Resolution Inferences in Electrical Networks** (US-9853690) are extensive and diverse, offering significant value propositions across the energy ecosystem. This patent's ability to provide granular, real-time grid insights unlocks numerous opportunities for commercialization.\n\n1.  **Advanced Distribution Management Systems (ADMS):** The core technology can be integrated into next-generation ADMS platforms, enabling functionalities like highly accurate fault location, isolation, and service restoration (FLISR), optimized voltage/VAR control, and real-time congestion management. This enhances the overall intelligence and automation of distribution networks.\n\n2.  **Predictive Maintenance and Asset Management Solutions:** By continuously monitoring power flow patterns at high resolution, the system can detect subtle anomalies indicative of equipment degradation (e.g., transformers, cables, switches) before they fail. This enables the development of predictive maintenance software that reduces unplanned outages, extends asset lifespan, and optimizes maintenance schedules, leading to significant cost savings for utilities.\n\n3.  **Enhanced Grid Integration for Distributed Energy Resources (DERs):** Commercial solutions built on this patent can provide the critical intelligence needed to seamlessly integrate large fleets of DERs (solar PV, wind, battery storage, electric vehicles). This includes optimizing DER dispatch, managing bidirectional power flows, and ensuring grid stability, which is vital for energy developers and aggregators.\n\n4.  **Demand Response and Energy Optimization Platforms:** The granular power flow estimates can power highly effective demand response programs, allowing utilities and energy service providers to precisely identify and manage load in specific areas during peak demand. This also supports commercial and industrial customers in optimizing their energy consumption and costs.\n\n5.  **Grid Planning and Investment Tools:** The high-resolution data provides unparalleled insights for long-term grid planning, allowing utilities to make more informed capital expenditure decisions on infrastructure upgrades, identify optimal locations for new substations or feeders, and accurately assess the impact of new developments.\n\n6.  **Cybersecurity and Anomaly Detection:** Real-time, high-resolution monitoring of power flows can be leveraged to detect unusual patterns that might indicate cyberattacks, physical tampering, or other security threats to critical infrastructure, leading to the development of advanced grid cybersecurity solutions.\n\n7.  **Microgrid and Community Energy Management:** For microgrids or local energy communities, this technology offers the precision needed to optimize local generation, storage, and consumption, ensuring stability and efficiency within a confined network.\n\nThese applications collectively position Generating High Resolution Inferences in Electrical Networks as a foundational technology for driving innovation and efficiency in the global energy market. Keywords: `ADMS applications`, `predictive maintenance solutions`, `DER integration platforms`, `demand response technology`, `grid planning software`, `energy cybersecurity`.","question":"What are the commercial applications of Generating High Resolution Inferences in Electrical Networks?"},{"answer":"The future developments expected for **Generating High Resolution Inferences in Electrical Networks** (US-9853690) are likely to build upon its foundational capabilities, pushing the boundaries of grid intelligence and automation. We can anticipate advancements in several key areas:\n\n1.  **Increased Automation and Autonomous Grid Operations:** As the accuracy and resolution of inferences improve, the system's output will increasingly be used to drive automated control actions. This could lead to a 'self-healing' grid that can automatically detect, isolate, and restore power in milliseconds without human intervention, significantly enhancing reliability and resilience. The inferences will power truly autonomous distribution management systems.\n\n2.  **Tighter Integration with Artificial Intelligence and Machine Learning:** While the patent already mentions a 'model,' future developments will likely see deeper integration of advanced AI/ML techniques. This includes explainable AI (XAI) to provide transparent insights into the model's decisions, reinforcement learning for optimal control strategies, and federated learning to leverage distributed data without compromising privacy. These will enhance predictive capabilities and adaptability.\n\n3.  **Edge Computing and Distributed Intelligence:** To handle the immense volume and velocity of high-resolution data, parts of the inference generation process may shift towards the 'edge' of the network. This means processing data closer to its source (e.g., at substations or even on smart meters), reducing latency and bandwidth requirements, and enabling faster, localized decision-making, which is crucial for microgrid operations and rapid fault response.\n\n4.  **Enhanced Cybersecurity Integration:** High-resolution power flow data provides a powerful baseline for detecting anomalies. Future developments will likely integrate advanced cybersecurity analytics that can identify subtle deviations from normal power flow patterns, indicative of cyberattacks or physical tampering, providing an additional layer of protection for critical infrastructure.\n\n5.  **Advanced Market Integration and Transactive Energy:** The granular data and precise power flow estimates can facilitate the development of more sophisticated transactive energy markets. This would allow for localized, real-time trading of electricity between prosumers (consumers who also produce power), optimized by the actual state of the distribution network, fostering greater market efficiency and participation.\n\n6.  **Multi-Energy System Integration:** Beyond just electricity, future developments might see the principles of Generating High Resolution Inferences in Electrical Networks extended to integrate with other energy vectors, such as gas and heat networks, to create a holistic, optimized multi-energy system. This would further enhance overall energy efficiency and sustainability across urban infrastructures. Keywords: `autonomous grid`, `AI in smart grid`, `edge computing for utilities`, `grid cybersecurity`, `transactive energy markets`, `multi-energy systems`.","question":"What are the future developments expected for Generating High Resolution Inferences in Electrical Networks?"}],"topics":["Generating High Resolution Inferences in Electrical Networks","electrical networks patent","power flow estimation","smart grid technology","high resolution data","intricate","dance","electrons"],"tech_cluster":null},"seo":{"title":"Generating High Resolution Inferences in Electrical Networks - Patent US-9853690","description":"Discover the US-9853690 patent: Generating High Resolution Inferences in Electrical Networks. Revolutionize grid management with granular data, real-time power flow, and enhanced reliability.","keywords":["Generating High Resolution Inferences in Electrical Networks","electrical networks patent","power flow estimation","smart grid technology","high resolution data","grid analytics","spatio-temporal resolution","utility innovation","energy management patent","US-9853690","patent US-9853690"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853690","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9853690","citation_suggestion":"Patentable. \"Generating high resolution inferences in electrical networks\" (US-9853690). https://patentable.app/patents/US-9853690","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853690","json":"https://patentable.app/api/llm-context/US-9853690","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T09:00:02.461Z"}