AI-POWERED VOLTAGE OPTIMIZATION SYSTEM FOR POWER NETWORKS

Abstract

The present invention relates to an AI-powered voltage optimization system designed to enhance stability, efficiency, and reliability in power networks. By using machine learning and artificial intelligence, the system continuously monitors voltage levels across the network, predicts fluctuations, and makes real-time adjustments. The system includes sensors to collect critical data, a data processing unit to execute predictive models, and voltage regulation devices to implement necessary adjustments. Three embodiments are described: a centralized system for large-scale grids, a decentralized system for distributed networks or microgrids, and a hybrid system combining centralized oversight with decentralized control for enhanced adaptability. Each embodiment offers unique benefits in terms of responsiveness, resilience, and optimization accuracy, making the invention suitable for various grid configurations, including those integrating renewable energy sources. This AI-powered system reduces energy losses, minimizes equipment stress, and improves grid resilience, addressing the challenges of modern power distribution networks.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The AI-powered voltage optimization system is designed to enhance voltage regulation in modern power networks by utilizing artificial intelligence and machine learning. This system monitors, predicts, and adjusts voltage levels across the network in real time, ensuring stability, efficiency, and resilience.

With a combination of sensors, data processing units, and voltage control devices, it proactively optimizes voltage based on dynamic load conditions, renewable energy inputs, and fault events. Three embodiments illustrate the system's versatility for different network configurations and operational requirements.

In first embodiment, the AI-powered voltage optimization system is implemented in a centralized configuration, ideal for large-scale power grids with multiple substations. The system includes a primary data processing unit housed at a central control center, which connects to a network of sensors installed across the grid. These sensors measure critical parameters such as voltage, current, and load levels at various substations and feeder points. The data collected is processed by machine learning models in the central unit, which uses historical and real-time data to predict voltage fluctuations and make optimized decisions.

The centralized system communicates directly with voltage regulation devices, such as on-load tap changers, capacitor banks, and FACTS devices, implementing adjustments as needed to maintain voltage within ideal limits. The system's AI algorithms, which may include neural networks and reinforcement learning, are trained to anticipate changes based on demand patterns and renewable energy fluctuations. This centralized approach is highly effective for large grids as it provides a comprehensive view of network conditions, enabling coordinated adjustments. However, a centralized setup may encounter latency when addressing localized issues, which the subsequent embodiments address.

The decentralized voltage optimization system operates in a distributed manner, making it suitable for microgrids or networks with significant distributed energy resources like solar or wind. Here, each sub-network or microgrid is equipped with its own data processing unit, which connects locally to sensors and voltage control devices within that specific area. This setup enables each decentralized unit to function independently, processing real-time data from its sensors and executing voltage adjustments autonomously based on local demand, generation, and environmental factors.

The decentralized units use machine learning algorithms, such as decision trees or clustering, to predict voltage changes specific to their areas. This localized control provides quick responsiveness to changes in load or renewable energy output, allowing for near-instantaneous adjustments. Additionally, the decentralized configuration enhances system resilience, as any fault in one network segment can be isolated without affecting the broader network. This approach is particularly valuable in regions with high renewable integration, as it allows for rapid adaptation to the variability inherent in renewable energy sources.

In another embodiment, the system combines centralized and decentralized elements to achieve both high-level coordination and localized adaptability. A central data processing unit oversees the network, aggregating data from decentralized units situated at key nodes across the grid. The central unit coordinates voltage settings and optimizes overall network stability, while the decentralized units independently manage localized voltage adjustments based on specific regional conditions.

In regular operating conditions, the decentralized units handle voltage adjustments within their respective zones. However, during critical events-such as peak demand or fault conditions-the central unit overrides local controls to implement a coordinated, network-wide response. This hybrid system uses AI algorithms in both the central and local units, enabling each to learn and optimize based on feedback. The central unit uses deep learning to identify broad trends across the grid, while decentralized units use reinforcement learning to optimize region-specific voltage settings. The hybrid approach is ideal for interconnected grids with a combination of centralized infrastructure and distributed energy, providing flexibility, reliability, and a coordinated response to network-wide events.

These three embodiments demonstrate the system's adaptability for various power network configurations, from large-scale centralized grids to decentralized microgrids and hybrid systems. Each embodiment emphasizes real-time voltage regulation, predictive adaptability, and resilience, illustrating the AI-powered system's capability to enhance voltage stability in diverse network environments.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.An AI-powered voltage optimization system for power networks, comprising:
a plurality of sensors configured to measure voltage, current, and load data across multiple points in the power network;
a data processing unit operatively connected to the sensors, wherein the data processing unit executes one or more machine learning models to predict voltage fluctuations and determine optimal voltage levels for each point in the network; and
a voltage control mechanism configured to adjust voltage settings based on output from the data processing unit, thereby optimizing voltage levels across the power network in real time.

2.The system of claim 1, wherein the machine learning models include neural networks trained on historical and real-time data to enhance prediction accuracy of voltage requirements based on demand and supply variations.

3.The system of claim 1, wherein the voltage control mechanism comprises on-load tap changers, power converters, or Flexible AC Transmission Systems (FACTS) devices.

4.The system of claim 1, wherein the data processing unit further comprises an adaptive learning module that adjusts the machine learning model parameters based on feedback from the power network.

5.The system of claim 1, wherein the data processing unit is configured to isolate fault conditions in the power network and modify voltage levels in affected areas to stabilize the network.

6.The system of claim 2, wherein the neural networks utilize reinforcement learning techniques to continuously improve voltage optimization based on system performance metrics.

7.The system of claim 1, further comprising a user interface that displays real-time voltage levels, optimization recommendations, and fault diagnostics to a network operator.

8.The system of claim 1, wherein the machine learning model is configured to optimize voltage settings to balance load requirements, minimize energy costs, and improve energy efficiency.

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