System for intelligent control of power flow using predictive grid stabilization techniques

Abstract

A system for the intelligent control of electrical power flow using methods for predicting grid stability, wherein the system comprises: a plurality of sensor units distributed over an electrical grid and configured to continuously measure electrical parameters such as voltage magnitude, current, phase angle, frequency, and power factor from a plurality of grid nodes; a data acquisition unit electrically connected to the plurality of sensor units and configured to convert analog signals into synchronized, time-stamped digital data streams; a communication interface unit configured to transmit the synchronized digital data streams over a communication network;a central processing unit operationally linked to the communication interface unit, the central processing unit comprising at least one processor and memory storing executable instructions configured to create a dynamic state representation of the power grid based on synchronized digital data streams; a predictive computing unit integrated with the central unit, configured to generate future grid state estimations through time series analysis, state estimation, and adaptive learning-based computations; a control signal generation unit configured to derive control commands based on future grid state estimations;and a multitude of actuating units coupled to grid-side electrical components, the actuating units being configured to adjust the power flow by controlling switching devices, transformer stage positions and reactive power compensation devices, thereby maintaining grid stability under varying load and generation conditions.

Description

Technical field of the invention The present invention relates generally to electrical energy systems and in particular to a system with an associated device for the intelligent control of electrical power flow in distributed or centralized power grids. The invention relates in particular to predictive systems for grid stability improvement that utilize real-time sensors, adaptive control, and predictive analytical calculations to dynamically control electrical power flow, reduce instabilities, and increase the operational reliability of electrical transmission and distribution networks. Background of the invention Modern power grids are increasingly characterized by fluctuating load patterns, the integration of intermittent renewable energy sources, bidirectional power flows, and distributed generation units. This complexity introduces instability risks such as voltage dips, frequency deviations, harmonic distortion, and cascading outages. Conventional grid control mechanisms typically rely on reactive strategies that only react after disturbances have occurred, thus limiting their effectiveness in preventing widespread outages. Furthermore, existing SCADA (Supervisory Control and Data Acquisition) systems lack the necessary predictive intelligence to anticipate system loads under dynamic load and generation scenarios. Therefore, there is a need for an intelligent system capable of forecasting grid behavior and proactively controlling power flow using predictive stability techniques. The evolution of electrical energy systems from conventional, centralized grids to highly dynamic, decentralized, and renewable energy-integrated grids has necessitated the development of advanced mechanisms for maintaining grid stability and regulating power flow. Traditional power grids were primarily designed for the unidirectional transmission of electricity from large, centralized power plants to consumers, with relatively predictable demand patterns and controllable generation sources. However, the increasing integration of renewable energy sources such as solar and wind power, as well as decentralized energy sources and bidirectional energy exchange, has significantly increased the complexity of grid operation. Modern grids, known as smart grids, utilize digital communication, sensor, and control technologies to enable real-time monitoring and control of power flow across the entire network.Despite these advances, ensuring a stable and efficient flow of electricity under highly fluctuating conditions remains a key challenge. Existing solutions for load flow control and grid stability are largely based on SCADA (Supervisory Control and Data Acquisition) systems, energy management systems, and widespread monitoring infrastructures. These systems utilize measurements from sensors such as phase sensors, which provide synchronized data on voltage, current, and phase angles at geographically distributed grid locations. The collected data is processed to assess the system's health and enable grid operators to make informed decisions regarding load balancing, fault location, and corrective actions. Additionally, automatic transfer switches, protective relays, and digital controllers are used to reconfigure the grid in response to disturbances. While these systems have improved situational awareness and response time, they remain largely reactive, reacting to disturbances only after they have occurred rather than predicting them. Another class of existing solutions includes control strategies such as frequency control, voltage control, and large-scale damping control. These aim to ensure system stability by modulating active and reactive power using static reactive power compensators, high-voltage direct current links, and energy storage systems. These techniques stabilize the system by counteracting oscillations and maintaining generator synchronization. However, such control mechanisms typically operate based on predefined models and thresholds, which limits their adaptability to rapidly changing grid conditions. The increasing variability of renewable energy generation leads to uncertainties that are difficult to capture with static or deterministic control models. Smart grids have implemented advanced control systems that can monitor and automatically adjust energy flows in real time. These systems utilize communication networks and distributed intelligence to coordinate various grid components, including distributed energy generation facilities, storage systems, and load management mechanisms. Predictive analytics and machine learning are also employed to forecast energy demand and generation, enabling proactive control measures. For example, control systems can predict fluctuations in solar or wind power generation based on weather data and adjust energy distribution accordingly. Energy storage systems, such as batteries and supercapacitors, are used to absorb excess energy during periods of low demand and release it during peak demand, thus balancing fluctuations in power flow. Despite these improvements, existing solutions still have some drawbacks. A key limitation is the inability of conventional predictive models to accurately represent the nonlinear, stochastic, and time-varying behavior of modern power grids. Grid conditions can change rapidly due to unpredictable factors such as weather fluctuations, sudden load changes, or equipment failures. Traditional models often do not adapt in real time, resulting in suboptimal or delayed control measures. Furthermore, many machine learning methods are based on historical datasets and may not be effectively applied to unknown scenarios, especially rare or extreme events. This limitation reduces the reliability of predictive control mechanisms in critical situations. Another significant disadvantage lies in the lack of transparency and interpretability of advanced predictive systems. Many machine learning models function as black boxes, making it difficult for network operators to understand the decision-making process. This lack of explainability can impair trust and acceptance in safety-critical applications, where operators must validate control measures before implementation. Furthermore, some predictive systems merely classify network states as stable or unstable without providing detailed insights into the underlying causes or corrective actions. This limits their practical use in real-time operation. Scalability and computational complexity pose challenges even for existing grid stability solutions. Large-scale power grids consist of thousands of interconnected components, making real-time simulations and optimizations computationally intensive. Techniques such as grid reduction are frequently used to simplify models. However, such simplifications can compromise accuracy and fail to capture local phenomena that may propagate into system-wide disturbances. Therefore, balancing computational efficiency with model accuracy remains a constant challenge. The integration of decentralized energy sources further complicates control strategies. Unlike centralized generation, decentralized sources are geographically dispersed and often operate independently, making coordinated control difficult. Communication delays, data inconsistencies, and cybersecurity vulnerabilities can impair system performance. Although frameworks for decentralized control have been proposed, their implementation requires a robust communication infrastructure and standardized protocols, which are not equally available in all network environments. Moreover, the decentralized nature of such systems can lead to conflicting control actions if they are not properly synchronized. Economic and infrastructural constraints hinder the widespread adoption of advanced grid stability solutions. The deployment of smart grid technologies, including intelligent metering infrastructure, communication networks, and energy storage systems, requires significant investment. Many existing grids, particularly in developing countries, rely on outdated infrastructure that is not readily compatible with modern control technologies. Retrofitting such systems to support advanced, predictive, and automated control mechanisms can be both technically challenging and financially burdensome. Furthermore, existing control systems often lack the capability for fully autonomous operation. Human intervention is still required in many cases to validate and implement control measures, leading to delays that can exacerbate instabilities. In the case of rapid disturbances such as frequency surges or cascading failures, even minor delays can result in significant system failures. The absence of tightly integrated prediction and control mechanisms capable of executing coordinated actions in real time remains a critical gap in current technologies. Furthermore, interoperability issues between heterogeneous network components pose a significant challenge. Different manufacturers and technologies often employ proprietary communication protocols and control architectures, hindering seamless integration. This lack of standardization impedes the development of unified control strategies and limits the effectiveness of system-wide optimization efforts. As the power grid continues to evolve through the integration of new technologies, ensuring compatibility and interoperability becomes increasingly complex. Overall, existing solutions for load flow control and grid stability have made significant progress through the use of smart grid technologies, real-time monitoring, and predictive analytics, but they still face limitations regarding adaptability, scalability, interpretability, infrastructure dependency, and response time. These shortcomings underscore the need for more advanced systems that coherently and adaptively integrate predictive intelligence and real-time control, thereby enabling proactive and robust management of modern power grids. Summary of the invention The present invention provides a system for the intelligent control of electrical power flow using predictive grid stability methods. Grid parameters are continuously acquired, processed, and analyzed in real time to predict potential instability states. The system includes a predictive computing unit that generates future grid state estimates based on historical data, real-time measurements, and adaptive learning methods. A control unit dynamically regulates the power flow across transmission paths by coordinating the control of switching elements, tap changers, and reactive power compensation devices. The system further comprises a machine-integrated device for local use that can communicate with grid components and perform predictive control operations at the node or substation level. The present invention aims to provide a system for the intelligent control of electrical power flow. This system utilizes predictive grid stability methods, thus enabling proactive management of electrical networks by predicting potential instability conditions and dynamically adjusting the power distribution to ensure operational reliability. The invention overcomes the limitations of conventional reactive control systems by integrating predictive calculations with real-time data acquisition and control, thereby ensuring the continuous stability of voltage, frequency, and load distribution throughout the entire network. A further objective of the invention is to provide a system for the continuous acquisition and processing of synchronized electrical parameters from multiple network nodes. Advanced analytical methods are employed to generate precise, predictive forecasts of network behavior under varying load and generation conditions. The invention aims to improve situational awareness in the power grid by creating a dynamic representation of the system states, thereby enabling the early detection of anomalies such as overload conditions, voltage fluctuations, and frequency deviations. A further objective of the invention is to provide a control device that dynamically regulates the electrical power flow by coordinating the control of grid-connected components, including switching devices, tap changers, and reactive power compensation elements, based on predictive data. The invention aims to achieve optimal load distribution, minimize transmission losses, prevent cascade failures, and maintain system stability both during normal operation and in the event of faults. A further objective of the invention is to provide a device in the form of a machine-integrated structure suitable for local use in substations, transmission nodes, or distribution networks. The device is configured to perform predictive calculations in real time and to implement control measures independently or in coordination with other devices. The invention aims to enable scalable implementation through distributed intelligence across the entire network, thereby reducing dependence on central control systems and improving response times to disturbances. A further objective of the invention is the precise synchronization of data acquisition and control processes, so that control measures are carried out in accordance with the grid frequency cycles, thereby improving the accuracy and effectiveness of load flow adjustments. The invention also aims to provide robust communication functions for secure and reliable data exchange between system components, thus ensuring seamless coordination across geographically distributed grid segments. A further objective of the invention is to provide a robust and thermally efficient device configuration suitable for operation in demanding electrical environments and incorporating protective mechanisms to safeguard the internal circuitry against voltage spikes, electromagnetic interference, and environmental influences. The invention further aims to support modular installation and integration into existing network infrastructures, thereby reducing implementation complexity and facilitating retrofitting in legacy systems. A further objective of the invention is to increase the overall efficiency and resilience of electrical energy systems by minimizing downtime, reducing operating costs, and improving energy utilization through intelligent and predictive control strategies. The invention is intended to support the integration of renewable energy sources and decentralized generation by effectively controlling their variability and ensuring stable interaction with the main grid. A further objective of the invention is to provide a system that reduces dependence on manual intervention through automated decision-making and execution of control measures, thereby minimizing response time to critical events and improving the reliability of network operation. The invention also aims to improve adaptability to changing network conditions by continuously updating predictive models based on real-time data, thus ensuring sustained performance under dynamic and uncertain operating conditions. Ultimately, an objective of the invention is to provide an integrated solution that combines predictive analytics, real-time monitoring and adaptive control in a unified system architecture, thereby addressing the shortcomings of existing technologies and providing a comprehensive approach to intelligent control of electrical power flow and grid stability management. BRIEF DESCRIPTION OF THE IMAGE These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. 1 shows a block diagram of a system for intelligent control of electrical power flow using predictive grid stability methods. Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention. It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof. References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment. The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components. Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Fig. 1 shows a block diagram of a system for the intelligent control of electrical power flow using predictive grid stability methods. The system 100 comprises: a plurality of sensor units (102) distributed across a power grid and configured to continuously measure electrical parameters such as voltage amplitude, current, phase angle, frequency, and power factor from a plurality of grid nodes; a data acquisition unit (104) electrically connected to the plurality of sensor units, which converts analog signals into synchronized, timestamped digital data streams; and a communication interface unit (106) configured to transmit the synchronized digital data streams via a communication network.a central processing unit (108) operationally connected to the communication interface unit, the central processing unit comprising at least one processor and a memory that stores executable instructions configured to create a dynamic state representation of the power grid based on the synchronized digital data streams; a predictive computing unit (110) integrated into the central processing unit and configured to generate future grid state estimates by performing time series analyses, state estimations, and adaptive learning-based calculations; a control signal generation unit (112) configured to derive control commands based on the future grid state estimates;and a plurality of actuating units (114) coupled to network-side electrical components, the actuating units being configured to adjust the power flow by controlling switching devices, transformer stage positions and reactive power compensation devices, thereby maintaining network stability under varying load and generation conditions. In one embodiment, the numerous sensor units (102) further comprise synchronized phasor measuring devices configured to detect phase angle differences between geographically distributed nodes, wherein the data acquisition unit includes a synchronization circuit aligned with a global positioning signal to ensure the temporal coherence of the measured electrical parameters. In one embodiment, the central processing unit (108) is configured to perform a state estimation procedure that determines the node voltage magnitudes and phase angles using a weighted least squares calculation applied to the synchronized digital data streams, thereby generating a real-time network state matrix representation. In one embodiment, the predictive computing unit (110) is configured to perform a multi-horizon prediction of the grid parameters by combining autoregressive modeling with adaptive learning methods, wherein the predictive computing unit dynamically updates the model coefficients based on incoming data streams to take into account non-linear and time-varying grid behavior. In one embodiment, the predictive computing unit (110) is further configured to perform a contingency simulation by introducing simulated disturbances into the dynamic state representation and evaluating the resulting system responses to identify potential instability states before they occur. In one embodiment, the control signal generation unit (112) is configured to calculate optimal power flow adjustments by solving a constraint-based optimization problem that minimizes transmission losses while keeping voltage and frequency within predefined stability thresholds. In one embodiment, the numerous actuating units (114) comprise semiconductor switching devices configured to selectively change the current flow paths by modifying the network topology in response to the control commands generated by the control signal generation unit. In one embodiment, the numerous actuating units (114) further comprise transformer tap control units configured to adjust the voltage levels by changing the tap positions in response to predicted voltage deviations, thereby maintaining voltage stability throughout the network. In one embodiment, the numerous actuating units (114) further comprise reactive power compensation units configured to inject or absorb reactive power by means of capacitor banks or inductive elements in order to stabilize the power factor and voltage profiles. In one embodiment, the system further comprises a distributed processing unit integrated into a local device installed in a substation or transmission node. The distributed processing unit is configured to independently perform predictive calculations and generate control signals for local network segments while simultaneously maintaining coordination with the central processing unit. The present invention provides a system for the intelligent control of electrical power flow using predictive grid stability methods. System operation is controlled by a sequence of coordinated computing and control processes performed by a combination of sensor units, processing units, predictive computing devices, and actuator units. System operation begins with the continuous acquisition of electrical parameters by multiple sensor units distributed throughout the power grid. Each sensor unit measures instantaneous values ​​of voltage amplitude, current, phase angle, frequency, and power factor and generates analog signals representing these parameters. The data acquisition unit receives these analog signals and performs signal conditioning, including amplification, filtering, and noise reduction, followed by analog-to-digital conversion to generate high-resolution digital data streams.The data acquisition unit also includes a synchronization device that aligns all measurements with a common time reference derived from a GPS signal, thus ensuring that the acquired data represents a consistent snapshot of the network state. The synchronized digital data streams are transmitted via the communication interface to the central processing unit (CPU), where preprocessing operations are performed. These include outlier detection, interpolation of missing data points, and normalization of the measurement parameters to a standardized scale. The processed data is then used to generate a real-time state representation of the power grid. The CPU performs a weighted least squares state estimation procedure, minimizing measurement residuals to determine the most probable values ​​for node voltage magnitudes and phase angles. The procedure iteratively updates the state vector by calculating a Jacobian matrix representing the sensitivity of the measurements to state variables and subsequently correcting the estimation errors until the convergence criteria are met.This results in a dynamic network state matrix that accurately reflects the current operating state of the network. After determining the state of the network, the predictive computing unit forecasts network parameters over multiple time horizons. The forecasting system combines autoregressive modeling with adaptive learning methods. Historical data and real-time measurements are merged to predict future values ​​of voltage, frequency, and power flow. The system continuously updates its coefficients using recursive parameter estimation methods, thus adapting to changing network conditions. Additionally, the predictive computing unit features a pattern recognition mechanism that identifies temporal trends and correlations between network variables, enabling the early detection of potential instability states such as voltage dips, frequency fluctuations, or line overloads. The system also includes a contingency analysis, in which simulated disturbances are introduced into the dynamic state matrix to assess system resilience. These disturbances can include sudden load changes, power plant outages, or line faults. For each simulated scenario, the system calculates the resulting system response by iteratively solving the load flow equations, thus determining whether stability thresholds are exceeded. The results of these simulations are ranked by severity, and a risk index is assigned to each potential contingency. This predictive assessment enables the system to prioritize corrective actions before actual disturbances occur. Based on the predicted results, the control signal generation unit performs an optimization procedure to determine suitable control measures. The optimization problem is formulated to minimize transmission losses and deviations from the nominal voltage and frequency values ​​while simultaneously adhering to operational constraints such as line capacity limits, generator power limits, and stability reserves. The system uses iterative numerical methods to solve the constrained optimization problem. This results in a set of optimal control variables, including switching configurations, transformer stage positions, and reactive power injection levels. These control variables are then converted into control commands and transmitted to the actuators. The actuators receive control commands and implement physical adjustments in the grid. Semiconductor switching devices modify the grid topology by opening or closing circuits, thus redistributing the power flow. Transformer tap changers adjust the voltage by incrementally changing the tap positions, while reactive power compensation units inject or absorb reactive power to stabilize voltage profiles and improve the power factor. The execution of these control measures is synchronized with the grid frequency cycles via a synchronization unit to ensure that transitions occur at the optimal time and transient disturbances are minimized. The system also includes a feedback mechanism in which the effects of the implemented control measures are continuously monitored by the sensor units and reported back to the central processing unit. The updated measurements are used to refine the state estimation and prediction models, thus forming a closed-loop control system. Deviations between predicted and actual network behavior are analyzed to adjust the model parameters and thereby improve the accuracy and robustness of future predictions. This adaptive feedback mechanism enables the system to maintain high performance even under dynamic and uncertain operating conditions. In distributed deployment configurations, local devices with processing units independently execute portions of the predictive computing and control engineering for specific network segments. These local devices communicate with the central processing unit to ensure coordinated operation across the entire network. The distributed approach divides the network into manageable segments, each controlled by a local state estimation and predictive computing process, while global consistency is maintained through periodic synchronization of state information. This hierarchical control structure reduces the computational load on the central processing unit and improves response time to local disturbances. The system also includes an anomaly detection function that continuously evaluates deviations between predicted and measured parameters. If these deviations exceed predefined thresholds, the system detects the occurrence of anomalies and initiates corrective measures. Anomaly detection uses a statistical analysis of residual faults and temporal patterns to differentiate between temporary fluctuations and persistent instabilities. Upon detection of a critical anomaly, the system can isolate affected network segments by controlling switching devices, thereby preventing fault propagation and ensuring the overall stability of the system. Furthermore, the system is designed to enable the integration of decentralized energy sources by incorporating their generation profiles into the forecasting process. The variability of renewable energy generation is modeled using adaptive forecasting methods, and control measures are adjusted accordingly to maintain the balance between supply and demand. The system dynamically regulates the feed-in of electricity from renewable sources, thus ensuring stable interaction with the main grid while maximizing the use of clean energy. The system implemented in the present invention represents a comprehensive prediction and control system that integrates real-time data acquisition, dynamic state estimation, multi-horizon forecasting, contingency analysis, optimization-based control, and adaptive feedback. This integrated approach enables proactive control of electrical power flow, increases grid stability, and significantly reduces the probability of widespread outages. The continuous interaction between predictive calculation and control execution ensures that the system responds to changing grid conditions, thus providing a robust and intelligent solution for modern power grids. The system comprises multiple sensor elements distributed across electrical nodes, transmission lines, and load distributors. Each sensor element measures electrical parameters such as voltage amplitude, current, phase angle, frequency, and power factor. The sensor elements are electrically connected to a data acquisition unit that digitizes analog measurements and transmits time-synchronized data streams to a central processing unit. The central processing unit comprises a high-performance computing unit for applying methods to predict grid stability. These methods include time-series forecasting techniques, state estimation methods, and machine learning for pattern recognition to derive predictive indicators of grid behavior. The computing unit processes incoming data streams to create a dynamic state model of the grid. In this model, node voltages, line fluxes, and the system frequency are continuously estimated and projected into future time intervals. The predictive calculation also includes a contingency analysis, in which simulated disturbances are applied to the dynamic model to assess system resilience under various conditions. The system also includes a control unit operationally coupled to the central processing unit, which generates control signals based on predictive outputs. These control signals are transmitted to a variety of controllable electrical components, including semiconductor switches, phase-shifting transformers, elements of flexible AC transmission systems, and reactive power compensators. The control unit dynamically adjusts the power flow paths by changing the impedance characteristics, redistributing loads, and injecting or absorbing reactive power to ensure grid stability. The invention relates to a device for intelligent power flow control in the form of a machine-integrated structure. The device comprises a housing that accommodates electronic circuits, thermal management components, and communication interfaces. A processing circuit is mounted on a multilayer printed circuit board within the housing. This processing circuit includes a microprocessor, memory units, and a signal conditioning circuit. The device also has input terminals for receiving electrical signals from external sensors and output terminals for transmitting control signals to mains-connected devices. The device features a predictive computing circuit integrated into its processing circuitry, which performs real-time stability prediction procedures. A communication interface enables bidirectional data exchange between the device and remote network management systems via wired or wireless protocols. The device also includes a power supply that draws operating power from the mains while simultaneously ensuring electrical isolation and surge protection. The device also features a modular mounting system, allowing for installation in substations, control cabinets, or directly on the transmission infrastructure. The housing is made of electrically insulating and thermally conductive material to ensure operational reliability and efficient heat dissipation. Furthermore, the device incorporates an internal synchronization circuit that synchronizes data acquisition and control execution with grid frequency cycles, thus enabling precise timing of control measures. During operation, the system continuously monitors network parameters using sensors and transmits the collected data to the central processing unit. Predictive calculations generate forecasts of the network state and identify potential instability events such as overloads, voltage dips, or frequency deviations. Based on these forecasts, the control unit proactively regulates the power flow by issuing control signals to the network components. The device operates either as a distributed controller at the node level or as part of a coordinated network of devices, thus enabling scalable deployment across the entire power grid. The integration of predictive analytics with real-time control enables the system to transition from reactive to proactive network management. By anticipating disturbances before they occur, the invention significantly improves network stability, reduces the risk of outages, and optimizes the efficiency of power distribution. The device's design ensures robust operation even under harsh electrical conditions while offering flexibility for integration into existing network infrastructures. The present invention relates generally to electrical energy systems and control technologies, in particular to a system and an associated device for the intelligent control of electrical power flow in transmission and distribution networks using predictive grid stability methods. The invention specifically relates to real-time data acquisition, dynamic state estimation, predictive computation, and automated control of grid-connected components for maintaining voltage, frequency, and load balance under varying operating conditions. The described system is suitable for modern power grids with distributed energy sources, renewable energy generation, and bidirectional power flow, and aims to improve grid stability, operational efficiency, and resilience through proactive and adaptive control methods. The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims. The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A system for intelligent control of power flow using methods for predictive grid stability assurance. 102 Multiple sensor units. 104 Data acquisition unit. 106 Communication interface unit. 108 Central unit. 110 Predictive calculation unit. 112 Control signal generation unit. 114 Multiple actuation units.

Claims

A system for the intelligent control of electrical power flow using methods for predicting grid stability, wherein the system comprises: a plurality of sensor units distributed over an electrical grid and configured to continuously measure electrical parameters such as voltage magnitude, current, phase angle, frequency, and power factor from a plurality of grid nodes; a data acquisition unit electrically connected to the plurality of sensor units and configured to convert analog signals into synchronized, time-stamped digital data streams; a communication interface unit configured to transmit the synchronized digital data streams over a communication network;a central processing unit operationally linked to the communication interface unit, the central processing unit comprising at least one processor and memory storing executable instructions configured to create a dynamic state representation of the power grid based on synchronized digital data streams; a predictive computing unit integrated with the central unit, configured to generate future grid state estimations through time series analysis, state estimation, and adaptive learning-based computations; a control signal generation unit configured to derive control commands based on future grid state estimations;and a multitude of actuating units coupled to grid-side electrical components, the actuating units being configured to adjust the power flow by controlling switching devices, transformer stage positions and reactive power compensation devices, thereby maintaining grid stability under varying load and generation conditions. System according to claim 1, wherein the plurality of sensor units further comprises synchronized phasor measuring devices configured to detect phase angle differences across geographically distributed nodes, and wherein the data acquisition unit comprises a synchronization circuit aligned with a global positioning signal to ensure the temporal coherence of the measured electrical parameters. System according to claim 1, wherein the central processing unit is configured to perform a state estimation procedure that determines the node voltage magnitudes and phase angles using a weighted least squares calculation applied to the synchronized digital data streams, thereby generating a real-time network state matrix representation. System according to claim 1, wherein the predictive computing unit is configured to perform a multi-horizon prediction of the grid parameters by combining autoregressive modeling with adaptive learning methods, and wherein the predictive computing unit dynamically updates the model coefficients based on incoming data streams to account for nonlinear and time-varying grid behavior. System according to claim 1, wherein the predictive computing unit is further configured to perform a contingency simulation by introducing simulated disturbances into the dynamic state representation and evaluates the resulting system reactions in order to identify potential instability states before they occur. System according to claim 1, wherein the control signal generation unit is configured to calculate optimal power flow adjustments by solving a constrained optimization problem that minimizes transmission losses while keeping voltage and frequency within predefined stability thresholds. System according to claim 1, wherein the plurality of actuating units comprises semiconductor switching devices configured to selectively change the power flow paths by modifying the network topology in response to the control commands generated by the control signal generation unit. System according to claim 1, wherein the plurality of actuating units further comprises transformer stage control units configured to adjust the voltage levels by changing the stage positions in response to predicted voltage deviations and thereby maintain voltage stability throughout the network. System according to claim 1, wherein the plurality of actuating units further comprises reactive power compensation units configured to inject or absorb reactive power by means of capacitor banks or inductive elements in order to stabilize the power factor and voltage profiles. System according to claim 1, wherein the system further comprises a distributed processing unit integrated into a localized device installed in a substation or transmission node. The distributed processing unit is configured to independently perform predictive calculations and generate control signals for localized network segments while simultaneously maintaining coordination with the central processing unit.

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