Power supply grid power transformation voltage regulation control system based on artificial intelligence

Abstract

The invention discloses a power supply grid power transformation voltage regulation control system based on artificial intelligence, which relates to the field of power transformation voltage regulation systems and comprises a multi-source data acquisition module, an edge calculation processing module, an intelligent decision-making module, a self-adaptive execution module, a three-dimensional visualization module, an environment compensation module and a fault diagnosis module. High-precision multi-type sensors are adopted for multi-source data acquisition for high-speed sampling, a multi-core heterogeneous FPGA acceleration processor is adopted for edge calculation processing, intelligent decision-making multi-target optimization is adopted, rapid high-precision equipment is adaptively executed and controlled, three-dimensional visualization holographic projection and virtual inspection are performed, and environment compensation is performed to obtain parameters to correct equipment temperature rise. The fault diagnosis reasones the fault type through rules and a neural network. The power transformation voltage regulation performance of the power grid is remarkably improved, data processing and response are faster, active loss is reduced, and the voltage qualification rate is improved; fault diagnosis is accurate, reliability is high, and power failure accidents are reduced; operation and training are more efficient, the operation and maintenance cost is reduced, the new energy consumption rate is improved, and construction of a novel power system is powerfully supported.

Description

Technical Field

[0001] The present invention relates to the technical field of substation voltage regulation systems, and particularly to an artificial intelligence-based power supply grid substation voltage regulation control system. Background Art

[0002] Traditional substation voltage regulation systems rely on manual experience to set control parameters, with slow response speed and inability to adapt to the dynamic changes of the power grid. For example, at a certain substation during the summer load peak, the bus voltage deviation reached 8% due to the failure to adjust the transformer tap switch in time, affecting the power consumption of more than 2,000 households. Existing automated systems mostly adopt fixed threshold control strategies and are unable to coordinate and handle the power fluctuations between distributed power sources and loads in the scenario of new energy access. When a certain photovoltaic power station was connected to the grid, it once caused local voltage oscillation.

[0003] The existing technology makes insufficient use of multi-source data and only relies on basic parameters such as voltage and current for control. For example, at a 110 kV substation, the insulation aging occurred due to the failure to monitor the equipment temperature rise, and finally an interphase short circuit accident occurred. Traditional fault diagnosis relies on single-sensor data and is difficult to identify complex fault types. A main transformer winding deformation fault was missed due to the ineffective correlation between vibration signals and oil temperature data.

[0004] Existing systems lack intelligent optimization capabilities and are unable to achieve global optimal control under complex working conditions. For example, during the winter heating period of a certain power grid, the overall active power loss increased by 12% due to the failure to coordinate the voltage regulation strategies of each substation. When multi-site collaborative control is carried out, there are delays and security risks in data interaction. For a cross-provincial power grid dispatch, the control instruction lagged by 3 seconds due to communication delay, expanding the scope of the fault impact. Summary of the Invention

[0005] The artificial intelligence-based power supply grid substation voltage regulation control system proposed by the present invention is to solve the problems mentioned in the above existing technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An artificial intelligence-based power supply grid substation voltage regulation control system, comprising: Multi-source data acquisition module: Deploy voltage transformers, current transformers, temperature sensors, vibration sensors, electric field sensors and magnetic field sensors to collect data of bus voltage, load current, equipment temperature, mechanical vibration, electric field strength and magnetic field strength in real time; Edge computing processing module: Adopt a multi-core heterogeneous processor, integrate the fast Fourier transform FFT algorithm to carry out harmonic analysis, and the formula is: , introduce a quantum computing-assisted convolutional neural network Q-CNN to detect data anomalies in real time; Intelligent decision-making module: Construct a deep reinforcement learning DRL model to multi-objectively optimize active power loss, reactive power compensation and voltage stability, and the objective function is: , where λ1 and λ2 are weight coefficients, dynamically optimized by a genetic algorithm; the PPO algorithm is used to update the parameters of the policy network, and a meta-learning mechanism is introduced to adapt to changes in different power grid topologies and operating conditions; Adaptive execution module: Controls intelligent vacuum circuit breakers and on-load tap-changers, and dynamically adjusts control parameters through a fuzzy PID algorithm. The formula is: , proportionality coefficient K p , integral coefficient K i and derivative coefficient K d are adjusted in real time according to the load rate, grid harmonic content, and voltage fluctuation conditions; coordinated control is carried out with distributed power sources. By predicting the output of distributed power sources, the voltage regulation strategy of the power grid is adjusted in advance; 3D visualization module: Constructs a virtual model of the power grid based on digital twin technology, integrates the GIS geographic information system to display the equipment location, and uses WebGL technology to complete the switching between 2D and 3D views; introduces holographic projection technology to display the operating status and equipment parameters of the power grid, supports multi-person simultaneous interactive operations; conducts inspections in the virtual power grid model through virtual avatars to discover potential problems.

[0007] Furthermore, it also includes: Environmental compensation module: Obtains parameters by deploying weather stations, specifically temperature, humidity, air pressure, wind speed, and light intensity. An empirical formula with multi-factor coupling is used to correct the equipment temperature rise: ΔT comp =ΔT meas ⋅(1 + 0.003(T - 25) + 0.008(H - 60) + 0.001P - 0.002W + 0.0005L), where T is the environmental temperature, H is the relative humidity, P is the air pressure, W is the wind speed, and L is the light intensity; predicts the equipment insulation aging rate based on environmental parameters and formulates a maintenance plan in advance.

[0008] Furthermore, it also includes: Fault diagnosis module: Constructs an expert system that combines a knowledge graph and deep learning, integrates fault cases, and jointly infers the fault types through a rule engine and a deep neural network, specifically winding deformation, poor contact, and insulation breakdown; uses a method that combines wavelet packet decomposition and Hilbert-Huang transform (HHT) to extract the vibration signal features. The energy entropy calculation formula is: , p i is the energy proportion of the i-th frequency band; predicts the probability and trend of fault occurrence by mining historical fault data.

[0009] Furthermore, the data acquisition module supports multi-protocol fusion, is compatible with industrial protocols such as IEC61850, ModbusRTU, and DNP3, and uses a protocol conversion gateway based on blockchain to achieve unified data format and secure data transmission; it conducts quality assessment on data of different protocols and preferentially collects and processes quality assessment data.

[0010] Furthermore, the intelligent decision-making module introduces a transfer learning mechanism and a federated learning mechanism. The pre-trained model is initialized with historical data from 500 substations, and fine-tuning is completed with 50 hours of data from new sites; through the federated learning mechanism, multiple substations collaborate to optimize the model without sharing the original data.

[0011] Furthermore, the adaptive execution module simulates power grid disturbances through a real-time digital simulator RTDS to verify the effectiveness of control strategies. When a device in the execution module fails, the system automatically identifies the faulty device and adjusts the control strategy according to the fault type and severity, and uses standby devices or redundant control methods to maintain power grid operation.

[0012] Furthermore, the 3D visualization module supports augmented reality (AR) interaction and virtual reality (VR) immersive experiences. The virtual control interface is superimposed through Hololens2 glasses to complete contactless adjustment of device parameters; operators enter the virtual power grid environment through VR devices for equipment operation training and fault simulation.

[0013] Furthermore, it also includes: Data acquisition and preprocessing steps: Obtain raw data through multi-source sensors, and use a method combining median filtering and wavelet threshold denoising to remove impulse noise and high-frequency noise. The formula is: , and perform normalization processing to the range of [-1, 1]; fill in missing values and correct outliers in the data, and use a method based on Kalman filtering for data smoothing processing; Real-time state assessment steps: Use the Q-CNN model to detect data anomalies and calculate the power grid health index HI: , w i is the feature weight, f i is the i-th feature function; introduce the entropy weight method to dynamically adjust the feature weight; Control strategy generation steps: Based on the DRL model, output a control sequence, including transformer tap positions and breaker switching sequences; when generating the control strategy, consider factors such as the real-time topology of the power grid, load forecasting, and distributed power generation output forecasting, and use the model predictive control (MPC) algorithm to optimize the control sequence. Execution and feedback steps: Drive the execution module to adjust device parameters, and use the wide area measurement system (WAMS) to monitor the control effect in real time to form a closed-loop control; introduce a feedback linearization control method to compensate for nonlinear factors in the control process.

[0014] Furthermore, it also includes: Self-learning optimization step: Regularly collect control effect data, update the DRL model using an online learning algorithm, and the self-adaptive adjustment formula for the learning rate is: , where α0 is the initial learning rate, γ and β are decay coefficients, and an evolutionary strategy algorithm is introduced to optimize the hyperparameters of the model.

[0015] Furthermore, it also includes: Fault response step: When a severe fault is detected, trigger an emergency control strategy, preferentially disconnect non-essential loads. The load priority is determined by combining the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation method. Isolate the fault area and provide emergency power supply through distributed power sources and energy storage devices.

[0016] Compared with the existing technologies, the beneficial effects of the present invention are: This patent boosts the data processing speed by 5 times through quantum-enhanced edge computing and shortens the voltage fluctuation response time to 20 ms. Deep reinforcement learning achieves multi-objective optimization, reducing the active power loss of the power grid by 18% and increasing the voltage qualification rate from 95% to 99.2%. The environmental compensation module reduces the device temperature rise measurement error from ±0.5°C to ±0.1°C and extends the device life by 20%.

[0017] The intelligent decision-making module combines federated learning and transfer learning, shortening the new site model training time from 1000 hours to 50 hours. The fault diagnosis accuracy reaches 99%, the composite fault recognition ability is improved by 300%, and the annual power outage accidents are reduced by 70%. The hardware-in-the-loop test covers 95% of extreme working conditions, and the system reliability is increased by 40%.

[0018] The 3D visualization and AR interaction improve the operation efficiency by 60%, shortening the training cycle of inspection personnel from 2 weeks to 3 days. The adaptive execution module reduces the wear of switchgear through the no-arc switching technology and reduces the maintenance cost by 35%. The multi-protocol blockchain gateway ensures data security, reduces the communication delay from 10 ms to 5 ms, and supports the access of millions of devices. After the system is applied, the annual operation and maintenance cost of a provincial power grid is reduced by 120 million yuan, and the new energy consumption rate is increased to 98%, providing key technical support for the construction of a new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic block diagram of a power supply grid substation voltage regulation control system based on artificial intelligence proposed by the present invention; Figure 2 It is a schematic block diagram of a power supply grid substation voltage regulation control method based on artificial intelligence proposed by the present invention; Figure 3 It is a schematic diagram of the comparison of edge computing processing performance; Figure 4 Schematic diagram of the optimization effect trend of the intelligent decision-making module; Figure 5 Schematic diagram of the control response time distribution of the adaptive execution module; Figure 6 Schematic diagram of the accuracy rate comparison of the fault diagnosis module. Specific implementation manners

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0021] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention.

[0022] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. Next, the present invention will be further described in detail with reference to the accompanying drawings.

[0023] Refer to Figure 1-6 : A power grid voltage regulation and control system based on artificial intelligence, comprising: Multi-source data acquisition module: In terms of hardware deployment, a set of high-precision sensor networks has been carefully constructed, which covers a variety of advanced sensor types. The voltage transformer adopts a design scheme that combines cutting-edge electromagnetic induction technology and digital signal processing algorithms, with an accuracy of up to 0.05 level. It can accurately capture the minute changes in voltage signals in complex electrical environments. Even when the voltage fluctuation amplitude is extremely small, it can ensure the reliability of measurement results. The current transformer uses zero-flux technology and special core materials to achieve a high-precision measurement of 0.1 level. This design effectively suppresses the error sources of the transformer, enabling accurate measurement of the load current under both large-current and small-current operating conditions, providing a solid data foundation for subsequent data analysis. The temperature sensor selects the fiber Bragg grating temperature sensor. Based on the temperature sensing principle of fiber Bragg grating, it uses the optical characteristics of optical fibers to perceive temperature changes. Its accuracy can reach ±0.1°C. It can not only quickly respond to temperature changes, but also, due to the electromagnetic interference resistance of optical fibers, can stably and reliably measure the device temperature even in a strong electromagnetic interference environment. The vibration sensor adopts micro-nano sensing technology, integrating the advantages of micro-electro-mechanical systems (MEMS) and nano materials. Its accuracy reaches ±0.05mm / s, and it can keenly capture the subtle vibration signals during the operation of the device. Whether it is high-frequency mechanical vibration or low-frequency structural vibration, it can be accurately measured, providing key data for the condition monitoring and fault diagnosis of the device. In addition, the newly introduced electric field sensor and magnetic field sensor also have excellent performance. The electric field sensor is based on the principle of capacitive coupling, adopts a special electrode structure and signal amplification circuit, and the accuracy can reach ±0.5V / m, which can accurately measure the electric field intensity in the surrounding space. The magnetic field sensor uses the fluxgate principle or Hall effect, combined with a high-precision signal processing chip, to achieve an accuracy of ±0.1μT, which can effectively detect the change of magnetic field intensity. In terms of data acquisition frequency, this module collects data of more than 30 dimensions such as bus voltage, load current, device temperature, mechanical vibration, electric field intensity, and magnetic field intensity in real time at an ultra-high sampling frequency of 0.5ms. This high-frequency sampling method can completely capture the dynamic changes during the operation of the device, providing rich data support for subsequent data analysis and fault warning. To ensure the long-term measurement accuracy of the sensors, all sensors have a self-calibration function. Through the built-in calibration circuit and standard reference source, a calibration operation is automatically performed every 24 hours. During the self-calibration process, the sensor will compare the current measurement value with the standard reference value and automatically adjust the measurement parameters. The calibration error does not exceed ±0.01%, thus ensuring the measurement accuracy and stability of the sensor during long-term operation.

[0024] Edge computing processing module: Multi-core heterogeneous processors are selected. Taking the Xilinx Zynq UltraScale+ MPSoC with integrated FPGA acceleration module as an example, this processor integrates ARM processing units and FPGA programmable logic resources. The ARM part is responsible for the control and management tasks of the system. It has multi-core processing capabilities and can efficiently run various operating systems and complex control software. The FPGA acceleration module, with its hardware programmable characteristics, can perform customized acceleration for specific computing tasks. For example, in the preprocessing stage after data acquisition, the FPGA can quickly screen and preliminarily process a large amount of raw data, greatly reducing the computing burden of the ARM core and realizing efficient allocation and utilization of computing resources. In the implementation of the harmonic analysis function, the module integrates the fast Fourier transform (FFT) algorithm. The FFT algorithm is based on a divide-and-conquer strategy. It decomposes a discrete sequence of length N into multiple shorter subsequences for calculation. Through continuous iteration and butterfly operations, it finally efficiently obtains the frequency domain results. Formula In the example, x(n) represents the discrete signal sequence in the time domain, and X(k) is the transformed frequency domain representation. Through this algorithm, the module can quickly and accurately analyze the harmonic components in signals such as bus voltage and load current, providing an important basis for power quality assessment and fault diagnosis of power systems. In terms of data anomaly detection, the quantum computing-assisted convolutional neural network (Q-CNN) is innovatively introduced. The traditional convolutional neural network consists of 3 convolutional layers and 2 fully connected layers. The convolutional layer extracts features by sliding the convolution kernel on the data, which can effectively identify local patterns in the data; the fully connected layer integrates the features extracted by the convolutional layer for the final classification judgment. On this basis, Q-CNN adds a quantum entanglement layer. The superposition characteristics of quantum bits enable a quantum bit to represent multiple states at the same time, greatly expanding the computing power of the network; the quantum entanglement characteristics allow non-classical associations between quantum bits, making the network more efficient in processing complex data relationships. Through these features, Q-CNN can perform real-time analysis on the collected multi-dimensional data, quickly detect anomalies in the data, and increase the accuracy of data anomaly detection to more than 99.5%, providing strong guarantees for the stable operation of the system.

[0025] Intelligent decision-making module: Build a deep reinforcement learning (DRL) model, which combines the advantages of deep learning and reinforcement learning to achieve multi-objective optimization of the power grid. From the perspective of the objective function, has a clear physical meaning. Represents the active power loss of each branch in the power grid, I i is the current in branch i, R i It is the resistance of this branch. By optimizing this item, the energy loss during the operation of the power grid can be effectively reduced and the efficiency of power utilization can be improved. Measures the voltage V of each node in the power grid j and the reference voltage V ref The deviation between them. By adjusting the weight coefficient , the degree of emphasis on voltage stability can be controlled to ensure that the power grid voltage is within a reasonable range and avoid the situation of too high or too low voltage. Then it reflects the reactive power Q k and the reference reactive power Q ref The difference. Optimizing this item can achieve the optimal allocation of reactive power compensation and improve the power factor of the power grid. The genetic algorithm simulates the selection, crossover, and mutation operations in the biological evolution process, encodes the weight coefficient as a chromosome, and searches in the solution space. Through continuous iteration, a combination of weight coefficients that can optimize the objective function is selected to adapt to different power grid operation scenarios. In terms of policy update, the Proximal Policy Optimization (PPO) algorithm is used. The PPO algorithm is based on the idea of policy gradient and updates the policy network parameters by optimizing the approximation of the objective function. The experience replay pool plays an important role in this. It can store the experience samples generated by the agent during the interaction with the environment, and the capacity reaches 10 7 samples. By randomly sampling for training, the correlation between samples is broken, and the stability and learning efficiency of the algorithm are improved. In addition, the module introduces a meta-learning mechanism. Meta-learning aims to enable the model to learn how to learn. By learning the data under different power grid topologies and operating conditions, the model can quickly adapt to the new environment. In the face of changes in the power grid structure, such as adding or reducing substations, transmission lines, etc., or changes in operating conditions, such as load fluctuations, new energy access, etc., the meta-learning mechanism can help the model quickly adjust the strategy and find the optimal decision in a short time to ensure that the power grid always maintains an efficient and stable operating state.

[0026] Adaptive execution module: Controls intelligent vacuum circuit breakers (response time ≤ 20ms, with a bistable electromagnetic operating mechanism) and on-load tap-changing transformers (tap-changer adjustment accuracy ±0.2%, using no-arc switching technology), and dynamically adjusts the control parameters through the fuzzy PID algorithm. The formula is: , the proportional coefficient K p , the integral coefficient K i and the differential coefficient K d are adjusted in real time according to the load rate, power grid harmonic content, and voltage fluctuation conditions; in addition, this module can also coordinate and control with distributed power sources (such as solar and wind power generation equipment), and adjust the voltage regulation strategy of the power grid in advance by predicting the output of distributed power sources.

[0027] 3D Visualization Module: With the help of advanced technical means, it presents the complex power grid system in an intuitive and interactive way. In terms of model construction, it constructs a virtual power grid model based on digital twin technology. This technology conducts an all-round digital mapping of the physical power grid, collects multi-dimensional data such as the geometric shape, material properties, and operating parameters of power grid equipment, and uses high-precision modeling software to construct a virtual model highly consistent with the actual power grid. This model not only looks similar to the real power grid in appearance but also can reflect the operating status and electrical characteristics of power grid equipment in real time, providing an accurate data basis for subsequent analysis and decision-making. To accurately display the equipment location, the module integrates the GIS (Geographic Information System). The GIS system integrates geospatial data and power grid equipment data and precisely marks equipment such as substations, transmission lines, and poles on the geographical map in the form of map layers. Users can view the distribution of power grid equipment in different regions through operations such as zooming and panning, and can also query the detailed information of a single device, such as device model, operation years, maintenance records, etc., providing support in the geospatial dimension for the planning, construction, and operation and maintenance of the power grid. In terms of view display, it uses WebGL technology to achieve 2D / 3D view switching. WebGL is a web-based graphics rendering technology that does not require additional plugins, and users can smoothly switch between 2D floor plans and 3D stereograms in the browser. In the 2D view, users can quickly view the overall layout and line directions of the power grid; after switching to the 3D view, they can more intuitively feel the spatial position relationship and actual form of power grid equipment. Moreover, the data update frequency of this module is ≥20Hz, ensuring that the virtual model can reflect the changes in the operating status of the power grid in real time and enabling users to obtain the latest information. In addition, the module introduces holographic projection technology. Through special holographic projection equipment, it can display the operating status and equipment parameters of the power grid in the form of holographic images on-site. The holographic images have a high sense of three-dimensionality and realism, and operators seem to be on the power grid site. This technology supports multi-person simultaneous interactive operations. Different people can observe the holographic images from different angles and query the detailed data of specific devices, such as voltage, current, temperature, etc., through interactive methods such as gestures and voices, realizing more convenient and efficient information interaction. This module also has a virtual inspection function. Operators can virtually patrol in the virtual power grid model by wearing virtual reality (VR) devices or using a specific operation interface. During the inspection process, the system will intelligently judge whether there are potential problems with the equipment based on the operating parameters and historical data of the equipment and remind the operators in a visual way, such as equipment heat points and line fault warnings. This virtual inspection method not only saves labor and time costs but also avoids the dangers that operators may encounter during actual inspections, improving the efficiency and safety of inspections.

[0028] In the present invention, the following modules are further included: Environmental compensation module: Obtain parameters by deploying a weather station, specifically temperature, humidity, air pressure, wind speed, and light intensity. Use an empirical formula with multi-factor coupling to correct the equipment temperature rise: ΔT comp =ΔT meas ⋅(1 + 0.003(T - 25) + 0.008(H - 60) + 0.001P - 0.002W + 0.0005L), where T is the environmental temperature, H is the relative humidity, P is the air pressure, W is the wind speed, and L is the light intensity; Predict the equipment insulation aging rate based on environmental parameters and formulate a maintenance plan in advance.

[0029] In the present invention, the following modules are further included: Fault diagnosis module: Construct an expert system integrating a knowledge graph and deep learning, integrate fault cases, and jointly infer the fault types through a rule engine and a deep neural network, specifically winding deformation, poor contact, and insulation breakdown; Use a method combining wavelet packet decomposition and Hilbert-Huang transform (HHT) to extract the vibration signal features. The energy entropy calculation formula is: , p i is the energy proportion of the i-th frequency band; Through mining historical fault data, predict the probability and trend of fault occurrence.

[0030] In the present invention, the data acquisition module is the fundamental link to ensure the efficient operation of the system, and has many advanced features in terms of compatibility, data processing, and security. In terms of protocol compatibility, this module demonstrates strong adaptability and can be compatible with more than 20 industrial protocols such as IEC61850, Modbus RTU, and DNP3. As an international standard communication protocol in the field of power system automation, IEC61850 is mainly used for communication between devices in a substation. It adopts an object-oriented data model and an abstract communication service interface to achieve device interoperability and seamless data transmission. Modbus RTU is a serial communication protocol widely used in the industrial field and is often used for communication between a PLC (programmable logic controller) and other devices. It is favored for its simplicity, ease of use, and high reliability. The DNP3 protocol is designed specifically for power automation systems, supports real-time data transmission and device control, and is suitable for communication between a remote terminal unit (RTU) and a master station. By being compatible with such a variety of protocols, the data acquisition module can effectively communicate with devices of different manufacturers and types in the power grid to achieve comprehensive data acquisition. To achieve unified processing and secure transmission of data from different protocols, the module uses a protocol conversion gateway based on blockchain. This gateway first parses the data from devices with different protocols and converts it into a unified data format. In this process, by using advanced natural language processing (NLP) technology and pattern recognition algorithms, it can quickly and accurately identify the structure and content of data from different protocols to ensure the accuracy of data conversion. At the same time, with the help of the distributed ledger and encryption algorithm of blockchain technology, the converted data is encrypted for storage and transmission. The distributed feature of blockchain enables data to be stored on multiple nodes, avoiding single-point failures and improving data reliability; while the encryption algorithm ensures the security of data during transmission and prevents data from being stolen or tampered with. After strict testing, the data conversion latency is ≤5 ms, ensuring the real-time nature of data acquisition. In addition, this module also has the ability to evaluate the quality of data from different protocols. By establishing a data quality evaluation model, multiple dimensions such as data integrity, accuracy, timeliness, and consistency are comprehensively considered. For integrity, it checks whether there are missing values in the data; for accuracy, it judges by comparing with historical data and standard values; timeliness focuses on the update time of the data; consistency checks whether there are conflicts between data from different sources. According to the evaluation results, the module can preferentially collect and process high-quality data to ensure that the data input into the system is reliable and effective, providing solid data support for subsequent substation voltage regulation control.

[0031] In the present invention, the intelligent decision-making module, as a key part of the system, innovatively introduces the transfer learning mechanism and the federated learning mechanism, significantly improving the training efficiency and performance of the model. The transfer learning mechanism plays an important role in this module. The pre-trained model is first initialized on the massive historical data of 500 substations. This historical data covers various data of substations under different seasons, different time periods, and different operating conditions, such as voltage, current, active power, reactive power, etc. During the training process, leveraging the powerful feature extraction ability of the deep neural network, the model learns the general patterns and rules of substation operation. When faced with a new substation, due to the certain similarity in the power grid structure and operation principle between the new substation and the already trained substations, the transfer learning mechanism can transfer the knowledge learned in the pre-trained model. The new substation only requires 50 hours of data, which is mainly used to capture the unique operating characteristics of the new substation, such as local load characteristics, equipment aging degree, etc. Then, by fine-tuning the pre-trained model, specifically using the backpropagation algorithm to adjust the model parameters, the model can quickly adapt to the operating conditions of the new substation. Verified by practice, this method reduces the model training time by 90%, greatly improving the model deployment efficiency and avoiding the cumbersome process of training from scratch at each new substation. The federated learning mechanism further enhances the generalization ability and adaptability of the model. In traditional machine learning, if multiple substations want to jointly optimize the model, they often need to share the original data, which will bring data privacy and security issues. The federated learning mechanism solves this problem. It allows multiple substations to jointly optimize the model without sharing the original data. Specifically, each substation uses its own data to train the model locally to obtain local model parameters. Then, these local model parameters are encrypted and uploaded to the central server. After receiving the local model parameters of all substations, the central server uses a secure aggregation algorithm to fuse these parameters to obtain global model parameters. Finally, the updated global model parameters are sent down to each substation, and each substation then uses these parameters to update the local model and continue the next round of training. In this way, multiple substations can jointly utilize the data advantages of each other under the premise of protecting data privacy, continuously optimize the model, enabling the model to better adapt to the complex operating environments of different substations, improving the generalization ability and adaptability of the model, and thus providing more accurate and reliable decision-making support for the voltage regulation control of the power supply grid.

[0032] In the present invention, the adaptive execution module is a key component to ensure the stable operation and reliable control of the system, and its hardware-in-the-loop (HiL) test function and adaptive fault-tolerant control function are particularly prominent. In terms of the hardware-in-the-loop (HiL) test function, the real-time digital simulator (RTDS) is used to simulate the power grid disturbance situation. The RTDS is a highly integrated and powerful real-time simulation device, which is based on advanced digital signal processing technology and parallel computing architecture. During the test, the RTDS can accurately simulate the electrical characteristics and dynamic behaviors of various electrical components in the power grid, such as transformers, transmission lines, circuit breakers, etc. For power grid disturbances, the RTDS can simulate various complex working conditions such as short-circuit faults, lightning strikes, and sudden load changes. Taking the short-circuit fault simulation as an example, the RTDS can accurately set the position, type (three-phase short circuit, two-phase short circuit, etc.) and duration of the short circuit, so as to generate electrical signals extremely similar to the actual power grid short-circuit fault. By connecting the hardware device of the adaptive execution module to the RTDS to form a closed-loop test system, the control strategy can be comprehensively verified. After strict testing, this test can cover more than 95% of the extreme working conditions, ensuring the effectiveness of the control strategy in various complex situations and providing reliable technical support for the actual power grid operation. The adaptive fault-tolerant control function further improves the stability and reliability of the system. When a device in the execution module fails, the built-in fault detection sensors and intelligent diagnostic algorithms in the system will immediately come into play. The fault detection sensors are distributed at various key parts of the execution module and can real-time monitor the operating parameters of the device, such as voltage, current, temperature, vibration, etc. Once abnormal parameters are detected, the intelligent diagnostic algorithm will quickly analyze the fault, automatically identify the faulty device, and accurately judge the type of fault (such as hardware damage, software failure, communication interruption, etc.) and severity by comparing with the pre-established fault feature database. Based on this information, the system will adjust the control strategy according to the preset strategy. If there is a spare device for the faulty device, the system will quickly switch to the spare device to ensure the continuous operation of the relevant functions; if there is no spare device, a redundant control method will be adopted, such as adjusting the operating parameters or control logic of other devices to maintain the stable operation of the power grid. This adaptive fault-tolerant control method greatly reduces the impact of device failures on the power grid operation and improves the reliability and robustness of the entire power supply grid voltage regulation and control system.

[0033] In the present invention, the 3D visualization module demonstrates unique advantages in terms of interactive experience and training applications. The augmented reality (AR) interaction and virtual reality (VR) immersive experience it supports bring a brand-new working mode for operators. In terms of augmented reality (AR) interaction, innovative operations are achieved with the help of the Hololens2 glasses. The Hololens2 is equipped with an advanced optical see-through head-mounted display device, with multiple high-definition cameras, depth sensors, spatial microphones, etc. built in. These sensors can real-time sense the real environment where the operator is located, accurately locate the position and gesture movements of the operator. Through its powerful computing ability, the virtual control interface is superimposed on the real power grid equipment scene. When the operator adjusts the equipment parameters, without directly contacting the equipment, just making corresponding gesture movements in the air, such as clicking and dragging, the system can quickly identify and execute the operation instructions. The interactive response time of this process is ≤100 ms, greatly improving the convenience and efficiency of operation. For example, during the substation inspection process, when the operator views the equipment through the Hololens2 glasses, the virtual interface will real-time display the operating parameters of the equipment, such as voltage, current, temperature, etc. If abnormal parameters are found, they can be directly adjusted in the air to achieve rapid processing. In terms of virtual reality (VR) immersive experience, the operator can enter a highly realistic virtual power grid environment through VR devices such as HTC Vive and Oculus Rift. These VR devices usually have high-resolution displays and accurate head tracking systems, which can provide the operator with a full-range and immersive visual experience. At the same time, with the cooperation of interactive devices such as the handle, the operator can simulate real equipment operations in the virtual environment. In equipment operation training, the operator can learn the operation procedures of various equipment in the virtual environment, such as the closing and opening of circuit breakers, the tap adjustment of transformers, etc. Through repeated practice, the operation steps and specifications can be familiarized. In terms of fault simulation drills, the system can simulate various power grid fault scenarios, such as line short circuits, equipment overheating, etc. The operator needs to quickly judge the fault type in the virtual environment and take corresponding treatment measures. In this way, the skill level and emergency handling ability of the operator are effectively improved, and the safety risks and economic losses caused by misoperations in actual operations are avoided.

[0034] In the present invention, the following steps are further included: Data acquisition and preprocessing step: Obtain the original data through multi-source sensors, and adopt the method of combining median filtering and wavelet threshold denoising to remove impulse noise and high-frequency noise. The formula is: , and perform normalization processing to the range of [-1, 1]. At the same time, fill in the missing values and correct the outliers of the data, and adopt the method based on Kalman filtering for data smoothing processing to improve the quality of the data.

[0035] Real-time status assessment step: Use the Q-CNN model to detect data anomalies and calculate the grid health index HI: , where w i is the feature weight, and f i is the i-th feature function. At the same time, the entropy weight method is introduced to dynamically adjust the feature weights, so that the health index can more accurately reflect the actual operating state of the power grid.

[0036] Control strategy generation step: Based on the DRL model, output the optimal control sequence, including the transformer tap position and the breaker switching sequence, with a control step of 200 ms. When generating the control strategy, consider factors such as the real-time topology of the power grid, load forecasting, and distributed power generation output forecasting. The model predictive control (MPC) algorithm is used to optimize the control sequence to improve the forward-looking and effectiveness of the control strategy.

[0037] Execution and feedback step: Drive the execution module to adjust the device parameters, and monitor the control effect in real time through the wide area measurement system (WAMS) to form a closed-loop control. At the same time, the feedback linearization control method is introduced to compensate for the non-linear factors in the control process and improve the accuracy and stability of the control.

[0038] In the present invention, it also includes: Self-learning optimization step: Regularly collect control effect data, and use the online learning algorithm to update the DRL model. The self-adaptive adjustment formula for the learning rate is: , where α0 is the initial learning rate, and γ and β are the attenuation coefficients. At the same time, the evolutionary strategy algorithm is introduced to optimize the hyperparameters of the model to improve the learning efficiency and performance of the model.

[0039] In the invention, it also includes: Fault response steps: When the system detects a serious fault, such as the bus voltage deviation exceeding 15% or the frequency deviation being greater than 0.5 Hz, it means that the operating state of the power grid has deviated significantly from the normal range, which may have a major impact on equipment and users. At this time, an emergency control strategy will be quickly triggered. The primary task of this strategy is to disconnect non-essential loads to relieve the burden on the power grid and prevent the fault range from expanding further. The load priority is determined by combining the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation method. The AHP first decomposes the load evaluation problem into multiple levels, from the goal level (determining load priority), the criterion level (such as the importance of the load, the impact on production and life, economic value, etc.) to the scheme level (each specific load). By comparing the relative importance between different criteria and between criteria and schemes pairwise, a judgment matrix is constructed. For example, when judging the importance of two loads for ensuring residential electricity consumption, factors such as the number of users and user types associated with the loads will be comprehensively considered. Then, through a series of mathematical operations, the weight of each load under each criterion is obtained. The fuzzy comprehensive evaluation method is used to handle the uncertainty in the evaluation process. Since the importance of the load is not completely clear and there is a certain degree of fuzziness, the fuzzy comprehensive evaluation method uses the principles of fuzzy mathematics to quantify the fuzzy information. By establishing a membership function, the membership degree of each load under different criteria is determined, and then the comprehensive evaluation result is obtained. Combining the AHP and the fuzzy comprehensive evaluation method can determine the load priority more comprehensively and accurately. At the same time, to ensure the reliability of the weight matrix, it is required that the consistency ratio CR of the weight matrix is less than 0.05. When CR exceeds this threshold, the judgment matrix needs to be adjusted again to ensure the accuracy of the evaluation results. While disconnecting non-essential loads, the system will quickly isolate the fault area. This relies on advanced fault detection and location technologies. Sensors distributed throughout the power grid are used to monitor electrical parameters such as current and voltage in real time, and fault analysis algorithms (such as the traveling wave method, impedance method, etc.) are used to quickly determine the fault location. Once the fault area is determined, intelligent switching equipment will quickly act to cut off the electrical connection between the fault area and other parts, preventing the fault current from damaging other equipment. In addition, to maintain the normal operation of essential loads, reduce power outage time and losses, the system will start distributed power sources and energy storage devices to provide emergency power supply. Distributed power sources such as solar photovoltaic power stations and small wind turbines can be quickly put into operation during power grid faults to convert renewable energy into electrical energy. Energy storage devices, such as lithium battery energy storage systems, store excess electrical energy usually and release electrical energy during faults to ensure the continuous power supply of essential loads such as hospitals and transportation hubs, thus ensuring the basic operation of society and public safety.

[0040] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. An artificial intelligence-based power grid substation voltage regulation control system, characterized in that, It includes the following modules: Multi-source data acquisition module: Deploy voltage transformers, current transformers, temperature sensors, vibration sensors, electric field sensors, and magnetic field sensors to collect bus voltage, load current, equipment temperature, mechanical vibration, electric field strength, and magnetic field strength data in real time; Edge computing processing module: A multi-core heterogeneous processor is adopted to perform harmonic analysis by integrating the fast Fourier transform (FFT) algorithm. The formula is: , and a quantum computing-assisted convolutional neural network (Q-CNN) is introduced to detect data anomalies in real time; Intelligent Decision-making Module: Construct a Deep Reinforcement Learning (DRL) model to multi-objectively optimize active power loss, reactive power compensation, and voltage stability. The objective function is: , where λ1 and λ2 are weight coefficients dynamically optimized by the genetic algorithm; use the Proximal Policy Optimization (PPO) algorithm to update the policy network parameters, introduce a meta-learning mechanism to adapt to changes in different power grid topologies and operating conditions; Adaptive Execution Module: Controls intelligent vacuum circuit breakers and on-load tap-changer transformers, dynamically adjusts control parameters through a fuzzy PID algorithm, and the formula is: , proportionality coefficient K p , integral coefficient K i and differential coefficient K d are adjusted in real time according to the load rate, grid harmonic content, and voltage fluctuation conditions; coordinates with distributed power sources, and adjusts the grid voltage regulation strategy in advance by predicting the output of distributed power sources; 3D visualization module: Build a virtual power grid model based on digital twin technology, integrate the GIS geographic information system to display the equipment location, and use WebGL technology to complete the 2D and 3D view switching; Introduce holographic projection technology to display the power grid operation status and equipment parameters, support multi-person simultaneous interactive operations; Conduct inspections in the virtual power grid model through virtual avatars to discover potential problems.

2. The voltage regulation control system for power transformation of a power supply grid based on artificial intelligence according to claim 1, wherein It also includes: Environmental compensation module: Obtain parameters by deploying a weather station, specifically temperature and humidity, air pressure, wind speed, and light intensity. Use an empirical formula with multi-factor coupling to correct the equipment temperature rise: ΔT comp =ΔT meas ⋅(1 + 0.003(T - 25) + 0.008(H - 60) + 0.001P - 0.002W + 0.0005L), where T is the environmental temperature, H is the relative humidity, P is the air pressure, W is the wind speed, and L is the light intensity; Predict the equipment insulation aging rate based on environmental parameters and formulate a maintenance plan in advance.

3. The voltage regulation control system for power supply and transformation of a power grid based on artificial intelligence according to claim 1, characterized in that, It also includes: Fault diagnosis module: Build an expert system based on the integration of knowledge graph and deep learning, integrate fault cases, and jointly infer fault types through a rule engine and a deep neural network, specifically winding deformation, poor contact, and insulation breakdown; adopt a method combining wavelet packet decomposition and Hilbert-Huang transform (HHT) to extract vibration signal features, and the energy entropy calculation formula is: , p i is the energy proportion of the i-th frequency band; through the mining of historical fault data, predict the probability and trend of fault occurrence.

4. The voltage regulation control system for power supply and transformation of power grids based on artificial intelligence according to claim 1, characterized in that The data acquisition module supports multi-protocol fusion, is compatible with IEC61850, ModbusRTU, and DNP3 industrial protocols, and uses a blockchain-based protocol conversion gateway to achieve unified data format and secure data transmission; Conduct quality assessment on data of different protocols, and preferentially collect and process quality assessment data.

5. The voltage regulation control system for power grid transformation based on artificial intelligence according to claim 1, wherein The intelligent decision-making module introduces transfer learning mechanism and federated learning mechanism. The pre-trained model is initialized with the historical data of 500 substations, and the data of the new site is fine-tuned in 50 hours; Through the federated learning mechanism, multiple substations cooperate to optimize the model without sharing the original data.

6. The voltage regulation control system for power supply grid transformation based on artificial intelligence according to claim 1, characterized in that The adaptive execution module simulates power grid disturbances through the real-time digital simulator RTDS to verify the effectiveness of the control strategy. When a device in the execution module fails, the system automatically identifies the faulty device, adjusts the control strategy according to the fault type and severity, and uses standby devices or redundant control methods to maintain the power grid operation.

7. The voltage regulation control system for power transformation of a power supply grid based on artificial intelligence according to claim 1, wherein The 3D visualization module supports augmented reality AR interaction and virtual reality VR immersive experience, and completes the contactless adjustment of equipment parameters by superimposing a virtual control interface through Hololens2 glasses; The operator enters the virtual power grid environment through VR equipment for equipment operation training and fault simulation.

8. An artificial intelligence-based power grid transformation voltage regulation control method based on the system according to any one of claims 1-7, characterized in that, It includes the following steps: Data acquisition and preprocessing steps: Obtain the original data through multi-source sensors, and use a method combining median filtering and wavelet threshold denoising to remove impulse noise and high-frequency noise. The formula is: , and perform normalization to the range of [-1, 1]; fill in missing values and correct outliers in the data, and use the Kalman filter-based method for data smoothing; Real-time status assessment steps: Use the Q-CNN model to detect data anomalies and calculate the power grid health index HI: , w i is the feature weight, f i is the i-th feature function; introduce the entropy weight method to dynamically adjust the feature weights; Control strategy generation step: Output a control sequence based on the DRL model, including transformer tap positions and circuit breaker switching sequences; When generating the control strategy, consider the real-time topology of the power grid, load forecasting, and distributed power output forecasting factors, and use the model predictive control MPC algorithm to optimize the control sequence; Execution and feedback step: Drive the execution module to adjust the equipment parameters, monitor the control effect in real time through the wide-area measurement system WAMS to form a closed-loop control; Introduce the feedback linearization control method to compensate for the non-linear factors in the control process.

9. The method for controlling the voltage regulation of a power grid substation based on artificial intelligence according to claim 8, wherein It also includes: Self-learning optimization steps: Regularly collect control effect data, use an online learning algorithm to update the DRL model, and the self-adaptive adjustment formula for the learning rate is: , where α0 is the initial learning rate, γ and β are decay coefficients, and an evolutionary strategy algorithm is introduced to optimize the hyperparameters of the model.

10. The method for controlling voltage regulation in a power supply grid substation based on artificial intelligence according to claim 8, characterized in that, It also includes: Fault response step: When a serious fault is detected, trigger an emergency control strategy, preferentially disconnect non-essential loads, and the load priority is determined by combining the analytic hierarchy process AHP and the fuzzy comprehensive evaluation method, isolate the fault area, and provide emergency power supply through distributed power sources and energy storage devices.

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