Intelligent power distribution management system based on artificial intelligence

Abstract

The invention discloses a power distribution intelligent management system based on artificial intelligence, and the system comprises an acquisition module which collects the current power utilization data of a power utilization area; the acquisition switching module is respectively connected with the power distribution processing module and the acquisition module; the power distribution processing module is respectively connected with the acquisition switching module and the electric energy conversion module, and controls the acquisition switching module to work so as to send a control signal to the corresponding electric energy conversion module according to the input current power consumption data of the power consumption area; the electric energy conversion module is connected with the energy storage module and controls the energy storage module to charge and discharge according to the control signal; a preset AI model is stored in the power distribution processing module, current power consumption data and historical power consumption data of the power consumption area are input to the AI model, and future power consumption demands and equipment hidden danger information corresponding to the power consumption area are output. And generating a control signal according to the future power consumption demand of each power consumption area, the equipment hidden danger information and the future weather information corresponding to the power consumption area.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply systems, and in particular to an intelligent power distribution management system based on artificial intelligence. Background Art

[0002] At present, the power distribution system provides power supply according to the standard operating load of different power consumption areas to meet the needs of end users to a certain extent. However, it cannot adapt to the situation of insufficient power supply during peak power consumption hours and excess power during low power consumption hours, which easily leads to power shortage and waste of power resources for users. Secondly, the power distribution system uses multiple separate control lines for multiple power consumption areas to collect power consumption data for subsequent regulation and processing, but this method will lead to high line costs and a large number of control terminals required for the control panel, which will increase the overall electrical cost and reduce the regulation efficiency, affecting the regulation effect. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent power distribution management system based on artificial intelligence, which can improve the efficiency of intelligent regulation, greatly reduce the waste rate and amount of electric energy, minimize the power supply shortage during peak hours, and improve users' electricity demand and satisfaction.

[0004] In order to solve the above technical problems, the present invention provides an artificial intelligence-based power distribution intelligent management system, including a collection module, a collection switching module, a power distribution processing module, an electric energy conversion module and an energy storage module; the collection module is used to collect current power consumption data of different power consumption areas in different regions; the collection switching module is respectively connected to the power distribution processing module and the collection module, and is used to switch and collect current power consumption data of multiple different power consumption areas in the same region or current power consumption data of multiple different power consumption areas in different regions and send them to the power distribution processing module; the power distribution processing module is respectively connected to the collection switching module and the electric energy conversion module, and is used to control the collection switching module to work. The power distribution processing module stores a preset AI model, and the current power consumption data and historical power consumption data of the power consumption area are input into the AI ​​model. The AI ​​model outputs the future power demand and equipment hidden danger information corresponding to the power consumption area according to the current power consumption data and the historical power consumption data, and generates the control signal according to the future power demand, equipment hidden danger information and future weather information corresponding to each power consumption area.

[0005] As an improvement of the above-mentioned scheme, the collection switching module includes multiple collection switching modules, and the collection switching modules are used to switch and collect current electricity consumption data of multiple different electricity consumption areas in the same region or current electricity consumption data of multiple different electricity consumption areas in different regions; the collection switching module includes multiple collection switching circuits, and the collection switching circuits are arranged in a one-to-one correspondence with the collection modules.

[0006] As an improvement of the above scheme, the acquisition module includes a voltage transformer circuit and a current transformer circuit, and the voltage transformer circuit and the current transformer circuit are respectively used to collect voltage data and current data of the power consumption area, and the output ends of the voltage transformer circuit and the current transformer circuit are both connected to the acquisition switching circuit.

[0007] As an improvement of the above scheme, each two groups of the acquisition switching modules are a first acquisition switching module and a second acquisition switching module with opposite switching structures; any control end of the power distribution processing module is respectively connected to the first acquisition switching module and the second acquisition switching module, for switching and controlling the operation of the first acquisition switching module or the second acquisition switching module.

[0008] As an improvement of the above scheme, the first acquisition switching circuit in the first acquisition switching module includes a first acquisition main switch circuit and a first acquisition sub-switch circuit, and the second acquisition switching circuit in the second acquisition switching module includes a second acquisition main switch circuit and a second acquisition sub-switch circuit; the first acquisition main switch circuit includes a first switch tube and a first resistor, the first end of the first switch tube is connected to the working power supply end, the second end of the first switch tube is connected to the first acquisition sub-switch circuit and is grounded through the first resistor, and the control end of the first switch tube is connected to the power distribution processing module; the second acquisition main switch circuit includes a second switch tube and a second resistor, the first end of the second switch tube is connected to the working power supply end and the second acquisition sub-switch circuit through the second resistor, the second end of the second switch tube is grounded, and the control end of the second switch tube is connected to the power distribution processing module; the first acquisition sub-switch circuit and the second acquisition sub-switch circuit are respectively connected to the power distribution processing module and the acquisition module, when the first switch tube is turned on, the first acquisition sub-switch circuit turns on the loop between the power distribution processing module and the acquisition module, when the second switch tube is turned on, the second acquisition sub-switch circuit turns on the loop between the power distribution processing module and the acquisition module.

[0009] As an improvement of the above scheme, the first acquisition sub-switch circuit includes a third switch tube, a fourth switch tube, a third resistor and a fourth resistor; the control ends of the third switch tube and the fourth switch tube are both connected to the second end of the first switch tube, the first end of the third switch tube is connected to the output end of the voltage transformer circuit, the second end of the third switch tube is connected to the power distribution processing module and grounded through the third resistor; the first end of the fourth switch tube is connected to the voltage output end of the acquisition module, and the second end of the fourth switch tube is connected to the power distribution processing module through the fourth resistor.

[0010] As an improvement of the above scheme, the second acquisition sub-switch circuit includes a fifth switch tube, a sixth switch tube, a fifth resistor and a sixth resistor; the control ends of the fifth switch tube and the sixth switch tube are both connected to the first end of the second switch tube, the first end of the fifth switch tube is connected to the output end of the voltage transformer circuit, the second end of the fifth switch tube is connected to the power distribution processing module and grounded through the fifth resistor; the first end of the sixth switch tube is connected to the current output end of the acquisition module, and the second end of the sixth switch tube is connected to the power distribution processing module through the sixth resistor.

[0011] As an improvement of the above scheme, the DC end of the power conversion module is connected to the energy storage module, and the AC end of the power conversion module is connected to the power grid or the AC load, and is used to convert the AC power of the power grid into DC power to charge the energy storage module, and convert the DC power of the energy storage module into AC power to power the power grid or the AC load.

[0012] As an improvement of the above solution, the power conversion module includes a DC circuit breaker, a grid-connected inverter, an AC filter module, a transformer, an AC contactor and an AC circuit breaker connected in sequence; the DC circuit breaker is connected to the energy storage module, and the AC circuit breaker is connected to the power grid or the AC load.

[0013] As an improvement of the above-mentioned scheme, the power distribution processing module generates a control signal according to the future power demand of each power consumption area, the equipment hidden danger information and the future weather information corresponding to the power consumption area, including: obtaining the power consumption weight, equipment weight and weather weight of the power consumption area according to the regional location information of each power consumption area; calculating the power demand factor according to the power weight and the future power demand; calculating the equipment hidden danger factor according to the equipment hidden danger information and the equipment weight; calculating the weather factor according to the future weather information and the weather weight; the future weather information includes at least the future wind speed, future temperature and future humidity, and the weather weight includes the wind speed weight, the temperature weight and the humidity weight; calculating the safety margin coefficient of the power consumption area according to the power demand factor, the equipment hidden danger factor and the weather factor; calculating the control signal of the power consumption area according to the safety margin coefficient and the historical power consumption data.

[0014] As an improvement of the above solution, the power distribution processing module calculates a comprehensive factor according to the power demand factor, the equipment hidden danger factor and the weather factor; and calculates the safety margin coefficient according to the equipment hidden danger factor and the comprehensive factor;

[0015] The expression of the comprehensive factor includes: ;

[0016] The expression of the safety margin factor includes: ;

[0017] in, , , It is the electricity consumption area In time Corresponding electricity demand factors, equipment hidden danger factors and weather factors; It is the electricity consumption area In time The corresponding comprehensive factor; It is the electricity consumption area In time The corresponding safety margin factor; is the preset margin weight.

[0018] The artificial intelligence-based power distribution intelligent management system provided by the present invention integrates multiple functional modules (including but not limited to acquisition module, acquisition switching module, power distribution processing module, power conversion module and energy storage module) to achieve efficient power resource allocation, reduce power waste, and maximize power supply during peak power consumption periods, thereby improving users' power consumption experience and satisfaction. Below, in combination with the provided technical solution, the technical content and beneficial effects of the present invention are described in detail:

[0019] Collection module: This module is responsible for collecting real-time electricity consumption data from different regions and different electricity consumption areas. These data may include but are not limited to electricity consumption, electricity consumption time, electricity consumption mode, etc., which is the basis for the system to conduct intelligent analysis.

[0020] Collection switching module: This module acts as a bridge between data collection and processing, and can switch data collection between different power consumption areas when needed, whether it is multiple areas in the same region or cross-regional power consumption areas. This function ensures that the system can flexibly respond to changes in power demand in different areas.

[0021] Power distribution processing module: One of the core modules, with a built-in preset AI model. This module receives real-time power consumption data from the acquisition module, as well as historical data that can be selectively received through the acquisition switching module. Through in-depth analysis of these data, the AI ​​model not only predicts the future power demand of each power consumption area, but also identifies potential equipment hazards. In addition, considering the impact of future weather on power demand, this module will also incorporate weather forecast information as part of the basis for generating control signals.

[0022] Power conversion module: According to the control signal from the power distribution processing module, it performs specific power distribution and conversion tasks. This may involve converting part of the electrical energy into other forms of energy (such as chemical energy) for subsequent use, or converting the energy in the energy storage module back into electrical energy for user use when necessary.

[0023] Energy storage module: used to store excess electrical energy converted from different periods of time so that it can be released during peak power consumption or when the power grid is insufficient, to help balance the overall energy supply.

[0024] Beneficial effects:

[0025] 1. Improve the efficiency of intelligent regulation: Through real-time analysis and prediction of AI models, the system can more accurately grasp the demand change trend of each power consumption area, realize dynamic management and optimal configuration of electric energy, and greatly improve the efficiency and accuracy of regulation.

[0026] 2. Significantly reduce the rate and amount of power waste: Traditional power distribution methods often have difficulty accurately matching power demand, which easily leads to power waste. The AI-based intelligent management system can effectively avoid this problem and minimize the waste through scientific power distribution.

[0027] 3. Effectively alleviate power shortages during peak periods: The system's predictive capabilities and energy storage functions are combined to accumulate excess electricity during low-consumption periods and release the stored energy during peak demand periods, thereby effectively alleviating power shortages and ensuring that users' electricity needs can be met at all times.

[0028] 4. Improve users' electricity demand and satisfaction: Through the application of the above-mentioned technical means, not only can the stable supply of electricity be ensured, but also the electricity cost can be further optimized and unnecessary expenditure can be reduced, thereby improving users' electricity experience and satisfaction.

[0029] To sum up, the artificial intelligence-based power distribution intelligent management system provided by the present invention realizes the efficient management and utilization of power resources through intelligent data collection, analysis, processing and storage technology, which is of great significance for promoting energy conservation and emission reduction, optimizing power services, etc.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a structural diagram of the power distribution intelligent management system of the present invention;

[0032] Figure 2 It is a structural schematic diagram of the acquisition module and the acquisition switching module of the present invention;

[0033] Figure 3 It is a structural schematic diagram of the acquisition module, acquisition switching module and main controller of the present invention;

[0034] Figure 4 It is a structural schematic diagram of the electric energy conversion module and the energy storage module of the present invention;

[0035] Figure 5 It is a structural schematic diagram of the electric energy conversion module of the present invention.

[0036] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0039] It should be understood that, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the difference.

[0040] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0041] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0042] At present, the power distribution system provides power supply according to the standard operating load of different power consumption areas to meet the needs of end users to a certain extent. However, it cannot adapt to the situation of insufficient power supply during peak power consumption hours and excess power during low power consumption hours, which easily leads to power shortage and waste of power resources for users. Secondly, the power distribution system uses multiple separate control lines for multiple power consumption areas to collect power consumption data for subsequent regulation and processing, but this method will lead to high line costs and a large number of control terminals required for the control panel, which will increase the overall electrical cost and reduce the regulation efficiency, affecting the regulation effect.

[0043] To solve the above problems, Figure 1 As shown, the present invention provides an intelligent power distribution management system based on artificial intelligence, including a collection module 1, a collection switching module 2, a power distribution processing module 3, an electric energy conversion module 4 and an energy storage module 5;

[0044] The acquisition module 1 is used to acquire current power consumption data of different power consumption areas in different regions; wherein the current power consumption data includes voltage and current data.

[0045] The collection switching module 2 is connected to the power distribution processing module 3 and the collection module 1 respectively, and is used to switch and connect multiple collection modules 1 to collect current power consumption data of multiple different power consumption areas in the same region or current power consumption data of multiple different power consumption areas in different regions and send them to the power distribution processing module 3 to improve collection efficiency.

[0046] The power distribution processing module 3 is connected to the acquisition switching module 2 and the power conversion module 4 respectively, and is used to control the operation of the acquisition switching module 2, receive the current power consumption data of multiple power consumption areas collected, perform data processing according to the input current power consumption data of the power consumption area, and send corresponding control signals to the corresponding power conversion module 4, so as to synchronously control the operation of multiple power conversion modules 4 and improve the regulation efficiency.

[0047] The power conversion module 4 is connected to the energy storage module 5, and is used to control the energy storage module 5 to perform charging and discharging according to the control signal, and can store the excess power in the power grid into the energy storage module 5, and can transmit the power of the energy storage module 5 back to the power grid, so as to realize the power resource regulation of the power consumption area and meet the actual use needs of the power consumption area to the maximum extent. Among them, a corresponding power conversion module 4 and energy storage module 5 are provided for each power consumption area.

[0048] Among them, a preset AI model is stored in the power distribution processing module 3, and the current power consumption data and historical power consumption data of the power consumption area are input into the AI ​​model. The AI ​​model outputs the future power demand and equipment hidden danger information corresponding to the power consumption area based on the current power consumption data and the historical power consumption data. The power distribution processing module 3 generates a control signal according to the future power demand, equipment hidden danger information and future weather information corresponding to each power consumption area.

[0049] Specifically, the acquisition module 1 is responsible for collecting the current voltage and current data from different power consumption areas in different regions. It can be a smart meter installed at the user end, or it can be real-time data obtained by sensors. The acquisition switching module 2 is connected to the acquisition module 1 and the power distribution processing module 3, and can switch and connect multiple acquisition modules 1, thereby realizing efficient data collection of multiple power consumption areas. This can be achieved through a multiplexer or an intelligent switching device. The power distribution processing module 3: As the central processing unit of the system, it receives the power consumption data from the acquisition switching module 2 and performs data processing. The module stores a preset AI model, which can predict future power demand and equipment hidden danger information based on current power consumption data and historical power consumption data. In addition, the power distribution processing module 3 also generates a control signal based on the future power demand, equipment hidden danger information and future weather information of each power consumption area to control the operation of the power conversion module 4. The power conversion module 4 is connected to the energy storage module 5, and controls the energy storage module 5 to perform charging and discharging operations according to the control signal issued by the power distribution processing module 3. The power conversion module 4 can be a bidirectional inverter or an intelligent charge and discharge controller, which can realize efficient conversion and management of power. The energy storage module 5 is used to store excess power in the power grid and transmit the stored power back to the power grid when needed. The energy storage module 5 can be a battery energy storage system, a supercapacitor or a flywheel energy storage system.

[0050] The input data of the AI ​​model are current power consumption data (voltage, current), historical power consumption data, future weather information, etc. The output data are future power demand forecasts, equipment hidden danger information, etc. The model type can be a time series prediction model (such as LSTM, GRU), a machine learning model (such as random forest, support vector machine), or a deep learning model (such as neural network). Use a large amount of historical power consumption data and weather data for model training to improve the accuracy of the prediction.

[0051] Assume that there are three power consumption areas A, B and C, and each area is installed with a collection module 1. The collection module 1 collects voltage and current data every 15 minutes and transmits it to the distribution processing module 3 through the collection switching module 2. After receiving the data, the distribution processing module 3 uses the internal AI model to process the data of each power consumption area. For example, the LSTM model is used to predict the power demand in the next 24 hours. Assume that based on current and historical data, the AI ​​model predicts that area A will have a peak power demand at 8 o'clock tomorrow morning, while areas B and C will have lower power demand. The distribution processing module 3 generates a control signal based on the prediction result. The control signal instructs the power conversion module 4 to transfer excess power from the energy storage module 5 in areas B and C to area A to ensure that area A has sufficient power supply during peak power consumption. At the same time, the distribution processing module 3 also adjusts the control signal based on future weather information (such as high temperature or low temperature) to cope with the impact of extreme weather on power demand. After receiving the control signal, the power conversion module 4 controls the energy storage module 5 in area A to discharge and transmit the stored electric energy to the power grid to meet the power demand of area A. At the same time, the power conversion module 4 controls the energy storage modules 5 in areas B and C to charge and store the excess electric energy in the power grid for emergency use.

[0052] By intelligently predicting future electricity demand, the system can store excess electricity during low-power periods and release it during peak power periods, thereby effectively balancing supply and demand and improving power supply efficiency. Using the acquisition switching module 2, the system can efficiently collect data from multiple power consumption areas, reducing the number of separate control lines, reducing line costs and the number of control terminals required for the control board. The introduction of the AI ​​model enables the system to more accurately predict future electricity demand and adjust the distribution and storage of electricity in a timely manner, improving the effect and response speed of regulation. By intelligently managing the storage and distribution of electricity, the system can minimize the waste of electricity during low-power periods and ensure the stability of the power grid during peak power periods. The AI ​​model can detect abnormal operation of equipment, discover and report potential equipment hazards in advance, thereby reducing power outages and maintenance costs caused by equipment failures. By combining future weather information, the system can more flexibly adjust the power distribution strategy to cope with the impact of extreme weather such as high temperature and low temperature on electricity demand, ensuring that users' electricity needs are met under different weather conditions.

[0053] In summary, the artificial intelligence-based power distribution intelligent management system provided can effectively solve the problems of insufficient power supply during peak consumption periods and excess power during low consumption periods in traditional power distribution systems through intelligent prediction and flexible regulation, thereby improving power supply efficiency, reducing costs, and enhancing user experience.

[0054] Specifically, the AI ​​model training process in this application requires detailed consideration of data preparation, model selection, training strategy, evaluation method, and model optimization and deployment. The following is a detailed training process design:

[0055] First, historical electricity consumption data such as voltage, current, and power are collected from historical data of multiple electricity consumption areas. The historical electricity consumption data should include historical electricity consumption data in different time periods (such as peak hours and valley hours) and under different weather conditions.

[0056] Historical weather data: Collect historical weather data for each electricity consumption area, including temperature, humidity, wind speed, precipitation, etc.

[0057] Historical equipment status data: Collect historical equipment status data of equipment from the power grid and power-consuming equipment, including equipment operating time, fault records, maintenance records, etc.

[0058] Then, missing values ​​and outliers are processed for historical electricity consumption data, historical weather data, and historical operating status data by filling or deleting missing values ​​in the data. Interpolation methods or statistical methods based on historical data can be used for filling. Outliers are detected and processed. Statistical methods (such as standard deviation method) or machine learning methods (such as isolation forest) can be used for outlier detection. The data is then standardized to ensure that different features are at the same level. Z-score standardization or Min-Max standardization can be used.

[0059] Furthermore, the actual power demand of each power consumption area in different time periods is manually marked or automatically calculated based on historical power consumption data, historical weather data and historical operation status data. The degree of hidden dangers of each device in different time periods is marked based on the equipment's fault records and maintenance records.

[0060] Extract statistical features such as the maximum, minimum, average, and standard deviation of voltage, current, and power from historical electricity consumption data. Extract time-related features from historical equipment status data, such as hours, days of the week, holidays, etc. Extract weather features such as temperature, humidity, wind speed, and precipitation from historical weather data, and perform appropriate data processing (such as converting temperature to relative temperature difference). Extract features such as equipment operating time, number of failures, and maintenance records from historical equipment status data.

[0061] Use feature selection methods (such as recursive feature elimination, LASSO regression) to select the most relevant features, reduce redundant features, and improve the training efficiency and generalization ability of the model.

[0062] AI models include time series prediction models and classification models. Time series prediction models: such as LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), etc., are used to predict future electricity demand. Classification models: such as random forest, support vector machine, deep neural network, etc., are used to identify hidden dangers of equipment.

[0063] Design a multi-task learning model that can simultaneously predict power demand and identify equipment hazards. This model can share the underlying feature extraction layer, improving the efficiency and accuracy of the model.

[0064] Training set: used to train the model, accounting for 70% of the total data. Validation set: used to tune hyperparameters and evaluate model performance, accounting for 15% of the total data. Test set: used to finally evaluate model performance, accounting for 15% of the total data.

[0065] Use grid search or random search methods to tune the model's hyperparameters, such as the hidden layer size of LSTM, learning rate, batch size, etc. Use cross-validation methods to ensure the stability of the tuned hyperparameters on different datasets.

[0066] For electricity demand forecasting, the mean square error (MSE) or mean absolute error (MAE) is used as the loss function to minimize the error between the predicted value and the actual value.

[0067] Use the cross entropy loss function for equipment hidden danger identification to minimize classification errors. Standardize the data and convert it into the format required for model input. Use batch gradient descent or stochastic gradient descent for training to ensure that the model can effectively learn the characteristics of the data. During the training process, optimize the loss functions of both power demand forecasting and equipment hidden danger identification. Set regular checkpoints during the training process to save the intermediate state of the model for subsequent recovery or tuning.

[0068] The mean square error (MSE) is used to evaluate the error size of the model in predicting electricity demand.

[0069] The R² score is used to assess the fit of the model. The closer the R² is to 1, the better the model fit is.

[0070] Compare the model's prediction results with actual electricity consumption data to evaluate the accuracy of predictions in different time periods (such as peak hours and off-peak hours). Accuracy evaluates the accuracy of the model in classifying equipment hidden dangers. Precision and recall evaluate the accuracy and coverage of the model in identifying equipment hidden dangers.

[0071] Use existing pre-trained models (such as pre-trained LSTM models) for transfer learning, and quickly improve the model's prediction ability by fine-tuning based on the pre-trained model. Use multiple models (such as LSTM, GRU, and random forest) for integrated learning, and use ensemble methods (such as voting and stacking) to improve the model's stability and prediction accuracy.

[0072] Use interpretability methods (such as LIME or SHAP) to explain the model's prediction results to ensure that the model's decision-making process is transparent and easy for grid managers to understand and trust.

[0073] Deploy the trained model to the power distribution processing module 3 to ensure that the model can run efficiently in the actual environment. Use containerization technology (such as Docker) to deploy the model to improve the flexibility and maintainability of deployment. Monitor the operating status and performance of the model in real time and collect feedback data from the model in actual applications.

[0074] Use anomaly detection methods (such as statistical or machine learning methods) to detect model prediction errors, and adjust model parameters or retrain the model in a timely manner. Regularly update the model's data set, including new electricity consumption data, weather data, and equipment status data. Use incremental learning methods (such as online learning) to continuously optimize the model to ensure that the model can adapt to the dynamic changes of the power grid. During the training process, dynamically select the most relevant features, adjust the feature selection strategy based on the performance feedback of the model, and improve the adaptability and accuracy of the model. Use reinforcement learning methods to optimize the decision-making process of the model. By defining reward functions (such as rewards for successfully smoothing loads during peak hours), the model can continuously learn and optimize in actual operation to improve the control effect. Combine electricity demand forecasting with power distribution optimization to design a closed-loop control system. The model not only predicts future electricity demand, but also optimizes the distribution and storage strategy of electricity based on the prediction results to improve the overall performance of the system.

[0075] Through the above detailed training process design, it can be ensured that the AI-based distribution intelligent management system has efficient, accurate and reliable performance in practical applications, effectively solve the shortcomings of traditional distribution systems, and improve user experience and power grid operation efficiency.

[0076] In some embodiments, the power distribution processing module 3 generates a control signal based on the future power demand of each power consumption area, the equipment hidden danger information and the future weather information corresponding to the power consumption area, including: obtaining the power consumption weight, equipment weight and weather weight of the power consumption area according to the regional location information of each power consumption area; calculating the power demand factor according to the power weight and future power demand; calculating the equipment hidden danger factor according to the equipment hidden danger information and the equipment weight; calculating the weather factor according to the future weather information and the weather weight; the future weather information includes at least the future wind speed, future temperature and future humidity, and the weather weight includes the wind speed weight, the temperature weight and the humidity weight; calculating the safety margin coefficient of the power consumption area according to the power demand factor, the equipment hidden danger factor and the weather factor; calculating the control signal of the power consumption area according to the safety margin coefficient and the historical power consumption data.

[0077] The electricity location information is the location information of each electricity consumption area, which may include geographic coordinates and geographic location type (such as city center, suburbs, rural areas, etc.). The equipment location information is the equipment location information in each electricity consumption area, which may include the equipment installation location and equipment type (such as large industrial equipment, residential equipment, etc.). The weather location information is the location information of the weather station in each electricity consumption area, ensuring that the weather data obtained is more accurate.

[0078] Determine the power consumption weight based on the location information of the power consumption area and the historical power consumption data. For example, the power consumption area in the city center may have a higher power consumption weight. Determine the equipment weight based on the location information and failure frequency of the equipment. For example, the equipment weight of large industrial equipment may be higher. Determine the weather weight based on the location information of the power consumption area and the accuracy of the weather station. The weather weight can be further subdivided into wind speed weight, temperature weight, and humidity weight.

[0079] Exemplarily, the power distribution processing module 3 calculates a comprehensive factor according to a power demand factor, an equipment hidden danger factor and a weather factor; and calculates the safety margin coefficient according to the equipment hidden danger factor and the comprehensive factor.

[0080] The expression of the comprehensive factor includes: ;

[0081] The expression of the safety margin factor includes: ;

[0082] in, , , It is the electricity consumption area In time Corresponding electricity demand factors, equipment hidden danger factors and weather factors; It is the electricity consumption area In time The corresponding comprehensive factor; It is the electricity consumption area In time The corresponding safety margin factor; is the preset margin weight.

[0083] Furthermore, the expression of the control signal includes:

[0084] ;

[0085] It is a threshold value that is dynamically adjusted based on historical power consumption data and the overall load of the grid. Its expression includes: ;

[0086] It should be noted that It is the electricity consumption area In the past The average comprehensive factor over the time period, is the standard deviation, is a preset adjustment factor that balances the strictness and flexibility of the threshold.

[0087] It should be noted that when the power consumption obtained based on the collected power consumption data is within the first preset threshold range, it means that the current power consumption area is in a low power consumption working state; when the power consumption is within the second preset threshold range, it means that the current power consumption area is in a normal power consumption working state; when the power consumption is within the third preset threshold range, it means that the current power consumption area is in a peak power consumption working state. The power distribution processing module 3 issues relevant control instructions to the power conversion module 4 according to the current working state of the power consumption area to control the power conversion module 4 to perform corresponding work.

[0088] For example, when the power consumption area is in a low-peak power consumption state, the power distribution processing module 3 controls the power conversion module 4 to store the excess power in the power grid of the power consumption area into the energy storage module 5 for subsequent use, which greatly reduces the waste rate and amount of power. When the power consumption area is in a peak power consumption state, the power distribution processing module 3 controls the power conversion module 4 to feed the power of the energy storage module 5 back to the power grid to increase the amount of power available during the peak power consumption period, so as to minimize the power shortage during the peak power consumption period and improve the power demand and satisfaction of users. By synchronously performing intelligent control processing on multiple power consumption areas, the efficiency and effect of intelligent control can be improved.

[0089] Preferably, the power distribution processing module 3 includes a main controller and a communication module. The main controller is connected to other devices or apparatuses or modules through a wired communication line module or a wireless communication module to realize data interaction processing.

[0090] like Figure 2-3As shown, the acquisition module 1 includes a voltage transformer circuit 11 and a current transformer circuit 12, and the voltage transformer circuit 11 and the current transformer circuit 12 are respectively used to collect voltage data and current data of the power consumption area, and the output ends of the voltage transformer circuit 11 and the current transformer circuit 12 are connected to the acquisition switching circuit 22. It should be noted that the voltage transformer circuit 11 and the current transformer circuit 12 are both conventional circuits in the prior art, and their working principles and structures are not described one by one here.

[0091] The acquisition switching module 2 includes a plurality of acquisition switching modules 21, and the acquisition switching module 21 is used to switch and collect the current power consumption data of multiple different power consumption areas in the same area or the current power consumption data of multiple different power consumption areas in different areas. By switching and controlling the acquisition switching module 21, data collection can be performed on more power consumption areas to improve the efficiency of intelligent regulation. Among them, the acquisition switching module 21 includes a plurality of acquisition switching circuits 22 with the same structure, and the acquisition switching circuits 22 are set one by one with the acquisition module 1 to obtain the power consumption data of the power consumption area.

[0092] Each two groups of the acquisition switching modules 21 are the first acquisition switching module 211 and the second acquisition switching module 212 with opposite switching structures; any control end of the main controller U1 is connected to the first acquisition switching module 211 and the second acquisition switching module 212 respectively, and is used to switch and control the operation of the first acquisition switching module 211 or the second acquisition switching module 212. Through multiple pairs of the first acquisition switching module 211 and the second acquisition switching module 212, it is possible to realize synchronous switching and collect electricity consumption data of a large number of power consumption areas, thereby improving the efficiency of synchronous processing and intelligent control.

[0093] Specifically, the first acquisition switching circuit 221 in the first acquisition switching module 211 includes a first acquisition main switch circuit 2211 and a first acquisition sub-switch circuit 2212, and the second acquisition switching circuit 222 in the second acquisition switching module 212 includes a second acquisition main switch circuit 2221 and a second acquisition sub-switch circuit 2222; the first acquisition main switch circuit 2211 includes a first switch tube Q1 and a first resistor R1, the first end of the first switch tube Q1 is connected to the working power supply end VDD, the second end of the first switch tube Q1 is connected to the first acquisition sub-switch circuit 2212 and is grounded through the first resistor R1, and the control end of the first switch tube Q1 is connected to the main controller U1; the second acquisition main switch circuit Circuit 2221 includes a second switch tube Q7 and a second resistor R7, a first end of the second switch tube Q7 is connected to the working power supply terminal VDD through the second resistor R7 and is connected to the second acquisition sub-switch circuit 2222, a second end of the switch tube is grounded, and a control end of the second switch tube Q7 is connected to the main controller U1; the first acquisition sub-switch circuit 2212 and the second acquisition sub-switch circuit 2222 are respectively connected to the main controller U1 and the acquisition module 1, when the first switch tube Q1 is turned on, the first acquisition sub-switch circuit 2212 turns on the loop between the main controller U1 and the acquisition module 1, and when the second switch tube Q7 is turned on, the second acquisition sub-switch circuit 2222 turns on the loop between the main controller U1 and the acquisition module 1.

[0094] It should be noted that if Figure 3 As shown, the main controller includes a first pin group (including the first sub-pin A1 to the nth sub-pin An), a second pin group (including the first auxiliary pin S1 to the nth auxiliary pin Sn) and a third pin group (including the first slave pin C1 to the nth slave pin cn). When the first slave pin C1 of the third pin group of the main controller U1 outputs a high-level signal, the first switch tube Q1 is turned on to make the first acquisition auxiliary switch circuit 2212 conduct the loop between the main controller U1 and the acquisition module 1, the first auxiliary pin S1 in the second pin group of the main controller U1 receives the voltage data of the corresponding power application area, and the first sub-pin A1 in the first pin group receives the current data of the corresponding power application area. At this time, the level of the control end of the second switch tube Q7 is high, the second switch tube Q7 is not turned on, the second acquisition auxiliary switch circuit 2222 disconnects the loop between the main controller U1 and the acquisition module 1, and the main controller U1 temporarily does not obtain the power consumption data of the power consumption area for intelligent regulation.

[0095] Through the first slave pin C1 to the nth slave pin Cn in the third pin group of the main controller U1, the n first acquisition switching modules 211 and the n second acquisition switching modules 212 can be synchronously switched to control the acquisition work, which can reduce the demand on the control end, and can further collect the power consumption data of more power consumption areas, so as to synchronously perform intelligent regulation, improve the efficiency and effect of intelligent regulation, and thus meet the power regulation needs of multiple power consumption areas. By sharing the first auxiliary pin S1 to the nth auxiliary pin Sn in the second pin group and the first sub-pin A1 to the nth sub-pin An in the first pin group, the power consumption data inputted by the first acquisition switching module 211 and the second acquisition switching module 212 can be respectively received, so as to improve the pin multiplexing effect, simplify the line control structure, reduce the line cost, make the overall electrical cost low, and improve the regulation efficiency and regulation effect.

[0096] Preferably, the specific number of the acquisition switching circuits 22 in the acquisition switching module 21 can be set and adjusted according to actual conditions, such as according to the number of power consumption areas that need to be collected and processed.

[0097] Preferably, the first switch tube Q1 is preferably an NPN transistor, the second switch tube Q7 is preferably a PNP transistor, and the main controller U1 is preferably a PLC controller, but this is not limited to this.

[0098] Among them, the first acquisition sub-switch circuit 2212 includes a third switch tube Q2, a fourth switch tube Q3, a third resistor R2 and a fourth resistor R3; the control ends of the third switch tube Q2 and the fourth switch tube Q3 are both connected to the second end of the first switch tube Q1, the first end of the third switch tube Q2 is connected to the output end of the voltage transformer circuit 11, the second end of the third switch tube Q2 is connected to the main controller U1 and grounded through the third resistor R2; the first end of the fourth switch tube Q3 is connected to the voltage output end of the acquisition module 1, and the second end of the fourth switch tube Q3 is connected to the main controller U1 through the fourth resistor R3.

[0099] It should be noted that when the first switch tube Q1 is turned on, the levels of the control ends of the third switch tube Q2 and the fourth switch tube Q3 are both in a high level state, and the third switch tube Q2 and the fourth switch tube Q3 are turned on to work, so that the pin S1 of the main controller U1 is connected to the voltage output end of the voltage transformer in the acquisition module 1, and the first sub-pin A1 is connected to the current output end of the current transformer in the acquisition module 1, so that the main controller U1 can obtain the power consumption data of the power consumption area and calculate the relevant power consumption for intelligent control processing.

[0100] Preferably, the third switch tube Q2 and the fourth switch tube Q3 are both preferably N-channel junction field effect transistors, but this is not limited to this.

[0101] The second acquisition sub-switch circuit 2222 includes a fifth switch tube Q8, a sixth switch tube Q9, a fifth resistor R8 and a sixth resistor R9; the control ends of the fifth switch tube Q8 and the sixth switch tube Q9 are both connected to the first end of the second switch tube Q7, the first end of the fifth switch tube Q8 is connected to the output end of the voltage transformer circuit 11, the second end of the fifth switch tube Q8 is connected to the main controller U1 and grounded through the fifth resistor R8; the first end of the sixth switch tube Q9 is connected to the current output end of the acquisition module 1, and the second end of the sixth switch tube Q9 is connected to the main controller U1 through the sixth resistor R9.

[0102] It should be noted that when the second switch tube Q7 is turned on, the levels of the control ends of the fifth switch tube Q8 and the sixth switch tube Q9 are both in a high level state, and the fifth switch tube Q8 and the sixth switch tube Q9 are turned on to work, so that the pin S1 of the main controller U1 is connected to the voltage output end of the voltage transformer circuit 11 in the acquisition module 1, and the first sub-pin A1 is connected to the current output end of the current transformer circuit 12 in the acquisition module 1, so that the main controller U1 can obtain the power consumption data of the power consumption area and calculate the relevant power consumption for intelligent control processing.

[0103] Preferably, the fifth switch tube Q8 and the sixth switch tube Q9 are both preferably P-channel junction field effect transistors, but this is not limited to this.

[0104] like Figure 4 As shown, the DC end of the power conversion module 4 is connected to the energy storage module 5, and the AC end of the power conversion module 4 is connected to the power grid, which is also connected to the power consumption area. The power conversion module 4 is used to convert the AC power of the power grid into DC power to charge the energy storage module 5, and convert the DC power of the energy storage module 5 into AC power to power the power grid. Among them, the power conversion module 4 is preferably a PCS energy storage inverter, but it is not limited to this. The AC end of the power conversion module 4 can also be directly connected to the AC load for user use to meet power demand.

[0105] like Figure 5 As shown, the power conversion module 4 includes a DC circuit breaker 41, a grid-connected inverter 42, an AC filter module 43, a transformer 44, an AC contactor 45 and an AC circuit breaker 46 connected in sequence; the DC circuit breaker 41 is connected to the energy storage module 5, and the AC circuit breaker 46 is connected to the grid. Among them, the AC filter module 43 includes inductance, capacitance filtering and the like.

[0106] The conversion between AC and DC can be achieved by controlling the on and off of the DC circuit breaker 41 or the AC circuit breaker 46. When the power consumption area is in the low-peak power consumption state, the DC circuit breaker 41 can be controlled to be turned on, and the excess electric energy in the power grid can be stored in the energy storage module 5 for subsequent use, which greatly reduces the waste rate and amount of electric energy. When the power consumption area is in the peak power consumption state, the AC circuit breaker 46 can be controlled to work, and the excess electric energy in the energy storage module 5 can be fed back to the power grid for use in the power consumption area, thereby increasing the amount of electric energy available during the peak power consumption period, thereby maximizing the alleviation of the power shortage phenomenon during the peak power consumption period and improving the user's power demand and satisfaction.

[0107] To sum up, the present invention can switch and collect current electricity consumption data of multiple different electricity consumption areas in the same area or current electricity consumption data of multiple different electricity consumption areas in different areas to improve the regulation efficiency; according to the electricity consumption data of multiple electricity consumption areas, the power output and storage of different electricity consumption areas can be intelligently regulated at the same time, thereby improving the intelligent regulation efficiency, greatly reducing the waste rate and amount of electricity, alleviating the power supply shortage during peak hours to the greatest extent, and improving users' electricity demand and satisfaction.

[0108] Secondly, the line control structure can be simplified, the line cost can be reduced, and the demand for the multiplexed control terminal can be reduced by optimizing the multiplexing control terminal, so that the overall electrical cost is low and the control efficiency and control effect are improved.

[0109] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An intelligent power distribution management system based on artificial intelligence, characterized in that: It includes a collection module, a collection switching module, a power distribution processing module, a power conversion module and an energy storage module; The acquisition module is used to collect current electricity consumption data of different electricity consumption areas in different regions; The collection switching module is connected to the power distribution processing module and the collection module respectively, and is used to switch and collect the current power consumption data of multiple different power consumption areas in the same area or the current power consumption data of multiple different power consumption areas in different areas and send them to the power distribution processing module; The power distribution processing module is connected to the acquisition switching module and the power conversion module respectively, and is used to control the acquisition switching module to work, so as to send a control signal to the corresponding power conversion module according to the current power consumption data of the input power consumption area; The electric energy conversion module is connected to the energy storage module and is used to control the energy storage module to perform charging and discharging operations according to a control signal; Among them, a preset AI model is stored in the power distribution processing module, and the current power consumption data and historical power consumption data of the power consumption area are input into the AI ​​model. The AI ​​model outputs the future power demand and equipment hidden danger information corresponding to the power consumption area according to the current power consumption data and the historical power consumption data, and generates the control signal according to the future power demand, equipment hidden danger information and future weather information corresponding to each of the power consumption areas, including: obtaining the power consumption weight, equipment weight and weather weight of the power consumption area according to the regional location information of each power consumption area; calculating the power demand factor according to the power weight and future power demand; calculating the equipment hidden danger factor according to the equipment hidden danger information and the equipment weight; calculating the weather factor according to the future weather information and the weather weight; the future weather information includes at least the future wind speed, future temperature and future humidity, and the weather weight includes the wind speed weight, the temperature weight and the humidity weight; calculating the safety margin coefficient of the power consumption area according to the power demand factor, the equipment hidden danger factor and the weather factor; calculating the control signal of the power consumption area according to the safety margin coefficient and the historical power consumption data.

2. The system according to claim 1, characterized in that The acquisition switching module includes a plurality of acquisition switching modules, and the acquisition switching modules are used to switch and collect current electricity consumption data of a plurality of different electricity consumption areas in the same region or current electricity consumption data of a plurality of different electricity consumption areas in different regions; The acquisition switching module includes a plurality of acquisition switching circuits, and the acquisition switching circuits are arranged in a one-to-one correspondence with the acquisition modules.

3. The system according to claim 1, characterized in that The acquisition module includes a voltage transformer circuit and a current transformer circuit, which are respectively used to collect voltage data and current data of the power consumption area, and the output ends of the voltage transformer circuit and the current transformer circuit are both connected to the acquisition switching module.

4. The system according to claim 2, characterized in that Each two groups of acquisition switching modules are a first acquisition switching module and a second acquisition switching module with opposite switching structures; Any control end of the power distribution processing module is connected to the first acquisition switching module and the second acquisition switching module respectively, and is used to switch and control the operation of the first acquisition switching module or the second acquisition switching module.

5. The system according to claim 4, characterized in that The first acquisition switching circuit in the first acquisition switching module includes a first acquisition main switch circuit and a first acquisition auxiliary switch circuit, and the second acquisition switching circuit in the second acquisition switching module includes a second acquisition main switch circuit and a second acquisition auxiliary switch circuit; The first acquisition main switch circuit includes a first switch tube and a first resistor, the first end of the first switch tube is connected to the working power supply end, the second end of the first switch tube is connected to the first acquisition auxiliary switch circuit and grounded through the first resistor, and the control end of the first switch tube is connected to the power distribution processing module; The second acquisition main switch circuit includes a second switch tube and a second resistor, a first end of the second switch tube is connected to the working power supply end through the second resistor and is connected to the second acquisition auxiliary switch circuit, a second end of the second switch tube is grounded, and a control end of the second switch tube is connected to the power distribution processing module; The first acquisition sub-switch circuit and the second acquisition sub-switch circuit are respectively connected to the power distribution processing module and the acquisition module. When the first switch tube is turned on, the first acquisition sub-switch circuit turns on the loop between the power distribution processing module and the acquisition module. When the second switch tube is turned on, the second acquisition sub-switch circuit turns on the loop between the power distribution processing module and the acquisition module.

6. The system according to claim 5, characterized in that The first acquisition auxiliary switch circuit includes a third switch tube, a fourth switch tube, a third resistor and a fourth resistor; The control ends of the third switch tube and the fourth switch tube are both connected to the second end of the first switch tube, the first end of the third switch tube is connected to the output end of the voltage transformer circuit, and the second end of the third switch tube is connected to the power distribution processing module and grounded through a third resistor; The first end of the fourth switch tube is connected to the voltage output end of the acquisition module, and the second end of the fourth switch tube is connected to the power distribution processing module through a fourth resistor.

7. The system according to claim 5, characterized in that The second acquisition auxiliary switch circuit includes a fifth switch tube, a sixth switch tube, a fifth resistor and a sixth resistor; The control ends of the fifth switch tube and the sixth switch tube are both connected to the first end of the second switch tube, the first end of the fifth switch tube is connected to the output end of the voltage transformer circuit, and the second end of the fifth switch tube is connected to the power distribution processing module and grounded through a fifth resistor; The first end of the sixth switch tube is connected to the current output end of the acquisition module, and the second end of the sixth switch tube is connected to the power distribution processing module through a sixth resistor.

8. The system according to claim 1, characterized in that The DC end of the electric energy conversion module is connected to the energy storage module, and the AC end of the electric energy conversion module is connected to the power grid or the AC load, and is used to convert the AC power of the power grid into DC power to charge the energy storage module, and convert the DC power of the energy storage module into AC power to power the power grid or the AC load.

9. The system according to claim 1, characterized in that The power distribution processing module calculates a comprehensive factor based on power demand factors, equipment hidden danger factors and weather factors; Calculate the safety margin coefficient according to the equipment hidden danger factor and the comprehensive factor; The expression of the comprehensive factor includes: ; The expression of the safety margin factor includes: ; in, , , It is the electricity consumption area In time Corresponding electricity demand factors, equipment hidden danger factors and weather factors; It is the electricity consumption area In time The corresponding comprehensive factor; It is the electricity consumption area In time The corresponding safety margin factor; is the preset margin weight.

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