Power supply control optimization method and system based on artificial intelligence

Abstract

The invention discloses a power supply control optimization method and system based on artificial intelligence. The system comprises a data acquisition module, a data processing module, an intelligent decision module, a control execution module and a feedback optimization module based on a system architecture. Compared with the prior art, the system has the advantages that the real-time data acquisition capability is high; data processing is efficient and accurate; intelligent decision making is accurate and efficient; and the feedback optimization mechanism is perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply control, and in particular to a power supply control optimization method and system based on artificial intelligence. Background Art

[0002] With the continuous growth of power demand and the complexity of power systems, traditional power control methods can no longer meet the requirements of modern power systems for efficient, stable and safe operation. In existing technologies, power control mostly relies on manual experience or simple control algorithms, which have problems such as slow response speed, low control accuracy and high energy consumption. Especially in large-scale, multi-variable power systems, traditional control methods are difficult to achieve global optimization and dynamic adjustment of power systems. Summary of the invention

[0003] The technical problems to be solved by the present invention are: 1) Insufficient real-time data collection: the existing system is often unable to collect various parameter data of the power supply system in real time and comprehensively, resulting in a lack of sufficient data support for control decisions;

[0004] 2) Limited data processing capabilities: Traditional data processing methods are difficult to process large-scale and complex data and cannot effectively extract useful features from the data;

[0005] 3) Low level of intelligent decision-making: lack of efficient intelligent decision-making modules, unable to make accurate control decisions based on real-time data;

[0006] 4) Imperfect feedback mechanism: The existing system lacks a perfect feedback mechanism and cannot adjust the control strategy in real time according to the system operating status.

[0007] To solve the above technical problems, the technical solution provided by the present invention is: a power control optimization system based on artificial intelligence, including a data acquisition module, a data processing module, an intelligent decision module, a control execution module, and a feedback optimization module based on the system architecture. The data acquisition module is the front end of the system, responsible for collecting real-time data from various parts of the power system, and is composed of a sensor network, a communication interface, and a data storage unit. The sensor is responsible for monitoring physical quantities such as voltage, current, and power factor. The communication interface realizes communication with various devices in the power system, and the data storage unit is used to temporarily store the collected data; the data processing module is the core processing unit of the system, responsible for preprocessing, cleaning, feature extraction and storage of the collected data, and is composed of a data preprocessing unit, a feature extraction unit, and a data storage unit; the intelligent decision module is the intelligent core of the system, responsible for prediction and optimization calculation based on the processed data, and is composed of a prediction model, an optimization algorithm, and a decision engine; the control execution module is the execution unit of the system, responsible for adjusting the control parameters of the power system according to the output results of the intelligent decision module, and is composed of a controller and an actuator; the feedback optimization module is the optimization feedback unit of the system, responsible for receiving real-time feedback data of the power system and continuously optimizing the intelligent decision module, and is composed of a feedback data receiving unit, a model optimization unit, and a data storage unit.

[0008] An artificial intelligence-based power control optimization method thereof includes the following steps:

[0009] Step 1: Collect various parameter data of the power supply system in real time through the data acquisition module;

[0010] Step 2: Use the data processing module to preprocess and extract features from the collected data;

[0011] Step 3: Input the processed data into the intelligent decision-making module for prediction and optimization calculation;

[0012] Step 4: According to the output result of the intelligent decision-making module, the control parameters of the power supply system are adjusted through the control execution module;

[0013] Step 5: Receive real-time feedback data from the power system and continuously optimize the intelligent model through the feedback optimization module.

[0014] The advantages of the present invention over the prior art are: 1) Strong real-time data acquisition capability: The present invention can collect various parameter data of the power supply system in real time and comprehensively, including voltage, current, power factor, etc., through the data acquisition module, to provide sufficient data support for intelligent decision-making;

[0015] 2) Efficient and accurate data processing: The data processing module uses advanced preprocessing, cleaning, and feature extraction technologies to efficiently process large-scale and complex data and extract feature information that is useful for control decisions;

[0016] 3) Intelligent decision-making is accurate and efficient: The intelligent decision-making module is based on prediction models and optimization algorithms, and can make accurate control decisions based on processed data to achieve global optimization and dynamic adjustment of the power system;

[0017] 4) Improved feedback optimization mechanism: The feedback optimization module can receive real-time feedback data from the power supply system, continuously optimize the intelligent decision-making module, and improve the overall performance and stability of the system.

[0018] Furthermore, the data acquisition module communicates with sensors and actuators in the power supply system through wired or wireless means to obtain real-time data; the data acquisition module transmits the collected data to the data processing module through the data interface for further processing.

[0019] Furthermore, the data processing module receives and pre-processes the raw data transmitted by the data acquisition module, and transmits the processed data and features to the intelligent decision-making module for prediction and optimization.

[0020] Furthermore, the intelligent decision-making module receives the data and features processed by the data processing module through the channel, and transmits the optimized power distribution plan to the control execution module for execution.

[0021] Furthermore, the control execution module receives the optimized power distribution plan, sends the control instructions to the actuators in the power system for execution, and receives feedback of the execution results.

[0022] Furthermore, the feedback optimization module receives real-time feedback data of the power supply system, and uses the feedback data to optimize the prediction model and optimization algorithm to improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the architecture of the patent of this invention.

[0024] Figure 2 It is a schematic diagram of the work flow of the patent of this invention.

[0025] As shown in the figure: 1. Data acquisition module, 2. Data processing module, 3. Intelligent decision-making module, 4. Control execution module, 5. Feedback optimization module, 6. Data acquisition module. DETAILED DESCRIPTION

[0026] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0027] Embodiment 1

[0028] like Figure 1 and Figure 2 As shown, this system mainly includes the following modules:

[0029] 1. Data acquisition module

[0030] The functions of the data acquisition module mainly include: 1 Real-time monitoring: uninterrupted monitoring of various parameters of the power supply system; 2 Data acquisition: digital processing of the data obtained by the sensor and transmission to subsequent modules; 3 Preliminary verification: preliminary verification of the collected data to ensure the accuracy and completeness of the data.

[0031] 2. Data Processing Module

[0032] The functions of the data processing module mainly include: 1) Data preprocessing: cleaning, denoising, filling missing values ​​and other processing of the original data to improve data quality; 2) Feature extraction: extracting features useful for prediction and optimization from the preprocessed data; 3) Data storage: storing the processed data and features in the database for use by subsequent modules.

[0033] 3. Intelligent Decision-making Module

[0034] The main functions of the intelligent decision-making module include: 1) Load forecasting: using machine learning or deep learning algorithms to predict future load conditions based on historical data and current status; 2) Power distribution optimization: formulating the optimal power distribution plan based on load forecast results and the actual capacity of the power system; 3) Fault warning and diagnosis: monitoring and analyzing abnormal conditions of the power system, and providing early warning and diagnosis of potential faults.

[0035] 4. Control Execution Module

[0036] The main functions of the control execution module include: 1) Control parameter adjustment: According to the output results of the intelligent decision-making module, adjust the control parameters of each device in the power supply system, such as voltage, current, etc.; 2) Execute control instructions: Convert the adjusted control parameters into specific control instructions and send them to the actuators in the power supply system for execution.

[0037] 5. Feedback Optimization Module

[0038] The main functions of the feedback optimization module include: 1) Feedback data reception: receiving real-time feedback data from the power supply system, such as actual load conditions, equipment operating status, etc.; 2) Model optimization: continuously optimizing the prediction model and optimization algorithm in the intelligent decision-making module based on feedback data to improve the accuracy of prediction and optimization; 3) Data storage: storing feedback data and optimized model parameters in the database for subsequent use.

[0039] Embodiment 2

[0040] Based on the first embodiment, Figure 1 and Figure 2 As shown in the figure, the overall working principle of the system: This system is based on artificial intelligence technology, and realizes intelligent control and management of the power supply system by integrating modules such as data acquisition, data processing, intelligent decision-making, control execution and feedback optimization. The system uses the sensor network to collect various parameter data of the power supply system in real time. After preprocessing and feature extraction by the data processing module, it is input into the intelligent decision-making module for prediction and optimization calculation. The intelligent decision-making module predicts future load conditions based on historical data and current status, and formulates the optimal power distribution plan. The control execution module adjusts the control parameters of each device in the power supply system according to the output results of the intelligent decision-making module to realize accurate distribution and dynamic adjustment of power supply. At the same time, the feedback optimization module receives real-time feedback data from the power supply system, continuously optimizes the intelligent decision-making module, and improves the overall performance and prediction accuracy of the system.

[0041] Embodiment 3

[0042] Based on the second embodiment, Figure 1 and Figure 2 As shown, the system workflow is:

[0043] 1) Data collection

[0044] The data acquisition module monitors various parameters of the power system in real time through the sensor network, such as voltage, current, power factor, etc. The collected data is transmitted to the data processing module through the communication interface for further processing.

[0045] 2) Data processing

[0046] The data processing module performs preprocessing operations such as cleaning, denoising, and filling missing values ​​on the collected raw data to improve data quality. Then, features useful for prediction and optimization are extracted from the preprocessed data. The processed data and features are stored in the database for use by subsequent modules.

[0047] 3) Intelligent Decision Making

[0048] The intelligent decision-making module uses machine learning or deep learning algorithms to predict loads based on historical data and current status. It formulates the optimal power distribution plan based on the prediction results and the actual capacity of the power system. At the same time, it monitors and analyzes abnormal conditions of the power system, and warns and diagnoses potential faults in advance.

[0049] 4) Control execution

[0050] The control execution module receives the power distribution plan output by the intelligent decision-making module. According to the plan, the control parameters of each device in the power system, such as voltage and current, are adjusted. The adjusted control parameters are converted into specific control instructions and sent to the actuators in the power system for execution.

[0051] 5) Feedback optimization

[0052] The feedback optimization module receives real-time feedback data from the power system, such as actual load conditions, equipment operating status, etc. The prediction model and optimization algorithm in the intelligent decision-making module are continuously optimized based on the feedback data to improve the accuracy of prediction and optimization. The optimized model parameters are stored in the database for subsequent use.

[0053] 6) Loop Iteration

[0054] The system continuously repeats the above workflow to form a closed-loop control. Through continuous data collection, processing, decision-making, execution and feedback optimization, dynamic adjustment and intelligent management of the power supply system can be achieved.

[0055] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A power supply control optimization system based on artificial intelligence, comprising a data acquisition module (1), a data processing module (2), an intelligent decision-making module (3), a control execution module (4), and a feedback optimization module (5) based on a system architecture, characterized in that: The data acquisition module (1) is the front end of the system, responsible for collecting real-time data from various parts of the power supply system, and is composed of a sensor network, a communication interface and a data storage unit. The sensor is responsible for monitoring physical quantities such as voltage, current, power factor, etc. The communication interface realizes communication with various devices in the power supply system, and the data storage unit is used to temporarily store the collected data; the data processing module (2) is the core processing unit of the system, responsible for preprocessing, cleaning, feature extraction and storage of the collected data, and is composed of a data preprocessing unit, a feature extraction unit and a data storage unit; the intelligent decision module (3) is the intelligent core of the system, responsible for prediction and optimization calculation based on the processed data, and is composed of a prediction model, an optimization algorithm and a decision engine; the control execution module (4) is the execution unit of the system, responsible for adjusting the control parameters of the power supply system according to the output results of the intelligent decision module, and is composed of a controller and an actuator; the feedback optimization module (5) is the optimization feedback unit of the system, responsible for receiving real-time feedback data of the power supply system and continuously optimizing the intelligent decision module, and is composed of a feedback data receiving unit, a model optimization unit and a data storage unit.

2. A power control optimization method based on artificial intelligence, characterized in that The following steps are involved: Step 1: Real-time acquisition of various parameter data of the power supply system through the data acquisition module (1); Step 2: Using the data processing module (2) to pre-process and extract features from the collected data; Step 3: Input the processed data into the intelligent decision-making module (3) for prediction and optimization calculation; Step 4: According to the output result of the intelligent decision-making module (3), the control parameters of the power supply system are adjusted through the control execution module (4); Step 5: Receive real-time feedback data from the power system and continuously optimize the intelligent model through the feedback optimization module (5).

3. The power control optimization system based on artificial intelligence according to claim 1, characterized in that: The data acquisition module (1) communicates with sensors and actuators in the power supply system via wired or wireless means to acquire real-time data; the data acquisition module (1) transmits the acquired data to the data processing module (2) via a data interface for further processing.

4. The power control optimization system based on artificial intelligence according to claim 1, characterized in that: The data processing module (2) receives and pre-processes the raw data transmitted by the data acquisition module (1), and transmits the processed data and features to the intelligent decision-making module (3) for prediction and optimization.

5. The power control optimization system based on artificial intelligence according to claim 1, characterized in that: The intelligent decision-making module (3) receives the data and features processed by the data processing module (2) through a channel, and transmits the optimized power distribution plan to the control execution module (4) for execution.

6. The power control optimization system based on artificial intelligence according to claim 1, characterized in that: The control execution module (4) receives the optimized power distribution plan, sends the control instruction to the actuator in the power system for execution, and receives feedback of the execution result.

7. The power control optimization system based on artificial intelligence according to claim 1, characterized in that: The feedback optimization module (5) receives real-time feedback data from the power supply system and uses the feedback data to optimize the prediction model and optimization algorithm to improve the overall performance of the system.

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