Reverse power monitoring system and method based on source network load storage

Abstract

The invention belongs to the technical field of reverse power monitoring, and particularly relates to a reverse power monitoring system and method based on source network load storage, and the system comprises a monitoring module which is used for monitoring a plurality of parameters of a power supply, a power grid, a load and energy storage; the edge calculation module is used for analyzing and filtering the monitoring data of the monitoring module in real time according to edge calculation so as to extract and transmit key data; the data acquisition and preprocessing module is used for preprocessing and fusing the data extracted by the edge calculation module; and the data analysis and processing module is used for deeply analyzing and processing the data processed by the data acquisition and preprocessing module. According to the invention, by constructing various models, prediction, early warning and fault diagnosis of reverse power can be realized, a scientific regulation and control strategy can be provided for optimized operation of the source network load storage system, the operation efficiency and stability of the system are significantly improved, and the occurrence frequency of a reverse power phenomenon is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of reverse power monitoring, and in particular to a reverse power monitoring system and method based on source-grid-load-storage. Background Art

[0002] With the continuous adjustment and optimization of the energy structure, the proportion of distributed energy in the power system is increasing. Under the coordinated operation of power generation, grid, load, and storage, the widespread integration of distributed power sources, such as photovoltaics and wind power, has brought cleaner and more flexible energy supply, but it has also posed new challenges to the operation and management of the power system. The frequency of reverse power phenomena has increased significantly. This is when the electricity generated by distributed power sources, after meeting local load demand, begins to feed back into the grid. This reverse power phenomenon can be caused by a variety of factors, such as the power generated by distributed power sources exceeding expectations or a sudden decrease in local load. If this phenomenon is not monitored and controlled in a timely and effective manner, it may negatively impact the safe and stable operation of the power grid, causing voltage fluctuations and increases, affecting the normal operation of relay protection devices, increasing harmonic pollution in the grid, and even threatening the lifespan and reliability of grid equipment. Furthermore, unreasonable reverse power can disrupt power market trading order and affect the efficient allocation and utilization of energy. Therefore, a reverse power monitoring system and method based on power generation, grid, load, and storage is invented. Summary of the Invention

[0003] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0004] A reverse power monitoring system based on source, grid, load and storage, comprising:

[0005] Monitoring module, used to monitor multiple parameters of power supply, grid, load and energy storage;

[0006] The edge computing module is used to perform real-time analysis and filtering of the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data;

[0007] Data acquisition and preprocessing module, used to preprocess and fuse the data extracted by the edge computing module;

[0008] The data analysis and processing module is used to conduct in-depth analysis and processing of the data processed by the data acquisition and preprocessing module, so as to predict and analyze the causes of reverse power and generate optimization strategies;

[0009] The result output and feedback module is used to output and feedback the data generated by the data analysis and processing module;

[0010] The performance monitoring module is used to monitor the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, alarms can be issued in time to ensure the stable and efficient operation of the system.

[0011] The data analysis and processing module includes:

[0012] The data exploratory analysis module is used to first calculate the basic statistical characteristics of the data in the collection and preprocessing modules, and then present the data intuitively;

[0013] Model building module, used to build prediction models and fault diagnosis models based on the data from the data exploratory analysis module;

[0014] Run the optimization module to generate optimization strategies based on the data of the prediction model and fault diagnosis model.

[0015] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the monitoring module includes:

[0016] A power supply monitoring module is used to monitor the electrical parameters and power quality indicators of the power supply output in real time. The electrical parameters include active power, reactive power, voltage, current, and frequency. The power quality indicators include harmonic content, voltage fluctuation, and flicker.

[0017] The power grid monitoring module is used to collect the operating parameters and fault information of the power grid in real time. The operating parameters include voltage, current, power flow, and power factor. The fault information includes short circuit faults and open circuit faults.

[0018] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the monitoring module further includes:

[0019] The load monitoring module is used to collect and monitor load data in real time, including real-time power, power consumption, and load characteristics;

[0020] The energy storage monitoring module is used to monitor the parameters of the energy storage system in real time, including the charge and discharge status, battery voltage, current, temperature, and remaining power.

[0021] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the data acquisition and preprocessing module includes:

[0022] Data acquisition module, used to collect data extracted by the edge computing module;

[0023] The data preprocessing module is used to preprocess the data collected by the data acquisition module, and the preprocessing includes cleaning, denoising, and format conversion.

[0024] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the data acquisition and preprocessing module further includes:

[0025] The data fusion module is used to integrate and fuse the data pre-processed by the data pre-processing module. Based on data association analysis and feature extraction technology, it can explore the inherent connections between different types of data, eliminate data redundancy and contradictions, and form a more comprehensive and accurate data set.

[0026] The data storage module is used to store the data integrated and fused by the data fusion module.

[0027] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the data exploratory analysis module includes:

[0028] Statistical feature calculation module, used to calculate the basic statistical features of data to understand the central tendency, dispersion degree and distribution range of the data;

[0029] The data visualization module is used to intuitively present data using visualization tools, which include line charts, bar charts, scatter plots, and heat maps.

[0030] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the model building module includes:

[0031] Prediction model construction is used to build a reverse power prediction model based on time series analysis and machine learning algorithms. This model can predict the probability, magnitude, and duration of reverse power in the future by learning from historical power data and related influencing factors.

[0032] The fault diagnosis model building module is used to build a fault diagnosis model based on decision tree, support vector machine and Bayesian network algorithm to analyze the cause of reverse power.

[0033] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the operation optimization module includes:

[0034] A multi-strategy generation module is used to formulate operation optimization strategies for various source-grid-load-storage systems based on data from prediction models and fault diagnosis models;

[0035] The simulation evaluation module is used to use the simulation module to simulate and evaluate different operation optimization strategies, and compare the effects of various strategies, including the reverse power reduction range, system operation cost, and power quality improvement index, to select the optimal strategy.

[0036] As a preferred solution of the reverse power monitoring system based on source, grid, load and storage described in the present invention, the result output and feedback module includes:

[0037] The report generation module is used to present the model prediction results and optimization strategies in the form of reports, including data statistics charts, model evaluation indicators, and strategy implementation suggestions, providing decision makers with intuitive and comprehensive information;

[0038] The database is used to store model prediction results and optimization strategies to accumulate experience and provide feedback for subsequent data analysis.

[0039] The specific steps are as follows:

[0040] Step 1: Monitor multiple parameters of power supply, grid, load and energy storage through the monitoring module;

[0041] Step 2: The edge computing module performs real-time analysis and filtering on the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data;

[0042] Step 3: The data extracted by the edge computing module is collected through the data acquisition module. After collection, the data collected by the data acquisition module will be preprocessed by the data preprocessing module. After preprocessing, the data preprocessed by the data preprocessing module will be integrated and fused by the data fusion module. Based on data association analysis and feature extraction technology, the inherent connection between different types of data is explored, data redundancy and contradiction are eliminated, and a more comprehensive and accurate data set is formed. Afterwards, the data integrated and fused data by the data fusion module will be stored through the data storage module;

[0043] Step 4: Calculate the basic statistical characteristics of the data through the statistical feature calculation module to understand the central trend, discrete degree and distribution range of the data. After calculation, the data will be visually presented through the data visualization module using visualization tools. After presentation, the reverse power prediction model will be constructed based on time series analysis and machine learning algorithms through the prediction model construction, so that the probability, size and duration of reverse power in the future can be predicted by learning historical power data and related influencing factors. After that, the fault diagnosis model construction module will be used to construct a fault diagnosis model based on the decision tree, support vector machine and Bayesian network algorithm to analyze the cause of reverse power. Then, the multi-strategy generation module will be used to formulate a variety of source-grid-load-storage system operation optimization strategies based on the data of the prediction model and the fault diagnosis model. After formulation, the simulation evaluation module will be used to simulate and evaluate different operation optimization strategies, and the effects of each strategy will be compared to select the optimal strategy.

[0044] Step 5: The model prediction results and optimization strategies are presented in the form of reports through the report generation module, providing intuitive and comprehensive information for decision makers. Afterwards, the model prediction results and optimization strategies are stored in the database to accumulate experience and provide feedback for subsequent data analysis.

[0045] Step 6: The performance monitoring module monitors the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, an alarm can be issued in time to ensure the stable and efficient operation of the system.

[0046] Compared with existing technologies:

[0047] By constructing multiple models, the present invention is able to predict, warn and diagnose reverse power, thereby providing a scientific control strategy for the optimized operation of the source-grid-load-storage system, significantly improving the operating efficiency and stability of the system, and reducing the frequency of reverse power phenomena. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0049] Figure 2 Schematic diagram of the data analysis and processing module flow of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0051] The present invention provides a reverse power monitoring system based on source, grid, load and storage. Figure 1-Figure 2 ;

[0052] It includes: a monitoring module for monitoring multiple parameters of power supply, power grid, load and energy storage; an edge computing module for real-time analysis and filtering of the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data; a data acquisition and preprocessing module for preprocessing and fusing the data extracted by the edge computing module; a data analysis and processing module for in-depth analysis and processing of the data processed by the data acquisition and preprocessing module, so as to predict and analyze the causes of reverse power and generate optimization strategies; a result output and feedback module for outputting and feeding back the data generated by the data analysis and processing module; a performance monitoring module for preprocessing and fusing the data extracted by the edge computing module; a data analysis and processing module for preprocessing and fusing the data processed by the data acquisition and preprocessing module; a data analysis and processing module for preprocessing the data processed by the data acquisition and preprocessing module; a data analysis and processing module for preprocessing the data processed by the data analysis and processing ... The block is used to monitor the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, an alarm can be issued in time to ensure the stable and efficient operation of the system; by setting up an edge computing module, the amount of data transmission can be greatly reduced, the communication pressure can be reduced, the real-time performance of data processing can be improved, and the system can respond to abnormal events such as reverse power more quickly; by setting up a data fusion module, not only can the data quality be improved, but also a better data foundation can be provided for subsequent data analysis and processing, making the analysis results more reliable and decision-making value; by setting up a performance monitoring module, the operating status of each module can be fully monitored to ensure the stable and efficient operation of the system.

[0053] The monitoring module includes: a power supply monitoring module, which is used to monitor the electrical parameters and power quality indicators of the power supply output in real time. The electrical parameters include active power, reactive power, voltage, current, and frequency. The power quality indicators include harmonic content, voltage fluctuation and flicker; a power grid monitoring module, which is used to collect the operating parameters and fault information of the power grid in real time. The operating parameters include voltage, current, power flow, and power factor. The fault information includes short circuit faults and open circuit faults; a load monitoring module, which is used to collect and monitor the load data in real time. The data includes real-time power, power consumption, and load characteristics; and an energy storage monitoring module, which is used to monitor the parameters of the energy storage system in real time. The parameters include charge and discharge status, battery voltage, current, temperature, and remaining power.

[0054] The data acquisition and preprocessing module includes: a data acquisition module, which is used to collect data extracted by the edge computing module; a data preprocessing module, which is used to preprocess the data collected by the data acquisition module, and the preprocessing includes cleaning, denoising, and format conversion; a data fusion module, which is used to integrate and fuse the data preprocessed by the data preprocessing module, so as to explore the intrinsic connections between different types of data based on data association analysis and feature extraction technology, eliminate data redundancy and contradictions, and form a more comprehensive and accurate data set; a data storage module, which is used to store the data integrated and fused by the data fusion module.

[0055] The data analysis and processing module includes: a data exploratory analysis module, which is used to first calculate the basic statistical characteristics of the data in the acquisition and preprocessing module and then intuitively present the data; a model construction module, which is used to construct a prediction model and a fault diagnosis model based on the data of the data exploratory analysis module; and an operation optimization module, which is used to generate an optimization strategy based on the data of the prediction model and the fault diagnosis model.

[0056] The data exploratory analysis module includes: a statistical feature calculation module, which is used to calculate the basic statistical features of the data to understand the central tendency, dispersion degree and distribution range of the data; a data visualization module, which is used to intuitively present the data using visualization tools, and the visualization tools include line charts, bar charts, scatter plots, and heat maps.

[0057] The model construction module includes: a prediction model construction module, which is used to construct a reverse power prediction model based on time series analysis and machine learning algorithms, so that the probability, size and duration of reverse power in the future can be predicted by learning historical power data and related influencing factors; a fault diagnosis model construction module, which is used to construct a fault diagnosis model based on decision trees, support vector machines, and Bayesian network algorithms, so that the causes of reverse power can be analyzed.

[0058] The operation optimization module includes: a multi-strategy generation module for formulating operation optimization strategies for multiple source-grid-load-storage systems based on data from the prediction model and the fault diagnosis model; a simulation evaluation module for using the simulation module to simulate and evaluate different operation optimization strategies, and compare the effects of each strategy, including reverse power reduction, system operation cost, and power quality improvement indicators, to select the optimal strategy;

[0059] The result output and feedback module includes: a report generation module, which is used to present the model prediction results and optimization strategies in the form of reports, including data statistical charts, model evaluation indicators, and strategy implementation suggestions, providing decision makers with intuitive and comprehensive information; a database, which is used to store the model prediction results and optimization strategies to accumulate experience for subsequent data analysis and achieve feedback.

[0060] A reverse power monitoring method based on source-grid-load-storage has the following specific steps:

[0061] Step 1: Monitor multiple parameters of power supply, grid, load and energy storage through the monitoring module;

[0062] Step 2: The edge computing module performs real-time analysis and filtering on the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data;

[0063] Step 3: The data extracted by the edge computing module is collected through the data acquisition module. After collection, the data collected by the data acquisition module will be preprocessed by the data preprocessing module. After preprocessing, the data preprocessed by the data preprocessing module will be integrated and fused by the data fusion module. Based on data association analysis and feature extraction technology, the inherent connection between different types of data is explored, data redundancy and contradiction are eliminated, and a more comprehensive and accurate data set is formed. Afterwards, the data integrated and fused data by the data fusion module will be stored through the data storage module;

[0064] Step 4: Calculate the basic statistical characteristics of the data through the statistical feature calculation module to understand the central trend, discrete degree and distribution range of the data. After calculation, the data will be visually presented through the data visualization module using visualization tools. After presentation, the reverse power prediction model will be constructed based on time series analysis and machine learning algorithms through the prediction model construction, so that the probability, size and duration of reverse power in the future can be predicted by learning historical power data and related influencing factors. After that, the fault diagnosis model construction module will be used to construct a fault diagnosis model based on the decision tree, support vector machine and Bayesian network algorithm to analyze the cause of reverse power. Then, the multi-strategy generation module will be used to formulate a variety of source-grid-load-storage system operation optimization strategies based on the data of the prediction model and the fault diagnosis model. After formulation, the simulation evaluation module will be used to simulate and evaluate different operation optimization strategies, and the effects of each strategy will be compared to select the optimal strategy.

[0065] Step 5: The model prediction results and optimization strategies are presented in the form of reports through the report generation module, providing intuitive and comprehensive information for decision makers. Afterwards, the model prediction results and optimization strategies are stored in the database to accumulate experience and provide feedback for subsequent data analysis.

[0066] Step 6: The performance monitoring module monitors the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, an alarm can be issued in time to ensure the stable and efficient operation of the system.

[0067] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A reverse power monitoring system based on source, grid, load and storage, characterized in that: include: Monitoring module, used to monitor multiple parameters of power supply, grid, load and energy storage; The edge computing module is used to perform real-time analysis and filtering of the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data; Data acquisition and preprocessing module, used to preprocess and fuse the data extracted by the edge computing module; The data analysis and processing module is used to conduct in-depth analysis and processing of the data processed by the data acquisition and preprocessing module, so as to predict and analyze the causes of reverse power and generate optimization strategies; The result output and feedback module is used to output and feedback the data generated by the data analysis and processing module; The performance monitoring module is used to monitor the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, alarms can be issued in time to ensure the stable and efficient operation of the system. The data analysis and processing module includes: The data exploratory analysis module is used to first calculate the basic statistical characteristics of the data in the collection and preprocessing modules, and then present the data intuitively; Model building module, used to build prediction models and fault diagnosis models based on the data from the data exploratory analysis module; Run the optimization module to generate optimization strategies based on the data of the prediction model and the fault diagnosis model; The model building module includes: Prediction model construction is used to build a reverse power prediction model based on time series analysis and machine learning algorithms. This model can predict the probability, magnitude, and duration of reverse power in the future by learning from historical power data and related influencing factors. A fault diagnosis model building module is used to build a fault diagnosis model based on decision tree, support vector machine, and Bayesian network algorithms to analyze the causes of reverse power generation; The operation optimization module includes: A multi-strategy generation module is used to formulate operation optimization strategies for various source-grid-load-storage systems based on data from prediction models and fault diagnosis models; The simulation evaluation module is used to simulate and evaluate different operation optimization strategies using the simulation module, and compare the effects of each strategy, including the reverse power reduction range, system operation cost, and power quality improvement index, to select the optimal strategy; The result output and feedback module includes: The report generation module is used to present the model prediction results and optimization strategies in the form of reports, including data statistics charts, model evaluation indicators, and strategy implementation suggestions, providing decision makers with intuitive and comprehensive information; The database is used to store model prediction results and optimization strategies to accumulate experience and provide feedback for subsequent data analysis.

2. A reverse power monitoring system based on source-grid-load-storage according to claim 1, characterized in that: The monitoring module includes: A power supply monitoring module is used to monitor the electrical parameters and power quality indicators of the power supply output in real time. The electrical parameters include active power, reactive power, voltage, current, and frequency. The power quality indicators include harmonic content, voltage fluctuation, and flicker. The power grid monitoring module is used to collect the operating parameters and fault information of the power grid in real time. The operating parameters include voltage, current, power flow, and power factor. The fault information includes short circuit faults and open circuit faults.

3. The reverse power monitoring system based on source-grid-load-storage according to claim 2, characterized in that: The monitoring module also includes: The load monitoring module is used to collect and monitor load data in real time, including real-time power, power consumption, and load characteristics; The energy storage monitoring module is used to monitor the parameters of the energy storage system in real time, including the charge and discharge status, battery voltage, current, temperature, and remaining power.

4. The reverse power monitoring system based on source-grid-load-storage according to claim 1, characterized in that: The data acquisition and preprocessing module includes: Data acquisition module, used to collect data extracted by the edge computing module; The data preprocessing module is used to preprocess the data collected by the data acquisition module, and the preprocessing includes cleaning, denoising, and format conversion.

5. The reverse power monitoring system based on source-grid-load-storage according to claim 4, characterized in that: The data acquisition and preprocessing module also includes: The data fusion module is used to integrate and fuse the data pre-processed by the data pre-processing module. Based on data association analysis and feature extraction technology, it can explore the inherent connections between different types of data, eliminate data redundancy and contradictions, and form a more comprehensive and accurate data set. The data storage module is used to store the data integrated and fused by the data fusion module.

6. The reverse power monitoring system based on source-grid-load-storage according to claim 1, characterized in that: The data exploratory analysis module includes: Statistical feature calculation module, used to calculate the basic statistical features of data to understand the central tendency, dispersion degree and distribution range of the data; The data visualization module is used to intuitively present data using visualization tools, which include line charts, bar charts, scatter plots, and heat maps.

7. A reverse power monitoring method based on source-grid-load-storage, characterized in that: The specific steps are as follows: Step 1: Monitor multiple parameters of power supply, grid, load and energy storage through the monitoring module; Step 2: The edge computing module performs real-time analysis and filtering on the monitoring data of the monitoring module based on edge computing, so as to extract and transmit key data; Step 3: The data extracted by the edge computing module is collected through the data acquisition module. After collection, the data collected by the data acquisition module will be preprocessed by the data preprocessing module. After preprocessing, the data preprocessed by the data preprocessing module will be integrated and fused by the data fusion module. Based on data association analysis and feature extraction technology, the inherent connection between different types of data is explored, data redundancy and contradiction are eliminated, and a more comprehensive and accurate data set is formed. Afterwards, the data integrated and fused data by the data fusion module will be stored through the data storage module; Step 4: Calculate the basic statistical characteristics of the data through the statistical feature calculation module to understand the central trend, discrete degree and distribution range of the data. After calculation, the data will be visually presented through the data visualization module using visualization tools. After presentation, the reverse power prediction model will be constructed based on time series analysis and machine learning algorithms through the prediction model construction, so that the probability, size and duration of reverse power in the future can be predicted by learning historical power data and related influencing factors. After that, the fault diagnosis model construction module will be used to construct a fault diagnosis model based on the decision tree, support vector machine and Bayesian network algorithm to analyze the cause of reverse power. Then, the multi-strategy generation module will be used to formulate a variety of source-grid-load-storage system operation optimization strategies based on the data of the prediction model and the fault diagnosis model. After formulation, the simulation evaluation module will be used to simulate and evaluate different operation optimization strategies, and the effects of each strategy will be compared to select the optimal strategy. Step 5: The model prediction results and optimization strategies are presented in the form of reports through the report generation module, providing intuitive and comprehensive information for decision makers. Afterwards, the model prediction results and optimization strategies are stored in the database to accumulate experience and provide feedback for subsequent data analysis. Step 6: The performance monitoring module monitors the operating performance of each module in real time and can set performance indicator thresholds. When the module performance indicators exceed the thresholds, an alarm can be issued in time to ensure the stable and efficient operation of the system.

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