System and method for monitoring heavy load and overload of low-voltage branch load

Abstract

The invention relates to the technical field of low-voltage branch load monitoring, in particular to a low-voltage branch load overload monitoring system and method, and the system comprises an acquisition module which is used for collecting low-voltage branch load data in real time; the transmission module is used for transmitting the collected load data to a cloud server; the monitoring module is used for checking the load data on the cloud platform in real time and carrying out analysis and early warning on the load data; and the adjusting module is used for adjusting the three-phase load through an intelligent algorithm and keeping balance. The system has the beneficial effects that the functions of data acquisition, transmission and load regulation are integrated, so that the system is convenient to install and maintain. In addition, a mature Internet of Things communication technology is used, data can be transmitted to a cloud end in real time for monitoring, and therefore the response speed and reliability of the system are both improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage branch load monitoring, and in particular to a low-voltage branch load heavy overload monitoring system and method. Background Art

[0002] At present, there is a lack of effective monitoring methods for the low-voltage branch loads of distribution network transformers. When the low-voltage branch loads are overloaded, the low-voltage branch circuit breakers of the distribution transformers are often tripped frequently due to the failure to monitor and handle them in time, which in turn leads to serious user complaints. Usually in actual work, the monitoring of low-voltage branch loads can only rely on the staff to measure the loads on site with the help of clamp meters, but there are the following difficulties in on-site measurement:

[0003] 1. The peak load period of the line is often during non-working hours, and the staff often need to work overtime to complete the work;

[0004] 2. Manual measurement can only obtain load data at a certain point in time, and the data error is large, which is insufficient to provide effective support for load adjustment work. Summary of the invention

[0005] In view of the above problems or problems existing in the prior art, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides the following technical solutions: a low-voltage branch load heavy overload monitoring system, comprising a collection module for collecting low-voltage branch load data in real time;

[0008] A transmission module, used to transmit the collected load data to a cloud server;

[0009] Monitoring module, used to view load data in real time on the cloud platform, and to analyze and warn of load data;

[0010] The regulation module is used to adjust the three-phase load through intelligent algorithms to maintain balance.

[0011] As a preferred solution of the low-voltage branch load overload monitoring system of the present invention, it also includes a remote control module for the administrator to adjust the load through the cloud platform.

[0012] As a preferred solution of the low-voltage branch load heavy overload monitoring system of the present invention, the transmission module transmits the collected load data to the cloud server through the Internet of Things communication unit.

[0013] As a preferred solution of the low-voltage branch load overload monitoring system of the present invention, the cloud platform is used to standardize the processing of various load data and establish a prediction model using a multivariate linear regression analysis algorithm.

[0014] As a preferred solution of the low-voltage branch load overload monitoring system of the present invention, the system also includes an early warning module for issuing an early warning notification through a cloud platform when a load overload risk is predicted.

[0015] As a preferred solution of the low-voltage branch load overload monitoring system of the present invention, wherein: the administrator performs load adjustment operations through the cloud platform including manual load adjustment or automatic load adjustment.

[0016] As a preferred solution of the low-voltage branch load overload monitoring system of the present invention, manual load adjustment includes allowing the administrator to perform manual adjustment according to different load requirements.

[0017] As a preferred solution of the low-voltage branch load heavy overload monitoring system of the present invention, the load data includes but is not limited to the ABCN four-wire current value and the three-phase imbalance rate.

[0018] As a preferred solution of the low-voltage branch load heavy overload monitoring system of the present invention, the monitoring module uses an integrated learning CNN-GRU power load prediction model to analyze and predict load data.

[0019] In a second aspect, the present invention provides the following technical solutions: a method for monitoring a heavy overload of a low-voltage branch load, comprising collecting low-voltage branch load data in real time through a collection module;

[0020] The collected load data is transmitted to the cloud server in real time through the transmission module;

[0021] Use the monitoring module to view load data in real time on the cloud monitoring platform, conduct data analysis and early warning;

[0022] The regulation module uses intelligent algorithms to automatically adjust the three-phase load to maintain balance;

[0023] Manual or automatic load adjustment via remote control module.

[0024] Beneficial effects of the present invention: The present invention integrates the functions of data collection, transmission and load regulation, making it easy to install and maintain. It also uses mature Internet of Things communication technology, which can transmit data to the cloud in real time for monitoring, thus improving the response speed and reliability of the system. Even smarter, it can automatically adjust the load through remote-controlled phase-changing switches and intelligent algorithms, thus reducing the need for manual intervention. Moreover, it can also provide load warnings and adjustment suggestions in a timely manner based on real-time monitoring and data analysis on the cloud platform, making the entire system run more efficiently and safely. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0026] Figure 1 The figure is a flowchart of a method for monitoring a heavy overload of a low voltage branch load. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0029] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0030] Example 1

[0031] This embodiment provides a low-voltage branch load heavy overload monitoring system, which includes.

[0032] Collection module, used to collect low-voltage branch load data in real time;

[0033] Furthermore, the load data includes but is not limited to the current values ​​of the four lines ABCN and the three-phase imbalance rate.

[0034] A transmission module, used to transmit the collected load data to a cloud server;

[0035] Furthermore, the transmission module transmits the collected load data to the cloud server through the Internet of Things communication unit, wherein the Internet of Things communication unit is such as NB-IoT or LoRa.

[0036] Monitoring module, used to view load data in real time on the cloud platform, and to analyze and warn of load data;

[0037] Furthermore, the monitoring module uses the CNN-GRU power load forecasting model with integrated learning to analyze and predict the load data.

[0038] It should be noted that the CNN-GRU power load forecasting model with integrated learning uses the kernel principal component analysis method to process multi-dimensional input data, reduce data nonlinearity and effectively extract the main influencing factors as subsequent inputs, construct a CNN and GRU combined model for training and prediction, and improve the prediction performance of the prediction model.

[0039] Furthermore, the cloud platform is used to standardize the processing of various load data and establish a prediction model using a multivariate linear regression analysis algorithm.

[0040] It should be noted that the multiple linear regression analysis algorithm assumes that the predicted object Y is affected by multiple independent variables X1, X2...Xp, and assumes that the relationship between each independent variable and Y is linearly related, then the theoretical multiple regression model is established as follows:

[0041] Y=β0+β1X1+β2X2+…+β p X p +ε

[0042] Where: β0 is a constant term; β1, β2…β p is the partial regression coefficient; ε is the random error.

[0043] After regression analysis and modeling prediction, the predicted load curve is compared with the expected power consumption index. When different expected power consumption indicators fluctuate above and below the predicted amount, load adjustment is performed.

[0044] The regulation module is used to adjust the three-phase load through intelligent algorithms to maintain balance.

[0045] It should be noted that the intelligent algorithm can automatically adjust the control strategy according to the dynamic changes of the system. After the control terminal self-checks normally and finds that there is a three-phase imbalance in the power grid, the control terminal reads the load power consumption data sent by each phase-changing switch, and uses the load prediction results of the above-mentioned CNN-GRU power load prediction model. After considering multiple factors such as three-phase current balance and phase-changing economic cost, the best phase-changing execution plan is obtained through the existing multi-objective optimization algorithm to achieve the optimal load balance.

[0046] Furthermore, it also includes a remote control module for administrators to adjust the load through the cloud platform.

[0047] Furthermore, the administrator performs load adjustment operations including manual load adjustment or automatic load adjustment through the cloud platform.

[0048] Furthermore, manual load adjustment includes allowing an administrator to make manual adjustments based on different load requirements.

[0049] Furthermore, the system also includes an early warning module for issuing early warning notifications through the cloud platform when a load overload risk is predicted.

[0050] In summary, the beneficial effect of the low-voltage branch load overload monitoring system of the present invention is that it is easy to install and maintain by integrating the functions of data collection, transmission and load regulation. It also uses mature Internet of Things communication technology, which can transmit data to the cloud in real time for monitoring, so that the response speed and reliability of the system are improved. What's more intelligent is that it can automatically adjust the load through remote-controlled phase-changing switches and intelligent algorithms, thus reducing the need for manual intervention. Moreover, it can also provide load warnings and adjustment suggestions in a timely manner based on real-time monitoring and data analysis of the cloud platform, making the entire system run more efficiently and safely.

[0051] Example 2

[0052] Reference Figure 1 , which is a second embodiment of the present invention, provides a low-voltage branch load overload monitoring method, which includes:

[0053] S1. Collect low-voltage branch load data in real time through the acquisition module;

[0054] S2, transmit the collected load data to the cloud server in real time through the transmission module;

[0055] S3, use the monitoring module to view load data in real time on the cloud monitoring platform, conduct data analysis and early warning;

[0056] S4, the three-phase load is automatically adjusted by the regulation module using intelligent algorithms to maintain balance;

[0057] S5. Manual or automatic load adjustment is performed through the remote control module.

[0058] In summary, the beneficial effect of the low-voltage branch load overload monitoring system of the present invention is to achieve efficient management and control of the low-voltage branch load through a series of intelligent steps. First, it can collect load data in real time to ensure the timeliness and accuracy of the data. Then, the data is quickly transmitted to the cloud through the Internet of Things technology, which improves the response speed of the system. Then, the cloud monitoring platform can view and analyze this data in real time, discover problems in time and issue warnings, and enhance the prediction and prevention capabilities of the system.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A low voltage branch load overload monitoring system, characterized in that: include, Collection module, used to collect low-voltage branch load data in real time; A transmission module, used for transmitting the collected load data to a cloud server; A monitoring module, used to view the load data in real time on the cloud platform, and to analyze and issue early warnings for the load data; The regulation module is used to adjust the three-phase load through intelligent algorithms to maintain balance.

2. The low voltage branch load overload monitoring system according to claim 1, characterized in that: It also includes a remote control module for administrators to adjust the load through the cloud platform.

3. The low voltage branch load overload monitoring system according to claim 2, characterized in that: The transmission module transmits the collected load data to the cloud server through the Internet of Things communication unit.

4. The low voltage branch load overload monitoring system according to claim 3, characterized in that: The cloud platform is used to standardize the processing of various load data and establish a prediction model using a multivariate linear regression analysis algorithm.

5. The low voltage branch load overload monitoring system according to claim 4, characterized in that: The system also includes an early warning module for issuing an early warning notification through a cloud platform when a load overload risk is predicted.

6. The low voltage branch load overload monitoring system according to claim 5, characterized in that: The load adjustment operation performed by the administrator through the cloud platform includes manual load adjustment or automatic load adjustment.

7. The low voltage branch load overload monitoring system according to claim 6, characterized in that: The manual load adjustment includes allowing an administrator to perform manual adjustment according to different load requirements.

8. The low voltage branch load overload monitoring system according to claim 7, characterized in that: The load data include but are not limited to the ABCN four-wire current values ​​and the three-phase unbalance rate.

9. The low voltage branch load overload monitoring system according to claim 8, characterized in that: The monitoring module uses an integrated learning CNN-GRU power load prediction model to analyze and predict the load data.

10. The low voltage branch load overload monitoring method according to claim 9, characterized in that: Collect low-voltage branch load data in real time through the acquisition module; The collected load data is transmitted to the cloud server in real time through the transmission module; The load data can be viewed in real time on the cloud monitoring platform through the monitoring module to perform data analysis and early warning; The regulation module uses intelligent algorithms to automatically adjust the three-phase load to maintain balance; Manual or automatic load adjustment via remote control module.

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