Transformer loss monitoring system based on load characteristic identification terminal
The transformer loss monitoring system based on load feature identification terminals solves the problem that traditional detection methods cannot achieve online monitoring, realizes real-time and accurate monitoring of transformer losses, and improves the operating efficiency of the power grid and the level of equipment management.
Patent Information
- Application Number
- CN202510908159.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-02
- Publication Date
- 2025-11-14
AI Technical Summary
Traditional offline detection methods cannot achieve online monitoring of transformers, and there is a large deviation between calculated values and actual values, making it difficult to meet the needs of accurate monitoring and real-time evaluation of the actual operating energy efficiency of transformers.
A transformer loss monitoring system based on load feature identification terminals is adopted, including an RPA self-connecting robot, a data acquisition module, a loss calculation module, a data center, and an IoT module. By monitoring and calculating the high-voltage and low-voltage side parameters of the transformer in real time, accurate online monitoring of transformer losses is achieved.
It enables real-time online monitoring of transformer losses, improves the accuracy and timeliness of monitoring, supports lean management of transformers, optimizes asset allocation, extends equipment life, ensures safe operation, and serves the efficient, reliable and economical operation of the power grid.
Smart Images
Figure CN120948911A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of transformer monitoring technology, specifically relating to a transformer loss monitoring system based on a load feature identification terminal. Background Technology
[0002] With global focus on climate change, energy conservation and emission reduction have become crucial tasks for various industries. Transformer losses account for approximately 40%-50% of power transmission and distribution losses, indicating significant potential for energy conservation. As the power grid evolves towards intelligent systems, it requires more precise data support. Transformers, as fundamental equipment in power transmission and distribution, play a vital role in optimizing grid scheduling and diagnosing faults. Over long-term operation, transformers experience increased losses due to loose connections, corrosion, and component aging, leading to increased temperature, decreased efficiency, and shortened lifespan. Loss monitoring can promptly detect potential faults and signs of aging, enabling the development of appropriate maintenance plans. Reducing transformer losses directly reduces energy waste and saves electricity costs. Accurate loss monitoring data also helps businesses optimize power usage, improve energy efficiency, and enhance economic benefits. Traditional offline monitoring methods cannot achieve online monitoring and suffer from significant discrepancies between calculated and actual values, as well as the inability to distinguish between no-load and load losses, making it difficult to meet the needs for accurate monitoring and real-time evaluation of transformer operating efficiency. Summary of the Invention
[0003] This application provides a transformer loss monitoring system based on a load feature identification terminal to solve or partially solve the problems mentioned in the background art.
[0004] This application provides a transformer loss monitoring system based on a load feature identification terminal, wherein the load feature identification terminal includes an RPA self-connecting robot, a data acquisition module, a loss calculation module, a data center, and an IoT module;
[0005] The RPA self-connecting robot monitors the network port and, when a local area network environment is established, obtains parameter configuration information based on the SSH remote protocol.
[0006] The data acquisition module obtains parameters from the low-voltage and high-voltage sides of the transformer based on parameter configuration information and stores them in the data center.
[0007] The loss calculation module collects data from the data center based on a preset strategy and calculates the transformer loss.
[0008] Data centers are used for data storage;
[0009] The IoT module remotely uploads transformer loss information to the cloud main station.
[0010] Preferably, the specific method for the RPA self-connecting robot to obtain parameter configuration information is as follows:
[0011] S1: Real-time monitoring of the network port; when a local area network environment is established, the SSH remote protocol is initiated to connect to the configuration device.
[0012] S2: Obtain the self-feature information code M and parameter configuration version number N, and generate the retrieval information "M" + "N+1";
[0013] S3: Based on the retrieval information retrieval configuration device, obtain the configuration file whose filename includes the retrieval information;
[0014] S4: After deleting the content related to the search information from the file name of the configuration file, store the configuration file in the preset location.
[0015] Preferably, in the load feature recognition terminal, the self-feature information code M of the configuration file can be a combination of its file name and text content at a specific position in the configuration file content, and the parameter configuration version number is the text content at a specific position in the configuration file. In the configuration device, the retrieval information is combined with the original file name of the configuration file.
[0016] Preferably, the parameter configuration information includes:
[0017] Parameter types include: current and voltage on the high-voltage side and low-voltage side of the transformer, power factor angle on the low-voltage side, and percentage of no-load current;
[0018] The data collection frequency is once every 15 minutes.
[0019] Preferably, the acquisition parameters include acquisition time, and the load terminal synchronizes its time with the acquisition target device before each acquisition of parameters;
[0020] The target acquisition device is a high-configuration intelligent fuse.
[0021] Preferably, the high-performance intelligent fuse has a built-in current sensor and a voltage sensor, and the detection accuracy of the current sensor and the voltage sensor is 0.5 class.
[0022] Preferably, the specific method by which the loss calculation module collects data from the data center based on a preset strategy to calculate transformer losses is as follows:
[0023] S10: The sampling module collects parameters from the high-voltage and low-voltage sides of the transformer every 15 minutes and stores them in the data center;
[0024] S20: Every hour, the loss calculation module retrieves four sets of data from the data center for the previous hour.
[0025] S30: The loss calculation module calculates the transformer loss value every 15 minutes, using the following formula:
[0026]
[0027] P 损 =P A +P B +P C
[0028] ΔA=∑P 损 ΔT
[0029] Among them, P 单相 Q 单相 These represent the single-phase active power loss and single-phase reactive power loss of the transformer, S. N P is the rated capacity of the transformer. 损 For the total active power loss of the transformer, P A P B P C Let U be the active power loss of phases A, B, and C, U1 and U2 be the high-voltage and low-voltage side voltages respectively, and I1 and I2 be the high-voltage and low-voltage side currents and the low-voltage side power factor angle respectively. k % and I0% are the percentage of transformer short-circuit impedance voltage and no-load current, respectively. ΔA is the transformer loss value every 15 minutes, and ΔT represents 15 minutes.
[0030] Preferably, the parameter configuration information includes a communication method and communication parameters, wherein the communication method is RS485.
[0031] Compared with the prior art, the beneficial effects of this application are as follows:
[0032] This application enables real-time, online, and rapid monitoring of transformer losses, a core element in achieving lean and intelligent transformer management. Its core value lies in accurately understanding equipment energy efficiency status, driving cost savings, ensuring safe operation (fault prevention), optimizing asset allocation (extending lifespan), and ultimately serving the efficient, reliable, and economical operation of the power grid. Attached Figure Description
[0033] The present application will be further described below with reference to the accompanying drawings and embodiments.
[0034] Figure 1 This is a schematic diagram of the system composition of this application.
[0035] Figure 2 This is a schematic diagram of the method flow of this application. Detailed Implementation
[0036] The specification and claims use certain terms to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The term "comprising" throughout the specification and claims is an open-ended term and should be interpreted as "comprising but not limited to." "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error.
[0037] In the description of this application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.
[0038] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0039] Example 1
[0040] like Figures 1 to 2 As shown, this application provides a transformer loss monitoring system based on a load feature identification terminal, wherein the load feature identification terminal includes an RPA self-connecting robot, a data acquisition module, a loss calculation module, a data center, and an IoT module;
[0041] The RPA self-connecting robot monitors the network port and, when a local area network environment is established, obtains parameter configuration information based on the SSH remote protocol.
[0042] The data acquisition module obtains parameters from the low-voltage and high-voltage sides of the transformer based on parameter configuration information and stores them in the data center.
[0043] The loss calculation module collects data from the data center based on a preset strategy and calculates the transformer loss.
[0044] Data centers are used for data storage;
[0045] The IoT module remotely uploads transformer loss information to the cloud main station.
[0046] Specifically, the IoT module can be a 4G, 5G communication module or a LoRa communication module.
[0047] When the load feature identification terminal of this application is connected to the target acquisition device, the communication parameters of the load feature identification terminal need to be configured. The traditional configuration method is to use a configuration device (computer) to form a local area network with the load feature identification terminal, and then modify or replace the configuration file through SSH software, which requires a long configuration time.
[0048] For the reasons mentioned above, this application deploys an RPA robot in the load feature recognition terminal to complete the modification or replacement of configuration files. RPA (Robotic Process Automation) is a software program that automates repetitive and rule-based tasks by simulating human-computer interaction.
[0049] Specifically, the method for the RPA self-connecting robot to obtain parameter configuration information is as follows:
[0050] S1: Real-time monitoring of the network port; when a local area network environment is established, the SSH remote protocol is initiated to connect to the configuration device.
[0051] S2: Obtain the self-feature information code M and parameter configuration version number N, and generate the retrieval information "M" + "N+1";
[0052] S3: Based on the retrieval information retrieval configuration device, obtain the configuration file whose filename includes the retrieval information;
[0053] S4: After deleting the content related to the search information from the file name of the configuration file, store the configuration file in the preset location.
[0054] The RPA self-connecting robot integrates an SSH communication module to monitor the network port of the load feature identification terminal. When the configuration device connects to the load feature identification terminal to achieve a local area network environment, it retrieves the configuration file within the configuration device based on the retrieval information to transfer and replace the configuration file. The self-feature information code M is used to identify the load feature identification terminal, and the parameter configuration version number is used to distinguish the version number of the configuration file. Within the load feature identification terminal, the self-feature information code M of the configuration file can be a combination of its file name and text content at a specific location within the configuration file. The parameter configuration version number is the text content at a specific location within the configuration file. In the configuration device, the retrieval information is combined with the original file name of the configuration file.
[0055] Specifically, the parameter configuration information includes:
[0056] The parameter types include the current and voltage on the high-voltage side and low-voltage side of the transformer, the power factor angle on the low-voltage side, and the percentage of no-load current. The sampling frequency is once every 15 minutes.
[0057] Specifically, the acquisition parameters include the acquisition time. Before each acquisition, the load terminal synchronizes its time with the target acquisition device to ensure the timeliness of the acquired parameters.
[0058] Specifically, the target acquisition device is a high-performance intelligent fuse, which is installed on the incoming and outgoing lines of the transformer. It has built-in current and voltage sensors, and the detection accuracy of the current and voltage sensors is 0.5 class.
[0059] Specifically, the loss calculation module collects data from the data center based on a preset strategy and calculates the transformer loss using the following method:
[0060] S10: The sampling module collects parameters from the high-voltage and low-voltage sides of the transformer every 15 minutes and stores them in the data center;
[0061] S20: Every hour, the loss calculation module retrieves four sets of data from the data center for the previous hour.
[0062] S30: The loss calculation module calculates the transformer loss value every 15 minutes, using the following formula:
[0063]
[0064] P 损 =P A +P B +P C
[0065] ΔA=∑P 损 ΔT
[0066] Among them, P 单相 Q 单相 These represent the single-phase active power loss and single-phase reactive power loss of the transformer, S. N P is the rated capacity of the transformer. 损 For the total active power loss of the transformer, P A P B P C Let U be the active power loss of phases A, B, and C, U1 and U2 be the high-voltage and low-voltage side voltages respectively, and I1 and I2 be the high-voltage and low-voltage side currents and the low-voltage side power factor angle respectively. k % and I0% represent the percentage of short-circuit impedance voltage and no-load current of the transformer, respectively. ΔA is the transformer loss value every 15 minutes, ΔT represents 15 minutes, and the transformer's rated capacity S is... N Transformer short-circuit impedance voltage percentage Uk % is a preset value.
[0067] Specifically, the parameter configuration information includes communication method and communication parameters, wherein the communication method is RS485.
[0068] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.
Claims
1. A transformer loss monitoring system based on a load characteristic identification terminal, characterized in that, The load feature identification terminal includes an RPA self-connecting robot, a cross-collection module, a loss calculation module, a data center, and an IoT module; The RPA self-connecting robot monitors the network port and, when a local area network environment is established, obtains parameter configuration information based on the SSH remote protocol. The data acquisition module obtains parameters from the low-voltage and high-voltage sides of the transformer based on parameter configuration information and stores them in the data center. The loss calculation module collects data from the data center based on a preset strategy and calculates the transformer loss. Data centers are used for data storage; The IoT module remotely uploads transformer loss information to the cloud main station.
2. The transformer loss monitoring system based on a load feature identification terminal according to claim 1, characterized in that: The specific method for the RPA self-connecting robot to obtain parameter configuration information is as follows: S1: Real-time monitoring of the network port; when a local area network environment is established, the SSH remote protocol is initiated to connect to the configuration device. S2: Obtain the self-feature information code M and parameter configuration version number N, and generate the retrieval information "M" + "N+1"; S3: Based on the retrieval information retrieval configuration device, obtain the configuration file whose filename includes the retrieval information; S4: After deleting the content related to the search information from the file name of the configuration file, store the configuration file in the preset location.
3. The transformer loss monitoring system based on a load feature identification terminal according to claim 2, characterized in that: Within the load feature recognition terminal, the configuration file's own feature information code M can be a combination of its filename and text content at a specific location within the configuration file. The parameter configuration version number is the text content at a specific location within the configuration file. In the configuration device, the retrieval information is combined with the original filename of the configuration file.
4. A transformer loss monitoring system based on a load feature identification terminal according to claim 2, characterized in that: The parameter configuration information includes: Parameter types include: current and voltage on the high-voltage side and low-voltage side of the transformer, power factor angle on the low-voltage side, and percentage of no-load current; The data collection frequency is once every 15 minutes.
5. A transformer loss monitoring system based on a load feature identification terminal according to claim 4, characterized in that: The parameters to be collected include the collection time. Before each collection of parameters, the load terminal synchronizes its time with the target device. The target acquisition device is a high-configuration intelligent fuse.
6. A transformer loss monitoring system based on a load feature identification terminal according to claim 5, characterized in that: The high-end intelligent fuse has built-in current and voltage sensors, and the detection accuracy of the current and voltage sensors is 0.5 class.
7. A transformer loss monitoring system based on a load feature identification terminal according to claim 4, characterized in that: The specific method by which the loss calculation module collects data from the data center based on a preset strategy and calculates transformer losses is as follows: S10: The sampling module collects parameters from the high-voltage and low-voltage sides of the transformer every 15 minutes and stores them in the data center; S20: Every hour, the loss calculation module retrieves four sets of data from the data center for the previous hour. S30: The loss calculation module calculates the transformer loss value every 15 minutes, using the following formula: P 损 =P A +P B +P C ΔA=∑P 损 ΔT Among them, P 单相 Q 单相 These represent the single-phase active power loss and single-phase reactive power loss of the transformer, S. N P is the rated capacity of the transformer. 损 For the total active power loss of the transformer, P A P B P C Let U be the active power loss of phases A, B, and C, U1 and U2 be the high-voltage and low-voltage side voltages respectively, and I1 and I2 be the high-voltage and low-voltage side currents and the low-voltage side power factor angle respectively. k % and I0% are the percentage of transformer short-circuit impedance voltage and no-load current, respectively. ΔA is the transformer loss value every 15 minutes, and ΔT represents 15 minutes.
8. A transformer loss monitoring system based on a load feature identification terminal according to claim 4, characterized in that: The parameter configuration information includes the communication method and communication parameters, wherein the communication method is RS485.