Main transformer intelligent partial discharge sensing system and method thereof

Abstract

The invention provides a main transformer intelligent partial discharge sensing system and method, and the system comprises a monitoring device used for monitoring an electric appliance unit, and a cloud platform used for receiving and storing data. The method comprises the steps that a data set carrying a timestamp is packaged and then uploaded to a cloud platform, the cloud platform receives the data set and stores the data set to a server, the timestamp is read after the data set is received, and a data value of the data and an operation state of a current corresponding electric appliance unit are displayed at a local platform end according to the sequence of the timestamp; three partial discharge detection technologies of ultrahigh frequency, high frequency and ultrasonic wave are integrated, the type, position and severity of partial discharge can be identified more accurately by comprehensively analyzing the signal characteristics of the three different frequency bands, and more comprehensive information is provided for fault location and diagnosis of the transformer.

Description

Technical Field

[0001] The present invention relates to the electrical appliance monitoring device industry, and therefore provides a main transformer intelligent partial discharge sensing system and method thereof. Background Art

[0002] Condition monitoring of high-voltage electrical equipment, especially transformer equipment, is a crucial technical means that can realize continuous and real-time monitoring of the insulation performance of running equipment. This method can deeply understand the potential hidden dangers inside the equipment and ensure that the operating status of the equipment is timely and accurately grasped without power outages. In this way, we can significantly reduce the economic losses caused by power outages and ensure that the maintenance of transformers can be carried out in a safe, orderly and well-planned environment.

[0003] In current technical practice, transformer partial discharge (PD) monitoring usually adopts a single PD detection method. However, the frequency bands covered by these detection methods are different and are often in the frequency band area that is susceptible to interference. Therefore, in practical applications, they are easily interfered by the noise of the corresponding frequency band, which in turn has an adverse effect on the accuracy and reliability of the evaluation results. Summary of the invention

[0004] The purpose of the invention is to at least solve the problem that the existing partial discharge monitoring in the prior art has only a single monitoring means. The present invention provides a main transformer intelligent partial discharge sensing system and method thereof.

[0005] The technical solutions provided are:

[0006] A main transformer intelligent partial discharge sensing system and method thereof, comprising:

[0007] A monitoring device for monitoring a transformer, the monitoring device comprising at least one frequency sensor for capturing electromagnetic wave signals inside an electrical unit and at least one current value sensor for monitoring the operating current inside the electrical unit, the monitoring device encapsulates a data set carrying a timestamp and uploads it through the current time data of the frequency sensor and the current value sensor;

[0008] A cloud platform for receiving and storing data, the cloud platform receives the data set and stores it in the server, reads the timestamp after receiving the data set, and displays the data value of the data and the current operating status of the corresponding electrical unit on the local platform in the order of the timestamps;

[0009] The frequency sensor monitors and captures electromagnetic wave information of multiple frequencies inside the electrical unit, and the current value sensor monitors and captures current information of multiple locations inside the electrical unit.

[0010] Furthermore, the frequency sensor is composed of an ultra-high frequency sensing unit, a high frequency local sensing unit and an ultrasonic sensing unit, and the monitoring device includes a ground current sensor for monitoring current, a load voltage current sensor, and a component current monitoring sensor.

[0011] Furthermore, the monitoring device also includes a vibration monitoring sensor for the internal vibration of the electrical unit and a temperature and humidity monitoring sensor or an infrared thermal imaging monitoring probe for the internal temperature and humidity of the electrical unit.

[0012] Furthermore, the data set includes numerical information of electromagnetic wave signals and numerical information of current signals.

[0013] Furthermore, the cloud platform also includes an early warning prediction module, which is used to select data within a set time for weighted averaging after multiple data sets are uploaded to the cloud platform to obtain a data reference mean. The subsequently uploaded electromagnetic wave signal numerical information or current signal numerical information is compared with the data reference mean. When the difference exceeds the set threshold, the early warning prediction module pushes the alarm information to the local platform.

[0014] Compared with the prior art, the beneficial effects are:

[0015] 1. Through the integrated monitoring design of various means, the simultaneous acquisition and independent processing of multi-channel signals are realized, ensuring that the signals of each channel do not interfere with each other and improving the accuracy and stability of data acquisition.

[0016] 2. It integrates three partial discharge detection technologies: ultra-high frequency, high frequency, and ultrasonic. By comprehensively analyzing the signal characteristics of these three different frequency bands, it can more accurately identify the type, location, and severity of partial discharge, providing more comprehensive information for transformer fault location and diagnosis.

[0017] 3. Combined with cloud computing technology, rapid processing and analysis of monitoring data is achieved, and the data center can provide instant feedback on test results to ensure the timeliness and accuracy of monitoring work.

[0018] 4. The built-in intelligent algorithm can automatically determine whether the transformer is in an abnormal state based on historical data and real-time monitoring results, and issue fault warnings in a timely manner, providing operation and maintenance personnel with valuable response time and reducing fault losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a communication diagram of the system modules of the present invention; DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0021] The following is based on the attached Figure 1 The present invention is further described:

[0022] Provided are a main transformer intelligent partial discharge sensing system and method thereof, a monitoring device for monitoring electrical equipment that is integrated with the monitored electrical equipment, a cloud platform for receiving and storing data, and a local terminal for viewing the current state of the monitored transformer.

[0023] The local end can be a mobile app, a web page or an online playback platform.

[0024] The monitoring device for monitoring the transformer is specifically, the monitoring device includes at least one frequency sensor for capturing electromagnetic wave signals inside the electrical unit and at least one current value sensor for monitoring the operating current inside the electrical unit, wherein the frequency sensor is composed of an ultra-high frequency sensor unit, a high frequency local sensor unit and an ultrasonic sensor unit, the monitoring device includes a ground current sensor for monitoring the current, a load voltage current sensor, and a component current monitoring sensor, the ground current sensor includes a ground detector for monitoring multiple components with grounding functions in the monitored electrical equipment, and the component current monitoring sensor can be a current value sensor with a current value monitoring function for monitoring the current values ​​of multiple independent power consumption or partial power consumption units in the monitored electrical equipment.

[0025] The operation process of the system is as follows: the monitoring device obtains the electromagnetic wave data of different frequencies and the flow value data of the internal device of the electrical unit at the current moment through the frequency sensor and the flow value sensor, and then uploads them through the remote transmission module after packaging. The packaging package carries a timestamp. The cloud platform receives the data set and stores it to the server. After receiving the data set, the timestamp is read, and the data value of the data and the current operation status of the corresponding electrical unit are displayed on the local platform in the order of the timestamps.

[0026] The frequency sensor monitors and captures electromagnetic wave information of multiple frequencies inside the electrical unit, and the current value sensor monitors and captures current information of multiple locations inside the electrical unit. The data set includes electromagnetic wave signal numerical information and current signal numerical information.

[0027] The monitoring device of the main transformer intelligent partial discharge sensing system also includes a vibration monitoring sensor for the vibration inside the electrical unit and a temperature and humidity monitoring sensor or an infrared thermal imaging monitoring probe for the temperature and humidity inside the electrical unit. The vibration monitoring sensor, the temperature and humidity monitoring sensor or the infrared thermal imaging monitoring probe is combined with a frequency sensor and a flow value sensor to collect multiple devices and multiple data and upload them to the cloud platform together. The cloud platform combines multiple data and presents them to the local end.

[0028] The cloud platform also includes an early warning prediction module, which is used to select data within a set time for weighted averaging after multiple data sets are uploaded to the cloud platform to obtain a data reference mean. The subsequently uploaded electromagnetic wave signal numerical information or current signal numerical information is compared with the data reference mean. When the difference exceeds the set threshold, the early warning prediction module pushes the alarm information to the local platform.

[0029] Embodiment 2 provides a main transformer intelligent partial discharge sensing method, which includes:

[0030] S1. Determine the internal space size and grid coding of the monitored electrical unit. In this step, the monitored electrical unit (such as transformers, switch cabinets, etc.) must first be fully measured in terms of physical dimensions, including but not limited to its length, width, height, and internal structure complexity assessment. The accuracy of the measurement data is critical for subsequent analysis because it directly affects the accuracy and coverage of the monitoring system. After the size measurement is completed, the next step is to encode the internal space of the monitored electrical unit according to the preset coordinate grid system. This process is similar to dividing a grid in two-dimensional or three-dimensional space, and each grid unit (or "point") is assigned a unique coordinate code. Such a grid map is not only convenient for data visualization and management, but more importantly, it allows the monitoring system to accurately locate any position inside the electrical unit, thereby achieving accurate monitoring of specific areas or components. The encoded internal grid map of the monitored electrical appliance provides an accurate geographic coordinate reference for the subsequent collection of electrical information and thermal signal information.

[0031] S2. Collect electrical information. Electrical information collection is one of the core functions of the monitoring system, which aims to capture abnormal electrical phenomena that may occur during the operation of electrical units. This includes but is not limited to ultra-high frequency (UHF) partial discharge signals, high frequency (HF) partial discharge signals, and ultrasonic (US) partial discharge signals. These signals are usually early signs of equipment insulation aging, defects or failures. By installing specialized sensors (such as UHF antennas, HF detectors, ultrasonic sensors, etc.), these signals can be captured in real time and converted into digital data that can be analyzed and processed. The collected electrical information is used to assess whether the equipment has potential partial discharge hazards.

[0032] S3. Collect thermal signal information. The collection of thermal signal information is mainly achieved through infrared cameras, which is a non-contact temperature measurement technology that can safely and efficiently capture the heat release inside the monitored electrical unit. The infrared camera captures the infrared radiation emitted by the surface of the object and converts it into a thermal image, showing the temperature distribution in different areas. When monitoring the electrical unit, the infrared camera will measure the temperature of each point according to the preset grid map and record the thermal radiation value at the current moment. This information is extremely important for identifying overheating areas, evaluating heat dissipation performance, and monitoring the thermal stress status of the equipment.

[0033] S4. Data transmission and abnormal alarms. The collected electrical information, thermal signal information and grid diagrams are packaged and timestamped to ensure the integrity and timeliness of the data. These data are continuously transmitted to the cloud platform via wired or wireless means, and the powerful processing capabilities of cloud computing are used for real-time analysis. The cloud platform has built-in intelligent algorithms that can quickly judge the electric field values ​​corresponding to the electrical information. Once the preset threshold is exceeded, it is considered an abnormal situation and the alarm information is immediately pushed to the local end (such as the monitoring room, the mobile device of the maintenance personnel) so that timely measures can be taken.

[0034] S5. Establish a reference for thermal radiation values. In order to more accurately evaluate the changes in the thermal state of the electrical unit, determine the preset comparison time period, and store the thermal radiation values ​​of each point in the grid map in time order as a reference for the thermal radiation values ​​within the time period. This step is to establish a reference benchmark for the thermal radiation values ​​so that the changes in the thermal radiation values ​​can be compared and analyzed later. The preset comparison time period can be adjusted according to actual conditions, such as setting it to one day, one week, or one month. And during this period, the thermal radiation values ​​of each point in the grid map are continuously monitored and recorded. These thermal radiation values ​​stored in time order constitute a reference database for thermal radiation values, which provides a benchmark for subsequent comparison and analysis. In this way, the thermal radiation values ​​collected later can be compared with the reference values ​​to determine whether there is a partial discharge phenomenon inside the equipment. The data accumulated over a long period of time forms a normal thermal behavior pattern of the equipment, which helps to identify abnormal thermal conditions.

[0035] S6. Comparison of thermal radiation values ​​and push notification of alarms. When the preset time period is exceeded, the system will compare the newly reported thermal radiation values ​​of each point with the thermal radiation value reference at the same time. This step is achieved by comparing the historical data of each point with the current data one by one, aiming to detect significant changes in thermal radiation values. When the thermal value at the current moment exceeds the thermal value at the same moment in the previous time period, it means that the point may be overheated due to partial discharge. If the comparison result shows that the information difference exceeds the set threshold, it indicates that there may be abnormal thermal phenomena, such as overheating, local cooling failure, etc. At this time, the system will push the alarm information to the local end again. This continuous monitoring and comparison mechanism helps to timely discover and deal with potential heat-related problems, prevent them from evolving into more serious faults, and thus ensure the safe and stable operation of the electrical unit.

[0036] In order to eliminate the influence of weather on calorific value,

[0037] The thermal signal information also includes obtaining a weighted average of multiple thermal radiation values ​​at the same time every day.

[0038] Evaluate and substitute:

[0039] z is the standardized value, x is the original value, μ is the mean value, p is the temperature difference given by weather influence, and σ is the standard deviation;

[0040] In the process of comparing the thermal radiation values ​​of each point reported again with the thermal radiation values ​​of each point corresponding to the thermal radiation value reference at the same time,

[0041] The re-reported thermal radiation values ​​of each point are compared with the weighted average.

[0042] To eliminate the impact of weather on electrical units.

[0043] In the process of comparing the re-reported thermal radiation values ​​of each point with the thermal radiation values ​​of each point corresponding to the thermal radiation value reference at the same time, the re-reported thermal radiation values ​​of each point are compared with the weighted average. This comparison can help determine whether the change in the thermal radiation value exceeds the set threshold, thereby determining whether there is a partial discharge phenomenon.

[0044] The specific steps of the comparison process are as follows:

[0045] Calculate the weighted mean of the thermal radiation values ​​at the same time of day.

[0046] Compare the re-reported thermal radiation values ​​of each point with the weighted mean.

[0047] If the difference in the comparison information exceeds the set threshold, an alarm message is pushed to the local end.

[0048] In this way, the main transformer intelligent partial discharge sensing method can realize intelligent monitoring of partial discharge inside electrical equipment, timely discover partial discharge phenomena inside the equipment, and provide guarantee for the safe operation of the power system.

[0049] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A main transformer intelligent partial discharge sensing system, characterized in that: include A monitoring device for monitoring an electrical unit, the monitoring device comprising at least one frequency sensor for capturing electromagnetic wave signals inside the electrical unit and at least one current value sensor for monitoring the operating current inside the electrical unit, the monitoring device encapsulates a data set carrying a timestamp and uploads it through the current time data of the frequency sensor and the current value sensor; A cloud platform for receiving and storing data, the cloud platform receives the data set and stores it in the server, reads the timestamp after receiving the data set, and displays the data value of the data and the current operating status of the corresponding electrical unit on the local platform in the order of the timestamps; The frequency sensor monitors and captures electromagnetic wave information of multiple frequencies inside the electrical unit, and the current value sensor monitors and captures current information of multiple locations inside the electrical unit.

2. According to claim 1, a main transformer intelligent partial discharge sensing system is characterized in that: The frequency sensor is composed of an ultra-high frequency sensing unit, a high frequency local sensing unit and an ultrasonic sensing unit, and the monitoring device includes a ground current sensor for monitoring current, a load voltage current sensor, and a component current monitoring sensor.

3. According to claim 1, a main transformer intelligent partial discharge sensing system is characterized in that: The monitoring device also includes a vibration monitoring sensor for the internal vibration of the electrical unit and a temperature and humidity monitoring sensor or an infrared thermal imaging monitoring probe for the internal temperature and humidity of the electrical unit.

4. The main transformer intelligent partial discharge sensing system according to claim 2 is characterized in that: The data set includes numerical information of electromagnetic wave signals and numerical information of current signals.

5. The main transformer intelligent partial discharge sensing system according to claim 4 is characterized in that: The cloud platform also includes an early warning prediction module, which is used to select data within a set time for weighted averaging after multiple data sets are uploaded to the cloud platform to obtain a data reference mean. The subsequently uploaded electromagnetic wave signal numerical information or current signal numerical information is compared with the data reference mean. When the difference exceeds the set threshold, the early warning prediction module pushes the alarm information to the local platform.

6. A method for sensing partial discharge of a main transformer, characterized in that: include S1. Determine the size of the internal space of the monitored electrical unit, and encode the internal space of the monitored electrical unit with a preset coordinate grid, and obtain an internal grid map of the monitored electrical appliance carrying the coordinate points after encoding; S2. Collecting electrical information, the electrical information includes at least one of a UHF partial discharge signal, a high frequency partial discharge signal, and an ultrasonic partial discharge signal; S3. Collect thermal signal information, shoot the heat release inside the monitored electrical unit through an infrared camera, and record the thermal radiation value of each point in the grid diagram inside the monitoring electrical unit at the current moment; S4. The electrical information, thermal signal information and grid map are packaged and continuously transmitted to the cloud platform with a timestamp. The cloud platform determines whether the electric field value corresponding to the electrical information exceeds the set threshold. If the set threshold is exceeded, an alarm message is pushed to the local end; S5. Determine a preset comparison time period, and store the thermal radiation values ​​of each point in the grid map in time order within the time period as a reference for the thermal radiation value; S6. After the time period has passed, the re-reported thermal radiation values ​​of each point are compared with the thermal radiation values ​​of each point corresponding to the thermal radiation values ​​at the same time. When the comparison information difference exceeds the set threshold, the alarm information is pushed to the local end.

7. A main transformer intelligent partial discharge sensing method according to claim 6, characterized in that: The thermal signal information also includes obtaining a weighted average of multiple thermal radiation values ​​at the same time every day. Evaluate and substitute: z is the standardized value, x is the original value, μ is the mean value, p is the temperature difference given by weather influence, and σ is the standard deviation; In the process of comparing the thermal radiation values ​​of each point reported again with the thermal radiation values ​​of each point corresponding to the thermal radiation value reference at the same time, The re-reported thermal radiation values ​​of each point are compared with the weighted mean.

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