A capacitor health monitoring system

Abstract

The application discloses a capacitor operation condition monitoring system, comprising: a data acquisition module for acquiring current value, voltage value and coil temperature in the capacitor operation process through a signal sensor, the signal sensor comprising a current sensor, a voltage sensor and a temperature sensor; a data transmission module for transmitting the current value, the voltage value and the coil temperature to a monitoring analysis module of a background according to a preset radiation degree algorithm; and a monitoring analysis module for analyzing and processing the current value, the voltage value and the coil temperature according to a preset SVM multi-classification model to obtain a capacitor operation state, wherein the preset SVM multi-classification model is obtained through parameter optimization processing by improving a CSA algorithm, and the capacitor operation state comprises normal operation, overvoltage, overcurrent and high-temperature operation. The application can solve the technical problem that the prior art has the defect of poor monitoring efficiency, so that the actual monitoring requirement cannot be met.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of capacitors, in particular to a capacitor operation condition monitoring system. BACKGROUND

[0002] In the power system, maintaining the reactive power balance under the normal operation of the power grid is the basic condition to guarantee the power supply quality. Using power capacitors for reactive power compensation can improve the power factor, reduce the active power loss of the power grid, and at the same time, can improve the capacity utilization rate of the transformer and the power capacitor, and stabilize the operating voltage. Therefore, the safe operation of the capacitor plays a very important role in guaranteeing the power supply quality and efficiency of the power system. However, various faults may occur during the operation of the existing capacitor, such as overvoltage, overcurrent or temperature overload, etc. Overvoltage and overcurrent can cause the capacitor to heat up seriously and accelerate the aging of the capacitor insulating medium, reduce the insulation strength, and thus cause breakdown discharge. When the power capacitor has breakdown discharge inside, the insulating oil inside the capacitor will decompose to produce a large amount of gas, causing the internal pressure of the capacitor box to increase, the plastic deformation of the box wall, and then the external drum, and the "bulging" phenomenon occurs. Therefore, it is necessary to monitor the operation process of the capacitor in real time.

[0003] The existing capacitor operation process monitoring technology lacks certain automatic and intelligent processing mechanism, and the actual application process has limitations in real time and efficiency, which cannot meet the existing capacitor operation monitoring requirements. SUMMARY

[0004] The present application provides a capacitor operation condition monitoring system to solve the technical problem that the prior art has poor monitoring efficiency, which cannot meet the actual monitoring requirements.

[0005] Therefore, the first aspect of the present application provides a capacitor operation condition monitoring system, comprising:

[0006] a data acquisition module, a data transmission module and a monitoring analysis module;

[0007] The data acquisition module is configured to acquire the current value, the voltage value and the coil temperature during the operation of the capacitor through a signal sensor, wherein the signal sensor comprises a current sensor, a voltage sensor and a temperature sensor.

[0008] The data transmission module is configured to send the current value, the voltage value and the coil temperature to the monitoring analysis module of the background according to a preset radiation degree algorithm.

[0009] The monitoring analysis module is configured to analyze and process the current value, the voltage value and the coil temperature according to a preset SVM multi-classification model to obtain a capacitor operation state, wherein the preset SVM multi-classification model is obtained by performing parameter optimization processing on a CSA algorithm, and the capacitor operation state includes normal operation, overvoltage, overcurrent and high-temperature operation.

[0010] Preferably, the data acquisition module comprises a signal sensor, an A / D conversion module and a power supply module.

[0011] The signal sensor is configured to acquire the current value, the voltage value and the coil temperature during capacitor operation.

[0012] The A / D conversion module is configured to perform digital-to-analog conversion on the acquired data.

[0013] The power supply module is configured to provide working power for the sub-modules in the data acquisition module.

[0014] Preferably, the data transmission module is specifically configured to:

[0015] Calculate the data transmission rate of each node according to a preset radiation algorithm and a preset transmission task;

[0016] Transmit the current value, the voltage value and the coil temperature to the monitoring analysis module of the background based on the data transmission rate.

[0017] Preferably, the monitoring analysis module is specifically configured to:

[0018] Perform parameter optimization training on an initial SVM multi-classification model based on an optimal penalty factor and a kernel function parameter in an improved CSA algorithm to obtain a preset SVM multi-classification model;

[0019] Analyze and process the current value, the voltage value and the coil temperature according to the preset SVM multi-classification model to obtain a capacitor operation state, wherein the capacitor operation state includes normal operation, overvoltage, overcurrent and high-temperature operation.

[0020] Preferably, the monitoring analysis module further comprises a storage module.

[0021] The storage module is configured to store the current value, the voltage value and the coil temperature, and store the capacitor operation state.

[0022] Preferably, the monitoring analysis module further comprises a main control board.

[0023] The main control board comprises a positioning module, a communication module and a power supply module.

[0024] The master control board is used for providing positioning information for the system, configuring a communication protocol of the system, and providing working power for the system.

[0025] Preferably, the master control board further comprises a display module.

[0026] The display module is used for displaying the current value, the voltage value and the coil temperature in the capacitor running process, and displaying the network state of the signal sensor in the data acquisition module.

[0027] Preferably, the communication module of the master control board is an ESP8266 module or an ESP32 module.

[0028] From the above technical solutions, the embodiments of the present application have the following advantages:

[0029] In the present application, a capacitor running condition monitoring system is provided, comprising a data acquisition module, a data transmission module and a monitoring analysis module; the data acquisition module is used for collecting current value, voltage value and coil temperature in the capacitor running process through a signal sensor, the signal sensor comprising a current sensor, a voltage sensor and a temperature sensor; the data transmission module is used for sending the current value, the voltage value and the coil temperature to the monitoring analysis module of the background according to a preset radiation degree algorithm; the monitoring analysis module is used for analyzing and processing the current value, the voltage value and the coil temperature according to a preset SVM multi-classification model to obtain the capacitor running state, the preset SVM multi-classification model being obtained by parameter optimization processing through an improved CSA algorithm, and the capacitor running state comprising normal running, overvoltage, overcurrent and high-temperature running.

[0030] The capacitor running condition monitoring system provided by the present application can quickly and stably transmit the obtained various capacitor running data to the monitoring analysis module of the background for analysis by using the preset radiation degree algorithm, so that efficient real-time monitoring can be realized; and the data analysis can be performed by using the preset SVM multi-classification model optimized based on the CSA algorithm, so that the accuracy of data analysis can be ensured and the reliability of analysis results can be improved. Therefore, the present application can solve the technical problem that the prior art has the defect of poor monitoring efficiency, so that the actual monitoring requirements cannot be met. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A structural schematic diagram of a capacitor running condition monitoring system provided by an embodiment of the present application;

[0032] Figure 2 A structural relationship schematic diagram of a master control board provided by an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] For the convenience of understanding, please refer to Figure 1 The embodiment of the capacitor operating condition monitoring system provided by the present application comprises a data acquisition module 101, a data transmission module 102 and a monitoring analysis module 103.

[0035] The data acquisition module 101 is used to acquire the current value, voltage value and coil temperature in the capacitor operating process through a signal sensor, which comprises a current sensor, a voltage sensor and a temperature sensor.

[0036] Further, the data acquisition module 101 comprises a signal sensor, an A / D conversion module and a power supply module.

[0037] The signal sensor is used to acquire the current value, voltage value and coil temperature in the capacitor operating process.

[0038] The A / D conversion module is used to convert the acquired data into digital signals.

[0039] The power supply module is used to provide working power for the sub-modules in the data acquisition module.

[0040] The data analysis through the acquisition of the current value, voltage value and coil temperature in the capacitor operating process can accurately control the operating state of the capacitor and avoid faults or disturbances. The operating data of the capacitor can be acquired through the signal sensor. In addition to various signal sensors, a mutual inductor can also be used, and a Hall sensor can also be selected in the sensor. The temperature sensor is not limited to a platinum resistance temperature sensor, a thermocouple temperature sensor and a thermistor temperature sensor, etc., and can be selected according to the actual situation, which is not limited here.

[0041] The A / D conversion module in the data acquisition module is mainly used to convert the data format of the acquired various data, so as to facilitate subsequent unified processing and analysis. The power supply module is mainly used to provide necessary power for the devices or sub-modules in the module. The specific structure composition can be increased or reduced according to the actual data acquisition requirements, which can improve the data acquisition efficiency and guarantee the data accuracy.

[0042] The data transmission module 102 is used to send the current value, voltage value and coil temperature to the monitoring analysis module in the background according to a preset radiation degree algorithm.

[0043] Further, the data transmission module 102 is specifically configured to:

[0044] calculate the data transmission rate of each node according to the preset radiosity algorithm and a preset transmission task;

[0045] send the current value, the voltage value and the coil temperature to the monitoring and analyzing module in the background based on the data transmission rate.

[0046] The preset radiosity algorithm is a global illumination algorithm, which is based on the theory of thermal radiation, because radiosity depends on the transfer of light energy between two surfaces; after a scene is decomposed into multiple regions, the amount of light energy transfer can be calculated by using the reflectivity of the known reflective surface and the form factor of the two patches. The form factor is a dimensionless quantity that is calculated based on the geometric orientation of the two patches, and can be regarded as the proportion of all possible emission regions of the first patch that are covered by the second patch; more precisely, radiosity is the energy per unit time leaving a curved patch, which is a combination of emitted and reflected energy.

[0047] In the embodiment, the radiosity algorithm is used for data transmission, which can optimize the allocation of transmission tasks of multiple transmission nodes and improve the overall transmission speed. When a given task A i When data transmission is performed at a certain substation node, the data transmission rate R ij can be expressed by the following formula:

[0048]

[0049] where B is the bandwidth, H ij is the channel gain when node i communicates with node j, d ij is the transmission distance between node i and node j, the farther the distance, the weaker the channel gain; is the power when node i sends task data, δ is the background noise power, e ij is the connection state of node i and node j, e ij = 1 when node i and node j are connected, and e ij = 0 when node i and node j are not connected. D i represents the data size of task A i , B i represents the result data size generated after the calculation of task A i , and the data transmission time is:

[0050]

[0051] The energy consumed by the transmission is represented as:

[0052]

[0053] where k is the effective capacitance coefficient, a constant, r ij The required computing resource allocation, C i The required computing resource allocation, C i The required computing resource allocation, C i The required CPU cycle number.

[0054] The monitoring and analysis module 103 is configured to analyze and process the current value, the voltage value and the coil temperature according to the preset SVM multi-classification model to obtain the capacitor operating state, wherein the preset SVM multi-classification model is obtained by performing parameter optimization processing on an initial SVM multi-classification model through the improved CSA algorithm, and the capacitor operating state includes normal operation, overvoltage, overcurrent and high-temperature operation.

[0055] Further, the monitoring and analysis module 103 is specifically configured to:

[0056] perform parameter optimization training on the initial SVM multi-classification model based on the optimal penalty factor and the kernel function parameter in the improved CSA algorithm to obtain the preset SVM multi-classification model;

[0057] analyze and process the current value, the voltage value and the coil temperature according to the preset SVM multi-classification model to obtain the capacitor operating state, wherein the capacitor operating state includes normal operation, overvoltage, overcurrent and high-temperature operation.

[0058] The optimal penalty factor c and the kernel function parameter g are defined, and based on the two parameters, the initial SVM multi-classification model can be subjected to parameter optimization training to obtain the optimized preset SVM multi-classification model. The solving process of the optimal penalty factor c and the kernel function parameter g is as follows:

[0059]

[0060] wherein, is the position of the i th bird nest in the t th generation, is the position of the i th bird nest in the t+1 th generation, is the adaptive step size, x b-up is the optimal solution guided Gaussian mutation mechanism, L(λ) is a random search path, and obeys a levy probability distribution. The adaptive step size is expressed as:

[0061]

[0062]

[0063] wherein, are the maximum step size and the minimum step size, respectively, d id is a ratio of a distance between the optimal position and the current nest position and a maximum distance, x max x is a maximum distance between the optimal position and the remaining nest position b The current optimal position is denoted as xopt.

[0064] The optimal solution guided Gaussian variation mechanism is denoted as x b-up The expression is as follows:

[0065]

[0066] Wherein, N(0, 1) is a random function obeying a standard Gaussian distribution.

[0067] The levy probability distribution is expressed as:

[0068] Levy ~ u = t -λ 1 < λ ≤ 3

[0069] Wherein, t is a current iteration number, and λ is a power number.

[0070] The accuracy of the preset SVM multi-classification model is expressed as:

[0071]

[0072] Wherein, ACC is a model accuracy, TP is a number of samples correctly judged by the model as normal capacitors, TN is a number of samples correctly judged by the model as fault capacitors, FN is a number of samples incorrectly judged by the model as normal capacitors, and FP is a number of samples incorrectly judged by the model as fault capacitors.

[0073] In addition to the normal state, the operating state of the voltage device includes a fault state such as overvoltage, overcurrent and high-temperature operation. The operating state can be accurately determined through the model and the obtained power data, and the fault condition can be monitored in time to make a response.

[0074] Further, the device further comprises a storage module 104.

[0075] The storage module is used for storing current values, voltage values and coil temperatures, and storing capacitor operating states. It can be understood that, in addition to storing important capacitor operating data and model recognition results, the storage module can also store data such as the preset SVM multi-classification model optimized by training.

[0076] Further, the device further comprises a main control board 105.

[0077] The main control board comprises a positioning module 1051, a communication module 1052 and a power supply module 1053.

[0078] The main control board is used for providing positioning information for the system, configuring the system communication protocol, and providing working power supply for the system. The main control board is a service panel for the system. In addition to being able to give specific positioning information and configure the system communication protocol in the data transmission process, it also needs to provide the working power supply required by various device modules in the system.

[0079] Further, the main control board 105 further comprises a display module 1054.

[0080] The display module is used for displaying the current value, voltage value and coil temperature in the capacitor running process, and displaying the network state of the signal sensor in the data acquisition module. The display module facilitates timely display of data or network state to the operator, facilitates human-computer interaction, and improves the timeliness of information transmission. The display module can adopt a 16bit TFT-LCD screen.

[0081] Further, the communication module 1052 of the main control board 105 is an ESP8266 module or an ESP32 module. The relationship between the main control board and the data acquisition sensor and the sub-module in the actual application process can be described as shown in the figure. Figure 2

[0082] The capacitor running condition monitoring system provided by the embodiment of the application can quickly and stably transmit the obtained various capacitor running data to the monitoring and analysis module of the background for analysis by using the preset radiation degree algorithm, and can realize efficient real-time monitoring. The data analysis can ensure the accuracy of data analysis and improve the reliability of analysis results by using the preset SVM multi-classification model optimized based on the CSA algorithm. Therefore, the embodiment of the application can solve the defect that the monitoring efficiency is poor in the prior art, and solve the technical problem that the actual monitoring demand cannot be met.

[0083] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there can be another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0084] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.​

[0085] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0086] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for executing all or part of the steps of the methods described in each embodiment of the present application by a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (English full name: Read-Only Memory, English abbreviation: ROM), a random access memory (English full name: Random Access Memory, English abbreviation: RAM), a magnetic disk or an optical disk, etc.

[0087] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A capacitor operating status monitoring system, characterized in that, include: Data acquisition module, data transmission module, and monitoring and analysis module; The data acquisition module is used to acquire the current value, voltage value and coil temperature of the capacitor during operation through signal sensors, including a current sensor, a voltage sensor and a temperature sensor. The data transmission module is used to send the current value, the voltage value, and the coil temperature to the monitoring and analysis module in the background according to a preset radiance algorithm. The monitoring and analysis module is used to analyze and process the current value, the voltage value and the coil temperature according to the preset SVM multi-classification model to obtain the capacitor operating status. The preset SVM multi-classification model is obtained by parameter optimization processing through an improved CSA algorithm. The capacitor operating status includes normal operation, overvoltage, overcurrent and high temperature operation.

2. The capacitor operation status monitoring system according to claim 1, characterized in that, The data acquisition module includes: a signal sensor, an A / D conversion module, and a power supply module; The signal sensor collects the current value, voltage value, and coil temperature of the capacitor during operation. The A / D conversion module performs digital-to-analog conversion on the collected data. The power module provides operating power to the sub-modules in the data acquisition module.

3. The capacitor operation status monitoring system according to claim 1, characterized in that, The data transmission module is specifically used for: The data transmission rate of each node is calculated based on the preset radiometric algorithm and preset transmission tasks; Based on the data transmission rate, the current value, the voltage value, and the coil temperature are sent to the monitoring and analysis module in the background.

4. The capacitor operation status monitoring system according to claim 1, characterized in that, The monitoring and analysis module is specifically used for: A pre-defined SVM multi-classification model is obtained by optimizing the parameters of the initial SVM multi-classification model through improved CSA algorithm by optimizing the optimal penalty factor and kernel function parameters. The current value, voltage value, and coil temperature are analyzed and processed according to a preset SVM multi-classification model to obtain the capacitor operating status, which includes normal operation, overvoltage, overcurrent, and high temperature operation.

5. The capacitor operation status monitoring system according to claim 1, characterized in that, Also includes: Storage module; The storage module is used to store the current value, the voltage value, and the coil temperature, as well as the operating status of the capacitor.

6. The capacitor operation status monitoring system according to claim 1, characterized in that, Also includes: Main control board; The main control board includes a positioning module, a communication module, and a power supply module; The main control board is used to provide positioning information to the system, configure the system communication protocol, and provide operating power to the system.

7. The capacitor operation status monitoring system according to claim 6, characterized in that, The main control board also includes: a display module; The display module is used to display the current value, voltage value, and coil temperature during capacitor operation, and to display the network status of the signal sensor in the data acquisition module.

8. The capacitor operation status monitoring system according to claim 6, characterized in that, The communication module of the main control board is either an ESP8266 module or an ESP32 module.

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