Oil level detection method and system based on correlation analysis

Abstract

The present invention discloses an oil level detection method and system based on correlation analysis, which relates to the technical field of oil level detection, and includes the following steps: collecting and preprocessing the static pressure signal in the transformer oil tank; inputting the preprocessed static pressure signal into a preset programmable logic controller to calculate the initial oil level height; performing correlation analysis on the characteristics of the transformer, the ambient temperature and the oil level height to obtain the influencing data of the oil level height; constructing an oil level height deviation model according to the influencing data of the oil level height, obtaining the correction factor of the oil level height and correcting the initial oil level height to obtain the final oil level height. The present invention obtains the oil level height influence coefficient through correlation analysis and uses it to correct the oil level height, thereby realizing a more comprehensive and accurate detection of the oil level height, which can more accurately reflect the real situation of the oil level in the actual operation of the transformer, and is helpful to discover potential problems in advance and take effective measures.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment detection, and more specifically, to an oil level detection method and system based on correlation analysis. Background Art

[0002] Power equipment, especially oil-immersed power transformers, need to maintain an appropriate oil level during operation. The oil level not only affects the heat dissipation effect and insulation performance of the transformer, but is also directly related to its safe operation and life. If the oil level is too high, the internal oil pressure of the transformer may increase, causing oil overflow; if the oil level is too low, the transformer may be running at low load or the temperature drops suddenly, resulting in less oil in the main body tank, causing insulation failure. At the same time, with the rapid expansion of the scale of the power grid and the continuous improvement of the automation level, unmanned substations have become an inevitable development trend. The online monitoring and safety early warning of power equipment also need to constantly adapt to the new development situation, further improve their own functions, improve monitoring capabilities and diagnostic levels, reduce the cost of the monitoring system, and develop real-time monitoring and unmanned monitoring systems. At present, many online monitoring methods based on electrical quantities have been proposed for different power equipment. Compared with the electrical quantity detection method, the use of non-electrical quantities to diagnose faults in power equipment is more effective to a certain extent. Because before the power equipment fails, the electrical quantity of the equipment has not changed significantly, but some non-electrical quantity change information such as the content and temperature of various gases contains information that the fault is about to occur or has occurred.

[0003] For example, the invention patent with announcement number: CN 106525194 B announces a real-time monitoring method for the oil level of the main transformer based on the equivalence of the extreme oil temperature. By obtaining the measured data of the oil temperature and oil level of a main transformer device in the last 30 days and performing linear fitting, the measured data oil temperature-oil level fitting straight line function and the oil level value at 90 degrees Celsius oil temperature are obtained; then the measured data and the oil level value at 90 degrees Celsius oil temperature are used to obtain the 30-day measured data oil level equivalent fitting straight line, and the equivalent oil level value at the set temperature for 30 days is calculated; linear fitting is performed with time as the independent variable and the equivalent oil level value as the dependent variable to determine the fitting straight line function and slope of time and oil level; the slope is determined to obtain the oil level state of the main transformer, and the user receives the determination result to complete the real-time monitoring. The present invention dynamically analyzes the oil temperature-oil level relationship in combination with the actual situation, reduces the error between the oil level values ​​after equivalent, and ensures that the results are accurate and reliable.

[0004] The above disclosed technical solutions have at least the following technical problems: traditional oil level monitoring methods, such as manual observation method, float type oil level gauge, etc., have limitations such as low accuracy, susceptibility to human factors, and inability to monitor in real time. The oil level will change with temperature factors, resulting in unstable sensor readings or reduced accuracy. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an oil level detection method and system based on correlation analysis, which performs correlation analysis on the characteristics of the transformer, the ambient temperature and the initial oil level height to obtain various factors that may affect the oil level height, so as to solve the problem that the oil height may change with temperature factors, resulting in unstable sensor readings or reduced accuracy.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The oil level detection method based on correlation analysis includes the following steps: collecting and preprocessing the static pressure signal in the transformer oil tank; inputting the preprocessed static pressure signal into a preset programmable logic controller to calculate the initial oil level height; performing correlation analysis on the characteristics of the transformer, the ambient temperature and the oil level height to obtain the influencing data of the oil level height; constructing an oil level height deviation model according to the influencing data of the oil level height to obtain the correction factor of the oil level height and correcting the initial oil level height to obtain the final oil level height; if the final oil level height is lower than or higher than the preset safety range, the system sends an alarm signal to remind the operator to take timely measures.

[0008] In a preferred embodiment, the static pressure signal in the transformer oil tank is collected and preprocessed, specifically: a pressure sensor is selected according to the maximum pressure variation range inside the transformer oil tank; the static pressure signal in the oil tank is collected by the pressure sensor installed on the transformer oil tank; the collected static pressure signal is converted into an electrical signal for output; the impedance conversion requirement is obtained according to the performance requirements of the transformer, and the impedance conversion requirement includes input impedance, output impedance and required conversion ratio; based on the impedance conversion requirement, an impedance conversion circuit is constructed and matched with the impedance of the transformer; a low-pass filter is used to filter the electrical signal, and a band-pass filter is used to suppress the DC bias of the signal; an isolation amplifier is used to completely isolate the input side and the output side of the signal electrically, and the signal is amplified by the amplifier.

[0009] In a preferred embodiment, the pre-processed static pressure signal is input into a preset programmable logic controller to calculate the initial oil level height, specifically: the pre-processed static pressure signal is input into the input module of the PLC; the input module performs A / D conversion on the static pressure signal to convert the static pressure signal into a digital signal; the digital signal is calibrated according to the calibration data of the sensor and the input range of the PLC; a mathematical relationship between the static pressure and the oil level height is established according to the density of the oil, the acceleration of gravity and the geometric shape parameters of the oil tank; an algorithm program is written in the PLC, and the received static pressure signal is substituted into the mathematical relationship to calculate the initial oil level height.

[0010] In a preferred embodiment, the correlation analysis of the transformer characteristics, ambient temperature and oil level height is performed to obtain the influencing data of the oil level height, specifically: collecting transformer characteristic data and oil level height data under different ambient temperatures; cleaning the collected transformer characteristic data and oil level height data to remove outliers and duplicate data; using the ambient temperature as the horizontal coordinate and the oil level height as the vertical coordinate to draw a scatter plot, in which each point represents the data at an observation time; if the points are linearly distributed, there is a linear relationship between the ambient temperature and the oil level height; if the distribution of the points is a curve or other shape, there is a nonlinear relationship between the ambient temperature and the oil level height; calculating the Pearson correlation coefficient between the transformer characteristics, the ambient temperature and the oil level height; mapping the Pearson correlation coefficient in combination with the transformer characteristics into a matrix to obtain a correlation matrix; and obtaining the influencing data of the oil level height by comparing the correlation matrix with a preset correlation threshold.

[0011] In a preferred embodiment, the influencing data of the oil level height include transformer load factor, gas influence coefficient and thermal balance coefficient; the specific method for obtaining the transformer load factor is as follows: setting the data sampling frequency higher than the fundamental frequency of the power system; obtaining the transformer working data within time t and performing data cleaning, the transformer working data including current, voltage and power factor; calculating the real-time load of the transformer according to the current, voltage and power factor monitored in real time; sorting the calculated real-time load in chronological order to construct a load time series; calculating the load rate at each time point according to the rated capacity of the transformer; arranging the calculated load rate data in chronological order to construct a load rate time series; for each data point in the load rate time series, calculating the load difference between it and the previous data point; combining the load rate, load difference and transformer rated power to obtain the transformer load factor based on a preset transformer load factor formula.

[0012] In a preferred embodiment, the specific method for obtaining the gas influence coefficient is as follows: obtaining the type and concentration of dissolved gas in oil by gas chromatography; constructing a linear regression model based on the obtained gas type and concentration to analyze the relationship between different gas concentrations and oil, and obtaining the influence index of different gas concentrations; calculating the gas influence coefficient based on the preset gas influence coefficient calculation formula according to the influence index of different gas concentrations combined with the solubility of gas in oil.

[0013] In a preferred embodiment, the specific method for obtaining the thermal balance coefficient is as follows: obtaining the parameters of the transformer oil including specific heat capacity, density and thermal expansion coefficient; obtaining the thermal conductivity of the oil based on heat exchange according to the temperature difference between the temperature of the transformer oil tank and the ambient temperature; taking the ratio of the thermal conductivity of the oil to the heat flow as the thermal index; and obtaining the thermal balance coefficient based on a preset thermal balance coefficient calculation formula according to the thermal index combined with the specific heat capacity and the thermal expansion coefficient.

[0014] The system of the oil level detection method based on correlation analysis includes the following modules: a static pressure signal acquisition module, an initial oil level height calculation module, an oil level height influence data analysis module, an oil level height deviation model construction and correction module and an oil level monitoring module; the static pressure signal acquisition module is used to collect the static pressure signal in the transformer oil tank and perform preprocessing; the initial oil level height calculation module is used to input the preprocessed static pressure signal into a preset programmable logic controller to calculate the initial oil level height; the oil level height influence data analysis module is used to perform correlation analysis on the characteristics of the transformer, the ambient temperature and the oil level height to obtain the influence data of the oil level height; the oil level height deviation model construction and correction module is used to construct an oil level height deviation model according to the influence data of the oil level height, obtain the correction factor of the oil level height and correct the initial oil level height to obtain the final oil level height; the oil level monitoring module is used to send an alarm signal if the final oil level height is lower than or higher than the preset safety range, reminding the operator to take timely measures.

[0015] Technical effects and advantages of the oil level detection method and system based on correlation analysis of the present invention:

[0016] 1. The present invention does not simply focus on the oil level itself, but comprehensively considers the impact of various factors closely related to the operating status of the transformer on the oil level. For example, the initial oil level is obtained by collecting the static pressure signal in the transformer oil tank and performing a series of preprocessing. On this basis, the correlation analysis of the transformer characteristics, ambient temperature and oil level is further performed to obtain the influencing data of the oil level. These influencing data cover many aspects such as transformer load factor, gas influence coefficient and thermal balance coefficient. Based on these comprehensive factors, an oil level deviation model is constructed to correct the oil level, thereby achieving a more comprehensive and accurate detection of the oil level, which can more accurately reflect the actual oil level situation in the actual operation of the transformer, and help to discover potential problems in advance and take effective measures.

[0017] 2. The present invention predicts the change trend of the oil level by monitoring and analyzing the transformer load factor, gas influence factor and other related coefficients. For example, the change of the transformer load factor can reflect the change of the internal temperature of the transformer and thus affect the oil level. The change of the gas influence coefficient can predict the oil level fluctuation caused by gas-related factors, etc. This helps to detect oil level abnormalities in advance and prevent transformer failures caused by excessively high or low oil levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the oil level detection method based on correlation analysis of the present invention.

[0019] Figure 2 It is a schematic diagram of the structure of the oil level detection system based on correlation analysis of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The present invention provides an oil level detection method based on correlation analysis, comprising the following steps:

[0022] S1, collects the static pressure signal in the transformer tank and performs preprocessing;

[0023] The purpose of collecting the static pressure signal in the oil tank through the pressure sensor installed on the transformer oil tank is to monitor and analyze the working status and safety performance inside the transformer. Specifically, the static pressure signal reflects the oil pressure change in the transformer oil tank.

[0024] The static pressure signal in the transformer oil tank is collected and preprocessed, specifically:

[0025] Select the pressure sensor according to the maximum pressure variation range inside the transformer tank;

[0026] The static pressure signal in the oil tank is collected through the pressure sensor installed on the transformer oil tank;

[0027] Convert the collected static pressure signal into an electrical signal for output;

[0028] Obtaining the demand for impedance conversion according to the performance requirements of the transformer, wherein the demand for impedance conversion includes input impedance, output impedance and required conversion ratio;

[0029] Based on the requirements of impedance conversion, an impedance conversion circuit is constructed and matched with the impedance of the transformer;

[0030] The electrical signal is filtered by a low-pass filter, and the DC bias of the signal is suppressed by a band-pass filter;

[0031] An isolation amplifier is used to completely isolate the input side and output side of the signal electrically, and the signal is amplified by the amplifier.

[0032] S2, inputting the pre-processed static pressure signal into a preset programmable logic controller to calculate the initial oil level;

[0033] The pre-processed static pressure signal is input into a preset programmable logic controller to calculate the initial oil level, specifically:

[0034] Input the pre-processed static pressure signal into the input module of the PLC;

[0035] The input module performs A / D conversion on the static pressure signal and converts the static pressure signal into a digital signal;

[0036] Calibrate the digital signal according to the sensor's calibration data and the PLC's input range;

[0037] According to the density of the oil, the acceleration of gravity and the geometric parameters of the oil tank, the mathematical relationship between the static pressure and the oil level is established;

[0038] Write an algorithm program in the PLC, substitute the received static pressure signal into the mathematical relationship, and calculate the initial oil level height.

[0039] The specific calculation formula of the initial oil level is:

[0040]

[0041] Where h is the initial oil level, P is the static pressure at the bottom of the tank, is the density of the transformer oil and g is the acceleration due to gravity, which is approximately 9.81 m / s² on the Earth's surface.

[0042] S3, performing correlation analysis on the characteristics of the transformer, the ambient temperature and the oil level to obtain the influence data of the oil level;

[0043] The correlation analysis of the transformer characteristics, ambient temperature and oil level is performed to obtain the influencing data of the oil level, specifically:

[0044] Collect transformer characteristic data and oil level data at different ambient temperatures;

[0045] Clean the collected transformer characteristic data and oil level data to remove abnormal values ​​and duplicate data;

[0046] Use the ambient temperature as the horizontal axis and the oil level as the vertical axis to draw a scatter plot, where each point represents the data at an observation time.

[0047] If the points are distributed linearly, there is a linear relationship between the ambient temperature and the oil level; if the points are distributed in a curve or other shape, there is a nonlinear relationship between the ambient temperature and the oil level;

[0048] Calculate the Pearson correlation coefficient between transformer characteristics, ambient temperature and oil level;

[0049] Mapping the Pearson correlation coefficient in combination with the transformer characteristics into a matrix to obtain a correlation matrix;

[0050] By comparing the correlation matrix with the preset correlation threshold, the influence data of the oil level height is obtained.

[0051] Correlation analysis has the following benefits for oil level analysis:

[0052] Intuitiveness: By drawing a scatter plot of ambient temperature and oil level, the relationship between the two can be intuitively displayed, whether it is a linear or nonlinear relationship. The presentation of the correlation matrix also makes the correlation between different variables clearer, making it easier to understand and analyze.

[0053] Objectivity: Using statistical indicators such as the Pearson correlation coefficient to measure the correlation between variables avoids errors caused by subjective judgment. The results of correlation analysis are based on statistical calculations of a large amount of data and are objective and reliable.

[0054] Comprehensiveness: This method not only considers the impact of ambient temperature on oil level, but also other factors such as transformer characteristics, making the analysis more comprehensive. Through the correlation matrix, the correlation relationship between multiple variables can be displayed at one time, which is convenient for comprehensive analysis.

[0055] Predictive: If the linear or nonlinear relationship between ambient temperature and oil level is determined, as well as the effect of transformer characteristics on oil level, these relationships can be used for prediction. For example, given the ambient temperature and transformer characteristics, future oil level changes can be predicted, allowing preventive or adjustment measures to be taken in advance.

[0056] Efficiency: Correlation analysis is a statistical method that can process a large amount of data in a short period of time and obtain analysis results. Compared with traditional experimental methods, this method can save a lot of time and resources and improve analysis efficiency.

[0057] Guidance: Based on the results of correlation analysis, the operation and maintenance of the transformer can be guided. For example, if it is found that the ambient temperature has a greater impact on the oil level, measures can be taken to control the ambient temperature to maintain the stability of the oil level. At the same time, according to the different characteristics of the transformer, corresponding maintenance strategies can be formulated to improve the operating efficiency and safety of the transformer.

[0058] In summary, the oil level detection method based on correlation analysis has the advantages of intuitiveness, objectivity, comprehensiveness, predictability, efficiency and guidance, and can provide strong support for the operation and maintenance of transformers.

[0059] S4, constructing an oil level deviation model according to the influencing data of the oil level, obtaining a correction factor of the oil level and correcting the initial oil level to obtain a final oil level;

[0060] The influencing data of the oil level height include transformer load factor, gas influence factor and thermal balance factor;

[0061] The transformer load factor is an important parameter for evaluating the operating status of the transformer. In practical applications, changes in the transformer load factor will have many effects on the operation of the transformer, including the oil level. When the load increases, the temperature inside the transformer will rise, causing the transformer oil to expand and the oil level to rise. Conversely, when the load decreases, the temperature inside the transformer will drop, causing the transformer oil to shrink and the oil level to drop. Therefore, understanding the relationship between the transformer load factor and the oil level is of great significance to ensure the normal operation of the transformer.

[0062] The specific method for obtaining the transformer load factor is as follows:

[0063] Set the data sampling frequency higher than the fundamental frequency of the power system;

[0064] Obtaining transformer operating data within time t and performing data cleaning, wherein the transformer operating data includes current, voltage and power factor;

[0065] Calculate the real-time load of the transformer based on the current, voltage and power factor monitored in real time;

[0066] Sort the calculated real-time loads in chronological order and construct a load time series;

[0067] According to the rated capacity of the transformer, calculate the load rate at each time point;

[0068] Arrange the calculated load rate data in chronological order to construct a load rate time series;

[0069] For each data point in the load rate time series, calculate the load difference between it and the previous data point;

[0070] The transformer load factor is obtained based on a preset transformer load factor formula in combination with the load rate, the load difference and the transformer rated power.

[0071] The specific calculation formula of the load rate is:

[0072]

[0073] The specific calculation formula of the transformer load factor is:

[0074]

[0075] in, is the load factor, is the transformer current at time t, is the voltage of the transformer at time t, is the rated power of the transformer, is the rated voltage of the transformer, is the power factor, is the transformer load factor, is the load of the transformer at time t+1, is the load of the transformer at time t, and n is the total number of time series.

[0076] The transformer load factor has the following benefits for oil testing:

[0077] Predict oil level changes: By monitoring and analyzing changes in transformer load factors, the changing trend of transformer oil levels can be predicted. This is of great significance for timely detection of oil level anomalies and prevention of transformer failures caused by excessively high or low oil levels.

[0078] Assessing transformer status: The transformer load factor not only reflects the load of the transformer, but also indirectly reflects the operating status of the transformer. Through the relationship between the load factor and the oil level, the thermal stability and insulation performance of the transformer can be further evaluated, so as to timely discover potential safety hazards.

[0079] Optimize maintenance strategy: Understanding the relationship between transformer load factor and oil level height helps to formulate a more scientific and reasonable transformer maintenance strategy. For example, according to the change of load factor, the transformer inspection and maintenance work can be reasonably arranged to ensure that the transformer is always in good operating condition.

[0080] Improve operating efficiency: By optimizing load distribution and scheduling, the load factor of the transformer can be kept within a reasonable range, which can reduce the energy consumption and loss of the transformer and improve the operating efficiency of the transformer. At the same time, it also helps to reduce transformer failures caused by overload or underload and extend the service life of the transformer.

[0081] In summary, the benefits of understanding the transformer load factor for oil testing are mainly reflected in predicting oil level changes, evaluating transformer status, optimizing maintenance strategies, and improving operating efficiency. These benefits are of great significance to ensuring the normal operation of the transformer and extending its service life.

[0082] In the field of transformers, the gas influence coefficient is an indicator used to quantify the impact of changes in gas content on transformer oil performance. This coefficient may involve multiple aspects, such as the relationship between gas content and oil level, the relationship between gas dissolution and transformer performance, and the impact of gas on transformer insulation performance. Due to differences in requirements in different research or application backgrounds, the specific definition, calculation method, and application of the gas influence coefficient may also be different. In practical applications, in order to obtain an accurate gas influence coefficient, it is necessary to combine the specific research background and objectives, and establish a corresponding mathematical model or relationship by collecting and analyzing data. This coefficient is of great significance for evaluating the impact of gas in transformer oil and optimizing the operation and maintenance strategies of transformers.

[0083] The specific method for obtaining the gas influence coefficient is as follows:

[0084] The types and concentrations of dissolved gases in oil are obtained by gas chromatography;

[0085] According to the types and concentrations of the acquired gases, a linear regression model is constructed to analyze the relationship between different gas concentrations and oil, and the influence index of different gas concentrations is obtained;

[0086] The gas influence coefficient is calculated based on the influence index of different gas concentrations and the solubility of the gas in the oil based on a preset gas influence coefficient calculation formula.

[0087] The specific calculation formula of the gas influence coefficient is:

[0088]

[0089] in, is the gas influence coefficient, N is the number of gas types, is the influence index of the i-th gas, is the concentration of the ith gas, is the solubility of the ith gas.

[0090] The influence of gas influence coefficient on transformer oil level is mainly reflected in the following aspects:

[0091] Gas dissolution and oil level changes: When the dissolved gas content in transformer oil changes, these gases will affect the volume and density of the oil. Especially under high temperature or high load conditions, the gas in the oil will precipitate due to reduced solubility, resulting in changes in the oil volume, which in turn affects the oil level. If the gas content increases, the oil volume may expand and the oil level rises; conversely, when the gas content decreases, the oil volume may shrink and the oil level drops.

[0092] Relationship between gas pressure and oil level: Inside the transformer, changes in gas pressure will also affect the oil level. When the gas pressure increases, it will push the oil upward, causing the oil level to rise. Conversely, when the gas pressure decreases, the oil may move downward and the oil level will drop. This change is closely related to the gas influence coefficient, because the gas influence coefficient reflects the degree of influence of changes in gas content on oil performance.

[0093] Gas composition and oil level stability: The gas composition dissolved in transformer oil will also affect the stability of the oil level. For example, some gases (such as hydrogen, methane, etc.) have a high solubility in oil and change greatly with temperature. Changes in the content of these gases may cause significant changes in the volume of the oil, which in turn affects the stability of the oil level. The gas influence coefficient can help us understand the degree of influence of different gas components on the stability of the oil level, so that we can take corresponding measures to maintain the stability of the oil level. Predicting potential transformer failures: The gas influence coefficient is closely related to the internal state of the transformer. By analyzing the changing trend of the gas influence coefficient, possible transformer failures can be predicted. For example, when the gas influence coefficient fluctuates abnormally, it may mean that there are problems such as partial discharge, overheating or insulation aging inside the transformer. This helps to take measures in advance to prevent failures and reduce maintenance costs.

[0094] Optimize transformer operation and maintenance strategies: Understanding the gas impact coefficient helps to formulate more scientific and reasonable transformer operation and maintenance strategies. For example, according to the changes in the gas impact coefficient, the load distribution of the transformer can be adjusted to avoid overload operation; at the same time, according to the changes in gas composition and concentration, a reasonable oil replacement and oil filtration plan can be formulated to ensure the cleanliness and performance of the oil.

[0095] Improve transformer operation efficiency and safety: By optimizing the management and control of the gas influence coefficient, the transformer operation efficiency and safety can be improved. On the one hand, it can reduce the energy consumption and loss of the transformer and improve the energy utilization efficiency; on the other hand, it can reduce transformer failures caused by abnormal gas content and ensure the stable operation of the power system.

[0096] The thermal balance coefficient is a parameter that describes the energy distribution, temperature distribution or heat exchange efficiency inside the system when the system reaches thermal equilibrium. The thermal balance coefficient reflects the uniformity of the temperature distribution inside the system. If the temperature distribution inside the system is uneven, it may cause the oil to expand or contract in a local area, thus affecting the oil level.

[0097] The specific method for obtaining the thermal equilibrium coefficient is as follows:

[0098] Obtaining transformer oil parameters including specific heat capacity, density and thermal expansion coefficient;

[0099] The thermal conductivity of the oil is obtained based on heat exchange according to the temperature difference between the temperature of the transformer oil tank and the ambient temperature;

[0100] The ratio of the thermal conductivity of the oil to the heat flow rate is taken as the thermal state index;

[0101] The thermal equilibrium coefficient is obtained according to the thermal index combined with the specific heat capacity and the thermal expansion coefficient based on a preset thermal equilibrium coefficient calculation formula.

[0102] The specific calculation formula of the thermal equilibrium coefficient is:

[0103]

[0104] in, is the thermal equilibrium coefficient, is the internal temperature of the transformer tank, is the ambient temperature, S is the cross-sectional area of ​​the oil tank, q is the specific heat capacity, R is the heat flow, L is the thickness of the oil tank, and c is the thermal expansion coefficient.

[0105] The influence of thermal balance coefficient on transformer oil level is mainly reflected in the following aspects:

[0106] Temperature distribution and oil level changes: The thermal balance coefficient reflects the uniformity of the temperature distribution inside the system. When the temperature distribution inside the system is uneven, it will cause the oil to expand or contract in local areas. Specifically, the oil in the area with higher temperature will expand due to heat, causing the oil level in that area to rise; while the oil in the area with lower temperature will cool and contract, causing the oil level to drop. This uneven temperature distribution will cause the overall fluctuation of the oil level in the transformer tank, thus affecting the stable operation of the transformer.

[0107] Heat exchange efficiency and oil level stability: The thermal balance coefficient is also closely related to the heat exchange efficiency of the system. The higher the heat exchange efficiency, the more uniform the temperature distribution inside the system, and the more uniform the thermal expansion and contraction of the oil, which helps to maintain the stability of the oil level. On the contrary, if the heat exchange efficiency is low, the temperature distribution inside the system is uneven, the thermal expansion and contraction of the oil will be more significant, resulting in greater fluctuations in the oil level.

[0108] The oil level height deviation model is specifically:

[0109]

[0110] in, is the correction factor, is the transformer load factor, is the gas influence coefficient, is the thermal equilibrium coefficient, is the weight factor of the preset transformer load factor, is the preset weight factor of the gas influence coefficient, is the weight factor of the preset thermal equilibrium coefficient.

[0111] The final oil level is:

[0112]

[0113] in, is the final oil level, P is the static pressure at the bottom of the tank, is the density of transformer oil, g is the acceleration due to gravity, is the correction factor.

[0114] S5: If the final oil level is lower or higher than the preset safety range, the system will send out an alarm signal to remind the operator to take timely measures;

[0115] Install an oil level monitoring device: Install an oil level gauge or oil level sensor on the transformer tank to monitor the oil level in real time. Ensure that the oil level monitoring device is accurate and reliable, and calibrate and maintain it regularly.

[0116] Set safety range: According to the transformer operation requirements and safety standards, set the oil level safety range. This range usually includes the minimum safety oil level and the maximum safety oil level.

[0117] Signal acquisition: The oil level monitoring device transmits the real-time oil level data to the system controller, which processes and analyzes the received data.

[0118] Alarm judgment: The system controller compares the real-time oil level with the preset safety range. If the oil level is lower than the minimum safety level or higher than the maximum safety level, the system controller will judge it as an abnormal situation.

[0119] Alarm signal output: Once the system controller determines that an abnormal situation occurs, it will immediately output an alarm signal. The alarm signal may include sound and light alarms, display of alarm information on the display screen, etc.

[0120] Receiving alarm information: After receiving the alarm signal, the operator should immediately go to the transformer site for confirmation. By observing the display information on the oil level monitoring device or system controller, the actual oil level height can be understood.

[0121] Find the cause: The operator should check whether there is oil leakage, oil seepage or oil level gauge failure in the transformer tank. At the same time, the load condition, temperature distribution and heat dissipation conditions of the transformer should also be checked to determine the specific cause of the abnormal oil level.

[0122] According to the reasons found, the operator should take appropriate measures to deal with it.

[0123] For example, if the oil level drops due to oil leakage, the leak should be found and repaired as soon as possible; if the oil temperature and oil level rise due to excessive load, the load distribution should be adjusted or the heat dissipation conditions should be increased.

[0124] The operator should record the processing process and results for subsequent analysis and summary.

[0125] At the same time, abnormal situations should be fed back to relevant departments or leaders for further analysis and improvement of transformer operation and maintenance strategies.

[0126] Embodiment 2, a system of an oil level detection method based on correlation analysis, comprises the following modules: a static pressure signal acquisition module, an initial oil level height calculation module, an oil level height influence data analysis module, an oil level height deviation model construction and correction module and an oil level monitoring module;

[0127] Static pressure signal acquisition module: used to collect the static pressure signal in the transformer tank and perform preprocessing;

[0128] Initial oil level calculation module: used to input the pre-processed static pressure signal into the preset programmable logic controller to calculate the initial oil level;

[0129] Oil level height impact data analysis module: used to perform correlation analysis on transformer characteristics, ambient temperature and oil level height, and obtain oil level height impact data;

[0130] Oil level deviation model construction and correction module: used to construct an oil level deviation model based on the influencing data of the oil level, obtain the correction factor of the oil level and correct the initial oil level to obtain the final oil level;

[0131] Oil level monitoring module: If the final oil level is lower or higher than the preset safety range, the system will send out an alarm signal to remind the operator to take timely measures.

[0132] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0133] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0134] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0135] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0137] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The oil level detection method based on correlation analysis is characterized in that: The following steps are involved: Collect the static pressure signal in the transformer tank and pre-process it; The pre-processed static pressure signal is input into a preset programmable logic controller to calculate the initial oil level height; Conduct correlation analysis on transformer characteristics, ambient temperature and oil level to obtain the impact data of oil level; The influencing data of the oil level height includes a gas influence coefficient; The specific method for obtaining the gas influence coefficient is as follows: The types and concentrations of dissolved gases in oil are obtained by gas chromatography; According to the types and concentrations of the acquired gases, a linear regression model is constructed to analyze the relationship between different gas concentrations and oil, and the influence index of different gas concentrations is obtained; The gas influence coefficient is calculated based on the influence index of different gas concentrations and the solubility of the gas in the oil based on the preset gas influence coefficient calculation formula; An oil level deviation model is constructed based on the influencing data of the oil level, the correction factor of the oil level is obtained, and the initial oil level is corrected to obtain the final oil level; If the final oil level is lower or higher than the preset safety range, the system will send out an alarm signal to remind the operator to take timely measures.

2. The oil level detection method based on correlation analysis according to claim 1, characterized in that: The static pressure signal in the transformer oil tank is collected and preprocessed, specifically: Select the pressure sensor according to the maximum pressure variation range inside the transformer tank; The static pressure signal in the oil tank is collected through the pressure sensor installed on the transformer oil tank; Convert the collected static pressure signal into an electrical signal for output; Obtaining the demand for impedance conversion according to the performance requirements of the transformer, wherein the demand for impedance conversion includes input impedance, output impedance and required conversion ratio; Based on the requirements of impedance conversion, an impedance conversion circuit is constructed and matched with the impedance of the transformer; The electrical signal is filtered by a low-pass filter, and the DC bias of the signal is suppressed by a band-pass filter; An isolation amplifier is used to completely isolate the input side and output side of the signal electrically, and the signal is amplified by the amplifier.

3. The oil level detection method based on correlation analysis according to claim 2 is characterized in that: The pre-processed static pressure signal is input into a preset programmable logic controller to calculate the initial oil level, specifically: Input the pre-processed static pressure signal into the input module of the PLC; The input module performs A / D conversion on the static pressure signal and converts the static pressure signal into a digital signal; Calibrate the digital signal according to the sensor's calibration data and the PLC's input range; According to the density of the oil, the acceleration of gravity and the geometric parameters of the oil tank, the mathematical relationship between the static pressure and the oil level is established; Write an algorithm program in the PLC, substitute the received static pressure signal into the mathematical relationship, and calculate the initial oil level height.

4. The oil level detection method based on correlation analysis according to claim 3 is characterized in that: The correlation analysis of the transformer characteristics, ambient temperature and oil level is performed to obtain the influencing data of the oil level, specifically: Collect transformer characteristic data and oil level data at different ambient temperatures; Clean the collected transformer characteristic data and oil level data to remove abnormal values ​​and duplicate data; Use the ambient temperature as the horizontal axis and the oil level as the vertical axis to draw a scatter plot, where each point represents the data at an observation time. If the points are distributed linearly, there is a linear relationship between the ambient temperature and the oil level; if the points are distributed in a curve or other shape, there is a nonlinear relationship between the ambient temperature and the oil level; Calculate the Pearson correlation coefficient between transformer characteristics, ambient temperature and oil level; Mapping the Pearson correlation coefficient in combination with the transformer characteristics into a matrix to obtain a correlation matrix; By comparing the correlation matrix with the preset correlation threshold, the influence data of the oil level height is obtained.

5. The oil level detection method based on correlation analysis according to claim 4 is characterized in that: The influencing data of the oil level height include transformer load factor and thermal balance factor; The specific method for obtaining the transformer load factor is as follows: Set the data sampling frequency higher than the fundamental frequency of the power system; Obtaining transformer operating data within time t and performing data cleaning, wherein the transformer operating data includes current, voltage and power factor; Calculate the real-time load of the transformer based on the current, voltage and power factor monitored in real time; Sort the calculated real-time loads in chronological order and construct a load time series; According to the rated capacity of the transformer, calculate the load rate at each time point; Arrange the calculated load rate data in chronological order to construct a load rate time series; For each data point in the load rate time series, calculate the load difference between it and the previous data point; The transformer load factor is obtained based on a preset transformer load factor formula in combination with the load rate, the load difference and the transformer rated power.

6. The oil level detection method based on correlation analysis according to claim 5 is characterized in that: The specific method for obtaining the thermal equilibrium coefficient is as follows: Obtaining transformer oil parameters including specific heat capacity, density and thermal expansion coefficient; The thermal conductivity of the oil is obtained based on heat exchange according to the temperature difference between the temperature of the transformer oil tank and the ambient temperature; The ratio of the thermal conductivity of the oil to the heat flow rate is taken as the thermal state index; The thermal equilibrium coefficient is obtained according to the thermal index combined with the specific heat capacity and the thermal expansion coefficient based on a preset thermal equilibrium coefficient calculation formula.

7. The oil level detection method based on correlation analysis according to claim 5, characterized in that: The specific calculation formula of the load rate is: The specific calculation formula of the transformer load factor is: in, is the load factor, is the transformer current at time t, is the voltage of the transformer at time t, is the rated power of the transformer, is the rated voltage of the transformer, is the power factor, is the transformer load factor, is the load of the transformer at time t+1, is the load of the transformer at time t, and n is the total number of time series.

8. The oil level detection method based on correlation analysis according to claim 1, characterized in that: The specific calculation formula of the gas influence coefficient is: in, is the gas influence coefficient, N is the number of gas types, is the influence index of the i-th gas, is the concentration of the ith gas, is the solubility of the ith gas.

9. A system using the oil level detection method based on correlation analysis as claimed in any one of claims 1 to 8, characterized in that: It includes the following modules: static pressure signal acquisition module, initial oil level calculation module, oil level impact data analysis module, oil level deviation model construction and correction module and oil level monitoring module; Static pressure signal acquisition module: used to collect the static pressure signal in the transformer tank and perform preprocessing; Initial oil level calculation module: used to input the pre-processed static pressure signal into the preset programmable logic controller to calculate the initial oil level; Oil level height impact data analysis module: used to perform correlation analysis on transformer characteristics, ambient temperature and oil level height, and obtain oil level height impact data; Oil level deviation model construction and correction module: used to construct an oil level deviation model based on the influencing data of the oil level, obtain the correction factor of the oil level and correct the initial oil level to obtain the final oil level; Oil level monitoring module: If the final oil level is lower or higher than the preset safety range, the system will send out an alarm signal to remind the operator to take timely measures.

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