Insulation detection system supporting direct-current high-voltage energy storage system

Abstract

The invention discloses an insulation detection system supporting a direct-current high-voltage energy storage system, which belongs to the technical field of insulation detection and comprises a data acquisition module, a state analysis module, a detection module and an alarm module. Wherein the data acquisition module is used for acquiring leakage current data and environmental parameter data in real time; the state analysis module is used for calculating an insulation state evaluation value according to the leakage current data and the environmental parameter data; the detection module is used for selecting an alternating current superposition method, a direct current leakage current method or a resistance voltage division method to perform insulation detection according to the insulation state evaluation value; and the alarm module is used for judging whether the detection result is abnormal or not and giving an alarm when the detection result is abnormal. By comprehensively monitoring the direct current leakage current and the environmental parameters and dynamically selecting the optimal insulation detection method, efficient and accurate evaluation of the insulation state of the direct current high-voltage energy storage system is achieved, insulation faults are effectively prevented, and safe and stable operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of insulation detection, and in particular to an insulation detection system supporting a direct current high voltage energy storage system. Background Art

[0002] DC high-voltage energy storage systems have been widely used in renewable energy generation, electric vehicle charging stations, and other fields that require high energy density and long-term energy storage due to their high efficiency and stable energy storage characteristics. However, with the increase in system voltage levels, insulation failure has become a key factor affecting the safe operation of DC high-voltage energy storage systems.

[0003] Traditional insulation detection methods, such as the single AC superposition method, DC leakage current method, or resistance voltage division method, often have many limitations when detecting the insulation status of DC high-voltage energy storage systems. These methods usually lack comprehensive consideration of environmental factors, such as temperature, humidity, etc., which may have a significant impact on insulation performance. In addition, different detection conditions (such as system operating status, load conditions, etc.) may also lead to differences in insulation test results. Therefore, traditional insulation detection methods often find it difficult to fully and accurately evaluate the insulation status of DC high-voltage energy storage systems. Summary of the invention

[0004] To solve the above problems, the present invention provides an insulation detection system supporting a DC high-voltage energy storage system. By comprehensively monitoring the DC leakage current and environmental parameters and dynamically selecting the optimal insulation detection method, an efficient and accurate assessment of the insulation status of the DC high-voltage energy storage system is achieved, insulation failures are effectively prevented, and safe and stable operation of the system is ensured.

[0005] The above objectives can be achieved through the following solutions:

[0006] An insulation detection system supporting a DC high-voltage energy storage system includes a data acquisition module, a state analysis module, a detection module and an alarm module; wherein the data acquisition module is used to collect leakage current data and environmental parameter data in real time; the state analysis module is used to calculate an insulation state evaluation value based on the leakage current data and the environmental parameter data; the detection module is used to select an AC superposition method, a DC leakage current method or a resistance voltage division method for insulation detection based on the insulation state evaluation value; and the alarm module is used to determine whether a detection result is abnormal and to issue an alarm when the detection result is abnormal.

[0007] Furthermore, the data acquisition module includes: an environmental parameter acquisition unit and a leakage current acquisition unit; wherein the leakage current acquisition unit is used to acquire leakage current data in real time; and the environmental parameter acquisition unit is used to acquire environmental parameter data in real time.

[0008] Furthermore, the state analysis module includes: a feature extraction unit, a model construction unit, and a state calculation unit; wherein the feature extraction unit is used to preprocess and extract features from the leakage current data and the environmental parameter data to obtain a feature data set; the model construction unit is used to establish an evaluation function for characterizing the insulation state evaluation value; and the state calculation unit is used to calculate the insulation state evaluation value based on the evaluation function and the feature data set.

[0009] Furthermore, the preprocessing and feature extraction of the leakage current data and the environmental parameter data to obtain a feature data set includes: extracting factors affecting insulation detection from a pre-acquired insulation resistance influencing factor library to obtain influencing features; denoising the leakage current data and the environmental parameter data to obtain a basic data set; normalizing the data in the basic data set to obtain a standard data set; and calculating the corresponding data in the standard data set according to the influencing features to obtain a feature data set.

[0010] Furthermore, the establishment of an evaluation function for characterizing the insulation state evaluation value includes: establishing a linear regression model according to the influencing feature and initializing the regression coefficient; collecting historical detection results and historical feature data sets to construct a training set; using the training set to train the linear regression model to determine the final regression coefficient; using the final regression coefficient and the feature data corresponding to the influencing feature to obtain an evaluation function for the insulation state evaluation value. ,have: ;

[0011] In the formula, For the The regression coefficient corresponding to the influencing feature is For the feature data corresponding to the influencing features, is the error term.

[0012] Furthermore, the detection module includes: a state judgment unit, an AC superposition detection unit, a DC leakage current detection unit, and a resistance voltage division detection unit; wherein the state judgment unit is used to judge the size of the insulation state evaluation value and a preset first threshold and a preset second threshold; the DC leakage current detection unit is used to perform insulation detection using a DC leakage current method when the insulation state evaluation value is less than the first threshold; the resistance voltage division detection unit is used to perform insulation detection using a resistance voltage division method when the insulation state evaluation value is greater than or equal to the first threshold and less than the second threshold; the AC superposition detection unit is used to perform insulation detection using an AC superposition method when the insulation state evaluation value is greater than or equal to the second threshold.

[0013] Furthermore, the insulation detection using the DC leakage current method includes: normalizing the leakage current data to obtain a first detection value by calculation, and for the first detection value ,have: ;

[0014] In the formula, is the proportional adjustment coefficient, is the normalized value of the leakage current data.

[0015] Furthermore, the insulation detection using the resistance voltage division method includes: using the resistance voltage division principle to divide the high-voltage end voltage of the DC high-voltage energy storage system according to a preset ratio; measuring the voltage value after the voltage division, and calculating the voltage value of the high-voltage end of the DC high-voltage energy storage system according to the preset ratio and the voltage value after the voltage division; collecting the current value of the DC high-voltage energy storage system, and calculating the first insulation resistance value according to the voltage value of the high-voltage end; normalizing the first insulation resistance value to calculate a second detection value, and for the second detection value ,have: ;

[0016] In the formula, is the normalized value of the first insulation resistance value.

[0017] Furthermore, the insulation detection using the AC superposition method includes: superimposing an AC voltage on the DC high-voltage energy storage system to obtain an AC response current; using the AC response current to calculate a second insulation resistance value; normalizing the second insulation resistance value to calculate a third detection value, and for the third detection value ,have: ;

[0018] In the formula, is the normalized value of the second insulation resistance value.

[0019] Furthermore, the alarm module includes: an abnormality judgment unit, an abnormality alarm unit and an abnormality optimization unit; wherein the abnormality judgment unit is used to judge whether the first detection value, the second detection value and the third detection value are greater than a third threshold value; the abnormality alarm unit is used to issue an alarm when the first detection value is greater than the third threshold value; the abnormality alarm unit is also used to issue an alarm when the second detection value or the third detection value is greater than the third threshold value; the abnormality optimization unit is used to optimize the linear regression coefficient of the evaluation function according to the current detection result and the current feature data set to obtain a new evaluation function when the abnormal alarm unit issues an alarm.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. The present invention can integrate environmental parameters and DC leakage current data to dynamically select the optimal insulation detection method to improve detection efficiency and accuracy; through real-time monitoring and data analysis, the system can timely detect and warn of potential insulation faults, thereby ensuring the safe and stable operation of the DC high-voltage energy storage system;

[0022] 2. The present invention realizes a comprehensive and accurate evaluation of the insulation status by comprehensively monitoring the DC leakage current and environmental parameters (such as temperature, humidity, etc.) and using advanced evaluation functions; this method effectively breaks through the limitations of the traditional single detection method, significantly improves the accuracy and reliability of the evaluation, and provides a solid guarantee for the safe operation of the system.

[0023] 3. When an abnormal insulation state is detected, the system can automatically issue an alarm to remind the operator to take timely measures; in addition, the system also has an abnormal optimization function, which can automatically optimize the evaluation function when an alarm is issued to better adapt to the insulation state of the current system and ensure the accuracy of early warning and alarm;

[0024] 4. Through real-time monitoring and early warning mechanisms, the present invention can timely discover and properly handle potential insulation faults, effectively avoiding system failures and safety accidents caused by insulation problems. This not only greatly improves the overall safety and stability of the DC high-voltage energy storage system, but also lays a solid foundation for the long-term stable operation of the system.

[0025] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a framework diagram of an insulation detection system supporting a DC high-voltage energy storage system according to an embodiment of the present invention.

[0028] Figure 2It is a schematic diagram of the process execution of the insulation detection system supporting the DC high-voltage energy storage system according to an embodiment of the present invention.

[0029] Figure 3 It is a structural schematic diagram of an insulation detection system supporting a DC high-voltage energy storage system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0031] Reference Figure 1 An embodiment of the present invention proposes an insulation detection system that supports a DC high-voltage energy storage system. By comprehensively monitoring the DC leakage current and environmental parameters and dynamically selecting the optimal insulation detection method, an efficient and accurate assessment of the insulation status of the DC high-voltage energy storage system is achieved, insulation failures are effectively prevented, and safe and stable operation of the system is ensured.

[0032] The system of this embodiment specifically includes: a data acquisition module, a state analysis module, a detection module and an alarm module; wherein:

[0033] The data acquisition module is used to collect leakage current data and environmental parameter data in real time;

[0034] Specifically, the leakage current data and environmental parameter data (such as temperature, humidity and other factors that can affect the insulation status assessment) in the real-time acquisition system provide basic data for insulation status assessment.

[0035] The state analysis module is used to calculate the insulation state evaluation value according to the leakage current data and the environmental parameter data;

[0036] Specifically, based on the collected leakage current and environmental parameter data, the insulation status assessment value is calculated through an internal algorithm to quantify the insulation performance of the system.

[0037] The detection module is used to select an AC superposition method, a DC leakage current method or a resistance voltage division method to perform insulation detection according to the insulation state evaluation value;

[0038] Specifically, according to the insulation status evaluation value, the optimal insulation detection method (AC superposition method, DC leakage current method or resistance voltage division method) is intelligently selected for detection to improve detection efficiency and accuracy.

[0039] The alarm module is used to determine whether the detection result is abnormal, and to issue an alarm when the detection result is abnormal.

[0040] Specifically, the detection results are judged as abnormal, and once an abnormal insulation state is found, an alarm signal is immediately issued to remind the operator to take timely measures.

[0041] For example, Figure 2 As shown, it is assumed that a DC high-voltage energy storage system is in operation and is equipped with the above-mentioned insulation detection system; the leakage current acquisition unit monitors the system leakage current of 5μA in real time, and the environmental parameter acquisition unit measures the current ambient temperature of 25°C and the humidity of 50%; after receiving the above data, the state analysis module performs preprocessing and feature extraction to obtain feature data closely related to the insulation state; according to the feature data and the established evaluation function, the current insulation state evaluation value is calculated; the detection module determines whether the insulation state evaluation value is less than the first threshold value, and if so, performs insulation detection according to the DC leakage current method to obtain the first detection value; if not, it determines whether the insulation state evaluation value is less than the first threshold value. If it exceeds the second threshold, insulation detection is performed according to the resistance voltage division method to obtain a second detection value; if not, insulation detection is performed according to the AC superposition method to obtain a third detection value; then, the alarm module determines whether the first detection value, the second detection value or the third detection value is greater than the third threshold, if not, the relevant data continues to be collected for re-judgment; if so, the alarm module will immediately issue an alarm signal, and optimize the linear regression coefficient of the evaluation function through the abnormal optimization unit to obtain a new evaluation function to better adapt to the insulation status of the current system; through the above process, the system can realize efficient and accurate evaluation of the insulation status of the DC high-voltage energy storage system to ensure the safe and stable operation of the system.

[0042] Furthermore, if Figure 3 As shown, the data acquisition module includes: an environmental parameter acquisition unit and a leakage current acquisition unit; wherein:

[0043] The leakage current collection unit is used to collect leakage current data in real time;

[0044] Specifically, the leakage current acquisition unit is responsible for collecting the leakage current data of the DC high-voltage energy storage system in real time; leakage current is one of the important indicators for evaluating the insulation state, which reflects the current leakage of the insulating material under the action of the electric field.

[0045] Exemplarily, the leakage current acquisition unit usually includes a high-precision current sensor or ammeter, which can monitor and measure the tiny current passing through the insulating material in real time; these data will be recorded and used for subsequent insulation status analysis and evaluation; assuming that in a DC high-voltage energy storage system, the leakage current acquisition unit collects the following data during continuous monitoring: Initial moment: leakage current is 1μA, after 1 hour of operation: leakage current increases to 2μA, after 12 hours of operation: leakage current stabilizes at 3μA, the changing trend of these data will help the system determine whether the insulation status has deteriorated, so as to take necessary maintenance measures in time.

[0046] The environmental parameter acquisition unit is used to acquire environmental parameter data in real time.

[0047] Specifically, the environmental parameter acquisition unit is responsible for real-time acquisition of environmental parameter data that affect insulation performance, including but not limited to temperature, humidity, air pressure, etc. These environmental parameters have a significant impact on the performance of insulation materials and must therefore be taken into consideration in insulation status assessment.

[0048] For example, the environmental parameter acquisition unit may integrate various sensors, such as temperature sensors, humidity sensors, and air pressure sensors, which can monitor changes in the surrounding environment in real time and convert the collected data into electrical signals or digital signals for subsequent processing and analysis. In an actual DC high-voltage energy storage system, the environmental parameter acquisition unit collects the following data at different time periods of the day: 10 a.m.: temperature 25°C, humidity 50%, 3 p.m.: temperature 30°C, humidity 70%, 9 p.m.: temperature 20°C, humidity 60%; these data will be used for subsequent insulation status evaluation to more accurately reflect the impact of the environment on insulation performance.

[0049] Furthermore, if Figure 3 As shown, the state analysis module includes: a feature extraction unit, a model building unit, and a state calculation unit; wherein:

[0050] The feature extraction unit is used to preprocess and extract features from the leakage current data and the environmental parameter data to obtain a feature data set;

[0051] Specifically, the feature extraction unit is responsible for preprocessing and feature extraction of the collected leakage current data and environmental parameter data; this includes denoising, normalization, and calculation based on features closely related to the insulation state to obtain a feature data set for subsequent evaluation.

[0052] For example, the feature extraction unit may extract features such as the rate of change and peak value of the current from the leakage current data, and extract features such as the average value and fluctuation range of temperature and humidity from the environmental parameter data; then, these data are normalized to ensure that they are comparable on the same scale.

[0053] The model building unit establishes an evaluation function for characterizing the insulation state evaluation value;

[0054] Specifically, the model building unit establishes an evaluation function for characterizing the insulation status evaluation value based on historical insulation detection data; this can be achieved through machine learning algorithms such as linear regression, support vector machine, neural network, etc.

[0055] For example, the model building unit may establish a linear regression model based on historical data, in which the insulation status evaluation value is a linear combination of features such as the leakage current change rate, temperature average, humidity fluctuation range, etc.; through training, the model will learn the mapping relationship between these features and the insulation status, and give the final regression coefficient.

[0056] The state calculation unit is used to calculate the insulation state evaluation value according to the evaluation function and the characteristic data set.

[0057] Specifically, the state calculation unit calculates the insulation state evaluation value according to the evaluation function and the characteristic data; this evaluation value will be used to determine whether the insulation state of the system is normal.

[0058] Exemplarily, the state calculation unit substitutes the extracted feature data into the evaluation function to calculate the insulation state evaluation value; if the evaluation value is lower than a certain threshold, it indicates that the insulation state is good; if the evaluation value is higher than a certain threshold, it indicates that there may be problems with the insulation state and further inspection is required.

[0059] Furthermore, the preprocessing and feature extraction of the leakage current data and the environmental parameter data to obtain a feature data set includes:

[0060] Extract factors affecting insulation detection from a pre-acquired insulation resistance influencing factor library to obtain influencing features;

[0061] Specifically, it is necessary to analyze which features are closely related to the insulation status; this is usually based on research on domain knowledge and historical data; for example, in a DC high-voltage energy storage system, the size of the leakage current, its changing trend, and environmental parameters such as temperature and humidity may affect the performance of the insulation material; therefore, these parameters are identified as influencing features.

[0062] Exemplarily, leakage current characteristics may include the average value, maximum value, minimum value, change rate, etc. of the leakage current; environmental parameter characteristics may include the average value, maximum value, minimum value, fluctuation range of temperature, the average value, maximum value, minimum value, etc. of humidity.

[0063] Performing denoising processing on the leakage current data and the environmental parameter data to obtain a basic data set;

[0064] Specifically, the raw data may contain noise, which may come from sensor instability, electromagnetic interference, etc.; denoising processing aims to eliminate such noise and retain the real data signal; commonly used denoising methods include filtering, smoothing, etc.

[0065] For example, for leakage current data, median filtering or mean filtering can be used to remove sudden noise points; for environmental parameter data, sliding average or exponential smoothing can be used to eliminate data fluctuations and noise.

[0066] Normalizing the data in the basic data set to obtain a standard data set;

[0067] Specifically, normalization is the process of converting data to a uniform scale for comparison and calculation; this usually involves scaling the data to a range of 0 to 1 or -1 to 1; normalization helps eliminate the impact between data of different dimensions and improves the accuracy and stability of the algorithm.

[0068] For example, for leakage current data, if its range is 0 to 10 μA, each data point can be divided by 10 μA to obtain normalized data; for temperature data, if its range is -20 to 50°C, each data point can be subtracted from -20 and then divided by 70 to obtain normalized data.

[0069] The corresponding data in the standard data set is calculated according to the influencing characteristics to obtain a characteristic data set.

[0070] Specifically, according to the previously determined impact characteristics, the normalized standard data is calculated to obtain characteristic data; these characteristic data will be used for subsequent status analysis and evaluation.

[0071] For example, for leakage current characteristics, the normalized average value and change rate of leakage current can be calculated as characteristic data; for environmental parameter characteristics, the normalized average value and fluctuation range of temperature and the average value of humidity can be calculated as characteristic data.

[0072] Furthermore, the establishment of an evaluation function for characterizing the insulation state evaluation value includes:

[0073] Establishing a linear regression model according to the influencing characteristics and initializing the regression coefficients;

[0074] Specifically, a linear regression model is established based on the influencing characteristics obtained from the previous analysis (such as the average value and change rate of leakage current, the average value and fluctuation range of temperature, the average value of humidity, etc.); the linear regression model is a simple and commonly used statistical method to describe the linear relationship between independent variables (influencing characteristics) and dependent variables (insulation status evaluation values); when establishing the model, a regression coefficient needs to be assigned to each influencing characteristic, and these coefficients can be random or set based on some prior knowledge at the initial stage.

[0075] Collect historical detection results and historical feature data sets to build a training set;

[0076] Using the training set to train the linear regression model, thereby determining a final regression coefficient;

[0077] Specifically, historical detection results and historical feature data sets (i.e., known influencing feature values ​​and corresponding insulation status assessment values) are used to train a linear regression model; the training process usually involves using some optimization algorithm (such as the least squares method) to adjust the regression coefficient so that the error between the model prediction value and the actual value is minimized.

[0078] Using the final regression coefficient and the characteristic data corresponding to the influencing feature, an evaluation function is obtained, and for the insulation state evaluation value ,have: ;

[0079] In the formula, For the The regression coefficient corresponding to the influencing feature is For the feature data corresponding to the influencing features, is the error term.

[0080] Specifically, once the final regression coefficients are obtained, these coefficients and real-time or future characteristic data can be substituted into a linear regression model to obtain a prediction or estimate of the insulation state assessment value.

[0081] For example, it is assumed that there are three influencing characteristics: the average value of the leakage current ( ), the average temperature ( ) and the average humidity ( ); then the following linear regression model can be established: ; Assume that there are several sets of historical data, each of which contains , , The value and corresponding Value; these data can be used to train a linear regression model and obtain the final regression coefficient by minimizing the prediction error , , ; Through training, we get the final regression coefficient , , ; The following evaluation function can be used to calculate the insulation condition evaluation value: .

[0082] Furthermore, if Figure 3 As shown, the detection module includes: a state judgment unit, an AC superposition detection unit, a DC leakage current detection unit, and a resistance voltage division detection unit; wherein,

[0083] The state judgment unit is used to judge the magnitude of the insulation state evaluation value and a preset first threshold and a preset second threshold;

[0084] Specifically, the state judgment unit is responsible for judging the magnitude of the insulation state evaluation value and the preset first threshold and second threshold to determine which insulation detection method to adopt.

[0085] Exemplarily, if the insulation status evaluation value is less than the first threshold value (such as 0.5), it indicates that the insulation status is very good, and a simple DC leakage current method can be selected for detection; if the evaluation value is between the first threshold value and the second threshold value (such as 0.5 to 0.8), the resistance voltage division method is selected for detection; if the evaluation value is greater than or equal to the second threshold value (such as 0.8), a more reliable AC superposition method is selected for detection.

[0086] The DC leakage current detection unit is used to perform insulation detection using a DC leakage current method when the insulation state evaluation value is less than the first threshold value;

[0087] Specifically, when the insulation state evaluation value is less than the first threshold, the DC leakage current detection unit performs insulation detection according to the DC leakage current method; this usually involves measuring the magnitude of the leakage current and performing normalization processing to obtain a detection value.

[0088] The resistance voltage division detection unit is used to perform insulation detection using a resistance voltage division method when the insulation state evaluation value is greater than or equal to the first threshold value and less than the second threshold value;

[0089] Specifically, when the insulation state evaluation value is between the first threshold and the second threshold, the resistance voltage division detection unit performs insulation detection according to the resistance voltage division method; this involves measuring the high voltage terminal voltage using the resistance voltage division principle and calculating the insulation resistance value.

[0090] The AC superposition detection unit is used to perform insulation detection using an AC superposition method when the insulation state evaluation value is greater than or equal to the second threshold.

[0091] Specifically, when the insulation state evaluation value is greater than or equal to the second threshold, the AC superposition detection unit performs insulation detection according to the AC superposition method; this involves superimposing an AC voltage on the DC high-voltage energy storage system and measuring the AC response current to calculate the insulation resistance value.

[0092] Furthermore, the insulation detection using the DC leakage current method includes:

[0093] The leakage current data is normalized to obtain a first detection value. ,have: ;

[0094] In the formula, is the proportional adjustment coefficient, is the normalized value of the leakage current data.

[0095] Specifically, specialized test equipment or instruments are required to measure the leakage current of a device under DC voltage; this usually involves connecting the device to a test circuit, then applying a known DC voltage and measuring the resulting current; this current value is the leakage current data, which reflects the insulation material's ability to block current.

[0096] Specifically, normalization is a commonly used data preprocessing technology, which can convert data of different dimensions or different ranges to the same standard or range for comparison and analysis; in insulation testing, normalization of leakage current data can eliminate the influence of different equipment, different test conditions or different insulation materials on the leakage current value, thereby obtaining a relatively standardized test value; there are many methods of normalization, such as linear normalization, logarithmic normalization, etc. Here, we use the linear normalization method, that is, dividing the leakage current data by a reference value or maximum value to obtain a value between 0 and 1 as the first test value; the proportional adjustment coefficient It is used to unify the first detection value, the second detection value and the third detection value onto the same quantitative scale, so as to facilitate unified comparison.

[0097] For example, assuming that the leakage current data of the device is 1mA, and we know that under the same conditions, the maximum leakage current value of this type of device is usually 10mA; then 1mA can be divided by 10mA to obtain the normalized first detection value is 0.1; that is: = =1mA / 10mA=0.1This is the first detection value It can be used as an indicator to evaluate the insulation status of the equipment. In practical applications, one or more thresholds can be set according to the specific conditions and insulation requirements of the equipment to determine whether the insulation status of the equipment is qualified. If the value exceeds a certain threshold, further inspection or maintenance of the equipment may be required to ensure that its insulation performance meets the requirements.

[0098] Furthermore, the insulation detection using the resistance voltage division method includes:

[0099] Using the resistor voltage division principle, the high voltage terminal voltage of the DC high voltage energy storage system is divided according to a preset ratio;

[0100] Specifically, two series resistors (R1 and R2) need to be selected and connected in parallel with the high voltage end of the DC high voltage energy storage system (assuming its resistance is Riso); by adjusting the resistance values ​​of R1 and R2, the high voltage end voltage can be divided according to a preset ratio; the divided voltage value (VM1) will appear at both ends of R2.

[0101] Measuring the voltage value after voltage division, and calculating the voltage value of the high voltage end of the DC high voltage energy storage system according to the preset ratio and the voltage value after voltage division;

[0102] Specifically, use a voltmeter to measure the voltage value (VM1) across R2; then, according to the voltage division formula of the series resistor (VM1=(R2 / (R1+R2))*Vhigh, where Vhigh is the voltage at the high-voltage end), the voltage value at the high-voltage end can be inferred.

[0103] Collecting the current value of the DC high-voltage energy storage system, and calculating the first insulation resistance value according to the voltage value of the high-voltage end;

[0104] Specifically, after obtaining the voltage value Vhigh of the high-voltage end, the insulation resistance Riso, ie, the first insulation resistance value, is calculated using Ohm's law R=V / I in combination with the current I passing through the circuit (which can be obtained by measurement or calculation).

[0105] The first insulation resistance value is normalized to obtain a second detection value. ,have: ;

[0106] In the formula, is the normalized value of the first insulation resistance value.

[0107] Specifically, the normalization process is to convert the first insulation resistance value into a standardized value for comparison and analysis; here, the linear normalization method can be used, that is, the first insulation resistance value is divided by a reference value (such as the maximum insulation resistance value of the type of equipment) to obtain ; Then, using the formula Calculate the second detection value .

[0108] For example, suppose there is a DC high-voltage energy storage system with a high-voltage terminal voltage of 500V, and R1=49MΩ and R2=1MΩ are selected as voltage-dividing resistors; the voltage value (VM1) across R2 is measured to be 10V; according to the voltage-dividing formula, the high-voltage terminal voltage Vhigh=(R1+R2)*VM1 / R2=(49MΩ+1MΩ)x10V / 1MΩ=500V can be calculated, which is consistent with the assumed value; suppose the measured current value passing through the parallel circuit is 0.0002A (200μA); then, the insulation resistance (Riso) value = Vhigh / I=500V / 0.0002A=2.5GΩ; suppose the maximum insulation resistance value of this type of equipment is 10GΩ; then, =2.5GΩ / 10GΩ=0.25; then, the second detection value =1- =1-0.25=0.75.

[0109] Furthermore, the insulation detection using the AC superposition method includes:

[0110] Superimposing an AC voltage on a DC high-voltage energy storage system to obtain an AC response current;

[0111] Specifically, an AC voltage signal with known frequency and amplitude needs to be superimposed on the high voltage end or test point of the DC high voltage energy storage system; this AC voltage signal will cause a current response in the insulating material, namely, an AC response current.

[0112] Calculating a second insulation resistance value using the AC response current;

[0113] Specifically, the insulation resistance value under the action of the AC voltage, ie, the second insulation resistance value, can be calculated by measuring the AC response current; this calculation process may involve analysis of the AC circuit and application of Ohm's law.

[0114] The second insulation resistance value is normalized to obtain a third detection value. ,have: ;

[0115] In the formula, is the normalized value of the second insulation resistance value.

[0116] Specifically, in order to facilitate comparison and analysis, the second insulation resistance value needs to be normalized; this usually involves dividing the insulation resistance value by a reference value (such as the maximum insulation resistance value of this type of equipment) to obtain a standardized value. ; Then, using the formula Calculate the third detection value .

[0117] For example, suppose there is a DC high-voltage energy storage system with a rated voltage of 500V; it is decided to use the AC superposition method for insulation detection, and the superimposed AC voltage signal frequency is 101Hz and the amplitude is 50V; a 101Hz, 50V AC voltage signal is superimposed on the high-voltage end of the DC high-voltage energy storage system; an ammeter is used to measure the resulting AC response current, assuming that the measured AC response current effective value is 1mA; in this example, the AC response current can be regarded as the current passing through the insulation resistor; therefore, Ohm's law can be used to calculate the insulation resistance value (i.e., the second insulation resistance value) under the action of the AC voltage, and the calculation process is as follows: RisoAC=Vac / Iac, where RisoAC is the second insulation resistance value, Vac is the superimposed AC voltage amplitude (50V), and Iac is the AC response current effective value (1mA, i.e., 0.001A); substituting the numerical values ​​into the calculation, the result is: RisoAC=50V / 0.001A=50kΩ; assuming that the maximum insulation resistance value of this type of equipment is 1MΩ (i.e., 1000kΩ), then the normalized value for: =RisoAC / Risomax, substitute the numerical value and calculate =50kΩ / 1000kΩ=0.05, then =1- =1-0.05=0.95; Through the above steps and calculations, the third detection value is obtained , which can be used as an indicator to evaluate the insulation performance of DC high-voltage energy storage systems; in practical applications, the frequency and amplitude of the superimposed AC voltage signal, as well as the normalized reference value, can be adjusted according to specific needs and equipment characteristics.

[0118] Furthermore, if Figure 3 As shown, the alarm module includes: an abnormality judgment unit, an abnormality alarm unit and an abnormality optimization unit; wherein:

[0119] The abnormality judgment unit is used to judge whether the first detection value, the second detection value and the third detection value are greater than a third threshold;

[0120] Specifically, this unit is responsible for comparing the first detection value ( )、Second detection value( ) and the third detection value ( ) and the preset third threshold ( ); If any detection value is greater than the third threshold, it is considered that the insulation state may be abnormal; By adjusting the second detection value ( ) and the third detection value ( ) is the reference value in the normalization process, so that the preset third threshold ( ) can also be used as the first detection value ( )、Second detection value( ) and the third detection value ( ) to determine the abnormality.

[0121] The abnormal alarm unit is used to issue an alarm when the first detection value is greater than a third threshold value; the abnormal alarm unit is also used to issue an alarm when the second detection value or the third detection value is greater than a third threshold value;

[0122] Specifically, when the abnormality judgment unit detects an abnormality (i.e. any detection value is greater than the third threshold), this unit will trigger an alarm mechanism and send an alarm signal to the operator or system; this can be through sound, light, email, text message or other forms of notification.

[0123] The abnormality optimization unit is used to optimize the linear regression coefficient of the evaluation function according to the current detection result and the current feature data set to obtain a new evaluation function when the abnormality alarm unit issues an alarm.

[0124] Specifically, when the abnormal alarm unit sounds an alarm, the abnormal optimization unit starts working; it optimizes the linear regression coefficient of the evaluation function based on the current insulation detection data (including the first, second and third detection values ​​and possible other related data); the evaluation function is usually used to convert the detection value into an insulation status indicator or risk level that is easier to understand; the optimized evaluation function can more accurately reflect the relationship between the insulation status and the detection value.

[0125] For example, it is assumed that there is an insulation detection system, in which the third threshold ( ) is set to 0.8; the evaluation function of the system is originally a linear regression model obtained by fitting a set of historical data, which is used to convert the detection value into an insulation status indicator; suppose that in a certain detection, the second detection value is 0.85; the abnormality judgment unit sets the second detection value Compared with the third threshold 0.8, it is found that and are all greater than 0.8, so it is judged that the insulation state is abnormal; the abnormal alarm unit immediately sounds an alarm after receiving the signal from the abnormal judgment unit; this may be through the buzzer in the control room to sound an alarm, and at the same time send an alarm information to the maintenance personnel by email; at the same time as the alarm is issued, the abnormal optimization unit starts working; it first collects the current insulation detection data (including T1, T2, T3 and possible other parameters such as temperature, humidity, etc.); then, use these data to refit or adjust the linear regression coefficient of the evaluation function; for example, the accuracy of the model may be improved by adding new data points or adjusting weights; the optimized evaluation function will be able to more accurately predict the insulation state or risk level based on future detection values; through this process, the alarm module can not only issue an alarm in time when the insulation state is abnormal, but also improve the accuracy and reliability of the entire insulation detection system by continuously optimizing the evaluation function.

[0126] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean direct connection of the lines, and the indirect connection mode can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0127] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptation of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. Support the insulation detection system of DC high voltage energy storage system, characterized by: Data acquisition module, status analysis module, detection module and alarm module; among which: The data acquisition module is used to collect leakage current data and environmental parameter data in real time; The state analysis module is used to calculate the insulation state evaluation value according to the leakage current data and the environmental parameter data; The detection module is used to select an AC superposition method, a DC leakage current method or a resistance voltage division method to perform insulation detection according to the insulation state evaluation value; The alarm module is used to determine whether the detection result is abnormal, and to issue an alarm when the detection result is abnormal.

2. The insulation detection system supporting a DC high voltage energy storage system according to claim 1, characterized in that: The data acquisition module includes: an environmental parameter acquisition unit and a leakage current acquisition unit; wherein: The leakage current collection unit is used to collect leakage current data in real time; The environmental parameter acquisition unit is used to acquire environmental parameter data in real time.

3. The insulation detection system supporting a DC high voltage energy storage system according to claim 1, characterized in that: The state analysis module includes: a feature extraction unit, a model building unit, and a state calculation unit; wherein: The feature extraction unit is used to preprocess and extract features from the leakage current data and the environmental parameter data to obtain a feature data set; The model building unit is used to establish an evaluation function for characterizing the insulation state evaluation value; The state calculation unit is used to calculate the insulation state evaluation value according to the evaluation function and the characteristic data set.

4. The insulation detection system supporting a DC high voltage energy storage system according to claim 3, characterized in that: The preprocessing and feature extraction of the leakage current data and the environmental parameter data to obtain a feature data set includes: Extract factors affecting insulation detection from a pre-acquired insulation resistance influencing factor library to obtain influencing features; Performing denoising processing on the leakage current data and the environmental parameter data to obtain a basic data set; Normalizing the data in the basic data set to obtain a standard data set; The corresponding data in the standard data set is calculated according to the influencing characteristics to obtain a characteristic data set.

5. The insulation detection system supporting a DC high voltage energy storage system according to claim 4, characterized in that: The establishment of an evaluation function for characterizing the insulation status evaluation value comprises: Establishing a linear regression model according to the influencing characteristics and initializing the regression coefficients; Collect historical detection results and historical feature data sets to build a training set; Using the training set to train the linear regression model, thereby determining a final regression coefficient; Using the final regression coefficient and the characteristic data corresponding to the influencing feature, an evaluation function is obtained, and for the insulation state evaluation value ,have: ； In the formula, For the The regression coefficient corresponding to the influencing feature is For the feature data corresponding to the influencing features, is the error term.

6. The insulation detection system supporting a DC high voltage energy storage system according to claim 5, characterized in that: The detection module includes: a state judgment unit, an AC superposition detection unit, a DC leakage current detection unit, and a resistance voltage division detection unit; wherein: The state judgment unit is used to judge the magnitude of the insulation state evaluation value and a preset first threshold and a preset second threshold; The DC leakage current detection unit is used to perform insulation detection using a DC leakage current method when the insulation state evaluation value is less than the first threshold value; The resistance voltage division detection unit is used to perform insulation detection using a resistance voltage division method when the insulation state evaluation value is greater than or equal to the first threshold value and less than the second threshold value; The AC superposition detection unit is used to perform insulation detection using an AC superposition method when the insulation state evaluation value is greater than or equal to the second threshold.

7. The insulation detection system supporting a DC high voltage energy storage system according to claim 6, characterized in that: The insulation detection using the DC leakage current method includes: The leakage current data is normalized to obtain a first detection value. ,have: ： In the formula, is the proportional adjustment coefficient, is the normalized value of the leakage current data.

8. The insulation detection system supporting a DC high voltage energy storage system according to claim 7, characterized in that: The insulation detection using the resistance voltage division method includes: Using the resistor voltage division principle, the high voltage terminal voltage of the DC high voltage energy storage system is divided according to a preset ratio; Measuring the voltage value after voltage division, and calculating the voltage value of the high voltage end of the DC high voltage energy storage system according to the preset ratio and the voltage value after voltage division; Collecting the current value of the DC high-voltage energy storage system, and calculating the first insulation resistance value according to the voltage value of the high-voltage end; The first insulation resistance value is normalized to obtain a second detection value. ,have: ； In the formula, is the normalized value of the first insulation resistance value.

9. The insulation detection system supporting a DC high voltage energy storage system according to claim 8, characterized in that: The insulation detection using the AC superposition method comprises: Superimposing an AC voltage on a DC high-voltage energy storage system to obtain an AC response current; Calculating a second insulation resistance value using the AC response current; The second insulation resistance value is normalized to obtain a third detection value. ,have: ； In the formula, is the normalized value of the second insulation resistance value.

10. The insulation detection system supporting a DC high voltage energy storage system according to claim 9, characterized in that: The alarm module includes: an abnormality judgment unit, an abnormality alarm unit and an abnormality optimization unit; wherein, The abnormality judgment unit is used to judge whether the first detection value, the second detection value and the third detection value are greater than a third threshold; The abnormal alarm unit is used to issue an alarm when the first detection value is greater than a third threshold value; the abnormal alarm unit is also used to issue an alarm when the second detection value or the third detection value is greater than a third threshold value; The abnormality optimization unit is used to optimize the linear regression coefficient of the evaluation function according to the current detection result and the current feature data set to obtain a new evaluation function when the abnormality alarm unit issues an alarm.

Images