A Fault Prediction Method Based on Impedance Change of Parallel Capacitor Device

Abstract

The present invention relates to a fault prediction method based on the impedance change of a parallel capacitor device. The method calculates the equivalent impedance of each sampling point by establishing a mathematical model of a target parallel capacitor device and monitoring the voltage and current parameters of the circuit in real time. A statistical analysis method is used to calculate the mean and standard deviation of the equivalent impedance, and then a time series of standard deviations of impedance samples is constructed. By comparing the standard deviations of consecutive sampling cycles, the impedance fluctuation rate is determined, so that the state of the capacitor device is monitored in real time and fault prediction is performed. Fault judgment is based on the impedance fluctuation rate. If the fluctuation exceeds the set threshold, the system will trigger an alarm to indicate a potential equipment abnormality. This method can effectively realize early diagnosis of parallel capacitor device faults, improve the reliability and safety of the power system, and avoid production interruptions and economic losses caused by equipment failures.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical fault detection, and in particular to a fault prediction method based on impedance change of a parallel capacitor device. Background Art

[0002] In the power system, capacitors are one of the key components, mainly used to improve the power factor of the power grid, optimize load distribution, reduce power loss, and improve voltage quality. Due to its important role in the operation of the power grid, the reliability of capacitors directly affects the stability and efficiency of the entire power system.

[0003] Capacitors are affected by many factors during operation, including ambient temperature, grid load fluctuations, electrical stress, and physical aging. These factors may cause the performance of capacitors to gradually degrade, such as capacitance drop, insulation deterioration, etc. Especially in high-voltage power grids, capacitor failures may lead to serious safety accidents, such as short circuits in the power system, voltage collapse, and even large-scale power outages.

[0004] At present, the health status monitoring of capacitors mainly relies on regular physical inspections and parameter tests, such as evaluating their health status by measuring indicators such as capacitance and loss angle. In terms of capacitor health monitoring and fault prediction, existing technologies mainly rely on the following methods:

[0005] 1. Physical inspection and periodic testing: This traditional method involves regular physical inspection and electrical testing of capacitors, such as capacitance measurement and loss angle analysis. By comparing with historical data, its health status is analyzed. Disadvantages: It requires shutdown inspection, resulting in grid operation interruption. In addition, this method is difficult to capture early signs of failure and can usually only be detected when a failure is about to occur.

[0006] 2. Online monitoring system: Various sensors (such as temperature sensors, voltage and current sensors) are used to monitor the key parameters of capacitors in real time. Disadvantages: Although real-time monitoring of capacitors can be achieved, the installation and maintenance costs of these systems are high. In addition, due to environmental interference and the limitations of the equipment itself, the accuracy and reliability of the data are often challenged.

[0007] 3. Non-destructive testing technology: such as using acoustic emission technology, thermal imaging technology and partial discharge monitoring to detect internal abnormalities of capacitors. Disadvantages: These technologies are usually expensive and complex, requiring professionals to operate and interpret the results, and are difficult to be widely used in all power facilities.

[0008] In summary, the shortcomings of the existing technology mainly include:

[0009] Cost issues: Many advanced monitoring technology equipment are expensive, which is a heavy economic burden for large-scale deployment. Operational complexity: Advanced technologies such as partial discharge monitoring and acoustic emission monitoring require professional operation and complex data analysis, and have high requirements for operators. Interference issues: In actual applications, environmental factors such as temperature changes, humidity, and electromagnetic interference may affect the accuracy of monitoring data. Insufficient real-time performance: Some traditional monitoring methods cannot provide real-time data, which may not provide timely warnings when the capacitor deteriorates rapidly, increasing the risk of failure. Limited early warning capabilities: Existing technologies often find it difficult to predict the occurrence of failures. In most cases, alarms can only be issued when a failure is about to occur, making it difficult to achieve preventive maintenance.

[0010] In addition, although some advanced monitoring technologies, such as non-destructive detection methods based on acoustic emission or electromagnetic wave technology, have been proposed and implemented to try to achieve online monitoring of capacitors, these technologies often have high equipment costs, and complex signal processing and environmental interference greatly limit their widespread application.

[0011] Therefore, developing a method that can monitor the capacitor status in real time and online and effectively predict its potential faults is of great significance to ensure the stable operation of the power system. The fault prediction technology based on impedance change provided by the present invention comes into being under this background and aims to provide an economical and effective solution. Summary of the invention

[0012] In view of the above problems, the present invention aims to propose: a fault prediction method based on impedance change of a parallel capacitor device, comprising the following steps:

[0013] S1. Establish a mathematical model: Establish impedance for the target parallel capacitor device Mathematical model of

[0014] S2. Collect voltage and current parameters: Set multiple sampling points, monitor the voltage and current parameters of the circuit in real time, and calculate the voltage-current ratio as the equivalent impedance of each sampling point to meet the following requirements:

[0015] ;

[0016] in, Indicates the sampling point number, represents the sampling time, Indicates the serial number is The sampling point is The equivalent impedance at time , is the total number of sampling points; is the initial sampling time, is the sampling interval, for The total number of sampling cycles at the time;

[0017] S3. Calculate the impedance fluctuation rate of the sampling point: specifically including:

[0018] S31. Construct the equivalent impedance data set of sampling points: The equivalent impedance of each sampling point Constructed as an equivalent impedance series ;

[0019] S32. Calculate the mean of the equivalent impedance series ,satisfy:

[0020] ;

[0021] S33. Calculate the sample standard deviation of the equivalent impedance series, satisfying:

[0022] ;

[0023] in, Indicated in The sample standard deviation of the equivalent impedance of all sampling points at the moment;

[0024] S34, constructing a time series sample standard deviation series: constructing the sample standard deviation of the equivalent impedance sequence calculated in each sampling period into a series form to obtain a time series sample standard deviation series;

[0025] S35. Calculate the mean of the standard deviation of the time series samples as the impedance fluctuation rate of the sampling point ;

[0026] S4. Abnormal diagnosis based on impedance fluctuation rate: At this moment, the judgment conditions are:

[0027] ;

[0028] in, Indicated in The sample standard deviation of the sampling points calculated at each moment; Indicates the fluctuation allowable value;

[0029] If the above judgment conditions are met , then it is determined that the target parallel capacitor device is normal;

[0030] If the above judgment conditions are not met , it is determined that the target parallel capacitor device is abnormal;

[0031] S5. Providing an alarm based on an abnormality: when it is determined that the target parallel capacitor device is abnormal, an alarm signal is output to indicate that a fault exists in the target parallel capacitor device.

[0032] Furthermore, in step S1, the impedance The mathematical model satisfies:

[0033] ;

[0034] in, Indicates the first The equivalent impedance of the resistive elements is A resistive element; Indicates the first The equivalent capacitive reactance of the capacitive elements is A capacitive element, Represents the frequency response characteristic attenuation index; Indicates the first The equivalent inductive reactance of the inductive components is An inductive component.

[0035] Furthermore, in step S34, the time series sample standard deviation sequence satisfies:

[0036] ;

[0037] in, is the standard deviation of the time series samples, Indicates the sampling cycle number.

[0038] Further, in step S35, the impedance fluctuation rate of the sampling point satisfy:

[0039] ;

[0040] in, Indicated in The impedance fluctuation rate at the sampling point at time.

[0041] Furthermore, step S3 also includes the following steps:

[0042] S36. Analyze the fluctuation trend of the standard deviation series of time series samples and calculate the slope of the fluctuation trend ,satisfy:

[0043] ;

[0044] in, It represents the slope of the fluctuation trend, which is numerically equal to the sample standard deviation at Subsampling and The slope of change between subsamples.

[0045] Furthermore, step S4 also includes: if the judgment condition is met , then further determine:

[0046] ;

[0047] in, represents the slope of the benchmark fluctuation trend, Indicates the allowable deviation of the fluctuation trend slope.

[0048] Furthermore, if the above judgment conditions are met , then the target parallel capacitor device is determined to be stable; if the above judgment conditions are not met , then it is determined that the target parallel capacitor device is unstable.

[0049] Furthermore, according to the judgment condition and judgment conditions , output the judgment results for the target parallel capacitor device, including: normal and stable, normal but unstable, abnormal.

[0050] The beneficial effects of the present invention are:

[0051] 1. Early warning capability: By real-time monitoring of the impedance changes of parallel capacitor devices and calculating the fluctuation rate of equivalent impedance, the present invention can instantly identify abnormal conditions before capacitor failure occurs. This early warning mechanism can greatly reduce the operation interruption and maintenance costs caused by sudden failures and ensure the stable operation of the power system.

[0052] 2. Reduce maintenance costs: Traditional capacitor fault detection methods often require equipment shutdown and manual detection, while the present invention can be performed without interrupting equipment operation through online monitoring and data analysis. This can significantly reduce the economic losses caused by downtime required for maintenance and detection.

[0053] 3. Accurate fault location: By analyzing the value and fluctuation of the impedance at each sampling point, the present invention can not only accurately determine whether there is a fault in the equipment, but also make a preliminary judgment on the type and severity of the fault through the change trend of the impedance, thereby achieving accurate fault location and classification.

[0054] 4. Improve equipment life: Through continuous health status monitoring and fault prediction of parallel capacitor devices, small problems in the early stage can be discovered and repaired in time to prevent these small problems from turning into major faults, thereby extending the service life of the equipment.

[0055] 5. Optimize operation and maintenance decision support: The data analysis and fault prediction results provided by the present invention can provide scientific data support for the operation and maintenance team, helping them to make more accurate and efficient operation and maintenance decisions, optimize maintenance resources and scheduling, and improve the overall power system operation efficiency.

[0056] 6. Strong environmental adaptability: The present invention is applicable to parallel capacitor devices under various environmental conditions, whether it is a high temperature, low temperature or high humidity environment, and can accurately monitor and predict faults, thereby enhancing the reliability of the system in harsh environments.

[0057] Through these beneficial effects, the present invention not only improves the monitoring and maintenance efficiency of power equipment, but also enhances the safety and reliability of the power grid, providing a strong guarantee for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0059] Figure 2 It is a schematic diagram of the sub-steps of step S3 of the present invention. DETAILED DESCRIPTION

[0060] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0061] Embodiment 1

[0062] according to Figure 1 As shown, this embodiment provides a fault prediction method based on impedance change of a parallel capacitor device, comprising the following steps:

[0063] S1. Establish a mathematical model: Establish impedance for the target parallel capacitor device Mathematical model of impedance The mathematical model satisfies:

[0064] ;

[0065] in, Indicates the first The equivalent impedance of the resistive elements is A resistive element; Indicates the first The equivalent capacitive reactance of the capacitive elements is A capacitive element, Represents the frequency response characteristic attenuation index; Indicates the first The equivalent inductive reactance of the inductive components is An inductive component.

[0066] S2. Collect voltage and current parameters: Set multiple sampling points, monitor the voltage and current parameters of the circuit in real time, and calculate the voltage-current ratio as the equivalent impedance of each sampling point to meet the following requirements:

[0067] ;

[0068] in, Indicates the sampling point number, represents the sampling time, Indicates the serial number is The sampling point is The equivalent impedance at time , is the total number of sampling points; is the initial sampling time, is the sampling interval, for The total number of sampling cycles at the time;

[0069] S3. Calculate the impedance fluctuation rate of the sampling point: Figure 2 As shown, specifically including:

[0070] S31. Construct the equivalent impedance data set of sampling points: The equivalent impedance of each sampling point Constructed as an equivalent impedance series ;

[0071] S32. Calculate the mean of the equivalent impedance series ,satisfy:

[0072] ;

[0073] S33. Calculate the sample standard deviation of the equivalent impedance series, satisfying:

[0074] ;

[0075] in, Indicated in The sample standard deviation of the equivalent impedance of all sampling points at the moment;

[0076] S34, constructing a time series sample standard deviation series: constructing the sample standard deviation of the equivalent impedance sequence calculated in each sampling period into a series form to obtain a time series sample standard deviation series; the time series sample standard deviation series satisfies:

[0077] ;

[0078] in, is the standard deviation of the time series samples, Indicates the sampling cycle number.

[0079] S35. Calculate the mean of the standard deviation of the time series samples as the impedance fluctuation rate of the sampling point ; In step S35, the impedance fluctuation rate of the sampling point satisfy:

[0080] ;

[0081] in, Indicated in The impedance fluctuation rate at the sampling point at time.

[0082] S36. Analyze the fluctuation trend of the standard deviation series of time series samples and calculate the slope of the fluctuation trend ,satisfy:

[0083] ;

[0084] in, It represents the slope of the fluctuation trend, which is numerically equal to the sample standard deviation at Subsampling and The slope of change between subsamples.

[0085] S4. Abnormal diagnosis based on impedance fluctuation rate: At this moment, the judgment conditions are:

[0086] ;

[0087] in, Indicated in The sample standard deviation of the sampling points calculated at each moment; Indicates the fluctuation allowable value;

[0088] If the above judgment conditions are met , then it is determined that the target parallel capacitor device is normal;

[0089] If the above judgment conditions are not met , it is determined that the target parallel capacitor device is abnormal;

[0090] If the judgment conditions are met , then further determine:

[0091] ;

[0092] in, represents the slope of the benchmark fluctuation trend, Indicates the allowable deviation of the fluctuation trend slope.

[0093] If the above judgment conditions are met , then the target parallel capacitor device is determined to be stable;

[0094] If the above judgment conditions are not met , then it is determined that the target parallel capacitor device is unstable.

[0095] S5. Providing an alarm based on an abnormality: when it is determined that the target parallel capacitor device is abnormal, an alarm signal is output to indicate that a fault exists in the target parallel capacitor device.

[0096] According to the judgment conditions and judgment conditions , output the judgment results for the target parallel capacitor device, including: normal and stable, normal but unstable, abnormal. Specific embodiment 2

[0098] System configuration and initialization:

[0099] In a typical large industrial facility, multiple parallel capacitor devices are deployed to optimize the energy efficiency and power quality of the entire facility. These devices are mainly installed at the power input of key production lines to reduce energy losses and improve voltage stability.

[0100] In the equipment control center, a high-precision data acquisition system is installed, including current transformers and voltage transformers, to monitor the real-time voltage and current data of the circuit.

[0101] Build a mathematical model:

[0102] A complex impedance model is built for each parallel capacitor device, taking into account all combinations of resistance, capacitance and inductance. The model expression is:

[0103] ;

[0104] in, Indicates the first The equivalent impedance of the resistive elements is A resistive element; Indicates the first The equivalent capacitive reactance of the capacitive elements is A capacitive element, Represents the frequency response characteristic attenuation index; Indicates the first The equivalent inductive reactance of the inductive components is An inductive component.

[0105] Data collection and real-time monitoring:

[0106] By collecting voltage and current data from each monitoring point at regular intervals, the instantaneous impedance values ​​of these data points are calculated. The impedance of each sampling point is determined by the ratio of voltage to current:

[0107] ;

[0108] in, Indicates the sampling point number, represents the sampling time, Indicates the serial number is The sampling point is The equivalent impedance at time , is the total number of sampling points; is the initial sampling time, is the sampling interval, for The total number of sampling cycles at the time;

[0109] The data acquisition system automatically updates the data every 10 seconds to ensure that the acquired data can reflect the real-time status of the equipment.

[0110] Impedance Fluctuation Analysis:

[0111] The collected impedance data are processed using statistical methods to calculate the equivalent impedance mean and standard deviation for each sampling period. These statistics are very useful for identifying potential equipment anomalies.

[0112] The calculation method is as follows:

[0113] The mean satisfies:

[0114] ;

[0115] The sample standard deviation satisfies:

[0116] ;

[0117] in, Indicated in The sample standard deviation of the equivalent impedance of all sampling points at the moment;

[0118] These calculations help monitor the performance of the device and promptly detect unusual fluctuations in impedance, which may be caused by device aging or external interference.

[0119] Failure prediction and response:

[0120] The calculated impedance fluctuation rate is compared with the preset fluctuation allowance value. If the current fluctuation rate exceeds the normal range, the system automatically triggers a fault alarm.

[0121] The fault response procedure includes immediately notifying the equipment maintenance team to conduct inspection and analyze the possible cause of the fault based on system logs and real-time data.

[0122] After discovering the source of the fault, the maintenance team will make the necessary repairs or adjustments to restore the normal operation of the system and update the maintenance records of all related components through the system.

[0123] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A fault prediction method based on impedance change of parallel capacitor device, characterized in that: The following steps are involved: S1. Establish a mathematical model: Establish impedance for the target parallel capacitor device Mathematical model of impedance The mathematical model satisfies: ； in, Indicates the first The equivalent impedance of the resistive elements is A resistive element; Indicates the first The equivalent capacitive reactance of the capacitive elements is A capacitive element, Represents the frequency response characteristic attenuation index; Indicates the first The equivalent inductive reactance of the inductive components is An inductive element; S2. Collect voltage and current parameters: Set multiple sampling points, monitor the voltage and current parameters of the circuit in real time, and calculate the voltage-current ratio as the equivalent impedance of each sampling point, satisfying: ； in, Indicates the sampling point number, represents the sampling time, Indicates the serial number is The sampling point is The equivalent impedance at time , is the total number of sampling points; is the initial sampling time, is the sampling interval, for The total number of sampling cycles at the time; S3. Calculate the impedance fluctuation rate of the sampling point: specifically including: S31. Construct the equivalent impedance data set of sampling points: The equivalent impedance of each sampling point Constructed as an equivalent impedance series ; S32. Calculate the mean of the equivalent impedance series ,satisfy: ； S33. Calculate the sample standard deviation of the equivalent impedance series, satisfying: ； in, Indicated in The sample standard deviation of the equivalent impedance of all sampling points at the moment; S34, constructing a time series sample standard deviation series: constructing the sample standard deviation of the equivalent impedance sequence calculated in each sampling period into a series form to obtain a time series sample standard deviation series; S35. Calculate the mean of the standard deviation of the time series samples as the impedance fluctuation rate of the sampling point ; Sampling point impedance fluctuation rate satisfy: ； in, Indicated in The impedance fluctuation rate of the sampling point at the time; S36. Analyze the fluctuation trend of the standard deviation series of time series samples and calculate the slope of the fluctuation trend ,satisfy: ； in, It represents the slope of the fluctuation trend, which is numerically equal to the sample standard deviation at Subsampling and The slope of change between subsampling; S4. Abnormal diagnosis based on impedance fluctuation rate: At this moment, the judgment conditions are: ； in, Indicated in The sample standard deviation of the sampling points calculated at each moment; Indicates the fluctuation allowable value; If the above judgment conditions are met , then it is determined that the target parallel capacitor device is normal; If the above judgment conditions are not met , then it is determined that the target parallel capacitor device is abnormal; If the judgment conditions are met , then further determine: ； in, Indicates the slope of the benchmark fluctuation trend, Indicates the allowable deviation of the fluctuation trend slope; If the above judgment conditions are met , then the target parallel capacitor device is determined to be stable; if the above judgment conditions are not met , then the target parallel capacitor device is determined to be unstable; S5. Providing an alarm based on an abnormality: when it is determined that the target parallel capacitor device is abnormal, an alarm signal is output to indicate that a fault exists in the target parallel capacitor device.

2. A fault prediction method based on impedance change of parallel capacitor device according to claim 1, characterized in that: In step S34, the time series sample standard deviation sequence satisfies: ； in, is the standard deviation of the time series samples, Indicates the sampling cycle number.

3. A fault prediction method based on impedance change of parallel capacitor device according to claim 2, characterized in that: According to the judgment conditions and judgment conditions , output the judgment results for the target parallel capacitor device, including: normal and stable, normal but unstable, abnormal.

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