Photovoltaic dc circuit breaker fault diagnosis method and system

Abstract

The application provides a photovoltaic DC circuit breaker fault diagnosis method and system, relates to the technical field of fault diagnosis, and specifically comprises the following steps: obtaining key parameters of equipment, calculating instantaneous power, current rate of change, temperature rate of change and energy consumption per unit time based on the key parameters, and introducing total harmonic distortion correction power factor when calculating instantaneous power; the correlation between each key index and the fault evaluation index is analyzed by using the Pearson correlation coefficient method, and the fault evaluation index is calculated; a dynamic threshold adjustment mechanism is introduced to determine whether the photovoltaic DC circuit breaker has a fault. Through the dynamic threshold adjustment mechanism and the Pearson correlation coefficient analysis, the fault diagnosis capability of the photovoltaic DC circuit breaker is improved. Compared with the traditional method, the present scheme realizes high-frequency data acquisition and real-time analysis, ensures accurate evaluation of the operating state, effectively identifies potential fault risks, and reduces the probability of equipment damage.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, specifically to a fault diagnosis method and system for photovoltaic DC circuit breakers. Background Technology

[0002] With the widespread application of renewable energy, photovoltaic power generation systems have developed rapidly worldwide. In this process, photovoltaic DC circuit breakers, as key protective components, directly impact the safe operation of the entire system due to their stability and reliability. However, current fault detection and diagnosis technologies for photovoltaic DC circuit breakers still have significant shortcomings. Traditional methods often rely on periodic manual inspections and maintenance, failing to achieve real-time monitoring of equipment status. The limitation of this approach is that equipment failures often occur during unplanned operational phases, and manual inspections cannot cope with dynamic changes in equipment operation, leading to delayed fault detection and potentially causing more serious equipment damage and economic losses. Furthermore, existing technologies typically employ simple alarm mechanisms, lacking in-depth analysis of complex fault modes, making fault diagnosis often reliant on experience and prone to false alarms or missed alarms.

[0003] Furthermore, traditional fault diagnosis methods also have problems with data analysis and processing. Existing technologies typically focus on monitoring single parameters, such as temperature, current, or voltage, while neglecting the interrelationships and combined effects between various key parameters. This one-sidedness leads to an inability to comprehensively assess the overall health of the equipment, limiting the ability to identify potential faults. In addition, traditional methods lack dynamic threshold adjustment mechanisms during fault assessment, failing to adapt to changes in the equipment's operating environment and fluctuations in its condition, resulting in lag and inaccuracy in fault diagnosis. Overall, existing technologies cannot meet the demands of today's photovoltaic power generation systems in terms of safety, economy, and efficiency, and a more advanced and systematic fault diagnosis method is urgently needed to address these challenges.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for diagnosing faults in photovoltaic DC circuit breakers, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A fault diagnosis method for photovoltaic DC circuit breakers includes the following steps:

[0008] Step 1: Monitor the operating status of the photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature;

[0009] Step 2: Calculate instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor.

[0010] Step 3: Analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results.

[0011] Step 4: Introduce a dynamic threshold adjustment mechanism to compare the fault assessment index with the fault assessment threshold, and determine whether the photovoltaic DC circuit breaker has a fault based on the comparison result.

[0012] Furthermore, a time synchronization mechanism is implemented to ensure that data from all the following sensors are collected at the same timestamp, and the collection frequency is set to 10 times per second;

[0013] A voltage sensor is used to obtain the voltage between the photovoltaic module and the DC circuit breaker, and the voltage value is recorded as follows: ;

[0014] A current sensor is used to obtain the current flowing through the circuit, and the current value is recorded as... ;

[0015] Use a temperature sensor to acquire the component's temperature and record the temperature value as... .

[0016] Furthermore, the instantaneous power is calculated using the following formula:

[0017]

[0018] in, The instantaneous power at the current moment, This is the voltage value at the current moment. This represents the current value at the current moment. The power factor at the current moment, For total harmonic distortion;

[0019] The acquisition method is as follows: the voltage signal is sampled using a digital instrument at a sampling frequency that is 10 times the highest frequency of the signal and satisfies the Nyquist sampling theorem. For the acquired time-domain signal, a fast Fourier transform is used to convert it into a frequency-domain signal to obtain the amplitude of each harmonic component. For each harmonic, its effective value is calculated according to the following formula:

[0020]

[0021] in, It is the effective value of the harmonic. The time required for a signal to complete one full waveform. Within the time of one complete waveform The signal value at time [time] The time variable is the time interval of one complete waveform;

[0022] Using the above formula for calculating the effective value of harmonics, the effective value of the fundamental frequency is calculated. Passing the exam The effective value of harmonics, The index representing harmonics, where, , This represents the total number of fundamental and harmonic frequencies, and the calculation result is substituted into the following... Calculation formula:

[0023]

[0024] in, For total harmonic distortion, It is the effective value of the fundamental frequency, that is, the effective value of the first harmonic. It is the first The effective value of harmonics, ;

[0025] Calculate the rate of change of current using the following formula:

[0026]

[0027] in, The rate of change of current, This represents the current value at the current moment. This indicates the time interval from the current time is... Historical current values, For time intervals, These are preset weighting factors used to adjust the relative impact of different rates of change on fault assessment;

[0028] Calculate the rate of temperature change using the following formula:

[0029]

[0030] in, For the rate of temperature change, The temperature value at the current moment. This indicates the time interval from the current time is... Historical temperature values, For time intervals, These are preset weighting factors used to adjust the relative impact of different rates of change on fault assessment;

[0031] The index of the historical sampling time that is n time intervals away from the current time is assigned to 1, and the sampling index of the current time is assigned to the natural number N. The indices of the historical sampling times that are between the two are assigned to 2, 3, and so on. For N-1, the energy consumption is approximated using the trapezoidal rule:

[0032]

[0033] in, For energy consumption, Let represent the instantaneous power at the i-th sampling time, where i is the index of the sampling time, and , This represents the time interval between two adjacent sampling times.

[0034] Furthermore, the correlation between instantaneous power, current rate of change, temperature rate of change, energy consumption, and fault assessment index is analyzed using the Pearson correlation coefficient method. The specific logic behind this analysis is as follows:

[0035] The Pearson correlation coefficient is a statistic that measures the strength and direction of the linear relationship between two variables, and its value ranges as follows: The specific steps are as follows:

[0036] Collect a set of historical key parameters and corresponding fault assessment indices for the equipment. The key parameters are instantaneous power, current change rate, temperature change rate, and energy consumption.

[0037] The Pearson correlation coefficient is calculated using the following formula:

[0038]

[0039] in, The Pearson correlation coefficient is used. Indicates the first The key parameters of the nth sample, i.e., the nth The instantaneous power, current change rate, temperature change rate, or energy consumption in each sample are used to perform correlation analysis with the corresponding fault assessment index. Indicates the first Fault assessment index for each sample Key parameters The mean, This represents the average of the fault assessment index.

[0040] The Pearson correlation coefficient value obtained through calculation To analyze the relationship between various key parameters and the fault assessment index:

[0041] when When the value is 1, it indicates that there is a positive correlation between the parameter and the fault assessment index, which means that the fault assessment index also increases when the parameter increases.

[0042] when When the value is 0, it indicates that there is a negative correlation between the parameter and the fault assessment index, meaning that the fault assessment index decreases when the parameter is increased.

[0043] when When there is no correlation, it means that there is no linear relationship between this parameter and the fault assessment index;

[0044] Among them, when The closer the value is to 1, the stronger the correlation.

[0045] The fault assessment index is calculated using the following formula:

[0046]

[0047] in, This is a fault assessment index. The instantaneous power at the current moment, The rate of change of current, For the rate of temperature change, For energy consumption, , , and This is a preset proportional coefficient; the specific value is determined based on the correlation analysis results.

[0048] Furthermore, a dynamic threshold adjustment mechanism is introduced to obtain the data from the current time backward. The key parameters collected in this study are voltage, current, and temperature. A corresponding fault assessment index is calculated based on these key parameters, and the average value of the first m key parameters is also calculated. and standard deviation The fault assessment threshold at the current moment is calculated using the following formula:

[0049]

[0050] in, It is the fault assessment threshold at the current moment. It is the mean of the fault assessment index. The standard deviation of the fault assessment index. It is an adjustment factor;

[0051] Fault assessment index Compare with fault assessment thresholds:

[0052] like If the current fault assessment index is higher than the normal range, it indicates that there is a risk of failure, and the equipment should be further inspected and maintained.

[0053] like This indicates that the current device is operating normally and no fault has been detected.

[0054] The present invention also provides a photovoltaic DC circuit breaker fault diagnosis system, which is used to perform the above-described photovoltaic DC circuit breaker fault diagnosis method, including:

[0055] The data acquisition module is used to monitor the operating status of photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature.

[0056] The key performance indicator calculation module calculates instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor.

[0057] The correlation analysis module is used to analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results.

[0058] The threshold comparison module is used to introduce a dynamic threshold adjustment mechanism, which compares the fault assessment index with the fault assessment threshold, and determines whether the photovoltaic DC circuit breaker has a fault based on the comparison result.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention effectively enhances the fault diagnosis capability of photovoltaic DC circuit breakers by introducing a dynamic threshold adjustment mechanism and Pearson correlation coefficient analysis. Compared to traditional methods, this solution achieves high-frequency data acquisition and real-time analysis, ensuring accurate assessment of operating status, effectively identifying potential fault risks, and thus reducing the probability of equipment damage. Furthermore, by comparing the calculated fault assessment index with the dynamic threshold, maintenance measures can be triggered promptly, ensuring the safe and efficient operation of the photovoltaic system, thereby improving the overall reliability and economic benefits of photovoltaic power generation. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0062] Figure 2 This is a schematic diagram of the overall system modules of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0064] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0065] Example:

[0066] Please see Figure 1 The present invention provides a technical solution:

[0067] A fault diagnosis method for photovoltaic DC circuit breakers includes the following steps:

[0068] Step 1: Monitor the operating status of the photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature;

[0069] In this embodiment, a time synchronization mechanism is implemented to ensure that the data of all the following sensors are collected at the same timestamp, and the collection frequency is set to 10 times per second;

[0070] A voltage sensor is used to obtain the voltage between the photovoltaic module and the DC circuit breaker, and the voltage value is recorded as follows: ;

[0071] A current sensor is used to obtain the current flowing through the circuit, and the current value is recorded as... ;

[0072] Use a temperature sensor to acquire the component's temperature and record the temperature value as... .

[0073] The advantage of Step 1 lies in its ability to acquire key parameters through real-time monitoring of the photovoltaic panels and DC circuit breakers, thereby achieving a comprehensive understanding of the equipment's status. This contrasts sharply with existing technologies, which often rely on periodic manual inspections and fail to capture real-time dynamic changes in the equipment, potentially leading to undetected faults. Therefore, real-time monitoring not only improves the timeliness and accuracy of data acquisition but also provides a reliable data foundation for subsequent fault analysis, significantly reducing the potential risks caused by equipment failures.

[0074] In this patented solution, step 1 provides crucial data support for the overall solution, making subsequent steps more scientific and effective. By collecting and analyzing these key parameters in real time, operational anomalies of the equipment can be identified more accurately, enhancing the sensitivity and accuracy of fault diagnosis. Furthermore, the fault assessment mechanism based on real-time data improves the system's intelligence level, making fault detection of the photovoltaic DC circuit breaker more reliable in complex operating environments, thereby enhancing the safety and economy of the photovoltaic system.

[0075] Step 2: Calculate instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor.

[0076] In this embodiment, the instantaneous power is calculated using the following formula:

[0077]

[0078] in, The instantaneous power at the current moment, This is the voltage value at the current moment. This represents the current value at the current moment. The power factor at the current moment is usually a value between 0 and 1, used to represent the ratio of active power to apparent power in an AC circuit. The specific value depends on the nature of the load and the operating conditions of the system. For total harmonic distortion;

[0079] The acquisition method is as follows: the voltage signal is sampled using a digital instrument at a sampling frequency that is 10 times the highest frequency of the signal and satisfies the Nyquist sampling theorem. For the acquired time-domain signal, a fast Fourier transform is used to convert it into a frequency-domain signal to obtain the amplitude of each harmonic component. For each harmonic, its effective value is calculated according to the following formula:

[0080]

[0081] in, It is the effective value of the harmonic. The time required for a signal to complete one full waveform. Within the time of one complete waveform The signal value at time [time] The time variable is the time interval of one complete waveform;

[0082] Using the above formula for calculating the effective value of harmonics, the effective value of the fundamental frequency is calculated. Passing the exam The effective value of harmonics, The index representing harmonics, where, , This represents the total number of fundamental and harmonic frequencies, and the calculation result is substituted into the following... Calculation formula:

[0083]

[0084] in, For total harmonic distortion, It is the effective value of the fundamental frequency, that is, the effective value of the first harmonic. It is the first The effective value of harmonics, ;

[0085] Calculate the rate of change of current using the following formula:

[0086]

[0087] in, The rate of change of current, This represents the current value at the current moment. This indicates the time interval from the current time is... Historical current values, For time intervals, These are preset weighting factors used to adjust the relative impact of different rates of change on fault assessment;

[0088] Calculate the rate of temperature change using the following formula:

[0089]

[0090] in, For the rate of temperature change, The temperature value at the current moment. This indicates the time interval from the current time is... Historical temperature values, For time intervals, These are preset weighting factors used to adjust the relative impact of different rates of change on fault assessment;

[0091] The index of the historical sampling time that is n time intervals away from the current time is assigned to 1, and the sampling index of the current time is assigned to the natural number N. The indices of the historical sampling times that are between the two are assigned to 2, 3, and so on. For N-1, the energy consumption is approximated using the trapezoidal rule:

[0092]

[0093] in, For energy consumption, Let represent the instantaneous power at the i-th sampling time, where i is the index of the sampling time, and , This represents the time interval between two adjacent sampling times.

[0094] Step 2's advantage lies in its ability to deeply analyze the operating status of photovoltaic DC circuit breakers through calculations of key parameters, such as instantaneous power, current change rate, temperature change rate, and energy consumption per unit time. This contrasts sharply with existing technologies, which often rely solely on static monitoring data and lack dynamic evaluation of equipment performance. Therefore, this step not only improves the accuracy of instantaneous power calculation by introducing total harmonic distortion (THD) correction to the power factor, but also provides a more comprehensive reflection of the equipment's health and operating efficiency, thereby effectively enhancing the accuracy of fault prediction.

[0095] In this patented solution, step 2 provides a more detailed analysis of the overall operating parameters, enhancing the scientific rigor and reliability of subsequent steps. By dynamically monitoring instantaneous power and its changes, combined with the rates of change of current and temperature, this information can promptly identify potential anomalies, further improving the system's sensitivity to and diagnostic capabilities for faults. Simultaneously, through in-depth analysis of these key parameters, the solution can adjust and optimize maintenance strategies in real time, making the operation of the photovoltaic DC circuit breaker safer and more efficient.

[0096] Step 3: Analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results.

[0097] In this embodiment, the Pearson correlation coefficient method is used to analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption, and fault assessment index. The specific logic behind this is as follows:

[0098] The Pearson correlation coefficient is a statistic that measures the strength and direction of the linear relationship between two variables, and its value ranges as follows: The specific steps are as follows:

[0099] Collect a set of historical key parameters of the equipment, and calculate the corresponding fault assessment index based on this set of historical key parameters. The key parameters are instantaneous power, current change rate, temperature change rate, and energy consumption.

[0100] The Pearson correlation coefficient is calculated using the following formula:

[0101]

[0102] in, The Pearson correlation coefficient is used. Indicates the first The key parameters of the nth sample, i.e., the nth The instantaneous power, current change rate, temperature change rate, or energy consumption in each sample are used to perform correlation analysis with the corresponding fault assessment index. Indicates the first Fault assessment index for each sample Key parameters The mean, This represents the average of the fault assessment index.

[0103] The Pearson correlation coefficient value obtained through calculation To analyze the relationship between various key parameters and the fault assessment index:

[0104] when When the value is 1, it indicates that there is a positive correlation between the parameter and the fault assessment index, which means that the fault assessment index also increases when the parameter increases.

[0105] when When the value is 0, it indicates that there is a negative correlation between the parameter and the fault assessment index, meaning that the fault assessment index decreases when the parameter is increased.

[0106] when When there is no correlation, it means that there is no linear relationship between this parameter and the fault assessment index;

[0107] Among them, when The closer the value is to 1, the stronger the correlation.

[0108] The fault assessment index is calculated using the following formula:

[0109]

[0110] in, This is a fault assessment index. The instantaneous power at the current moment, The rate of change of current, For the rate of temperature change, For energy consumption, , , and The preset proportional coefficient, the specific value of which is determined based on the correlation analysis results, will be used to measure the instantaneous power. Pearson correlation index and current change rate with fault assessment index And fault assessment index, temperature change rate Pearson correlation index with fault assessment index, energy consumption The Pearson correlation index and the fault assessment index are respectively labeled as r1, r2, r3, and r4. r1, r2, r3, and r4 are scaled proportionally to convert them into the preset scaling coefficients in this formula, and it is ensured that the sum of the absolute values ​​of these four preset scaling coefficients is equal to 1.

[0111] Step 3's advantage lies in its ability to accurately identify which key parameters are significantly related to fault risk by utilizing Pearson correlation coefficient analysis to examine the correlation between instantaneous power, current rate of change, temperature rate of change, energy consumption, and the fault assessment index. This contrasts sharply with existing technologies, where traditional methods often rely on empirical judgment and lack systematic data analysis. Quantitative analysis reduces the influence of subjective factors, improves the scientific rigor and accuracy of fault diagnosis, and thus more effectively provides early warnings of potential equipment failures.

[0112] In this patented solution, step 3 provides a crucial statistical basis for the overall solution, enabling more accurate judgments in fault assessment. By calculating the fault assessment index and combining it with the analysis results of relevant parameters, the fault diagnosis strategy can be dynamically adjusted, allowing the system to respond more sensitively to equipment anomalies. Furthermore, this data-driven analysis method enhances the system's intelligence level, enabling photovoltaic DC circuit breakers to achieve more efficient fault detection and maintenance in variable operating environments, thereby ensuring the safety and stability of the photovoltaic power generation system.

[0113] Step 4: Introduce a dynamic threshold adjustment mechanism to compare the fault assessment index with the fault assessment threshold, and determine whether the photovoltaic DC circuit breaker has a fault based on the comparison result;

[0114] In this embodiment, a dynamic threshold adjustment mechanism is introduced to obtain the threshold value traced back from the current time. The key parameters collected in this study are voltage, current, and temperature. A corresponding fault assessment index is calculated based on these key parameters, and the average value of the first m key parameters is also calculated. and standard deviation The fault assessment threshold at the current moment is calculated using the following formula:

[0115]

[0116] in, It is the fault assessment threshold at the current moment. It is the mean of the fault assessment index. The standard deviation of the fault assessment index. It is an adjustment coefficient, the specific value of which is determined based on the equipment's historical data and experience. This operation can automatically adjust the sensitivity of fault judgment based on the equipment's historical operating status.

[0117] Fault assessment index Compare with fault assessment thresholds:

[0118] like If the current fault assessment index is higher than the normal range, it indicates that there is a risk of failure, and the equipment should be further inspected and maintained.

[0119] like This indicates that the current device is operating normally and no fault has been detected.

[0120] Step 4's advantage lies in introducing a dynamic threshold adjustment mechanism, enabling the fault assessment index to adapt to the current equipment status during real-time monitoring. This contrasts with existing technologies, where traditional methods typically use fixed thresholds for fault diagnosis, making them susceptible to environmental changes, equipment aging, and other factors that can lead to misjudgments or missed diagnoses. By dynamically adjusting the threshold, fault risks can be identified more accurately, improving the system's responsiveness and accuracy.

[0121] In this patented solution, step 4 significantly enhances the intelligence and adaptability of the overall solution. By calculating the fault assessment threshold in real time and comparing it with the fault assessment index, targeted fault detection and maintenance are achieved. This not only effectively reduces the risk of fault occurrence but also saves time and costs in actual operation, improving the operating efficiency and safety of the photovoltaic DC circuit breaker. Furthermore, the application of dynamic thresholds ensures that the system maintains efficient fault identification capabilities under various operating conditions, thus providing a strong guarantee for the long-term stable operation of the photovoltaic power generation system.

[0122] Please see Figure 2 A photovoltaic DC circuit breaker fault diagnosis system, comprising:

[0123] The data acquisition module is used to monitor the operating status of photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature.

[0124] The key performance indicator calculation module calculates instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor.

[0125] The correlation analysis module is used to analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results.

[0126] The threshold comparison module is used to introduce a dynamic threshold adjustment mechanism, which compares the fault assessment index with the fault assessment threshold, and determines whether the photovoltaic DC circuit breaker has a fault based on the comparison result.

[0127] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0128] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A fault diagnosis method for a photovoltaic DC circuit breaker, characterized in that, The specific steps include: Step 1: Monitor the operating status of the photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature; Step 2: Calculate instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor. Step 3: Analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results. Step 4: Introduce a dynamic threshold adjustment mechanism to compare the fault assessment index with the fault assessment threshold, and determine whether the photovoltaic DC circuit breaker has a fault based on the comparison result; The Pearson correlation coefficient method is used to analyze the correlation between instantaneous power, current rate of change, temperature rate of change, energy consumption, and fault assessment index. The specific logic behind this analysis is as follows: The Pearson correlation coefficient is a statistic that measures the strength and direction of the linear relationship between two variables, and its value ranges as follows: The specific steps are as follows: Collect a set of historical key parameters and corresponding fault assessment indices for the equipment. The key parameters are instantaneous power, current change rate, temperature change rate, and energy consumption. The Pearson correlation coefficient is calculated using the following formula: in, The Pearson correlation coefficient is used. Indicates the first The key parameters of the nth sample, i.e., the nth The instantaneous power, current change rate, temperature change rate, or energy consumption in each sample are used to perform correlation analysis with the corresponding fault assessment index. Indicates the first Fault assessment index for each sample Key parameters The mean, This represents the average of the fault assessment index. The Pearson correlation coefficient value obtained through calculation To analyze the relationship between various key parameters and the fault assessment index: when When the value is 1, it indicates that there is a positive correlation between the parameter and the fault assessment index, which means that the fault assessment index also increases when the parameter increases. when When the value is 0, it indicates that there is a negative correlation between the parameter and the fault assessment index, meaning that the fault assessment index decreases when the parameter is increased. when When there is no correlation, it means that there is no linear relationship between this parameter and the fault assessment index; Among them, when The closer the value is to 1, the stronger the correlation. The fault assessment index is calculated using the following formula: in, This is a fault assessment index. The instantaneous power at the current moment, The rate of change of current, For the rate of temperature change, For energy consumption, , , and This is a preset proportional coefficient; the specific value is determined based on the correlation analysis results. Introducing a dynamic threshold adjustment mechanism to obtain data from the current time backwards. The key parameters collected in this study are voltage, current, and temperature. A corresponding fault assessment index is calculated based on these key parameters, and the average value of the first m key parameters is also calculated. and standard deviation The fault assessment threshold at the current moment is calculated using the following formula: in, It is the fault assessment threshold at the current moment. It is the mean of the fault assessment index. The standard deviation of the fault assessment index. It is an adjustment factor; Fault assessment index Compare with fault assessment thresholds: like If the current fault assessment index is higher than the normal range, it indicates that there is a risk of failure, and the equipment needs further testing and maintenance. like This indicates that the current device is operating normally and no fault has been detected.

2. The method for fault diagnosis of a photovoltaic DC circuit breaker according to claim 1, characterized in that: Implement a time synchronization mechanism to ensure that data from all the following sensors are collected at the same timestamp, and set the collection frequency to 10 times per second; A voltage sensor is used to obtain the voltage between the photovoltaic module and the DC circuit breaker, and the voltage value is recorded as follows: ; A current sensor is used to obtain the current flowing through the circuit, and the current value is recorded as... ; Use a temperature sensor to acquire the component's temperature and record the temperature value as... .

3. The method for fault diagnosis of a photovoltaic DC circuit breaker according to claim 2, characterized in that: Calculate instantaneous power using the following formula: in, The instantaneous power at the current moment, This is the voltage value at the current moment. This represents the current value at the current moment. The power factor at the current moment, For total harmonic distortion; The acquisition method is as follows: the voltage signal is sampled using a digital instrument at a sampling frequency that is 10 times the highest frequency of the signal and satisfies the Nyquist sampling theorem. For the acquired time-domain signal, a fast Fourier transform is used to convert it into a frequency-domain signal to obtain the amplitude of each harmonic component. For each harmonic, its effective value is calculated according to the following formula: in, It is the effective value of the harmonic. The time required for a signal to complete one full waveform. Within the time of one complete waveform The signal value at time [time] The time variable is the time interval of one complete waveform; Using the above formula for calculating the effective value of harmonics, the effective value of the fundamental frequency is calculated. Passing the exam The effective value of harmonics, The index represents the harmonics, where, , This represents the total number of fundamental and harmonic frequencies, and the calculation result is substituted into the following... Calculation formula: in, For total harmonic distortion, It is the effective value of the fundamental frequency, that is, the effective value of the first harmonic. It is the first The effective value of harmonics, ; Calculate the rate of change of current using the following formula: in, The rate of change of current, This represents the current value at the current moment. This indicates the time interval from the current time is... Historical current values, For time intervals, These are preset weighting factors; Calculate the rate of temperature change using the following formula: in, For the rate of temperature change, The temperature value at the current moment. This indicates the time interval from the current time is... Historical temperature values, For time intervals, These are preset weighting factors; The index of the historical sampling time that is n time intervals away from the current time is assigned to 1, and the sampling index of the current time is assigned to the natural number N. The indices of the historical sampling times that are between the two are assigned to 2, 3, and so on. For N-1, the energy consumption is approximated using the trapezoidal rule: in, For energy consumption, Let represent the instantaneous power at the i-th sampling time, where i is the index of the sampling time, and , This represents the time interval between two adjacent sampling times.

4. A fault diagnosis system for photovoltaic DC circuit breakers, characterized in that: The photovoltaic DC circuit breaker fault diagnosis system described above is used to execute the photovoltaic DC circuit breaker fault diagnosis method according to any one of claims 1-3, comprising: The data acquisition module is used to monitor the operating status of photovoltaic panels and DC circuit breakers in real time and obtain their key parameters, including voltage, current and temperature. The key indicator calculation module calculates instantaneous power, current change rate, temperature change rate, and energy consumption per unit time based on key parameters. When calculating instantaneous power, total harmonic distortion is introduced to correct the power factor. The correlation analysis module is used to analyze the correlation between instantaneous power, current change rate, temperature change rate, energy consumption and fault assessment index using the Pearson correlation coefficient method, and calculate the fault assessment index based on the correlation analysis results. The threshold comparison module is used to introduce a dynamic threshold adjustment mechanism, which compares the fault assessment index with the fault assessment threshold, and determines whether the photovoltaic DC circuit breaker has a fault based on the comparison result.

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