Leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring

Abstract

The application discloses a leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring and belongs to the technical field of leakage current monitoring. The leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring comprises a sensing module, a data acquisition and preprocessing module, a central processing module, a multi-stage dynamic threshold module, a complex pattern recognition module and a multi-stage early warning module. The application solves the problems that only a single threshold is set in the existing leakage current monitoring technology, real-time early warning cannot be achieved, and complex leakage current patterns cannot be identified. According to the real-time monitored environmental parameters and the working state of the inverter, the threshold of the leakage current is dynamically adjusted. The machine learning and pattern recognition algorithm are combined with the comprehensive analysis results of zero sequence current, phase and frequency parameters to identify complex leakage current patterns. The multi-stage early warning module can send early warnings of different levels according to the degree of the leakage current.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of leakage current monitoring, in particular to a leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring. BACKGROUND

[0002] With the rapid development of new energy technology, especially the widespread application of photovoltaic power generation systems, the safety of power systems has become a focus of attention. At present, most leakage current monitoring systems use a fixed single threshold setting method. Although this method is simple and easy to implement, it has many shortcomings in actual application. First of all, due to the complex and variable working environment of photovoltaic systems, a single fixed threshold is difficult to adapt to various working conditions, which can easily lead to false positives or false negatives. For example, in a high temperature or high humidity environment, a fixed threshold can cause the system to frequently misreport, while in a low temperature or low humidity environment, a fixed threshold can not be able to detect leakage current in time, thereby increasing the safety hazard.

[0003] Secondly, the existing leakage current monitoring system usually lacks real-time early warning function. When the system detects leakage current, it can only issue a warning after the fact, and cannot provide early warning at the first time of leakage current occurrence, which greatly affects the response speed and emergency handling capacity of the system.

[0004] In addition, the existing leakage current monitoring technology cannot effectively identify complex leakage current patterns. Many leakage currents are not just simple current abnormalities, but are accompanied by changes in phase, frequency, and harmonics. Traditional monitoring systems can only detect simple current overruns, but cannot comprehensively analyze these complex changes. SUMMARY

[0005] The purpose of the present application is to provide a leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring. By introducing a multi-level dynamic threshold module, according to the real-time monitoring of environmental parameters and the working state of the inverter, machine learning and pattern recognition algorithms are used, combined with the comprehensive analysis results of zero sequence current, phase and frequency parameters, complex leakage current patterns are identified, through real-time monitoring and dynamic adjustment of the threshold, the system can timely detect and handle leakage current, the application of multi-sensor fusion and complex pattern recognition algorithms improves the monitoring accuracy and fault diagnosis ability of the system, through the design of multi-level dynamic threshold adjustment and multi-level early warning mechanism, the false positive rate is effectively reduced, the threshold is dynamically adjusted according to the environmental parameters and working state, and different operating conditions are adapted, solving the problems raised in the above background technology.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] The leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring comprises:

[0008] a sensor module installed around the photovoltaic inverter for monitoring the operation indicators of the photovoltaic inverter;

[0009] a data acquisition and preprocessing module connected to the sensor module for performing preliminary filtering and denoising on the sensor data;

[0010] a central processing module connected to the data acquisition and preprocessing module for real-time analysis and processing of the preprocessed data using a high-performance processor;

[0011] a multi-level dynamic threshold module connected to the central processing module for real-time monitoring and analysis of environmental parameters and working conditions to dynamically adjust the threshold of leakage current;

[0012] a complex pattern recognition module connected to the central processing module for identifying complex leakage current patterns through machine learning and pattern recognition algorithms combined with the comprehensive analysis results of zero-sequence current, phase, and frequency parameters;

[0013] a multi-level early warning module connected to the central processing module for issuing different levels of alarms according to the degree of leakage current after determining the presence of leakage current.

[0014] Preferably, the sensor module comprises:

[0015] a temperature sensor for real-time monitoring of environmental temperature;

[0016] a humidity sensor for real-time monitoring of environmental humidity;

[0017] a voltage sensor for real-time monitoring of changes in grid voltage;

[0018] a current sensor for real-time monitoring of the load size of the inverter;

[0019] a state sensor for real-time monitoring of the working mode of the inverter.

[0020] Preferably, the central processing module is further used for:

[0021] monitoring and calculating zero-sequence current, phase, and frequency parameters to determine the leakage current mode and fault type and location;

[0022] wherein the zero-sequence current is continuously monitored to determine whether leakage current occurs, and when the zero-sequence current exceeds a preset threshold, it is determined that there is leakage current; the phase difference of each phase current is monitored to determine whether the three-phase current is balanced, and an unbalanced phase difference indicates the presence of leakage current; the frequency fluctuation is monitored to determine the stability of the power grid, and a large frequency fluctuation indicates a problem with the power grid;

[0023] the amplitude and harmonic components of the current are calculated for load monitoring, system stability evaluation, fault diagnosis, energy efficiency evaluation, and model building.

[0024] Preferably, the multi-level dynamic threshold module is further used for:

[0025] dynamically calculating and adjusting the threshold of the leakage current according to environmental parameters and working conditions;

[0026] wherein the environmental parameters at least include temperature, humidity and grid voltage, and the working conditions of the inverter at least include load size and working mode;

[0027] storing the threshold under different working conditions in a database;

[0028] periodically comparing the current threshold with historical data to verify the rationality of the current threshold and summarize long-term trends of the threshold changes;

[0029] providing a manual verification function to allow users to manually adjust the threshold.

[0030] Preferably, the dynamic calculation and adjustment of the threshold of the leakage current according to the environmental parameters and the working conditions is specifically:

[0031] increasing the threshold in a high-temperature environment and decreasing the threshold in a low-temperature environment;

[0032] decreasing the threshold in a high-humidity environment and increasing the threshold in a low-humidity environment;

[0033] increasing the threshold when the grid voltage is large;

[0034] increasing the threshold when the load is large;

[0035] decreasing the threshold in standby mode.

[0036] Preferably, the dynamic calculation and adjustment of the threshold of the leakage current according to the environmental parameters and the working conditions is specifically:

[0037] calculating and adjusting the current threshold through linear adjustment, nonlinear adjustment and fuzzy logic algorithm:

[0038] wherein linear adjustment is adopted for single temperature change, voltage change and load change;

[0039] nonlinear adjustment is adopted for single humidity change and working mode change;

[0040] fuzzy logic algorithm is adopted for two or more than two compound factors among temperature change, humidity change, voltage change, load change and working mode change.

[0041] Preferably, the flow of the fuzzy logic algorithm is specifically:

[0042] Converting the precise values of temperature, humidity, voltage, load and working mode input variables into membership values of fuzzy sets;

[0043] Matching the membership values of input variables according to the fuzzy rule base, determining the applicable rules;

[0044] Synthesizing the outputs of the matched rules to obtain fuzzy outputs;

[0045] Converting the fuzzy outputs into precise thresholds to adjust the threshold of leakage current.

[0046] Preferably, the complex pattern recognition module is further used for:

[0047] Extracting the zero sequence component of three-phase current, the phase information of each phase current, the frequency information of current, the amplitude of current and harmonic components;

[0048] Selecting a neural network (ANN) recognition algorithm, training the model using historical data, adjusting model parameters, optimizing model performance, and verifying the accuracy and generalization ability of the model using test set data;

[0049] Using the trained model to perform pattern recognition on real-time data.

[0050] Preferably, the complex pattern recognition module is further used for:

[0051] Comparing the recognition result with a preset threshold to determine whether there is a complex leakage current pattern;

[0052] Triggering a corresponding early warning mechanism according to the recognition result.

[0053] Preferably, the system further comprises:

[0054] A user interface connected to the central processing module for displaying system status and historical data;

[0055] A communication module connected to the central processing module for realizing data exchange with external devices.

[0056] Compared with the prior art, the present application has the following advantages:

[0057] 1、The present application introduces a multi-level dynamic threshold module, which dynamically adjusts the threshold of leakage current according to the real-time monitored environmental parameters and the working state of the inverter. By dynamically adjusting the threshold, the system can accurately identify the leakage current under different working conditions, avoiding false positives or false negatives caused by fixed thresholds, and improving the adaptability and accuracy of the system.

[0058] 2、The application adopts machine learning and pattern recognition algorithm, combines the comprehensive analysis results of zero sequence current, phase and frequency parameters, identifies complex leakage current mode, can identify and process complex leakage current mode, improves the accuracy and timeliness of fault diagnosis, and ensures the safe operation of the system.

[0059] 3、The application can issue different levels of alarms according to the degree of leakage current, can give early warning at the first time of leakage current, ensures the real-time performance and response speed of the system, timely reminds the operator to take corresponding measures, and reduces the potential safety risk.

[0060] 4、The application can find and handle leakage current in time, reduce potential safety hazards, improve the overall safety of the system, and the application of multi-sensor fusion and complex pattern recognition algorithm improves the monitoring accuracy and fault diagnosis capability of the system, and ensures the stable operation of the system under various working conditions.

[0061] 5、The application reduces the false alarm rate, avoids unnecessary downtime and maintenance, and improves the operation efficiency of the system; the threshold is dynamically adjusted according to the environmental parameters and working state, which adapts to different operating conditions and improves the adaptability and flexibility of the system. BRIEF DESCRIPTION OF DRAWINGS

[0062] Fig. 1 The application is a module diagram of a leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring;

[0063] Fig. 2 The application is a flowchart of a fuzzy logic algorithm; DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0065] In order to solve the problem that only a single threshold is set in the existing leakage current monitoring technology, real-time early warning cannot be realized, and complex leakage current modes cannot be identified, please refer to Figs. 1-2 The following technical solutions are provided in the embodiment:

[0066] The leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring comprises:

[0067] A sensing module is installed around the photovoltaic inverter to monitor the operation indicators of the photovoltaic inverter.

[0068] A data acquisition and preprocessing module is connected to the sensing module to perform preliminary filtering and denoising on the sensor data, ensuring data quality.

[0069] A central processing module is connected to the data acquisition and preprocessing module and uses a high-performance processor to perform real-time analysis and processing on the preprocessed data.

[0070] A multi-level dynamic threshold module is connected to the central processing module to monitor and analyze environmental parameters and working conditions in real time and dynamically adjust the threshold of leakage current. Dynamic adjustment can ensure accurate identification of leakage current under different working conditions, avoid false positives or false negatives caused by fixed thresholds, and improve the adaptability and accuracy of the system.

[0071] A complex pattern recognition module is connected to the central processing module and uses machine learning and pattern recognition algorithms combined with comprehensive analysis of zero-sequence current, phase, and frequency parameters to identify complex leakage current patterns.

[0072] A multi-level warning module is connected to the central processing module to compare real-time calculated current parameters with dynamic thresholds to determine whether there is leakage current. When abnormal current is detected, different levels of alarms are issued according to the degree of leakage current to ensure real-time performance and response speed of the system.

[0073] A user interface is connected to the central processing module to display system status and historical data, improving system operability and maintainability, facilitating real-time monitoring and management of inverter operation status, and enhancing user experience and management efficiency of the system.

[0074] A communication module is connected to the central processing module to enable data exchange with external devices, improving system integration and expandability.

[0075] The sensing module includes:

[0076] A temperature sensor is used to monitor environmental temperature in real time, allowing timely detection of overheating and preventing equipment damage.

[0077] A humidity sensor is used to monitor environmental humidity in real time, preventing electrical faults caused by excessive humidity.

[0078] A voltage sensor is used to monitor changes in grid voltage, allowing timely detection and handling of voltage abnormalities.

[0079] A current sensor is used to monitor the load size of the inverter, ensuring safe operation within a safe range and avoiding overloading.

[0080] State sensors are used to monitor the working mode of the inverter, ensuring that the system can work normally in different modes.

[0081] Multi-sensor fusion can comprehensively reflect the operating state of the inverter and environmental conditions, providing comprehensive data information for system analysis and decision-making.

[0082] The central processing module is also used for:

[0083] Monitoring and calculating zero sequence current, phase and frequency parameters to determine the leakage current mode and fault type and fault location;

[0084] Among them, the zero sequence current is continuously monitored to determine whether the leakage current occurs. When the zero sequence current exceeds the preset threshold, it is determined that there is leakage current. The phase difference of each phase current is monitored to determine whether the three-phase current is balanced. The unbalanced phase difference indicates the existence of leakage current. The frequency fluctuation is monitored to determine the stability of the power grid. Large frequency fluctuation indicates that the power grid has a problem;

[0085] The amplitude and harmonic components of the current are calculated for load monitoring, system stability evaluation, fault diagnosis, energy efficiency evaluation and model construction.

[0086] By comprehensively analyzing the zero sequence current, phase, frequency and current amplitude and harmonic components, the leakage current mode and fault type can be more accurately identified, and the diagnostic ability of the system can be improved.

[0087] The multi-level dynamic threshold module is further used for:

[0088] Real-time monitoring of environmental parameters, including at least temperature, humidity and grid voltage;

[0089] Real-time monitoring of the working state of the inverter, including at least load size and working mode;

[0090] According to the environmental parameters and working conditions, the threshold of leakage current is dynamically calculated and adjusted;

[0091] The threshold values under different working conditions are stored in the database for system management and query;

[0092] Periodically compare the current threshold with historical data to ensure the rationality of the threshold;

[0093] Provide manual verification function to allow users to manually adjust the threshold to ensure stable operation of the system.

[0094] According to the environmental parameters and working conditions, the threshold of leakage current is dynamically calculated and adjusted, specifically:

[0095] In high temperature environment, the threshold is increased to avoid false positives, in low temperature environment, the threshold is reduced; in high humidity environment, the threshold is reduced to improve sensitivity, in low humidity environment, the threshold is increased; when the grid voltage is large, the threshold is increased to avoid false positives; when the load is large, the threshold is increased to avoid false positives; in standby mode, the threshold is reduced to improve sensitivity.

[0096] According to the environmental parameters and working conditions, the threshold of leakage current is dynamically calculated and adjusted, specifically:

[0097] Linear adjustment is adopted for single temperature change, voltage change and load change;

[0098] Nonlinear adjustment is adopted for single humidity change and working mode change;

[0099] Fuzzy logic algorithm is adopted for adjustment of two or more composite factors among temperature change, humidity change, voltage change, load change and working mode change.

[0100] The flow of fuzzy logic algorithm is as follows:

[0101] The precise value of temperature, humidity, voltage, load and working mode input variable is converted into the membership value of fuzzy set;

[0102] According to the fuzzy rule base, the membership value of the input variable is matched to determine the applicable rule;

[0103] The output of the matched rule is synthesized to obtain the fuzzy output;

[0104] The fuzzy output is converted into a precise threshold to adjust the threshold of leakage current.

[0105] Suppose the current temperature is 26°C and the humidity is 65%.

[0106] An embodiment is provided as follows:

[0107] Data collection: temperature: 26°C, humidity: 65%

[0108] Temperature normalization: (26-0) / (50-0)=0.52 (assuming the temperature range is 0-50°C)

[0109] Humidity normalization: (65-0) / (100-0)=0.65

[0110] The temperature and humidity data are normalized to the range of 0-1 for subsequent processing;

[0111] Temperature membership:

[0112] Low temperature: 0

[0113] Medium temperature: 0.52

[0114] High temperature: 0.48

[0115] Humidity membership:

[0116] Low humidity: 0

[0117] Medium humidity: 0.35

[0118] High humidity: 0.65

[0119] Match corresponding rules from defined fuzzy rules:

[0120] Rule 1: If temperature is high and humidity is high, increase threshold by 25%.

[0121] Rule 2: If temperature is high and humidity is low, increase threshold by 15%.

[0122] Rule 3: If temperature is low and humidity is high, decrease threshold by 10%.

[0123] Fuzzy composition:

[0124] Rule 1 weight: 0.48 * 0.65 = 0.312

[0125] Rule 2 weight: 0.48 * 0.35 = 0.168

[0126] Rule 3 weight: 0 * 0.65 = 0

[0127] Defuzzification:

[0128] Fuzzy output: (0.312 * 25%) + (0.168 * 15%) + (0 * -10%) = 7.8% + 2.52% +0% = 10.32%

[0129] Threshold adjustment percentage: 10.32%

[0130] Threshold adjustment:

[0131] Assume initial threshold is 30mA

[0132] Adjusted threshold: 30mA * (1 + 10.32%) = 30mA * 1.1032 ≈ 33.096mA

[0133] Through the above steps, the goal of adjusting the leakage current threshold according to temperature and humidity is achieved.

[0134] The complex pattern recognition module is further used for:

[0135] extracting the zero-sequence component of three-phase current, phase information of each phase current, frequency information of current, amplitude of current and harmonic components;

[0136] selecting a neural network (ANN) recognition algorithm, training the model using historical data, adjusting model parameters, optimizing model performance, and verifying the accuracy and generalization ability of the model using test set data;

[0137] using the trained model to perform pattern recognition on real-time data;

[0138] comparing the recognition result with the preset threshold to determine whether there is a complex leakage current mode;

[0139] triggering the corresponding warning mechanism according to the recognition result to ensure the real-time performance and response speed of the system.

[0140] Working principle: The system first collects environmental and operating data in real time through the sensor module installed around the inverter. These data are filtered and denoised by the data acquisition and preprocessing module to ensure the accuracy and consistency of the data. The central processing module uses a high-performance processor to analyze the preprocessed data in real time, calculates key parameters such as zero-sequence current, phase and frequency, and determines the leakage current mode and fault type. The multi-level dynamic threshold module dynamically adjusts the leakage current threshold according to the real-time monitoring of environmental parameters and the working state of the inverter. Through linear adjustment, nonlinear adjustment and fuzzy logic algorithm, the system can flexibly adjust the threshold according to the needs under different working conditions, avoiding false positives or false negatives.

[0141] The complex pattern recognition module uses machine learning and pattern recognition algorithms to analyze the comprehensive results of zero-sequence current, phase and frequency parameters to identify complex leakage current patterns. The system compares the recognition result with the preset threshold to determine whether there is a complex leakage current mode, and triggers the multi-level warning module according to the recognition result to issue different levels of alarms.

[0142] In addition, the system is also equipped with a user interface and communication module for displaying system status and historical data, and realizing data exchange with external devices to ensure the real-time performance and response speed of the system.

[0143] It should be noted that in this paper, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0144] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the application.

Claims

1. A leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring, characterized in that, include: The sensing module is installed around the photovoltaic inverter to monitor its operating parameters. The data acquisition and preprocessing module is connected to the sensing module and is used to perform preliminary filtering and noise reduction on the sensor data. The central processing module is connected to the data acquisition and preprocessing module and uses a high-performance processor to perform real-time analysis and processing of the preprocessed data. The system monitors and calculates zero-sequence current, phase, and frequency parameters to determine leakage current modes, fault types, and fault locations. Specifically, it continuously monitors zero-sequence current to determine if leakage current has occurred; when the zero-sequence current exceeds a preset threshold, leakage current is identified. It monitors the phase difference between each phase current to determine if the three-phase current is balanced; an unbalanced phase difference indicates leakage current. It monitors frequency fluctuations to assess grid stability; excessive frequency fluctuations indicate a problem in the grid. It calculates the amplitude and harmonic components of the current for load monitoring, system stability assessment, fault diagnosis, energy efficiency assessment, and model building. A multi-level dynamic threshold module, connected to the central processing module, is used to monitor and analyze environmental parameters and operating conditions in real time, and dynamically adjust the leakage current threshold. Based on environmental parameters and operating conditions, it dynamically calculates and adjusts the leakage current threshold using linear adjustment, nonlinear adjustment, and fuzzy logic algorithms. Specifically, linear adjustment is used for single temperature, voltage, and load changes; nonlinear adjustment is used for single humidity and operating mode changes; and fuzzy logic algorithms are used to adjust for changes in two or more composite factors among temperature, humidity, voltage, load, and operating mode changes. The environmental parameters include at least temperature, humidity, and grid voltage; the inverter's operating status includes at least load size and operating mode; thresholds under different operating conditions are stored in a database; the current threshold is periodically compared with historical data to verify its rationality and summarize the long-term trend of threshold changes; a manual verification function is provided, allowing users to manually adjust the threshold. The specific process of the fuzzy logic algorithm is as follows: convert the precise values ​​of the input variables, such as temperature, humidity, voltage, load, and operating mode, into the membership values ​​of fuzzy sets; match the membership values ​​of the input variables according to the fuzzy rule base to determine the applicable rules; synthesize the outputs of the matched rules to obtain the fuzzy output; convert the fuzzy output into a precise threshold and adjust the leakage current threshold. The complex pattern recognition module, connected to the central processing module, uses machine learning and pattern recognition algorithms combined with comprehensive analysis results of zero-sequence current, phase, and frequency parameters to identify complex leakage current patterns. It also extracts the zero-sequence component of the three-phase current, the phase information of each phase current, the frequency information of the current, the amplitude of the current, and harmonic components. A neural network recognition algorithm is selected, a model is trained using historical data, model parameters are adjusted, model performance is optimized, and the accuracy and generalization ability of the model are verified using test set data. The trained model is then used to perform pattern recognition on real-time data. A multi-level early warning module, connected to the central processing module, is used to issue different levels of alarms based on the degree of leakage current after determining that leakage current exists.

2. The leakage current monitoring and early warning system based on new energy photovoltaic inverter current monitoring according to claim 1, characterized in that, The sensing module includes: Temperature sensor used to monitor ambient temperature in real time; Humidity sensor for real-time monitoring of ambient humidity; Voltage sensors are used to monitor changes in grid voltage in real time. Current sensor is used to monitor the load size of the inverter in real time; Status sensors are used to monitor the inverter's operating mode in real time.

3. The leakage current monitoring and early warning system based on the current monitoring of a new energy photovoltaic inverter according to claim 2, characterized in that, The aforementioned method of dynamically calculating and adjusting the leakage current threshold based on environmental parameters and operating conditions specifically includes: The threshold is increased in high-temperature environments and decreased in low-temperature environments; The threshold is lowered in high humidity environments and raised in low humidity environments. Increase the threshold when the grid voltage is high; Increase the threshold when the load is high; Lower the threshold in standby mode.

4. The leakage current monitoring and early warning system based on the current monitoring of a new energy photovoltaic inverter according to claim 3, characterized in that, The complex pattern recognition module is also used for: The identification results are compared with preset thresholds to determine whether complex leakage current patterns exist. The corresponding early warning mechanism is triggered based on the identification results.

5. The leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter according to claim 4, characterized in that, The system also includes: The user interface, connected to the central processing module, is used to display system status and historical data; The communication module connects to the central processing module and is used to exchange data with external devices.

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