Intelligent early warning method and system based on power distribution network relay protection remote constant value comparison

Abstract

The invention relates to the technical field of intelligent electric power, in particular to an intelligent early warning method and system based on power distribution network relay protection remote constant value comparison, and the method comprises the steps: collecting equipment data of a power distribution network, and calculating an optimal protection constant value through an optimal constant value formula; comparing the optimal constant value with the remote constant value, performing intelligent early warning, and automatically adjusting the optimal constant value based on an early warning result; and constructing a visual interface to display an early warning result and the adjusted optimal constant value in real time, and storing the collected equipment data and the optimal constant value generated by analysis. The method comprises the following steps: collecting equipment data of a power distribution network, and calculating an optimal protection constant value through an optimal constant value formula; comparing the optimal constant value with the remote constant value, performing intelligent early warning, and automatically adjusting the optimal constant value based on an early warning result; the early warning sensitivity and accuracy are improved, the intelligent level of the early warning mechanism is enhanced, the sensitivity and response speed of the system are improved, and safe operation of the power distribution network is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power technology, and in particular to an intelligent early warning method and system based on remote setting comparison of distribution network relay protection. Background Art

[0002] Distribution network relay protection technology plays a vital role in the power system. It can respond quickly when power equipment fails or is abnormal to protect the equipment from damage. With the expansion of the scale and increase of complexity of the power grid, the traditional relay protection setting method faces more and more challenges. The earliest relay protection equipment relied on fixed setting and could not cope with the uncertainty caused by complex load changes and equipment aging. With the development of automation and information technology, the setting optimization of relay protection has gradually introduced computer algorithms, intelligent sensors and real-time data monitoring technologies to improve the accuracy and reliability of the response. Remote setting management has gradually become a research hotspot. By remotely collecting equipment operation data in real time and dynamically adjusting protection settings based on data analysis, the safety of the power system can be effectively improved.

[0003] The existing remote constant value comparison methods still have many shortcomings. There is a lack of accurate optimal constant value calculation methods. Constant value setting mostly relies on experience or simple mathematical models. It fails to fully consider the complexity of equipment operating conditions and lacks an effective constant value optimization mechanism. Although some systems have automatic adjustment functions, the rationality and sensitivity of the adjustment strategy are low, making it difficult to adapt to the complex and changeable power grid environment. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is: the lack of accurate optimal constant value calculation method, the constant value setting mostly relies on experience or simple mathematical models, fails to fully consider the complexity of equipment operating conditions, and lacks an effective constant value optimization mechanism. Although some systems have automatic adjustment functions, the rationality and sensitivity of the adjustment strategy are low, and it is difficult to adapt to the complex and changeable power grid environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent early warning method based on remote setting comparison of distribution network relay protection, comprising: collecting equipment data of the distribution network, and calculating the optimal protection setting through the optimal setting formula; comparing the optimal setting and the remote setting and conducting intelligent early warning, and automatically adjusting the optimal setting based on the early warning result; constructing a visual interface to display the early warning results and the adjusted optimal setting in real time, and storing the optimal setting generated by the collected equipment data and analysis.

[0007] As a preferred solution of the intelligent early warning method based on remote setting comparison of distribution network relay protection described in the present invention, wherein: the collection of equipment data of the distribution network includes using an integrated current transformer CT, a load sensor, a vibration signal collector and a smart meter to collect equipment data;

[0008] The device data includes current signal, load current, response curve, current and load data;

[0009] The collected current signal is converted into a digital signal using a data acquisition device DAQ, and the high-frequency noise of the data signal is removed using a low-pass filter. The data signal after the high-frequency noise is removed is converted into a short-circuit current S using MATLAB. i (t);

[0010] The time domain current signal is converted into a frequency domain signal using the fast Fourier transform FFT, and the extracted frequency domain signal is analyzed using the harmonic analysis algorithm. Based on the analysis results of the harmonic frequency, the nonlinear current change L is calculated. i (t);

[0011] Collect the rated capacity of the equipment from the equipment manual and use the load capacity correction formula to calculate the equipment capacity C i (t);

[0012] Calculate the time constant T of the response curve using a nonlinear fitting algorithm i (t).

[0013] As a preferred solution of the intelligent early warning method based on remote setting comparison of distribution network relay protection according to the present invention, wherein: the optimal protection setting calculated by the optimal setting formula includes using linear processing to calculate the result A of the nonlinear combination of short-circuit current and equipment capacity at time t 1 (t), the formula is:

[0014]

[0015] Where n represents the total number of devices, S i (t) represents the short-circuit current of the ith device at time t, C i (t) represents the capacity of the i-th device at time t, where t represents time;

[0016] The result of using the integral to calculate the nonlinear current change and time constant at time t A 2 (t), the formula is:

[0017]

[0018] Where M represents the total time to collect device data, T i(t) represents the time constant of the ith device at time t, L i (t) represents the nonlinear current change of the i-th device at time t;

[0019] The normalized processing result B(t) of the current at time t is calculated using the normalization function. The formula is:

[0020]

[0021] Where θ represents the adjustment factor, L i (t) represents the nonlinear current change of the i-th device at time t;

[0022] The result A is based on the nonlinear combination of short-circuit current and equipment capacity at calculation time t. 1 (t), the result of nonlinear current change and time constant A 2 (t) and the normalized processing result of the current B(t) are used to construct the optimal protection setting formula, which is expressed as follows:

[0023]

[0024] Where V top (t) represents the i-th optimal protection setting at time t.

[0025] As a preferred solution of the intelligent early warning method based on remote setting comparison of distribution network relay protection according to the present invention, wherein: the comparison of the optimal setting and the remote setting and the intelligent early warning include collecting the currently running protection setting V through the remote protection setting management platform run (t);

[0026] Calculate the difference ΔV(t) between the optimal set value and the operating set value;

[0027] The result of calculating the exponential warning function F(t) is expressed as:

[0028]

[0029] Among them I i (t) represents the current of the ith device at time t, P i (t) represents the load of the i-th device at time t;

[0030] Calculate the result Z(t) of the normalized function that handles the nonlinearity of the device load and current. The formula is:

[0031]

[0032] Among them I i (t) represents the current of the i-th device at time t;

[0033] Based on the result F(t) of the exponential warning function and the result Z(t) of the normalization function, the final optimal fixed value adjustment formula is constructed:

[0034]

[0035] Where V new (t) represents the final optimal value.

[0036] As a preferred solution of the intelligent early warning method based on remote setting comparison of distribution network relay protection according to the present invention, wherein: the final optimal setting and the remote setting are compared with the intelligent early warning means to set a judgment threshold Y based on historical equipment data;

[0037] Calculate the final difference ΔV'(t) between the final optimal setting and the operating setting;

[0038] Compare the judgment threshold Y with the final difference ΔV'(t). If Y<ΔV'(t), it is determined to be the protection setting value V run (t) abnormal, automatically send an alarm to notify the operator, if Y ≥ ΔV'(t), it is determined to be the protection setting value V run (t) Normal, continuously check the protection setting value V run (t).

[0039] As a preferred solution of the intelligent early warning method based on remote set value comparison of distribution network relay protection described in the present invention, wherein: the construction of a visual interface to display the early warning results and the adjusted optimal set value in real time refers to using the front-end technology React to build a visual interface, using the chart library D3.js to display the set value comparison results, and designing an early warning panel at the top of the interface to display the currently triggered early warning information;

[0040] Customers who have passed real-name verification are allowed to view the information.

[0041] As a preferred solution of the intelligent early warning method based on remote constant value comparison of distribution network relay protection described in the present invention, wherein: the storing of the collected device data and the optimal constant generated by the analysis refers to storing the collected device data and the optimal constant generated by the analysis in a database, sorting the database in chronological order and marking the corresponding tags, synchronously backing up the device data and the optimal constant generated by the analysis in the cloud, and regularly performing integrity checks on the backup data.

[0042] Another object of the present invention is to provide an intelligent early warning system based on remote set value comparison of distribution network relay protection, to improve the flexibility and automation level of set value adjustment, to ensure the stability and reliability of the system under complex working conditions, by constructing a real-time monitoring and adjustment module, automatically adjusting the protection set value, performing remote comparison in real time, and displaying early warning information and system status through a visual interface, to help operators discover problems in a timely manner and respond, thereby effectively improving the safe operation capability of the network.

[0043] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent early warning system based on remote setting comparison of distribution network relay protection, comprising: a collection and calculation module, an early warning adjustment module and a visualization storage module;

[0044] The collection and calculation module is used to collect equipment data of the distribution network and calculate the optimal protection setting value through the optimal setting formula;

[0045] The warning adjustment module is used to compare the optimal set value and the remote set value and perform intelligent warning, and automatically adjust the optimal set value based on the warning result;

[0046] The visualization storage module is used to construct a visualization interface to display the warning results and the adjusted optimal set values ​​in real time, and to store the optimal set values ​​generated by the collected equipment data and analysis.

[0047] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the intelligent early warning method based on remote constant value comparison of distribution network relay protection as described above are implemented.

[0048] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the intelligent early warning method based on remote setting comparison of distribution network relay protection as described above are implemented.

[0049] The beneficial effects of the present invention are as follows: the present invention collects equipment data of the distribution network, calculates the optimal protection setting through the optimal setting formula; compares the optimal setting and the remote setting with intelligent early warning, and automatically adjusts the optimal setting based on the early warning result; improves the sensitivity and accuracy of the early warning, enhances the intelligence level of the early warning mechanism, improves the sensitivity and response speed of the system, and ensures the safe operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 The figure is a flow chart of the intelligent early warning method based on remote setting comparison of distribution network relay protection.

[0052] Figure 2 This is a structural diagram of the intelligent early warning system based on remote setting comparison of distribution network relay protection. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0056] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides an intelligent early warning method based on remote setting comparison of distribution network relay protection. The intelligent early warning method based on remote setting comparison of distribution network relay protection includes:

[0057] S1: Collect equipment data of the distribution network and calculate the optimal protection setting value through the optimal setting formula;

[0058] Specifically, collecting equipment data of the distribution network means using an integrated current transformer CT, a load sensor, and a vibration signal collector to collect equipment data;

[0059] Equipment data includes current signal, load current and response curve data;

[0060] The collected current signal is converted into a digital signal using a data acquisition device DAQ, and the high-frequency noise of the data signal is removed using a low-pass filter. The data signal after the high-frequency noise is removed is converted into a short-circuit current S using MATLAB. i (t);

[0061] The time domain current signal is converted into a frequency domain signal using the fast Fourier transform FFT, and the extracted frequency domain signal is analyzed using the harmonic analysis algorithm. Based on the analysis results of the harmonic frequency, the nonlinear current change L is calculated. i (t);

[0062] Collect the rated capacity of the equipment from the equipment manual and use the load capacity correction formula to calculate the equipment capacity C i (t), the formula is:

[0063]

[0064] Where E i,ratd is the rated capacity of the equipment, I(t) is the front load current, I max is the maximum rated current;

[0065] Calculate the time constant T of the response curve using a nonlinear fitting algorithm i (t);

[0066] Through the integrated collection of multi-source data, multiple key parameters in the operation of the power grid can be captured at the same time, further improving the system's ability to perceive the power grid status. The diverse data collection methods significantly improve the system's fault tolerance and adaptability, and can adapt to complex power grid environments. The denoising step not only improves the accuracy of the signal, but also reduces the error of subsequent calculations. Especially in the calculation process of short-circuit current, accurate data input is directly related to the accuracy of the optimal protection setting, which in turn affects the protection effect of the entire system. Harmonic analysis can detect abnormal fluctuations in the operation of the power grid and provide an important basis for the adjustment of the setting. The collaborative analysis process helps to improve the system's detection accuracy of abnormal conditions and enhance its adaptability to complex conditions. The nonlinear fitting algorithm can accurately capture the transition process of the equipment from dynamic to stable, thereby providing a scientific basis for the setting of the setting. The application of the nonlinear fitting algorithm enables the entire system to provide a more sensitive and timely response when facing complex load changes.

[0067] Furthermore, the optimal protection setting value is calculated by the optimal setting value formula, which means that the existing formula uses a linear weighted method to calculate the basic optimal protection setting value V 0 (t) as a basis, the formula is:

[0068]

[0069] Where n is the total number of devices, Si (t) is the short-circuit current of the ith device at time t, C i (t) is the capacity of the ith device at time t, T i (t) is the time constant of the ith device at time t;

[0070] The result of the nonlinear combination of short-circuit current and equipment capacity at time t is calculated using nonlinear processing A 1 (t), the formula is:

[0071]

[0072] Where n is the total number of devices, S i (t) is the short-circuit current of the ith device at time t, C i (t) is the capacity of the ith device at time t, where t is time;

[0073] The short-circuit current and capacity of the equipment change dynamically with time. If the short-circuit current and equipment capacity are not taken as dynamic functions of time, the system's set value cannot be adjusted in real time, which may lead to over-protection or under-protection. In order to accurately reflect the state of the system, the set value formula must handle the dynamic changes of the short-circuit current and capacity of the equipment. Static processing or fixed set value methods are no longer applicable in modern power systems because they cannot cope with complex power fluctuations. The impact of equipment capacity on the system set value is nonlinear, especially when the equipment is close to its maximum capacity, its impact on the system will increase exponentially. Linear processing cannot accurately reflect the impact of high-load equipment on the system set value, which may lead to protection failure. Dynamic processing improves the real-time performance of the system, ensures that the set value can be adjusted immediately with the operating status of the power system, and enhances the adaptability and stability of the system.

[0074] The result of using the integral to calculate the nonlinear current change and time constant at time t A 2 (t), the formula is:

[0075]

[0076] Where M is the total time to collect device data, T i (t) is the time constant of the ith device at time t, L i (t) is the nonlinear current change of the i-th device at time t;

[0077] The normalized function is used to calculate the current at time t, and the formula is:

[0078]

[0079] Where θ is the adjustment factor, which is the adjustment of the normalized current, L i(t) is the nonlinear current variation of the i-th device at time t, and M is the total time for collecting device data;

[0080] In order to better capture the changes in current during the entire operating time period, integration is the only mathematical method that can cumulatively process this change. Through time integration, we can uniformly incorporate current fluctuations at different times into the calculation to ensure that the generated normalized result can accurately reflect the current changes in the entire time interval. If we only process the instantaneous current value at a certain moment, we cannot reflect the global fluctuations in system operation, the system's constant value generation may be inaccurate, and it is easy to ignore long-term fluctuation effects. In the power system, short-term current fluctuations may not have much impact on the system, but long-term current fluctuations may accumulate and affect the stability and safety of the system. Time integration is the best way to capture and process this cumulative effect. Time integration can gradually accumulate short-term current fluctuations, thereby providing a global The overall perspective helps to determine the impact of current fluctuations on the long-term stability of the system. The final normalized result can reflect the global characteristics of the system operation and ensure the rationality and accuracy of the normalized calculation results over a long period of time. Other simple statistical methods, such as taking the average and the maximum value, will lose the fluctuation information in the time dimension and cannot replace the integral processing. In the traditional power system protection constant value calculation, the current fluctuation is usually calculated by the maximum value or the current at a fixed moment. The existing technology is usually unable to handle the global effect of the current accumulation in time. Through time integration, we can accurately handle the dynamic changes of the current in the entire time interval. Whether the current fluctuates rapidly or changes slowly, the integral operation can accurately capture these changes and incorporate these changes into the optimal constant value generation process of the system.

[0081] The result A is based on the nonlinear combination of short-circuit current and equipment capacity at calculation time t. 1 (t), the result of nonlinear current change and time constant A 2 (t) and the normalized result B(t) are used to construct the optimal protection setting formula, which is:

[0082]

[0083] Where V top (t) is the i-th optimal protection value at time t;

[0084] The power system is dynamically changing, and the current and capacity fluctuate over time. Therefore, it is necessary to deal with their dynamic changes. Static processing can no longer adapt to modern complex systems. Compared with the static processing of existing technologies, the dynamic processing method ensures the real-time and accuracy of the system constant generation and improves the system's adaptive ability. The cumulative effect of current fluctuations directly affects the stability of the system. Integration is the only effective way to capture this cumulative effect. Without the processing of cumulative effects, the system constant will ignore the long-term impact of small fluctuations. Compared with the instantaneous processing of existing technologies, the cumulative effect of time integration takes into account the global current fluctuations and ensures the stability of the system constant generation. In order to ensure that the constant is within a safe range, normalization is an indispensable step. The introduction of adjustment factors and logarithmic functions improves the formula's ability to cope with complex fluctuations. Compared with traditional linear normalization methods, nonlinear normalization and dynamic adjustment factors can better cope with complex system fluctuations and ensure that the system constant is within a safe range.

[0085] Linear processing is used to calculate the combined result of short-circuit current and equipment capacity at time t, which can effectively handle the nonlinear changes in the equipment status in the power grid system. This processing method not only improves the calculation accuracy, but also enables complex power grid models to be described in a relatively simple way, thereby improving calculation efficiency. The use of the integral method to calculate the nonlinear current change at time t can more accurately capture the trend of current changes over time. The application of integral calculation allows the constant value calculation to take into account the cumulative effect of current changes, thereby improving the accuracy of protection constants. The introduction of normalization function can eliminate the numerical differences between different devices and unify the current changes of each device to facilitate further comparison and analysis. Normalization processing improves the system's adaptability to multi-device status data and ensures the calculation results. The consistency and comparability of the results are the result of the power system operation state with strong nonlinear characteristics. The sub-linear processing can better simulate these nonlinear changes, while the integral calculation ensures that the cumulative effect of the current change is taken into consideration, providing a more accurate optimal protection setting in a complex power environment, reducing the error rate, and improving the reliability of the protection system. By introducing the adjustment factor, the system can dynamically adjust the optimal protection setting under different equipment and different operating environments, thereby improving the adaptability and reliability of the system. This formula not only takes into account the time-varying characteristics of the current, but also can be dynamically adjusted according to the actual capacity of the equipment to ensure the optimization of the protection setting under a complex operating environment, providing a scientific and accurate calculation basis for the protection setting, and reducing the equipment damage and power grid accidents that may be caused by improper setting of the setting.

[0086] S2: Compare the optimal setting value with the remote setting value and make intelligent warnings, and automatically adjust the optimal setting value based on the warning results;

[0087] Specifically, automatically adjusting the final optimal setting through the final optimal setting adjustment formula refers to collecting the currently running protection setting V through the remote protection setting management platform. run (t);

[0088] Calculate the difference ΔV(t) between the optimal protection setting and the operating setting. The formula is:

[0089] ΔV(t)=|V run (t)-V top (t)|,

[0090] The existing formula uses the automatic adjustment setting to calculate the optimal protection setting V for the existing adjustment i,1 (t) as a basis, the formula is:

[0091] V 1 (t) = V run (t)+k·ΔV(t),

[0092] Where k is the adjustment coefficient;

[0093] Calculate the result of the exponential warning function F(t), the formula is:

[0094]

[0095] Among them I i (t) is the current of the ith device at time t, P i (t) is the load of the ith device at time t;

[0096] The integration operation captures the dynamic changes of the system over the entire time period, rather than relying solely on a certain instantaneous state. Through integration, the system can reflect the cumulative effects of parameters such as current and load over a long period of time, ensuring that the adjustment process takes into account historical data and the global operating status of the system. Relying only on instantaneous values ​​may lead to over- or under-reaction to system fluctuations. Without the processing of time accumulation effects, the impact of small fluctuations on the system may be ignored, reducing the accuracy of adjustments. Existing technologies usually only consider instantaneous current or load values, ignoring dynamic accumulation effects, and are unable to capture long-term fluctuations in the system, resulting in too frequent adjustments or over-reaction to short-term fluctuations. Through time integration, the system can capture the global trend of current fluctuations and load changes, and make adjustments based on historical accumulated data. The cumulative response from a global perspective can effectively avoid instantaneous over-adjustments and improve system stability.

[0097] Calculate the result of the normalized function Z(t) for the nonlinear treatment of the device load and current, the formula is:

[0098]

[0099] Among them Ii (t) is the current of the i-th device at time t;

[0100] There is a nonlinear relationship between current and equipment capacity. Especially under high load conditions, the impact of the system load on the set value will be multiplied. Through nonlinear processing, the system can more accurately capture changes in the equipment load state and make timely adjustments under high load. Linear processing cannot reflect the actual impact of high-load equipment, which can easily lead to insufficient system adjustment under high load conditions, increasing the risk of system failure. In existing technologies, the processing of load and current is usually linear, which cannot reflect the behavior of complex equipment under different load conditions, resulting in the system being unable to adjust in time when the load is close to the maximum capacity, increasing the risk of failure. Through nonlinear processing of the current and capacity of the equipment, the formula can adaptively handle the dynamic changes of high-load equipment, ensuring that the system makes fast and accurate adjustments under high load conditions. This improvement significantly improves the stability of the system under complex load environments. Through normalization processing, the formula can balance the impact of current and load, avoiding excessive response of the system when the current fluctuation is small. Traditional systems usually lack normalization processing, resulting in excessive or slow response of the system to changes in load and current. Traditional systems usually lack normalization processing, resulting in excessive or slow response of the system to changes in load and current.

[0101] Based on the result F(t) of the exponential warning function and the result Z(t) of the normalization function, the final optimal fixed value adjustment formula is constructed:

[0102]

[0103] Where V new (t) is the final optimal value;

[0104] Existing technologies may rely on preset static adjustment strategies and cannot reflect system changes in real time. Fast and accurate adjustments are achieved through real-time calculation and dynamic response differences. Dynamic adjustment is necessary because the state of the power system is constantly changing, and optimal performance cannot be achieved by preset parameters alone. Traditional systems may ignore the cumulative impact of short-term fluctuations on long-term performance. The time integral in the formula combines short-term and long-term data to more comprehensively evaluate the system state. The comprehensive processing of long-term and short-term data provides a more comprehensive perspective, which is particularly critical for preventing system overloads or failures. Compared with the possible linear processing of existing technologies, the nonlinear processing of this formula more accurately reflects the actual operating conditions, especially when the equipment is close to the load limit. Nonlinear processing is necessary because it can more accurately simulate the operating characteristics of real equipment, especially under extreme conditions.

[0105] Remote management is extremely convenient and practical, especially in large-scale power grids. Manually collecting the operating data of each device is time-consuming and labor-intensive, while the remote platform can obtain information from all devices in real time, improving the automation level of the system. The centralized management of the platform also reduces the possibility of data omissions or errors, and improves the reliability of constant value monitoring. The automatic calculation of constant value differences not only improves the accuracy of detection, but also can identify small abnormal changes, thereby discovering potential problems earlier. The safety of the power grid is of great significance. Measures can be taken before problems occur to avoid accidents. By introducing a dynamic calculation model, parameters can be continuously adjusted to ensure that the parameter settings at each time point are optimal. By adjusting these set values ​​in real time, system risks caused by improper parameter settings can be better prevented. Different devices have different sensitivities to faults under different working conditions. Through the introduction of this function, different sensitivity levels can be set according to the actual operating conditions of the equipment. The system can identify potential problems in advance before a fault occurs and issue warnings in time, thereby avoiding the setting caused by delayed response. In the event of equipment damage or power outage, the system can dynamically adjust the protection setting according to the changes in current and load through the dynamic response function to ensure the safety of the equipment in emergency situations. The real-time response capability enables the power grid to make timely adjustments when facing sudden load changes and reduce the occurrence of equipment failures. The nonlinear characteristics of the exponential function enable the system to quickly increase the warning level and issue accurate warning signals when encountering sudden abnormalities. Compared with the linear function, the exponential warning function can better adapt to the changes in equipment status under complex working conditions and significantly improve the accuracy and timeliness of the warning. Through the normalization function, the system can standardize the current changes of the equipment to make the data of different equipment comparable, ensuring that the system processes all equipment under the same standards, improving the uniformity and accuracy of the system. Compared with the traditional setting method, this formula is highly flexible and can adjust the protection setting in time according to the current working conditions of the equipment. The dynamic optimization capability effectively improves the safety of the equipment under different load conditions, reduces the error of human adjustment, and enhances the intelligence and automation level of power grid protection.

[0106] Furthermore, the final optimal set value and the remote set value are compared with the intelligent early warning indicator to set the judgment threshold Y based on the historical equipment data;

[0107] Calculate the final difference ΔV'(t) between the final optimal setting and the operating setting;

[0108] Compare the judgment threshold Y with the final difference ΔV'(t). If Y<ΔV'(t), it is determined to be the protection setting value V run (t) abnormal, automatically send an alarm to notify the operator, if Y ≥ ΔV'(t), it is determined to be the protection setting value V run(t) Normal, continuously check the protection setting value V run (t);

[0109] Through historical data, the system can set more flexible thresholds based on the long-term operating trends of the equipment, thereby improving the accuracy of judgment. The data-driven analysis method ensures that the system can adapt to the set value requirements of different equipment and different working conditions, reducing the possibility of false alarms. The threshold is automatically generated based on historical equipment data. This method has extremely strong intelligent characteristics. The intelligent threshold setting not only improves the accuracy of the set value comparison, but also can dynamically adapt to changing working conditions according to the operating trends of the equipment, thereby reducing the possibility of false alarms and missed alarms. Compared with traditional manual monitoring methods, automatic alarms can notify as soon as a fault occurs, reducing response delays, thereby gaining time for the rapid repair of the power grid. The system will continue to check the set value status to ensure that the problem is handled in a timely manner after the alarm is issued to ensure the safe operation of the equipment.

[0110] S3: Build a visual interface to display the warning results and the adjusted optimal values ​​in real time, and store the optimal values ​​generated by the collected equipment data and analysis;

[0111] Specifically, building a visual interface to display the warning results and the adjusted optimal set value in real time means using the front-end technology React to build a visual interface, using the chart library D3.js to display the set value comparison results, and designing a warning panel at the top of the interface to display the currently triggered warning information;

[0112] Allow customers who have passed real-name verification to access;

[0113] The modular design not only improves the readability and maintainability of the code, but also facilitates the later upgrade and function expansion of the system. React's virtual DOM update mechanism can ensure the real-time display of warning information and fixed value adjustment results, greatly improving the system's response speed and user experience. The interactive function enables users to more intuitively understand the system's warning information and optimal fixed value adjustment results. The warning panel can not only display the triggered warning type, but also provide corresponding operation suggestions, which can help operators make decisions quickly, reduce the probability of accidents, and improve the safety and stability of the power grid. Real-name verification can not only limit access rights, but also record the user's operation history, which is convenient for tracing and review when problems occur. This security measure greatly improves the data security of the system and prevents attacks and data leakage from malicious users. The system allows users to easily obtain key information through charts and warning panels, and provides detailed data explanations and analysis results, so that users can have a comprehensive understanding of the system's operating status, which not only improves the user-friendliness of the system, but also enhances its practicality in complex power grid environments.

[0114] Further, storing the collected device data and the optimal set value generated by the analysis means storing the collected device data and the optimal set value generated by the analysis in a database, sorting the database in chronological order and marking corresponding tags, synchronously backing up the device data and the optimal set value generated by the analysis in the cloud, and regularly performing integrity checks on the backup data;

[0115] The database is sorted in chronological order, so that operators can easily find and analyze historical data. The time-sorted storage structure greatly enhances the traceability of the data, making it easier for the system to quickly find relevant information and take corresponding measures when problems occur. Through tags, operators can quickly locate the data and analysis results of specific equipment. This tagged storage method enhances the flexibility of the system, so that the system can still maintain an efficient operating state when processing large-scale data. Tag management helps to improve the accuracy and operability of data analysis and reduce errors caused by human factors. Data can be quickly restored through cloud backup to ensure the continuity of power grid management. Cloud backup is also scalable and can dynamically expand storage capacity according to the actual needs of power grid management to meet the growing storage needs of equipment data and optimal values. By comparing the original data and the backup data, the system can promptly discover and repair errors or anomalies in the data to ensure the consistency of data between different storage nodes. This mechanism greatly improves the reliability of the data and avoids system failures or protection failures caused by data errors. Regular integrity detection ensures the security and stability of data storage and reduces the complexity of data maintenance. The scalability of the system enables it to flexibly expand storage space and computing resources as the equipment data and optimal values ​​grow, ensuring the long-term sustainable development of the system.

[0116] The above is a schematic scheme of an intelligent early warning method based on remote constant value comparison of distribution network relay protection in this embodiment. It should be noted that the technical scheme of the system of the intelligent early warning method based on remote constant value comparison of distribution network relay protection and the technical scheme of the intelligent early warning method based on remote constant value comparison of distribution network relay protection mentioned above belong to the same concept. The details of the technical scheme of the intelligent early warning system based on remote constant value comparison of distribution network relay protection in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the intelligent early warning method based on remote constant value comparison of distribution network relay protection mentioned above.

[0117] Example 2, reference Figure 2 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides an intelligent early warning system based on remote setting comparison of distribution network relay protection, including a collection and calculation module, an early warning adjustment module and a visualization storage module;

[0118] The collection and calculation module is used to collect equipment data of the distribution network and calculate the optimal protection setting value through the optimal setting formula;

[0119] The warning adjustment module is used to compare the optimal set value and the remote set value and perform intelligent warning, and automatically adjust the optimal set value based on the warning result;

[0120] This embodiment further provides a computing device, which is applicable to the case of an intelligent early warning method based on remote setting comparison of distribution network relay protection, including:

[0121] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent early warning method based on remote setting comparison of distribution network relay protection as proposed in the above embodiment.

[0122] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent early warning method based on remote setting comparison of distribution network relay protection as proposed in the above embodiment is implemented.

[0123] The storage medium proposed in this embodiment and the intelligent early warning method based on remote setting comparison of distribution network relay protection proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0124] The visualization storage module is used to construct a visualization interface to display the warning results and the adjusted optimal set values ​​in real time, and to store the optimal set values ​​generated by the collected equipment data and analysis.

[0125] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0127] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0128] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] Example 3 is an embodiment of the present invention. This embodiment provides an intelligent early warning method based on remote setting comparison of distribution network relay protection. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0130] 1. Experimental objectives include: verifying the proposed intelligent early warning method compared with the existing technology, focusing on evaluating its response speed, set value adjustment accuracy and system recovery time under different current loads and abnormal working conditions.

[0131] 2. Experimental design, including: using Simulink experimental platform for simulation to simulate different load conditions of distribution network equipment; the scheme uses dynamic constant adjustment, nonlinear fitting and harmonic analysis to monitor and adjust protection constants in real time. The existing technology uses the traditional fixed constant method without real-time adjustment.

[0132] 3. Experimental steps, including: creating the same load environment and equipment model in the simulation platform, collecting data using the proposed intelligent early warning method and the existing technology, obtaining current, load, and voltage changes, and recording the system's response under different loads.

[0133] The experimental data comparison table shows:

[0134]

[0135]

[0136] The experimental results are analyzed, including: 1. Warning triggering time: Compared with the existing technology, the new technology triggers warnings faster under high load and abnormal conditions. The existing technology has a significant warning delay due to fixed values;

[0137] 2. Effect of constant value adjustment: The new technology can quickly adapt to current and load changes by dynamically adjusting the constant value, while the fixed constant value of the existing technology shows obvious shortcomings under complex load conditions and cannot be adjusted.

[0138] 3. System stability: The new technology provides better system stability under high load conditions. The flexibility of automatic adjustment improves the system's ability to respond to emergencies and reduces the risk of overload or equipment damage.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent early warning method based on remote setting comparison of distribution network relay protection is characterized by: include, Collect equipment data of the distribution network and calculate the optimal protection setting value through the optimal setting formula; Compare the optimal setting value with the remote setting value and make intelligent warnings, and automatically adjust the optimal setting value based on the warning results; Build a visual interface to display the warning results and the adjusted optimal values ​​in real time, and store the optimal values ​​generated by collecting equipment data and analysis.

2. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 1 is characterized in that: The collecting of equipment data of the distribution network includes using an integrated current transformer CT, a load sensor, a vibration signal collector and a smart meter to collect equipment data; The device data includes current signal, load current, response curve, current and load data; The collected current signal is converted into a digital signal using a data acquisition device DAQ, and the high-frequency noise of the data signal is removed using a low-pass filter. The data signal after the high-frequency noise is removed is converted into a short-circuit current S using MATLAB. i (t); The time domain current signal is converted into a frequency domain signal using the fast Fourier transform FFT, and the extracted frequency domain signal is analyzed using the harmonic analysis algorithm. Based on the analysis results of the harmonic frequency, the nonlinear current change L is calculated. i (t); Collect the rated capacity of the equipment from the equipment manual and use the load capacity correction formula to calculate the equipment capacity C i (t); Calculate the time constant T of the response curve using a nonlinear fitting algorithm i (t).

3. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 2 is characterized in that: The optimal protection setting value calculated by the optimal setting value formula includes using sub-linear processing to calculate the result A1(t) of the nonlinear combination of short-circuit current and equipment capacity at time t, which is expressed as: Where n represents the total number of devices, S i (t) represents the short-circuit current of the ith device at time t, C i (t) represents the capacity of the i-th device at time t, where t represents time; The result of the nonlinear current change and time constant at time t is calculated by integration, A2(t), and the formula is expressed as: Where M represents the total time to collect device data, T i (t) represents the time constant of the ith device at time t, L i (t) represents the nonlinear current change of the i-th device at time t; The normalized processing result B(t) of the current at time t is calculated using the normalization function. The formula is: Where θ represents the adjustment factor, L i (t) represents the nonlinear current change of the i-th device at time t; The optimal protection setting formula is constructed based on the calculation result A1(t) of the nonlinear combination of short-circuit current and equipment capacity at time t, the result A2(t) of nonlinear current change and time constant, and the normalized current result B(t). The formula is expressed as follows: Where V top (t) represents the i-th optimal protection setting at time t.

4. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 3 is characterized in that: The comparison of the optimal setting value and the remote setting value and the intelligent early warning include collecting the current protection setting value V through the remote protection setting value management platform run (t); Calculate the difference ΔV(t) between the optimal set value and the operating set value; The result of calculating the exponential warning function F(t) is expressed as: Among them I i (t) represents the current of the ith device at time t, P i (t) represents the load of the i-th device at time t; Calculate the result Z(t) of the normalized function that handles the nonlinearity of the device load and current. The formula is: Among them I i (t) represents the current of the i-th device at time t; Based on the result F(t) of the exponential warning function and the result Z(t) of the normalization function, the final optimal fixed value adjustment formula is constructed: Where V new (t) represents the final optimal value.

5. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 4 is characterized in that: The final optimal setting value and the remote setting value are compared with the intelligent early warning device to set the judgment threshold Y based on the historical equipment data; Calculate the final difference ΔV'(t) between the final optimal setting and the operating setting; Compare the judgment threshold Y with the final difference ΔV'(t). If Y<ΔV'(t), it is determined to be the protection setting value V run (t) abnormal, automatically send an alarm to notify the operator, if Y ≥ ΔV'(t), it is determined to be the protection setting value V run (t) Normal, continuously check the protection setting value V run (t).

6. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 5 is characterized in that: The construction of a visual interface to display the warning results and the adjusted optimal set value in real time refers to using the front-end technology React to build a visual interface, using the chart library D3.js to display the set value comparison results, and designing a warning panel at the top of the interface to display the currently triggered warning information; Customers who have passed real-name verification are allowed to view the information.

7. The intelligent early warning method based on remote setting comparison of distribution network relay protection according to claim 6 is characterized in that: The storing of the collected device data and the optimal set values ​​generated by the analysis refers to storing the collected device data and the optimal set values ​​generated by the analysis in a database, sorting the database in chronological order and marking the corresponding tags, synchronously backing up the device data and the optimal set values ​​generated by the analysis to the cloud, and regularly performing integrity checks on the backup data.

8. A system based on the intelligent early warning method based on remote setting comparison of distribution network relay protection according to any one of claims 1 to 7, characterized in that: include: Collection and calculation module, early warning adjustment module and visualization storage module; The collection and calculation module is used to collect equipment data of the distribution network and calculate the optimal protection setting value through the optimal setting formula; The warning adjustment module is used to compare the optimal set value and the remote set value and perform intelligent warning, and automatically adjust the optimal set value based on the warning result; The visualization storage module is used to construct a visualization interface to display the warning results and the adjusted optimal set values ​​in real time, and to store the optimal set values ​​generated by the collected equipment data and analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent early warning method based on remote setting comparison of distribution network relay protection described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent early warning method based on remote setting comparison of distribution network relay protection described in any one of claims 1 to 7 are implemented.

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