Fault self-healing method, device and equipment for power distribution network, medium and program product

Abstract

The invention relates to a power distribution network-oriented fault self-recovery method, device and equipment, a medium and a program product. The method comprises the following steps: monitoring the working temperature of the power distribution network in real time, and outputting a temperature discrimination signal based on a comparison result between the real-time temperature of the power distribution network and a preset temperature threshold value; monitoring the working voltage of the power distribution network in real time to obtain voltage fluctuation data of the power distribution network, analyzing the voltage fluctuation data, and outputting a voltage discrimination signal; inputting the temperature discrimination signal and the voltage discrimination signal into the trained influence model, and outputting the influence degree of the voltage anomaly on the temperature anomaly; and switching the power distribution network to a standby port to output current and performing safety threshold regulation and control on the voltage under the condition that the voltage abnormity occurs in the power distribution network and it is determined that the voltage abnormity can influence the temperature abnormity. Therefore, temperature abnormity caused by voltage fluctuation can be avoided, an efficient and reliable solution is provided for power distribution network management under complex working conditions, and the reliability and sustainability of the system are improved.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a fault self-healing method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power distribution networks. Background Technology

[0002] The distribution network is an important component of the power system. To ensure the highly intelligent operation of the distribution network, it is necessary to monitor the feeder operation data in real time and provide timely warnings and rapid handling of abnormal situations.

[0003] Existing power distribution network management systems have significant shortcomings in practical applications: for example, they rely too heavily on fixed thresholds for judgment and lack adaptability to dynamically changing environments, leading to misjudgments under complex or extreme conditions. Furthermore, traditional power grid alarm mechanisms are too simplistic, only issuing warnings after a problem occurs, rather than providing effective real-time control measures, thus increasing safety risks.

[0004] Therefore, there is an urgent need for a fault self-healing solution for distribution networks to ensure the long-term stable operation of distribution networks. Summary of the Invention

[0005] Therefore, it is necessary to provide a fault self-healing method, device, computer equipment, computer-readable storage medium, and computer program product for distribution networks that can ensure the long-term stable operation of distribution networks, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a fault self-healing method for distribution networks, the method comprising:

[0007] The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0008] The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data of the distribution network. The voltage fluctuation data is analyzed and a voltage discrimination signal is output. The voltage discrimination signal includes a voltage qualified signal and a voltage unqualified signal.

[0009] The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the influence of voltage anomaly on temperature anomaly is output.

[0010] If a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

[0011] In one embodiment, the analysis of the voltage fluctuation data includes:

[0012] Based on the voltage fluctuation data, the fluctuation amplitude and fluctuation frequency are extracted;

[0013] Based on the fluctuation amplitude and the fluctuation frequency, determine the amplitude anomaly coefficient and frequency anomaly coefficient of the voltage data;

[0014] The amplitude anomaly coefficient and the frequency anomaly coefficient are normalized to obtain the voltage anomaly coefficient.

[0015] In one embodiment, the voltage anomaly coefficient is used to determine whether the voltage signal is qualified;

[0016] When the voltage coefficient is greater than or equal to the first preset threshold, the output voltage discrimination signal is a voltage failure signal;

[0017] When the voltage coefficient is less than the first preset threshold, the output voltage discrimination signal is a voltage qualified signal.

[0018] In one embodiment, before inputting the temperature discrimination signal and the voltage discrimination signal into the trained influence model and outputting the degree of influence of the voltage anomaly on the temperature anomaly, the method further includes:

[0019] An impact model is constructed, which uses support vector regression to learn the nonlinear relationship between voltage anomalies and temperature anomalies, and selects radial basis functions as kernel functions to model the nonlinear relationship between the voltage anomalies and the temperature anomalies.

[0020] The impact model is trained using real-time data to obtain a well-trained impact model.

[0021] In one embodiment, inputting the temperature discrimination signal and the voltage discrimination signal into a trained influence model and outputting the degree of influence of voltage anomaly on temperature anomaly includes:

[0022] The mapping relationship between voltage anomalies and temperature anomalies is determined by the trained influence model, and temperature data is predicted based on the real-time input voltage anomaly coefficient.

[0023] The predicted temperature data is normalized to obtain normalized temperature data.

[0024] Based on the normalized temperature data and the voltage anomaly coefficient, the degree of influence of voltage anomaly on temperature anomaly is determined; wherein, if the degree of influence of voltage anomaly on temperature anomaly is greater than a second preset threshold, it is determined that voltage anomaly has an influence on temperature anomaly; if the degree of influence of voltage anomaly on temperature anomaly is not greater than the second preset threshold, it is determined that voltage anomaly has no influence on temperature anomaly.

[0025] In one embodiment, the voltage safety threshold regulation includes:

[0026] The operating voltage of the power distribution network is monitored in real time, and the operating voltage is compared with a preset safe voltage range to determine whether the operating voltage exceeds the safe voltage range.

[0027] If the operating voltage is greater than the upper limit threshold of the safe voltage range, the output current is adjusted to reduce the operating voltage to the safe voltage range.

[0028] If the operating voltage is less than the lower threshold of the safe voltage range, then boosting is initiated to raise the operating voltage to the safe voltage range.

[0029] Secondly, this application also provides a fault self-healing device for distribution networks, the device comprising:

[0030] The temperature monitoring module is used to monitor the operating temperature of the power distribution network in real time, and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0031] The voltage monitoring module is used to monitor the working voltage of the distribution network in real time, obtain voltage fluctuation data of the distribution network, analyze the voltage fluctuation data, and output voltage discrimination signals, which include: voltage qualified signal and voltage unqualified signal;

[0032] The processing module is used to input the temperature discrimination signal and the voltage discrimination signal into the trained influence model and output the degree of influence of voltage anomaly on temperature anomaly.

[0033] The execution module is used to switch the power distribution network to the backup port to output current and to regulate the voltage to a safety threshold when a voltage abnormality occurs in the power distribution network and it is determined that the voltage abnormality will affect the temperature abnormality.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0036] The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data of the distribution network. The voltage fluctuation data is analyzed and a voltage discrimination signal is output. The voltage discrimination signal includes a voltage qualified signal and a voltage unqualified signal.

[0037] The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the influence of voltage anomaly on temperature anomaly is output.

[0038] If a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0040] The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0041] The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data of the distribution network. The voltage fluctuation data is analyzed and a voltage discrimination signal is output. The voltage discrimination signal includes a voltage qualified signal and a voltage unqualified signal.

[0042] The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the influence of voltage anomaly on temperature anomaly is output.

[0043] If a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

[0044] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0045] The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0046] The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data of the distribution network. The voltage fluctuation data is analyzed and a voltage discrimination signal is output. The voltage discrimination signal includes a voltage qualified signal and a voltage unqualified signal.

[0047] The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the influence of voltage anomaly on temperature anomaly is output.

[0048] If a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

[0049] The aforementioned fault self-healing method, device, computer equipment, computer-readable storage medium, and computer program product for power distribution networks monitor the operating temperature of the power distribution network in real time and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature pass signal and a temperature fail signal. This allows for the judgment of the real-time monitored operating temperature of the power distribution network and the determination of whether the corresponding temperature signal is abnormal. The operating voltage of the power distribution network is monitored in real time to obtain voltage fluctuation data. This voltage fluctuation data is analyzed, and a voltage discrimination signal is output, including a voltage pass signal and a voltage fail signal. This allows for the judgment of the real-time monitored operating voltage of the power distribution network and the determination of whether the corresponding voltage signal is abnormal. The temperature discrimination signal and the voltage discrimination signal are input into a trained influence model, which outputs the degree of influence of voltage anomalies on temperature anomalies. This allows for the determination of whether voltage anomalies will affect temperature anomalies and the corresponding degree of influence, facilitating subsequent fault self-healing processing. When a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage. This avoids temperature anomalies caused by voltage fluctuations, providing an efficient and reliable solution for distribution network management under complex operating conditions, reducing operating costs and safety risks, and improving the reliability and sustainability of the distribution network system. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1This is a flowchart illustrating a fault self-healing method for a distribution network in one embodiment.

[0052] Figure 2 A flowchart illustrating the process of obtaining the voltage anomaly coefficient provided in an embodiment of this application;

[0053] Figure 3 This is a flowchart illustrating a fault self-healing method for a distribution network in another embodiment;

[0054] Figure 4 This is a structural block diagram of a fault self-healing device for a power distribution network in one embodiment;

[0055] Figure 5 This is a structural block diagram of a fault self-healing device for a power distribution network in another embodiment;

[0056] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that the terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more. The term "and / or" as used in this application refers to one of the solutions, or any combination of multiple solutions.

[0059] In one exemplary embodiment, such as Figure 1 As shown, a fault self-healing method for distribution networks is provided. This method can be applied to distribution network systems and may include the following steps S101 to S104. Wherein:

[0060] Step S101: Monitor the operating temperature of the power distribution network in real time, and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold.

[0061] The temperature discrimination signals include: temperature qualified signal and temperature unqualified signal.

[0062] In this embodiment, the operating temperature of the power distribution network can be monitored in real time by temperature sensors pre-deployed in the power distribution network system, and the real-time temperature can be compared with a preset temperature threshold. Based on the comparison result, a temperature qualified signal and a temperature unqualified signal can be generated.

[0063] It should be understood that the temperature sensor used in this embodiment needs to have high sensitivity and fast response capabilities in order to accurately capture temperature changes.

[0064] It should be noted that this embodiment does not limit the specific value of the preset temperature threshold. The corresponding temperature threshold can be set according to the type of power distribution network and the specifications provided by the manufacturer. For example, the temperature threshold may include a minimum operating temperature and a maximum operating temperature. When the temperature threshold is exceeded, it may lead to a decrease in the performance of the power distribution network or even damage.

[0065] For example, if the real-time temperature of the distribution network is within a preset temperature threshold range, a temperature compliance signal will be generated. This indicates that the current operating environment of the distribution network is safe and will not adversely affect its performance or lifespan. Once the distribution network temperature is detected to exceed the preset temperature threshold range, a temperature non-compliance signal will be generated. At this time, an alarm mechanism may be triggered, such as an audible alarm or a flashing red light, to remind users that the distribution network has entered a dangerous state and to take appropriate measures to reduce the temperature or stop using the distribution network.

[0066] Step S102: Monitor the operating voltage of the distribution network in real time, obtain the voltage fluctuation data of the distribution network, analyze the voltage fluctuation data, and output a voltage discrimination signal.

[0067] The voltage discrimination signals include: voltage qualified signal and voltage unqualified signal.

[0068] For example, Figure 2 A flowchart illustrating the process of obtaining the voltage anomaly coefficient provided in this application embodiment is shown below. Figure 2 As shown, the operating voltage of the distribution network is monitored in real time, and the degree of voltage fluctuation is analyzed. Based on the analysis results, the voltage of the distribution network is divided into qualified voltage signals and unqualified voltage signals. For example, the fluctuation amplitude and fluctuation frequency can be extracted based on the voltage fluctuation data; the amplitude anomaly coefficient and frequency anomaly coefficient of the voltage data can be determined based on the fluctuation amplitude and fluctuation frequency; the amplitude anomaly coefficient and frequency anomaly coefficient are normalized to obtain the voltage anomaly coefficient.

[0069] The voltage anomaly coefficient is used to determine whether the voltage signal is qualified. When the voltage coefficient is greater than or equal to the first preset threshold, the output voltage discrimination signal is a voltage unqualified signal; when the voltage coefficient is less than the first preset threshold, the output voltage discrimination signal is a voltage qualified signal.

[0070] For example, the process of obtaining the amplitude anomaly coefficient is as follows:

[0071] First, the voltage signal acquired in real time Perform continuous wavelet transform to obtain wavelet coefficients at different time frequency scales. ;in, Indicates the time point of data collection. Indicates the scale parameter. The time parameter is represented; the scale parameter and the time parameter are discretized to obtain the decomposed wavelet coefficient matrix. ,in Indicates the first Each scale; for each scale, the local energy distribution of the wavelet coefficients is calculated, and the calculation expression is:

[0072]

[0073] in, This indicates the total number of data collection points. Indicates the first Energy at scale.

[0074] Secondly, the local nonlinear amplitude offset value is calculated at each scale. The calculation expression is:

[0075]

[0076] in, Indicates the first The median of the scale wavelet coefficients. For adaptive adjustment factor, This represents the local nonlinear amplitude offset value.

[0077] Finally, at different scales, the amplitude anomaly coefficients at all scales are calculated using the fluctuation amplitude of the wavelet coefficients. The calculation expression is as follows:

[0078]

[0079] in, The total number of scales. Indicates the first The ratio of wavelet energy distribution at different scales is calculated using the following expression: , Represents the total energy across all scales. This represents the amplitude anomaly coefficient.

[0080] For example, the process of obtaining the frequency anomaly coefficient is as follows:

[0081] First, real-time voltage signals are acquired and preprocessed using a normalization formula to obtain a normalized voltage sequence; then, the difference sequence of the voltage signal is calculated. The calculation expression is:

[0082]

[0083] in, Indicates the time point of data collection. This represents the normalized voltage data.

[0084] Secondly, based on time windows Calculate the fluctuation frequency The calculation expression is:

[0085]

[0086] in, Indicates the first Fluctuation frequency within a time window Indicates a time window. Indicates a time window; This represents the number of sample points within each time window. This indicates the number of non-zero data points within the time window.

[0087] Then, input the fluctuation frequency sequence. The mean clustering algorithm performs frequency feature clustering to obtain... Cluster centers and clustering tags The clustering objective is to minimize the following cost function:

[0088]

[0089] in, Indicates fluctuation frequency Belongs to the One cluster, otherwise , Indicates the number of clusters, This represents the total number of clusters. Indicates the first Cluster centers, Indicates fluctuation frequency With the Cluster centers Euclidean distance.

[0090] The expression for calculating the offset value of each cluster center is as follows:

[0091]

[0092] in, This represents the offset value of the cluster center. This represents the average frequency of fluctuation. It represents the standard deviation of the fluctuation frequency.

[0093] The expression for calculating the frequency anomaly coefficient based on the cluster centers is as follows:

[0094]

[0095] in, Represents the frequency anomaly coefficient. Indicates the first The percentage of cluster centers is calculated using the following expression: .

[0096] The formula for calculating the voltage anomaly coefficient is as follows:

[0097]

[0098] in, Indicates the voltage anomaly coefficient. Represents the frequency anomaly coefficient. Indicates the amplitude anomaly coefficient. and This indicates a preset scaling factor, and and All are greater than 0.

[0099] In this embodiment, by monitoring the operating temperature and voltage of the power distribution network in real time, and utilizing high-sensitivity sensors and advanced data analysis techniques, such as continuous wavelet transform and differential sequence analysis, potential safety hazards can be accurately identified.

[0100] Optionally, when the temperature or voltage exceeds the preset safety range, the system can quickly generate an unqualified signal and trigger a multi-level alarm mechanism, including audible alarms and flashing red lights, to ensure that users can respond in a timely manner and take measures to prevent serious accidents such as overheating, fire or explosion.

[0101] It should be noted that by monitoring the operating voltage of the distribution network in real time, and combining continuous wavelet transform and differential sequence analysis, amplitude anomaly coefficients and frequency anomaly coefficients are calculated. Further normalization is then performed to obtain the voltage anomaly coefficient, thereby accurately determining whether the distribution network voltage status is acceptable. This method not only effectively identifies subtle fluctuations and their frequency changes in voltage data, but also accurately quantifies the impact of these fluctuations on the safety and performance of the distribution network through adaptive adjustment factors and clustering algorithms. Finally, the voltage signal status (acceptable or unacceptable) is marked based on the comparison result of the voltage anomaly coefficient with a preset threshold, achieving intelligent monitoring and early warning of the distribution network voltage status.

[0102] Step S103: Input the temperature discrimination signal and the voltage discrimination signal into the trained influence model, and output the degree of influence of voltage anomaly on temperature anomaly.

[0103] For example, the mapping relationship between voltage anomalies and temperature anomalies is determined by a trained influence model, and temperature data is predicted based on the real-time input voltage anomaly coefficient. The predicted temperature data is normalized to obtain normalized temperature data. Based on the normalized temperature data and the voltage anomaly coefficient, the degree of influence of voltage anomalies on temperature anomalies is determined. If the degree of influence of voltage anomalies on temperature anomalies is greater than a second preset threshold, it is determined that voltage anomalies have an influence on temperature anomalies; if the degree of influence of voltage anomalies on temperature anomalies is not greater than the second preset threshold, it is determined that voltage anomalies have no influence on temperature anomalies.

[0104] In step S104, when a voltage anomaly occurs in the distribution network and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the standby port to output current and performs safety threshold regulation on the voltage.

[0105] Optionally, if a voltage anomaly is confirmed and it adversely affects the temperature, the system will automatically execute an emergency response procedure. This involves disconnecting the negative protection board and quickly switching to the backup connection port to directly output current (this operation aims to cut off the potential source of risk and prevent further damage caused by the voltage anomaly). The system will then adjust the current output to reduce overvoltage or initiate a voltage boost to restore undervoltage, ensuring stable operation of the distribution network under various operating conditions. It should be understood that this process not only effectively isolates the faulty area but also ensures the continued safe operation of the rest of the system.

[0106] Optionally, after implementing the above emergency measures, the system will continuously monitor the operating voltage of the distribution network in real time and compare it with the preset safe voltage range. If the operating voltage is detected to be outside the normal range, the system will take targeted adjustment measures to restore normal operation.

[0107] For example, voltage safety threshold regulation includes: real-time monitoring of the operating voltage of the distribution network and comparing the operating voltage with a preset safe voltage range to determine whether the operating voltage exceeds the safe voltage range; if the operating voltage is greater than the upper threshold of the safe voltage range, adjusting the output current to reduce the operating voltage to the safe voltage range; if the operating voltage is less than the lower threshold of the safe voltage range, starting voltage boosting to increase the operating voltage to the safe voltage range.

[0108] In this embodiment, when the voltage exceeds the upper threshold, the system intelligently adjusts the current output to reduce the voltage to a safe range. This dynamic adjustment mechanism prevents damage to the distribution network and safety hazards caused by overvoltage, while maintaining the stability and reliability of the system. When the voltage falls below the lower threshold, the system automatically initiates a voltage boosting program to restore the voltage to the normal operating range. This step ensures that the distribution network always maintains a healthy power level, avoiding performance degradation and permanent damage due to undervoltage.

[0109] Optionally, throughout the entire process of regulating voltage to safety thresholds, the system also records all critical data and operation logs for subsequent analysis and optimization. For example, by reviewing historical data, the root causes of potential voltage anomalies can be identified, and preventative measures can be taken to reduce the likelihood of similar problems in the future. These functions work together to not only improve the automation and safety of the distribution network management system but also significantly extend the lifespan and overall performance of the distribution network.

[0110] In the aforementioned fault self-healing method for distribution networks, the operating temperature of the distribution network is monitored in real time. Based on the comparison between the real-time temperature and a preset temperature threshold, a temperature discrimination signal is output, including a qualified temperature signal and a failed temperature signal. This allows for the judgment of the real-time monitored operating temperature of the distribution network and the determination of whether the corresponding temperature signal is abnormal. The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data. This voltage fluctuation data is analyzed, and a voltage discrimination signal is output, including a qualified voltage signal and a failed voltage signal. This allows for the judgment of the real-time monitored operating voltage of the distribution network and the determination of whether the corresponding voltage signal is abnormal. The temperature discrimination signal and the voltage discrimination signal are input into a trained influence model, which outputs the degree of influence of voltage anomaly on temperature anomaly. This allows for the determination of whether voltage anomaly will affect temperature anomaly, and the corresponding degree of influence, facilitating subsequent fault self-healing processing. When a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage. This can avoid temperature anomalies caused by voltage fluctuations, providing an efficient and reliable solution for distribution network management under complex operating conditions, reducing operating costs and safety risks, and improving the reliability and sustainability of the distribution network system.

[0111] In another exemplary embodiment, such as Figure 3 As shown, a fault self-healing method for distribution networks is provided. This method can be applied to distribution network systems and may include the following steps S301 to S306. Wherein:

[0112] Step S301: Monitor the operating temperature of the power distribution network in real time, and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold.

[0113] Step S302: Monitor the operating voltage of the distribution network in real time, obtain the voltage fluctuation data of the distribution network, analyze the voltage fluctuation data, and output a voltage discrimination signal.

[0114] For the specific implementation process and technical effects of steps S301 to S302 in this embodiment, please refer to [link / reference needed]. Figure 1 The relevant descriptions of steps S101 to S102 in the method embodiment shown will not be repeated here.

[0115] Step S303: Construct the impact model.

[0116] The impact model uses support vector regression to learn the nonlinear relationship between voltage anomalies and temperature anomalies, and selects radial basis functions as kernel functions to model the nonlinear relationship between voltage anomalies and temperature anomalies.

[0117] In this embodiment, a support vector regression model combined with a radial basis function kernel function is used to intelligently predict and quantify the specific impact of voltage anomalies on temperature, further enhancing the system's active protection capability.

[0118] In this embodiment, real-time distribution network temperature data is first acquired, and the temperature data is normalized to obtain normalized distribution network temperature data. The normalized distribution network temperature data and the feature vector of the voltage anomaly coefficient are used as input to construct an impact model. The impact model uses support vector regression to learn the nonlinear relationship between voltage anomaly and temperature anomaly and generates a prediction model. The radial basis function is selected as the kernel function to model the nonlinear relationship between voltage anomaly and temperature anomaly.

[0119] Step S304: Train the influence model using real-time data to obtain a trained influence model.

[0120] In this embodiment, the mapping relationship between voltage anomalies and temperature anomalies is obtained by training an impact model with real-time data; the temperature data is predicted by the real-time input voltage anomaly coefficient using the trained impact model; and the degree of influence of voltage anomalies on temperature anomalies is calculated using the impact degree formula.

[0121] For example, the process of obtaining the influence formula is as follows:

[0122] First, a trained impact model is used for real-time prediction to obtain predicted temperature data. This predicted temperature data is then processed using a normalization formula to obtain normalized temperature data. Finally, the normalized temperature data is compared with the voltage anomaly coefficient using an impact degree formula to calculate the influence of the voltage anomaly on the temperature anomaly. The calculation expression is as follows:

[0123]

[0124] in, This indicates the degree of influence of voltage anomalies on temperature anomalies. This represents the normalized temperature data. This represents the voltage anomaly coefficient.

[0125] In this embodiment, real-time temperature data of the distribution network is normalized and combined with the feature vector of the voltage anomaly coefficient as model input. Historical data is used to train the model to learn the complex nonlinear relationship between voltage anomalies and temperature anomalies, thereby establishing a predictive model for the impact of voltage anomalies on temperature anomalies.

[0126] In practical applications, by inputting the voltage anomaly coefficient in real time, the impact model can predict potential temperature changes and calculate the specific degree of influence of voltage anomalies on temperature anomalies. If the calculated degree of influence is greater than or equal to a second preset threshold, it indicates that the voltage anomaly has indeed had a significant impact on the temperature; otherwise, it is considered to have no significant impact. This method not only improves the accuracy and predictability of monitoring the health status of the distribution network, but also enables the timely detection of potential safety hazards and the implementation of measures to prevent temperature anomalies caused by voltage fluctuations, thereby ensuring the safe and stable operation of the distribution network system.

[0127] Step S305: Input the temperature discrimination signal and the voltage discrimination signal into the trained influence model, and output the degree of influence of voltage anomaly on temperature anomaly.

[0128] Step S306: When a voltage anomaly occurs in the distribution network and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the standby port to output current and performs safety threshold regulation on the voltage.

[0129] In this embodiment, please refer to the detailed implementation process and technical effects of steps S305 to S306. Figure 1 The relevant descriptions of steps S103 to S104 in the method embodiment shown will not be repeated here.

[0130] This embodiment employs advanced data analysis techniques, such as continuous wavelet transform, differential sequence analysis, and support vector regression models, to accurately quantify the impact of voltage anomalies on temperature, enabling intelligent monitoring and early warning of the distribution network's health status. Through in-depth analysis of voltage and temperature data, potential problems can be identified in advance, and preventative maintenance measures can be implemented through adaptive adjustment mechanisms, significantly extending the service life of the distribution network.

[0131] It should be understood that the method in this embodiment can not only monitor and respond to voltage and temperature anomalies in real time, but also optimize the operating status of the distribution network through intelligent predictive models, ensuring its stable operation under various conditions. This provides users with a convenient and efficient user experience, and offers solid technical support for the full lifecycle management of the distribution network from production to decommissioning, maximizing the utilization of distribution network performance while reducing operating costs and safety risks, and comprehensively improving the reliability and sustainability of the distribution network system.

[0132] It should be understood that the formulas involved in the above embodiments are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world formulas. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0133] This embodiment uses a high-sensitivity temperature sensor to monitor the operating temperature of the distribution network in real time and compares it with a preset temperature threshold to generate a temperature pass / fail signal, thereby identifying potential safety hazards. By monitoring the operating voltage of the distribution network in real time, continuous wavelet transform and differential sequence analysis are used to calculate the amplitude anomaly coefficient and frequency anomaly coefficient. After normalization, a voltage anomaly coefficient is obtained to determine whether the voltage status is acceptable. Furthermore, a support vector regression model is constructed, using a radial basis function as the kernel function, to learn the nonlinear relationship between voltage anomalies and temperature anomalies, predicting and quantifying the impact of voltage anomalies on temperature. Once a significant impact is confirmed, an emergency response procedure is automatically executed: when a voltage anomaly is detected, the system immediately switches to the backup port to directly output current, while simultaneously adjusting the current output to reduce overvoltage and initiating voltage boosting to restore undervoltage, ensuring the voltage remains within a safe range. Throughout this process, not only can voltage and temperature anomalies in the distribution network be detected and responded to in a timely manner, but the intelligent control mechanism also ensures the stable operation of the distribution network under complex operating conditions, effectively preventing temperature anomalies caused by voltage fluctuations, improving the reliability and safety of the distribution network management system, extending the service life of the distribution network, and reducing safety hazards.

[0134] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0135] Based on the same inventive concept, this application also provides a fault self-healing device for distribution networks to implement the aforementioned fault self-healing method for distribution networks. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the fault self-healing device for distribution networks provided below can be found in the limitations of the fault self-healing method for distribution networks described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 4 As shown, a fault self-healing device for power distribution networks is provided, comprising: a temperature monitoring module 401, a voltage monitoring module 402, a processing module 403, and an execution module 404, wherein:

[0137] Temperature monitoring module 401 is used to monitor the operating temperature of the power distribution network in real time, and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0138] The voltage monitoring module 402 is used to monitor the working voltage of the distribution network in real time, obtain the voltage fluctuation data of the distribution network, analyze the voltage fluctuation data, and output voltage discrimination signals, including voltage qualified signals and voltage unqualified signals.

[0139] Processing module 403 is used to input the temperature discrimination signal and the voltage discrimination signal into the trained influence model and output the degree of influence of voltage anomaly on temperature anomaly.

[0140] The execution module 404 is used to switch the power distribution network to the backup port to output current and to regulate the voltage to a safety threshold when a voltage abnormality occurs in the power distribution network and it is determined that the voltage abnormality will affect the temperature abnormality.

[0141] For example, the voltage monitoring module 402 is specifically used to: extract the fluctuation amplitude and fluctuation frequency based on voltage fluctuation data; determine the amplitude anomaly coefficient and frequency anomaly coefficient of the voltage data according to the fluctuation amplitude and fluctuation frequency; and normalize the amplitude anomaly coefficient and frequency anomaly coefficient to obtain the voltage anomaly coefficient.

[0142] For example, the voltage anomaly coefficient is used to determine whether the voltage signal is qualified; when the voltage coefficient is greater than or equal to the first preset threshold, the output voltage discrimination signal is a voltage unqualified signal; when the voltage coefficient is less than the first preset threshold, the output voltage discrimination signal is a voltage qualified signal.

[0143] For example, voltage safety threshold regulation includes: real-time monitoring of the operating voltage of the distribution network and comparing the operating voltage with a preset safe voltage range to determine whether the operating voltage exceeds the safe voltage range; if the operating voltage is greater than the upper threshold of the safe voltage range, adjusting the output current to reduce the operating voltage to the safe voltage range; if the operating voltage is less than the lower threshold of the safe voltage range, starting voltage boosting to increase the operating voltage to the safe voltage range.

[0144] In another exemplary embodiment, such as Figure 5 As shown, a fault self-healing device for power distribution networks is provided. Figure 4 Based on the device shown, it may also include:

[0145] Model building module 405 is used to build an impact model. The impact model uses support vector regression to learn the nonlinear relationship between voltage anomalies and temperature anomalies, and selects radial basis function as kernel function to model the nonlinear relationship between voltage anomalies and temperature anomalies.

[0146] Training module 406 is used to train the influence model using real-time data to obtain a trained influence model.

[0147] For example, the processing module 403 is specifically used to: determine the mapping relationship between voltage anomaly and temperature anomaly through a trained influence model, and predict temperature data based on the real-time input voltage anomaly coefficient; normalize the predicted temperature data to obtain normalized temperature data; determine the degree of influence of voltage anomaly on temperature anomaly based on the normalized temperature data and voltage anomaly coefficient; wherein, if the degree of influence of voltage anomaly on temperature anomaly is greater than a second preset threshold, it is determined that voltage anomaly has an influence on temperature anomaly; if the degree of influence of voltage anomaly on temperature anomaly is not greater than the second preset threshold, it is determined that voltage anomaly has no influence on temperature anomaly.

[0148] Each module in the aforementioned fault self-healing device for power distribution networks can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0149] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a fault self-healing method for power distribution networks. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0150] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0151] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0152] The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal.

[0153] The system monitors the operating voltage of the distribution network in real time, obtains voltage fluctuation data of the distribution network, analyzes the voltage fluctuation data, and outputs voltage discrimination signals, including voltage qualified signals and voltage unqualified signals.

[0154] The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the output is the degree of influence of voltage anomaly on temperature anomaly.

[0155] When a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

[0156] In one embodiment, analyzing voltage fluctuation data includes:

[0157] Based on voltage fluctuation data, the fluctuation amplitude and fluctuation frequency are extracted; based on the fluctuation amplitude and fluctuation frequency, the amplitude anomaly coefficient and frequency anomaly coefficient of the voltage data are determined; the amplitude anomaly coefficient and frequency anomaly coefficient are normalized to obtain the voltage anomaly coefficient.

[0158] In one embodiment, the voltage anomaly coefficient is used to determine whether the voltage signal is qualified; when the voltage coefficient is greater than or equal to a first preset threshold, the output voltage discrimination signal is a voltage unqualified signal; when the voltage coefficient is less than the first preset threshold, the output voltage discrimination signal is a voltage qualified signal.

[0159] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0160] An impact model is constructed, which uses support vector regression to learn the nonlinear relationship between voltage anomalies and temperature anomalies, and selects radial basis functions as kernel functions to model the nonlinear relationship between voltage anomalies and temperature anomalies; the impact model is trained using real-time data to obtain a well-trained impact model.

[0161] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0162] The mapping relationship between voltage anomalies and temperature anomalies is determined by a trained impact model, and temperature data is predicted based on the real-time input voltage anomaly coefficient. The predicted temperature data is then normalized to obtain normalized temperature data. Based on the normalized temperature data and the voltage anomaly coefficient, the degree of influence of voltage anomalies on temperature anomalies is determined. If the degree of influence of voltage anomalies on temperature anomalies is greater than a second preset threshold, then voltage anomalies are determined to have an impact on temperature anomalies; if the degree of influence of voltage anomalies on temperature anomalies is not greater than the second preset threshold, then voltage anomalies are determined to have no impact on temperature anomalies.

[0163] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0164] The system monitors the operating voltage of the power distribution network in real time and compares it with the preset safe voltage range to determine whether the operating voltage exceeds the safe voltage range. If the operating voltage is greater than the upper threshold of the safe voltage range, the output current is adjusted to reduce the operating voltage to the safe voltage range. If the operating voltage is less than the lower threshold of the safe voltage range, the boost voltage is activated to increase the operating voltage to the safe voltage range.

[0165] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method steps of the various embodiments described above.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the method steps of the various embodiments described above.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0168] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0169] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A fault self-healing method for distribution networks, characterized in that, The method includes: The system monitors the operating temperature of the power distribution network in real time and outputs a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and a preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal. The operating voltage of the distribution network is monitored in real time to obtain voltage fluctuation data of the distribution network. The voltage fluctuation data is analyzed and a voltage discrimination signal is output. The voltage discrimination signal includes a voltage qualified signal and a voltage unqualified signal. The temperature discrimination signal and the voltage discrimination signal are input into the trained influence model, and the influence of voltage anomaly on temperature anomaly is output. If a voltage anomaly occurs in the distribution network, and it is determined that the voltage anomaly will affect the temperature anomaly, the distribution network switches to the backup port to output current and performs safety threshold regulation on the voltage.

2. The method according to claim 1, characterized in that, The analysis of the voltage fluctuation data includes: Based on the voltage fluctuation data, the fluctuation amplitude and fluctuation frequency are extracted; Based on the fluctuation amplitude and the fluctuation frequency, determine the amplitude anomaly coefficient and frequency anomaly coefficient of the voltage data; The amplitude anomaly coefficient and the frequency anomaly coefficient are normalized to obtain the voltage anomaly coefficient.

3. The method according to claim 2, characterized in that, The voltage anomaly coefficient is used to determine whether the voltage signal is qualified; When the voltage coefficient is greater than or equal to the first preset threshold, the output voltage discrimination signal is a voltage failure signal; When the voltage coefficient is less than the first preset threshold, the output voltage discrimination signal is a voltage qualified signal.

4. The method according to any one of claims 1 to 3, characterized in that, Before inputting the temperature discrimination signal and the voltage discrimination signal into the trained influence model and outputting the degree of influence of voltage anomaly on temperature anomaly, the method further includes: An impact model is constructed, which uses support vector regression to learn the nonlinear relationship between voltage anomalies and temperature anomalies, and selects radial basis functions as kernel functions to model the nonlinear relationship between the voltage anomalies and the temperature anomalies. The impact model is trained using real-time data to obtain a well-trained impact model.

5. The method according to claim 4, characterized in that, The step of inputting the temperature discrimination signal and the voltage discrimination signal into the trained influence model and outputting the degree of influence of voltage anomaly on temperature anomaly includes: The mapping relationship between voltage anomalies and temperature anomalies is determined by the trained influence model, and temperature data is predicted based on the real-time input voltage anomaly coefficient. The predicted temperature data is normalized to obtain normalized temperature data. Based on the normalized temperature data and the voltage anomaly coefficient, the degree of influence of voltage anomaly on temperature anomaly is determined; wherein, if the degree of influence of voltage anomaly on temperature anomaly is greater than a second preset threshold, it is determined that voltage anomaly has an influence on temperature anomaly; if the degree of influence of voltage anomaly on temperature anomaly is not greater than the second preset threshold, it is determined that voltage anomaly has no influence on temperature anomaly.

6. The method according to claim 1, characterized in that, The voltage safety threshold regulation includes: The operating voltage of the power distribution network is monitored in real time, and the operating voltage is compared with a preset safe voltage range to determine whether the operating voltage exceeds the safe voltage range. If the operating voltage is greater than the upper threshold of the safe voltage range, the output current is adjusted to reduce the operating voltage to the safe voltage range. If the operating voltage is less than the lower threshold of the safe voltage range, then boosting is initiated to raise the operating voltage to the safe voltage range.

7. A fault self-healing device for power distribution networks, characterized in that, The device includes: The temperature monitoring module is used to monitor the operating temperature of the power distribution network in real time, and output a temperature discrimination signal based on the comparison between the real-time temperature of the power distribution network and the preset temperature threshold. The temperature discrimination signal includes a temperature qualified signal and a temperature unqualified signal. The voltage monitoring module is used to monitor the working voltage of the distribution network in real time, obtain voltage fluctuation data of the distribution network, analyze the voltage fluctuation data, and output voltage discrimination signals, which include: voltage qualified signal and voltage unqualified signal; The processing module is used to input the temperature discrimination signal and the voltage discrimination signal into the trained influence model and output the degree of influence of voltage anomaly on temperature anomaly. The execution module is used to switch the power distribution network to the backup port to output current and to regulate the voltage to a safety threshold when a voltage abnormality occurs in the power distribution network and it is determined that the voltage abnormality will affect the temperature abnormality.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images