Intelligent detection and maintenance method and device for fuse of new energy automobile charging station

Abstract

The invention provides an intelligent detection and maintenance method and device for a fuse of a new energy automobile charging station, and relates to the technical field of new energy, and the method comprises the steps: obtaining real-time electrical parameters of the fuse, carrying out the time-frequency domain analysis, extracting an aging feature vector, carrying out the health grade division, building a cooperative protection strategy under an electrical coupling path, and carrying out the detection and maintenance of the fuse when the current exceeds a threshold value. And a protection control instruction is generated based on the fusing time sequence control rule and the action priority distribution rule, and accurate selective fusing is realized. According to the invention, the accuracy of evaluating the health state of the fuse is improved.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to an intelligent detection and maintenance method and device for fuses in new energy vehicle charging stations. Background Technology

[0002] As a key infrastructure for the development of the electric vehicle industry, the safe and stable operation of new energy vehicle charging stations is crucial to ensuring the quality of charging services. In the power system of a charging station, fuses are important electrical protection components responsible for cutting off the circuit in the event of overload or short-circuit faults, preventing equipment damage and safety accidents. With the rapid growth in the demand for charging new energy vehicles, the electrical load on charging stations is increasing, leading to a continuous increase in the frequency and intensity of fuse use. Their health directly affects the operational safety and service reliability of the charging station.

[0003] Traditional fuse maintenance methods rely primarily on periodic manual inspections and experience-based judgment, lacking the ability to monitor and scientifically assess the health status of fuses in real time. In practical applications, the degree of fuse aging is difficult to accurately determine, often relying on visual inspection or simple electrical measurements to infer its performance condition, failing to promptly identify potential faults. Furthermore, current fuse protection systems are mostly independent operation modes, failing to fully consider the electrical coupling relationships between fuses in the charging station's power grid system, which can easily lead to uncoordinated protection actions and affect the overall power supply reliability of the charging station. Summary of the Invention

[0004] The present invention provides a method, apparatus, electronic device, storage medium, and computer program product for intelligent detection and maintenance of fuses in new energy vehicle charging stations, which improves the accuracy of assessing the health status of fuses in new energy charging stations to a certain extent.

[0005] In a first aspect, the present invention provides an intelligent detection and maintenance method for fuses in new energy vehicle charging stations, the method comprising:

[0006] The real-time electrical status parameters of multiple fuses in the new energy station are obtained, and the time-frequency domain joint analysis of the real-time electrical status parameters is performed to extract a set of feature vectors reflecting the aging degree of the fuses.

[0007] Based on the set of feature vectors and the preset health status assessment rules, each fuse is classified into health levels to obtain the health status identifier of each fuse.

[0008] Based on the health status identifier and the topological connection relationship of the new energy station, the electrical coupling path between each fuse is determined, and a collaborative protection strategy for the fuse group is established based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels.

[0009] When the current waveform data of any fuse exceeds the action threshold of the corresponding health level of the fuse, protection control instructions are generated for the fuse and other fuses that are electrically coupled to it, in accordance with the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the collaborative protection strategy.

[0010] Based on the protection control command, the corresponding fuse is controlled to perform a melting action or maintain its operating state.

[0011] In one embodiment of the present invention, the real-time electrical state parameters include current waveform data, voltage waveform data, and temperature rise data; a time-frequency domain joint analysis is performed on the real-time electrical state parameters to extract a set of feature vectors reflecting the aging degree of the fuse, including:

[0012] The current waveform data is decomposed in the time domain to obtain current change components at multiple time scales. The amplitude envelope is extracted and the fluctuation trend is analyzed for each current change component to obtain time domain feature components.

[0013] The voltage waveform data and temperature rise data are respectively subjected to frequency domain transformation to obtain frequency domain feature components; the frequency domain feature components include the harmonic content obtained based on the voltage waveform data and the spectral energy obtained based on the temperature rise data;

[0014] A mapping relationship is established between the time-domain feature components and the spectral energy, wherein the mapping relationship reflects the thermal accumulation response characteristics corresponding to the current change components;

[0015] According to the mapping relationship, the time-domain feature components and the frequency-domain feature components are fused and encoded to generate the feature vector set. Each feature vector in the feature vector set corresponds to the comprehensive aging state of a fuse at the current moment.

[0016] In one embodiment of the present invention, based on the feature vector set and a preset health status assessment rule, each fuse is classified into health levels to obtain a health status identifier for each fuse, including:

[0017] The feature vector corresponding to each fuse is extracted from the feature vector set. The feature components of each dimension in the feature vector are numerically normalized to obtain a normalized feature vector. The deviation value between the feature components of each dimension in the normalized feature vector and the corresponding dimension components of the standard health feature vector defined in the preset health status assessment rules is calculated. The deviation values ​​of each dimension are weighted and summed to obtain a comprehensive deviation measure value that reflects the overall health deviation of the fuse.

[0018] Obtain the comprehensive deviation measurement value range corresponding to multiple predefined health levels in the health status assessment rules. The multiple health levels are arranged in descending order of health degree, and each health level corresponds to a comprehensive deviation measurement value range.

[0019] The comprehensive deviation metric value of each fuse is compared one by one with the comprehensive deviation metric value intervals corresponding to the multiple health levels. The comprehensive deviation metric value of the fuse falls into the comprehensive deviation metric value interval. The health level corresponding to the comprehensive deviation metric value interval is taken as the health level of the fuse, and a health status identifier containing the fuse identifier and the health level is generated.

[0020] In one embodiment of the present invention, based on the health status identifier and the topological connection relationship of the new energy station, the electrical coupling path between each fuse is determined, and a collaborative protection strategy for the fuse group is established based on the electrical coupling path, including:

[0021] A fuse node network is constructed based on the topological connection relationship of the new energy station. Each node in the fuse node network corresponds to a fuse. The health status identifier is assigned as a node attribute to the corresponding node. By tracing the electrical connection path between each node in the fuse node network, node pairs with power transmission association are identified, and the electrical connection path between each node pair is marked as an electrical coupling path.

[0022] The nodes on each electrical coupling path are sorted according to the direction of power flow. Based on the sorting results and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path. The fuse timing control rule defines the order in which the fuses corresponding to each node on the electrical coupling path will operate when the fuse corresponding to any node on the electrical coupling path fails.

[0023] Calculate the degradation degree quantification value corresponding to the health status identifier of each node on each electrical coupling path, and assign action priority to each node on each electrical coupling path according to the degradation degree quantification value to generate action priority allocation rules;

[0024] Fuse with the same electrical coupling path or with intersecting paths are divided into fuse groups. For each fuse group, the fuse timing control rules and action priority allocation rules corresponding to all electrical coupling paths within the fuse group are integrated to establish a collaborative protection strategy for the fuse group.

[0025] In one embodiment of the present invention, the nodes on each electrical coupling path are sorted according to the direction of power flow, and based on the sorting result and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path, including:

[0026] Obtain the current direction data and voltage phase data of the fuses corresponding to each node on each electrical coupling path, determine the direction of power transmission on the electrical coupling path based on the current direction data and voltage phase data, sort the nodes on the electrical coupling path according to the direction of power transmission from the source end to the load end, and obtain a sorting result including the node position number.

[0027] Extract the health status identifier of each node in the sorting result and establish a correspondence table between the node position number and the health status identifier;

[0028] Based on the correspondence table, key nodes whose health status indicators indicate a degree of deterioration exceeding a preset evaluation benchmark are identified, and the upstream node set and downstream node set of the key nodes are determined from the sorting results.

[0029] When the fuse corresponding to the critical node needs to perform a protective action, the fuse corresponding to the node whose health status indicator in the upstream node set of the critical node is lower than the preset degradation value will delay its action, and the fuses corresponding to all nodes in the downstream node set of the critical node will act in sequence from the nearest to the farthest node according to the node position number.

[0030] The timing constraints of the actions corresponding to all key nodes on each electrical coupling path are summarized to form the fuse timing control rules for the electrical coupling path.

[0031] In one embodiment of the present invention, a degradation degree quantification value corresponding to the health status identifier of each node on each electrical coupling path is calculated, and an action priority is assigned to each node on each electrical coupling path based on the degradation degree quantification value, including:

[0032] The historical number of operations and cumulative current carrying time of the fuses corresponding to each node on each electrical coupling path are numerically normalized to obtain normalized operation components and normalized duration components. The normalized operation components and normalized duration components are weighted and summed to obtain a quantitative value of the degree of degradation.

[0033] The degradation quantification values ​​of all nodes on each electrical coupling path are sorted to obtain the degradation ranking of each node;

[0034] Based on the degradation ranking, an action priority mapping table is established for each electrical coupling path. The action priority mapping table defines the mapping relationship between the degradation ranking of the node and the action priority.

[0035] Based on the action priority mapping table, a unique action priority identifier is assigned to each node on each electrical coupling path, and an action priority allocation rule containing node identifier, degradation degree quantification value and action priority identifier is generated.

[0036] In one embodiment of the present invention, the intelligent detection and maintenance of fuses in new energy vehicle charging stations further includes:

[0037] If at least two nodes in a plurality of nodes in the electrical coupling path have the same degradation ranking, the topology position correction coefficient of the nodes with the same degradation ranking in the electrical coupling path is calculated. The topology position correction coefficient is obtained by calculating the number of electrical connection hops between the node and the power energy node in the electrical coupling path.

[0038] The action priority value assigned to the node with the smaller topology position correction coefficient value is set to a value lower than that assigned to the node with the larger topology position correction coefficient value.

[0039] In one embodiment of the present invention, based on the protection control command, controlling the corresponding fuse to perform a fusing action or maintain an operating state includes:

[0040] Extract the target fuse identifier, action type identifier, and execution time identifier contained in the protection control instruction; determine the fuse object that needs to be controlled based on the target fuse identifier; determine whether the fuse object should perform a fuse-breaking action or remain in operation based on the action type identifier; and determine the time node when the fuse object starts to perform the corresponding action based on the execution time identifier.

[0041] Establish a fuse control queue, and add the fuse object, the corresponding action type identifier, and the execution time identifier as control tasks to the fuse control queue;

[0042] The system monitors the time difference between the execution time identifier of each control task in the fuse control queue and the current system time. When the time difference meets the preset triggering condition, the system extracts the corresponding control task from the fuse control queue, reads the action type identifier in the control task, and if the action type identifier indicates that a fuse action is to be executed, a fuse trigger signal is sent to the fuse object corresponding to the control task. If the action type identifier indicates that the operation is to be maintained, a status maintenance signal is sent to the fuse object corresponding to the control task, and the control task is marked as executed in the fuse control queue.

[0043] Secondly, the present invention provides an intelligent detection and maintenance device for fuses in new energy vehicle charging stations, the intelligent detection and maintenance device for fuses in new energy vehicle charging stations comprising:

[0044] The status analysis module is used to acquire real-time electrical status parameters of multiple fuses in the new energy station, perform time-frequency domain joint analysis on the real-time electrical status parameters, and extract a set of feature vectors reflecting the aging degree of the fuses.

[0045] The level classification module is used to classify the health level of each fuse according to the feature vector set and the preset health status assessment rules, and obtain the health status identifier of each fuse.

[0046] The strategy generation module is used to determine the electrical coupling path between each fuse based on the health status identifier and the topological connection relationship of the new energy station, and to establish a collaborative protection strategy for the fuse group based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels.

[0047] The instruction generation module is used to generate protection control instructions for the fuse and other fuses that are electrically coupled to it, according to the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the cooperative protection strategy, when the current waveform data of any fuse is detected to exceed the action threshold of the corresponding health level of the fuse.

[0048] The instruction execution module is used to control the corresponding fuse to perform a melting action or maintain the operating state based on the protection control instruction.

[0049] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the intelligent detection and maintenance method for fuses in new energy vehicle charging stations as described in any of the above claims.

[0050] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent detection and maintenance method for fuses in new energy vehicle charging stations as described in any of the preceding claims.

[0051] Fifthly, the present invention provides a computer program product, including a computer program, which, when executed by a processor, causes the computer to perform the intelligent detection and maintenance method for fuses in new energy vehicle charging stations described in any of the above claims.

[0052] The technical solution provided by this invention firstly, by performing time-frequency domain joint analysis on the real-time electrical status parameters of fuses and extracting a set of feature vectors, can accurately assess the aging degree of fuses, improving the accuracy and scientific nature of fuse health status assessment. Furthermore, by establishing an electrical coupling path based on the fuse health status identifier and the topological connection relationship of the charging station, a collaborative protection strategy for fuse groups is realized, avoiding the chain reactions and system instability that may result from traditional independent protection of a single fuse. Secondly, by setting fuse timing control rules and action priority allocation rules, the system can intelligently judge and prioritize the use of fuses with poor health status to perform protection actions, extending the service life of fuses with good health status and improving the overall safety and reliability of the charging station. Attached Figure Description

[0053] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating an intelligent detection and maintenance method for fuses in a new energy vehicle charging station, provided as an embodiment of the present invention.

[0055] Figure 2 This is a flowchart illustrating a method for maintaining fuses in a new energy vehicle charging station according to an embodiment of the present invention.

[0056] Figure 3 This is a flowchart illustrating the execution of protection control instructions according to an embodiment of the present invention.

[0057] Figure 4 This is a schematic diagram of the structure of an intelligent detection and maintenance device for fuses in a new energy vehicle charging station, provided in an embodiment of the present invention.

[0058] Figure 5 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0059] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0060] Please see Figure 1 and Figure 2 The present invention provides an intelligent detection and maintenance method for fuses in new energy vehicle charging stations. This method may include the following steps.

[0061] Step S110: Obtain the real-time electrical status parameters of multiple fuses in the new energy station, perform time-frequency domain joint analysis on the real-time electrical status parameters, and extract a set of feature vectors reflecting the aging degree of the fuses.

[0062] In this embodiment, a fuse refers to an electrical component installed in the electrical circuit of a new energy vehicle charging station for short-circuit and overload protection. The parameters generated in real time during fuse operation that reflect its electrical characteristics and operating status are called real-time electrical status parameters.

[0063] In this embodiment, joint time-domain and frequency-domain analysis refers to a comprehensive analysis method that combines time-domain analysis to study the variation of electrical state parameters over time, and frequency-domain analysis to study the distribution of electrical state parameters with frequency.

[0064] In this embodiment, the feature vector set is a collection of feature parameters that reflect the aging degree of a fuse. Each feature vector in the feature vector set represents the aging degree of a fuse, and each element in the feature vector represents the aging degree of the fuse in different dimensions.

[0065] Step S120: Based on the feature vector set and the preset health status assessment rules, classify each fuse into health levels to obtain the health status identifier of each fuse.

[0066] In this embodiment, the health status assessment rules are preset standards and criteria used to determine the health level of the fuse. The health status of the fuse can be classified into three levels: healthy, sub-healthy, and deteriorated. It is understood that in other embodiments, the health status level of the fuse may also be divided into two, four, or five levels, or multiple levels as needed. The more levels, the more detailed the classification criteria; the fewer levels, the simpler the classification.

[0067] In this embodiment, the health status identifier of a fuse includes a tuple of fuse identification information and health level identifier information. Each fuse has a unique identification information, allowing the system to view the health status of all fuses by directly querying the health status identifier table.

[0068] Step S130: Based on the health status identifier and the topological connection relationship of the new energy station, determine the electrical coupling path between each fuse, and establish a collaborative protection strategy for the fuse group based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels.

[0069] In this embodiment, the topology connection relationship of the new energy station refers to the electrical connection path of each electrical device in the charging station. The electrical coupling path refers to the electrical connection path between fuses within the new energy station that are associated with power transmission, as determined by the topology connection relationship.

[0070] In this embodiment, the timing control rules for fuses specify the order of action when a fuse fails on the same coupling path. The action priority allocation rules specify the priority rules for the protective actions of fuses with different health levels.

[0071] Step S140: When the current waveform data of any fuse exceeds the action threshold of the corresponding health level of the fuse, according to the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the collaborative protection strategy, a protection control command is generated for the fuse and other fuses that have an electrical coupling relationship with it.

[0072] Step S150: Based on the protection control command, control the corresponding fuse to perform a melting action or maintain the operating state.

[0073] In the above embodiments, by performing time-frequency domain joint analysis on the real-time electrical status parameters of the fuses and extracting feature vector sets, the aging degree of the fuses can be accurately assessed, improving the accuracy and scientific nature of fuse health status assessment. Furthermore, by establishing an electrical coupling path based on the fuse health status identifier and the topological connection relationship of the charging station, a collaborative protection strategy for fuse groups is realized, avoiding the chain reactions and system instability that may result from traditional independent protection of a single fuse. Secondly, by setting fuse timing control rules and action priority allocation rules, the system can intelligently judge and prioritize the use of fuses with poor health status to perform protection actions, extending the service life of fuses with good health status and improving the overall safety and reliability of the charging station.

[0074] In some embodiments, the real-time electrical state parameters include current waveform data, voltage waveform data, and temperature rise data; performing time-frequency domain joint analysis on the real-time electrical state parameters to extract a set of feature vectors reflecting the aging degree of the fuse may include:

[0075] The current waveform data is decomposed in the time domain to obtain current change components at multiple time scales. The amplitude envelope is extracted and the fluctuation trend is analyzed for each current change component to obtain time domain feature components.

[0076] The voltage waveform data and temperature rise data are transformed in the frequency domain to obtain the energy distribution spectrum of multiple frequency bands. The energy concentration and harmonic component identification are performed on the energy distribution spectrum of each frequency band to obtain the frequency domain feature components.

[0077] A mapping relationship is established between the time-domain feature components and the frequency-domain feature components, and the mapping relationship reflects the thermal accumulation response characteristics corresponding to the current change components;

[0078] According to the mapping relationship, the time-domain feature components and the frequency-domain feature components are fused and encoded to generate the feature vector set. Each feature vector in the feature vector set corresponds to the comprehensive aging state of a fuse at the current moment.

[0079] In some cases, joint time-frequency domain analysis of the real-time electrical condition parameters of fuses is an effective method for assessing their aging status. By extracting a set of feature vectors reflecting the aging status of fuses, the health status of fuses can be accurately determined and their remaining service life predicted.

[0080] First, real-time electrical status parameters of the fuse need to be collected, including current waveform data, voltage waveform data, and temperature rise data. During the acquisition process, current and voltage data are obtained using a high-precision sampler at a sampling frequency of no less than 10kHz to ensure that transient changes are captured; temperature rise data is measured using an infrared thermal imager or an array of temperature sensors mounted on the fuse surface, with a sampling interval of 1 second. For example, temperature rise data refers to the temperature increase of the fuse's casing or internal key components relative to the ambient temperature during operation, obtained in real time by temperature sensors mounted on or inside the fuse surface, reflecting the fuse's heat loss and heat dissipation performance under load current.

[0081] In this embodiment, the acquired current waveform data is decomposed in the time domain, which can be achieved using Empirical Mode Decomposition (EMD) or wavelet decomposition. Taking EMD as an example, the current waveform is decomposed into multiple intrinsic mode functions (IMFs), each IMF representing a current change component at a different time scale. Specifically, for the current data I(t) within each time window, n IMF components and a residual term are obtained after EMD decomposition.

[0082] Then, the amplitude envelope of each IMF component is extracted, and the instantaneous amplitude is obtained using Hilbert transform. The statistical characteristics of each IMF component within the time window are calculated, including peak value, waveform factor, impulse factor, and margin factor. When analyzing fluctuation trends, the short-term trend slope is calculated using the sliding window method, and trend abrupt change points are extracted. The final time-domain feature components include the amplitude characteristics, fluctuation characteristics, and trend characteristics of each IMF.

[0083] In this embodiment, the voltage waveform data and temperature rise data are subjected to frequency domain transformation using Fast Fourier Transform (FFT) or wavelet packet decomposition. The spectrum is divided into low-frequency band (0-100Hz), mid-frequency band (100-1000Hz), and high-frequency band (above 1000Hz). For harmonic component identification, fundamental frequency separation technology is used to extract each harmonic component in the voltage, with a focus on the content of the 3rd, 5th, and 7th harmonics and their time-varying patterns. Simultaneously, for the frequency domain analysis of the temperature rise data, the focus is on energy changes in the low-frequency band (0-10Hz), as this frequency band energy is typically closely related to the thermal accumulation process of the fuse. Through the above analysis, frequency domain characteristic components including harmonic content and spectral entropy are obtained.

[0084] In this embodiment, a Kalman filter or Bayesian network model is used to establish the mapping relationship between the time-domain feature components and the frequency-domain feature components. A dynamic thermal response model is constructed using the instantaneous energy of the current IMF component as input and the spectral energy response of the temperature rise data as output. This model can characterize the changes in the thermal accumulation characteristics and heat dissipation performance of the fuse under different current fluctuation conditions.

[0085] In practical applications, there are significant differences in the thermal response characteristics of newly installed fuses and fuses that have been in use for many years. New fuses exhibit a regular response in terms of temperature rise spectrum energy changes after current fluctuations; while aging fuses, due to contact oxidation and material fatigue, show characteristics such as hysteresis in thermal response and accelerated heat accumulation. These characteristics can be accurately captured through mapping relationships.

[0086] In this embodiment, based on the established mapping relationship, the time-domain feature components and frequency-domain feature components are fused and encoded. The fusion process employs an autoencoder structure, taking the time-domain and frequency-domain features as input, compressing them into a low-dimensional feature representation through a multi-layer encoder, and then reconstructing the original features through a decoder. After training, the low-dimensional feature vector output by the intermediate layer of the encoder is the desired fused feature.

[0087] In the final set of feature vectors, each feature vector is a fixed-dimensional vector (usually 32 or 64-dimensional), corresponding to the overall aging state of a fuse at a specific moment. Each dimension of the feature vector characterizes different aging characteristics of the fuse, including the degree of contact oxidation, the degree of melt fatigue, and changes in thermal response characteristics.

[0088] After the feature vector is generated, the aging index can be calculated by comparing it with a pre-established health benchmark library. The aging index is represented by a standardized value of 0-1, where 0 represents a brand new state and 1 represents a fully aged state. When the aging index exceeds a preset threshold (usually 0.7), the system will issue a warning signal and recommend replacing the fuse.

[0089] In some embodiments, based on the feature vector set and a preset health status assessment rule, each fuse is classified into health levels to obtain a health status identifier for each fuse, including:

[0090] The feature vector corresponding to each fuse is extracted from the feature vector set. The feature components of each dimension in the feature vector are numerically normalized to obtain a normalized feature vector. The deviation value between the feature components of each dimension in the normalized feature vector and the corresponding dimension components of the standard health feature vector defined in the preset health status assessment rules is calculated. The deviation values ​​of each dimension are weighted and summed to obtain a comprehensive deviation measure value that reflects the overall health deviation of the fuse.

[0091] Obtain the comprehensive deviation measurement value range corresponding to multiple predefined health levels in the health status assessment rules. The multiple health levels are arranged in descending order of health degree, and each health level corresponds to a unique comprehensive deviation measurement value range.

[0092] The comprehensive deviation metric value of each fuse is compared one by one with the comprehensive deviation metric value intervals corresponding to the multiple health levels. The comprehensive deviation metric value of the fuse falls into the comprehensive deviation metric value interval. The health level corresponding to the comprehensive deviation metric value interval is taken as the health level of the fuse, and a health status identifier containing the fuse identifier and the health level is generated.

[0093] In this embodiment, normalization refers to the process of converting the numerical range of the feature vector of each fuse to a preset range (such as [0, 1]) to eliminate the influence of features of different dimensions on subsequent feature extraction.

[0094] In this embodiment, the normalization formula for the normalization process is:

[0095]

[0096] in, Let x represent the value of the element at the i-th position after normalization, M represent the maximum value of the elements in this feature vector, and x i This represents the value of the i-th element in the feature vector. The normalized value is limited to the range [0, 1], which helps improve the convergence speed and stability of the model.

[0097] In practical applications, batch normalization can be performed on the feature vectors in the feature vector set to further improve efficiency. The formula for batch normalization is:

[0098]

[0099] in, and These represent the mean and standard deviation of each element in the feature vector set of this batch, respectively.

[0100] In this embodiment, each element in the standard health feature vector corresponds to a health range. The deviation value between each dimension feature component in the normalized feature vector and the corresponding dimension component of the standard health feature vector defined in the preset health status assessment rules can be calculated as follows: when an element in the feature vector exceeds the upper limit of the standard health feature vector for the corresponding dimension, the deviation value for that dimension is obtained by subtracting the upper limit value of the element in the standard feature vector from the element's value; when an element in the feature vector is below the lower limit of the standard health feature vector for the corresponding dimension, the deviation value for that dimension is obtained by subtracting the element's value from the lower limit value of the element in the standard feature vector; when an element in the feature vector is between the upper and lower limits of the element's value in the standard feature vector for the corresponding dimension, the deviation value is set to 0. Then, a weighted summation operation is performed on the deviation values ​​of each dimension to obtain a comprehensive deviation metric reflecting the overall health deviation of the fuse.

[0101] In a specific embodiment, a feature vector corresponding to each fuse is extracted from the feature vector set. Assume that the feature vector of a certain fuse contains five feature components: contact resistance measurement, operating time measurement, temperature rise measurement, mechanical wear measurement, and insulation resistance measurement. The feature components of each dimension in the feature vector are then normalized using a maximum-minimum normalization method. This involves subtracting the minimum value of that dimension from the original value of each feature component across all fuse samples, and then dividing by the difference between the maximum and minimum values ​​of that dimension. This method maps the numerical range of each feature component to the interval between 0 and 1, resulting in a normalized feature vector.

[0102] The deviation values ​​between each dimension feature component in the normalized feature vector and the corresponding dimension component of the standard health feature vector defined in the preset health status assessment rules are calculated. The standard health feature vector is obtained through statistical analysis of a large amount of operating data from fuses in a healthy state, and represents the standard values ​​of various characteristic parameters of the fuse in a healthy state. For each dimension feature component, the absolute difference between that dimension component of the normalized feature vector and that dimension component of the standard health feature vector is calculated to obtain the deviation value for that dimension.

[0103] The deviation values ​​of each dimension are weighted and summed to obtain a comprehensive deviation metric reflecting the overall health deviation of the fuse. The weight coefficients for each dimension's deviation value are determined based on the degree of influence of each dimension's characteristics on the fuse's health status; the greater the influence, the larger the weight coefficient. For example, the weight coefficient for contact resistance measurement is set to 0.3, the weight coefficient for operating time measurement is set to 0.25, the weight coefficient for temperature rise measurement is set to 0.2, the weight coefficient for mechanical wear measurement is set to 0.15, and the weight coefficient for insulation resistance measurement is set to 0.1, with the sum of all weight coefficients equal to 1. The comprehensive deviation metric is obtained by multiplying the deviation value of each dimension by its corresponding weight coefficient and then summing them.

[0104] The comprehensive deviation metric value range corresponding to multiple predefined health levels in the health status assessment rules is obtained. These health levels are arranged in descending order of health severity as Excellent, Good, Average, Poor, and Dangerous. Each health level corresponds to a unique comprehensive deviation metric value range. The comprehensive deviation metric value range for the Excellent level is a left-closed, right-open interval from 0 to 0.15; for the Good level, it is a left-closed, right-open interval from 0.15 to 0.3; for the Average level, it is a left-closed, right-open interval from 0.3 to 0.5; for the Poor level, it is a left-closed, right-open interval from 0.5 to 0.7; and for the Dangerous level, it is a closed interval from 0.7 to 1.

[0105] The overall deviation metric value of each fuse is compared one by one with the overall deviation metric value intervals corresponding to the multiple health levels to determine whether the overall deviation metric value of the fuse falls within a certain overall deviation metric value interval. For example, if the calculated overall deviation metric value of a fuse is 0.42, and through comparison, it is found that this value is greater than or equal to 0.3 and less than 0.5, it is determined that the overall deviation metric value falls within the left-closed and right-open interval of 0.3 to 0.5, and the health level corresponding to this overall deviation metric value interval is determined as the general level, which is used as the health level of the fuse.

[0106] A health status identifier is generated, containing the fuse's identifier and health level. This identifier is stored in a structured data format, including fields for fuse identifier, health level, comprehensive deviation metric, and assessment time. All fuse health status identifiers are then aggregated and stored to form a complete fuse health status database, providing data support for subsequent fuse maintenance decisions and replacement plans.

[0107] In some embodiments, the electrical coupling path between each fuse is determined based on the health status identifier and the topological connection relationship of the new energy station, and a collaborative protection strategy for the fuse group is established based on the electrical coupling path, which may include:

[0108] A fuse node network is constructed based on the topological connection relationship of the new energy station. Each node in the fuse node network corresponds to a fuse. The health status identifier is assigned as a node attribute to the corresponding node. By tracing the electrical connection path between each node in the fuse node network, node pairs with power transmission association are identified, and the electrical connection path between each node pair is marked as an electrical coupling path.

[0109] The nodes on each electrical coupling path are sorted according to the direction of power flow. Based on the sorting results and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path. The fuse timing control rule defines the order in which the fuses corresponding to each node on the electrical coupling path will operate when the fuse corresponding to any node on the electrical coupling path fails.

[0110] Calculate the degradation degree quantification value corresponding to the health status identifier of each node on each electrical coupling path, and assign action priority to each node on each electrical coupling path according to the degradation degree quantification value to generate action priority allocation rules;

[0111] Fuse with the same electrical coupling path or with intersecting paths are divided into fuse groups. For each fuse group, the fuse timing control rules and action priority allocation rules corresponding to all electrical coupling paths within the fuse group are integrated to establish a collaborative protection strategy for the fuse group.

[0112] In this embodiment, a fuse node network is constructed based on the topological connections of the renewable energy station. In practical applications, the installation location and electrical connection relationships of each fuse can be extracted using the primary electrical system diagram of the renewable energy station. For example, in a renewable energy station containing photovoltaic arrays, energy storage units, and transformers, fuses are distributed at various key connection points, and each fuse is assigned a unique identifier. The constructed fuse node network represents each fuse as a node, and the connections between nodes reflect their electrical connections in the actual power system.

[0113] In this embodiment, a health status identifier is assigned as a node attribute to the corresponding node. The health status identifier can be obtained by analyzing the operating parameters of the fuse, including but not limited to factors such as fuse temperature, fusible element condition, number of operations, and service life. The health status can be divided into four levels: "Good," "Slightly Deteriorated," "Moderately Deteriorated," and "Severely Deteriorated." These status identifiers are directly associated with the corresponding nodes in the fuse node network, providing basic data for subsequent analysis.

[0114] In this embodiment, node pairs with associated power transmission are identified by tracing the electrical connection paths between nodes in the fuse node network. Specifically, a depth-first search or breadth-first search algorithm can be used to explore all possible electrical connection paths starting from each node. When it is determined that there is a possibility of power transmission between two nodes, they are marked as a node pair, and the electrical connection path between them is recorded as the electrical coupling path.

[0115] In this embodiment, the nodes on each electrical coupling path are sorted according to the direction of power flow. In renewable energy stations, power typically flows from generation units (such as photovoltaic arrays) to loads or grid connection points. Based on the fundamental principles of power systems, the nodes on the electrical coupling path are sorted along the direction of power flow to ensure that subsequent protection strategies can be executed according to the physical characteristics of power flow.

[0116] In this embodiment, based on the sorting results and the health status identifiers of each node, a fuse timing control rule is generated for each electrical coupling path. The fuse timing control rule defines the order in which each fuse on the path will operate when any fuse on the path fails. For example, when the electrical coupling path contains three fuse nodes A, B, and C, and the power flow is A→B→C, if node B fails, the rule might be defined as operating B first, then A, and finally C, thus minimizing the scope of the fault's impact.

[0117] In this embodiment, the degradation level corresponding to the health status identifier of each node on each electrical coupling path is calculated using a quantified value. The four levels—"Good," "Slightly Degraded," "Moderately Degraded," and "Severely Degraded"—can be quantified as values ​​of 0, 0.3, 0.6, and 0.9, respectively. This quantification method allows for numerical processing of health status, facilitating subsequent calculations and comparisons.

[0118] In this embodiment, the action priority is assigned to each node on each electrical coupling path based on the quantified value of the degree of degradation. The priority allocation follows these principles: fuses with poorer health conditions have lower reliability and should be given priority in operation; the importance of the fuse's location in the electrical path must also be considered. Combining these two factors, a comprehensive priority index for each node can be calculated, and action priority allocation rules can be generated accordingly.

[0119] In this embodiment, fuses with the same electrical coupling path or whose paths intersect are grouped into fuse groups. In practical applications, there may be multiple electrical coupling paths that intersect or share nodes. By analyzing the topological relationships of all electrical coupling paths, these highly correlated paths can be identified, and the fuses involved can be grouped into the same group. This grouping method ensures that fuses with strong mutual influence can work together.

[0120] In this embodiment, for each fuse group, the fuse timing control rules and action priority allocation rules corresponding to all electrical coupling paths within the group are integrated to establish a collaborative protection strategy for the fuse group. During the integration process, potential rule conflicts need to be resolved. For example, when a fuse is assigned different action priorities in different electrical coupling paths, coordination needs to be achieved by comprehensively considering factors such as the importance of each path and the magnitude of the current.

[0121] In this embodiment, in the practical application of the collaborative protection strategy, the collaborative control of the fuse group can be achieved through intelligent power distribution terminals or protection devices. When any fuse is detected to have a fault or abnormal state, the system controls the operation of each fuse according to the preset collaborative protection strategy, following a predetermined timing and priority, thereby achieving precise protection of the renewable energy station, minimizing the scope of fault impact, and improving system reliability.

[0122] It is understandable that, in some other embodiments, the collaborative protection strategy can also be dynamically adjusted according to the operating status of the renewable energy station. For example, when there is sufficient sunlight and the photovoltaic system is operating at full capacity, it may be necessary to adjust the protection strategy of certain fuse groups to adapt to changes in the direction and magnitude of power flow. The system can periodically reassess the health status of the fuses, update node attributes, and adjust the collaborative protection strategy accordingly to achieve dynamic optimization of the protection scheme.

[0123] In some embodiments, the nodes on each electrical coupling path are sorted according to the direction of power flow, and based on the sorting result and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path, which may include:

[0124] Obtain the current direction data and voltage phase data of the fuses corresponding to each node on each electrical coupling path, determine the direction of power transmission on the electrical coupling path based on the current direction data and voltage phase data, sort the nodes on the electrical coupling path according to the direction of power transmission from the source end to the load end, and obtain a sorting result including the node position number.

[0125] Extract the health status identifier of each node in the sorting result and establish a correspondence table between the node position number and the health status identifier;

[0126] Based on the correspondence table, key nodes whose health status indicators indicate a degree of deterioration exceeding a preset evaluation benchmark are identified, and the upstream node set and downstream node set of the key nodes are determined from the sorting results.

[0127] When the fuse corresponding to the critical node needs to perform a protective action, the fuse corresponding to the node whose health status indicator in the upstream node set of the critical node is lower than the preset degradation value will delay its action, and the fuses corresponding to all nodes in the downstream node set of the critical node will act in sequence from the nearest to the farthest node according to the node position number.

[0128] The timing constraints of the actions corresponding to all key nodes on each electrical coupling path are summarized to form the fuse timing control rules for the electrical coupling path.

[0129] In this embodiment, the current direction data and voltage phase data of the corresponding fuses at each node on the electrical coupling path are acquired. Specifically, power quality monitoring devices installed at each node are used to collect parameters such as the amplitude and direction of the three-phase current, as well as the amplitude and phase of the voltage at the node in real time. For example, for an electrical coupling path containing nodes A, B, C, and D, the current direction vectors I_A, I_B, I_C, and I_D and the voltage phase angles φ_A, φ_B, φ_C, and φ_D at each node are acquired respectively.

[0130] In this embodiment, the direction of electrical energy transmission along the electrical coupling path is determined based on the acquired current direction data and voltage phase data. The direction of electrical energy flow can be determined by the direction of active power, i.e., P=UI. cosφ, where φ is the phase difference between voltage and current. When cosφ is positive, electrical energy flows out of the node; when cosφ is negative, electrical energy flows into the node. By analyzing the power flow between adjacent nodes, the direction of electrical energy transmission along the entire path can be determined.

[0131] In this embodiment, the nodes on the electrical coupling path are sorted according to the direction of power transmission from the source to the load, resulting in a sorting result including the node position numbers. Taking the aforementioned electrical coupling path as an example, assuming that the power flow direction analysis determines that the power flows from node A through B and C to D, the sorting result is: node A (position number 1), node B (position number 2), node C (position number 3), and node D (position number 4).

[0132] Next, the health status identifier of each node in the sorting results is extracted, and a correspondence table between the node location number and the health status identifier is established. The health status identifier can be calculated based on multi-dimensional information such as the node device's operating parameters, historical fault data, and service life, using a health assessment algorithm, and is usually expressed as a percentage or a grade system. For example, the following correspondence table can be established: Node A (Location No. 1, Health Status 95%), Node B (Location No. 2, Health Status 75%), Node C (Location No. 3, Health Status 60%), Node D (Location No. 4, Health Status 85%).

[0133] In this embodiment, based on the correspondence table, critical nodes whose health status indicators show a degradation level exceeding a preset assessment benchmark are identified. Assuming the preset assessment benchmark is that a health status below 70% is considered a critical node, then in the example above, node C (health status 60%) is identified as a critical node. The upstream and downstream node sets of the critical nodes are determined from the ranking results. For node C, its upstream node set is {node A, node B}, and its downstream node set is {node D}.

[0134] In this embodiment, a differentiated fuse control strategy is formulated when the fuse corresponding to a critical node needs to perform a protective action. The fuses corresponding to nodes in the upstream node set of the critical node whose degradation level is lower than a preset degradation value are delayed in operation. Assuming the preset degradation value is 30% (i.e., health status is higher than 70%), the fuses of nodes A and B should delay operation. The delay time can be set according to the node's health status; for example, delay time t = base delay time × (node ​​health status / 100%).

[0135] Simultaneously, the fuses corresponding to all nodes in the downstream node set of the critical node activate sequentially from nearest to farthest node location. In this example, the fuse of node D should activate within a certain time interval after the fuse of node C activates. The time interval can be determined based on the system response time and safety margin, typically ranging from tens to hundreds of milliseconds.

[0136] Finally, the timing constraints corresponding to all critical nodes on each electrical coupling path are summarized to form the fuse timing control rules for the electrical coupling path. For paths containing multiple critical nodes, the protection requirements of each critical node must be comprehensively considered, possible conflict conditions must be resolved, and a unified control rule must be formed. The final generated fuse timing control rule can be expressed as a series of "if-then" conditional statements, clearly specifying the action sequence and timing requirements of each fuse under different fault scenarios.

[0137] It is understood that in other embodiments, different preset evaluation benchmarks and preset degradation values ​​can be set for specific power grid structures to adapt to the safety requirements and operating characteristics of different power grid areas. Furthermore, as the power grid state changes, node health status identifiers can be updated periodically, and circuit breaker timing control rules can be dynamically adjusted to ensure that the power grid protection system always remains in optimal condition.

[0138] In some embodiments, calculating a degradation degree quantification value corresponding to the health status identifier of each node on each electrical coupling path, and assigning action priorities to each node on each electrical coupling path based on the degradation degree quantification value, may include:

[0139] The historical number of operations and cumulative current carrying time of the fuses corresponding to each node on each electrical coupling path are numerically normalized to obtain normalized operation components and normalized duration components. The normalized operation components and normalized duration components are weighted and summed to obtain a quantitative value of the degree of degradation.

[0140] The degradation quantification values ​​of all nodes on each electrical coupling path are sorted to obtain the degradation ranking of each node;

[0141] Based on the degradation ranking, an action priority mapping table is established for each electrical coupling path. The action priority mapping table defines the mapping relationship between the degradation ranking of the node and the action priority.

[0142] Based on the action priority mapping table, a unique action priority identifier is assigned to each node on each electrical coupling path, and an action priority allocation rule containing node identifier, degradation degree quantification value and action priority identifier is generated.

[0143] In some cases within power systems, the health status of nodes along the electrical coupling path directly affects the safe and stable operation of the entire system. By quantitatively analyzing the health status of nodes and assigning reasonable action priorities, orderly protective actions can be implemented when system faults occur, thereby improving system reliability.

[0144] In this embodiment, historical data of each node on the electrical coupling path is acquired, including the historical number of times the corresponding fuse of each node has been activated and the cumulative current carrying time. For example, in a certain distribution network, there is an electrical coupling path from a substation to an end user, which includes four nodes: A, B, C, and D. Their historical activation counts are 15, 8, 12, and 5, respectively, and their cumulative current carrying times are 2000 hours, 1500 hours, 1800 hours, and 1200 hours, respectively.

[0145] In this embodiment, the historical action count is numerically normalized using a maximum-minimum normalization method: Normalized action component = (Current node action count - Minimum action count) / (Maximum action count - Minimum action count). Similarly, the cumulative throughput duration is normalized: Normalized duration component = (Current node throughput duration - Minimum throughput duration) / (Maximum throughput duration - Minimum throughput duration). The normalized action component and normalized duration component are weighted and summed to obtain the degradation degree quantification value. Assuming the action component weight is set to 0.6 and the duration component weight is set to 0.4, then: Degradation degree quantification value = 0.6 × Normalized action component + 0.4 × Normalized duration component.

[0146] In one specific implementation, the degradation levels of each node are ranked differently, and action priorities are directly assigned based on the degradation level ranking. Assuming that a smaller value indicates a higher priority, the action priority allocation is as follows: Node A: Priority 1; Node C: Priority 2; Node B: Priority 3; Node D: Priority 4.

[0147] In this embodiment, an action priority allocation rule is generated, comprising node identifier, degradation level quantification value, and action priority identifier: Node A: degradation level quantification value 1, action priority 1; Node C: degradation level quantification value 0.72, action priority 2; Node B: degradation level quantification value 0.33, action priority 3; Node D: degradation level quantification value 0, action priority 4. This action priority allocation rule will serve as the basis for the system to execute protection actions when facing fault conditions, ensuring that the system can implement protection measures according to the predetermined priority order, thereby improving the reliability and security of system operation. In practical applications, the weight coefficients of the action component and duration component can be adjusted according to the specific scenario.

[0148] In some embodiments, the intelligent detection and maintenance method for fuses in new energy vehicle charging stations may further include:

[0149] If at least two nodes in a plurality of nodes in the electrical coupling path have the same degradation ranking, the topology position correction coefficient of the nodes with the same degradation ranking in the electrical coupling path is calculated. The topology position correction coefficient is obtained by calculating the number of electrical connection hops between the node and the power energy node in the electrical coupling path.

[0150] The action priority value assigned to the node with the smaller topology position correction coefficient value is set to a value lower than that assigned to the node with the larger topology position correction coefficient value.

[0151] In this embodiment, the electrical connection hop count refers to the number of intermediate nodes traversed from the power source node in the distribution network along the electrical connection path to the target fuse node. It is calculated through graph theory analysis of the distribution network topology and reflects the topological distance of the fuse node from the power source node within the distribution network. A topology position correction coefficient is determined based on the hop count; for example, the correction coefficient can be set equal to the hop count value. When multiple nodes with the same degradation level appear, the node with the smaller topology position correction coefficient value is assigned a lower action priority value than the node with the larger topology position correction coefficient value. For example, if the degradation level quantification values ​​of nodes B and C are both 0.5, then according to the topology position correction coefficient, the correction coefficient for node B is 1, and the correction coefficient for node C is 2. Therefore, the action priority of node B should be higher than that of node C.

[0152] Please see Figure 3 In some embodiments, controlling the corresponding fuse to perform a blowing action or maintain its operating state based on the protection control command may include:

[0153] Extract the target fuse identifier, action type identifier, and execution time identifier contained in the protection control instruction; determine the fuse object that needs to be controlled based on the target fuse identifier; determine whether the fuse object should perform a fuse-breaking action or remain in operation based on the action type identifier; and determine the time node when the fuse object starts to perform the corresponding action based on the execution time identifier.

[0154] Establish a fuse control queue, and add the fuse object, the corresponding action type identifier, and the execution time identifier as control tasks to the fuse control queue;

[0155] The system monitors the time difference between the execution time identifier of each control task in the fuse control queue and the current system time. When the time difference meets the preset triggering condition, the system extracts the corresponding control task from the fuse control queue, reads the action type identifier in the control task, and if the action type identifier indicates that a fuse action is to be executed, a fuse trigger signal is sent to the fuse object corresponding to the control task. If the action type identifier indicates that the operation is to be maintained, a status maintenance signal is sent to the fuse object corresponding to the control task, and the control task is marked as executed in the fuse control queue.

[0156] In this embodiment, key identification information contained in the protection control command is extracted. Upon receiving the protection control command, the target fuse identifier, action type identifier, and execution time identifier are extracted by parsing the command message structure. The target fuse identifier uniquely identifies the fuse object to be controlled and can be a unique code such as "FB-001". The action type identifier can be set to a binary value, such as "0" indicating that the operation is maintained and "1" indicating that the fuse action is performed. The execution time identifier is usually in timestamp format, such as "2023-10-20 14:30:25.500", accurate to the millisecond level. The parsing process can be implemented using regular expressions or a specific format parser to ensure the accuracy of the extracted information.

[0157] In this embodiment, the corresponding fuse object is searched in the fuse object library based on the extracted target fuse identifier. The fuse object library can be stored using a hash table structure, with the fuse identifier as the key and the corresponding fuse control interface as the value, resulting in a search complexity of O(1). If the search fails, an error log is recorded and an error code is returned; if the search succeeds, a reference to the fuse object is obtained to prepare for subsequent control operations.

[0158] In this embodiment, the operation that the fuse should perform is determined based on the action type identifier. When the action type identifier is "1", it indicates that the fuse needs to perform a fuse-breaking action; when the action type identifier is "0", it indicates that the fuse needs to remain in operation. Based on the execution time identifier, the difference between the current system time and the target execution time is calculated to determine the execution time point.

[0159] In this embodiment, the control queue adopts a priority queue data structure, with execution time as the priority sorting basis, and the most recently executed task located at the front of the queue. Each control task contains three key pieces of information: a circuit breaker object reference, an action type identifier, and an execution time identifier, along with a task status identifier, initially in the "not executed" state. The queue supports dynamic insertion of new tasks while maintaining the time priority order, achieving orderly task management.

[0160] In a specific embodiment, the task structure can be defined as follows: Fuse object reference: points to the fuse instance to be controlled; Action type: an enumeration value indicating whether to perform circuit breaking or keep running; Execution time: a timestamp specifying the precise time of task execution; Task status: an enumeration value including "not executed", "executed", "execution failed", etc.

[0161] In this embodiment, after the constructed control task is added to the circuit breaker control queue, a monitoring thread is started to continuously monitor the queue status. The monitoring thread checks the difference between the execution time of the task at the head of the queue and the current system time at fixed intervals (e.g., every millisecond). The preset trigger condition can be set to a time difference less than or equal to zero, that is, the current time has reached or exceeded the task execution time.

[0162] When the triggering condition is met, the corresponding control task is retrieved from the queue, and its action type identifier is read. If the action type identifier is "1" (execute circuit breaker action), the circuit breaking interface of the fuse object is called to send a circuit breaker trigger signal, causing the fuse to perform an open operation; if the action type identifier is "0" (maintain running state), a status maintenance signal is sent to the fuse to ensure its continued normal operation. Signal sending can be implemented through the predefined control interface of the fuse object, such as the `triggerBreak()` or `maintainStatus()` methods.

[0163] After executing a control operation, the result must be recorded and the task status updated. If the operation is successfully completed, the task is marked as "executed" in the fuse control queue; if an exception occurs during the operation, it is marked as "execution failed" and the error information is recorded. A retry mechanism or alarm notification can be selected. Simultaneously, an execution log can be recorded, containing key information such as fuse identifier, execution time, operation type, and execution result, facilitating subsequent analysis and troubleshooting.

[0164] In practical applications, such as power distribution systems, it may be necessary to dynamically adjust the status of multiple fuses based on load changes. For example, when a sudden increase in the load on a power supply line in a certain area is detected, exceeding a safety threshold, the system may generate a set of protection control commands to perform tiered control on fuses of different priority load lines. This ensures normal power supply to key facilities while preventing overall system overload. These control commands are parsed and added to a control queue, executed according to a predetermined sequence to achieve system safety protection.

[0165] The above implementation scheme can precisely control the fuse to perform the melting action or maintain the operating state, thereby achieving refined protection and control of the power system and improving system safety and reliability.

[0166] Please see Figure 4 One embodiment of the present invention provides an intelligent detection and maintenance device for fuses in new energy vehicle charging stations. The intelligent detection and maintenance device for fuses in new energy vehicle charging stations may include: a status analysis module, a level classification module, a strategy generation module, an instruction generation module, and an instruction execution module.

[0167] The status analysis module is used to acquire real-time electrical status parameters of multiple fuses in the new energy station, perform time-frequency domain joint analysis on the real-time electrical status parameters, and extract a set of feature vectors reflecting the aging degree of the fuses.

[0168] The rating module is used to classify the health level of each fuse according to the set of feature vectors and the preset health status assessment rules, so as to obtain the health status identifier of each fuse.

[0169] The strategy generation module is used to determine the electrical coupling path between each fuse based on the health status identifier and the topological connection relationship of the new energy station, and to establish a collaborative protection strategy for the fuse group based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels.

[0170] The instruction generation module is used to generate protection control instructions for the fuse and other fuses that are electrically coupled to it, when the current waveform data of any fuse is detected to exceed the action threshold of the corresponding health level of the fuse, according to the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the cooperative protection strategy.

[0171] The instruction execution module is used to control the corresponding fuse to perform a melting action or maintain the operating state based on the protection control instruction.

[0172] The specific functions and effects of the intelligent fuse detection and maintenance device for new energy vehicle charging stations can be explained by referring to other embodiments in this manual, and will not be repeated here. Each module in the intelligent fuse detection and maintenance device for new energy vehicle charging stations can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0173] Please see Figure 5 One embodiment of the present invention can provide an electronic device, the electronic device comprising:

[0174] A memory, and one or more processors communicatively connected to the memory;

[0175] The memory stores instructions that can be executed by the one or more processors. These instructions are executed by the one or more processors to enable the one or more processors to implement the intelligent detection and maintenance method for fuses in new energy vehicle charging stations as described in any of the above embodiments.

[0176] One embodiment of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent detection and maintenance method for fuses in new energy vehicle charging stations described in any of the above embodiments.

[0177] The embodiments of this specification also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the intelligent detection and maintenance method for fuses in new energy vehicle charging stations described in any of the above embodiments.

[0178] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments described herein, and are not intended to limit the scope of the invention.

[0179] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0180] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0181] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0182] It is understood that the processor in this invention can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method implementation can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0183] It is understood that the memory in this invention can be volatile memory or non-volatile memory, or may include both. Specifically, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0184] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0185] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0187] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If the aforementioned functions are implemented as 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 solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for intelligent detection and maintenance of fuses in new energy vehicle charging stations, characterized in that, include: The real-time electrical status parameters of multiple fuses in the new energy station are obtained, and the time-frequency domain joint analysis of the real-time electrical status parameters is performed to extract a set of feature vectors reflecting the aging degree of the fuses. Based on the set of feature vectors and the preset health status assessment rules, each fuse is classified into health levels to obtain the health status identifier of each fuse. Based on the health status identifier and the topological connection relationship of the new energy station, the electrical coupling path between each fuse is determined, and a collaborative protection strategy for the fuse group is established based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels. When the current waveform data of any fuse exceeds the action threshold of the corresponding health level of the fuse, protection control instructions are generated for the fuse and other fuses that are electrically coupled to it, in accordance with the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the collaborative protection strategy. Based on the protection control command, the corresponding fuse is controlled to perform a melting action or maintain its operating state.

2. The method according to claim 1, characterized in that, The real-time electrical status parameters include current waveform data, voltage waveform data, and temperature rise data; A joint time-frequency domain analysis is performed on the real-time electrical state parameters to extract a set of feature vectors reflecting the aging degree of the fuse, including: The current waveform data is decomposed in the time domain to obtain current change components at multiple time scales. The amplitude envelope is extracted and the fluctuation trend is analyzed for each current change component to obtain time domain feature components. The voltage waveform data and temperature rise data are respectively subjected to frequency domain transformation to obtain frequency domain feature components; the frequency domain feature components include the harmonic content obtained based on the voltage waveform data and the spectral energy obtained based on the temperature rise data; A mapping relationship is established between the time-domain feature components and the spectral energy, wherein the mapping relationship reflects the thermal accumulation response characteristics corresponding to the current change components; According to the mapping relationship, the time-domain feature components and the frequency-domain feature components are fused and encoded to generate the feature vector set. Each feature vector in the feature vector set corresponds to the comprehensive aging state of a fuse at the current moment.

3. The method according to claim 1, characterized in that, Based on the set of feature vectors and the preset health status assessment rules, each fuse is classified into health levels to obtain the health status identifier of each fuse, including: The feature vector corresponding to each fuse is extracted from the feature vector set. The feature components of each dimension in the feature vector are numerically normalized to obtain a normalized feature vector. The deviation value between the feature components of each dimension in the normalized feature vector and the corresponding dimension components of the standard health feature vector defined in the preset health status assessment rules is calculated. The deviation values ​​of each dimension are weighted and summed to obtain a comprehensive deviation measure value that reflects the overall health deviation of the fuse. Obtain the comprehensive deviation measurement value range corresponding to multiple predefined health levels in the health status assessment rules. The multiple health levels are arranged in descending order of health degree, and each health level corresponds to a comprehensive deviation measurement value range. The comprehensive deviation metric value of each fuse is compared one by one with the comprehensive deviation metric value intervals corresponding to the multiple health levels. The comprehensive deviation metric value of the fuse falls into the comprehensive deviation metric value interval. The health level corresponding to the comprehensive deviation metric value interval is taken as the health level of the fuse, and a health status identifier containing the fuse identifier and the health level is generated.

4. The method according to claim 1, characterized in that, Based on the health status indicators and the topological connections of the new energy stations, the electrical coupling paths between each fuse are determined, and a collaborative protection strategy for the fuse group is established based on these electrical coupling paths, including: A fuse node network is constructed based on the topological connection relationship of the new energy station. Each node in the fuse node network corresponds to a fuse. The health status identifier is assigned as a node attribute to the corresponding node. By tracing the electrical connection path between each node in the fuse node network, node pairs with power transmission association are identified, and the electrical connection path between each node pair is marked as an electrical coupling path. The nodes on each electrical coupling path are sorted according to the direction of power flow. Based on the sorting results and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path. The fuse timing control rule defines the order in which the fuses corresponding to each node on the electrical coupling path will operate when the fuse corresponding to any node on the electrical coupling path fails. Calculate the degradation degree quantification value corresponding to the health status identifier of each node on each electrical coupling path, and assign action priority to each node on each electrical coupling path according to the degradation degree quantification value to generate action priority allocation rules; Fuse with the same electrical coupling path or with intersecting paths are divided into fuse groups. For each fuse group, the fuse timing control rules and action priority allocation rules corresponding to all electrical coupling paths within the fuse group are integrated to establish a collaborative protection strategy for the fuse group.

5. The method according to claim 4, characterized in that, The nodes on each electrical coupling path are sorted according to the direction of power flow. Based on the sorting result and the health status identifier of each node, a fuse timing control rule is generated for each electrical coupling path, including: Obtain the current direction data and voltage phase data of the fuses corresponding to each node on each electrical coupling path, determine the direction of power transmission on the electrical coupling path based on the current direction data and voltage phase data, sort the nodes on the electrical coupling path according to the direction of power transmission from the source end to the load end, and obtain a sorting result including the node position number. Extract the health status identifier of each node in the sorting result and establish a correspondence table between the node position number and the health status identifier; Based on the correspondence table, key nodes whose health status indicators indicate a degree of deterioration exceeding a preset evaluation benchmark are identified, and the upstream node set and downstream node set of the key nodes are determined from the sorting results. When the fuse corresponding to the critical node needs to perform a protective action, the fuse corresponding to the node whose health status indicator in the upstream node set of the critical node is lower than the preset degradation value will delay its action, and the fuses corresponding to all nodes in the downstream node set of the critical node will act in sequence from the nearest to the farthest node according to the node position number. The timing constraints of the actions corresponding to all key nodes on each electrical coupling path are summarized to form the fuse timing control rules for the electrical coupling path.

6. The method according to claim 4, characterized in that, Calculate the degradation level quantification value corresponding to the health status identifier of each node on each electrical coupling path, and assign action priorities to each node on each electrical coupling path based on the degradation level quantification value, including: The historical number of operations and cumulative current carrying time of the fuses corresponding to each node on each electrical coupling path are numerically normalized to obtain normalized operation components and normalized duration components. The normalized operation components and normalized duration components are weighted and summed to obtain a quantitative value of the degree of degradation. The degradation quantification values ​​of all nodes on each electrical coupling path are sorted to obtain the degradation ranking of each node; Based on the degradation ranking, an action priority mapping table is established for each electrical coupling path. The action priority mapping table defines the mapping relationship between the degradation ranking of the node and the action priority. Based on the action priority mapping table, a unique action priority identifier is assigned to each node on each electrical coupling path, and an action priority allocation rule containing node identifier, degradation degree quantification value and action priority identifier is generated.

7. The method according to claim 6, characterized in that, The method further includes: If at least two nodes in a plurality of nodes in the electrical coupling path have the same degradation ranking, the topology position correction coefficient of the nodes with the same degradation ranking in the electrical coupling path is calculated. The topology position correction coefficient is obtained by calculating the number of electrical connection hops between the node and the power energy node in the electrical coupling path. The action priority value assigned to the node with the smaller topology position correction coefficient value is set to a value lower than that assigned to the node with the larger topology position correction coefficient value.

8. The method according to claim 1, characterized in that, Based on the protection control command, control the corresponding fuse to perform a blowing action or maintain its operating state, including: Extract the target fuse identifier, action type identifier, and execution time identifier contained in the protection control instruction; determine the fuse object that needs to be controlled based on the target fuse identifier; determine whether the fuse object should perform a fuse-breaking action or remain in operation based on the action type identifier; and determine the time node when the fuse object starts to perform the corresponding action based on the execution time identifier. Establish a fuse control queue, and add the fuse object, the corresponding action type identifier, and the execution time identifier as control tasks to the fuse control queue; The system monitors the time difference between the execution time identifier of each control task in the fuse control queue and the current system time. When the time difference meets the preset triggering condition, the system extracts the corresponding control task from the fuse control queue, reads the action type identifier in the control task, and if the action type identifier indicates that a fuse action is to be executed, a fuse trigger signal is sent to the fuse object corresponding to the control task. If the action type identifier indicates that the operation is to be maintained, a status maintenance signal is sent to the fuse object corresponding to the control task, and the control task is marked as executed in the fuse control queue.

9. An intelligent detection and maintenance device for fuses in new energy vehicle charging stations, characterized in that, The intelligent detection and maintenance device for fuses in the new energy vehicle charging station includes: The status analysis module is used to acquire real-time electrical status parameters of multiple fuses in the new energy station, perform time-frequency domain joint analysis on the real-time electrical status parameters, and extract a set of feature vectors reflecting the aging degree of the fuses. The level classification module is used to classify the health level of each fuse according to the feature vector set and the preset health status assessment rules, and obtain the health status identifier of each fuse. The strategy generation module is used to determine the electrical coupling path between each fuse based on the health status identifier and the topological connection relationship of the new energy station, and to establish a collaborative protection strategy for the fuse group based on the electrical coupling path. The collaborative protection strategy includes the fuse blowing timing control rules for multiple fuses on the same electrical coupling path and the action priority allocation rules for fuses with different health levels. The instruction generation module is used to generate protection control instructions for the fuse and other fuses that are electrically coupled to it, according to the fuse timing control rules and action priority allocation rules of the electrical coupling path where the fuse is located in the cooperative protection strategy, when the current waveform data of any fuse is detected to exceed the action threshold of the corresponding health level of the fuse. The instruction execution module is used to control the corresponding fuse to perform a melting action or maintain the operating state based on the protection control instruction.

10. An electronic device, characterized in that, The device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the intelligent detection and maintenance method for fuses in new energy vehicle charging stations as described in any one of claims 1 to 8.

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