Intelligent Electric Cabinet Electric Fault Detection System and Device

Abstract

The present invention discloses an intelligent electric cabinet electric fault detection system and device, which relates to the technical field of power detection. The system realizes real-time monitoring of power loads through a data acquisition module, and uses a sensor group and an intelligent electric cabinet installed in the electric cabinet to collect power data in real time. Then, through data preprocessing on the cloud platform and combined with dimensionless processing technology, the system improves the standardization and processing efficiency of data and obtains a dimensionless data set. The anomaly detection module uses the power fluctuation anomaly determination model to aggregate the dimensionless data set, calculates the power fluctuation anomaly value, compares it with the abnormal fluctuation threshold, quickly determines whether there is a fault in the power load, and generates an abnormal fluctuation load set. This function significantly improves the efficiency and accuracy of fault detection, overcomes the problem of insufficient fault fluctuation perception ability of traditional electric cabinets, and lays a foundation for timely response to faults.

Description

Technical Field

[0001] The present invention relates to the technical field of power detection, and specifically to an intelligent electric cabinet electric fault detection system and device. Background Art

[0002] Intelligent electric cabinets belong to the field of intelligent management of power systems, which is one of the important directions of modern energy management. In this field, the intelligent power distribution system is one of the core sub - fields, focusing on the monitoring, management, and optimized scheduling of power distribution equipment. In the intelligent power distribution system, the real - time detection and processing of power faults are key links. As an integrated device, the intelligent electric cabinet electric fault detection system is responsible for the real - time monitoring of power loads, fault determination, and dynamic adjustment. It not only pays attention to the safe operation of power equipment but also ensures the stability of power supply to key equipment through mechanisms such as load priority shedding, achieving the efficient utilization of power resources.

[0003] At the current stage, the current power distribution management system usually relies on manual intervention or static rule configuration when dealing with sudden overload faults. This method has obvious lag and low efficiency. When traditional electric cabinets experience load overload or equipment failures, they cannot automatically distinguish between key and non - key loads, nor can they dynamically adjust the power supply plan according to real - time conditions. In this case, the system often fails to guarantee key loads or non - key loads continuously occupy limited resources. In addition, the execution logic of the traditional system for load recovery is simple, lacking an intelligent recovery mechanism, which is prone to resource waste or insufficient recovery, thus affecting the overall power supply efficiency. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent electric cabinet electric fault detection system and device, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: It includes a data acquisition module, an anomaly detection module, a dynamic power - cut decision - making module, and a fault recovery and comprehensive analysis module;

[0006] The data acquisition module installs a sensor group in the intelligent electric cabinet to collect the power data of the power load in real - time. At the same time, it constructs a cloud platform, transmits the power data to the cloud platform through wireless communication connection, and pre - processes the power data in the cloud platform to obtain a dimensionless data set;

[0007] The abnormal detection module constructs a power fluctuation abnormal determination model to calculate and output the power fluctuation abnormal value. It initially compares and evaluates the set abnormal fluctuation threshold with the power fluctuation abnormal value to identify the power load fault situation, summarizes the faulty power loads into an abnormal fluctuation load set, calculates and outputs the load priority index for the faulty power loads, and then performs load priority ranking based on the load priority index to obtain the load priority set;

[0008] The dynamic power cut decision module calculates the real-time total load rate of the intelligent electric cabinet, and based on the output result of the real-time total load rate, determines the execution situation of the power cut decision, judges the total power cut amount, and summarizes the cut-off power loads to generate a cut-off set;

[0009] After the load cut mechanism is executed, the fault recovery and comprehensive analysis module continuously monitors the change of the power load rate in the intelligent electric cabinet, constructs a recovery strategy formula, calculates and outputs the recovered load power, and summarizes it into a recovery set. At the same time, through the load dynamic adjustment formula, it calculates the comprehensive load rate of the power load after recovery, and generates a constraint mechanism for the comprehensive load rate of the power load after recovery.

[0010] Preferably, the data acquisition module includes a load acquisition unit, a data transmission unit, and a data processing unit;

[0011] The load acquisition unit installs a sensor group in the intelligent electric cabinet and combines it with the intelligent electric cabinet to collect the power data of the power loads connected to the intelligent electric cabinet in real time. The power loads represent the power equipment connected to the intelligent electric cabinet;

[0012] The sensor group includes a temperature sensor and a humidity sensor;

[0013] The power data includes real-time current, real-time voltage, power load power, total input power of the electric cabinet, temperature difference inside and outside the intelligent electric cabinet, ambient humidity, cumulative fault time of the power load, and total operation time of the power load equipment;

[0014] The data transmission unit integrates the sensor group with the intelligent electric cabinet, uses the wireless communication module of the intelligent electric cabinet, and the 5G communication network to wirelessly connect the cloud platform with the intelligent electric cabinet and transmit the power data to the cloud platform;

[0015] The data processing unit receives power data in real time in the cloud platform and preprocesses the power data. The preprocessing includes data cleaning and denoising. After preprocessing, the cumulative failure time of the power load and the total operating time of the power load equipment in the power data are combined and calculated to obtain the equipment maintenance reliability index. The equipment maintenance reliability index and the remaining power data are dimensionless processed to obtain a dimensionless data set;

[0016] The dimensionless data set includes real-time current, real-time voltage, power load power, total input power of the electric cabinet, temperature difference inside and outside the intelligent electric cabinet, ambient humidity, and equipment maintenance reliability index.

[0017] Preferably, the anomaly detection module includes a total load rate anomaly analysis unit and a load priority dynamic allocation unit;

[0018] The anomaly analysis unit includes a fluctuation anomaly analysis unit and a fluctuation anomaly evaluation unit;

[0019] The fluctuation anomaly analysis unit constructs a power fluctuation anomaly determination model, extracts the real-time current and real-time voltage in the dimensionless data set, inputs them into the power fluctuation anomaly determination model, and calculates and outputs the power fluctuation anomaly value;

[0020] The fluctuation anomaly evaluation unit sets an anomaly fluctuation threshold based on the normal standard of the power load, initially compares and evaluates the anomaly fluctuation threshold with the power fluctuation anomaly value, analyzes and identifies the fluctuation situation of the power load, summarizes all power loads with abnormal fluctuations, and obtains an abnormal fluctuation load set. The specific evaluation content is as follows;

[0021] When the power fluctuation anomaly value > the anomaly fluctuation threshold, it indicates that there is an anomaly in the current power load fluctuation. At this time, the current power load is assigned to the abnormal fluctuation load set, and a priority adjustment mechanism is triggered;

[0022] When the power fluctuation anomaly value ≤ the anomaly fluctuation threshold, it indicates that there is an anomaly in the current power load fluctuation. At this time, continuous detection is carried out.

[0023] Preferably, the load priority dynamic allocation unit automatically divides the key power loads and non-power loads in the abnormal fluctuation load set obtained through the preliminary comparison and evaluation, and eliminates the key power loads;

[0024] In a dimensionless dataset of power loads in a set of non-critical abnormal fluctuations, extract the power load power and equipment maintenance reliability index in the abnormal fluctuation load set. For non-critical power loads in the abnormal fluctuation load set, calculate and output the load priority index. Based on the output result of the load priority index, perform load priority sorting from low to high. After sorting the power loads, summarize them to obtain the load priority set.

[0025] Preferably, the dynamic power cut decision module includes a dynamic load rate analysis unit and a power cut decision unit;

[0026] The dynamic load rate analysis unit calculates the safe power supply capacity of the intelligent electric cabinet by extracting the temperature difference inside and outside the intelligent electric cabinet and the ambient humidity in the dimensionless dataset and combining them for calculation;

[0027] By summarizing the power load powers of all power loads in the current intelligent electric cabinet and calculating the ratio with the safe power supply capacity of the intelligent electric cabinet, obtain the real-time total load rate. Based on the output result of the real-time total load rate, conduct a comparative evaluation to determine the situation of the total load rate, monitor the operating state of the electric cabinet in real time, and generate a trigger mechanism for power cut decision based on the comparative evaluation result. The specific evaluation content is as follows:

[0028] When the real-time total load rate > 90%, it indicates that there is an overload fault risk in the total load rate of the intelligent electric cabinet. At this time, trigger the power cut decision;

[0029] When the real-time total load rate ≤ 90%, it indicates that the total load rate of the intelligent electric cabinet is normal, and there is no need for power cut at this time.

[0030] Preferably, when the power cut decision unit determines that there is an overload risk in the total load rate of the intelligent electric cabinet, it executes the power cut decision. The power cut decision calculates the total power cut amount based on the current real-time total load rate, and then automatically cuts the non-critical power loads in the load priority set from low to high until the total load rate of the intelligent electric cabinet is normal, stops the power cut, and summarizes the cut power loads to generate a cut set.

[0031] Preferably, the fault recovery and comprehensive analysis module includes a fault recovery unit, a power comprehensive analysis unit, and a load constraint unit;

[0032] After the power cut decision is executed, the fault recovery unit continuously collects the power load power and calculates the real-time total load rate for the second time;

[0033] Re-detect the overload fault elimination situation of the intelligent electric cabinet after the execution of the cut-off power decision. If it is detected that the overload fault has been eliminated, then execute the load restoration decision. The load restoration decision calculates and outputs the restored load power by constructing a restoration strategy formula, selects the power load with the lowest priority from the cut-off set to gradually restore power supply, and summarizes the restored power loads to obtain a restoration set.

[0034] Preferably, the power comprehensive analysis unit summarizes the total power of the initial power loads, the total load power of the power loads in the restoration set, and the total load power of the power loads in the cut-off set, calculates the ratio with the safe power supply capacity of the intelligent electric cabinet to obtain the comprehensive load rate, analyzes the comprehensive load situation of the intelligent electric cabinet after overload fault restoration, and sets constraint conditions to limit the comprehensive power load of the intelligent electric cabinet within a safe range.

[0035] Preferably, the load constraint unit sets the constraint conditions for the power loads of the intelligent electric cabinet according to the comprehensive load rate, and after the execution of the cut-off power decision, constrains the comprehensive load rate of the intelligent electric cabinet within the normal range. The specific content of the constraint conditions is as follows:

[0036] When the comprehensive load rate > 90%, it indicates that there is an overload fault in the intelligent electric cabinet. Send a first warning message to the mobile user terminal through the cloud platform for warning, and at the same time automatically execute the cut-off power decision for the second time;

[0037] When 70% ≤ comprehensive load rate ≤ 90%, it indicates that the intelligent electric cabinet is within the normal range. At this time, continuously detect the intelligent electric cabinet;

[0038] When the comprehensive load rate < 70%, it indicates that there is resource waste in the intelligent electric cabinet. Send a second warning message to the mobile user terminal through the cloud platform for warning, and at the same time automatically execute the load restoration decision for the second time.

[0039] The intelligent electric cabinet electric fault detection device includes a data acquisition device, a data processing device, a priority control and cut-off device, and an interaction device;

[0040] The data acquisition device collects power data in real time through a sensor group and the intelligent electric cabinet, and then transmits the power data to the cloud platform through a transmission device;

[0041] The data processing device preprocesses the power data in the cloud platform to obtain a dimensionless data set, and completes relevant formula calculations through a real-time calculation engine;

[0042] The priority control and cut-off device performs cut-off and restoration operations on the power loads with a lower priority limit through the intelligent circuit breakers of the intelligent electric cabinet;

[0043] The interactive device remotely connects the cloud platform with the mobile device of the client to synchronously display the display and interaction operation status, excision results, and fault information.

[0044] The present invention provides an intelligent electric cabinet electric fault detection system and device, which have the following beneficial effects:

[0045] (1) The system realizes real-time monitoring of the power load through the data acquisition module, and uses the sensor group and intelligent electric cabinet installed in the electric cabinet to collect power data in real time. Then, through data preprocessing on the cloud platform and combining with the dimensionless processing technology, the system improves the standardization and processing efficiency of the data and obtains a dimensionless data set. The anomaly detection module combines the dimensionless data set through the power fluctuation anomaly determination model set, calculates the power fluctuation anomaly value, and compares it with the anomaly fluctuation threshold F1 to quickly determine whether there is a fault in the power load and generate an anomaly fluctuation load set. This function significantly improves the efficiency and accuracy of fault detection, overcomes the problem of insufficient fault fluctuation perception ability of traditional electric cabinets, and lays a foundation for timely response to faults.

[0046] (2) When the system identifies a fault or overload situation, the system comprehensively evaluates the load status through the dynamic excision power decision module by using the real-time calculated total load rate and safe power supply capacity. When it is detected that the total load rate > 90%, the system triggers the excision power decision, and based on the non-critical loads in the load priority set, starts to gradually excise from the loads with lower priorities until the total load rate ≤ 90%. Compared with the traditional static excision method, this module can automatically adjust the power supply status of non-critical loads, release the limited power resources of the system while ensuring the continuous operation of critical loads, and improve the flexibility and rationality of power distribution. This dynamic excision mechanism effectively avoids the impact on the continuity of core services caused by the power outage of critical loads.

[0047] (3) After the execution of the excision power decision is completed and the overload fault is detected and relieved, the fault recovery and comprehensive analysis module starts the load recovery mechanism. The recovery power is calculated through the recovery strategy formula, and non-critical loads are gradually recovered from the excision set cut. During the recovery process, the system gives priority to loads with higher priorities and real-time monitors the comprehensive load rate after recovery. When 70% ≤ comprehensive load rate ≤ 90%, the system operates stably; if the comprehensive load rate > 90%, the system automatically executes the secondary excision power decision; if the comprehensive load rate < 70%, the secondary load recovery decision is triggered to avoid resource waste. This comprehensive analysis and dynamic adjustment mechanism not only ensures the safety of the system during the recovery stage, but also effectively improves the utilization rate of power resources and reduces the operation risks caused by overload or resource waste. Description of the Drawings

[0048] Figure 1Schematic diagram of the power failure detection system for the intelligent electric cabinet of the present invention;

[0049] Figure 2 Schematic connection diagram of the power failure detection device for the intelligent electric cabinet of the present invention. Specific implementation manner

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , the present invention provides a power failure detection system for an intelligent electric cabinet. To achieve the above objectives, the present invention is realized through the following technical solutions: including a data acquisition module, an anomaly detection module, a dynamic power cut decision module, and a fault recovery and comprehensive analysis module;

[0053] The data acquisition module installs a sensor group in the intelligent electric cabinet to collect power data of the power load in real time. At the same time, a cloud platform is constructed, and the power data is transmitted to the cloud platform through wireless communication connection, and the power data is preprocessed in the cloud platform to obtain a dimensionless data set;

[0054] The anomaly detection module constructs a power fluctuation anomaly determination model, calculates and outputs the power fluctuation anomaly value N, preliminarily compares and evaluates the set anomaly fluctuation threshold F1 with the power fluctuation anomaly value N to identify the power load fault situation, summarizes the faulty power loads into an abnormal fluctuation load set, calculates and outputs the load priority index Ppri for the faulty power loads, and then sorts the load priorities based on the load priority index Ppri to obtain the load priority set Low;

[0055] The dynamic power cut decision module calculates the real-time total load rate Lt of the intelligent electric cabinet, and based on the output result of the real-time total load rate Lt, determines the execution situation of the power cut decision, judges the total power cut amount Pcut, and summarizes the cut-off power loads to generate a cut set cut;

[0056] After the load cut mechanism is executed, the fault recovery and comprehensive analysis module continuously monitors the change of the power load rate in the intelligent electric cabinet, constructs a recovery strategy formula, calculates and outputs the recovery load power Prestore, and summarizes it into a recovery set. At the same time, through the load dynamic adjustment formula, the comprehensive load rate Lt of the power load after recovery newand for the comprehensive load rate Lt of the power load after recovery new Generate a constraint mechanism.

[0057] In this embodiment, the system installs a sensor group in the intelligent electric cabinet through the data acquisition module, combines the self-check function of the intelligent electric cabinet, collects power data in real time, and transmits the data to the cloud platform through wireless communication for preprocessing to generate a dimensionless data set, providing high-quality data support for subsequent analysis. The anomaly detection module, based on the power fluctuation anomaly determination model, quickly identifies the power fluctuation anomaly value N, judges and summarizes the abnormal load set by comparing with the abnormal fluctuation threshold F1, and calculates the load priority index Ppri at the same time, providing a basis for subsequent load priority sorting and dynamic removal. The dynamic removal power decision module triggers the removal power decision in case of overload by calculating the real-time total load rate Lt, preferentially removing non-critical loads with low priority to ensure the power supply stability of critical loads. After the overload is lifted, the fault recovery and comprehensive analysis module gradually restores the removed loads through the recovery strategy formula, and calculates the comprehensive load rate Lt after recovery in real time new and controls the system load within the optimal range of 70%-90% through the constraint mechanism. Through the specific implementation of the above modules, the system not only realizes the accurate detection, rapid response and dynamic adjustment of power faults, but also significantly improves the utilization rate of power supply resources and the safety of the power system. It can efficiently identify power load fluctuation anomalies, optimize power distribution strategies, and avoid power interruption of critical loads; through dynamic removal and gradual recovery mechanisms, it reduces the risk of secondary overload and resource waste; combined with the cloud platform and real-time monitoring technology, it realizes the intelligent management of the entire life cycle of power loads.

[0058] Embodiment 2

[0059] Specifically: The data acquisition module includes a load acquisition unit, a data transmission unit, and a data processing unit;

[0060] The load acquisition unit, by installing a sensor group in the intelligent electric cabinet and combining the intelligent electric cabinet, collects the power data of the power load connected to the intelligent electric cabinet in real time, and the power load represents the power equipment connected to the intelligent electric cabinet;

[0061] The sensor group includes a temperature sensor and a humidity sensor;

[0062] The power data includes the real-time current It, the real-time voltage Vt, the power load power P, the total input power Ptotal of the electric cabinet, the temperature difference Te inside and outside the intelligent electric cabinet, the environmental humidity Hc, the cumulative fault time tfault of the power load, and the total operation time tlife of the power load equipment;

[0063] The real-time current It, real-time voltage Vt, power of the electrical load P, total input power of the electrical cabinet Ptotal, cumulative fault time tfault of the electrical load, and total operating time tlife of the electrical load equipment are obtained in real time through the self-check function of the intelligent electrical cabinet;

[0064] The temperature difference Te inside and outside the intelligent electrical cabinet and the ambient humidity Hc are collected through temperature sensors and humidity sensors;

[0065] The data transmission unit integrates the sensor group with the intelligent electrical cabinet, and through the wireless communication module of the intelligent electrical cabinet, uses the 5G communication network to wirelessly communicate and connect the cloud platform with the intelligent electrical cabinet, and transmits the power data to the cloud platform;

[0066] The data processing unit receives the power data in real time in the cloud platform and preprocesses the power data. The preprocessing includes data cleaning and denoising. After preprocessing, the cumulative fault time tfault of the electrical load and the total operating time tlife of the electrical load equipment in the power data are combined and calculated to obtain the equipment maintenance reliability index Rm. The specific algorithm formula is: The dimensionless processing is performed on the equipment maintenance reliability index Rm and the remaining power data to obtain a dimensionless data set;

[0067] The dimensionless data set includes the real-time current It, real-time voltage Vt, power of the electrical load P, total input power of the electrical cabinet Ptotal, temperature difference Te inside and outside the intelligent electrical cabinet, ambient humidity Hc, and equipment maintenance reliability index Rm.

[0068] In this embodiment, the data acquisition module of the system realizes the accurate acquisition, efficient transmission, and intelligent processing of the power load data in the intelligent electrical cabinet through the collaborative work of the load acquisition unit, data transmission unit, and data processing unit. The load acquisition unit installs a sensor group in the intelligent electrical cabinet and combines the self-check function of the intelligent electrical cabinet to collect power data in real time. These data are collected in real time through the self-check function of the intelligent electrical cabinet and sensor devices, covering the key parameters of the operating state of the power equipment and environmental conditions. The data transmission unit uses the 5G wireless communication network to transmit the power data collected in real time to the cloud platform to realize remote sharing and centralized management of the data. The data processing unit receives the transmitted power data in real time in the cloud platform and performs preprocessing, including data cleaning and denoising, removing abnormal data and interference signals, and performing dimensionless processing on all data to generate a dimensionless data set, providing standardized data support for subsequent analysis.

[0069] Embodiment 3

[0070] Specifically: The anomaly detection module includes a total load rate anomaly analysis unit and a load priority dynamic allocation unit;

[0071] The anomaly analysis unit includes a fluctuation anomaly analysis unit and a fluctuation anomaly evaluation unit;

[0072] The fluctuation anomaly analysis unit constructs a power fluctuation anomaly determination model, extracts the real-time current It and real-time voltage Vt from the dimensionless dataset, inputs them into the power fluctuation anomaly determination model, and calculates and outputs the power fluctuation anomaly value N;

[0073] The power fluctuation anomaly value N is calculated and output through the following power fluctuation anomaly determination model;

[0074]

[0075] In the formula, N i represents the power fluctuation anomaly value of the i-th power load, Iavg represents the average value of normal current operation, and Vavg represents the average value of normal current operation;

[0076] The fluctuation anomaly evaluation unit sets the anomaly fluctuation threshold F1 based on the normal standard of the power load, preliminarily compares and evaluates the anomaly fluctuation threshold F1 with the power fluctuation anomaly value N, analyzes and identifies the fluctuation situation of the power load, summarizes all power loads with abnormal fluctuations, and obtains the abnormal fluctuation load set. The specific evaluation content is as follows;

[0077] When the power fluctuation anomaly value N > the anomaly fluctuation threshold F1, it indicates that there is an anomaly in the current power load fluctuation. At this time, the current power load is assigned to the abnormal fluctuation load set, and the priority adjustment mechanism is triggered;

[0078] When the power fluctuation anomaly value N ≤ the anomaly fluctuation threshold F1, it indicates that there is an anomaly in the current power load fluctuation. At this time, continuous detection is carried out.

[0079] The load priority dynamic allocation unit automatically divides the key power loads and non-power loads in the abnormal fluctuation load set obtained from the preliminary comparison and evaluation, and eliminates the key power loads to obtain the non-critical abnormal fluctuation set Yc;

[0080] The key power load refers to the power load that is directly related to the system security and the continuity of the core business;

[0081] The non-critical power load refers to the power load that will not affect the system security and the continuity of the core business after the power supply is cut off;

[0082] In the dimensionless data set of power loads in the non-critical abnormal fluctuation set Yc, extract the power load P and the equipment maintenance reliability index Rm in the abnormal fluctuation load set. For the non-critical power loads in the abnormal fluctuation load set, calculate and output the load priority index Ppri. Based on the output results of the load priority index Ppri, perform a load priority ranking from low to high. Then, summarize the sorted power loads to obtain the load priority set Low;

[0083] The load priority index Ppri is calculated and output through the following algorithm formula;

[0084]

[0085] In the formula, Ppri i represents the load priority index of the i-th power load in the abnormal fluctuation load set, where i ∈ Low indicates that the power load belongs to the non-critical abnormal fluctuation set Yc, and Rm i represents the equipment maintenance reliability index of the i-th power load in the abnormal fluctuation load set, and P i represents the power load of the i-th power load in the abnormal fluctuation load set, represents the load power ratio. α and β respectively represent the preset weight values of the power load P and the load power ratio, and α + β = 1. Their specific values are set by the user. Ptotal represents the total power of the power load.

[0086] In this embodiment, the system realizes the real-time detection, fault judgment and dynamic priority adjustment of the abnormal state of the power load in the intelligent electric cabinet through the collaborative operation of the total load rate abnormal analysis unit and the load priority dynamic allocation unit in the abnormal detection module. The fluctuation abnormal analysis unit in the total load rate abnormal analysis unit extracts the real-time current It and the real-time voltage Vt from the dimensionless dataset, and calculates and outputs the power fluctuation abnormal value N by using the power fluctuation abnormal judgment model. Subsequently, the fluctuation abnormal evaluation unit compares and analyzes the power fluctuation abnormal value N with the abnormal fluctuation threshold F1, quickly identifies the abnormal fluctuation load, and summarizes the results into the abnormal fluctuation load set. Through this mechanism, the system can accurately identify the abnormal state in the power load. The load priority dynamic allocation unit subdivides the loads in the abnormal fluctuation load set, eliminates the key loads that have a direct impact on the system security or the continuity of the core business, and only performs dynamic priority allocation for the non-critical loads. Based on the power load P and the equipment maintenance reliability index Rm, the priority index Ppri of the non-critical load is calculated, and a priority set Low sorted from low to high is generated. This set provides a clear decision basis for subsequent power cut-off and load recovery. Through the implementation of the abnormal detection module, the system realizes the rapid identification and dynamic priority adjustment of power load faults, significantly improving the efficiency and accuracy of fault detection. Its beneficial effects include: accurately identifying the power fluctuation abnormal load, avoiding misjudgment or missed judgment problems in traditional detection technologies; effectively protecting the key loads to ensure the system security and the continuity of the core business; optimizing the management of non-critical loads through priority allocation, providing a scientific basis for subsequent dynamic cut-off and gradual recovery.

[0087] Embodiment 4

[0088] Specifically, the dynamic power cut-off decision module includes a dynamic load rate analysis unit and a cut-off power decision unit;

[0089] The dynamic load rate analysis unit combines and calculates the internal and external temperature difference Te and the environmental humidity Hc of the intelligent electric cabinet by extracting them from the dimensionless dataset to obtain the safe power supply capacity Psafe of the intelligent electric cabinet;

[0090] The safe power supply capacity Psafe of the intelligent electric cabinet is calculated and output through the following algorithm formula;

[0091] Psafe = Pmax·(1 - Q T ·Te - Q H ·Hc);

[0092] In the formula, Pmax represents the upper limit value of the power supply capacity of the intelligent electric cabinet, Q T represents the influence coefficient of the internal and external temperature difference of the intelligent electric cabinet, Q H represents the influence coefficient of the environmental humidity;

[0093] By summarizing the power load powers P of all power loads in the current intelligent electric cabinet and calculating the ratio with the safe power supply capacity Psafe of the intelligent electric cabinet, the real-time total load rate Lt is obtained, and based on the output result of the real-time total load rate Lt, a comparison and evaluation are carried out to determine the situation of the total load rate, the operation status of the electric cabinet is monitored in real time, and a trigger mechanism for generating a cut-off power decision is generated based on the comparison and evaluation result. The specific evaluation content is as follows:

[0094] When the real-time total load rate Lt > 90%, it means that there is a risk of overload failure in the total load rate of the intelligent electric cabinet. At this time, the cut-off power decision is triggered;

[0095] When the real-time total load rate Lt ≤ 90%, it means that the total load rate of the intelligent electric cabinet is normal, and there is no need to cut off at this time. The real-time total load rate Lt is calculated by the following algorithm formula;

[0096]

[0097] In the formula, n represents the total number of power loads in the intelligent electric cabinet.

[0098] When the cut-off power decision unit determines that there is an overload risk in the total load rate of the intelligent electric cabinet, it executes the cut-off power decision. The cut-off power decision calculates the total cut-off power Pcut based on the current real-time total load rate Lt, and then according to the total cut-off power Pcut, the non-critical power loads in the load priority set Low are automatically cut off from low to high until the total load rate of the intelligent electric cabinet is normal, the cut-off is stopped, and the cut-off power loads are summarized to generate a cut-off set cut;

[0099] The total cut-off power Pcut is calculated by the following algorithm formula;

[0100]

[0101] In the formula, P j represents the power load power of the jth power load in the load priority set Low, subjectto represents the constraint condition, here it means to stop cutting off when Lt ≤ 90%, and j ∈ Low(min(Ppri i’ )) means to cut off the power load with the lowest priority in the load priority set Low.

[0102] In this embodiment, the dynamic power removal decision module of the system realizes the automatic load adjustment and power removal optimization of the intelligent cabinet under the risk of overload through the linkage work of the dynamic load rate analysis unit and the power removal decision unit. The dynamic load rate analysis unit extracts the temperature difference Te inside and outside the intelligent cabinet and the ambient humidity Hc in the dimensionless data set, and obtains the safe power supply capacity Psafe of the intelligent cabinet by combining calculation. The safe power supply capacity Psafe takes into account the influence of environmental factors through the formula and dynamically adjusts the upper limit of power supply. Subsequently, the total load rate Lt is calculated in real time by calculating the ratio of the total power P of all power loads to the safe power supply capacity Psafe. When Lt>90% is detected, the system determines that there is an overload risk and triggers the power removal decision unit. The power removal decision unit cuts off non-critical loads from the load priority set Low in order from low to high priority according to the calculated total power removal Pcut until the total load rate Lt≤90%. The removed load power is summarized to form a removal set cut, which provides a basis for subsequent recovery decisions. Through the implementation of the dynamic power removal decision module, the intelligent power cabinet can respond quickly and accurately when facing overload risks, avoiding system failure or damage due to overload. Its beneficial effects include: dynamically adjusting the power supply capacity to improve the adaptability of the power cabinet under different environmental conditions; by cutting off non-critical loads, ensuring the continuous power supply of critical loads, ensuring system safety and continuity of core business; the automated power removal mechanism reduces manual intervention and improves adjustment efficiency and accuracy.

[0103] Example 5

[0104] Specifically: the fault recovery and comprehensive analysis module includes a fault recovery unit, a power comprehensive analysis unit and a load constraint unit;

[0105] After the fault recovery unit has completed the execution of the power cut-off decision, it continues to collect the power load power P and performs a secondary calculation of the real-time total load rate Lt;

[0106] Then detect the overload fault removal of the smart cabinet after the power removal decision is executed. If the overload fault is detected to be removed, execute the load recovery decision. The load recovery decision constructs a recovery strategy formula to calculate and output the restored load power Prestore, selects the power load with the smallest priority from the cut-off set cut, and gradually restores the power supply. The restored power loads are summarized to obtain the recovery set Pre;

[0107] The restored load power Prestore is calculated and output by the following recovery strategy formula;

[0108] Prestore = min{P k Lt≤80%, k∈cut(min(Ppri i))};

[0109] Wherein, min represents the lower limit value, cut represents the cut set, and k ∈ cut(min(Ppri i )) indicates that the restored power load is the one with the lowest priority in the cut set Cut, and P k represents the power load power of the kth power load in the cut set cut.

[0110] The power comprehensive analysis unit sums up the total power of the initial power load, the total load power of the power loads in the restoration set Pre, and the total load power of the power loads in the cut set cut, and then calculates the ratio with the safe power supply capacity Psafe of the intelligent electric cabinet to obtain the comprehensive load rate Lt new , analyzes the comprehensive load condition of the intelligent electric cabinet after overload fault recovery, and sets constraint conditions to limit the comprehensive power load of the intelligent electric cabinet within a safe range;

[0111] The comprehensive load rate Lt new is calculated and output through the following algorithm formula;

[0112]

[0113] Wherein, P p represents the power load power of the power loads in the restoration set, and ∑ p∈pre P p represents the total load power of the power loads in the restoration set Pre, and ∑ k∈cut P k represents the total load power of the power loads in the cut set cut, represents the total power of the initial power load.

[0114] The load constraint unit sets the constraint conditions for the power load of the intelligent electric cabinet according to the comprehensive load rate Lt new , and after the constraint cut power decision is executed, the comprehensive load rate Lt of the intelligent electric cabinet new is constrained within the normal range to avoid secondary overload and resource waste. The specific content of the constraint conditions is as follows;

[0115] When the comprehensive load rate Lt new > 90%, it indicates that there is an overload fault in the intelligent electric cabinet. A first warning message is sent to the mobile user terminal through the cloud platform for warning, and at the same time, the cut power decision is automatically executed again;

[0116] When 70% ≤ the comprehensive load rate Lt new ≤ 90%, it indicates that the intelligent electric cabinet is within the normal range. At this time, the intelligent electric cabinet is continuously detected;

[0117] When the comprehensive load rate Ltnew When it is less than 70%, it indicates that there is resource waste in the intelligent electric cabinet. A second warning message is sent to the mobile user terminal through the cloud platform for warning, and at the same time, the load recovery decision is automatically executed again.

[0118] In this embodiment, through the coordinated operation of the fault recovery unit, the power comprehensive analysis unit, and the load constraint unit, the fault recovery and comprehensive analysis module comprehensively realizes the dynamic management and intelligent analysis of the intelligent electric cabinet from fault detection to power restoration. After the power cut decision execution is completed, the fault recovery unit continuously collects the power load P and performs secondary calculation of the real-time total load rate Lt. When it is detected that the overload fault is lifted, the module triggers the load recovery decision, calculates the restored load power Prestore according to the recovery strategy formula, preferentially restores the low-priority loads from the cut set cut, and gradually summarizes them into the restoration set Pre. The power comprehensive analysis unit comprehensively analyzes the initial load power, the restoration set Pre, and the cut set cut, and dynamically calculates the comprehensive load rate Ltnew in combination with the safe power supply capacity Psafe to evaluate the overall power supply state after the overload fault is lifted. The load constraint unit sets dynamic constraint conditions based on the comprehensive load rate Ltnew: when Ltnew > 90%, a secondary cut decision is triggered to avoid overload; when 70% ≤ Ltnew ≤ 90%, the system runs stably; when Ltnew < 70%, a secondary restoration decision is triggered to avoid resource waste, and a warning message is sent to the mobile user terminal through the cloud platform to achieve remote monitoring and management. Through the specific implementation of the fault recovery and comprehensive analysis module, the intelligent electric cabinet can dynamically adjust the load configuration after fault recovery, ensuring the safety and efficiency of the system operation. Its beneficial effects include: optimizing the recovery process of non-critical loads through the step-by-step recovery mechanism to avoid the risk of secondary overload caused by too fast recovery; effectively preventing resource waste through the dynamic constraint of the comprehensive load rate and improving the energy utilization efficiency; enhancing the intelligent management level of the power system by combining remote monitoring and automatic warning.

[0119] Embodiment 6

[0120] Please refer to Figure 1 and Figure 2 , the electric fault detection device of the intelligent electric cabinet, including a data acquisition device, a data processing device, a priority control and cut-off device, and an interaction device;

[0121] The data acquisition device collects power data in real time through the sensor group and the intelligent electric cabinet, and then transmits the power data to the cloud platform through the transmission device;

[0122] The data processing device preprocesses the power data in the cloud platform to obtain a dimensionless data set, and completes the relevant formula calculations through the real-time calculation engine;

[0123] The priority control and cutting device performs cutting and restoration operations on the power load below the priority lower limit through the intelligent circuit breaker of the intelligent electrical cabinet;

[0124] The interaction device remotely connects the cloud platform with the mobile device of the client to synchronously display the operation status, cutting results, and fault information.

[0125] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention.

Claims

1. Intelligent electric cabinet electric fault detection system, characterized in that: It includes data acquisition module, anomaly detection module, dynamic power removal decision module and fault recovery and comprehensive analysis module; The data acquisition module collects power data of the power load in real time by installing a sensor group in the intelligent power cabinet, and at the same time builds a cloud platform, transmits the power data to the cloud platform through wireless communication connection, and pre-processes the power data in the cloud platform to obtain a dimensionless data set; The abnormality detection module constructs an abnormal power fluctuation determination model, calculates the output power fluctuation abnormal value, sets an abnormal fluctuation threshold and performs preliminary comparative evaluation with the power fluctuation abnormal value, identifies the power load fault situation, and summarizes the faulty power loads into an abnormal fluctuation load set, calculates the output load priority index of the faulty power load, and then performs load priority sorting based on the load priority index to obtain a load priority set; The dynamic power removal decision module calculates the real-time total load rate of the intelligent power cabinet, and determines the execution of the power removal decision according to the output result of the real-time total load rate, determines the total amount of power removal, and summarizes the cut-off power load to generate a removal set; The fault recovery and comprehensive analysis module continuously monitors the change of the power load rate in the intelligent electric cabinet after the load shedding mechanism is executed, constructs a recovery strategy formula, calculates and outputs the restored load power, and summarizes it into a recovery set. At the same time, the comprehensive load rate of the power load after recovery is adjusted through the load dynamic adjustment formula, and generates a constraint mechanism for the comprehensive load rate of the power load after recovery; The fault recovery and comprehensive analysis module includes a fault recovery unit, a power comprehensive analysis unit and a load constraint unit; After the fault recovery unit has completed the execution of the power cut-off decision, it continuously collects the power load power and performs a secondary calculation of the real-time total load rate; Then detect the overload fault removal status of the intelligent power cabinet after the power removal decision is executed. If the overload fault is detected to be removed, execute the load recovery decision. The load recovery decision calculates and outputs the restored load power by constructing a recovery strategy formula, selects the power load with the lowest priority from the removal set to gradually restore power supply, and summarizes the restored power loads to obtain a recovery set; The power comprehensive analysis unit summarizes the total power of the initial power load, the total load power of the power loads in the recovery set, and the total load power of the power loads in the removal set, and calculates the ratio with the safe power supply capacity of the smart cabinet to obtain the comprehensive load rate, analyzes the comprehensive load of the smart cabinet after the overload fault is restored, and sets constraints to limit the comprehensive power load of the smart cabinet to a safe range; The load constraint unit sets the constraint conditions of the power load of the smart cabinet according to the comprehensive load rate, and constrains the comprehensive load rate of the smart cabinet within a normal range after the constraint power cut decision is executed. The specific contents of the constraint conditions are as follows; When the comprehensive load rate > 90%, it indicates that there is an overload fault in the intelligent electric cabinet. A first warning message is sent to the mobile user terminal through the cloud platform for warning, and at the same time, the power cut-off decision is automatically executed twice; When 70% ≤ comprehensive load rate ≤ 90%, it indicates that the intelligent electric cabinet is within the normal range, and at this time, the intelligent electric cabinet is continuously detected; When the comprehensive load rate < 70%, it indicates that there is resource waste in the intelligent electric cabinet. A second warning message is sent to the mobile user terminal through the cloud platform for warning, and at the same time, the load recovery decision is automatically executed twice.

2. The intelligent electric cabinet electric fault detection system according to claim 1, wherein: The data acquisition module includes a load acquisition unit, a data transmission unit, and a data processing unit; The load acquisition unit installs a sensor group in the intelligent electric cabinet, and combines the intelligent electric cabinet to collect the power data of the power load connected to the intelligent electric cabinet in real time. The power load represents the power equipment connected to the intelligent electric cabinet; The sensor group includes a temperature sensor and a humidity sensor; The power data includes real-time current, real-time voltage, power load power, total input power of the electric cabinet, temperature difference inside and outside the intelligent electric cabinet, ambient humidity, cumulative fault time of the power load, and total operation time of the power load equipment; The data transmission unit integrates the sensor group with the intelligent electric cabinet, and uses the 5G communication network through the wireless communication module of the intelligent electric cabinet to wirelessly communicate and connect the cloud platform with the intelligent electric cabinet, and transmit the power data to the cloud platform; The data processing unit receives the power data in real time in the cloud platform and preprocesses the power data. The preprocessing includes data cleaning and denoising processing. After preprocessing, the cumulative fault time of the power load and the total operation time of the power load equipment in the power data are combined and calculated to obtain the equipment maintenance reliability index. After dimensionless processing of the equipment maintenance reliability index and the remaining power data, a dimensionless data set is obtained; The dimensionless data set includes real-time current, real-time voltage, power load power, total input power of the electric cabinet, temperature difference inside and outside the intelligent electric cabinet, ambient humidity, and equipment maintenance reliability index.

3. The intelligent electric cabinet electric fault detection system according to claim 2, wherein: The anomaly detection module includes a total load rate anomaly analysis unit and a load priority dynamic allocation unit; The total load rate anomaly analysis unit includes a fluctuation anomaly analysis unit and a fluctuation anomaly evaluation unit; The fluctuation anomaly analysis unit constructs a power fluctuation anomaly determination model, extracts the real-time current and real-time voltage in the dimensionless data set, inputs them into the power fluctuation anomaly determination model, and calculates and outputs the power fluctuation anomaly value; The fluctuation anomaly evaluation unit is used to set an abnormal fluctuation threshold based on the normal standard of the power load. After preliminary comparison and evaluation of the abnormal fluctuation threshold and the power fluctuation anomaly value, the fluctuation situation of the power load is analyzed and judged, and all power loads with abnormal fluctuations are summarized to obtain an abnormal fluctuation load set. The specific evaluation content is as follows; When the power fluctuation anomaly value > the abnormal fluctuation threshold, it indicates that there is an abnormality in the current power load fluctuation. At this time, the current power load is assigned to the abnormal fluctuation load set, and the priority adjustment mechanism is triggered; When the power fluctuation outlier ≤ the abnormal fluctuation threshold, it indicates that there is an abnormality in the current power load fluctuation, and continuous detection is carried out at this time.

4. The intelligent electric cabinet electric fault detection system according to claim 3, characterized in that: The load priority dynamic allocation unit automatically divides the key power loads and non-key power loads in the abnormal fluctuation load set obtained through preliminary comparison and evaluation, and eliminates the key power loads; Based on the dimensionless data set of the power loads in the non-key abnormal fluctuation set, the power load power and the equipment maintenance reliability index in the abnormal fluctuation load set are extracted. For the non-key power loads in the abnormal fluctuation load set, the load priority index is calculated and output, and the load priorities are sorted from low to high based on the output result of the load priority index. The sorted power loads are summarized to obtain the load priority set.

5. The intelligent electric cabinet electric fault detection system according to claim 1, wherein: The dynamic power cut decision module includes a dynamic load rate analysis unit and a power cut decision unit; The dynamic load rate analysis unit combines and calculates the temperature difference inside and outside the intelligent electric cabinet and the environmental humidity extracted from the dimensionless data set to obtain the safe power supply capacity of the intelligent electric cabinet; By summarizing the power load powers of all power loads in the current intelligent electric cabinet and calculating the ratio with the safe power supply capacity of the intelligent electric cabinet, the real-time total load rate is obtained. Based on the output result of the real-time total load rate, a comparison and evaluation are carried out to determine the situation of the total load rate, the operation status of the electric cabinet is monitored in real time, and a trigger mechanism for the power cut decision is generated based on the comparison and evaluation result. The specific evaluation content is as follows. When the real-time total load rate > 90%, it indicates that there is an overload fault risk in the total load rate of the intelligent electric cabinet, and the power cut decision is triggered at this time; When the real-time total load rate ≤ 90%, it indicates that the total load rate of the intelligent electric cabinet is normal, and no power cut is required at this time.

6. The intelligent electric cabinet electric fault detection system according to claim 5, characterized in that: When the power cut decision unit determines that there is an overload risk in the total load rate of the intelligent electric cabinet, it executes the power cut decision. The power cut decision calculates the total power cut amount based on the current real-time total load rate, and then automatically cuts the non-key power loads in the load priority set from low to high until the total load rate of the intelligent electric cabinet is normal, stops the power cut, and summarizes the cut power loads to generate a cut set.

7. The intelligent electric cabinet electric fault detection device is applied to the intelligent electric cabinet electric fault detection system according to any one of claims 1-6, and is characterized in that: It includes a data acquisition device, a data processing device, a priority control and cut device, and an interaction device; The data acquisition device collects power data in real time through the sensor group and the intelligent electric cabinet, and then transmits the power data to the cloud platform through the transmission device; The data processing device preprocesses the power data in the cloud platform to obtain a dimensionless data set, and completes relevant formula calculations through the real-time calculation engine; The priority control and cut device performs cut and recovery operations on the power loads below the priority lower limit through the intelligent circuit breaker of the intelligent electric cabinet; The interaction device remotely connects the cloud platform with the mobile device of the client to synchronously display the operation status, cut result, and fault information.

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