Electrical equipment fault prediction system and method based on data fusion

Abstract

The present invention relates to the field of data management technology, and specifically discloses an electrical equipment fault prediction system and method based on data fusion. The system accurately captures the electromagnetic signal characteristic parameters of the electrical equipment operating environment, scientifically evaluates the electromagnetic interference index, and thus intelligently adjusts the data acquisition strategy, effectively improving the accuracy and efficiency of data acquisition. At the same time, by monitoring the operating status parameters of the electrical equipment, it can also deeply extract its characteristics and calculate the data acquisition compliance index, providing a strong basis for data preprocessing. Finally, by highly integrating the multi-dimensional operating status parameters of the electrical equipment, it can accurately identify the operating degradation factors, realize the accurate prediction of the fault level of the electrical equipment, and issue early warnings in time, which not only significantly improves the accuracy and timeliness of fault prediction, but also effectively reduces the operation and maintenance costs, enhances the stability and safety of the power system, and provides a strong technical guarantee for the intelligent management and maintenance of electrical equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an electrical equipment fault prediction system and method based on data fusion. Background Art

[0002] With the continuous expansion of the scale of power systems and the increase in equipment complexity, the safe and stable operation of power equipment has become particularly important. Therefore, the development of a more efficient and accurate fault prediction system has become a hot topic in current research. By integrating data from different sources and of different natures, more valuable information can be extracted, providing a more reliable basis for fault decision-making of electrical equipment.

[0003] For example, the invention patent with publication number CN118297579A discloses a method, device, storage medium and electronic device for predicting equipment faults, and relates to the field of fault prediction technology. The method includes: obtaining multi-source heterogeneous data collected from gas-insulated switchgear; predicting faults of the gas-insulated switchgear based on a target fault prediction model and multi-source heterogeneous data to obtain prediction results, wherein the target fault prediction model is trained based on multiple multi-source heterogeneous data samples with different data sources and different data structures, and the prediction results are used to reflect the operating status of the gas-insulated switchgear. The prediction results include normal, tip discharge, particle discharge, air gap discharge, suspended discharge, loose defect of busbar guide rod base, loose defect of disconnector base, poor contact defect of disconnector plum blossom contact, and gas leakage fault.

[0004] For example, the invention patent with announcement number CN103996077B announces an electrical equipment fault prediction method based on multidimensional time series. This method proposes a multidimensional time series data mining method for the online electrical measurement data with high density sampling in the power system. Through the historical time series training data, a time series decomposition algorithm, a characteristic event generation algorithm and a fault association relationship mining algorithm based on association rules are established. The change characteristics of other equipment associated with this fault, namely the "precursor events", are mined. This relationship forms an equipment fault prediction model, which, combined with online monitoring data, provides strong support for fault prediction and judgment of complex nonlinear electrical equipment.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: in the process of analyzing electrical equipment failures in the prior art, data collection often follows a fixed pattern, thereby introducing more data noise, making it impossible for the collected data to truly reflect the actual status of the equipment, and thus resulting in a large deviation between the prediction of electrical equipment failures and the actual failures. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an electrical equipment fault prediction system and method based on data fusion, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an electrical equipment fault prediction system based on data fusion, including: an electromagnetic interference evaluation module, which is used to obtain the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, and evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment, thereby determining whether to adjust the data acquisition strategy of the preset electrical equipment; an operating parameter monitoring module, which is used to monitor and collect the operating status parameters of the electrical equipment through the data acquisition strategy of the electrical equipment, and obtain the characteristic parameters of the operating status parameters, analyze the data acquisition compliance index of the electrical equipment, thereby determining whether to pre-process the operating status parameters of the electrical equipment; an operating parameter analysis module, which is used to determine the operating degradation factor of the electrical equipment by fusing the operating status parameters of the electrical equipment, thereby predicting the fault level of the electrical equipment and giving a prompt.

[0008] As a further solution, the determination of whether to adjust the data collection strategy of the preset electrical equipment is as follows: the electromagnetic signal interference threshold of the operating environment of the electrical equipment is extracted from the equipment database, and the electromagnetic signal interference index of the operating environment of the electrical equipment is compared with the electromagnetic signal interference threshold of the operating environment of the electrical equipment; if the electromagnetic signal interference index of the operating environment of the electrical equipment is less than or equal to the electromagnetic signal interference threshold of the operating environment of the electrical equipment, it is determined that the data collection strategy of the preset electrical equipment is not adjusted; if the electromagnetic signal interference index of the operating environment of the electrical equipment is greater than the electromagnetic signal interference threshold of the operating environment of the electrical equipment, it is determined that the data collection strategy of the preset electrical equipment is adjusted.

[0009] As a further solution, the preset data collection strategy of the electrical equipment is adjusted. The specific adjustment process is: the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is compared with the historical reference electromagnetic wave frequency to obtain a comparison result, and based on the comparison result and the electromagnetic signal interference index of the operating environment of the electrical equipment, a first optimization set of data collection strategies is matched from the equipment database, thereby updating the data collection strategy of the electrical equipment.

[0010] As a further solution, the determination of whether to preprocess the operating status parameters of the electrical equipment is as follows: extracting the data collection compliance threshold of the electrical equipment from the equipment database and comparing it with the data collection compliance index of the electrical equipment; if the data collection compliance index of the electrical equipment is greater than the data collection compliance threshold of the electrical equipment, then it is determined that the operating status parameters of the electrical equipment are not preprocessed; if the data collection compliance index of the electrical equipment is less than or equal to the data collection compliance threshold of the electrical equipment, then it is determined that the operating status parameters of the electrical equipment are preprocessed; the specific preprocessing process is as follows: according to the data collection compliance index of the electrical equipment, matching an optimized set of operating status parameters from the equipment database, thereby preprocessing the operating status parameters of the electrical equipment according to the optimized set of operating status parameters, and updating the operating status parameters of the electrical equipment.

[0011] As a further solution, the electrical equipment fault level is predicted and a prompt is given. The specific prediction and prompt process is: matching the operation degradation factor of the electrical equipment with the fault level corresponding to each operation degradation factor interval stored in the equipment database, thereby predicting the fault level of the electrical equipment; at the same time, locating the maximum value of the operation degradation index of each detection area to which the electrical equipment belongs in the operation degradation factor of the electrical equipment, marking the detection area corresponding to the maximum value of the operation degradation index as an electrical equipment fault warning area, thereby comprehensively considering the fault level of the electrical equipment and the fault warning area of ​​the electrical equipment for a warning prompt.

[0012] The second aspect of the present invention provides an electrical equipment fault prediction method based on data fusion, including: step one, obtaining the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, evaluating the electromagnetic signal interference index of the operating environment of the electrical equipment, and thereby determining whether to adjust the data acquisition strategy of the preset electrical equipment; step two, monitoring and acquiring the operating status parameters of the electrical equipment through the data acquisition strategy of the electrical equipment, and acquiring the characteristic parameters of the operating status parameters, analyzing the data acquisition compliance index of the electrical equipment, and thereby determining whether to pre-process the operating status parameters of the electrical equipment; step three, determining the operating degradation factor of the electrical equipment by fusing the operating status parameters of the electrical equipment, thereby predicting the fault level of the electrical equipment and providing a prompt.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides an electrical equipment fault prediction system and method based on data fusion. The system accurately captures the electromagnetic signal characteristic parameters of the electrical equipment operating environment and scientifically evaluates the electromagnetic interference index, thereby intelligently adjusting the data collection strategy, effectively improving the accuracy and efficiency of data collection. At the same time, by monitoring the operating status parameters of the electrical equipment, it can also deeply extract its characteristics and calculate the data collection compliance index, providing a strong basis for data preprocessing. Finally, by highly integrating the multi-dimensional operating status parameters of the electrical equipment, it can accurately identify the operating degradation factors, realize the accurate prediction of the fault level of the electrical equipment, and issue early warnings in time, which not only significantly improves the accuracy and timeliness of fault prediction, but also effectively reduces the operation and maintenance costs, enhances the stability and safety of the power system, and provides a strong technical guarantee for the intelligent management and maintenance of electrical equipment.

[0015] (2) The present invention can accurately evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment by acquiring the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment. This evaluation result first establishes an accurate qualitative and quantitative analysis model between the data detection of the electrical equipment and the equipment failure, so that the complex relationship between the equipment failure and the electromagnetic environment can be deeply understood. On this basis, the present invention further conducts an in-depth study on the interference factors that affect data acquisition, which not only improves the understanding of the operating status of the electrical equipment, but also promotes the improvement of the data feature extraction method. Through these improvement measures, the preset electrical equipment data acquisition strategy can be more effectively adjusted to adapt to different electromagnetic interference environments, thereby ensuring the accuracy and reliability of data acquisition.

[0016] (3) The present invention integrates the operating status parameters of electrical equipment to achieve accurate determination of the operating degradation factor of electrical equipment, breaking the limitation of traditional single data collection. It combines multiple detection methods and fully considers the performance of electrical equipment under various energized states. By comprehensively analyzing diverse data, it can more comprehensively capture the details of changes in the insulation status of the equipment and better reflect the changing process and trend of the insulation status of the electrical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 2 The figure is a schematic flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the first aspect of the present invention provides an electrical equipment fault prediction system based on data fusion, including: an electromagnetic interference evaluation module, an operation parameter monitoring module and an operation parameter analysis module.

[0022] The first aspect of the present invention provides an electrical equipment fault prediction system based on data fusion, which also includes an equipment database, wherein the equipment database is used to store historical reference electromagnetic wave frequencies, historically defined magnetic field intensity fluctuation values, historically defined electric field intensity fluctuation values, electromagnetic wave frequency influence coefficients, magnetic field intensity fluctuation value influence coefficients, electric field intensity fluctuation value influence coefficients, electromagnetic signal interference thresholds of the operating environment of the electrical equipment, a first optimization set of data acquisition strategies, an electromagnetic signal interference index weight factor, a reference acquisition frequency matching rate of each type of data, a reference data acquisition average interval duration of each type of data, a reference average electrical signal strength of the acquisition device corresponding to each type of data, an acquisition frequency matching rate influence value, an acquisition average interval duration influence value, an average electrical signal strength influence value, a data acquisition compliance threshold of the electrical equipment, an optimization set of operating status parameters, an operating degradation factor correction value, an operating degradation index weight coefficient, an electromagnetic signal interference index weight coefficient, a temperature growth rate influence value, a maximum ultraviolet radiation intensity influence value, a maximum ultrasonic signal intensity influence value, a fault level, a temperature degradation index weight factor, an ultraviolet radiation intensity degradation index weight factor, and an ultrasonic signal intensity degradation index weight factor.

[0023] The electromagnetic interference assessment module is connected to the operating parameter monitoring module, the operating parameter monitoring module is connected to the operating parameter analysis module, and the electromagnetic interference assessment module, the operating parameter monitoring module and the operating parameter analysis module are all connected to the equipment database.

[0024] The electromagnetic interference evaluation module is used to obtain the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment, and thereby determine whether to adjust the preset data collection strategy of the electrical equipment.

[0025] It needs to be explained that the electrical equipment in this embodiment refers to a fully insulated and fully sealed pneumatic ring main switchgear. The pneumatic ring main switchgear is one of the key equipment in the power system and is composed of various high-voltage devices such as circuit breakers, disconnectors, grounding switches, current transformers, voltage transformers and lightning arresters.

[0026] In a specific embodiment, the present invention can accurately evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment by acquiring the electromagnetic signal characteristic parameters of the environment. This evaluation result first establishes an accurate qualitative and quantitative analysis model between the data detection of the electrical equipment and the equipment failure, so that the complex relationship between the equipment failure and the electromagnetic environment can be deeply understood. On this basis, the present invention further conducts an in-depth study on the interference factors affecting data acquisition, which not only improves the understanding of the operating status of the electrical equipment, but also promotes the improvement of the data feature extraction method. Through these improvement measures, the preset electrical equipment data acquisition strategy can be more effectively adjusted to adapt to different electromagnetic interference environments, thereby ensuring the accuracy and reliability of data acquisition.

[0027] Specifically, the determination of whether to adjust the data collection strategy of the preset electrical equipment is as follows: extracting the electromagnetic signal interference threshold of the operating environment of the electrical equipment from the equipment database, and comparing the electromagnetic signal interference index of the operating environment of the electrical equipment with the electromagnetic signal interference threshold of the operating environment of the electrical equipment; if the electromagnetic signal interference index of the operating environment of the electrical equipment is less than or equal to the electromagnetic signal interference threshold of the operating environment of the electrical equipment, then it is determined that the data collection strategy of the preset electrical equipment is not adjusted; if the electromagnetic signal interference index of the operating environment of the electrical equipment is greater than the electromagnetic signal interference threshold of the operating environment of the electrical equipment, then it is determined that the data collection strategy of the preset electrical equipment is adjusted; the electromagnetic signal interference threshold of the operating environment of the electrical equipment indicates the maximum value of the reasonable range of the electromagnetic signal interference index of the operating environment of the electrical equipment.

[0028] Specifically, the preset data collection strategy of the electrical equipment is adjusted, and the specific adjustment process is: the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is compared with the historical reference electromagnetic wave frequency to obtain a comparison result, and based on the comparison result and the electromagnetic signal interference index of the operating environment of the electrical equipment, a first optimization set of data collection strategies is matched from the equipment database, thereby updating the data collection strategy of the electrical equipment; the above-mentioned comparison results include comparison result one, comparison result two and comparison result three, the comparison result one refers to that the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is greater than the historical reference electromagnetic wave frequency, and the comparison result two refers to that the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is equal to Historical reference electromagnetic wave frequency, the comparison result three refers to the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is less than the historical reference electromagnetic wave frequency, the first optimization set of the data acquisition strategy, the specific matching rules are: the first optimization set of data acquisition strategies corresponding to the comparison result one, the comparison result two and the comparison result three are stored in the equipment database, and the specific parameters in the first optimization set of the data acquisition strategy are determined by the electromagnetic signal interference index. Therefore, by obtaining the comparison result of the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period and the historical reference electromagnetic wave frequency, the first optimization set of the data acquisition strategy is matched, and the specific parameters in the first optimization set of the data acquisition strategy are determined by the electromagnetic signal interference index of the operating environment of the electrical equipment.

[0029] In an example embodiment, it is assumed that the preset data collection strategy of the electrical equipment includes: the preset collection frequencies of the collection devices corresponding to the various types of data belonging to the electrical equipment are respectively The collection sensitivity of the collection device corresponding to each type of data of electrical equipment is , a is the number of each type of data, , h is the total number of data types. Assuming that the electromagnetic signal interference index of the operating environment of the electrical equipment is M, which is greater than the electromagnetic signal interference threshold K of the operating environment of the electrical equipment, and the comparison result is comparison result 1, the first optimization set of data collection strategies matched according to the electromagnetic signal interference index M of the operating environment of the electrical equipment includes: increasing the preset collection frequency of the collection device corresponding to each type of data of the electrical equipment by times, the updated data collection strategy for electrical equipment includes: the preset collection frequencies of the collection devices corresponding to the various types of data belonging to the electrical equipment are The collection sensitivity of the collection device corresponding to each type of data of electrical equipment is .

[0030] Furthermore, the electromagnetic signal interference index of the operating environment of the electrical equipment is evaluated, and the specific evaluation process is as follows: the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment include the real-time magnetic field strength of the operating environment of the electrical equipment during the detection period, the real-time electric field strength of the operating environment of the electrical equipment during the detection period, and the electromagnetic wave frequency of the operating environment of the electrical equipment during the detection period; the above-mentioned detection period represents the time period for performing electromagnetic interference signal analysis on the operating environment of the electrical equipment, and the specific duration is determined by the electrical equipment engineer; the above-mentioned real-time magnetic field strength represents the magnetic field strength of the operating environment of the electrical equipment at any time during the detection period, which can be measured by installing a magnetic field on the electrical equipment. The above-mentioned real-time electric field strength, the electric field strength of the operating environment of the electrical equipment at any time during the detection period, can be monitored by an electric field meter installed on the electrical equipment; the above-mentioned electromagnetic wave frequency, which represents the number of oscillations of the electromagnetic wave propagating in space in the operating environment of the electrical equipment during the detection period, is usually expressed in Hertz (Hz). Electromagnetic waves are a wave phenomenon that is alternately generated by changing electric and magnetic fields and propagates into space. In the operating environment of the electrical equipment, the electromagnetic wave frequency may be affected by the electromagnetic radiation of the electrical equipment itself, the electromagnetic radiation of other surrounding electrical equipment, and environmental factors, and can be monitored by a frequency meter installed on the electrical equipment.

[0031] The real-time magnetic field strength of the operating environment of the electrical equipment during the detection period is processed by standard deviation to obtain the magnetic field strength fluctuation value of the operating environment of the electrical equipment during the detection period; the real-time electric field strength of the operating environment of the electrical equipment during the detection period is processed by standard deviation to obtain the electric field strength fluctuation value of the operating environment of the electrical equipment during the detection period; the above-mentioned magnetic field strength fluctuation value is used to quantify the degree of fluctuation of the magnetic field strength of the operating environment of the electrical equipment during the detection period; the above-mentioned electric field strength fluctuation value is used to quantify the degree of fluctuation of the electric field strength of the operating environment of the electrical equipment during the detection period.

[0032] Comprehensively evaluate the electromagnetic wave frequency of the operating environment of the electrical equipment during the detection period, the magnetic field strength fluctuation value of the operating environment of the electrical equipment during the detection period, and the electric field strength fluctuation value of the operating environment of the electrical equipment during the detection period, and obtain the electromagnetic signal interference index of the operating environment of the electrical equipment. The electromagnetic signal interference index of the operating environment of the electrical equipment is used to comprehensively quantify the degree of interference of the electromagnetic signal of the operating environment of the electrical equipment on the data collection of the electrical equipment.

[0033] The electromagnetic signal interference index of the operating environment of the electrical equipment is evaluated by the following specific method:

[0034] ;

[0035] In the formula, is the electromagnetic signal interference index of the operating environment of the electrical equipment. is the electromagnetic wave frequency of the operating environment of the electrical equipment during the detection period, is the magnetic field intensity fluctuation value of the operating environment of the electrical equipment during the detection period, It is the fluctuation value of the electric field strength of the operating environment of the electrical equipment during the detection period. is the historical reference electromagnetic wave frequency stored in the device database, It is the historical definition of magnetic field intensity fluctuation value stored in the device database. The historical defined electric field intensity fluctuation values ​​stored in the equipment database, It is the electromagnetic wave frequency influence coefficient preset in the equipment database. It is the influence coefficient of magnetic field intensity fluctuation value preset in the equipment database. It is the influence coefficient of the electric field intensity fluctuation value preset in the equipment database.

[0036] The above-mentioned historical reference electromagnetic wave frequency represents the reference value of the electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period based on the historical electromagnetic signal characteristic parameters. The above-mentioned historically defined magnetic field intensity fluctuation value represents the maximum allowable value of the magnetic field intensity fluctuation value of the operating environment of the electrical equipment within the detection period based on the historical electromagnetic signal characteristic parameters. The above-mentioned historically defined electric field intensity fluctuation value represents the maximum allowable value of the electric field intensity fluctuation value of the operating environment of the electrical equipment within the detection period based on the historical electromagnetic signal characteristic parameters.

[0037] The above-mentioned electromagnetic wave frequency influence coefficient represents the numerical value of the influence degree of the unit value of the electromagnetic wave frequency on the electromagnetic signal interference index; the above-mentioned magnetic field intensity fluctuation value influence coefficient represents the numerical value of the influence degree of the unit value of the magnetic field intensity fluctuation value on the electromagnetic signal interference index; the above-mentioned electric field intensity fluctuation value influence coefficient represents the numerical value of the influence degree of the unit value of the electric field intensity fluctuation value on the electromagnetic signal interference index. The equipment database stores the corresponding relationship between the electromagnetic wave frequency and its corresponding influence coefficient, the corresponding relationship between the magnetic field intensity fluctuation value and its corresponding influence coefficient, and the electric field intensity fluctuation value and its corresponding influence coefficient. For example, the electromagnetic wave frequency, the magnetic field intensity fluctuation value and the electric field intensity fluctuation value are input into the equipment database, and the equipment database can match the electromagnetic wave frequency influence coefficient, the magnetic field intensity fluctuation value influence coefficient and the electric field intensity fluctuation value influence coefficient, and the value range is between 0 and 1.

[0038] It needs to be explained that within the detection cycle of the operating environment of the electrical equipment, there is a close correlation between the electromagnetic wave frequency, the magnetic field intensity fluctuation value and the electric field intensity fluctuation value. These correlations not only reveal the complexity of the electromagnetic environment, but also profoundly affect the data acquisition accuracy and stability of the electrical equipment, especially for those devices that rely on electromagnetic signals for data acquisition (such as ultraviolet sensors, ultrasonic sensors, etc.). Specifically, the electromagnetic wave frequency, as the basic characteristic of the electromagnetic field, its changes are often accompanied by fluctuations in the magnetic field and electric field strengths. When the electromagnetic wave frequency deviates significantly from the corresponding reference value, for example, the electromagnetic wave frequency is significantly greater than the corresponding reference value, it will produce more intensive electromagnetic field changes in space, which directly leads to an increase in the magnetic field intensity fluctuation value and the electric field intensity fluctuation value. This change not only enhances the energy density of the electromagnetic field, but also intensifies the coupling effect of the electromagnetic field on the surrounding environment, which may interfere with the data acquisition of the electrical equipment. For equipment that relies on electromagnetic signals for data acquisition, For example, ultraviolet sensors and ultrasonic sensors often work on the principle of receiving and processing electromagnetic waves of a specific frequency. When the electromagnetic wave frequency, magnetic field intensity fluctuation value or electric field intensity fluctuation value in the environment changes, these changes may interfere with the sensor's reception and recognition of the target signal, resulting in a decrease in the accuracy of data acquisition or damage to stability. For example, a strong magnetic field or a strong electric field may interfere with the photoelectric conversion process of the ultraviolet sensor, while high-frequency electromagnetic waves may interfere with the reception and analysis of sound waves by the ultrasonic sensor. In addition, changes in these electromagnetic parameters may also interfere with other electronic components inside the electrical equipment through electromagnetic induction, electromagnetic radiation, etc., further affecting the accuracy and reliability of data acquisition. Therefore, during the detection cycle of the operating environment of the electrical equipment, the monitoring and analysis of the electromagnetic wave frequency, magnetic field intensity fluctuation value and electric field intensity fluctuation value are particularly important. They not only reveal the real-time status of the electromagnetic environment, but also provide a key reference for data acquisition of electrical equipment.

[0039] The operating parameter monitoring module is used to monitor and collect the operating status parameters of the electrical equipment through the data collection strategy of the electrical equipment, and obtain the characteristic parameters of the operating status parameters, analyze the data collection compliance index of the electrical equipment, and thereby determine whether to pre-process the operating status parameters of the electrical equipment.

[0040] Specifically, the determination of whether to preprocess the operating status parameters of the electrical equipment is as follows: extracting the data collection compliance threshold of the electrical equipment from the equipment database and comparing it with the data collection compliance index of the electrical equipment. If the data collection compliance index of the electrical equipment is greater than the data collection compliance threshold of the electrical equipment, it is determined that the operating status parameters of the electrical equipment are not preprocessed; if the data collection compliance index of the electrical equipment is less than or equal to the data collection compliance threshold of the electrical equipment, it is determined that the operating status parameters of the electrical equipment are preprocessed. The specific preprocessing process is as follows: according to the data collection compliance index of the electrical equipment, matching an optimized set of operating status parameters from the equipment database, thereby preprocessing the operating status parameters of the electrical equipment according to the optimized set of operating status parameters, and updating the operating status parameters of the electrical equipment; the data collection compliance threshold of the electrical equipment represents the minimum value of the reasonable range of the data collection compliance index of the electrical equipment; the specific matching process of the optimized set of operating status parameters is as follows: storing the optimized set of operating status parameters corresponding to each data collection compliance index interval in the equipment database, querying the data collection compliance index interval in the equipment database to which the data collection compliance index of the electrical equipment belongs, and the optimized set of operating status parameters corresponding to the data collection compliance index interval is the optimized set of operating status parameters matched by the data collection compliance index of the electrical equipment.

[0041] In an example embodiment, assuming that the data collection compliance index of the electrical device is P, and the data collection compliance threshold of the electrical device is H, the matched operating state parameter optimization set includes: using a filtering algorithm to denoise the operating state parameters of the electrical device, wherein the filtering window size is , the number of iterations is Second-rate.

[0042] Furthermore, the data collection compliance index of the electrical equipment is analyzed, and the specific analysis process is as follows:

[0043] The characteristic parameters of the operating status parameters include the number of data collected during the operating cycle of each type of electrical equipment, the average interval time of data collection during the operating cycle of each type of electrical equipment, and the average electrical signal strength of the collection device corresponding to each type of data of the electrical equipment during the operating cycle; the above-mentioned operating cycle represents the time period for monitoring the operating status of the electrical equipment, and the specific duration is determined by the electrical equipment engineer; the above-mentioned number of data collected represents the total number of data collected during the operating cycle of each type of electrical equipment, which can be extracted from the data collection log in the collection device corresponding to each type of data; the above-mentioned average interval time of data collection represents the average value of the time interval between two adjacent data collections in each type of data during the operating cycle, and the time interval between two adjacent data collections in each type of data can be extracted from the data collection log in the collection device corresponding to each type of data, and the average value processing is performed to obtain the average interval time of data collection of each type of data belonging to the electrical equipment during the operating cycle; the above-mentioned average electrical signal strength represents the average strength of the electrical signal received by the collection device corresponding to each type of data during the operating cycle, which can be measured by the built-in signal strength sensor of the collection device.

[0044] The above-mentioned data include temperature data, ultraviolet radiation intensity data and ultrasonic signal intensity data.

[0045] The number of data collected during the operation cycle of the electrical equipment is ratioed with the duration corresponding to the operation cycle to obtain the collection frequency of the data collected during the operation cycle of the electrical equipment, which means the number of data collected during the operation cycle.

[0046] The preset collection frequency of the collection device corresponding to each type of data belonging to the electrical equipment during the operation cycle is obtained, and the ratio processing is performed with the collection frequency of each type of data belonging to the electrical equipment during the operation cycle to obtain the collection frequency matching rate of each type of data belonging to the electrical equipment during the operation cycle; the preset collection frequency of the collection device corresponding to each type of data belonging to the above-mentioned electrical equipment during the operation cycle can be obtained from the data collection strategy of the electrical equipment, which indicates the number of data collection times pre-set by the collection device for each type of data of the electrical equipment within the unit time of the operation cycle; the above-mentioned collection frequency matching rate indicates the degree of proximity between the preset collection frequency of the collection device for each type of data of the electrical equipment and the actual collection frequency, and the actual collection frequency is the collection frequency of each type of data belonging to the electrical equipment during the operation cycle.

[0047] The data collection compliance index of the electrical equipment is obtained by comprehensively analyzing the electromagnetic signal interference index of the operating environment of the electrical equipment, the collection frequency matching rate of various types of data belonging to the electrical equipment during the operating cycle, the average interval duration of data collection of various types of data belonging to the electrical equipment during the operating cycle, and the average electrical signal strength of the collection devices corresponding to various types of data belonging to the electrical equipment during the operating cycle. The data collection compliance index of the electrical equipment is used to comprehensively quantify the compliance degree of the collection of operating status parameters of the electrical equipment.

[0048] The data collection compliance index of the electrical equipment is analyzed by the following specific method:

[0049] ;

[0050] In the formula, Data collection compliance index for electrical equipment, is the electromagnetic signal interference index of the operating environment of the electrical equipment. is the electromagnetic signal interference index weight factor preset in the equipment database, a is the number of each type of data, , h is the total number of data types, is the collection frequency matching rate of the Class A data of the electrical equipment during the operation cycle, The average interval duration of data collection for the Class A data of electrical equipment during the operation cycle. is the average electrical signal strength of the collection device corresponding to the Class A data of the electrical equipment during the operation cycle, is the reference acquisition frequency matching rate of the type a data preset in the equipment database, The average interval length of reference data collection for category a data preset in the equipment database, is the reference average electrical signal strength of the acquisition device corresponding to the a-th type of data preset in the equipment database, It is the acquisition frequency matching rate impact value preset in the device database. It is the impact value of the average interval of data collection preset in the equipment database. It is the average electrical signal strength impact value preset in the device database.

[0051] The reference acquisition frequency matching rate of the above-mentioned Class a data represents the reference value of the acquisition frequency matching rate of the Class a data of the electrical equipment during its operating cycle; the reference data acquisition average interval duration of the above-mentioned Class a data represents the reference value of the data acquisition average interval duration of the Class a data of the electrical equipment during its operating cycle; the reference average electrical signal strength of the acquisition device corresponding to the above-mentioned Class a data represents the reference value of the average electrical signal strength of the acquisition device corresponding to the Class a data of the electrical equipment during its operating cycle.

[0052] The above-mentioned electromagnetic signal interference index weight factor indicates the proportion of the electromagnetic signal interference index to the data collection compliance index; the above-mentioned acquisition frequency matching rate influence value indicates the value of the influence degree of the acquisition frequency matching rate unit value on the data collection compliance index; the above-mentioned data collection average interval time influence value indicates the value of the influence degree of the data collection average interval time unit value on the data collection compliance index; the above-mentioned average electric signal strength influence value indicates the value of the influence degree of the average electric signal strength unit value on the data collection compliance index; the equipment database stores the correspondence between the electromagnetic signal interference index and its corresponding weight factor, the correspondence between the acquisition frequency matching rate and its corresponding influence value, the correspondence between the data collection average interval time and its corresponding influence value, and the correspondence between the average electric signal strength and its corresponding influence value. For example, the electromagnetic signal interference index, the acquisition frequency matching rate, the average data collection interval time and the average electric signal strength are input into the equipment database, and the equipment database can match the electromagnetic signal interference index weight factor, the acquisition frequency matching rate influence value, the data collection average interval time influence value and the average electric signal strength influence value, and the value range is between 0 and 1.

[0053] It should be explained that the higher the electromagnetic signal interference index of the operating environment, the more complex the electromagnetic environment in which the electrical equipment is located. This may cause the acquisition device to be subject to more interference when collecting data, thereby affecting the accuracy and integrity of the data. This interference may cause the actual acquisition frequency to deviate from the preset acquisition frequency, reduce the acquisition frequency matching rate, and make the acquisition frequency matching rate significantly deviate from the corresponding reference value. At the same time, in order to cope with the interference, the acquisition device may need to increase the number of data acquisition attempts or extend the acquisition time, increase the average interval time of data acquisition, and thus make the average interval time of data acquisition significantly deviate from the corresponding reference value. On the other hand, the average electrical signal strength parameter of the acquisition device reflects the strength of its output signal. This parameter directly affects the quality and efficiency of data acquisition. When the average electrical signal strength deviates significantly from the corresponding reference value, it may be difficult to accurately capture various types of data of the electrical equipment, which will also lead to a decrease in the acquisition frequency matching rate and may further extend the average interval time of data acquisition, thereby further aggravating the degree of deviation of the acquisition frequency matching rate from the corresponding reference value and the degree of deviation of the average interval time of data acquisition from the corresponding reference value. Ultimately, the changes in these parameters will have a comprehensive effect on the data acquisition compliance index of the electrical equipment, making the collected data unable to meet the established data acquisition compliance threshold requirements.

[0054] The operation parameter analysis module is used to determine the operation degradation factor of the electrical equipment by integrating the operation status parameters of the electrical equipment, thereby predicting the fault level of the electrical equipment and providing prompts.

[0055] The above-mentioned integration of the operating status parameters of the electrical equipment refers to conducting a comprehensive data analysis on the operating status parameters of the electrical equipment and comprehensively deriving the operating degradation factor of the electrical equipment.

[0056] In a specific embodiment, the present invention realizes accurate determination of the operating degradation factor of electrical equipment by integrating the operating status parameters of the electrical equipment, breaking the limitation of traditional single data collection, combining multiple detection methods, and fully considering the performance of electrical equipment under multiple energized states. By comprehensively analyzing diverse data, it is possible to more comprehensively capture the details of changes in the insulation status of the equipment and better reflect the changing process and trend of the insulation status of the electrical equipment.

[0057] Specifically, the electrical equipment fault level is predicted and prompted. The specific prediction and prompting process is: matching the operating degradation factor of the electrical equipment with the fault level corresponding to each operating degradation factor interval stored in the equipment database, thereby predicting the fault level of the electrical equipment; at the same time, locating the maximum value of the operating degradation index of each detection area to which the electrical equipment belongs in the operating degradation factor of the electrical equipment, marking the detection area corresponding to the maximum value of the operating degradation index as the electrical equipment fault warning area, thereby comprehensively considering the fault level of the electrical equipment and the electrical equipment fault warning area for warning prompts; the fault level of the above-mentioned electrical equipment, the specific matching prediction process is: querying the operating degradation factor interval in the equipment database to which the operating degradation factor of the electrical equipment belongs, then the fault level corresponding to the interval is the fault level of the electrical equipment. In an example embodiment, assuming that the fault level of the electrical equipment is level three, assuming that the operating degradation factor of the electrical equipment is ,Right now Therefore, the maximum value of the operation degradation index of each detection area of ​​the electrical equipment is the operation degradation index of the fourth detection area of ​​the electrical equipment. The fourth detection area is marked as the electrical equipment fault warning area. The specific warning prompt is: Emergency Notice! After analysis, the insulation performance of the current electrical equipment has a third-level fault risk. It is particularly noted that the fourth detection area has become a key area for fault warning. It is recommended to immediately conduct detailed inspection and maintenance of the insulation performance of this area and adjacent areas.

[0058] Specifically, the operation degradation factor of the electrical equipment is determined by:

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] In the formula, is the operational degradation factor of the electrical equipment, is the operational degradation index of the dth detection area of ​​the electrical equipment, is the weight coefficient of the operation degradation index preset in the equipment database, is the electromagnetic signal interference index of the operating environment of the electrical equipment. is the electromagnetic signal interference index weight coefficient preset in the equipment database, is the temperature growth rate of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the maximum ultraviolet radiation intensity of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the maximum ultrasonic signal strength of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the temperature growth rate of the dth detection area of ​​the electrical equipment during the operation cycle, is the maximum ultraviolet radiation intensity of the dth detection area of ​​the electrical equipment during the operation cycle, is the maximum ultrasonic signal strength of the dth detection area of ​​the electrical equipment during the operation cycle, It is the temperature growth rate influence value preset in the equipment database. It is the maximum UV radiation intensity impact value preset in the device database. is the maximum ultrasonic signal strength impact value preset in the equipment database, d is the number of each detection area, , g is the total number of detection areas, is the correction value of the operation degradation factor, Data collection compliance index for electrical equipment, Data collection compliance thresholds for electrical equipment, is the temperature degradation index of the dth detection area of ​​the electrical equipment, is the ultraviolet radiation intensity degradation index of the dth detection area of ​​the electrical equipment, is the ultrasonic signal strength degradation index of the dth detection area of ​​the electrical equipment, is the temperature degradation index weight factor preset in the equipment database, is the ultraviolet radiation intensity degradation index weight factor preset in the equipment database, is the ultrasonic signal strength degradation index weight factor preset in the equipment database, Indicates the maximum value of the operating degradation index of each detection area to which the electrical equipment belongs.

[0065] The operation degradation index of the dth detection area to which the above-mentioned electrical equipment belongs is used to quantify the degradation trend and degree of the insulation performance of the dth detection area to which the electrical equipment belongs; the temperature degradation index of the dth detection area to which the above-mentioned electrical equipment belongs is used to quantify the degradation trend and degree of the temperature parameter of the dth detection area to which the electrical equipment belongs; the ultraviolet radiation intensity degradation index of the dth detection area to which the above-mentioned electrical equipment belongs is used to quantify the degradation trend and degree of the ultraviolet radiation intensity parameter of the dth detection area to which the electrical equipment belongs; the ultrasonic signal intensity degradation index of the dth detection area to which the above-mentioned electrical equipment belongs is used to quantify the degradation trend and degree of the ultrasonic signal intensity parameter of the dth detection area to which the electrical equipment belongs.

[0066] The above-mentioned temperature degradation index weight factor indicates the proportion of the temperature degradation index to the operation degradation index; the above-mentioned ultraviolet radiation intensity degradation index weight factor indicates the proportion of the ultraviolet radiation intensity degradation index to the operation degradation index; the above-mentioned ultrasonic signal intensity degradation index weight factor indicates the proportion of the ultrasonic signal intensity degradation index to the operation degradation index. The equipment database stores the correspondence between the temperature degradation index and its corresponding weight factor, the correspondence between the ultraviolet radiation intensity degradation index and its corresponding weight factor, and the correspondence between the ultrasonic signal intensity degradation index and its corresponding weight factor. For example, by inputting the temperature degradation index, the ultraviolet radiation intensity degradation index and the ultrasonic signal intensity degradation index into the equipment database, the equipment database can match the temperature degradation index weight factor, the ultraviolet radiation intensity degradation index weight factor and the ultrasonic signal intensity degradation index weight factor, and the value range is between 0 and 1.

[0067] The above-mentioned temperature growth rate influence value represents the influence degree of the temperature growth rate unit value on the temperature degradation index; the above-mentioned maximum ultraviolet radiation intensity influence value represents the influence degree of the maximum ultraviolet radiation intensity unit value on the ultraviolet radiation intensity degradation index; the above-mentioned maximum ultrasonic signal intensity influence value represents the influence degree of the maximum ultrasonic signal intensity unit value on the ultrasonic signal intensity degradation index. The equipment database stores the correspondence between the temperature growth rate and its corresponding influence value, the correspondence between the maximum ultraviolet radiation intensity and its corresponding influence value, and the correspondence between the maximum ultrasonic signal intensity and its corresponding influence value. For example, the temperature growth rate, the maximum ultraviolet radiation intensity and the maximum ultrasonic signal intensity are input into the equipment database, and the equipment database can match the temperature growth rate influence value, the maximum ultraviolet radiation intensity influence value and the maximum ultrasonic signal intensity influence value, and the value range is between 0 and 1.

[0068] The above-mentioned operation degradation index weight coefficient indicates the proportion of the operation degradation index to the operation degradation factor; the above-mentioned electromagnetic signal interference index weight coefficient indicates the proportion of the electromagnetic signal interference index to the operation degradation factor; the equipment database stores the correspondence between the operation degradation index and its corresponding weight coefficient and the correspondence between the electromagnetic signal interference index and its corresponding weight coefficient. For example, by inputting the operation degradation index and the electromagnetic signal interference index into the equipment database, the equipment database can match the operation degradation index weight coefficient and the electromagnetic signal interference index weight coefficient, and the value range is between 0 and 1.

[0069] Electromagnetic signal interference is an important factor affecting the analysis of the operation degradation factor of electrical equipment. In order to ensure the accuracy of the analysis, it is necessary to remove the influence of the electromagnetic signal interference index when analyzing the operation degradation factor of electrical equipment. At the same time, in the specific analysis, the data collection compliance index of the electrical equipment will be checked first. If this index is less than or equal to the data collection compliance threshold of the electrical equipment, it means that there is a deviation between the data collection result and the actual situation. At this time, it is necessary to introduce an operation degradation factor correction value to correct the data to improve the accuracy of the analysis. On the contrary, if the data collection compliance index of the electrical equipment is greater than the data collection compliance threshold of the electrical equipment, it means that the data collection is accurate and there is no need to introduce an additional operation degradation factor correction value. The analysis can be carried out directly. This process is designed to ensure that the operation degradation factor analysis of electrical equipment can eliminate the influence of electromagnetic signal interference and provide reliable analysis results.

[0070] It needs to be explained that, in the current operating cycle, if the temperature growth rate continues to show an upward trend, that is, the temperature growth rate of the detection area in the operating cycle is greater than the temperature growth rate in the historical adjacent operating cycles, it indicates that there may be overheating in the detection area, and the temperature growth rate of the detection area in the operating cycle is at a high level. This may be due to the gradual degradation of the insulating material, poor contact or excessive load. This overheating phenomenon not only accelerates the aging process of the insulating material and reduces its insulation performance, but also may cause local discharge and other problems, thereby increasing the ultrasonic signal strength, making the maximum ultrasonic signal strength of the detection area in the operating cycle at a large level, and the maximum ultrasonic signal strength of the detection area in the operating cycle is greater than the maximum ultrasonic signal strength in the historical adjacent operating cycles. At the same time, overheating may cause discharge outside the equipment. Electrical hazards are manifested as the maximum ultraviolet radiation intensity of the detection area during the operating cycle being at a high level, and the maximum ultraviolet radiation intensity of the detection area during the operating cycle being greater than the maximum ultraviolet radiation intensity in the historical adjacent operating cycles. These external discharge phenomena may further aggravate the accumulation of dirt, corrosion or aging on the surface of the insulating material, forming a vicious circle, thereby accelerating the aging of the insulating materials of electrical equipment, inducing external discharge and local discharge, etc., aggravating the degree to which the temperature growth rate of the detection area during the operating cycle is greater than the temperature growth rate in the historical adjacent operating cycles, the degree to which the maximum ultrasonic signal intensity of the detection area during the operating cycle is greater than the maximum ultrasonic signal intensity in the historical adjacent operating cycles, and the degree to which the maximum ultraviolet radiation intensity of the detection area during the operating cycle is greater than the maximum ultraviolet radiation intensity in the historical adjacent operating cycles, gradually reducing the insulation performance of the equipment.

[0071] Furthermore, the operation degradation factor of the electrical equipment is determined by the following specific determination process:

[0072] The operating status parameters of the electrical equipment include the maximum temperature value of each detection area of ​​the electrical equipment during the operating cycle, the minimum temperature value of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during the operating cycle, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during the operating cycle; the above-mentioned detection areas are marked by the electrical equipment administrator; the maximum temperature value of each detection area of ​​the electrical equipment during the operating cycle indicates the maximum level of temperature values ​​of each detection area of ​​the electrical equipment during the operating cycle; the minimum temperature value of each detection area of ​​the electrical equipment during the operating cycle indicates the minimum level of temperature values ​​of each detection area of ​​the electrical equipment during the operating cycle; the above-mentioned maximum temperature value and minimum temperature value can be obtained by infrared detection, and the infrared thermal imager can capture the temperature distribution of each detection area of ​​the electrical equipment, and continuously monitor it during the entire operating cycle, thereby recording the highest and lowest temperature values; the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during the operating cycle indicates the maximum level of temperature values ​​of each detection area of ​​the electrical equipment The maximum level of ultraviolet radiation intensity in the domain during the operating cycle is obtained by detection by ultraviolet detection instruments. The ultraviolet detection instrument can capture and record the ultraviolet radiation intensity of each detection area of ​​the electrical equipment, and continuously monitor it during the entire operating cycle, thereby recording the maximum ultraviolet radiation intensity. Ultraviolet detection is mainly used to detect discharge phenomena outside the switchgear. High ultraviolet radiation intensity may indicate the existence of external discharge, which may be caused by accumulation of dirt, corrosion or aging on the surface of the insulating material; the maximum ultrasonic signal intensity of each detection area of ​​the above-mentioned electrical equipment during the operating cycle indicates the maximum value of the ultrasonic signal intensity of each detection area of ​​the electrical equipment during the operating cycle, which is obtained by detection by ultrasonic sensors. The ultrasonic sensor can capture and record the ultrasonic signals of each detection area, and continuously monitor it during the entire operating cycle, thereby recording the maximum ultrasonic signal intensity. Ultrasonic signals are usually related to local discharge phenomena, and local discharge is an important sign of deterioration of insulating materials. Therefore, high ultrasonic signal intensity may indicate the existence of local discharge, which may be caused by degradation or damage of insulating materials.

[0073] The maximum temperature value of each detection area of ​​the electrical equipment during the operating cycle is processed by the difference with the minimum temperature value of each detection area of ​​the electrical equipment during the operating cycle, and the processing result is processed by the ratio with the duration corresponding to the operating cycle to obtain the temperature growth rate of each detection area of ​​the electrical equipment during the operating cycle, which indicates the rate at which the temperature of each detection area of ​​the electrical equipment changes with time during the operating cycle.

[0074] The temperature growth rate of each detection area of ​​the electrical equipment in the historical adjacent operating cycles, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment in the historical adjacent operating cycles, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment in the historical adjacent operating cycles can all be obtained from the data analysis log of the electrical equipment.

[0075] According to the data collection compliance index of the electrical equipment, an operation degradation factor correction value is matched from the equipment database, which represents the numerical value of the degree of correction of the operation degradation factor of the electrical equipment. The specific matching process is: the operation degradation factor correction value corresponding to each data collection compliance index interval is stored in the equipment database, and the data collection compliance index interval stored in the equipment database to which the data collection compliance index of the electrical equipment belongs is queried. The operation degradation factor correction value corresponding to the data collection compliance index interval is the operation degradation factor correction value matched by the data collection compliance index of the electrical equipment.

[0076] The electromagnetic signal interference index of the operating environment of the electrical equipment, the correction value of the operating degradation factor, the temperature growth rate of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during the operating cycle, the temperature growth rate of each detection area of ​​the electrical equipment during historical adjacent operating cycles, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during historical adjacent operating cycles, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during historical adjacent operating cycles are comprehensively determined to obtain the operating degradation factor of the electrical equipment, which is used to comprehensively quantify the degradation trend and degree of the insulation performance of the electrical equipment.

[0077] In a specific embodiment, the present invention provides an electrical equipment fault prediction system based on data fusion. The system accurately captures the electromagnetic signal characteristic parameters of the electrical equipment operating environment and scientifically evaluates the electromagnetic interference index, thereby intelligently adjusting the data acquisition strategy, effectively improving the accuracy and efficiency of data acquisition. At the same time, by monitoring the operating status parameters of the electrical equipment, it can also deeply extract its characteristics and calculate the data acquisition compliance index, providing a strong basis for data preprocessing. Finally, the multi-dimensional operating status parameters of the electrical equipment are highly integrated, which can accurately identify the operating degradation factors, realize accurate prediction of the fault level of the electrical equipment, and issue early warnings in time, which not only significantly improves the accuracy and timeliness of fault prediction, but also effectively reduces the operation and maintenance costs, enhances the stability and safety of the power system, and provides a strong technical guarantee for the intelligent management and maintenance of electrical equipment.

[0078] Reference Figure 2As shown, the second aspect of the present invention provides an electrical equipment fault prediction method based on data fusion, including: step one, obtaining the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, evaluating the electromagnetic signal interference index of the operating environment of the electrical equipment, and thereby determining whether to adjust the data acquisition strategy of the preset electrical equipment; step two, monitoring and acquiring the operating status parameters of the electrical equipment through the data acquisition strategy of the electrical equipment, and acquiring the characteristic parameters of the operating status parameters, analyzing the data acquisition compliance index of the electrical equipment, and thereby determining whether to pre-process the operating status parameters of the electrical equipment; step three, determining the operating degradation factor of the electrical equipment by fusing the operating status parameters of the electrical equipment, and thereby predicting the fault level of the electrical equipment and providing a prompt.

[0079] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. The electrical equipment fault prediction system based on data fusion is characterized by: include: The electromagnetic interference evaluation module is used to obtain the electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment, and determine whether to adjust the preset data collection strategy of the electrical equipment; The operating parameter monitoring module is used to monitor and collect the operating status parameters of the electrical equipment through the data collection strategy of the electrical equipment, obtain the characteristic parameters of the operating status parameters, analyze the data collection compliance index of the electrical equipment, and determine whether to pre-process the operating status parameters of the electrical equipment; The operation parameter analysis module is used to determine the operation degradation factor of the electrical equipment by integrating the operation status parameters of the electrical equipment, thereby predicting the fault level of the electrical equipment and providing prompts; The specific determination process of determining the operation degradation factor of the electrical equipment is as follows: The operating status parameters of the electrical equipment include the maximum temperature value of each detection area of ​​the electrical equipment during the operating cycle, the minimum temperature value of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during the operating cycle, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during the operating cycle; Perform difference processing on the maximum temperature value of each detection area of ​​the electrical equipment within the operation cycle and the minimum temperature value of each detection area of ​​the electrical equipment within the operation cycle, perform ratio processing on the processing result and the duration corresponding to the operation cycle, and obtain the temperature growth rate of each detection area of ​​the electrical equipment within the operation cycle; Obtain the temperature growth rate of each detection area of ​​the electrical equipment in the historical adjacent operation cycles, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment in the historical adjacent operation cycles, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment in the historical adjacent operation cycles; According to the data collection compliance index of the electrical equipment, the operation degradation factor correction value is matched from the equipment database; The electromagnetic signal interference index of the operating environment of the electrical equipment, the correction value of the operating degradation factor, the temperature growth rate of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during the operating cycle, the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during the operating cycle, the temperature growth rate of each detection area of ​​the electrical equipment during historical adjacent operating cycles, the maximum ultraviolet radiation intensity of each detection area of ​​the electrical equipment during historical adjacent operating cycles, and the maximum ultrasonic signal intensity of each detection area of ​​the electrical equipment during historical adjacent operating cycles are comprehensively determined to obtain the operating degradation factor of the electrical equipment, which is used to comprehensively quantify the degradation trend and degree of the insulation performance of the electrical equipment.

2. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The electromagnetic signal interference index of the operating environment of the electrical equipment is evaluated, and the specific evaluation process is as follows: The electromagnetic signal characteristic parameters of the operating environment of the electrical equipment include the real-time magnetic field strength of the operating environment of the electrical equipment during the detection period, the real-time electric field strength of the operating environment of the electrical equipment during the detection period, and the electromagnetic wave frequency of the operating environment of the electrical equipment during the detection period; Performing standard deviation processing on the real-time magnetic field strength of the operating environment of the electrical equipment during the detection period to obtain the magnetic field strength fluctuation value of the operating environment of the electrical equipment during the detection period, and performing standard deviation processing on the real-time electric field strength of the operating environment of the electrical equipment during the detection period to obtain the electric field strength fluctuation value of the operating environment of the electrical equipment during the detection period; Comprehensively evaluate the electromagnetic wave frequency of the operating environment of the electrical equipment during the detection period, the magnetic field strength fluctuation value of the operating environment of the electrical equipment during the detection period, and the electric field strength fluctuation value of the operating environment of the electrical equipment during the detection period, and obtain the electromagnetic signal interference index of the operating environment of the electrical equipment. The electromagnetic signal interference index of the operating environment of the electrical equipment is used to comprehensively quantify the degree of interference of the electromagnetic signal of the operating environment of the electrical equipment on the data collection of the electrical equipment.

3. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The specific determination process of whether to adjust the preset data collection strategy of the electrical equipment is as follows: Extracting the electromagnetic signal interference threshold of the operating environment of the electrical equipment from the equipment database, and comparing the electromagnetic signal interference index of the operating environment of the electrical equipment with the electromagnetic signal interference threshold of the operating environment of the electrical equipment, if the electromagnetic signal interference index of the operating environment of the electrical equipment is less than or equal to the electromagnetic signal interference threshold of the operating environment of the electrical equipment, then determining not to adjust the preset data collection strategy of the electrical equipment; If the electromagnetic signal interference index of the operating environment of the electrical equipment is greater than the electromagnetic signal interference threshold of the operating environment of the electrical equipment, it is determined to adjust the preset data collection strategy of the electrical equipment.

4. The electrical equipment fault prediction system based on data fusion according to claim 3 is characterized in that: The preset data collection strategy of the electrical equipment is adjusted, and the specific adjustment process is as follows: The electromagnetic wave frequency of the operating environment of the electrical equipment within the detection period is compared with the historical reference electromagnetic wave frequency to obtain a comparison result. Based on the comparison result and the electromagnetic signal interference index of the operating environment of the electrical equipment, a first optimization set of data collection strategies is matched from the equipment database, thereby updating the data collection strategy of the electrical equipment.

5. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The data collection compliance index of the electrical equipment is analyzed, and the specific analysis process is as follows: The characteristic parameters of the operating status parameters include the number of data collected during the operating cycle of the electrical equipment, the average interval length of data collection during the operating cycle of the electrical equipment, and the average electrical signal strength of the collection device corresponding to the data of the electrical equipment during the operating cycle; The number of data collected during the operation cycle of the electrical equipment is compared with the duration of the operation cycle to obtain the frequency of data collected during the operation cycle. Obtaining the preset collection frequency of the collection device corresponding to each type of data belonging to the electrical equipment during the operation cycle, and performing ratio processing with the collection frequency of each type of data belonging to the electrical equipment during the operation cycle, to obtain the collection frequency matching rate of each type of data belonging to the electrical equipment during the operation cycle; The data collection compliance index of the electrical equipment is obtained by comprehensively analyzing the electromagnetic signal interference index of the operating environment of the electrical equipment, the collection frequency matching rate of various types of data belonging to the electrical equipment during the operating cycle, the average interval duration of data collection of various types of data belonging to the electrical equipment during the operating cycle, and the average electrical signal strength of the collection devices corresponding to various types of data belonging to the electrical equipment during the operating cycle. The data collection compliance index of the electrical equipment is used to comprehensively quantify the compliance degree of the collection of operating status parameters of the electrical equipment.

6. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The specific determination process of whether to pre-process the operating status parameters of the electrical equipment is as follows: Extracting the data collection compliance threshold of the electrical equipment from the equipment database and comparing it with the data collection compliance index of the electrical equipment, if the data collection compliance index of the electrical equipment is greater than the data collection compliance threshold of the electrical equipment, it is determined that the operating status parameters of the electrical equipment are not preprocessed; If the data collection compliance index of the electrical equipment is less than or equal to the data collection compliance threshold of the electrical equipment, it is determined that the operating status parameters of the electrical equipment are preprocessed. The specific preprocessing process is: according to the data collection compliance index of the electrical equipment, an optimized set of operating status parameters is matched from the equipment database, and then the operating status parameters of the electrical equipment are preprocessed according to the optimized set of operating status parameters, and the operating status parameters of the electrical equipment are updated.

7. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The specific determination method of the operation degradation factor of the electrical equipment is as follows: ； ； ； ； ； In the formula, is the operational degradation factor of the electrical equipment, is the operational degradation index of the dth detection area of ​​the electrical equipment, is the weight coefficient of the operation degradation index preset in the equipment database, is the electromagnetic signal interference index of the operating environment of the electrical equipment. is the electromagnetic signal interference index weight coefficient preset in the equipment database, is the temperature growth rate of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the maximum ultraviolet radiation intensity of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the maximum ultrasonic signal strength of the dth detection area of ​​the electrical equipment in the historical adjacent operation cycles, is the temperature growth rate of the dth detection area of ​​the electrical equipment during the operation cycle, is the maximum ultraviolet radiation intensity of the dth detection area of ​​the electrical equipment during the operation cycle, is the maximum ultrasonic signal strength of the dth detection area of ​​the electrical equipment during the operation cycle, It is the temperature growth rate influence value preset in the equipment database. It is the maximum UV radiation intensity impact value preset in the device database. is the maximum ultrasonic signal strength impact value preset in the equipment database, d is the number of each detection area, , g is the total number of detection areas, is the correction value of the operation degradation factor, Data collection compliance index for electrical equipment, Data collection compliance thresholds for electrical equipment, is the temperature degradation index of the dth detection area of ​​the electrical equipment, is the ultraviolet radiation intensity degradation index of the dth detection area of ​​the electrical equipment, is the ultrasonic signal strength degradation index of the dth detection area of ​​the electrical equipment, is the temperature degradation index weight factor preset in the equipment database, is the ultraviolet radiation intensity degradation index weight factor preset in the equipment database, is the ultrasonic signal strength degradation index weight factor preset in the equipment database, Indicates the maximum value of the operating degradation index of each detection area to which the electrical equipment belongs.

8. The electrical equipment fault prediction system based on data fusion according to claim 1 is characterized in that: The electrical equipment fault level is predicted and prompted, and the specific prediction and prompting process is as follows: Matching the operation degradation factor of the electrical equipment with the fault level corresponding to each operation degradation factor interval stored in the equipment database, thereby predicting the fault level of the electrical equipment; At the same time, the maximum value of the operation degradation index of each detection area to which the electrical equipment belongs in the operation degradation factor of the electrical equipment is located, and the detection area corresponding to the maximum value of the operation degradation index is marked as the electrical equipment fault warning area, thereby comprehensively analyzing the fault level of the electrical equipment and the fault warning area of ​​the electrical equipment for early warning prompts.

9. A method for the electrical equipment fault prediction system based on data fusion according to any one of claims 1 to 8, characterized in that: include: Step 1: Obtain electromagnetic signal characteristic parameters of the operating environment of the electrical equipment, evaluate the electromagnetic signal interference index of the operating environment of the electrical equipment, and determine whether to adjust the preset data collection strategy of the electrical equipment; Step 2: monitor and collect the operating status parameters of the electrical equipment through the data collection strategy of the electrical equipment, obtain the characteristic parameters of the operating status parameters, analyze the data collection compliance index of the electrical equipment, and determine whether to pre-process the operating status parameters of the electrical equipment; Step 3: By integrating the operating status parameters of the electrical equipment, the operating degradation factor of the electrical equipment is determined, thereby predicting the fault level of the electrical equipment and providing prompts.

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