Power transmission equipment hidden danger monitoring and suppression method, electronic equipment and storage medium

Abstract

The invention discloses a hidden danger monitoring and suppression method for power transmission equipment, electronic equipment and a storage medium, and the method comprises the steps: obtaining operation state parameters of the power transmission equipment; performing parameter cleaning and normalization processing on the operation state parameters to generate model input parameters; inputting the model input parameters into a pre-trained hidden danger detection model to obtain hidden danger detection information of the power transmission equipment; and adjusting the working condition of the power transmission equipment according to the hidden danger detection information. According to the hidden danger monitoring and suppression method for the power transmission equipment, operation parameters of the power transmission equipment are collected and processed, and processed model input parameters are input into a pre-trained hidden danger detection model for hidden danger detection; and judging whether to regulate and control the power transmission equipment according to the obtained hidden danger prediction probability or prediction index. Probability evaluation is carried out on operation hidden dangers of the equipment, and corresponding state control measures are taken to suppress the hidden dangers of the power transmission equipment when potential faults or potential safety hazards in the power transmission equipment are found.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for monitoring and suppressing hidden dangers of power transmission equipment, an electronic device and a storage medium. Background Art

[0002] Transmission equipment is the equipment and infrastructure used to transmit electric energy from power stations to power users in the power system. When regulating the operating status of transmission equipment, it is necessary to obtain the operating parameters of the transmission equipment to evaluate whether the transmission equipment has safety problems or unexpected operating conditions.

[0003] For the operation status control of power transmission equipment, the existing technical solution obtains the working status information of the power transmission equipment in real time, compares the current equipment working status information with the preset threshold, and then determines whether the current operation status of the power transmission equipment is stable. In the operation status control scheme of power transmission equipment in the existing technology, when analyzing the working status of the power transmission equipment, only the currently collected operating parameters can be analyzed; at the same time, the existing technology cannot predict the risks and hidden dangers that the power transmission equipment may have. Summary of the invention

[0004] The present invention provides a method for monitoring and suppressing hidden dangers of power transmission equipment, an electronic device and a storage medium, so as to detect the operating status of the equipment, predict the probability or index of hidden dangers of the equipment, and take measures to prevent and suppress the occurrence of faults.

[0005] In a first aspect, the present invention provides a method for monitoring and suppressing hidden dangers of power transmission equipment, the method comprising:

[0006] Obtaining operating status parameters of the power transmission equipment;

[0007] Performing parameter cleaning and normalization processing on the operating state parameters to generate model input parameters;

[0008] Inputting the model input parameters into a pre-trained hidden danger detection model to obtain hidden danger detection information of the power transmission equipment;

[0009] The operating state of the power transmission equipment is adjusted according to the hidden danger detection information.

[0010] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0011] at least one processor; and

[0012] a memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the hidden danger monitoring and suppression method for power transmission equipment described in any embodiment.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the hidden danger monitoring and suppression method for power transmission equipment described in any embodiment of the present invention when executed.

[0015] The hidden danger monitoring and suppression method, electronic device and storage medium of the power transmission equipment of the embodiment of the present invention, wherein in the hidden danger monitoring and suppression method of the power transmission equipment, when the state control of the power transmission equipment is performed, the operating parameters of the power transmission equipment are collected and processed, and the processed model input parameters are input into the pre-trained hidden danger detection model to perform hidden danger detection; and the obtained hidden danger detection prediction probability or prediction index is used to determine whether to regulate the power transmission equipment. The present invention is conducive to more comprehensive and stable monitoring of the power transmission equipment, and probability assessment of the operating hidden dangers of the equipment, and can timely discover potential faults or safety hazards in the power transmission equipment, so as to take corresponding state control measures to suppress the hidden dangers of the power transmission equipment, and effectively avoid or reduce the failure of the power transmission equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0018] Figure 2 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0019] Figure 3 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0020] Figure 4 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0021] Figure 5 A schematic diagram of a visualization effect of thermal distribution of a comprehensive hidden danger probability provided by an embodiment of the present invention;

[0022] Figure 6 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0023] Figure 7 The exponential distribution histograms of equipment failure prediction for two working conditions;

[0024] Figure 8 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0025] Fig. 9 It is a relationship diagram between different input parameters of power transmission equipment and changes in energy consumption hidden danger index;

[0026] Fig.10 A flow chart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention;

[0027] Fig.11 A schematic diagram of the structure of a hidden danger monitoring and suppression device for power transmission equipment provided by an embodiment of the present invention;

[0028] Fig.12 A schematic diagram of the structure of a hidden danger detection execution module provided in an embodiment of the present invention.

[0029] Fig.13 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Figure 1 This is a flowchart of a method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention. This embodiment can be applied to the state control of power transmission equipment. The method can be executed by a hidden danger monitoring and suppression device for power transmission equipment. The hidden danger monitoring and suppression device for power transmission equipment can be implemented in the form of hardware and / or software. Figure 1 As shown, the method includes:

[0033] S101. Obtain operating status parameters of a power transmission device.

[0034] Specifically, the operating status parameters of the power transmission equipment can reflect the operating status of the equipment at the current moment or in the past. The current operating status parameters of the power transmission equipment can reflect the current operating status of the power transmission equipment, and the historical operating status parameters of the power transmission equipment can reflect the operating status trend of the power transmission equipment over a period of time. Through the current operating status parameters and historical operating status parameters of the power transmission equipment, it is possible to predict and evaluate the system operating status to detect hidden dangers of the equipment.

[0035] Exemplarily, the operating state parameters of the power transmission equipment include voltage, current, operating temperature, vibration characteristics and ambient humidity. The historical operating parameters of the power transmission equipment include the average severity score of the fault events that occurred in the power transmission equipment in the past week of operation and the number of historical faults of the power transmission equipment in the past week of operation.

[0036] S102: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0037] Specifically, after the operating status parameters of the power transmission equipment are collected, the operating status parameters need to be cleaned and normalized.

[0038] Among them, parameter cleaning is to remove invalid, erroneous or duplicate data to ensure the accuracy and reliability of the data. Normalization processing is to convert the data into a unified dimension or range to generate high-quality model input parameters. The model input parameters can accurately reflect the operating status of the transmission equipment. After the model input parameters are input into the pre-trained hidden danger detection model, it is beneficial to improve the output efficiency of the hidden danger detection model and the accuracy of the output results.

[0039] Exemplarily, when performing parameter cleaning on operating status parameters, check whether there are null values ​​or unreasonable values ​​in the data to reduce invalid calculations when calculating the hidden danger detection model; clean or supplement missing values ​​in the data to improve the continuity of the hidden danger detection model calculation; clean outliers in the data to improve the accuracy of the hidden danger detection model calculation.

[0040] When normalizing the operating status parameters, the corresponding normalization method is selected according to the characteristics of the data and the requirements of the hidden danger detection model.

[0041] Exemplarily, when the operating state parameter varies greatly, a linear transformation is used to limit the range of the operating state parameter to reduce noise.

[0042] S103: Input model input parameters into a pre-trained hidden danger detection model to obtain hidden danger detection information of power transmission equipment.

[0043] Specifically, the hidden danger detection model is a pre-trained model, which can extract features from input parameters and predict possible hidden dangers in the power transmission equipment. The hidden danger detection model can include multiple types of hidden dangers, and the output result represents the possibility of the existence of the hidden danger in the power transmission equipment in the form of probability or prediction index.

[0044] Exemplarily, the hidden danger detection model includes an equipment failure prediction model. After the model input parameters are input into the equipment failure prediction model, the output result is an equipment failure prediction index. The greater the value of the equipment failure prediction index, the greater the probability of equipment failure. The value of the failure prediction index is the hidden danger detection information of the power transmission equipment.

[0045] Optionally, obtaining the hidden danger detection information of the power transmission equipment further includes: setting preset fault prediction index thresholds of different gradients, and outputting different hidden danger detection information of the power transmission equipment according to the size of the obtained fault prediction index compared with the preset fault prediction index threshold. Exemplarily, the equipment fault prediction index threshold is set, and when the equipment fault prediction index obtained by the hidden danger detection model is greater than or equal to the equipment fault prediction threshold, the output equipment hidden danger detection information is "equipment fault prediction index exceeds the standard".

[0046] S104. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0047] The hidden danger detection information includes the probability or index of a certain hidden danger in the power transmission equipment. When the hidden danger probability or index in the hidden danger detection information is at different preset threshold gradients, it indicates that the power transmission equipment is currently in different operating states. In order to ensure the efficient operation of the power transmission equipment, when the hidden danger detection information indicates that the equipment needs to be adjusted in working conditions, different degrees and types of adjustment measures are correspondingly performed. Optionally, the output power of the power transmission equipment is adjusted and / or the working state of the power transmission equipment is switched.

[0048] The embodiment of the present invention provides a method for monitoring and suppressing hidden dangers of power transmission equipment. When the state of the power transmission equipment is controlled, the operating parameters of the power transmission equipment are collected and processed, and the processed model input parameters are input into a pre-trained hidden danger detection model to perform hidden danger detection; the power transmission equipment is regulated by the obtained hidden danger detection prediction probability or prediction index. The present invention is conducive to more comprehensive and stable monitoring of power transmission equipment, probabilistic evaluation of the operating hidden dangers of the equipment, and can timely discover potential faults or safety hazards in the power transmission equipment, so as to take corresponding state control measures to suppress the hidden dangers of the power transmission equipment, and effectively avoid or reduce the failure of the power transmission equipment.

[0049] Based on the above embodiments, Figure 2 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method includes:

[0050] S201, obtaining voltage parameters, current parameters, operating temperature parameters, vibration parameters and environment parameters of the power transmission equipment during operation; and obtaining historical operating parameters of the power transmission equipment.

[0051] Specifically, when obtaining the operating status parameters of the power transmission equipment, the operating status parameters obtained are divided into two types:

[0052] One type of operating status parameter is the real-time parameter of the power transmission equipment during operation, including: voltage parameter, current parameter, operating temperature parameter, vibration parameter and the environment parameter of the power transmission equipment. The real-time parameters of the power transmission equipment during operation reflect the current operating status of the power transmission equipment. When the real-time operating status parameters of the power transmission equipment are input into the hidden danger detection model, the output result obtained can accurately and instantly reflect the possible safety hazards when the equipment is running in the current state.

[0053] Another operating status parameter is the historical operating parameters of the transmission equipment. The historical operating parameters of the transmission equipment record the operating parameters of the transmission equipment in the past time period. The historical operating parameters reflect the changing trend of the operating status of the transmission equipment within a time period. This trend can be used to predict the operating status of the transmission equipment in the future. When the historical operating parameters of the transmission equipment are input into the hidden danger detection model, the obtained safety hidden danger probability or index of the transmission equipment can reflect the operating status trend of the transmission equipment, and can regulate the operating status of the transmission equipment in a more predictive and comprehensive manner.

[0054] Exemplarily, the historical operating parameters of the power transmission equipment include: the average time interval between fault events that occurred during a historical week of operation of the power transmission equipment, the average severity score of fault events that occurred during a historical week of operation of the power transmission equipment, and the number of historical faults of the power transmission equipment during a historical week of operation.

[0055] S202: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0056] S203: Input the model input parameters into the pre-trained hidden danger detection model to obtain hidden danger detection information of the power transmission equipment.

[0057] S204. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0058] Based on the above embodiments, Figure 3 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the method includes:

[0059] S301. Obtain operating status parameters of a power transmission device.

[0060] S302: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0061] S303, inputting the model input parameters into the equipment comprehensive hidden danger probability calculation model to obtain the comprehensive hidden danger probability.

[0062] Specifically, in the algorithm of the comprehensive hidden danger probability calculation model, multiple model input parameters are weighted and nonlinearly transformed according to the types of model input parameters, and finally the comprehensive hidden danger probability is obtained. This includes: score transformation function, which converts the original model input parameters into scores for nonlinear transformation; parameter synthesis and sensitivity index, which multiplies the score of each parameter by its weight coefficient and adds all weighted scores, that is, multiple nonlinearly transformed parameters are combined into a hidden danger probability. By introducing weight coefficients and score transformation functions, the system can more accurately evaluate the impact of each parameter on the fault.

[0063] S304: Input the model input parameters into the equipment failure prediction model to obtain the equipment failure prediction index.

[0064] Specifically, the model input parameters required in the equipment failure prediction model include historical parameters of equipment operation. Exemplarily, the historical parameters of equipment operation in the model input parameters include: the average time interval of failure events that occurred in the historical operation of the power transmission equipment within one week, the average severity score of the failure events that occurred in the historical operation of the power transmission equipment within one week, and the number of historical failures of the power transmission equipment within one week.

[0065] The historical fault record sensitivity index of the power transmission equipment is obtained based on the historical parameters of the equipment operation in the model input parameters. The historical fault record sensitivity index comprehensively evaluates the frequency and severity of past faults of the power transmission equipment. The equipment failure prediction index is calculated by combining the historical fault record sensitivity index and the operating parameters of the equipment at the current moment.

[0066] The equipment failure prediction model not only inputs the current real-time model operating status parameters but also combines the equipment operating status trends over the past period of time. The equipment failure prediction model can accurately assess the possibility of future equipment failures and provide timely and accurate fault prediction information for operation and maintenance personnel, which is beneficial to the status control of transmission equipment.

[0067] S305 , inputting the model input parameters into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index.

[0068] Specifically, in the equipment energy consumption hidden danger index detection model, the safety hidden dangers in the equipment energy consumption are detected. The model input parameters include the power parameters of the equipment, the temperature parameters of the equipment and the environment. The energy consumption hidden danger index of the equipment is calculated by calculating the equipment heat dissipation constant, the influence values ​​of various environmental factors, the power parameters of the equipment and the temperature parameters. Among them, the equipment heat dissipation constant represents the heat dissipation performance of the equipment. The equipment heat dissipation constant is related to the heat dissipation coefficient of the equipment heat dissipation module, the area of ​​the heat dissipation module and the thermal conductivity of the heat dissipation module. The larger the equipment heat dissipation constant, the better the equipment heat dissipation performance. The environmental factor impact value represents the degree of influence of the environmental factors in the model input parameters on the equipment energy consumption hidden danger index. The larger the environmental factor impact value, the greater the degree of influence of the environmental factors on the equipment energy consumption hidden danger index. Optionally, the types of environmental factors include the temperature of the environment and the humidity of the environment. The equipment energy consumption hidden danger index detection model comprehensively evaluates the energy consumption hidden dangers of the transmission equipment, and expresses the energy consumption hidden dangers in the form of an index, which is conducive to evaluating the energy consumption hidden danger situation of the equipment and is conducive to the comprehensive regulation of the state of the transmission equipment.

[0069] S306. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0070] Based on the above embodiments, Figure 4 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the method includes:

[0071] S401. Obtain operating status parameters of a power transmission device.

[0072] S402: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0073] S403: Input the model input parameter values ​​and sensitivity indexes into the score transformation sub-model to obtain the score transformation function of each input parameter.

[0074] The expression of the score transformation sub-model is:

[0075] f i (x i (t)) = ln(1 + x i (t) γ );

[0076] Among them, x i (t) is the parameter value of the i-th selected parameter at time t; γ represents the sensitivity index; f i (x i (t)) is the score transformation function of the i-th selected parameter;.

[0077] Specifically, the score transformation sub-model transforms the original parameter value into a score that can reflect the degree of influence of the parameter on the hidden danger by performing nonlinear transformation on the operating state parameters. i (x i (t)) represents the score transformation function of the i-th selected parameter. This function converts the model input parameter into a score so that f i (x i (t)) can reflect the potential impact of the parameter on hidden dangers. γ represents the sensitivity index. The sensitivity index is a value set by the administrator based on experience, which is used to adjust the nonlinearity of the score transformation function. For example, the greater the rated output of the transmission equipment, the greater the sensitivity index is usually. That is, for equipment with a larger rated output, the impact of parameter changes on hidden dangers may be more significant, so a higher sensitivity is required to capture such changes. The score transformation sub-model helps the system to more accurately capture the potential impact of parameter changes on hidden dangers, thereby improving the accuracy of the assessment.

[0078] S404: Input the score transformation function of each input parameter and the weight coefficient of each input parameter into the comprehensive hidden danger probability solution sub-model to obtain the comprehensive hidden danger probability.

[0079] The expression of the comprehensive hidden danger probability solution sub-model is:

[0080]

[0081] Where M represents the total number of parameters selected in the input parameters when the comprehensive hidden danger probability calculation submodule works; λ i represents the weight coefficient of the i-th selected parameter; Prisk(t) represents the comprehensive hidden danger probability at time t after the system starts working.

[0082] Specifically, the comprehensive hidden danger probability solving sub-model combines multiple nonlinearly transformed input parameters (i.e., the results of the score transformation function) and calculates the comprehensive hidden danger probability of the transmission equipment at a certain moment (time t) after the system starts working by weighted summation and applying the Logistic function. i Represents the weight coefficient of the i-th selected parameter; the greater the impact of the parameter on the fault, the greater the weight coefficient of the parameter. The specific value is pre-set by the administrator based on experience. When the parameter is selected, the corresponding weight coefficient is a known value.

[0083] For example, in the comprehensive hidden danger probability calculation model, three parameters are selected, namely M=3. At time t, the three parameters are operating current x1(t)=150A, output voltage x2(t)=500kV, and operating temperature x3(t)=85°C. Set λ1=0.5, λ2=0.3, λ3=0.2, and γ=2. Then:

[0084] f1(x1(t))=ln(1+150 2 )=ln(1+22500)≈10.02;

[0085] f2(x2(t))=ln(1+500 2 )=ln(1+250000)≈12.43;

[0086] f3(x3(t))=ln(1+85 2 )=ln(1+7225)≈8.89;

[0087]

[0088] The final comprehensive hidden danger probability is 1-e -10.52 .

[0089] Figure 5A schematic diagram of a visualization effect of thermal distribution of a comprehensive hidden danger probability provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown in the figure, the comprehensive hidden danger probability is calculated 1000 times every hour within 100 hours after the equipment is put into operation. The smaller the comprehensive hidden danger probability is, the darker the color is in the thermal distribution visualization diagram of the comprehensive hidden danger probability. As shown in the figure, there are two moments with a small comprehensive hidden danger probability within 100 hours after the equipment is put into operation, namely: within 30 hours to 40 hours after the equipment is put into operation and within 70 hours to 80 hours after the equipment is put into operation. At these two moments, since the comprehensive hidden danger probability of the equipment is small, it means that the equipment is in good operating condition. On the contrary, when the comprehensive hidden danger probability is large, it means that the probability of hidden dangers in the operation of the equipment is higher. For example, the thermal distribution color of the comprehensive hidden danger probability within 10 hours after the equipment is put into operation is lighter, which means that the comprehensive hidden danger probability within 10 hours after the equipment is put into operation is higher.

[0090] S405 , inputting the model input parameters into the equipment failure prediction model to obtain the equipment failure prediction index.

[0091] S406: Input the model input parameters into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index.

[0092] S407. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0093] Based on the above embodiments, Figure 6 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, the method includes:

[0094] S501. Obtain operating status parameters of a power transmission device.

[0095] S502: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0096] S503: Input the model input parameters into the equipment comprehensive hidden danger probability calculation model to obtain the comprehensive hidden danger probability.

[0097] S504. Input the cumulative operating hours of the power transmission equipment, the number of historical failures of the power transmission equipment within a historical week of operation, the average severity score of the failure events that occurred in the power transmission equipment within a historical week of operation, the time attenuation factor, and the average time interval of the failure events that occurred in the power transmission equipment within a historical week of operation into the power transmission equipment historical failure record sensitivity index calculation submodel to obtain the power transmission equipment historical failure record sensitivity index.

[0098] The expression of the calculation sub-model of the historical fault record sensitivity index of power transmission equipment is:

[0099]

[0100] Among them, T total is the cumulative operating hours of the power transmission equipment; N is the number of historical failures of the power transmission equipment within one week of operation; S avg is the average severity score of the fault events that occurred in the transmission equipment within one week of operation; η is the time decay factor; t avg is the average time interval between failure events of power transmission equipment within one week of operation; R is the sensitivity index of historical failure records of power transmission equipment.

[0101] Specifically, S. avg It represents the average severity score of the fault events that occurred in the transmission equipment within one week of operation. The severity score of each fault event is assessed by the administrator based on the actual situation. The longer it takes to repair the fault, the higher the score, with a minimum of 1 point and a maximum of 10 points. η represents the time decay factor. The greater the cumulative operating hours, the smaller the time decay factor. The specific value is set by the administrator based on experience.

[0102] In the transmission equipment historical fault record sensitivity index calculation sub-model, calculate the correction value of the fault frequency The correction value of the failure frequency takes into account the cumulative operating time and number of failures of the equipment and is used to evaluate the relative frequency of equipment failures. avg It is the average severity score of the fault events that occurred in the transmission equipment within one week of operation. avg When calculating the inverse, the higher the severity score, the smaller the inverse, indicating that the fault has less impact on the equipment. From the perspective of index calculation, the value of the sensitivity index is increased. In the model, the time decay factor is adjusted according to the cumulative operating time of the equipment, reflecting the performance degradation or increased failure rate of the equipment as the operating time increases. The output of the transmission equipment historical fault record sensitivity index calculation sub-model is the transmission equipment historical fault record sensitivity index, which combines the cumulative operating time of the equipment, the number of historical faults, the average severity of the faults, the time decay factor and the average time interval of the faults to evaluate the risk level of the equipment based on the historical fault records.

[0103] S505, inputting the historical fault record sensitivity index of the power transmission equipment, the total load current of the power transmission equipment, the ambient temperature of the environment in which the power transmission equipment is located, the ambient temperature threshold, the voltage value of the transmission line between the power transmission equipment and the load, the output rated voltage of the power transmission equipment, and the vibration parameter value of the power transmission equipment during operation into the equipment fault prediction index solving sub-model to obtain the equipment fault prediction index

[0104] The expression of the equipment failure prediction index solution sub-model is:

[0105]

[0106] Among them, I load Indicates the total load current of the power transmission equipment; T a Indicates the ambient temperature of the environment where the power transmission equipment is located; T ref Indicates the ambient temperature threshold; V line Indicates the voltage value of the transmission line between the transmission equipment and the load; V ref It represents the output rated voltage of the power transmission equipment; H represents the vibration parameter value of the power transmission equipment during operation; F represents the equipment failure prediction index.

[0107] Specifically, T ref It represents the ambient temperature threshold, which is generally 25°C. H represents the vibration parameter value of the power transmission equipment during operation, that is, the vibration rate, and the unit is generally mm / s.

[0108] In the equipment failure prediction index solution sub-model, the total load current of the transmission equipment, the ambient temperature of the environment in which the transmission equipment is located, the ambient temperature threshold, the voltage value of the transmission line between the transmission equipment and the load, the output rated voltage of the transmission equipment, and the vibration parameters of the transmission equipment during operation are weighted and the square sum is calculated. The coefficient of each model input parameter is the weight of the model input parameter, which is set based on experience to reflect the importance of different parameters to equipment failure prediction. The weighted square sum of the model input parameters is divided by the adjusted historical fault record sensitivity index to obtain the equipment failure prediction index.

[0109] For example, in the equipment failure prediction model, it is assumed that the power transmission equipment has 20 failures in the past week, and the average severity score of the historical failures is S avg =7, mean time between failures t avg =2 hours, the time decay factor η is set to 0.01. Then:

[0110]

[0111] If it is known that I load =120A, T a =35℃,V line =480V, V ref =480V, H=2mm / s, F ref =150, then:

[0112]

[0113] That is, the final equipment failure prediction index is 333.47.

[0114] Figure 7 The exponential distribution histograms of equipment failure prediction for two working conditions are as follows: Figure 7 As shown in the figure, the equipment failure prediction index distribution of working condition 1 is concentrated in the larger F value area, representing high load, high temperature difference and other working conditions; the equipment failure prediction index distribution of working condition 2 is concentrated in the smaller F value area, representing low load, low temperature difference working conditions. That is, when the load and temperature difference of the power transmission equipment are large, the larger the equipment failure prediction index is, the greater the probability of failure of the power transmission equipment is.

[0115] S506: Input the model input parameters into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index.

[0116] S507. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0117] Based on the above embodiments, Figure 8 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Figure 8 As shown, the method includes:

[0118] S601. Obtain operating status parameters of a power transmission device.

[0119] S602: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0120] S603: Input the model input parameters into the equipment comprehensive hidden danger probability calculation model to obtain the comprehensive hidden danger probability.

[0121] S604: Input the model input parameters into the equipment failure prediction model to obtain the equipment failure prediction index.

[0122] S605. Obtain an impact value of the environmental factor according to the product of the adjustment factor of the environmental factor and the actual measured value of the environmental factor.

[0123] The expression of the impact value of environmental factors is:

[0124] E j =β j ·e j ;

[0125] Among them, β j represents the regulatory factor of the jth environmental factor; e j represents the measured value of the jth environmental factor; E j represents the impact value of the jth environmental factor;

[0126] Specifically, in the expression of the impact value of environmental factors,

[0127] Among them, different environmental factors affect the power transmission equipment in different ways and degrees. For each environmental factor, an adjustment factor needs to be set. The role of the adjustment factor is to adjust the range of the impact value of the environmental factor on the power transmission equipment to make it more in line with the actual situation or analysis requirements. The adjustment factor is pre-set by the administrator based on experience. The larger the historical average value of the environmental factor, the smaller the adjustment factor. Exemplary environmental factors include: temperature and humidity. The adjustment factor of the environmental factor is multiplied by the measured value of the environmental factor to obtain the impact value of the environmental factor. The impact value of the environmental factor quantifies the specific impact of the environmental factor on the energy consumption or performance of the power transmission equipment.

[0128] S606: Obtain a device heat dissipation constant according to the product of the heat dissipation coefficient, the surface area of ​​the heat dissipation region of the heat dissipation module of the power transmission equipment, and the thermal conductivity of the heat dissipation module of the power transmission equipment.

[0129] The expression of the device heat dissipation constant is:

[0130] C heat =h·A·k;

[0131] Wherein, h represents the heat dissipation coefficient; A represents the surface area of ​​the heat dissipation region of the heat dissipation module of the power transmission equipment; k represents the thermal conductivity of the heat dissipation module of the power transmission equipment; C heat Indicates the heat dissipation constant of the device;

[0132] Specifically, h represents the heat dissipation coefficient, which is measured by conducting a heat conduction experiment on the heat dissipation module of the power transmission equipment, and can also be selected from historical data. The size of the heat dissipation coefficient depends on the design, materials and external environmental conditions of the heat dissipation module. k represents the thermal conductivity of the heat dissipation module of the power transmission equipment, which is obtained by network table lookup based on the material of the heat dissipation module of the power transmission equipment. For the heat dissipation module of the power transmission equipment, the thermal conductivity depends on the properties of its constituent materials. The higher the thermal conductivity, the stronger the ability of the material to conduct heat. The surface area of ​​the heat dissipation area refers to the effective area for heat exchange between the heat dissipation module of the power transmission equipment and the external environment. The larger the surface area of ​​the heat dissipation area, the stronger the heat dissipation capacity is usually, because more heat can be dissipated into the environment through a larger area. The equipment heat dissipation constant is obtained by multiplying the heat dissipation coefficient, the surface area of ​​the heat dissipation area of ​​the heat dissipation module of the power transmission equipment, and the thermal conductivity of the heat dissipation module of the power transmission equipment to quantify the heat dissipation capacity of the heat dissipation module of the power transmission equipment.

[0133] S607. Input the impact value of environmental factors, equipment heat dissipation constant, output power of power transmission equipment, input power of power transmission equipment, correction coefficient, power transmission loss, ambient temperature of the environment in which the power transmission equipment is located, ambient temperature threshold and weight coefficient of each environmental factor into the equipment energy consumption hidden danger index solving sub-model to obtain the equipment energy consumption hidden danger index.

[0134] The expression of the equipment energy consumption hidden danger index solution sub-model is:

[0135]

[0136] Among them, P output Indicates the output power of the power transmission equipment; P input Indicates the input power of the power transmission equipment; δ indicates the correction factor; R loss Represents transmission loss; T a Indicates the ambient temperature of the environment where the power transmission equipment is located; T ref represents the ambient temperature threshold; J represents the total number of environmental factors; σ j represents the weight coefficient of the jth environmental factor; E eff Indicates the energy consumption potential risk index.

[0137] Specifically, δ represents the correction coefficient. The greater the age of the transmission equipment, the greater the correction coefficient. The specific value is set by the administrator based on experience. loss Represents the transmission loss, which can be calculated from historical data or the resistance of the power transmission line, or can be obtained by actual measurement; T a Indicates the ambient temperature of the environment where the power transmission equipment is located; T ref Indicates the ambient temperature threshold, which is generally 25°C; 273.15 is used to convert the ambient temperature from Celsius to the Kelvin temperature scale, avoiding the problem of zero or negative temperature, making the formula more mathematically stable, ensuring that the denominator is always positive, and avoiding the situation where the denominator is zero or negative when the Celsius temperature is close to or equal to absolute zero, thereby ensuring the validity and stability of the formula. j It represents the weight coefficient of the jth environmental factor, and the specific value is set by the administrator based on experience. In the sub-model for solving the equipment energy consumption hidden danger index, the energy consumption hidden danger index of the power transmission equipment is calculated by comprehensively considering the output power, input power, correction factor, transmission loss, equipment heat dissipation constant, ambient temperature, ambient temperature threshold, and the influence values ​​and weight coefficients of multiple environmental factors.

[0138] For example, if the known condition is P output =1000W, P input =1200W, R loss =150W, C heat =10, T a =35℃、T ref =25℃, δ=0.8, E1=0.2, σ1=0.5, E2=0.3, σ2=0.3 then:

[0139]

[0140] That is, the energy consumption potential risk index of the power transmission equipment finally obtained is 75.6%.

[0141] Fig. 9This is a graph showing the relationship between different input parameters of power transmission equipment and the change of energy consumption hidden danger index, such as Fig. 9 As shown in the figure, when the ambient temperature of the power transmission equipment changes, as the ambient temperature increases, the energy consumption hidden danger index of the power transmission equipment first increases, and when it rises to the maximum, the energy consumption hidden danger index decreases. The maximum value of the energy consumption hidden danger index obtained by different power transmission equipment when the ambient temperature changes is different. The power transmission equipment has an optimal operating temperature. At this temperature, the heat dissipation efficiency of the power transmission equipment is the highest and the energy consumption efficiency is also the highest. Exceeding this temperature threshold or being less than this temperature threshold will cause the energy consumption hidden danger index of the power transmission equipment to decrease. When the external energy of the power transmission equipment changes, as the external input energy increases, the value of the energy consumption hidden danger index of the power transmission equipment will continue to decrease. When the input power of the power transmission equipment changes, as the input power increases, the value of the energy consumption hidden danger index of the power transmission equipment will continue to decrease.

[0142] S608. Adjust the operating status of the power transmission equipment according to the hidden danger detection information.

[0143] Based on the above embodiments, Fig.10 A flowchart of another method for monitoring and suppressing hidden dangers of power transmission equipment provided by an embodiment of the present invention is shown in FIG. Fig.10 As shown, the method includes:

[0144] S701. Obtain operating status parameters of a power transmission device.

[0145] S702: Perform parameter cleaning and normalization processing on the operating status parameters to generate model input parameters.

[0146] S703: Input the model input parameter values ​​and sensitivity indexes into the score transformation sub-model to obtain the score transformation function of each input parameter.

[0147] S704: Input the score transformation function of each input parameter and the weight coefficient of each input parameter into the comprehensive hidden danger probability solution sub-model to obtain the comprehensive hidden danger probability.

[0148] S705 . Input the model input parameters into the equipment failure prediction model to obtain an equipment failure prediction index.

[0149] S706: Input the model input parameters into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index.

[0150] S707. When the comprehensive hidden danger probability is greater than or equal to the comprehensive hidden danger probability threshold, adjust the operating state of the transmission equipment; and / or, when the equipment failure prediction index is greater than or equal to the equipment failure prediction threshold, adjust the operating state of the transmission equipment; and / or, when the equipment energy consumption hidden danger index is less than the equipment energy consumption hidden danger threshold, adjust the operating state of the transmission equipment.

[0151] Among them, adjusting the operating state of the power transmission equipment includes: adjusting the output power of the power transmission equipment and / or switching the working state of the power transmission equipment; wherein the working state of the power transmission equipment includes: full load, overload and underload.

[0152] Specifically, the comprehensive hidden danger probability reflects multiple operating state parameters and their interrelationships as well as the possibility of various potential hidden dangers occurring in the current operating state of the power transmission equipment. When the comprehensive hidden danger probability is greater than or equal to the set comprehensive hidden danger probability threshold, it means that there is a high risk in the current operating state of the power transmission equipment, and measures need to be taken to reduce the hidden dangers. At this time, the operating risk of the equipment can be reduced by adjusting the output power of the power transmission equipment or switching its working state. Exemplarily, when the comprehensive hidden danger probability is greater than or equal to the set comprehensive hidden danger probability threshold, the power transmission equipment is switched from a full load or overload state to an underload state, or the output power of the power transmission equipment is reduced.

[0153] The equipment failure prediction index reflects the possibility of equipment failure in the future. When the equipment failure prediction index is greater than or equal to the set equipment failure prediction threshold, it means that if the transmission equipment continues to operate with this operating parameter, it may fail in the future and preventive measures need to be taken in advance. At this time, the possibility of failure can also be reduced by adjusting the output power of the transmission equipment or switching its working state.

[0154] The equipment energy consumption hidden danger index reflects the energy consumption efficiency of the power transmission equipment during operation. When the equipment failure prediction index is greater than or equal to the set equipment failure prediction threshold, it means that the energy consumption efficiency of the power transmission equipment is better at this time; when the equipment failure prediction index is less than the set equipment failure prediction threshold, the power transmission equipment is adjusted.

[0155] Based on the above embodiments, Fig.11 A schematic diagram of a hidden danger monitoring and suppression device for power transmission equipment provided by an embodiment of the present invention is shown in FIG. Fig.11 As shown, the hidden danger monitoring and suppression device of the power transmission equipment includes:

[0156] Power transmission equipment parameter collection terminal 01, parameter processing terminal 02, hidden danger detection terminal 03 and suppression control terminal 04. The power transmission equipment parameter collection terminal 01 includes: voltage parameter collection module 011, current parameter collection module 012, operating temperature parameter collection module 013, vibration parameter collection module 014, power transmission equipment environment parameter collection module 015 and power transmission equipment historical operation parameter collection module 016;

[0157] The parameter processing terminal 02 includes: a parameter cleaning module 021, a normalization processing module 022 and an input parameter generating module 023;

[0158] The hidden danger detection terminal 03 includes: an input parameter reading module 031, a hidden danger detection model reading module 032, a hidden danger detection execution module 033 and a hidden danger detection information generation module 034;

[0159] The suppression control terminal 04 includes: a suppression instruction generating module 041 and a control module 042 .

[0160] Specifically, in the power transmission equipment parameter acquisition terminal 01, the voltage parameter acquisition module 011 is used to acquire the input voltage and output voltage of the power transmission equipment; the current parameter acquisition module 012 is used to acquire the working current of the power transmission equipment; the working temperature parameter acquisition module 013 is used to acquire the working temperature of the power transmission equipment; the vibration parameter acquisition module 014 is used to acquire the vibration data of the power transmission equipment during operation; the environment parameter acquisition module 015 of the power transmission equipment is used to acquire the working environment parameters of the power transmission equipment; the historical operation parameter acquisition module 016 of the power transmission equipment is used to acquire the historical operation parameters of the power transmission equipment.

[0161] In the parameter processing terminal 02, the parameter cleaning module 021 is used to clean the operating parameters obtained in real time and remove data redundancy; the normalization processing module 022 is used to classify and normalize the cleaned operating parameters; the input parameter generation module 023 is used to screen data from the classified and normalized operating parameters to generate input parameters.

[0162] In the hidden danger detection terminal 03, the input parameter reading module 031 is used to read the input parameters from the input parameter generating module 023; the hidden danger detection model reading module 032 is used to read the hidden danger detection model pre-input by the administrator; the hidden danger detection execution module 033 is used to use the hidden danger detection model to perform hidden danger detection according to the input parameters; the hidden danger detection information generating module 034 generates and displays hidden danger detection information according to the hidden danger detection results; optionally, the hidden danger detection information generating module 034 is also used to compare the hidden danger detection results and the hidden danger detection threshold and generate a comparison result.

[0163] In the suppression control terminal 04, the suppression instruction generation module 041 is used to generate a suppression instruction according to the hidden danger detection information; the control module 042 is used to control the working parameters of the power transmission equipment according to the suppression instruction.

[0164] Fig.12 A schematic diagram of the structure of a hidden danger detection execution module provided by an embodiment of the present invention is shown in FIG. Fig.12 As shown, the hidden danger detection execution module 033 includes: an equipment comprehensive hidden danger probability model submodule 0331, an equipment failure prediction model submodule 0332, an equipment energy consumption hidden danger index detection model submodule 0333 and a hidden danger detection result output submodule 0334.

[0165] Among them, the equipment comprehensive hidden danger probability model submodule 0331 is used to use the equipment comprehensive hidden danger probability model in the hidden danger detection model and perform comprehensive hidden danger probability calculation according to the input parameters to calculate the comprehensive hidden danger probability; the equipment failure prediction model submodule 0332 is used to use the equipment failure prediction index model in the hidden danger detection model and perform equipment failure prediction according to the input parameters to calculate the equipment failure prediction index; the equipment energy consumption hidden danger index detection model submodule 0333 is used to use the equipment energy consumption hidden danger index detection model in the hidden danger detection model and perform energy consumption hidden danger index calculation according to the input parameters to calculate the equipment energy consumption hidden danger index; the hidden danger detection result output submodule 0334 is used to output the comprehensive hidden danger probability and the equipment failure prediction index as the hidden danger detection result.

[0166] The hidden danger monitoring and suppression device for power transmission equipment provided in the embodiment of the present invention can execute the hidden danger monitoring and suppression method for power transmission equipment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0167] Fig.13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device provided in an embodiment of the present invention is used to implement the hidden danger monitoring and suppression method of the power transmission equipment of the above-mentioned embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0168] like Fig.13 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0169] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0170] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the hidden danger monitoring and suppression method of the power transmission equipment.

[0171] In some embodiments, the hidden danger monitoring and suppression method of the power transmission equipment can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the hidden danger monitoring and suppression method of the power transmission equipment described above can be performed. Optionally, in other embodiments, the processor 11 can be configured to execute the hidden danger monitoring and suppression method of the power transmission equipment in any other appropriate manner (for example, by means of firmware).

[0172] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0173] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0174] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Optionally, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0175] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0176] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0177] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0178] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0179] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring and suppressing hidden dangers of power transmission equipment, characterized in that: The method comprises: Obtaining operating status parameters of the power transmission equipment; Performing parameter cleaning and normalization processing on the operating state parameters to generate model input parameters; Inputting the model input parameters into a pre-trained hidden danger detection model to obtain hidden danger detection information of the power transmission equipment; The operating state of the power transmission equipment is adjusted according to the hidden danger detection information.

2. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 1, characterized in that: The obtaining of the operating status parameters of the power transmission equipment includes: Acquire voltage parameters, current parameters, operating temperature parameters, vibration parameters of the power transmission equipment during operation, and parameters of the environment in which the power transmission equipment is located; Obtain historical operating parameters of the power transmission equipment.

3. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 1, characterized in that: Inputting the model input parameters into a pre-trained hidden danger detection model to obtain hidden danger detection information of the power transmission equipment includes: Inputting the model input parameters into the equipment comprehensive hidden danger probability calculation model to obtain the comprehensive hidden danger probability; Inputting the model input parameters into the equipment failure prediction model to obtain an equipment failure prediction index; The model input parameters are input into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index.

4. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 3, characterized in that: Inputting the model input parameters into the equipment comprehensive hidden danger probability calculation model to obtain the comprehensive hidden danger probability, including: Inputting the model input parameter values ​​and sensitivity indexes into the score transformation sub-model to obtain a score transformation function for each input parameter; Inputting the score transformation function of each input parameter and the weight coefficient of each input parameter into the comprehensive hidden danger probability solving sub-model to obtain the comprehensive hidden danger probability; The expression of the score transformation sub-model is: f i (x i (t))=ln(1+x i (t) γ ); Among them, x i (t) is the parameter value of the i-th selected parameter at time t; γ represents the sensitivity index; f i (x i (t)) is the score transformation function of the i-th selected parameter; The expression of the comprehensive hidden danger probability solution sub-model is: Wherein, M represents the total number of parameters selected in the input parameters when the comprehensive hidden danger probability calculation submodule works; λ i represents the weight coefficient of the i-th selected parameter; Prisk(t) represents the comprehensive hidden danger probability at time t after the system starts working.

5. The hidden danger monitoring and suppression method of power transmission equipment according to claim 3 is characterized in that: Inputting the model input parameters into the equipment failure prediction model to obtain the equipment failure prediction index includes: The accumulated operation hours of the transmission equipment, the number of historical failures of the transmission equipment in a historical week of operation, the average severity score of the failure events that occurred in the transmission equipment in a historical week of operation, the time decay factor and the average time interval of the failure events that occurred in the transmission equipment in a historical week of operation are input into the transmission equipment historical failure record sensitivity index calculation sub-model to obtain the transmission equipment historical failure record sensitivity index; Inputting the historical fault record sensitivity index of the power transmission equipment, the total load current of the power transmission equipment, the ambient temperature of the environment in which the power transmission equipment is located, the ambient temperature threshold, the voltage value of the transmission line between the power transmission equipment and the load, the output rated voltage of the power transmission equipment, and the vibration parameter value of the power transmission equipment during operation into the equipment fault prediction index solving sub-model to obtain the equipment fault prediction index; The expression of the transmission equipment historical fault record sensitivity index calculation sub-model is: Among them, T total is the cumulative operating hours of the power transmission equipment; N is the number of historical failures of the power transmission equipment within one week of operation; S avg is the average severity score of the fault events that occurred in the transmission equipment within one week of operation; η is the time decay factor; t avg is the average time interval of fault events that occurred in the power transmission equipment within one week of operation; R is the sensitivity index of historical fault records of the power transmission equipment; The expression of the equipment failure prediction index solution sub-model is: Among them, I load Indicates the total load current of the power transmission equipment; T a Indicates the ambient temperature of the environment where the power transmission equipment is located; T ref Indicates the ambient temperature threshold; V line Indicates the voltage value of the transmission line between the transmission equipment and the load; V ref It represents the output rated voltage of the power transmission equipment; H represents the vibration parameter value of the power transmission equipment during operation; F represents the equipment failure prediction index.

6. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 3, characterized in that: Inputting the model input parameters into the equipment energy consumption hidden danger index detection model to obtain the equipment energy consumption hidden danger index includes: Obtain the impact value of the environmental factor according to the product of the adjustment factor of the environmental factor and the measured value of the environmental factor; Obtaining a device heat dissipation constant according to the product of a heat dissipation coefficient, a surface area of ​​a heat dissipation region of a heat dissipation module of the power transmission equipment, and a thermal conductivity of the heat dissipation module of the power transmission equipment; Input the impact value of the environmental factor, the equipment heat dissipation constant, the output power of the power transmission equipment, the input power of the power transmission equipment, the correction coefficient, the power transmission loss, the ambient temperature of the environment where the power transmission equipment is located, the ambient temperature threshold and the weight coefficient of each environmental factor into the equipment energy consumption hidden danger index solving sub-model to obtain the equipment energy consumption hidden danger index; The expression of the impact value of the environmental factor is: AND j =β j ·And j ; Among them, β j represents the regulatory factor of the jth environmental factor; e j represents the measured value of the jth environmental factor; E j represents the impact value of the jth environmental factor; The expression of the heat dissipation constant of the device is: C heat =h·A·k; Wherein, h represents the heat dissipation coefficient; A represents the surface area of ​​the heat dissipation region of the heat dissipation module of the power transmission equipment; k represents the thermal conductivity of the heat dissipation module of the power transmission equipment; C heat Indicates the heat dissipation constant of the device; The expression of the equipment energy consumption hidden danger index solution sub-model is: Among them, P output Indicates the output power of the power transmission equipment; P input Indicates the input power of the power transmission equipment; δ indicates the correction factor; R loss Represents transmission loss; T a Indicates the ambient temperature of the environment where the power transmission equipment is located; T ref represents the ambient temperature threshold; J represents the total number of environmental factors; σ j represents the weight coefficient of the jth environmental factor; E eff Indicates the energy consumption potential risk index.

7. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 3, characterized in that: The step of adjusting the operating state of the power transmission equipment according to the hidden danger detection information further includes: When the comprehensive hidden danger probability is greater than or equal to the comprehensive hidden danger probability threshold, adjusting the operating state of the power transmission equipment; and / or, When the equipment failure prediction index is greater than or equal to the equipment failure prediction threshold, adjusting the operating state of the power transmission equipment; and / or, When the equipment energy consumption hidden danger index is less than the equipment energy consumption hidden danger threshold, the operating state of the power transmission equipment is adjusted.

8. The method for monitoring and suppressing hidden dangers of power transmission equipment according to claim 1, characterized in that: The step of adjusting the operating state of the power transmission device comprises: Adjusting the output power of the power transmission device and / or switching the working state of the power transmission device; The working status of the power transmission equipment includes: full load, overload and underload.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the hidden danger monitoring and suppression method for power transmission equipment according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the hidden danger monitoring and suppression method for power transmission equipment according to any one of claims 1 to 8 when executed.

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