Power equipment load performance state prediction and diagnosis method, electronic equipment, and storage medium

Abstract

The present invention provides a method for predicting and diagnosing the load performance status of power equipment, comprising the steps of: establishing a relationship model, predicting the maximum current carrying capacity of the equipment, predicting the maximum temperature at rated load, predicting the short-time emergency load current, and diagnosing the load health of the equipment. The present invention relates to an electronic device and a storage medium for executing the above method. The present invention solves the inherent problems existing in the status monitoring and diagnosis of power equipment, and is an important means to achieve the status maintenance management of power equipment and improve the lean level of power production and operation management. A relationship model between load current, temperature and ambient temperature is established, and four specific applications of load performance monitoring and diagnosis methods for power equipment are further designed and formulated: a method for predicting the maximum current carrying capacity of the equipment; a method for predicting the maximum temperature at rated load; a method for predicting the short-time emergency load current; and a diagnosis of the load health of the equipment. It is suitable for different working conditions and functional requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring of electric power equipment, and in particular to a method for predicting and diagnosing the load performance state of electric power equipment, an electronic device, and a storage medium. Background Art

[0002] In recent years, as global energy issues have become increasingly severe, countries around the world have carried out research on smart grids. The ultimate goal of smart grids is to build a panoramic real-time system covering the entire production process of the power system, including power generation, transmission, transformation, distribution, power consumption and dispatching. Power equipment status monitoring and diagnosis is an important part of the smart grid. The implementation of its key technologies can extend the service life of equipment, reduce the occurrence of sudden failures, and improve the power supply reliability of power equipment. Therefore, it has been widely used in power systems.

[0003] The online monitoring data of power equipment is growing in real time. Current sensors and temperature sensors have gradually become popular. The amount of monitoring data is huge and the speed of monitoring data generation is extremely fast. Especially on load current heating power equipment, such as power switchgear, power equipment and other equipment. In the industry, a large amount of load current and temperature data of field equipment has been collected, and most of them only use threshold judgment for alarm. The status monitoring of power equipment has the disadvantage of insufficient diagnosis and early warning functions, which greatly restricts the comprehensive promotion and application of power equipment status monitoring technology. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for predicting and diagnosing the load performance status of power equipment, which effectively solves the inherent problems existing in the status monitoring and diagnosis of power equipment, and is an important means to realize the status maintenance management of power equipment and improve the lean level of power production and operation management.

[0005] The present invention provides a method for predicting and diagnosing the load performance state of an electric power device, comprising the following steps:

[0006] Establishment of relationship model: Based on the theoretical analysis of current heating and heat dissipation process, establish mathematical model of power equipment load and temperature;

[0007] Predicting the maximum current carrying capacity of the equipment, by bringing the maximum allowable temperature of the power equipment into the load and temperature mathematical model of the power equipment, to obtain the maximum current carrying capacity of the power equipment;

[0008] The maximum temperature prediction at rated load is to bring the rated load current value of the power equipment into the load and temperature mathematical model of the power equipment to obtain the maximum temperature value of the power equipment at rated load;

[0009] Short-time emergency load current prediction, bringing the short-time emergency load demand constraint condition data into the load and temperature mathematical model of the power equipment to obtain the maximum temperature value of the power equipment under short-time emergency load;

[0010] Equipment load health diagnosis, the collected load current and ambient temperature data of the power equipment are brought into the load and temperature mathematical model of the power equipment, the ideal load temperature rise value of the power equipment is obtained, the ideal load temperature rise value is compared with the measured temperature data, and the equipment load health is judged by the comparison result.

[0011] Furthermore, in the step of establishing the relational model, the system dynamic model equation is:

[0012] ΔT(t)=k i ae -at *I(t) 2 , * is the convolution calculation

[0013] The static model equation of the system is:

[0014] ΔT(t)=k i I(t) 2

[0015] Wherein, ΔT(t)=T1(t)-T0(t); ΔT(t) is the temperature rise of the measured section of the power equipment at time t; T1(t) is the temperature of the temperature measuring point of the measured section of the power equipment at time t; T0(t) is the temperature of the environmental reference point of the measured section of the power equipment at time t; a is the thermal capacity temperature delay effect coefficient of the dynamic model; k i The load performance coefficient that removes the heat-induced temperature rise of the load.

[0016] Furthermore, in the step of predicting the maximum current carrying capacity of the equipment, the maximum allowable temperature of the power equipment is brought into the static model equation of the system, and the predicted maximum current carrying capacity is:

[0017] ΔT M =T M -T0(t)

[0018] Among them, T M is the maximum allowable temperature of the power equipment, I(t) M is the predicted value of the maximum current carrying capacity of the equipment, T M ≥T0(t).

[0019] Furthermore, in the step of predicting the maximum temperature at rated load, the maximum temperature value of the power equipment at rated load is:

[0020] T(t) M =ΔT M +T0(t), ΔTM =k i I e 2

[0021] Among them, I e is the rated load current value, T(t) M is the maximum temperature value of the equipment at rated load, I(t)≥I e .

[0022] Furthermore, in the short-time emergency load current prediction step, the maximum temperature value of the short-time emergency load of the power equipment is:

[0023] * is the convolution calculation

[0024] The static model equation of the system is:

[0025] T s =ΔT(t s )+T0(t s )

[0026] Among them, I s is the short-time emergency load current, t s is the emergency loading time, T s For s The temperature at the end, ΔT(t s ) is the maximum temperature value during the predicted short-term emergency load.

[0027] Furthermore, in the equipment load health diagnosis step, the ideal load temperature rise value of the power equipment is:

[0028] ΔT(t) LX =k i ae -at *I(t) 2 , * is the convolution calculation

[0029]

[0030] ΔT(t) SC =T(t) SC -T0(t)

[0031] Where, ΔT(t) LX is the ideal temperature rise value of the load of the power equipment, T(t) SC is the measured temperature data, and ξ is the equipment load health.

[0032] Furthermore, in the equipment load health diagnosis step, when ξ≥85%, the equipment health state is judged to be healthy, and the recommended maintenance / inspection cycle is 5 years; when 60%≤ξ<85%, the equipment health state is judged to be sub-healthy, and the recommended maintenance / inspection cycle is 3 years; when 35%≤ξ<60%, the equipment health state is judged to be a potential fault, and the recommended maintenance / inspection cycle is 1 year; when ξ<35%, the equipment health state is judged to be a serious potential fault, and the recommended maintenance / inspection cycle is 1 month.

[0033] Furthermore, in the step of establishing the relationship model, in the rough calculation process, k i is considered a constant; in the precise diagnostic calculation process, k i As a function of conductor temperature and ambient temperature, k is obtained by fitting the experimental data. i Empirical formula of the function.

[0034] An electronic device comprises: a processor; a memory; and a program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes a method for executing a load performance state prediction and diagnosis method of an electric power device.

[0035] A computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to predict and diagnose a method for load performance status of an electric power device.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Based on the relationship model between load current, temperature and ambient temperature, the present invention designs a method for monitoring and diagnosing the load performance of power equipment, which is applied to the operation and maintenance system of power equipment, thereby improving the management level of power equipment. It can well solve the inherent problems existing in the status monitoring and diagnosis of power equipment, and is an important means to realize the status maintenance management of power equipment and improve the lean level of power production and operation management. Its basic principle is to realize the real-time perception, analysis, diagnosis and evaluation and prediction of the operating status of power equipment through various sensor technologies, wide area communication technologies and information processing technologies. A relationship model between load current, temperature and ambient temperature is established, and four specific applications of load performance monitoring and diagnosis methods for power equipment are further designed and formulated: (1) Equipment maximum current carrying capacity prediction method; (2) Maximum temperature prediction method at rated load; (3) Short-term emergency load current prediction method; (4) Equipment load health diagnosis. It is suitable for different working conditions and functional requirements.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 This is a flow chart of the method for predicting and diagnosing the load performance status of electric power equipment of the present invention. DETAILED DESCRIPTION

[0041] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0042] Power equipment load performance status prediction and diagnosis methods, such as Figure 1 As shown, the following steps are included:

[0043] The relationship model is established based on the theoretical analysis of the current heating and heat dissipation process, that is, the relationship model between the load current, temperature and ambient temperature of the power equipment under ideal conditions. The load current and temperature rise of the power equipment have a definite correlation, and the mathematical model of the load and temperature of the power equipment is established.

[0044] In one embodiment, the model extreme value hypothesis method. When the physical variables in the model have measured data, one of the physical quantities (such as current, temperature or ambient temperature) can be set to an extreme value (maximum value, minimum value or rated value), and the other physical quantity (such as temperature rise or current) can be solved as an unknown. The solution obtained is the ideal value of the physical quantity under the ideal state of the equipment and a certain extreme condition, which is compared with the original design specification parameters of the equipment to diagnose the load performance status of the equipment. For example, the maximum current carrying capacity prediction method of the equipment; the maximum temperature prediction method at rated load.

[0045] In one embodiment, the model condition restriction method. This method requires the use of a dynamic (transient) relationship model. When the physical variables in the model have measured data, the time for the action of one of the physical quantities (such as current, temperature or ambient temperature) can be predicted (for example: loading a large current of 1500A for 2 hours from now on; another example: the high temperature ambient temperature reaches 105°C for 1 hour from now on, etc.), and the restrictive physical quantities (such as temperature or temperature rise) that affect the load or life of the equipment are solved as unknowns. The solution obtained is the ideal value of the physical quantity under a certain predictive restriction condition under the ideal state of the equipment. It is compared with the original design specification parameters of the equipment to determine whether the equipment can operate normally under this predictive restriction condition. For example, a short-term emergency load current prediction method.

[0046] In one embodiment, the ideal data comparison method. When the physical variables in the model have measured data, one of the physical quantities (such as temperature rise or current) can be solved as an unknown variable. Since the above model is an ideal relationship model of the device, the solution obtained is the ideal value of the physical quantity under the ideal state of the device. The ideal value is compared with the measured value, and the deviation result is judged to make a diagnosis of the health of the equipment load. For example, equipment load health diagnosis.

[0047] In one embodiment, in the relational model building step, the system dynamic (transient) model equation is:

[0048] ΔT(t)=k i ae -at *I(t) 2 , * is the convolution calculation

[0049] The static (steady-state) model equation of the system is:

[0050] ΔT(t)=k i I(t) 2

[0051] Wherein, ΔT(t)=T1(t)-T0(t); ΔT(t) is the temperature rise of the measured section of the power equipment at time t; T1(t) is the temperature of the temperature measuring point of the measured section of the power equipment at time t; T0(t) is the temperature of the environmental reference point of the measured section of the power equipment at time t; a is the thermal capacity temperature delay effect coefficient of the dynamic model; k i The load performance coefficient that removes the heat-induced temperature rise of the load.

[0052] In the rough calculation process, k i is considered a constant; in the precise diagnostic calculation process, k i As a function of conductor temperature and ambient temperature, k is obtained by fitting the experimental data. i Empirical formula of the function.

[0053] Prediction of the maximum current carrying capacity of the equipment, because the maximum current carrying capacity of the power equipment is limited by the actual maximum temperature of the power equipment. The maximum allowable temperature of the power equipment is brought into the static (steady-state) model of the system to obtain the maximum current carrying capacity of the power equipment. The formula is:

[0054] ΔT M =T M -T0(t)

[0055] Among them, T M is the maximum allowable temperature of the power equipment. Different power equipment T M The system can be configured to have different values, I(t) M is the predicted value of the maximum current carrying capacity of the equipment, T M ≥T0(t).

[0056] By comparing the predicted maximum current carrying capacity of the equipment with the rated current value of the original design of the equipment, the actual load capacity of the current power equipment can be determined, which may be higher or lower than the nominal load capacity of the equipment. In the equipment load scheduling, the predicted maximum current carrying capacity of the equipment calculated online should be used as the scheduling reference to maximize the protection of the safe operation of the power equipment and improve the energy efficiency of the equipment. At the same time, the higher the predicted maximum current carrying capacity, the better the health of the equipment.

[0057] The maximum temperature prediction at rated load is to bring the rated load current value of the power equipment into the load and temperature mathematical model of the power equipment to obtain the maximum temperature value of the power equipment at rated load. The formula is:

[0058] T(t) M =ΔT M +T0(t), ΔT M =k i I e 2

[0059] Among them, I e is the rated load current value, which is set according to the actual specifications of the power equipment (different power equipment I e The value is set differently, the system can be configured), T(t) M is the maximum temperature value of the equipment at rated load, I(t)≥I e The system condition of this method is the system static condition.

[0060] By comparing the predicted maximum temperature at rated load with the maximum allowable temperature of the equipment, the current temperature rise load performance of the power equipment can be determined. The higher the predicted temperature, even if it exceeds the maximum limit, it means that the equipment's load performance is reduced and the equipment is faulty. Conversely, the lower the predicted temperature, the better the equipment's load performance and the healthier the equipment.

[0061] Short-term emergency load current prediction, the short-term emergency load demand constraint data is brought into the load and temperature mathematical model of the power equipment to obtain the maximum temperature value of the short-term emergency load of the power equipment. The formula is:

[0062] * is the convolution calculation

[0063] The static model equation of the system is:

[0064] T s =ΔT(t s )+T0(t s )

[0065] Among them, I s is the short-time emergency load current, t s is the emergency loading time, T s For s The temperature at the end, ΔT(t s ) is the maximum temperature value of the predicted short-term emergency load. The system condition of this method is the system dynamic condition. Different power equipment coefficient a and k i The value settings are different and the system can be configured.

[0066] When a project needs to load a short-term emergency load, this step can be used to measure whether the equipment has such a load-bearing capacity. First, a short-term emergency load demand restriction condition is given (for example: load a large current of 1500A for 2 hours from now on), and then this conditional data is substituted into the relationship model to calculate the maximum temperature prediction value after 2 hours. If this value is less than or equal to the allowable maximum temperature value, it is determined that the equipment can withstand the short-term emergency load demand of the above requirement. Otherwise, it is not possible.

[0067] The feasibility of calculating short-term emergency loads based on online monitoring data of equipment provides a feasibility basis for power emergency dispatch in engineering projects and ensures safe and reliable operation of power equipment.

[0068] Equipment load health diagnosis, the collected load current and ambient temperature data of the power equipment are brought into the load and temperature mathematical model of the power equipment, the ideal load temperature rise value of the power equipment is obtained, the ideal load temperature rise value is compared with the measured temperature data, and the equipment load health is judged by the comparison results. Specifically, the ideal load temperature rise value of the power equipment is:

[0069] ΔT(t) LX =k i ae -at *I(t) 2 , * is the convolution calculation

[0070]

[0071] ΔT(t) SC =T(t) SC -T0(t)

[0072] Where, ΔT(t) LX is the ideal temperature rise value of the load of the power equipment, T(t) SC is the measured temperature data, ξ is the equipment load health, which is used to indicate the health status of the cabinet, 100% is the full scale, representing complete health, and 0 is the minimum value, representing a serious fault state. The system condition of this method is the system dynamic condition. Different power equipment coefficients a and k i The value settings are different and the system can be configured.

[0073] Set the equipment load health diagnostic threshold to make status diagnosis. Ideally, ξ≈100%; when ξ is small, it means that the load status of the equipment is abnormal. Preferably, the equipment load health diagnostic threshold is as follows:

[0074] Equipment load health Device health status Recommended maintenance / inspection cycle ξ ≥ 85% healthy 5 years 60%≤ξ<85% Sub-health 3 years 35%≤ξ<60% Fault hazards 1 year ξ<35% Serious potential malfunction January

[0075] When the equipment load current is not very large and the temperature is not very high, the equipment load health status can be diagnosed. In practical applications, predictions can be made before equipment failures occur; within a certain load range, equipment diagnosed as having potential failures can continue to operate without shutting down, and maintenance can be arranged only when necessary or possible.

[0076] An electronic device includes: a processor; a memory; and a program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes a method for executing a load performance state prediction and diagnosis method of an electric power device.

[0077] A computer-readable storage medium stores a computer program, which is executed by a processor to predict and diagnose a load performance state of an electric power device.

[0078] Based on the relationship model between load current, temperature and ambient temperature, the present invention designs a method for monitoring and diagnosing the load performance of power equipment, which is applied to the operation and maintenance system of power equipment, thereby improving the management level of power equipment. It can well solve the inherent problems existing in the status monitoring and diagnosis of power equipment, and is an important means to realize the status maintenance management of power equipment and improve the lean level of power production and operation management. Its basic principle is to realize the real-time perception, analysis, diagnosis and evaluation and prediction of the operating status of power equipment through various sensor technologies, wide area communication technologies and information processing technologies. A relationship model between load current, temperature and ambient temperature is established, and four specific applications of load performance monitoring and diagnosis methods for power equipment are further designed and formulated: (1) Equipment maximum current carrying capacity prediction method; (2) Maximum temperature prediction method at rated load; (3) Short-term emergency load current prediction method; (4) Equipment load health diagnosis. It is suitable for different working conditions and functional requirements.

[0079] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for predicting and diagnosing load performance status of power equipment, characterized in that The following steps are involved: S1, Relationship model construction: Based on the theoretical analysis of the current heating effect and heat dissipation mechanism, a mathematical model of the relationship between the load and temperature of power equipment is constructed. The specific expression is: The system dynamic model equation is: ΔT(t)=k i ae -at *I(t) 2 ,* is the convolution calculation The static model equation 1 of the system is: ΔT(t)=k i I(t) 2 Wherein, ΔT(t)=T1(t)-T0(t); ΔT(t) is the temperature rise of the measured section of the power equipment at time t; T1(t) is the temperature of the temperature measuring point of the measured section of the power equipment at time t; T0(t) is the ambient temperature of the power equipment at time t; a is the thermal capacity temperature delay effect coefficient of the dynamic model; k i is the load performance coefficient of the temperature rise caused by load; S2, prediction of the maximum current carrying capacity of the equipment: taking the maximum allowable temperature of the electric equipment as an input parameter, substituting it into the mathematical model, and calculating the maximum current carrying capacity of the electric equipment at the maximum allowable temperature; S3, rated load maximum temperature prediction: the rated load current value of the power equipment is used as an input parameter, substituted into the mathematical model, and the maximum temperature prediction value of the power equipment at rated load is calculated; S4, short-term emergency load capacity assessment: taking the short-term emergency load demand constraint as an input parameter, substituting it into the mathematical model, and calculating the maximum temperature prediction value of the power equipment under the short-term emergency load to assess its emergency load capacity; S5, equipment load health diagnosis: the collected load current and ambient temperature data of the power equipment are used as input parameters, substituted into the mathematical model, and the ideal temperature rise value of the power equipment under the current load is calculated. The ideal temperature rise value of the load is then compared and analyzed with the measured temperature data. Different situations are divided according to the comparison results, and the load health of the power equipment is calculated.

2. The method for predicting and diagnosing load performance status of electric power equipment according to claim 1, characterized in that: In the step S2, the maximum current carrying capacity prediction of the equipment, the maximum current carrying capacity of the power equipment at the maximum allowable temperature is calculated as follows: ΔT M =T M -T0(t),T M ≥T0(t) Among them, T M is the maximum allowable temperature of the electrical equipment; ΔT M is the maximum allowable temperature rise of the power equipment; T0(t) is the ambient temperature of the power equipment at time t; I(t) M k is the predicted value of the maximum current carrying capacity of the equipment; i is the load performance coefficient of the temperature rise caused by load.

3. The method for predicting and diagnosing load performance status of electric power equipment according to claim 1, characterized in that: In the step of predicting the maximum temperature under rated load in S3, the formula for calculating the predicted value of the maximum temperature under rated load of the power equipment is: T(t) M =ΔT M +T0(t),ΔT M =k i I e 2 Among them, I e is the rated load current value; T(t) M ΔT is the predicted value of the maximum temperature of the equipment under rated load; M is the predicted maximum temperature rise of the power equipment under rated load; T0(t) is the ambient temperature of the power equipment at time t; k i is the load performance coefficient of the temperature rise caused by load.

4. The method for predicting and diagnosing load performance status of electric power equipment according to claim 1, characterized in that: In the step S4, short-term emergency load capacity assessment, the maximum temperature prediction value of the power equipment under short-term emergency load is calculated as follows: * is the convolution calculation The static model equation 2 of the system is: T s =ΔT(t s )+T0(t s ) Among them, I s is the short-time emergency load current; t s is the emergency loading time; T0(t s ) The predicted value of the ambient temperature of the power equipment at the end of the emergency loading time is approximately the current ambient temperature value; T s is the predicted value of the maximum temperature of the short-term emergency load; ΔT(t s ) is the predicted value of the maximum temperature rise of short-term emergency load.

5. The method for predicting and diagnosing load performance status of electric power equipment according to claim 1, characterized in that: In the step of diagnosing the equipment load health in S5, the calculation formula of the load health of the power equipment is: ΔT(t) LX =k i ae -at *I(t) 2 ,* is the convolution calculation ΔT(t) SC =T(t) SC -T0(t) Where, ΔT(t) LX is the ideal temperature rise value of the load of the power equipment; T(t) SC is the measured temperature value; ΔT(t) SC is the measured temperature rise value; ξ is the equipment load health; NULL means none, that is, the health cannot be calculated; c is the allowable deviation of the temperature measurement system, and the default value is 3K.

6. An electronic device, characterized in that include: processor; Memory; And a program, wherein the program is stored in the memory and configured to be executed by a processor, and the program includes instructions for executing the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to perform the method according to any one of claims 1 to 5.

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