A remote intelligent maintenance system and method for power equipment

Abstract

The application relates to the technical field of maintenance operation management, and particularly discloses a power equipment remote intelligent maintenance system and method, which comprises the following steps: S1: obtaining the oil temperature of a transformer, screening out a first-level monitoring device and a second-level monitoring device, and pre-setting an initial detection time length and a detection node based on historical maintenance times; S2: obtaining equipment data between a current detection node and a previous detection node, calculating a fault value of a first-level equipment based on the equipment data, and screening out a pending equipment; S3: obtaining the resistivity of an insulation resistance in the pending equipment, calculating the conductivity and the electric field intensity, calculating the dielectric loss power of the insulation resistance in the pending equipment, and screening out a maintenance equipment. The application introduces a power equipment remote intelligent maintenance system and method to improve the traditional power equipment maintenance, so that the fault point can be quickly and accurately positioned, and the time and resource waste in the power equipment operation and maintenance process can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance, repair, operation and management, and in particular to a remote intelligent maintenance system and method for electric power equipment. Background Art

[0002] Traditional power equipment maintenance relies primarily on regular on-site inspections by maintenance personnel. This approach requires maintenance personnel to personally visit each equipment installation site and conduct inspections one by one according to a predetermined schedule.

[0003] Traditional power equipment maintenance requires significant manpower. To ensure comprehensive and accurate inspections, a large number of personnel are often required. Furthermore, power equipment is often widely distributed, potentially involving diverse locations and complex environmental conditions. This requires significant travel time for personnel, further increasing the manpower requirement. Furthermore, when power equipment fails, the drawbacks of traditional maintenance methods become even more pronounced. Lacking effective means to quickly and accurately locate the fault point, maintenance personnel are forced to rely on their own experience and limited detection tools for large-scale, blind investigations. This large-scale, blind investigation is not only inefficient but also wastes time and resources.

[0004] In the existing technology, due to the obvious deficiencies in manpower, material resources, time and resource utilization of traditional power equipment maintenance methods, it is difficult to meet the needs of efficient and stable operation of modern power systems. There is an urgent need to introduce a remote intelligent maintenance system and method for power equipment to improve the traditional power equipment maintenance, so as to quickly and accurately locate the fault point and reduce the waste of time and resources in the operation and maintenance process of power equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide a remote intelligent maintenance system and method for power equipment to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A remote intelligent maintenance method for power equipment includes the following steps:

[0008] S1: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment;

[0009] Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance.

[0010] S2: Acquire device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer;

[0011] Calculate the fault value GZ of the first-level equipment based on the equipment data, and set the fault value GZ to be greater than the preset fault threshold GZ sta The first-level equipment is recorded as pending equipment;

[0012] S3: Obtain the resistivity ρ of the insulation resistor in the device to be determined, calculate the conductivity σ = 1 / ρ, obtain the voltage U applied to the insulation resistor and the distance d between the electrodes, calculate the electric field strength E = U / d, and calculate the dielectric loss power P = σE of the insulation resistor in the device to be determined 2 V, where V represents the volume of the insulation resistance in the device to be determined;

[0013] Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

[0014] As a further solution of the present invention: in the step S1, a buffer time t is preset, when the oil temperature W is greater than the oil temperature threshold W s The timer starts counting until the oil temperature T is lower than the preset oil temperature threshold W. s If the timing is less than the buffer time t, the device will be unmarked as a first-level device.

[0015] As a further solution of the present invention: in step S1, the method of presetting the initial detection time T based on the historical maintenance times N corresponding to the primary equipment and the secondary equipment includes:

[0016] Get the average maintenance time T of the first-level equipment and the second-level equipment ave , calculate the initial detection time Among them, T max Represents the preset maximum duration and the maximum duration T max >T ave , λ represents the preset adjustment coefficient.

[0017] As a further solution of the present invention: in step S2, the method for calculating the fault value G of the primary device and the secondary device based on the device data specifically includes:

[0018] Get the mean value LH of the current in the current transformer ave, calculate the current fluctuation Where t1 represents the time at which the last detection node is located, t2 represents the time at which the current detection node is located, and LH(t) represents the equation of how the current LH in the current transformer changes with time t.

[0019] Calculating fault values Wherein, γ1 represents a preset first adjustment coefficient, γ2 represents a preset second adjustment coefficient, γ1>0, γ2>0.

[0020] As a further solution of the present invention: in step S2, the method for correcting the initial detection time T based on the fault value G includes:

[0021] Correct the initial detection time T to When the oil temperature T is lower than the preset oil temperature threshold W s Stop correction.

[0022] As a further solution of the present invention: in the step S3, if there are pending devices with equal dielectric loss power P, the pending device with a larger fault value is placed at a front position in the sorting.

[0023] As a further solution of the present invention: in step S3, the method for screening equipment to be maintained based on dielectric loss power P and fault value GZ includes:

[0024] Calculate the value to be maintained X=η1P+η2GZ, where η1 and η2 represent the first and second correction coefficients, and mark the pending device with the largest value to be maintained X as the device to be maintained.

[0025] A remote intelligent maintenance system for power equipment, comprising:

[0026] Filter module: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment;

[0027] Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance.

[0028] Detection module: acquires device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer;

[0029] Calculate the fault value GZ of the first-level equipment based on the equipment data, and set the fault value GZ to be greater than the preset fault threshold GZsta The first-level equipment is recorded as pending equipment;

[0030] Calculation module: obtain the resistivity ρ of the insulation resistance in the device to be determined, calculate the conductivity σ = 1 / ρ, obtain the voltage U applied to the insulation resistance and the distance d between the electrodes, calculate the electric field strength E = U / d, and calculate the dielectric loss power P = σE of the insulation resistance in the device to be determined 2 V, where V represents the volume of the insulation resistance in the device to be determined;

[0031] Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

[0032] Beneficial effects of the present invention: The present invention first detects the oil temperature in the transformer to preliminarily screen out the power equipment that needs to be monitored, reducing the number of items that need to be monitored, and then sets the initial detection time according to the number of historical maintenance times of the power equipment. The more historical maintenance times, the shorter the initial detection time and the more frequent the detection, which is conducive to reducing the time that the power equipment is shelved due to problems.

[0033] The fault value is then calculated by acquiring device data between the detection nodes. A significant difference in the amplitude of the unit's shaft vibration and the motor's stator and rotor vibrations indicates a high probability of a problem within the power equipment. Therefore, to prevent subsequent failures, further observation is required when the fault value exceeds a certain range. The purpose of selecting pending devices is to further narrow the scope of inspection. Finally, the dielectric loss power is calculated to select the equipment most in need of maintenance.

[0034] In summary, the present invention introduces a remote intelligent maintenance system and method for power equipment to improve traditional power equipment maintenance, thereby achieving rapid and accurate fault point positioning and reducing the waste of time and resources during the operation and maintenance of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings.

[0036] Figure 1 It is a flow chart of a remote intelligent maintenance method for electric power equipment according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, the present invention is a remote intelligent maintenance method for power equipment, comprising the following steps:

[0039] S1: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment;

[0040] Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance.

[0041] S2: Acquire device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer;

[0042] Calculate the fault value GZ of the first-level equipment based on the equipment data, and set the fault value GZ to be greater than the preset fault threshold GZ sta The first-level equipment is recorded as pending equipment;

[0043] S3: Obtain the resistivity ρ of the insulation resistor in the device to be determined, calculate the conductivity σ = 1 / ρ, obtain the voltage U applied to the insulation resistor and the distance d between the electrodes, calculate the electric field strength E = U / d, and calculate the dielectric loss power P = σE of the insulation resistor in the device to be determined 2 V, where V represents the volume of the insulation resistance in the device to be determined;

[0044] Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

[0045] It should be noted that transformers, as core equipment in power system operations, are crucial for the safe and efficient operation of the entire power grid. The initial phase of this invention will focus on real-time monitoring and analysis of transformer oil temperature. An abnormally high oil temperature indicates potential faults within the transformer, such as winding overheating, increased core loss, and poor heat dissipation. This abnormal temperature rise can be the result of a combination of factors, including equipment aging, load fluctuations, ambient temperature effects, and improper maintenance. This system identifies power equipment with abnormally high oil temperatures. Firstly, this significantly reduces blind monitoring. Traditional power equipment management often relies on regular inspections or empirically based judgments, which are not only inefficient but also prone to missing potential problems. Real-time oil temperature monitoring allows for focused monitoring of equipment with potential problems, avoiding aimless and unfocused comprehensive monitoring, thereby saving significant manpower, material, and financial resources. Secondly, it ensures that resources are focused on the areas that require the most attention. Given the vast number of devices in a power system, it is clearly unrealistic and unnecessary to monitor and maintain all equipment to the same degree. By screening out equipment with abnormally high oil temperature, the present invention can concentrate limited resources, such as professional maintenance personnel, repair tools, spare parts, etc., on equipment most likely to have problems, thereby improving resource utilization efficiency and maintenance effects.

[0046] The historical maintenance times of the power equipment are then processed. The greater the historical maintenance times, the more losses the equipment has experienced in its life cycle, the more potential sources of failure may exist, or it may have entered a period of performance degradation. Therefore, more frequent monitoring is required. The method of dynamically adjusting the detection time according to the historical maintenance frequency greatly improves the accuracy of monitoring. In this way, the present invention can effectively shorten the time window for problem discovery and reduce losses caused by failure to discover equipment failures in a timely manner, thereby significantly improving the reliability and operating efficiency of the power system.

[0047] After initial screening and detection strategy customization, the process of the present invention enters the equipment status monitoring and analysis phase. In this phase, the system further collects and analyzes equipment data between detection nodes. This data covers the vibration of core components of the steam turbine generator set, such as the shaft neck, motor stator, and rotor, as well as key parameters of the current in the current transformer. This data collection can reflect the operating status of the equipment in real time.

[0048] By analyzing these parameters, the present invention can reveal possible abnormalities within the equipment. For example, if the amplitude of the unit's journal vibration differs significantly from that of the motor's stator and rotor vibrations, this often indicates a disruption in the equipment's dynamic balance, potentially due to bearing wear, asymmetry, balancing weight loss, or other mechanical failures. Such abnormal vibration changes are a significant sign of equipment failure and warrant attention.

[0049] In order to accurately determine whether the equipment is in an abnormal state, when the monitored fault value exceeds these preset safety ranges, the system will immediately issue a warning signal and mark the relevant equipment as pending equipment.

[0050] By marking a device as pending, the present invention provides precise guidance for subsequent in-depth inspections. This means maintenance personnel can quickly locate potentially problematic devices and conduct targeted inspections and maintenance. In this way, the present invention not only improves the accuracy and efficiency of fault diagnosis but also significantly reduces the time and cost of equipment repairs, providing a strong guarantee for the stable operation of the power system.

[0051] Finally, this invention introduces the calculation of dielectric loss power as the final assessment and decision-making basis for the entire monitoring and maintenance process. Dielectric loss power, as a key indicator of insulation material degradation, is of great importance. It is not only directly related to the insulation condition of the equipment but also indirectly reflects the overall health and operating efficiency of the equipment. In power systems, factors such as aging, moisture, and contamination of insulation materials can lead to a significant increase in dielectric loss power, thereby threatening the safe and stable operation of the system.

[0052] The present invention calculates the dielectric loss power of each pending device. This process involves the real-time collection and analysis of multi-dimensional data such as voltage, current, and temperature during the operation of the equipment, ensuring the accuracy and reliability of the calculation results. And by horizontally comparing the dielectric loss power of different devices, the present invention can clearly identify which devices have degraded to the point where the insulation performance requires immediate maintenance, thereby accurately locking in the equipment that most needs maintenance at the moment - that is, the equipment to be maintained. It not only ensures the pertinence and effectiveness of maintenance work, so that limited maintenance resources can be accurately allocated to where they are most needed, but also greatly improves maintenance efficiency. By promptly discovering and maintaining equipment with poor insulation performance, the present invention effectively avoids chain reactions caused by equipment failures and large-scale power outages in the power system, providing a solid guarantee for the stable operation of the power system. At the same time, by optimizing maintenance plans and improving maintenance efficiency, the present invention also helps to extend the service life of equipment, reduce overall operation and maintenance costs, and achieve economical and efficient operation of the power system.

[0053] In another preferred embodiment of the present invention, a buffer time t is preset, and when the oil temperature W is greater than the oil temperature threshold W s The timer starts counting until the oil temperature T is lower than the preset oil temperature threshold W. s If the timing is less than the buffer time t, the device will be unmarked as a first-level device.

[0054] It is worth noting that when the system detects that the equipment is fluctuating due to changes in temperature, it will not immediately determine it as a first-level device that requires emergency maintenance, but will enter a short buffer period. During this buffer period, the system will continue to monitor the equipment and conduct a comprehensive analysis based on historical data and current environmental conditions. If the performance of the equipment gradually returns to normal during the buffer period, or the fluctuation amplitude does not reach the set threshold, the system will determine that the fluctuation is a normal phenomenon to avoid misjudging it as a first-level device. This ensures that under extreme environmental conditions, the present invention can still accurately identify equipment that truly requires emergency maintenance, thereby optimizing the allocation of maintenance resources and reducing unnecessary maintenance work.

[0055] In another preferred embodiment of the present invention, a method for presetting the initial detection duration T based on the number of historical maintenance times N corresponding to the primary equipment and the secondary equipment includes:

[0056] Get the average maintenance time T of the first-level equipment and the second-level equipment ave , calculate the initial detection time Among them, T max Represents the preset maximum duration and the maximum duration T max >T ave , λ represents the preset adjustment coefficient.

[0057] It is understandable that the historical maintenance times of power equipment are processed. The more historical maintenance times, the more losses the equipment has experienced in its life cycle, the more potential sources of failure may exist, or it has entered a performance degradation period. Therefore, more frequent monitoring is required. The method of dynamically adjusting the detection time according to the historical maintenance frequency greatly improves the accuracy of monitoring. In this way, the present invention can effectively shorten the time window for problem discovery and reduce the losses caused by the failure to discover equipment failures in a timely manner, thereby significantly improving the reliability and operating efficiency of the power system.

[0058] In another preferred embodiment of the present invention, the method for calculating the fault value G of the primary device and the secondary device based on the device data specifically includes:

[0059] Get the mean value LH of the current in the current transformer ave , calculate the current fluctuation Where t1 represents the time at which the last detection node is located, t2 represents the time at which the current detection node is located, and LH(t) represents the equation of how the current LH in the current transformer changes with time t.

[0060] Calculating fault values Wherein, γ1 represents a preset first adjustment coefficient, γ2 represents a preset second adjustment coefficient, γ1>0, γ2>0.

[0061] It's important to note that the core concept behind calculating fault values ​​for primary and secondary equipment based on device data is the fluctuation characteristics of current in current transformers, which serve as a key basis for assessing equipment operating status. This approach focuses not only on the instantaneous current value but also on its temporal trend. Through sophisticated data analysis, this method enables early warning and precise location of potential equipment failures.

[0062] Obtaining the average current in the current transformer: First, the system collects current data from the current transformer in real time and uses a specific algorithm to calculate the average current over a period of time. This average value serves as a baseline for subsequent analysis, measuring the degree of current fluctuation. The introduction of the average value makes the analysis results more stable and reliable, reducing misjudgments caused by accidental factors.

[0063] Next, the current fluctuation is quantified by comparing the current value at the last detection node (time t1) with the current detection node (time t2). This step reveals the dynamic changes in current over time by comparing the current values ​​at different time points. The magnitude and frequency of current fluctuations directly reflect the stability and reliability of the device's operating status.

[0064] Preset adjustment coefficients and calculate fault values: After obtaining current fluctuation data, the system combines the preset first adjustment coefficient (γ1) and second adjustment coefficient (γ2) for comprehensive calculation. These two adjustment coefficients are set based on extensive experimental data and expert experience. They are designed to weight different types and degrees of current fluctuations, thereby more accurately assessing the equipment's failure risk. By applying these adjustment coefficients to the current fluctuation data, the system can derive a quantitative fault value, G, which intuitively reflects the equipment's current operating status and potential failure risk.

[0065] In another preferred embodiment of the present invention, the method for correcting the initial detection time T based on the fault value G includes:

[0066] Correct the initial detection time T to When the oil temperature T is lower than the preset oil temperature threshold W s Stop correction.

[0067] It is understandable that the method of dynamically adjusting the initial detection duration based on the fault value greatly improves the accuracy of monitoring. In this way, the present invention can effectively shorten the time window for problem discovery and reduce the losses caused by untimely detection of equipment failures, thereby significantly improving the reliability and operational efficiency of the power system.

[0068] In another preferred embodiment of the present invention, if there are pending devices with equal dielectric loss power P, the pending device with a larger fault value is placed at a higher position in the sorting.

[0069] It is worth noting that the processing method for certain special circumstances enhances the ability of the present invention to resist certain special circumstances.

[0070] In another preferred embodiment of the present invention, a method for screening equipment to be maintained based on dielectric loss power P and fault value GZ includes:

[0071] Calculate the value to be maintained X=η1P+η2GZ, where η1 and η2 represent the first and second correction coefficients, and mark the pending device with the largest value to be maintained X as the device to be maintained.

[0072] The stator is a crucial component in the motor that generates the magnetic field. When the motor is running, the stator vibrates due to electromagnetic forces. Abnormal stator vibration can cause loosening and wear between the stator windings and the core, leading to serious faults such as short circuits, and thus increasing equipment losses.

[0073] The rotor rotates at high speeds in the motor, and its vibration is directly related to the motor's operational stability and reliability. Excessive rotor vibration not only affects the normal operation of the motor but can also increase friction between the rotor and other components, accelerating the physical wear of the equipment.

[0074] Dielectric loss power is also a key factor affecting the operating status of power equipment. Dielectric loss power refers to the energy consumed per unit volume of a dielectric due to dielectric conductivity and polarization under the action of an AC electric field. When dielectric loss power exceeds a certain range, it indicates that the insulation performance of the equipment may have deteriorated, posing a potential safety hazard.

[0075] A remote intelligent maintenance system for power equipment, comprising:

[0076] Filter module: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment;

[0077] Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance.

[0078] Detection module: acquires device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer;

[0079] Calculate the fault value GZ of the first-level equipment based on the equipment data, and set the fault value GZ to be greater than the preset fault threshold GZ sta The first-level equipment is recorded as pending equipment;

[0080] Calculation module: obtain the resistivity ρ of the insulation resistance in the device to be determined, calculate the conductivity σ = 1 / ρ, obtain the voltage U applied to the insulation resistance and the distance d between the electrodes, calculate the electric field strength E = U / d, and calculate the dielectric loss power P = σE of the insulation resistance in the device to be determined 2 V, where V represents the volume of the insulation resistance in the device to be determined;

[0081] Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

[0082] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A remote intelligent maintenance method for power equipment, characterized in that: The following steps are involved: S1: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment; Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance. S2: Acquire device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer; The method for calculating the fault value GZ of the primary equipment and the secondary equipment based on the equipment data specifically includes: Get the mean value LH of the current in the current transformer ave , calculate the current fluctuation , where t1 represents the time at which the last detection node is located, t2 represents the time at which the current detection node is located, and LH(t) represents the equation of how the current LH in the current transformer changes with time t; Calculating fault values , where γ1 represents the preset first adjustment coefficient, γ2 represents the preset second adjustment coefficient, γ1>0, γ2>0, the fault value GZ is greater than the preset fault threshold GZ sta The first-level equipment is recorded as pending equipment; The method for correcting the initial detection time T based on the fault value GZ includes: Correct the initial detection time T to When the oil temperature W is lower than the preset oil temperature threshold W s Stop correction when S3: Obtain the resistivity ρ of the insulation resistor in the device to be determined, calculate the conductivity σ=1 / ρ, obtain the voltage U applied to the insulation resistor and the distance d between the electrodes, calculate the electric field strength E=U / d, and calculate the dielectric loss power of the insulation resistor in the device to be determined , where V represents the volume of the insulation resistance in the device to be determined; Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

2. A remote intelligent maintenance method for power equipment according to claim 1, characterized in that: In step S1, a buffer time t is preset. When the oil temperature W is greater than the oil temperature threshold W s The timer starts counting when the oil temperature W is lower than the preset oil temperature threshold W s If the timing is less than the buffer time t, the device will be unmarked as a first-level device.

3. A remote intelligent maintenance method for power equipment according to claim 1, characterized in that: In step S1, the method of presetting the initial detection time T based on the historical maintenance times N corresponding to the primary equipment and the secondary equipment includes: Get the average maintenance time T of the first-level equipment and the second-level equipment ave , calculate the initial detection time , where T max Represents the preset maximum duration and the maximum duration T max >T ave , λ represents the preset adjustment coefficient.

4. A remote intelligent maintenance method for power equipment according to claim 1, characterized in that: In step S3, if there are pending devices with the same dielectric loss power P, the pending device with the larger fault value is placed at the front of the ranking.

5. A remote intelligent maintenance method for power equipment according to claim 1, characterized in that: In step S3, the method for screening equipment to be maintained based on dielectric loss power P and fault value GZ includes: Calculate the value to be repaired , where η1 and η2 represent the first and second correction coefficients, and the pending equipment with the largest pending maintenance value X is marked as the equipment to be maintained.

6. A remote intelligent maintenance system for power equipment, characterized in that: include: Filter module: Get the oil temperature W of the transformer. If the oil temperature W is higher than the preset oil temperature threshold W s It is recorded as the first-level monitoring equipment, and the power equipment directly connected to the first-level monitoring equipment through the transmission line is recorded as the second-level equipment; Based on the historical maintenance times N of the first-level and second-level equipment, the initial detection time T is preset. Several detection nodes are preset. The time interval between adjacent detection nodes is equal to the initial detection time T. The first detection node is the time of the most recent maintenance record in the historical maintenance. Detection module: acquires device data between the current detection node and the previous detection node, wherein the device data includes the journal vibration A of the steam turbine generator set, the stator vibration Vs and rotor vibration Vr of the motor, and the current LH in the current transformer; The method for calculating the fault value GZ of the primary equipment and the secondary equipment based on the equipment data specifically includes: Get the mean value LH of the current in the current transformer ave , calculate the current fluctuation , where t1 represents the time at which the last detection node is located, t2 represents the time at which the current detection node is located, and LH(t) represents the equation of how the current LH in the current transformer changes with time t; Calculating fault values , where γ1 represents the preset first adjustment coefficient, γ2 represents the preset second adjustment coefficient, γ1>0, γ2>0, the fault value GZ is greater than the preset fault threshold GZ sta The first-level equipment is recorded as pending equipment; The method for correcting the initial detection time T based on the fault value GZ includes: Correct the initial detection time T to When the oil temperature W is lower than the preset oil temperature threshold W s Stop correction when Calculation module: obtain the resistivity ρ of the insulation resistance in the device to be determined, calculate the conductivity σ=1 / ρ, obtain the voltage U applied to the insulation resistance and the distance d between the electrodes, calculate the electric field strength E=U / d, and calculate the dielectric loss power of the insulation resistance in the device to be determined , where V represents the volume of the insulation resistance in the device to be determined; Based on the dielectric loss power P and fault value GZ, the equipment to be maintained is screened and maintained.

Images