Electricity monitoring method for urban tunnels

Abstract

The present invention discloses a method for monitoring power in a city tunnel, comprising: setting a power parameter detection device in the city tunnel, detecting power parameters of power equipment in the city tunnel by the power parameter detection device; constructing a database in the monitoring terminal in advance, pre-storing a judgment model in the database, setting an advance warning threshold and multiple treatment intervals of the power parameter in the judgment model; the judgment module compares the power parameter with the advance warning threshold, and when the gap between the power parameter and the advance warning threshold falls into the first treatment interval, providing the probability of a possible failure in the first treatment interval, when the gap falls into the second treatment interval, providing the probability of a possible failure in the second treatment interval, and when the gap falls into the third treatment interval, providing the probability of a possible failure in the third treatment interval. The present invention can improve the reliability of monitoring the power system in the city tunnel, thereby improving the stability of the operation of the power system in the city tunnel.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power detection, and in particular to an urban tunnel electric power monitoring method. Background Art

[0002] The urban tunnel power system is an important part of urban planning. The urban tunnel power system consists of power equipment such as cables, wires, and transmission equipment installed in urban underground tunnels. The operation of these power equipment affects the stability and reliability of the power system. Therefore, it is necessary to monitor these power equipment to detect faults and eliminate hidden dangers in a timely manner. The existing power monitoring method mainly collects the power parameters of the power equipment and compares the power parameters with a fixed preset alarm value. If the preset alarm value is exceeded, an alarm prompt is issued. The lack of more complex analysis of power parameters leads to insufficient intelligence in fault monitoring. Therefore, the reliability of urban tunnel power system monitoring needs to be improved. Summary of the invention

[0003] An object of the present invention is to solve at least the above problems and / or disadvantages and to provide at least the advantages which will be described hereinafter.

[0004] An object of the present invention is to provide a method for monitoring power in an urban tunnel, which can improve the reliability of monitoring the power system in an urban tunnel, thereby improving the stability of the operation of the power system in an urban tunnel.

[0005] In order to achieve these purposes and other advantages according to the present invention, a method for monitoring power in an urban tunnel is provided, comprising:

[0006] Step 1: a power parameter detection device is set in the urban tunnel, and the power parameters of the power equipment in the urban tunnel are detected by the power parameter detection device, and the power parameter detection device is connected to the monitoring terminal for communication, and the power parameters are sent to the monitoring terminal;

[0007] Step 2: constructing a database in the monitoring terminal in advance, wherein a judgment model is pre-stored in the database, wherein an advance warning threshold value and a plurality of handling intervals of the power parameter are set in the judgment model, wherein the judgment model is pre-set with a probability of a failure that may occur in each handling interval, wherein the plurality of handling intervals are a first handling interval in which the difference between the advance warning threshold value and the first critical value is reached, a second handling interval in which the difference between the advance warning threshold value and the first critical value and the second critical value is between, and a third handling interval in which the difference between the advance warning threshold value and the second critical value is between;

[0008] Step 3, the judgment module compares the power parameter with the advance warning threshold, and calculates the difference between the power parameter and the advance warning threshold; when the difference between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the probability of a failure that may occur in the first handling interval; when the difference between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the probability of a failure that may occur in the second handling interval; when the difference between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the probability of a failure that may occur in the third handling interval;

[0009] Step 4: manually determine the actual maintenance plan for the power equipment based on the treatment interval in which the gap between the power parameter provided by the judgment model and the advance warning threshold falls and the probability of possible failure.

[0010] Preferably, in the urban tunnel power monitoring method, in the step 2, the database is constructed in the following manner: pre-collecting multiple historical power parameters of the power equipment and whether a fault occurs under each historical power parameter, dividing the multiple historical power parameters into the multiple treatment intervals, wherein each treatment interval contains several historical power parameters, dividing the number of historical power parameters in which faults occur among the several historical power parameters contained in each treatment interval by the total number of historical power parameters contained in each treatment interval, and calculating the probability of failure occurring in each treatment interval.

[0011] Preferably, in the urban tunnel power monitoring method, in the step two, the judgment model is pre-set with the time when a failure may occur in each handling interval; in the step three, when the gap between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the time when a failure may occur in the first handling interval; when the gap between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the time when a failure may occur in the second handling interval; when the gap between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the time when a failure may occur in the third handling interval.

[0012] Preferably, in the urban tunnel power monitoring method, in the step 2, the multiple historical power parameters collected are divided into multiple sampling periods, each sampling period includes a group of historical power parameters, and several groups of historical power parameters including historical power parameters of fault occurrence are selected. For each group of historical power parameters, the first change time of the group of historical power parameters from falling into the first disposal interval to the actual occurrence of the fault, the second time from falling into the second disposal interval to the actual occurrence of the fault, and the third change time from falling into the third disposal interval to the actual occurrence of the fault are analyzed, and the average value of the first change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the first disposal interval, the average value of the second change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the second disposal interval, and the average value of the third change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the third disposal interval.

[0013] Preferably, in the urban tunnel power monitoring method, the sampling period is one day.

[0014] Preferably, in the urban tunnel power monitoring method, in the step four, the actual maintenance plan for the power equipment is determined based on the treatment interval within which the gap between the power parameter provided by the judgment model and the advance warning threshold falls, the probability of a failure and the time when a failure may occur.

[0015] Preferably, in the urban tunnel power monitoring method, the power parameters include current, voltage and power.

[0016] The present invention has at least the following beneficial effects:

[0017] The present invention provides a method for monitoring power in a city tunnel, comprising: step 1, setting a power parameter detection device in the city tunnel, detecting power parameters of power equipment in the city tunnel by the power parameter detection device, the power parameter detection device being connected to a monitoring terminal for communication, and sending the power parameters to the monitoring terminal; step 2, constructing a database in the monitoring terminal in advance, wherein a judgment model is pre-stored in the database, an advance warning threshold and a plurality of handling intervals of the power parameter are set in the judgment model, the judgment model is pre-set with the probability of a failure occurring in each handling interval, the plurality of handling intervals being a first handling interval whose difference with the advance warning threshold reaches a first critical value, a second handling interval whose difference with the advance warning threshold is between the first critical value and the second critical value, and a second handling interval whose difference with the advance warning threshold is between the second critical value and the third critical value. The third disposal interval between the power parameters and the advance warning threshold; Step 3, the judgment module compares the power parameter with the advance warning threshold, and calculates the gap between the power parameter and the advance warning threshold. When the gap between the power parameter and the advance warning threshold falls into the first disposal interval, the judgment model provides the probability of a possible failure in the first disposal interval; when the gap between the power parameter and the advance warning threshold falls into the second disposal interval, the judgment model provides the probability of a possible failure in the second disposal interval; when the gap between the power parameter and the advance warning threshold falls into the third disposal interval, the judgment model provides the probability of a possible failure in the third disposal interval; Step 4, manually determine the actual maintenance plan for the power equipment based on the disposal interval in which the gap between the power parameter and the advance warning threshold falls and the probability of a possible failure provided by the judgment model. The present invention constructs a database in a monitoring terminal, and pre-stores a judgment model in the database. An advance warning threshold and multiple handling intervals and the probability of possible failure in each handling interval are set in the judgment model. The multiple handling intervals include a first handling interval whose gap with the advance warning threshold reaches a first critical value, a second handling interval whose gap with the advance warning threshold is between a first critical value and a second critical value, and a third handling interval whose gap with the advance warning threshold is between a second critical value and a third critical value. The judgment model judges whether the power parameter falls into the first handling interval, the second handling interval or the third handling interval according to the gap between the power parameter and the advance warning threshold, and provides the probability of possible failure matching the corresponding handling interval. The supervisor determines the actual maintenance plan for the power equipment according to the handling interval and the probability of possible failure provided by the judgment model.The present invention can perform a more accurate and comprehensive analysis of power parameters, and thus provide judgment data with more reference value, which is conducive to supervisors to formulate more accurate maintenance plans, so as to improve the reliability of supervision of urban tunnel power systems and improve the stability of operation of urban tunnel power systems.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the urban tunnel power monitoring method provided by the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring power in an urban tunnel, comprising:

[0022] Step 1: An electric power parameter detection device is set in the urban tunnel, and the electric power parameters of the electric power equipment in the urban tunnel are detected by the electric power parameter detection device. The electric power parameter detection device is communicatively connected with the monitoring terminal to send the electric power parameters to the monitoring terminal.

[0023] Step 2: Pre-build a database in the monitoring terminal, wherein a judgment model is pre-stored in the database, wherein an advance warning threshold value and multiple handling intervals of the power parameter are set in the judgment model, and the judgment model is pre-set with the probability of failure in each handling interval, wherein the multiple handling intervals are respectively a first handling interval in which the difference between the advance warning threshold value and the first critical value reaches a second handling interval in which the difference between the advance warning threshold value and the first critical value is between the second critical value and the second critical value, and a third handling interval in which the difference between the advance warning threshold value and the second critical value is between the second critical value and the third critical value.

[0024] Step three, the judgment module compares the power parameter with the advance warning threshold, and calculates the difference between the power parameter and the advance warning threshold; when the difference between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the probability of failure in the first handling interval; when the difference between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the probability of failure in the second handling interval; when the difference between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the probability of failure in the third handling interval.

[0025] Step 4: manually determine the actual maintenance plan for the power equipment based on the treatment interval in which the gap between the power parameter provided by the judgment model and the advance warning threshold falls and the probability of possible failure.

[0026] The present invention constructs a database in a monitoring terminal, and pre-stores a judgment model in the database. In the judgment model, an advance warning threshold and multiple treatment intervals and the probability of a possible failure in each treatment interval are set. The multiple treatment intervals include a first treatment interval whose gap with the advance warning threshold reaches a first critical value, a second treatment interval whose gap with the advance warning threshold is between the first critical value and the second critical value, and a third treatment interval whose gap with the advance warning threshold is between the second critical value and the third critical value. The judgment model judges whether the power parameter falls into the first treatment interval, the second treatment interval or the third treatment interval according to the gap between the power parameter and the advance warning threshold, and provides the probability of a possible failure matching the corresponding treatment interval. The supervisor determines the actual plan for overhauling the power equipment according to the treatment interval and the probability of a possible failure provided by the judgment model. For example, if the first critical value, the second critical value and the third critical value increase in sequence, the degree of deviation of the first treatment interval, the second treatment interval and the third treatment interval from the advance warning threshold increases in sequence, and the probability of a possible failure in the first treatment interval, the probability of a possible failure in the second treatment interval and the probability of a possible failure in the third treatment interval will increase in sequence. That is, the gap between the power parameter and the advance warning threshold falling into a certain treatment interval reflects the degree of deviation of the power parameter, and the probability of possible failure corresponding to the treatment interval can reflect the probability of current failure of the power equipment. These two values ​​can provide sufficient reference and basis for formulating maintenance plans, so as to prepare more accurate and effective maintenance plans.

[0027] To sum up, the present invention can conduct a more accurate and comprehensive analysis of power parameters, and thus provide judgment data with more reference value, which is conducive to supervisory personnel to formulate more accurate maintenance plans, so as to improve the reliability of supervision of urban tunnel power systems and improve the stability of operation of urban tunnel power systems.

[0028] In a preferred embodiment, in the urban tunnel power monitoring method, in the step 2, the database is constructed in the following manner: pre-collecting multiple historical power parameters of the power equipment and whether a fault occurs under each historical power parameter, dividing the multiple historical power parameters into the multiple treatment intervals, wherein each treatment interval contains several historical power parameters, dividing the number of historical power parameters in which faults occur among the several historical power parameters contained in each treatment interval by the total number of historical power parameters contained in each treatment interval, and calculating the probability of failure occurring in each treatment interval.

[0029] When constructing a database, the present invention collects historical power parameters in advance, finds those historical power parameters that have failed from the collected historical power parameters, and then divides these historical power parameters into multiple treatment intervals. In this way, each treatment interval corresponds to a number of historical power parameters, some of which have failed, while others have not. By dividing the number of historical power parameters that have failed by the total number of historical power parameters in the treatment interval, a numerical value can be obtained, which is used as the probability that a failure may occur in the treatment interval.

[0030] In a preferred embodiment, in the urban tunnel power monitoring method, in the step two, the judgment model is pre-set with the time when the failure may occur in each handling interval; in the step three, when the gap between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the time when the failure may occur in the first handling interval; when the gap between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the time when the failure may occur in the second handling interval; when the gap between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the time when the failure may occur in the third handling interval.

[0031] In addition to judging the treatment interval and the probability of the occurrence of a fault, the present invention can also judge the time when the fault may occur. Based on the above judgment, the supervisor can more accurately grasp the operation of the power equipment and make a maintenance plan that is more in line with the actual situation. For example, when the estimated time when the fault may occur is 1 month, the maintenance plan can be arranged within 1 month. When the estimated time when the fault may occur is 1 week, the maintenance plan needs to be arranged within 1 week.

[0032] In a preferred embodiment, in the urban tunnel power monitoring method, in the step 2, the multiple historical power parameters collected are divided into multiple sampling periods, each sampling period includes a group of historical power parameters, and several groups of historical power parameters including historical power parameters of faults are selected. For each group of historical power parameters, the first change time of the group of historical power parameters from falling into the first disposal interval to the actual occurrence of the fault, the second time from falling into the second disposal interval to the actual occurrence of the fault, and the third change time from falling into the third disposal interval to the actual occurrence of the fault are analyzed, and the average value of the first change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the first disposal interval, the average value of the second change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the second disposal interval, and the average value of the third change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the third disposal interval.

[0033] According to the above operation, a set of historical power parameters can be obtained in each sampling period. Among them, some groups of historical power parameters eventually correspond to faults, and under some historical power parameters, the power equipment maintains correct operation and no faults occur. For each group of historical power parameters in which faults occur, the time from the time when the historical power parameters of this group fall into the first disposal interval value or fall into the second disposal interval or fall into the third disposal interval to the actual occurrence of the fault is counted, and then three average values ​​are calculated based on the above three times as the time when the fault may occur in each disposal interval. For example, suppose there are 10 groups of historical power parameters. The first change time of a group of historical power parameters from falling into the first disposal interval to the actual occurrence of the fault is 90 days, the second change time from falling into the second disposal interval to the actual occurrence of the fault is 70 days, and the third change time from falling into the third disposal interval to the actual occurrence of the fault is 30 days. The first time, second time and third time of these 10 groups are counted, and then the average values ​​of the first change time, the second change time and the third change time are calculated to be 90 days, 71 days and 28 days respectively. The judgment model falls into the first disposal interval according to the gap between the power parameters and the advance warning threshold, and the possible time of failure is judged to be 90 days.

[0034] In a preferred embodiment, in the urban tunnel power monitoring method, the sampling period is one day. The power consumption usually changes in a daily cycle, so the sampling period is set to one day.

[0035] In a preferred embodiment, in the urban tunnel power monitoring method, in the step four, the actual maintenance plan for the power equipment is determined based on the treatment interval within which the gap between the power parameter provided by the judgment model and the advance warning threshold falls, the probability of a failure and the time when a failure may occur.

[0036] For example, when it falls into the first handling interval, the estimated probability of a failure is 30%, and the time when the failure may occur is 90 days, then the maintenance plan can be arranged within 80 days; when it falls into the second handling interval, the estimated probability of a failure is 50%, and the time when the failure may occur is 70 days, then the maintenance plan can be arranged within 50 days; when it falls into the third handling interval, the estimated probability of a failure is 80%, and the time when the failure may occur is 30 days, then the maintenance plan can be arranged within 1 week.

[0037] In a preferred embodiment, in the urban tunnel power monitoring method, the power parameters include current, voltage and power.

[0038] In summary, the present invention constructs a database in the monitoring terminal, and pre-stores a judgment model in the database, sets an advance warning threshold and multiple treatment intervals and the probability of failure in each treatment interval in the judgment model, and the multiple treatment intervals include a first treatment interval whose gap with the advance warning threshold reaches a first critical value, a second treatment interval whose gap with the advance warning threshold is between the first critical value and the second critical value, and a third treatment interval whose gap with the advance warning threshold is between the second critical value and the third critical value, and the judgment model judges whether the power parameter falls into the first treatment interval, the second treatment interval or the third treatment interval according to the gap between the power parameter and the advance warning threshold, and provides the probability of failure that matches the corresponding treatment interval, and the supervisor determines the actual maintenance plan for the power equipment according to the treatment interval and the probability of failure that are provided by the judgment model. The present invention can analyze the power parameters more accurately and comprehensively, and then provide judgment data with more reference value, so as to facilitate the supervisor to formulate a more accurate maintenance plan, so as to improve the reliability of the supervision of the urban tunnel power system and improve the stability of the operation of the urban tunnel power system.

[0039] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily realized. Therefore, without departing from the general concept defined by the claims and equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. A method for monitoring power in an urban tunnel, characterized in that: include: Step 1: a power parameter detection device is set in the urban tunnel, and the power parameters of the power equipment in the urban tunnel are detected by the power parameter detection device, and the power parameter detection device is connected to the monitoring terminal for communication, and the power parameters are sent to the monitoring terminal; Step 2: constructing a database in the monitoring terminal in advance, wherein a judgment model is pre-stored in the database, wherein an advance warning threshold value and a plurality of handling intervals of the power parameter are set in the judgment model, wherein the judgment model is pre-set with a probability of a failure that may occur in each handling interval, wherein the plurality of handling intervals are a first handling interval in which the difference between the advance warning threshold value and the first critical value is reached, a second handling interval in which the difference between the advance warning threshold value and the first critical value and the second critical value is between, and a third handling interval in which the difference between the advance warning threshold value and the second critical value is between; Step 3, the judgment module compares the power parameter with the advance warning threshold, and calculates the difference between the power parameter and the advance warning threshold; when the difference between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the probability of a failure that may occur in the first handling interval; when the difference between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the probability of a failure that may occur in the second handling interval; when the difference between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the probability of a failure that may occur in the third handling interval; Step 4: manually determine the actual maintenance plan for the power equipment based on the treatment interval in which the gap between the power parameter provided by the judgment model and the advance warning threshold falls and the probability of possible failure.

2. The urban tunnel power monitoring method according to claim 1, characterized in that: In the step 2, the database is constructed in the following manner: pre-collecting multiple historical power parameters of the power equipment and whether a fault occurs under each historical power parameter, dividing the multiple historical power parameters into the multiple treatment intervals, wherein each treatment interval contains several historical power parameters, dividing the number of historical power parameters in which faults occur among the several historical power parameters contained in each treatment interval by the total number of historical power parameters contained in each treatment interval, and calculating the probability of failure occurring in each treatment interval.

3. The urban tunnel power monitoring method according to claim 1, characterized in that: In the step 2, the judgment model is pre-set with the time when the failure may occur in each handling interval; in the step 3, when the gap between the power parameter and the advance warning threshold falls into the first handling interval, the judgment model provides the time when the failure may occur in the first handling interval; when the gap between the power parameter and the advance warning threshold falls into the second handling interval, the judgment model provides the time when the failure may occur in the second handling interval; when the gap between the power parameter and the advance warning threshold falls into the third handling interval, the judgment model provides the time when the failure may occur in the third handling interval.

4. The urban tunnel power monitoring method according to claim 3, characterized in that: In the step 2, the multiple historical power parameters collected are divided into multiple sampling periods, each sampling period includes a group of historical power parameters, and several groups of historical power parameters including historical power parameters of fault occurrence are selected. For each group of historical power parameters, the first change time of the group of historical power parameters from falling into the first disposal interval to the actual occurrence of the fault, the second time from falling into the second disposal interval to the actual occurrence of the fault, and the third change time from falling into the third disposal interval to the actual occurrence of the fault are analyzed, and the average value of the first change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the first disposal interval, the average value of the second change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the second disposal interval, and the average value of the third change time of the several groups of historical power parameters is calculated as the time when the fault may occur in the third disposal interval.

5. The urban tunnel power monitoring method according to claim 4, characterized in that: The sampling period is one day.

6. The urban tunnel power monitoring method according to claim 5, characterized in that: In the step four, the actual maintenance plan for the power equipment is determined based on the treatment interval in which the gap between the power parameter provided by the judgment model and the advance warning threshold falls, the probability of a failure and the time when a failure may occur.

7. The urban tunnel power monitoring method according to claim 1, characterized in that: The electrical parameters include current, voltage and power.

Images