A monitoring method for high and low voltage distribution cabinet and distribution cabinet

Abstract

The present invention relates to the field of intelligent monitoring, and provides a monitoring method for a high and low voltage distribution cabinet and a distribution cabinet, wherein a plurality of sensors are installed in the high and low voltage distribution cabinet, monitoring data is acquired in real time through the plurality of sensors, target data in the monitoring data is screened out, a target model is established according to the target data, and the current state of the high and low voltage distribution cabinet is identified through the target model. The method can monitor the operating state of the distribution cabinet in real time and identify its abnormal signals, and establish a target model to provide abnormality assessment, risk warning and problem location through real-time analysis and calculation of the monitoring data, so as to ensure the stable operation of the distribution system in an automatic monitoring manner, and effectively prevent the distribution cabinet from having risky failures, thereby improving the safety performance of its working process.

Description

Technical Field

[0001] The invention relates to the field of intelligent monitoring, and in particular to a monitoring method for a high and low voltage distribution cabinet. Background Art

[0002] High and low voltage distribution cabinets play an important role in the power system. Their main functions are the distribution, control and protection of electricity. Traditional distribution cabinets mainly rely on manual methods to implement fault monitoring, which makes it difficult to quickly locate faults. At the same time, they lack real-time monitoring methods and cannot detect potential risks in advance, resulting in a lag in fault handling. On the other hand, traditional monitoring systems mostly detect abnormal situations based on simple threshold judgments, which are prone to false alarms or missed alarms and cannot cope with the operating environment of complex power systems.

[0003] At present, the mainstream intelligent distribution cabinet monitoring system mainly utilizes modern sensing, wireless communication and data processing technologies, so that it can monitor the operating status of the distribution cabinet more comprehensively. However, the operating environment of the power system is complex and changeable, and abnormal situations often involve the linkage changes of multiple parameters. Therefore, it is necessary to realize the real-time collection of key parameters through the arrangement of multiple sensors, and set a reasonable alarm mechanism, so as to effectively improve the monitoring safety and stable operation of high and low voltage distribution cabinets. Summary of the invention

[0004] The purpose of the present invention is to propose a monitoring method for high and low voltage distribution cabinets to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0005] The present invention provides a monitoring method for a high and low voltage power distribution cabinet and a power distribution cabinet. A plurality of sensors are installed in the high and low voltage power distribution cabinet, and monitoring data is acquired in real time through the plurality of sensors. Target data in the monitoring data is screened out, and a target model is established according to the target data. The current state of the high and low voltage power distribution cabinet is identified through the target model. The method can monitor the operating state of the power distribution cabinet in real time and identify its abnormal signals. Through real-time analysis and calculation of the monitoring data, a target model is established to provide abnormality assessment, risk warning and problem location, and the stable operation of the power distribution system is ensured in an automatic monitoring manner. At the same time, risk failures of the power distribution cabinet are effectively prevented, and the safety performance of its working process is improved.

[0006] In order to achieve the above object, according to one aspect of the present invention, a monitoring method for a high and low voltage distribution cabinet is provided, the method comprising the following steps:

[0007] Install multiple sensors in the high and low voltage distribution cabinets to obtain monitoring data in real time;

[0008] Filter out target data from monitoring data and establish a target model based on the target data;

[0009] Through the target model, the current status of the high and low voltage distribution cabinets is identified.

[0010] Furthermore, multiple sensors are installed in the high and low voltage distribution cabinets. The method for obtaining monitoring data in real time through multiple sensors is specifically as follows: multiple points are planned inside the high and low voltage distribution cabinets, passive wireless temperature measurement sensors are installed at these points, and temperature data collected by the passive wireless temperature measurement sensors are obtained, and the temperature data is used as monitoring data.

[0011] Furthermore, the method for obtaining the temperature data collected by the passive wireless temperature measurement sensor is specifically as follows: the number of the passive wireless temperature measurement sensors is recorded as N, pws(i) represents the i-th passive wireless temperature measurement sensor (among the N passive wireless temperature measurement sensors), the temperature value collected by the passive wireless temperature measurement sensor pws(i) at time t(j) is recorded as h(i,j), and h(i,j) is used as monitoring data;

[0012] Wherein, i and j are serial numbers, i=1, 2, ..., N, j=1, 2, ..., M, time t(j) is the jth second in the monitoring period T, and the length of the monitoring period is M seconds.

[0013] Preferably, M is set to an integer in the interval [300, 1000].

[0014] Furthermore, the method for filtering out target data in the monitoring data is specifically as follows:

[0015] In the screening algorithm, i is traversed from i=1 to i=N, thereby obtaining N arrays Hn1, Hn2,…, Hn N , taking all the values ​​in these N arrays as target data.

[0016] Furthermore, the screening algorithm is:

[0017] S1, for the temperature data collected by the i-th passive wireless temperature sensor pws(i), that is, M temperature values ​​h(i,1), h(i,2),…, h(i,M), set a blank array Hn i , set variable x=1, set variable h, and set the initial value of h to Q1, where Q1 represents the ratio of the maximum value among h(i,1), h(i,2),…, h(i,M) to the average value of h(i,1), h(i,2),…, h(i,M);

[0018] S2, if h(i,x) is less than h(i,x+1), go to S3; if h(i,x+1) is less than h(i,x), go to S4; if h(i,x) is equal to h(i,x+1), go to S5;

[0019] S3, if h(i, x)+h > h(i, x + 1), then add h(i, x) to the array Hn i , and set the value of the variable h to Q1, then go to S5; if h(i, x)+h ≤ h(i, x + 1), then increase the value of the variable h by 1 / Q1 (h = h+(1 / Q1)), and go to S5;

[0020] S4, if h(i, x)-h < h(i, x + 1), then add h(i, x) to the array Hn i , and set the value of the variable h to Q1, then go to S5; if h(i, x)-h ≥ h(i, x + 1), then decrease the value of the variable h by 1 / Q1, and go to S5;

[0021] S5, if the value of the variable x is less than M - 1, then increase the value of the variable x by 1, and go to S2; if the value of the variable x is equal to or greater than M - 1, then go to S6;

[0022] S6, use the array Hn i as the target data.

[0023] The beneficial effect of this step is that in the monitoring system of high and low voltage switchgear, multiple sensors are deployed to collect data on the environment and equipment status in real time. Since the variation range and data volume of temperature data are large, it is necessary to screen out the target data from a large amount of monitoring data, so as to more effectively establish the target model. During the screening process, by setting Q1 as the screening ratio, combined with the absolute and relative changes of the monitoring data values, all target data are dynamically screened to ensure that the obtained target data can reflect the equipment status of the switchgear and ensure the performance of the subsequent established model.

[0024] Further, the method for establishing the target model based on the target data is specifically as follows:

[0025] For the N maximum values in the N arrays Hn1, Hn2, …, Hn N , subtract these N maximum values from the model variable X respectively to obtain N first model factors;

[0026] Use the average value of the N arrays Hn1, Hn2, …, Hn N as the N second model factors;

[0027] Divide the N first model factors by the N second model factors in order and one by one to obtain N third model factors, and accumulate these N third model factors as the target model Target(X).

[0028] The beneficial effect of this step is: taking the maximum value (extreme change) and the average value (central trend) in the target data as model factors, and constructing the target model by accumulating the state variable X, the model can effectively identify the state change of the distribution cabinet and quickly detect whether there is an abnormal trend in the current cabinet temperature. When the state data continues to approach the potential risk point (such as X tends to Ahn i ), the model value increases significantly, thereby capturing abnormal signals in real time and accurately, fully improving the response sensitivity of distribution cabinet monitoring, and intervening in time to ensure the safe operation of the distribution cabinet.

[0029] Furthermore, the method for identifying the current state of the high and low voltage distribution cabinets through the target model is specifically as follows:

[0030] Create an array Bn of length N, and use Bn(i) to represent the i-th value in the array Bn;

[0031] The calculation method of Bn(i) is as follows: i Each value in is input into the target model as a model variable in turn to obtain multiple model outputs, and the values ​​of these multiple model outputs are accumulated as Bn(i);

[0032] When more than half of the values ​​in the array Bn are greater than Q2, the current state of the high and low voltage distribution cabinet is marked as abnormal. Q2 represents the average value of the array Bn.

[0033] The beneficial effect of this step is: through the target data and model output, each sensor point is assigned a corresponding Bn value, which reflects the degree of abnormality of the data collected by the sensor. The Bn values ​​of all sensors are combined. If the mean value is at the end of the data segment, it means that most of the sensor points have temperature abnormalities, so it is necessary to issue an alarm message. By further combining the abnormal positioning of the sensor, a better risk warning effect can be achieved.

[0034] Furthermore, the current state of the high and low voltage distribution cabinets is identified through the target model, and an alarm message is issued when the current state of the high and low voltage distribution cabinets is identified as abnormal. Specifically, the alarm message is sent to the control center through the communication module in the high and low voltage distribution cabinet. The alarm message includes the array Bn, the start and end times of the monitoring period, the real-time parameters of the high and low voltage distribution cabinets, and the specific time when the abnormal state is identified.

[0035] On the other hand, the present invention relates to a high and low voltage power distribution cabinet, which applies the above-mentioned monitoring method of a high and low voltage power distribution cabinet to perform safety monitoring during its working process.

[0036] The beneficial effects of the present invention are as follows: the method can monitor the operating status of the distribution cabinet in real time and identify its abnormal signals, and through real-time analysis and calculation of the monitoring data, establish a target model to provide abnormality assessment, risk warning and problem location, thereby ensuring the stable operation of the distribution system in an automatic monitoring manner, and effectively preventing risky failures of the distribution cabinet and improving the safety performance of its working process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Shown is a flow chart of a monitoring method for high and low voltage distribution cabinets. DETAILED DESCRIPTION

[0038] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0039] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0040] like Figure 1 The flowchart of the monitoring method of a high and low voltage distribution cabinet according to the present invention is shown below. Figure 1 A monitoring method for high and low voltage distribution cabinets according to an embodiment of the present invention is described below.

[0041] The present invention provides a monitoring method for a high and low voltage distribution cabinet, the method comprising the following steps:

[0042] Install multiple sensors in the high and low voltage distribution cabinets to obtain monitoring data in real time;

[0043] Filter out target data from monitoring data and establish a target model based on the target data;

[0044] Through the target model, the current status of the high and low voltage distribution cabinets is identified.

[0045] Furthermore, multiple sensors are installed in the high and low voltage distribution cabinets. The method for obtaining monitoring data in real time through multiple sensors is specifically as follows: multiple points are planned inside the high and low voltage distribution cabinets, passive wireless temperature measurement sensors are installed at these points, and temperature data collected by the passive wireless temperature measurement sensors are obtained, and the temperature data is used as monitoring data.

[0046] Specifically, the method for planning multiple points inside the high and low voltage distribution cabinet is as follows: according to the historical maintenance records of the high and low voltage distribution cabinet or the thermal imaging data of the high and low voltage distribution cabinet, the positions in the high and low voltage distribution cabinet that are prone to overheating are marked, and these marked positions are used as the multiple points;

[0047] Alternatively, based on the actual structure of the high and low voltage distribution cabinets, the location where heat is concentrated can be used as the point for installing the passive wireless temperature sensor.

[0048] Furthermore, the method for obtaining the temperature data collected by the passive wireless temperature measurement sensor is specifically as follows: the number of the passive wireless temperature measurement sensors is recorded as N, pws(i) represents the i-th passive wireless temperature measurement sensor (among the N passive wireless temperature measurement sensors), the temperature value collected by the passive wireless temperature measurement sensor pws(i) at time t(j) is recorded as h(i,j), and h(i,j) is used as monitoring data;

[0049] Wherein, i and j are serial numbers, i=1, 2, ..., N, j=1, 2, ..., M, time t(j) is the jth second in the monitoring period T, and the length of the monitoring period is M seconds.

[0050] Furthermore, the method for filtering out target data in the monitoring data is specifically as follows:

[0051] In the screening algorithm, i is traversed from i=1 to i=N, thereby obtaining N arrays Hn1, Hn2,…, Hn N , taking all the values ​​in these N arrays as target data.

[0052] Furthermore, the screening algorithm is:

[0053] S1, for the temperature data collected by the i-th passive wireless temperature sensor pws(i), that is, M temperature values ​​h(i,1), h(i,2),…, h(i,M), set a blank array Hn i , set variable x=1, set variable h, and set the initial value of h to Q1, where Q1 represents the ratio of the maximum value among h(i,1), h(i,2),…, h(i,M) to the average value of h(i,1), h(i,2),…, h(i,M);

[0054] Q1=MAX{h(i,1),h(i,2),…,h(i,M)}÷Ave{h(i,1),h(i,2),…,h(i,M)}, MAX{h(i,1),h(i,2),…,h(i,M)}

[0055] Denote the maximum value among h(i, 1), h(i, 2), …, h(i, M), and Ave{h(i, 1), h(i, 2), …, h(i, M)} represents the average value of h(i, 1), h(i, 2), …, h(i, M);

[0056] S2, if h(i, x) is less than h(i, x + 1), then go to S3; if h(i, x + 1) is less than h(i, x), then go to S4; if h(i, x) is equal to h(i, x + 1), then go to S5;

[0057] S3, if h(i, x)+h > h(i, x + 1), then add h(i, x) to the array Hn i , and set the value of the variable h to Q1, then go to S5; if h(i, x)+h ≤ h(i, x + 1), then increase the value of the variable h by 1 / Q1 (h = h+(1 / Q1)), and go to S5;

[0058] S4, if h(i, x)-h < h(i, x + 1), then add h(i, x) to the array Hn i , and set the value of the variable h to Q1, then go to S5; if h(i, x)-h ≥ h(i, x + 1), then decrease the value of the variable h by 1 / Q1, and go to S5;

[0059] S5, if the value of the variable x is less than M - 1, then increase the value of the variable x by 1, and go to S2; if the value of the variable x is equal to or greater than M - 1, then go to S6;

[0060] S6, use the array Hn i as the target data.

[0061] Furthermore, the method for establishing the target model based on the target data is specifically as follows:

[0062] For the N maximum values in the N arrays Hn1, Hn2, …, Hn N , subtract these N maximum values from the model variable X respectively to obtain N first model factors;

[0063] Use the average value of the N arrays Hn1, Hn2, …, Hn N as N second model factors;

[0064] Divide the N first model factors by the N second model factors one by one in order to obtain N third model factors, and accumulate these N third model factors as the target model Target(X);

[0065] The specific mathematical expression is:

[0066]

[0067] In the formula, X is the model variable, MHn i Represents array Hn i The maximum value within, AHn i Represents array Hn i The average of all values ​​in .

[0068] Furthermore, the method for identifying the current state of the high and low voltage distribution cabinets through the target model is specifically as follows:

[0069] Create an array Bn of length N, and use Bn(i) to represent the i-th value in the array Bn;

[0070] The calculation method of Bn(i) is as follows: i Each value in is input into the target model as a model variable in turn to obtain multiple model outputs, and the values ​​of these multiple model outputs are accumulated as Bn(i);

[0071] Traverse i from i=1 to i=N to complete the filling of array Bn;

[0072] The specific mathematical expression is:

[0073]

[0074] In the formula, Hn i (p) represents the array Hn i The pth value in, U(i) represents the array Hn i The number of values ​​contained;

[0075] When more than half of the values ​​in the array Bn are greater than Q2, the current state of the high and low voltage distribution cabinet is marked as abnormal. Q2 represents the average value of the array Bn.

[0076] Furthermore, the current state of the high and low voltage distribution cabinets is identified through the target model, and an alarm message is issued when the current state of the high and low voltage distribution cabinets is identified as abnormal. Specifically, the alarm message is sent to the control center through the communication module in the high and low voltage distribution cabinet. The alarm message includes the array Bn, the start and end times of the monitoring period, the real-time parameters of the high and low voltage distribution cabinets, and the specific time when the abnormal state is identified.

[0077] On the other hand, the present invention relates to a high and low voltage power distribution cabinet, which applies the above-mentioned monitoring method of a high and low voltage power distribution cabinet to perform safety monitoring during its working process.

[0078] The present invention provides a monitoring method and a distribution cabinet for a high and low voltage distribution cabinet. By installing multiple sensors in the high and low voltage distribution cabinet, monitoring data is obtained in real time through the multiple sensors, target data in the monitoring data is screened out, a target model is established according to the target data, and the current state of the high and low voltage distribution cabinet is identified through the target model. The method can monitor the operating state of the distribution cabinet in real time and identify its abnormal signals. By real-time analysis and calculation of the monitoring data, a target model is established to provide abnormal evaluation, risk warning and problem location, and the stable operation of the distribution system is ensured in an automatic monitoring manner, while effectively preventing the distribution cabinet from having risky failures and improving the safety performance of its working process. Although the description of the present invention has been quite detailed and several embodiments are described in particular, it is not intended to be limited to any of these details or embodiments or any special embodiments, so as to effectively cover the predetermined scope of the present invention. In addition, the above describes the present invention with the foreseeable embodiments of the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that have not yet been foreseen can still represent equivalent changes to the present invention.

Claims

1. A monitoring method for high and low voltage distribution cabinets, characterized in that: The method comprises the following steps: Install multiple sensors in the high and low voltage distribution cabinets to obtain monitoring data in real time; Filter out target data from monitoring data and establish a target model based on the target data; Identify the current status of high and low voltage distribution cabinets through the target model; The method for obtaining the temperature data collected by the passive wireless temperature sensor is specifically as follows: the number of passive wireless temperature sensors is recorded as N, pws(i) represents the i-th passive wireless temperature sensor, the temperature value collected by the passive wireless temperature sensor pws(i) at time t(j) is recorded as h(i,j), and h(i,j) is used as monitoring data; wherein i and j are sequence numbers, i=1,2,…,N, j=1,2,…,M, time t(j) is the j-th second in the monitoring period T, and the length of the monitoring period is M seconds; The method of installing multiple sensors in the high and low voltage distribution cabinets and obtaining monitoring data in real time through the multiple sensors is specifically as follows: planning multiple points inside the high and low voltage distribution cabinets, installing passive wireless temperature measurement sensors at these points, obtaining temperature data collected by the passive wireless temperature measurement sensors, and using the temperature data as monitoring data; The specific method of filtering out the target data in the monitoring data is as follows: in the screening algorithm, i is traversed from i=1 to i=N, thereby obtaining N arrays Hn1, Hn2,…, Hn N , taking all the values ​​in these N arrays as target data; The screening algorithm is as follows: S1, for the temperature data collected by the i-th passive wireless temperature sensor pws(i), that is, M temperature values ​​h(i,1), h(i,2),…, h(i,M), set a blank array Hn i , set variable x=1, set variable h, set the initial value of h to Q1, Q1 represents the ratio of the maximum value among h(i,1),h(i,2),…,h(i,M) to the average value of h(i,1),h(i,2),…,h(i,M); S2, if h(i,x) is less than h(i,x+1), go to S3; if h(i,x+1) is less than h(i,x), go to S4; if h(i,x) is equal to h(i,x+1), go to S5; S3, if h(i,x)+h>h(i,x+1), then add h(i,x) to the array Hn i , and set the value of variable h to Q1, and go to S5; if h(i,x)+h≤h(i,x+1), increase the value of variable h by 1 / Q1, and go to S5; S4, if h(i, x) - h < h(i, x + 1), then add h(i, x) to the array Hn i , and at the same time set the value of the variable h to Q1, and go to S5; if h(i, x) - h ≥ h(i, x + 1), then decrease the value of the variable h by 1 / Q1, and go to S5; S5, if the value of variable x is less than M-1, increase the value of variable x by 1 and go to S2; if the value of variable x is equal to or greater than M-1, go to S6; S6, with array Hn i As target data; The specific method of establishing a target model based on target data is as follows: For N arrays Hn1, Hn2, ..., Hn N The N maximum values ​​in the model are deducted from the N maximum values ​​by the model variable X to obtain the N first model factors; N The average value of is taken as the N second model factors; the N first model factors are divided by the N second model factors in order and one by one to obtain N third model factors, and the N third model factors are accumulated as the target model Target(X); The method of identifying the current status of the high and low voltage distribution cabinets through the target model is as follows: Create an array Bn of length N, and use Bn(i) to represent the i-th value in the array Bn; The calculation method of Bn(i) is as follows: i Each value in is input into the target model as a model variable in turn to obtain multiple model outputs, and the values ​​of these multiple model outputs are accumulated as Bn(i); When more than half of the values ​​in the array Bn are greater than Q2, the current state of the high and low voltage distribution cabinet is marked as abnormal. Q2 represents the average value of the array Bn.

2. A high and low voltage distribution cabinet, characterized in that: A monitoring method for high and low voltage distribution cabinets as described in claim 1 is used for safety monitoring.

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