Misalignment monitoring system based on electric power acquisition platform

Abstract

The invention discloses a misalignment monitoring system based on an electric power acquisition platform, relates to the technical field of electric power acquisition platforms, and solves the problems that the specific fluctuation condition of misalignment cannot be fully identified and whether a fault occurs or not. Monitoring data associated with the acquisition items are monitored again, whether corresponding equipment is interfered or fluctuates is locked based on a specific time interval, and corresponding processing signals are generated in time to be displayed; in the case of continuous abnormity, the abnormity degree between the corresponding monitoring data is locked based on the association condition between the corresponding monitoring data, so that fault judgment is carried out, the misalignment condition can be effectively monitored, judged and synchronized, and the fault condition can be effectively identified.

Description

Technical Field

[0001] The present invention relates to the technical field of power collection platforms, and in particular to a misalignment monitoring system based on a power collection platform. Background Art

[0002] Its power collection platform is mainly composed of various types of smart meters, transformers, sensors and other equipment, which are deployed at power generation ends, transmission lines, substations, distribution systems and electricity customers. It is responsible for real-time collection of basic electrical parameters such as voltage, current, power, electricity, power factor, etc., and converts them into digital signals or transmittable electrical signals for subsequent transmission. Smart meters can accurately measure users' electricity consumption and record and store them according to set cycles. Current transformers and voltage transformers are used to proportionally convert large currents and high voltages to a suitable measurement range for reading by collection equipment.

[0003] The patent application with the publication number CN118137663A discloses a misalignment monitoring system based on an electric power collection platform, which relates to the field of electric power monitoring technology. It solves the technical problems in the prior art that it is difficult to timely detect whether there are abnormal conditions such as leakage current in the circuit system, and the technical problems that the experimental data cannot be effectively utilized due to the presence of noise during misalignment monitoring; it includes a data collection module, an industrial server and a data denoising module; the data collection module includes two switches for collecting the power usage data of the smart electric energy meter detected by the electric power collection platform and the power usage data of the substation; the industrial server is used to summarize and process the data information collected by the switch, and transmit the summarized power usage data to the leakage point diagnosis model after being processed by the data denoising module, so as to determine whether there is a leakage point within the substation range, and can analyze abnormal conditions such as leakage current in the full range to improve the accuracy of misalignment monitoring; at the same time, fewer computing resources are required, making the monitoring process more efficient and reducing costs.

[0004] When collecting power data, its power collection platform generally evaluates whether the collected power data is inaccurate based on a preset interval, and then displays the signal. However, this processing method can only identify that the corresponding data is inaccurate, and cannot fully identify the specific fluctuation of the inaccuracy and whether it is a fault. Its original processing method is not comprehensive and cannot achieve better inaccuracy monitoring and processing effects. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a misalignment monitoring system based on a power collection platform, which solves the problem of not being able to fully identify the specific fluctuation of the misalignment and whether it is a fault.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a misalignment monitoring system based on a power collection platform, comprising: The collection and monitoring end confirms the monitoring data generated by the power collection platform for different collection items, and transmits the monitoring data generated by different collection items in real time to the data verification end; The data verification end, based on the monitoring data generated by the corresponding collection item, checks such monitoring data with the monitoring data associated in the past time period, identifies whether such monitoring data has abnormalities based on the data characteristics of the monitoring data in the past time period, and performs abnormal calibration to determine the abnormal collection item. The specific method is as follows: The monitoring data confirmed by the corresponding collection item is calibrated as J i , where i represents different collection items, and this monitoring data J i Check with the preset standard interval of this collection item, where different standard intervals are preset for different collection items, and the endpoint values ​​of the standard intervals are all preset values; When J i ∉ standard interval, it means that the monitoring data is abnormal, and the collection item is marked as an abnormal collection item, and the marked abnormal collection item is transmitted to the abnormal processing end for data processing; When J i ∈ standard interval, it means that the monitoring data is normal and no processing is performed; The exception handling end, based on the marked exception collection item, identifies the monitoring data generated by the exception collection item at subsequent moments, and identifies whether the subsequent monitoring data continues to be abnormal from the current moment, and executes the periodic verification end or the fault detection end based on the different identification results. The specific method is as follows: Based on the marked abnormal collection item and the marked monitoring time, mark this monitoring time as an abnormal time, and confirm the monitoring data SJ associated with the subsequent adjacent time of this abnormal time. If SJ∈standard interval, it means that this abnormal collection item has a single point fluctuation, and then execute the periodic verification end; If SJ∉standard interval, it means that the data monitored by this abnormal collection item continues to be abnormal. If the subsequent data continues to be abnormal, the fault detection end is executed; The periodic verification end performs real-time verification on the monitoring data subsequently generated by the abnormal collection item in the case of single-point fluctuation. Based on the specific results of the real-time verification, the abnormal data in the subsequent monitoring data is confirmed. Based on the time characteristics between adjacent abnormal data, it is identified whether the abnormal collection item is a periodic misalignment anomaly or a volatility misalignment anomaly. The specific method is as follows: Based on the corresponding abnormal collection items and the marked abnormal time, the monitoring data associated with the abnormal time is verified in real time, and the associated monitoring data is checked with the preset standard interval, and the monitoring data that does not belong to the corresponding standard interval is marked as abnormal data, and the number of subsequent abnormal data is monitored. When the number of subsequent abnormal data exceeds three groups, the monitoring is stopped; Based on the confirmed abnormal data, the specific time associated with the corresponding abnormal data is locked and marked as the abnormal time, and the abnormal time sequence {Y1, Y2, ..., Y n}, where Y1 is the first set of abnormal moments confirmed, and Y n For the last set of abnormal moments confirmed, identify and confirm the time interval between adjacent abnormal moments: Jg=Y j+1 -Y j Confirm the time interval Jg, where j∈[1, n-1], and then confirm several groups of time intervals Jg in the abnormal time sequence; Select the maximum value Jgmax and the minimum value Jgmin from several groups of time intervals Jg, use Jgmax-Jgmin=Ps to determine the evaluation value Ps, and compare the evaluation value Ps with the preset time value S k Check, where k represents different collection items, each of which has a different preset time value S k , if Ps≤S k , it means that this abnormal collection item belongs to a periodic misalignment anomaly, and the periodic misalignment anomaly signal is directly generated through the signal end for display; If Ps>S k , it means that this abnormal collection item belongs to the volatility misalignment anomaly, and the volatility misalignment anomaly signal is directly generated through the signal end for display; The fault detection end, in response to the situation where the abnormal collection item continues to be abnormal, confirms the collection items associated with such abnormal collection items, and identifies whether the monitoring data associated with the abnormal collection item in the corresponding period is abnormal based on the numerical performance of the collection items in the corresponding period, and locks whether the equipment associated with the abnormal collection item has a fault based on the identification result. The specific method is as follows: Based on the confirmed abnormal collection items, the associated collection items are locked. There is an association relationship between each group of corresponding collection items, and association marks are set in advance; Based on the abnormal time when the abnormal collection item is marked, the abnormal data generated subsequently is confirmed, and based on the continuously confirmed process and abnormal time, the continuous period is locked. The initial time of this continuous period is the abnormal time, and its end time is the time when the process ends; The monitoring data associated with the abnormal collection item during the duration is marked as data to be checked, and the collection items associated with the abnormal collection item are marked as associated collection items, and the monitoring data associated with the associated collection items during the duration is marked as associated data; The associated data at different times during the continuous period are labeled as G q , the data to be checked at the corresponding time is marked as D q , where q represents different moments in this duration, using B q =G q ×C1 Confirm the standard data B corresponding to the related data q , where C1 is a preset fixed coefficient factor, q=1, 2, ..., m is proposed, and the Confirm its merging parameter HB; The combined parameter HB is checked against the preset value X1: if HB>X1, it means that the device associated with the abnormal collection item has a fault, and a device fault signal is directly generated and displayed through the signal end; if HB≤X1, it means that the device associated with the abnormal collection item has a numerical fluctuation, and a numerical fluctuation abnormal signal is generated and displayed.

[0007] The present invention provides a misalignment monitoring system based on a power collection platform. Compared with the prior art, it has the following beneficial effects: The present invention verifies the data monitored by different collection items in real time to assess whether there is abnormal data, and then confirms the abnormal state of the corresponding data based on the assessment result, and confirms the subsequent execution end based on the corresponding abnormal state, and the abnormal state is a single point abnormality or a continuous abnormality; In case of single-point anomaly, the monitoring data associated with such collection items are re-monitored, and based on the abnormal data confirmed during the continuous monitoring process, the time interval between the corresponding abnormal data is confirmed. Based on the specific time interval, whether the corresponding device is interfered or fluctuates on its own is locked, and the corresponding processing signal is generated in time for display; In case of persistent abnormalities, other collection items associated with this collection item are locked, and based on the monitoring data associated with the two groups of collection items in the corresponding time period, the degree of abnormality between the data is locked based on the correlation between the corresponding monitoring data, so as to make a fault judgment. This can not only effectively monitor and judge the misalignment, but also effectively identify the fault condition and determine the fault signal, thereby improving the overall effect of misalignment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0010] First embodiment See also Figure 1 , the present application provides an inaccuracy monitoring system based on a power collection platform, comprising a collection monitoring end, a data verification end, an abnormality processing end, a periodic verification end, a fault detection end and a signal end, wherein the collection monitoring end, the data verification end and the abnormality processing end are electrically connected from the output node to the input node in sequence, and the abnormality processing end is electrically connected to the periodic verification end or the fault detection end input node respectively, and the periodic verification end and the fault detection end are both electrically connected to the signal end input node; Among them, the collection monitoring end confirms the monitoring data generated by the power collection platform for different collection items, and transmits the monitoring data generated in real time by different collection items to the data verification end. In order to identify whether there is an inaccuracy problem in its collection process, it is necessary to verify the real-time monitoring data to assess whether there is an inaccuracy problem; Among them, the data verification end, based on the monitoring data generated by the corresponding collection item, checks such monitoring data with the monitoring data associated in the past time period, identifies whether such monitoring data is abnormal based on the data characteristics of the monitoring data in the past time period, and performs abnormal calibration to determine the abnormal collection item. The specific method of performing abnormal calibration is: The monitoring data confirmed by the corresponding collection item is calibrated as J i , where i represents different collection items, and this monitoring data J i Check with the preset standard interval of this collection item. Different standard intervals are preset for different collection items. The endpoint values ​​of the standard intervals are preset values, which are prepared in advance by relevant operators based on experience. Under normal circumstances, each corresponding collection item needs to fluctuate within the specified value range. When abnormal fluctuations occur, it means that the corresponding data has related abnormalities, and it is necessary to calibrate the abnormal items and process the calibrated abnormal items. When J i ∈ standard interval, it means that the monitoring data is normal and no processing is performed; When J i∉ standard interval, it means that the monitoring data is abnormal, and this collection item is marked as an abnormal collection item, and the marked abnormal collection item is transmitted to the abnormal processing end for data processing. Specifically, when the monitored data is normal, no processing is required. When the corresponding monitored data is abnormal, it means that there are relevant problems with the corresponding monitored data, and relevant processing is required. Abnormal analysis is performed to confirm the specific abnormal situation and identify whether the abnormality is an inaccuracy or a fault.

[0011] Among them, the exception processing end, based on the calibrated abnormal collection item, identifies the monitoring data generated by its abnormal collection item at subsequent moments, and identifies whether the subsequent monitoring data continues to be abnormal from the current moment, and executes the periodic verification end or the fault detection end based on the different identification results. The specific method of identification is: Based on the calibrated abnormal collection item and the calibrated monitoring time, this monitoring time is calibrated as the abnormal time, and the monitoring data SJ associated with the subsequent adjacent time of this abnormal time is confirmed. If SJ∈standard interval, it means that this abnormal collection item has a single point fluctuation (it may be a fluctuation inaccuracy), then the periodic verification end is executed; If SJ∉standard interval, it means that the data monitored by this abnormal collection item continues to be abnormal. If the subsequent data continues to be abnormal, the fault detection end is executed (assess whether the equipment associated with this collection item is faulty); Specifically, when it is determined that there is a corresponding abnormal collection item, there is only one set of abnormal data (that is, the data corresponding to subsequent different moments are in a normal state), which means that there is only a single-point abnormality in the abnormal collection item, that is, the corresponding numerical fluctuation occurs. When there is abnormal data at the corresponding moment, there is abnormal data at subsequent consecutive moments, which means that the data is continuously abnormal. For such continuous abnormal situations, it may be caused by inaccurate corresponding data, or it may be caused by a fault in the corresponding equipment, or it may be caused by a fault in the corresponding equipment. Therefore, the corresponding fault detection terminal is executed to monitor the relevant equipment to assess whether the corresponding equipment has a fault problem.

[0012] Among them, the periodic verification end, in view of the single-point fluctuation of this abnormal collection item, performs real-time verification on the monitoring data subsequently generated by this abnormal collection item, and based on the specific results of the real-time verification, confirms the abnormal data existing in the subsequent monitoring data, and identifies whether this abnormal collection item belongs to a periodic misalignment anomaly or a volatility misalignment anomaly based on the time characteristics between adjacent abnormal data. The specific method of identification is: Based on the corresponding abnormal collection items and the marked abnormal time, the monitoring data associated with the abnormal time is verified in real time, and the associated monitoring data is checked with the preset standard interval, and the monitoring data that does not belong to the corresponding standard interval is marked as abnormal data, and the number of subsequent abnormal data is monitored. When the number of subsequent abnormal data exceeds three groups, the monitoring is stopped; Based on the confirmed abnormal data, the specific time associated with the corresponding abnormal data is locked and marked as the abnormal time, and the abnormal time sequence {Y1, Y2, ..., Y n}, where Y1 is the first set of abnormal moments confirmed, and Y n For the last set of abnormal moments confirmed, identify and confirm the time interval between adjacent abnormal moments: Jg=Y j+1 -Y j Confirm the time interval Jg, where j∈[1, n-1], and then confirm several groups of time intervals Jg in the abnormal time sequence; Select the maximum value Jgmax and the minimum value Jgmin from several groups of time intervals Jg, use Jgmax-Jgmin=Ps to determine the evaluation value Ps, and compare the evaluation value Ps with the preset time value S k Check, where k represents different collection items, each of which has a different preset time value S k , if Ps≤S k , it means that this abnormal collection item belongs to the periodic misalignment anomaly, and the periodic misalignment anomaly signal is directly generated through the signal end for display. If Ps>S k , it means that this abnormal collection item belongs to the volatility misalignment anomaly, and the volatility misalignment anomaly signal is directly generated through the signal end for display; Specifically, external relevant personnel, based on the confirmed periodic misalignment abnormal signals, check the equipment associated with such collection items to identify whether such equipment is subject to relevant interference during operation, thereby causing periodic misalignment of the equipment. Under normal circumstances, when the corresponding equipment has such periodic misalignment, it is generally caused by external electromagnetic interference or other interference, resulting in periodic signal misalignment of the corresponding equipment. When there is a corresponding fluctuating misalignment abnormal signal, it is generally caused by the numerical fluctuation of the corresponding equipment itself. If such fluctuating misalignment abnormal signal is not continuously generated, generally no relevant processing is required. When it is continuously generated, it means that such equipment has specific abnormal problems, and it is necessary to promptly check the fluctuations of such equipment and deal with the fluctuation problems of such equipment.

[0013] Second embodiment In the specific implementation process, compared with the above-mentioned embodiment, this embodiment mainly performs fault detection on the data of the device associated with the abnormal collection item, identifies whether the device associated with the corresponding collection item has a fault problem, and displays the signal in time; Among them, the fault detection end, in view of the continuous abnormality of this abnormal collection item, confirms the collection item associated with such abnormal collection item, and identifies whether the monitoring data associated with this abnormal collection item in the corresponding period is abnormal based on the numerical performance of the collection item in the corresponding period, and locks whether the equipment associated with this abnormal collection item has a fault based on the identification result, wherein the specific method of identification is: Based on the confirmed abnormal collection items, the associated collection items are locked. There is an association relationship between each group of corresponding collection items, and the association tags are set in advance. The association tags are prepared in advance by relevant personnel. For example, if the value associated with the monitored collection item is the voltage value, the voltage value is associated with the corresponding current. If the monitored collection item is the corresponding speed, it is related to the working power of the corresponding equipment. Based on the abnormal time when the abnormal collection item is marked, the abnormal data generated subsequently is confirmed, and based on the continuously confirmed process and abnormal time, the continuous period is locked. The initial time of this continuous period is the abnormal time, and its end time is the time when the process ends; The monitoring data associated with the abnormal collection item during the duration is marked as data to be checked, and the collection items associated with the abnormal collection item are marked as associated collection items, and the monitoring data associated with the associated collection items during the duration is marked as associated data; The associated data at different times during the continuous period are labeled as G q , the data to be checked at the corresponding time is marked as D q , where q represents different moments in this duration, using B q =G q ×C1 Confirm the standard data B corresponding to the related data q , where C1 is a preset fixed coefficient factor, and its specific value is determined by the operator based on experience, and q=1, 2, ..., m is proposed. Confirm its merging parameter HB; The combined parameter HB is checked against the preset value X1: if HB>X1, it means that the equipment associated with the abnormal collection item has a fault, and a device fault signal is directly generated and displayed through the signal end; if HB≤X1, it means that the equipment associated with the abnormal collection item has a numerical fluctuation, and a numerical fluctuation abnormal signal is generated and displayed. The specific value of X1 is determined in advance by the operator based on experience.

[0014] Third embodiment The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0015] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0016] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A misalignment monitoring system based on a power collection platform, characterized in that: include: The collection and monitoring end confirms the monitoring data generated by the power collection platform for different collection items, and transmits the monitoring data generated by different collection items in real time to the data verification end; The data verification end, based on the monitoring data generated by the corresponding collection item, checks such monitoring data with the monitoring data associated in the past time period, identifies whether such monitoring data has abnormalities based on the data characteristics of the monitoring data in the past time period, and performs abnormal calibration to determine the abnormal collection item; The exception processing end, based on the marked abnormal collection items, identifies the monitoring data generated by the abnormal collection items at subsequent moments, and identifies whether the subsequent monitoring data continues to be abnormal from the current moment, and executes the periodic verification end or the fault detection end based on the different identification results; The periodic verification end performs real-time verification on the monitoring data subsequently generated by the abnormal collection item in the case of a single-point fluctuation. Based on the specific results of the real-time verification, the abnormal data in the subsequent monitoring data is confirmed. Based on the time characteristics between adjacent abnormal data, it is identified whether the abnormal collection item is a periodic misalignment anomaly or a fluctuating misalignment anomaly. At the fault detection end, in the event of a continuous abnormality in this abnormal collection item, the collection items associated with this abnormal collection item are confirmed, and based on the numerical performance of the collection items in the corresponding time period, it is identified whether the monitoring data associated with this abnormal collection item in the corresponding time period is abnormal, and based on the identification result, it is determined whether the equipment associated with this abnormal collection item has a fault.

2. The misalignment monitoring system based on the power collection platform according to claim 1 is characterized in that: The data verification end determines the abnormal collection item in a specific manner as follows: The monitoring data confirmed by the corresponding collection item is calibrated as J i , where i represents different collection items, and this monitoring data J i Check with the preset standard interval of this collection item, where different standard intervals are preset for different collection items, and the endpoint values ​​of the standard intervals are all preset values; When J i When it is ∉ standard interval, it means that the monitoring data is abnormal, and the collection item is marked as an abnormal collection item, and the marked abnormal collection item is transmitted to the abnormal processing end for data processing.

3. The misalignment monitoring system based on the power collection platform according to claim 2 is characterized in that: When J i ∈ standard interval, it means that the monitoring data is normal and no processing is performed.

4. The misalignment monitoring system based on the power collection platform according to claim 1 is characterized in that: The specific method for the exception handling end to identify whether the subsequent monitoring data continues to be abnormal is: Based on the calibrated abnormal collection item and the calibrated monitoring time, this monitoring time is marked as an abnormal time, and the monitoring data SJ associated with the subsequent adjacent times of this abnormal time is confirmed. If SJ∈standard interval, it means that this abnormal collection item has a single-point fluctuation, and the periodic verification end is executed.

5. The misalignment monitoring system based on the power collection platform according to claim 4 is characterized in that: If SJ∉standard interval, it means that the data subsequently monitored by this abnormal collection item continues to be abnormal. If the subsequent data continues to be abnormal, the fault detection end is executed.

6. The misalignment monitoring system based on the power collection platform according to claim 1 is characterized in that: The specific method for the periodic verification end to identify whether the abnormal collection item is a periodic misalignment anomaly or a fluctuating misalignment anomaly is: Based on the corresponding abnormal collection items and the marked abnormal time, the monitoring data associated with the abnormal time is verified in real time, and the associated monitoring data is checked with the preset standard interval, and the monitoring data that does not belong to the corresponding standard interval is marked as abnormal data, and the number of subsequent abnormal data is monitored. When the number of subsequent abnormal data exceeds three groups, the monitoring is stopped; Based on the confirmed abnormal data, the specific time associated with the corresponding abnormal data is locked and marked as the abnormal time, and the abnormal time sequence {Y1, Y2, ..., Y n }, where Y1 is the first set of abnormal moments confirmed, and Y n For the last set of abnormal moments confirmed, identify and confirm the time interval between adjacent abnormal moments: Jg=Y j+1 -Y j Confirm the time interval Jg, where j∈[1, n-1], and then confirm several groups of time intervals Jg in the abnormal time sequence; Select the maximum value Jgmax and the minimum value Jgmin from several groups of time intervals Jg, use Jgmax-Jgmin=Ps to determine the evaluation value Ps, and compare the evaluation value Ps with the preset time value S k Check, where k represents different collection items, each of which has a different preset time value S k , if Ps≤S k , it means that this abnormal collection item belongs to a periodic misalignment anomaly, and a periodic misalignment anomaly signal is directly generated through the signal end for display.

7. The misalignment monitoring system based on the power collection platform according to claim 6 is characterized in that: If Ps>S k , it means that this abnormal collection item belongs to the volatility misalignment anomaly, and the volatility misalignment anomaly signal is directly generated through the signal end for display.

8. The misalignment monitoring system based on the power collection platform according to claim 1 is characterized in that: The specific method of the fault detection end to lock whether the device associated with the abnormal collection item has a fault is: Based on the confirmed abnormal collection items, the associated collection items are locked. There is an association relationship between each group of corresponding collection items, and association marks are set in advance; Based on the abnormal time when the abnormal collection item is marked, the abnormal data generated subsequently is confirmed, and based on the continuously confirmed process and abnormal time, the continuous period is locked. The initial time of this continuous period is the abnormal time, and its end time is the time when the process ends; The monitoring data associated with the abnormal collection item during the duration is marked as data to be checked, and the collection items associated with the abnormal collection item are marked as associated collection items, and the monitoring data associated with the associated collection items during the duration is marked as associated data; The associated data at different times during the continuous period are labeled as G q , the data to be checked at the corresponding time is marked as D q , where q represents different moments in this duration, using B q =G q ×C1 Confirm the standard data B corresponding to the related data q , where C1 is a preset fixed coefficient factor, q=1, 2, ..., m is proposed, and the Confirm its merging parameter HB; The combined parameter HB is checked against the preset value X1: if HB>X1, it means that the device associated with the abnormal collection item has a fault, and a device fault signal is directly generated and displayed through the signal end; if HB≤X1, it means that the device associated with the abnormal collection item has a numerical fluctuation, and a numerical fluctuation abnormal signal is generated and displayed.

Images