Electric energy quality intelligent monitoring method and system for four-fusion integrated terminal

Abstract

The invention relates to the technical field of three-phase current data processing, in particular to an electric energy quality intelligent monitoring method and system for a four-fusion integrated terminal. The method comprises the following steps: acquiring a current sequence and a monitoring phase corresponding to each phase line; calculating the suspected abnormal degree of the current monitoring point in the current sequence of the monitoring phase, and judging whether the current monitoring point is an abnormal monitoring point or not; if the current monitoring point is an abnormal monitoring point, acquiring a continuation curve; calculating a continuation distribution characteristic value of the abnormal monitoring point according to each data point in the continuation curve; correcting the continuation distribution characteristic value based on the distance between trough points of the crest of the continuation curve and the height of the crest to obtain a corrected continuation distribution characteristic value; obtaining the current abnormal degree of the abnormal monitoring point based on the suspected abnormal degree of the abnormal monitoring point and the corrected continued distribution characteristic value; and judging whether the electric energy quality is abnormal according to the current abnormal degree. According to the invention, the electric energy quality of the four-fusible integrated terminal can be accurately monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of three-phase current data processing, and in particular to a method and system for intelligent power quality monitoring of a four-in-one terminal. Background Art

[0002] The four-in-one integrated terminal refers to an electric energy measurement and control terminal that integrates all photovoltaic four-in-one devices, takes circuit breakers as the basic carrier, and combines interface converters and current transformers to form an observable, measurable, controllable, and adjustable electric energy measurement and control terminal. It is suitable for energy edge autonomy in substations, photovoltaic four-in-one management, load regulation, and digital construction of substations. The four-in-one integrated terminal can realize the measurement, monitoring, and analysis of electric energy, and use the filtering data of the metering chip to complete harmonic collection, three-phase voltage and current imbalance, voltage fluctuation, flicker analysis, arc detection and other functions.

[0003] In terms of monitoring and analysis of three-phase current, the existing technology usually directly monitors the current size of each phase and sets a threshold to determine whether the three-phase current is balanced. However, in the power system of actual terminal applications, each phase of the three phases may carry a different load, and it is necessary to obtain the current judgment threshold based on the actual load of each phase of the three phases. The universality of fixed threshold monitoring is low, and when the load changes, the monitoring threshold cannot be changed according to the load change, which requires a lot of manual calibration, and the dynamic adjustment capability and monitoring efficiency are low. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for intelligent monitoring of power quality for a four-in-one terminal. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for intelligent monitoring of power quality for a four-in-one terminal, the method comprising: The current data of each phase in the three-phase circuit is collected and stored in the corresponding current sequence; one phase is selected as the phase to be monitored currently and recorded as the monitoring phase; Based on the current data of the current monitoring point in the current sequence of the monitoring phase and the other two corresponding current sequences, the suspected abnormality degree of the current data of the current monitoring point is obtained, and whether the current monitoring point is an abnormal monitoring point is determined; If the current monitoring point is an abnormal monitoring point, a preset amount of current data is collected after the abnormal monitoring point and a curve is formed, which is recorded as a continuation curve; the continuation distribution characteristic value of the abnormal monitoring point is calculated according to each data point in the continuation curve; Acquire the peak points in the continuation curve, and correct the continuation distribution characteristic value according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain a corrected continuation distribution characteristic value; The degree of current abnormality at the abnormal monitoring point is obtained based on the suspected abnormality degree of the abnormal monitoring point and the modified continuation distribution characteristic value; and whether there is abnormality in the power quality is determined according to the degree of current abnormality.

[0005] Preferably, obtaining the suspected abnormality degree of the current data of the current monitoring point based on the current data of the current monitoring point in the current sequence of the monitoring phase and the other two corresponding current sequences includes: The absolute value of the difference between the current data of the current monitoring point and the current data at the previous moment in the current sequence in which it is located is obtained and normalized to obtain the in-phase difference value; the absolute value of the difference between the current data of the current monitoring point and the current data at the same moment in the current sequence of the other two phases is obtained respectively and summed to obtain the summed result, and the summed result is normalized to obtain the out-of-phase difference value; the sum of the in-phase difference value and the out-of-phase difference value is the suspected abnormality degree of the current monitoring point.

[0006] Preferably, calculating the continuation distribution characteristic value of the abnormal monitoring point according to each data point in the continuation curve includes: Calculate the average of the absolute values ​​of the difference between the current data of each data point in the continuation curve and the current data of the abnormal monitoring point in the sequence where the abnormal monitoring point is located at the previous moment, and use the hyperbolic tangent function to normalize the average value, which is recorded as the first difference value; calculate the absolute value of the difference between the current data of every two adjacent data points in the continuation curve, which is recorded as the adjacent difference; obtain the product of the average value of the adjacent differences and the variance of all adjacent differences, which is recorded as the degree of change of the continuation curve; use an exponential function with a natural constant as the base to negatively correlate the degree of change of the continuation curve to obtain a mapping result; the product of the first difference value and the mapping result is the continuation distribution characteristic value of the abnormal monitoring point; wherein the difference between the current data of every two adjacent data points is the difference between the current data of the latter data point and the current data point of the previous data point in the adjacent data points.

[0007] Preferably, obtaining the peak point in the continuation curve includes: Use the AMPD peak finding algorithm to obtain all peak points in the continuation curve.

[0008] Preferably, the continuation distribution characteristic value is corrected according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain the corrected continuation distribution characteristic value, including: The inverse of the distance between the two trough points of the peak corresponding to the peak point is obtained, which is recorded as the inverse of the peak width; the sum of the height of a peak and the inverse of the peak width of the peak is calculated, which is recorded as the steepness of the peak; an exponential function with a natural constant as the base is used to negatively map the average value of the steepness of all peaks in the continuation curve to obtain a first mapping result; the product of the first mapping result and the continuation distribution eigenvalue is the modified continuation distribution eigenvalue.

[0009] Preferably, obtaining the current abnormality degree of the abnormal monitoring point based on the suspected abnormality degree of the abnormal monitoring point and the modified continuation distribution characteristic value includes: The product of the suspected abnormality degree of the abnormal monitoring point and the inverse of the modified continuation distribution characteristic value is calculated to obtain the current abnormality degree of the abnormal monitoring point.

[0010] Preferably, judging whether the power quality is abnormal according to the degree of current abnormality includes: An abnormal threshold is set. If the abnormal degree of the current at the abnormal monitoring point is greater than or equal to the abnormal threshold, the power quality is abnormal.

[0011] In the second aspect, the present invention also provides an intelligent power quality monitoring system for four-in-one terminals, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, steps of an intelligent power quality monitoring method for four-in-one terminals are implemented.

[0012] The embodiments of the present invention have at least the following beneficial effects: the present invention obtains the suspected abnormality degree of the current data of the current monitoring point by monitoring the current data of the current monitoring point in the current sequence of the phase and the other two corresponding current sequences, and judges whether the current monitoring point is an abnormal monitoring point, and makes a preliminary judgment on the current monitoring point, which can further improve the accuracy of abnormal monitoring; further, the continuation distribution characteristic value of the abnormal monitoring point is calculated based on the data after the abnormal monitoring point, and the continuation distribution characteristic value is corrected according to the continuation curve to obtain the corrected continuation distribution characteristic value, and the corrected continuation distribution characteristic value and the suspected abnormality degree of the abnormal monitoring point are combined to obtain the accurate current abnormality degree of the abnormal monitoring point. Compared with the prior art that directly uses a fixed threshold to monitor the three-phase current of the four-in-one terminal, it will result in the need to reset the threshold according to the load change when the load changes. The present invention can accurately judge whether the power quality of the three-phase current of the four-in-one terminal is abnormal, and does not need to set the threshold to obtain a relatively accurate power quality assessment result. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A method flow chart of a method for intelligent power quality monitoring of four integrated terminals provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a method and system for intelligent monitoring of power quality for a four-in-one terminal proposed in accordance with the present invention, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0016] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0017] The following is a detailed description of a specific scheme of a method and system for intelligent power quality monitoring of a four-in-one terminal provided by the present invention in conjunction with the accompanying drawings.

[0018] Embodiment 1: The main application scenarios of the present invention are: using four integrated terminals to monitor the power quality of the power system, and monitoring and analyzing the three-phase current data indicators therein. Because the traditional monitoring uses a fixed threshold and cannot dynamically adjust the monitoring threshold when the load changes to determine whether the three-phase current is balanced or abnormal, a dynamic monitoring and analysis method that can perform evaluation and analysis when the load changes is required. The three-phase current difference characteristics and the three-phase current load change characteristics are combined to reduce the defects of the conventional fixed threshold monitoring, which has weak dynamic monitoring capabilities, low efficiency, and large workload.

[0019] See also Figure 1 , which shows a method flow chart of a method for intelligent monitoring of power quality of a four-in-one terminal provided by an embodiment of the present invention, the method comprising the following steps: Step S1, collecting the current data of each phase line in the three-phase line and storing them in the corresponding current sequence respectively; selecting any phase as the phase to be currently monitored, recorded as the monitoring phase.

[0020] Four integrated terminals can collect three-phase current data of the power system under test, and use current transformers installed on each phase line of the power system under test to obtain the current data monitoring value of each phase line. The data monitoring and collection frequency is 10Hz, which can be adjusted by the implementer according to the actual situation.

[0021] The current data of each phase line is placed in a sequence, so each phase line corresponds to a current sequence. Further, the data in each current sequence is projected into a corresponding two-dimensional plane coordinate system. The horizontal axis is the acquisition time sequence, and the vertical axis is the current data value. The data points adjacent to the time sequence are connected to form a time curve corresponding to the current data monitored in the phase. At the same time, each newly collected monitoring current data is inserted into the end of the current sequence and connected in the curve. For each new current monitoring data stored in the current sequence, the power quality analysis is performed in combination with the time curve.

[0022] When monitoring the power quality, it is necessary to monitor the current data of each phase. In the embodiment of the present invention, the current data of one phase is taken as an example for explanation, and one phase is arbitrarily selected as the phase to be currently monitored, which is recorded as the monitoring phase.

[0023] Step S2, based on the current data of the current monitoring point in the current sequence of the monitoring phase and the other two corresponding current sequences, obtain the suspected abnormality degree of the current data of the current monitoring point, and determine whether the current monitoring point is an abnormal monitoring point.

[0024] The monitored current data of each phase is analyzed separately. For the new current data point of the monitored phase collected by real-time monitoring, that is, the current monitoring point, if there is a significant difference with the current data of the previous moment in the current sequence of the monitored phase, it means that the new current monitoring data collected in real time may be abnormal. On the other hand, the change of the monitored phase current may also be related to the change of voltage. Considering the relevant influence of voltage on current, it is necessary to exclude the possibility that the abnormal change of current is affected by voltage change. Because the voltage will have an impact on the comprehensive current of each phase in the three-phase current, the degree of difference between the current of this phase and the current of other phases is obtained here. The greater the difference, the less likely it is that the abnormal change of current is caused by voltage influence.

[0025] The degree of suspected abnormality of the current at the current monitoring point is thus obtained by comprehensively analyzing the difference between the current data of the previous monitoring phase and the current data of other phases at the same time. The greater the difference between the current data of the current monitoring point of the monitoring phase and the current data of the same phase at the previous moment, the stronger the suspected abnormality of the current; the greater the difference between the current of the current monitoring point phase and the current data of other phases at the same moment, the stronger the suspected abnormality of the current.

[0026] Furthermore, the absolute value of the difference between the current data of the current monitoring point and the current data of the previous moment in the current sequence where it is located is obtained and normalized to obtain the in-phase difference value; the absolute value of the difference between the current data of the current monitoring point and the current data of the same moment in the current sequence of the other two phases is obtained respectively and summed to obtain the summed result, and the summed result is normalized to obtain the out-of-phase difference value; the sum of the in-phase difference value and the out-of-phase difference value is the suspected abnormality degree of the current monitoring point. The specific calculation formula for the suspected abnormality degree is: , in, Indicates the suspected abnormality degree of the current data of the monitoring point at the current monitoring point; Indicates the value of the current data of the monitoring point at the current monitoring phase; Indicates the value of the current data of the previous moment of the current monitoring point in the current sequence where the current monitoring point is located; Indicates the value of the current data at the same time as the current monitoring point in the first corresponding voltage sequence of other phases except the monitoring phase; It represents the value of the current data at the same time as the current monitoring point in the second corresponding voltage sequence of other phases except the monitoring phase; norm represents the normalization operation, which is used to unify the magnitude.

[0027] represents the in-phase difference value, Indicates the difference between the current value at the current monitoring point and the current value at the previous moment. The greater the difference between the current value at the current monitoring point and the current value at the previous moment, the greater the deviation of the point, and the greater the suspected abnormality of the current at the corresponding monitoring point. represents the out-of-phase difference value, It indicates the difference between the current at the monitoring point at the current moment and the current data of other phases at the same moment. The larger the difference, the less likely it is that the abnormal current change is caused by voltage, and the stronger the suspected abnormal current is. At the same time, it also excludes the normal situation that the three-phase current changes simultaneously due to load.

[0028] Thus, the suspected abnormal degree of the current data of the current monitoring point of the monitored phase is obtained. The threshold is set to 0.7, which can be adjusted according to the scene requirements. When the suspected abnormal degree of the current value of the current monitoring point of the monitored phase is greater than or equal to 0.7, it is considered that the current value of the current monitoring point of the monitored phase may be abnormal, and the current monitoring point is set as the abnormal monitoring point. By analyzing the difference in current values ​​at different times of the same phase and different times of different phases, the suspected abnormal degree of the current value of the current monitoring point is obtained by linear normalization, and the abnormality of the measured phase current is judged based on the abnormal degree. Compared with fixed threshold monitoring, it can dynamically adapt to multi-load application scenarios.

[0029] Step S3, if the current monitoring point is an abnormal monitoring point, a preset amount of current data is collected after the abnormal monitoring point and a curve is formed, which is recorded as a continuation curve; and the continuation distribution characteristic value of the abnormal monitoring point is calculated according to each data point in the continuation curve.

[0030] Because the phase current offset may be caused by the load change of the phase, or it may be caused by abnormal environment or terminal failure. If the current value of the current monitoring point is detected as an abnormal current value, that is, when the current monitoring point is an abnormal monitoring point, it is necessary to further analyze the current value of the abnormal monitoring point.

[0031] Here, the judgment is mainly based on the characteristics of the current value change caused by the phase load change and the current value change caused by the fault, which is mainly reflected in the subsequent changes of the abnormal monitoring point. After the load changes, the current value of the monitoring phase will also stabilize as the load stabilizes, so the current imbalance caused by the load change is usually stable and long-term, and will not produce large fluctuations. Therefore, the subsequent changes in the current value of the monitoring phase caused by the load change are continuous. After the load changes, there is only a large offset at the abnormal monitoring point. The subsequent real-time monitoring of the current value should have a low fluctuation range, and the subsequent current values ​​are all different from the current values ​​before the change. The current change caused by the abnormal environment or fault is instantaneous and discontinuous. It may be classified as the original current value in the future. It is an abnormality that only changes instantaneously at the abnormal monitoring point. There may also be multiple discontinuous abnormal changes with large fluctuations in the future.

[0032] Furthermore, a preset number of current data are collected after the abnormal monitoring point, and the subsequently collected current data points are connected to the back end of the current monitoring point (abnormal monitoring point) to form a curve. The formed curve is recorded as a continuation curve, and further analysis is performed to obtain a continuation distribution characteristic value that characterizes the load change characteristics of the continuation curve. In this scheme, the number of subsequently collected current data is set to 30, that is, the preset number is 30.

[0033] Calculate the continuation distribution characteristic value of the abnormal monitoring point based on each data point in the continuation curve. Specifically, calculate the average value of the absolute value of the difference between the current data of each data point in the continuation curve and the current data of the abnormal monitoring point at the previous moment in the sequence where the abnormal monitoring point is located, and use the hyperbolic tangent function to normalize the average value, which is recorded as the first difference value; calculate the absolute value of the difference between the current data of every two adjacent data points in the continuation curve, which is recorded as the adjacent difference; obtain the product of the average value of the adjacent differences and the variance of all adjacent differences, which is recorded as the degree of change of the continuation curve; use an exponential function with a natural constant as the base to negatively map the degree of change of the continuation curve to obtain the mapping result; the product of the first difference value and the mapping result is the continuation distribution characteristic value of the abnormal monitoring point; wherein the difference between the current data of every two adjacent data points is the difference between the current data of the latter data point and the current data point of the previous data point in the adjacent data points. The specific calculation formula is: , Among them, Y represents the continuation distribution characteristic value of the abnormal monitoring point; tanh represents the hyperbolic tangent function, which is used to normalize the positive proportion and unify the magnitude in the formula; The current data of the i-th data point in the continuation curve representing the abnormal monitoring point; Indicates the value of the current data of the abnormal monitoring point (current monitoring point) at the previous moment in the current sequence where the abnormal monitoring point of the monitoring phase is located; Indicates the number of data points on the continuation curve; exp() represents an exponential function with the natural constant e as the base, which is used for negative correlation mapping and unifies the magnitude of the two parts of the formula; The current data of the i-1th data point in the continuation curve representing the suspected abnormal point; It represents the variance of the absolute value of the difference between the current data of every two adjacent data points in the continuation curve.

[0034] It represents the current data difference between the i-th data point in the continuation curve and the previous data point of the abnormal monitoring point. It is the mean of the differences between all data points in the continuation curve and the previous data point of the abnormal monitoring point. The mean indicates the difference level between the continuation curve as a whole and the previous data point of the abnormal monitoring point. The smaller the overall difference level is, the more the subsequent continuation of the abnormal monitoring point has returned to the normal current value level of the previous moment. The less it conforms to the continuation distribution with load change characteristics, the weaker the continuation distribution characteristic value is. Conversely, the larger the overall difference level is, the stronger the continuation distribution characteristic value is. is the first difference value.

[0035] Indicates the difference between the i-th data point and the previous data point in the continuation curve. When i=1, Indicates the current data of abnormal monitoring points, It is the cumulative mean of the difference between every two adjacent data points in the continuation curve. The smaller the mean, the smaller the change between every two data points in the continuation curve. Indicates the variance of the difference between every two adjacent data points in the continuation curve. The smaller the variance, the more uniform the overall data change level in the continuation curve. The smaller the degree of change between every two data points, the more uniform the overall change level. Therefore, the corresponding monitoring data points after the abnormal monitoring point are more stable and continuous, and have the characteristics of the distribution of the continuous data after the abnormal monitoring point caused by the load change. is the adjacent difference, To continue the curve change, This is the mapping result after negative correlation mapping.

[0036] In this way, the continuation distribution characteristic value of the continuation curve of the abnormal monitoring point in the monitoring phase is obtained. Based on the large overall difference level between the continuation distribution curve and the previous monitoring point, the continuation distribution characteristic value that can more accurately characterize the change characteristics of the load is obtained according to the change difference of adjacent data in the continuation curve and the uniformity of the change distribution.

[0037] Step S4, obtaining the peak points in the continuation curve, and correcting the continuation distribution characteristic value according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain a corrected continuation distribution characteristic value.

[0038] In step S3, the continuation distribution characteristic value of the continuation curve corresponding to the abnormal monitoring point is obtained. However, considering the actual application scenario of the terminal, there may be multiple load changes in a short period of time. In this case, there may be multiple load changes in the subsequent continuation curve obtained with the abnormal monitoring point, resulting in a low load change characteristic in the curve, and the continuation curve is identified as an abnormal change caused by an environmental abnormality or a fault other than a load change. Here, the obtained continuation distribution characteristic value is further corrected according to the transient characteristics of the three-phase current change caused by the fault.

[0039] Compared with the instantaneous current change caused by load change, which is a smooth change and does not form a higher change peak, the instantaneous change caused by abnormal environment or fault is more drastic and is prone to produce transient steep peaks. Here, the AMPD peak search algorithm is used to obtain all the peak points in the continuation curve, and the modified continuation distribution characteristics are obtained based on the distribution of the overall peak.

[0040] Furthermore, the continuation distribution characteristic value is corrected according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain the modified continuation distribution characteristic value. Specifically, the inverse of the distance between the two trough points of the peak corresponding to the peak point is obtained, which is recorded as the inverse of the peak width; the sum of the height of a peak and the inverse of the peak width of the peak is obtained, which is recorded as the steepness of the peak; the exponential function with the natural constant as the base is used to negatively correlate the average steepness of all peaks in the continuation curve to obtain the first mapping result; the product of the first mapping result and the continuation distribution characteristic value is the modified continuation distribution characteristic value. The specific calculation formula is: , in, represents the modified continuation distribution eigenvalue; Indicates the number of peaks in the continuation curve corresponding to the abnormal monitoring point; Indicates the height of the ath peak in the continuation curve corresponding to the abnormal monitoring point, It represents the distance between the two trough points corresponding to the a-th peak in the continuation curve corresponding to the abnormal monitoring point, which can also be said to be the width of the peak. Represents the inverse of the peak width, Indicates the steepness of the ath wave peak. The higher the height of the wave peak and the smaller the width, the steeper the wave peak. It represents the mean of the steepness of all the peaks in the continuation curve, indicating the overall steepness of all the peaks. The smaller the value, the flatter the peaks in the continuation curve, which corresponds to the data change characteristics caused by environmental anomalies or faults, and the more consistent with the continuation distribution characteristics after the abnormal monitoring point caused by load changes. Y represents the continuation distribution characteristic value of the abnormal monitoring point. The first mapping result is represented by . Thus, the corrected continuation distribution characteristic value can be obtained, and a more accurate continuation distribution characteristic value can be obtained.

[0041] Step S5, obtaining the current abnormality degree of the abnormal monitoring point based on the suspected abnormality degree of the abnormal monitoring point and the modified continuation distribution characteristic value; judging whether the power quality is abnormal according to the current abnormality degree.

[0042] After obtaining the corrected continuation distribution characteristics in step S4, the abnormal degree of the terminal three-phase current is obtained based on the corrected continuation distribution characteristics of the abnormal monitoring point. Specifically, the product of the suspected abnormal degree of the abnormal monitoring point and the inverse of the corrected continuation distribution characteristic value is calculated to obtain the current abnormal degree of the abnormal monitoring point. The calculation formula for the current abnormal degree is: , Where W represents the abnormal degree of current at the abnormal monitoring point; Indicates the suspected abnormality degree of the abnormal monitoring point; Indicates the modified continuation distribution characteristic value; the weaker the continuation distribution characteristic, the less consistent the subsequent distribution of the corresponding abnormal monitoring point is with the load change characteristics, and the stronger the current abnormality is, the inverse form is adopted .

[0043] The degree of current abnormality at the abnormal monitoring point is thus obtained. The stronger the current abnormality, the more likely it is that there is an abnormality in one phase of the three-phase current of the corresponding terminal that is not affected by load changes, indicating that the three-phase current is unbalanced, and the degree of power quality abnormality based on the three-phase current is stronger.

[0044] Finally, the abnormal threshold is set. Preferably, the value of the abnormal threshold in the present invention is 0.6. The implementer can adjust the value of the abnormal threshold according to the needs of scene changes. When an abnormal monitoring point is detected during the terminal monitoring process, and subsequent analysis shows that the current abnormality degree of the abnormal monitoring point is greater than or equal to the abnormal threshold, it is considered that the terminal power quality based on three-phase current monitoring is abnormal, and an alarm or remote prompt is used to prompt relevant maintenance personnel to handle it in time.

[0045] Embodiment 2: This embodiment provides a power quality intelligent monitoring system for a four-in-one terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of a power quality intelligent monitoring method for a four-in-one terminal are implemented. Since Embodiment 1 has already described a power quality intelligent monitoring method for a four-in-one terminal in detail, it will not be described in detail here.

[0046] In summary, the present invention obtains a more accurate three-phase current power quality assessment result by analyzing the difference characteristics between the real-time monitoring current and the related neighborhood and other phase currents, and further combining the continuous distribution characteristics of the current data.

[0047] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of power quality for a four-in-one terminal, characterized in that: The method includes: The current data of each phase in the three-phase circuit is collected and stored in the corresponding current sequence; one phase is selected as the phase to be monitored currently and recorded as the monitoring phase; Based on the current data of the current monitoring point in the current sequence of the monitoring phase and the other two corresponding current sequences, the suspected abnormality degree of the current data of the current monitoring point is obtained, and whether the current monitoring point is an abnormal monitoring point is determined; If the current monitoring point is an abnormal monitoring point, a preset amount of current data is collected after the abnormal monitoring point and a curve is formed, which is recorded as a continuation curve; the continuation distribution characteristic value of the abnormal monitoring point is calculated according to each data point in the continuation curve; Acquire the peak points in the continuation curve, and correct the continuation distribution characteristic value according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain a corrected continuation distribution characteristic value; The degree of current abnormality at the abnormal monitoring point is obtained based on the suspected abnormality degree of the abnormal monitoring point and the modified continuation distribution characteristic value; and whether there is abnormality in the power quality is determined according to the degree of current abnormality.

2. According to claim 1, a method for intelligent monitoring of power quality for a four-in-one terminal is characterized in that: The method of obtaining the suspected abnormality degree of the current data of the current monitoring point based on the current data of the current monitoring point in the current sequence of the monitoring phase and the other two corresponding current sequences includes: The absolute value of the difference between the current data of the current monitoring point and the current data at the previous moment in the current sequence in which it is located is obtained and normalized to obtain the in-phase difference value; the absolute value of the difference between the current data of the current monitoring point and the current data at the same moment in the current sequence of the other two phases is obtained respectively and summed to obtain the summed result, and the summed result is normalized to obtain the out-of-phase difference value; the sum of the in-phase difference value and the out-of-phase difference value is the suspected abnormality degree of the current monitoring point.

3. The method for intelligent power quality monitoring of a four-in-one terminal according to claim 1 is characterized in that: The step of calculating the continuation distribution characteristic value of the abnormal monitoring point according to each data point in the continuation curve includes: Calculate the average of the absolute values ​​of the difference between the current data of each data point in the continuation curve and the current data of the abnormal monitoring point in the sequence where the abnormal monitoring point is located at the previous moment, and use the hyperbolic tangent function to normalize the average value, which is recorded as the first difference value; calculate the absolute value of the difference between the current data of every two adjacent data points in the continuation curve, which is recorded as the adjacent difference; obtain the product of the average value of the adjacent differences and the variance of all adjacent differences, which is recorded as the degree of change of the continuation curve; use an exponential function with a natural constant as the base to negatively correlate the degree of change of the continuation curve to obtain a mapping result; the product of the first difference value and the mapping result is the continuation distribution characteristic value of the abnormal monitoring point; wherein the difference between the current data of every two adjacent data points is the difference between the current data of the latter data point and the current data point of the previous data point in the adjacent data points.

4. The method for intelligent power quality monitoring of a four-in-one terminal according to claim 1 is characterized in that: The step of obtaining the peak point in the continuation curve includes: Use the AMPD peak finding algorithm to obtain all peak points in the continuation curve.

5. The method for intelligent monitoring of power quality for a four-in-one terminal according to claim 1 is characterized in that: The step of correcting the continuation distribution characteristic value according to the distance between the trough points of the peak corresponding to each peak point and the height of the peak to obtain the corrected continuation distribution characteristic value includes: The inverse of the distance between the two trough points of the peak corresponding to the peak point is obtained, which is recorded as the inverse of the peak width; the sum of the height of a peak and the inverse of the peak width of the peak is calculated, which is recorded as the steepness of the peak; an exponential function with a natural constant as the base is used to negatively map the average value of the steepness of all peaks in the continuation curve to obtain a first mapping result; the product of the first mapping result and the continuation distribution eigenvalue is the modified continuation distribution eigenvalue.

6. The method for intelligent power quality monitoring for a four-in-one terminal according to claim 1 is characterized in that: The method of obtaining the current abnormality degree of the abnormal monitoring point based on the suspected abnormality degree of the abnormal monitoring point and the modified continuous distribution characteristic value includes: The product of the suspected abnormality degree of the abnormal monitoring point and the inverse of the modified continuation distribution characteristic value is calculated to obtain the current abnormality degree of the abnormal monitoring point.

7. The method for intelligent power quality monitoring for a four-in-one terminal according to claim 1 is characterized in that: The determining whether the power quality is abnormal according to the degree of current abnormality includes: An abnormal threshold is set. If the abnormal degree of the current at the abnormal monitoring point is greater than or equal to the abnormal threshold, the power quality is abnormal.

8. An intelligent power quality monitoring system for a four-in-one terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by the processor, the steps of the method for intelligent power quality monitoring for a four-in-one terminal as described in any one of claims 1 to 7 are implemented.

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