CN118294750B - Automatic point-to-point verification system based on relay protection - Google Patents
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Abstract
The invention relates to the technical field of relay protection, in particular to an automatic point-to-point verification system based on relay protection, which comprises the following steps: the waveform data acquisition module is used for monitoring the power grid in real time, collecting current and voltage waveform data of the power grid, recording time labels and amplitude values of each waveform, storing the data as a time sequence and generating a power grid real-time waveform record. According to the invention, the current and voltage waveforms in the power system are monitored in real time and recorded accurately, the dynamic change of the power system is effectively tracked and recorded, the real-time waveforms are compared with the stored standard waveforms, the matching and scoring waveform fitting degree is adjusted by utilizing the time axis, the waveforms deviating from the normal range are rapidly and accurately identified, the current state of the power grid can be rapidly analyzed, the potential fault type can be accurately diagnosed, and further, the protection parameters are adjusted according to the diagnosis results, so that the power system can timely and accurately respond when the power system fails.
Description
Technical Field
The invention relates to the technical field of relay protection, in particular to an automatic point-to-point verification system based on relay protection.
Background
The technical field of relay protection mainly relates to the use of relays to protect a power system from various faults and abnormal conditions. These relays are capable of reacting quickly upon detection of abnormal electrical conditions such as over-current, over-voltage, voltage imbalance, or ground faults, thereby protecting the power grid, generator, transformer, and other electrical equipment by opening the circuit or triggering other protection mechanisms. Relay protection is one of key technologies for ensuring stable and reliable operation of a power system, and reduces the risk of equipment damage and ensures power supply safety by accurately monitoring and controlling operation parameters of the power system.
The automatic point-to-point verification system based on relay protection is an electric power system monitoring tool, and utilizes an automatic technology to accurately verify and adjust relay protection equipment. The main purpose of the system is to ensure that all relay protection devices can respond accurately and timely when the power system fails. By automatic point-to-point verification, the reliability and the safety of the system can be obviously improved, and equipment damage or power interruption caused by improper relay setting can be reduced. Such systems are commonly used in power stations and substations to optimize the performance and response time of the relays, thereby protecting the power infrastructure and ensuring power supply continuity.
The prior art shows significant shortcomings in complex environments of power systems, particularly in the identification and handling of unusual or complex grid anomalies. The existing methods often rely on fixed parameters and preset thresholds that are manually set, which limits their flexibility and timeliness in rapidly changing grid conditions. Such fixed handling has limitations in adapting and fine-tuning the protective measures in real time, such as misassessing the type of fault or failing to identify the actual fault in time, which may lead to unnecessary blackouts and equipment damages, further affecting the continuity and reliability of the power supply. These problems highlight the urgent need for higher automation and adaptability technologies aimed at improving the operating efficiency and fault response capabilities of the power grid.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an automatic point-to-point verification system based on relay protection.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the automatic point-to-point verification system based on relay protection comprises:
the waveform data acquisition module is used for monitoring the power grid in real time, collecting current and voltage waveform data of the power grid, recording a time tag and an amplitude value of each waveform, storing the data as a time sequence and generating a power grid real-time waveform record;
The waveform matching module adopts time sequence analysis, compares the real-time waveform record of the power grid with a standard waveform template stored in a database, and verifies whether the waveform is aligned or not by dynamically adjusting the expansion and contraction of a waveform time axis to generate a waveform fitness score;
The fault diagnosis module determines deviation from a standard template by evaluating the waveform fitness score, classifies waveforms with the deviation exceeding a threshold value, and judges the waveforms as abnormal waveforms, so that the current state of the power grid is analyzed, potential fault types are identified, and the potential fault types are integrated into a fault type identification result;
And the protection parameter adjusting module adjusts the operation parameters of the relay according to the fault type identification result, wherein the operation parameters comprise the trigger time and the setting of a protection threshold, the relay is matched with the actual power grid state in response, the power grid is restored to the normal running state, and the adjusted protection parameter configuration is generated.
As a further scheme of the invention, the real-time waveform record of the power grid comprises a time stamp, a current value and a voltage value, the waveform fitness score specifically comprises a matching precision, an alignment index and a fitness score, the fault type identification result comprises a fault grade, a fault reason and a fault position, and the adjusted protection parameter configuration specifically comprises a trigger threshold, response time and a protection range.
As a further aspect of the present invention, the step of obtaining the time stamp and the amplitude value specifically includes:
In the real-time monitoring of current and voltage data, each acquired data point simultaneously records amplitude and time information, each waveform data point is provided with a time tag and a corresponding amplitude value, and the time and the amplitude of data acquisition are synchronously recorded through a timer and a sensor;
integrating the time and amplitude of each data point to generate a time-amplitude pairing, and adopting a formula
Wherein the method comprises the steps ofA time stamp representing each data point,Corresponding amplitude values;
the integrated time-amplitude pairing data is stored in a database, and the time tag and the amplitude value are generated by using batch processing and transaction management functions of the database.
As a further aspect of the present invention, the step of obtaining the waveform fitness score specifically includes:
extracting a stored standard waveform template from a database, and collecting real-time waveform records of a power grid;
the dynamic adjustment of the time axis is carried out on the real-time waveform data, including expansion and translation, which is aligned with the standard waveform template, and the formula is referred to:
wherein, Is the waveform data in real time and,AndIs an adjustment coefficient, and controls the expansion and translation respectively; applying a contrast analysis formula:
Calculating differences between waveforms, generating a waveform fitness score, wherein the parameters Representing template waveform data, adjusted real-time waveform data and total number of data points, respectively.
As a further aspect of the present invention, the standard template deviation obtaining step specifically includes:
comparing the real-time monitoring waveform with a preset standard waveform template, calculating the difference between the real-time monitoring waveform and the standard waveform template, obtaining the difference value of each point between the real-time waveform and the standard waveform by using differential operation, and setting the real-time waveform as And the standard waveform isDifference valueThe calculation formula is as follows:
the difference is calculated Cumulative summation is carried out to obtain total deviationThe total deviation is calculated using the sum formula:
wherein, As a total number of waveform data points,A value representing a waveform in real-time,The values representing the standard template waveforms are presented,Is a time-weighting factor that is used to determine,For the point in timeThe difference between the real-time waveform and the standard waveform.
As a further aspect of the present invention, the classification method of the abnormal waveform specifically includes:
setting an abnormality determination threshold Determining a threshold according to the deviation distribution of the historical data and the industry standard, and judging whether the waveform is abnormal or not by comparing the total deviationAnd (3) withThe judgment formula is as follows:
wherein, Is an anomaly threshold value determined based on historical data,Is an indication function according to a threshold judgment result;
When (when) Exceeding the limitIn the time-course of which the first and second contact surfaces,A value of 1 indicates abnormality, and a value of 0 indicates normal;
Using Classifying the waveforms according to the results of (a)Is marked as normal or abnormal, the abnormal waveform is analyzed, the fault type is identified, the abnormal waveform is matched with the fault model, and the model matching index is usedTo quantitatively evaluate the matching degree, the fault type of each waveform is determined by a model with the highest matching index, and the calculation formula is as follows:
wherein, Is the firstModel of seed fault at timeIs used for the characteristic weight of the (c),Is the abnormal waveform at timeIs used for the characteristic value of the (c),Is the first toA matching index of a fault model.
As a further aspect of the present invention, the step of obtaining the fault type identification result specifically includes:
collecting waveforms classified as anomalies and corresponding model matching indices For each fault type, selecting the waveform with the highest matching index as a representative for analysis;
Clustering the representative waveforms according to the characteristic values Matching indexUsing a clustering algorithm, waveform data points are fitted to features and fault severity (byRepresentation) are grouped, and the calculation formula is as follows:
wherein, Is the total number of abnormal waveforms,Is a weight factor assigned according to waveform severity,Is the firstThe abnormal waveform is at timeIs used for the characteristic value of the (c),The representative model is matched with the index of the model,Is the result of cluster analysis;
Summarizing the result of each cluster into a target fault type, defining the identification result of each type by the characteristic mode of the cluster center, and outputting the identification result as Each of which isRepresenting a type of fault by cluster analysisAnd integrating the obtained fault characteristic set into a fault type identification result.
As a further aspect of the present invention, the step of obtaining the adjusted protection parameter configuration specifically includes:
according to the fault type recognition result, analyzing the influence of the fault on the running state of the power grid, including abnormal frequency or voltage drop, and setting a preliminary value of a protection threshold according to the influence The method is characterized in that the method is initially set according to the fault severity and historical data statistics, and the calculation formula is as follows:
wherein, Is a coefficient that is adjusted according to the type of fault,Is a statistic of the extent of the fault impact;
Determining trigger time The triggering time is set according to the current load of the power grid and the emergency of fault recovery, and a dynamic adjustment formula is used for calculatingThe formula is:
wherein, Is a constant factor which is a function of the time,Is the current load capacity of the power grid,Is the adjustment coefficient of the light source,In order to protect the threshold value,Is the trigger time;
Combining the adjusted protection threshold Trigger timeOutputting the adjusted protection parameter configuration, wherein the protection parameter configuration is applied to the relay, and the response is matched with the power grid state to recover the power grid to the normal operation state.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, the current and voltage waveforms in the power system are monitored in real time and recorded accurately, so that the dynamic change of the power system is effectively tracked and recorded. By comparing the real-time waveform with the stored standard waveform, the waveform deviating from the normal range can be rapidly and accurately identified by adjusting the accuracy matching and the grading waveform fitting degree through a time axis, so that the current state of the power grid can be rapidly analyzed, and the potential fault type can be accurately diagnosed. Furthermore, the protection parameters are adjusted according to the diagnosis results, so that the power system can timely and accurately react when the power system fails, the automation level and the response speed of the power grid are obviously improved, the damage and interruption risk of the power equipment caused by improper equipment setting or response delay are greatly reduced, and the safety and the stability of the system are enhanced.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of the acquisition of time stamps and amplitude values according to the present invention;
FIG. 3 is a flow chart of the acquisition of a waveform fitness score according to the present invention;
FIG. 4 is a flow chart of the deviation acquisition of the standard template of the present invention;
FIG. 5 is a flow chart of a method for classifying abnormal waveforms according to the present invention;
FIG. 6 is a flow chart of the acquisition of the fault type recognition result of the present invention;
fig. 7 is a flow chart of the adjusted protection parameter configuration acquisition of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, an automatic peer-to-peer verification system based on relay protection includes:
the waveform data acquisition module is used for monitoring the power grid in real time, collecting current and voltage waveform data of the power grid, recording a time tag and an amplitude value of each waveform, storing the data as a time sequence and generating a power grid real-time waveform record;
The waveform matching module adopts time sequence analysis, compares the real-time waveform record of the power grid with a standard waveform template stored in a database, and verifies whether the waveform is aligned or not by dynamically adjusting the expansion and contraction of a waveform time axis to generate a waveform fitting degree score;
The fault diagnosis module determines deviation from a standard template by evaluating the waveform fitness score, classifies waveforms with the deviation exceeding a threshold value, and judges the waveforms as abnormal waveforms, so that the current state of the power grid is analyzed, potential fault types are identified, and the potential fault types are integrated into a fault type identification result;
And the protection parameter adjusting module adjusts the operation parameters of the relay according to the fault type identification result, wherein the operation parameters comprise the trigger time and the setting of a protection threshold, the relay is matched with the actual power grid state in response, the power grid is restored to the normal operation state, and the adjusted protection parameter configuration is generated.
The real-time waveform record of the power grid comprises a time stamp, a current value and a voltage value, the waveform fitness score comprises matching precision, an alignment index and a fitness score, the fault type identification result comprises a fault grade, a fault reason and a fault position, and the adjusted protection parameter configuration comprises a trigger threshold, response time and a protection range.
Referring to fig. 2, the steps for obtaining the time stamp and the amplitude value specifically include:
In the real-time monitoring of current and voltage data, each acquired data point simultaneously records amplitude and time information, each waveform data point is provided with a time tag and a corresponding amplitude value, and the time and the amplitude of data acquisition are synchronously recorded through a timer and a sensor;
integrating the time and amplitude of each data point to generate a time-amplitude pairing, and adopting a formula
Wherein the method comprises the steps ofA time stamp representing each data point,Corresponding amplitude values;
the integrated time-amplitude pairing data is stored in a database, and the time tag and the amplitude value are generated by using batch processing and transaction management functions of the database.
At the time of data acquisition, the acquisition time of each data point is recorded using a precise time synchronization system (e.g., GPS clock). Each time a sample is taken, the sensor generates a time stamp, recorded as。
The current or voltage sensor measures the current or voltage amplitude of each data point. The readings of the voltage or current values measured by the sensor at a particular point in time are recorded as。
The time tags are associated with corresponding amplitude values to form a one-to-one mapping for data analysis and storage.
For each data point, there is already(Time tag)(Amplitude value). In order to make the data easy to process and query, it is necessary to integrate into a single string representation.
The time stamp and amplitude value are connected with a specific character (e.g., dash '-') using a string concatenation function 'concat'. By doing so, the clarity and traceability of the data can be maintained, and the retrieval and analysis of the future data are facilitated.
Assume that at a certain measurement instant, a time tagIs "12:00:01.123" (representing a 12 th order of 1 second 123 ms).
Assuming corresponding amplitude values50.5 (In volts or amperes, depending on whether voltage or current is measured).
The data points collected are at time "12:00:01.123" with a voltage value of 50.5.
These two parameters are processed using a 'concat' function: concat ("12:00:01.123", '-', "50.5").
The output will be "12:00:01.123-50.5".
By such processing, the time and amplitude information of each data point is effectively integrated into a character string which retains all critical measurement data and is uniform in format, thereby facilitating subsequent data processing and analysis. The core of this approach is to increase the ease of use and queriability of data while maintaining data integrity.
Referring to fig. 3, the step of obtaining the waveform fitness score specifically includes:
extracting a stored standard waveform template from a database, and collecting real-time waveform records of a power grid;
the dynamic adjustment of the time axis is carried out on the real-time waveform data, including expansion and translation, which is aligned with the standard waveform template, and the formula is referred to:
wherein, Is the waveform data in real time and,AndIs an adjustment coefficient, and controls the expansion and translation respectively; applying a contrast analysis formula:
Calculating differences between waveforms, generating a waveform fitness score, wherein the parameters Representing template waveform data, adjusted real-time waveform data and total number of data points, respectively.
Standard waveform templates are called from the database.
Assume that the templates stored in the database areWherein, the method comprises the steps of, wherein,Represent the firstVoltage or current values for the individual data points.
Database query operations involve SQL query statements, such as ' SELECTwaveformFROMtemplatesWHEREid = ' model_id '.
Monitoring the grid in real time to obtain waveform records of current and voltage。
Representing the first in real-time waveformVoltage or current values for the individual data points.
With the sensor data acquisition system, data points are recorded every millisecond, each data point including a time stamp and an amplitude value.
For real-time waveformsPerforming time axis expansion and translation to maximize and templateIs used for the alignment of the two parts.
Determination of the telescoping parameters by an optimization algorithm, e.g. gradient descentAnd translation parameters。
Referring to the formula:
Initialization of And。
Iterative adjustmentAndBy minimizingTo improve alignment.
Comparing the adjusted real-time waveformsWaveform of templateA fitness score is calculated.
Representing the total number of data points in the waveform.
The calculation formula is as follows:
For each of Calculation ofAndAbsolute difference between them.
Summing all the differences and dividing byThe average difference is obtained.
Subtracting this average difference from 1 yields a waveform fitness score.
Referring to fig. 4, the standard template deviation obtaining step specifically includes:
comparing the real-time monitoring waveform with a preset standard waveform template, calculating the difference between the real-time monitoring waveform and the standard waveform template, obtaining the difference value of each point between the real-time waveform and the standard waveform by using differential operation, and setting the real-time waveform as And the standard waveform isDifference valueThe calculation formula is as follows:
the difference is calculated Cumulative summation is carried out to obtain total deviationThe total deviation is calculated using the sum formula:
wherein, As a total number of waveform data points,A value representing a waveform in real-time,The values representing the standard template waveforms are presented,Is a time-weighting factor that is used to determine,For the point in timeThe difference between the real-time waveform and the standard waveform.
It is assumed that the number of the sub-blocks,At a point in time for a real-time waveformIs a function of the number of (c),At the same time point for standard waveform templateIs a numerical value of (2).
For each point in timeCalculating a real-time waveformAnd standard waveformThe difference between them to obtain。
Difference valueThe calculation formula of (2) is as follows:
wherein, Representing the real-time waveform and the standard template waveform at a point in timeIs a deviation of (2).
It is assumed that the number of the sub-blocks,Is the time ofWeight factor of (1), is assumed to beWhereinIs the total number of waveform data points, representing a linear increase in time over time.
Each time point calculated using the calculationIs the difference of (2)Combining time weight factorsCalculate the total deviation。
Total deviation ofThe calculation formula of (2) is as follows:
wherein, Representation for all time pointsFrom 1 toIs added up to the sum of (a),Indicating a point in timeAnd the product of the difference of (c) and its corresponding weight.
Assuming real-time waveformsThe values at four time points areStandard waveformThe value at the same time point is。
Calculating the difference for each time point：
Calculating the total deviationAssume a total point in time：
Referring to fig. 5, the method for classifying abnormal waveforms specifically includes:
setting an abnormality determination threshold Determining a threshold according to the deviation distribution of the historical data and the industry standard, and judging whether the waveform is abnormal or not by comparing the total deviationAnd (3) withThe judgment formula is as follows:
wherein, Is an anomaly threshold value determined based on historical data,Is an indication function according to a threshold judgment result;
When (when) Exceeding the limitIn the time-course of which the first and second contact surfaces,A value of 1 indicates abnormality, and a value of 0 indicates normal;
Using Classifying the waveforms according to the results of (a)Is marked as normal or abnormal, the abnormal waveform is analyzed, the fault type is identified, the abnormal waveform is matched with the fault model, and the model matching index is usedTo quantitatively evaluate the matching degree, the fault type of each waveform is determined by a model with the highest matching index, and the calculation formula is as follows:
wherein, Is the firstModel of seed fault at timeIs used for the characteristic weight of the (c),Is the abnormal waveform at timeIs used for the characteristic value of the (c),Is the first toA matching index of a fault model.
It is assumed that the number of the sub-blocks,The values for the real-time waveforms, obtained directly from the sensor or data acquisition system,For the values of the standard template waveforms, selected from historical data or preset by an expert,For the time weight factor, reference is made to the formula:
wherein, Is the total time point, and is designed to have a greater influence on the data points at later stages.
For each time pointCalculating the difference between the real-time waveform and the standard waveform。
Using time weighting factorsAnd carrying out weighted summation on the differences of all the time points to obtain total deviation:
If there are waveform data points Then for each of. Calculate eachThereafter, a weighted sum is used to calculate。
And setting an abnormality judgment threshold according to historical data analysis and expert knowledge.
Based on the calculated total deviationComparison ofWhether or not it is greater than a set threshold。
If it isThe waveform is marked as abnormalOtherwise marked as normal。
It is assumed that the number of the sub-blocks,Is the first toThe matching index of the seed fault model,For a specific fault typeThe weight of the time point is preset according to the typical characteristics of the fault.
For each waveform marked as abnormal, a matching index is calculated with each fault model, referring to the formula:
Wherein the method comprises the steps of Is of waveform atIs a function of the actual measurement of (a).
Select the one with highestThe fault model of the value serves as the fault type of the waveform.
Referring to fig. 6, the steps for obtaining the fault type identification result specifically include:
collecting waveforms classified as anomalies and corresponding model matching indices For each fault type, selecting the waveform with the highest matching index as a representative for analysis;
Clustering the representative waveforms according to the characteristic values Matching indexUsing a clustering algorithm, waveform data points are fitted to features and fault severity (byRepresentation) are grouped, and the calculation formula is as follows:
wherein, Is the total number of abnormal waveforms,Is a weight factor assigned according to waveform severity,Is the firstThe abnormal waveform is at timeIs used for the characteristic value of the (c),The representative model is matched with the index of the model,Is the result of cluster analysis;
Summarizing the result of each cluster into a target fault type, defining the identification result of each type by the characteristic mode of the cluster center, and outputting the identification result as Each of which isRepresenting a type of fault by cluster analysisAnd integrating the obtained fault characteristic set into a fault type identification result.
Assuming that all abnormal waveforms are identified by the previous steps, and a model matching index is calculated for each waveform. The waveforms are stored in a data set, each waveform corresponding to a time series of characteristic valuesWhereinRepresenting the time point index.
Weighting factorIs assigned according to the severity of each abnormal waveform or the degree of impact on the system. It is assumed that the severity of the waveform is assessed by an estimate of the loss or impact caused by the fault, which is an economic loss, potential downtime, or safety risk rating.
EigenvaluesIs obtained by analyzing time-series data of each abnormal waveform, including amplitude, frequency, phase change, and the like. Each of which isReflecting the time pointIs a waveform characteristic of (a).
Will beAnd (d) sumFor cluster analysis, the objective is to group together abnormal waveforms with similar characteristics. Each clusterIs calculated by referring to the formula:
wherein, Representing the weighted matching indexRepresenting the characteristic value.
Each clusterIs generalized to a specific type of faultThis is defined by analyzing the feature patterns of each cluster center. For example, if a cluster consists mainly of high frequency oscillating waveforms, it is classified as "high frequency failure".
Assuming three abnormal waveforms, the severity of each waveform is evaluated separately asAnd 0.7, corresponding model matching indexRespectively isAnd 0.95. Each waveform at a certain momentIs of the characteristic value of (2)100,150, And 130, respectively. The weighted eigenvalue of each waveformThe calculation is as follows:
For the first waveform:
For the second waveform:
For the third waveform:
The cluster analysis will be based on these weighted eigenvalues To perform, thereby classifying waveforms and defining fault types。
Referring to fig. 7, the adjusted protection parameter configuration obtaining step specifically includes:
According to the fault type recognition result, analyzing the influence of the fault on the running state of the power grid, including abnormal frequency or voltage drop, and setting a preliminary value of a protection threshold according to the influence The method is characterized in that the method is initially set according to the fault severity and historical data statistics, and the calculation formula is as follows:
wherein, Is a coefficient that is adjusted according to the type of fault,Is a statistic of the extent of the fault impact;
Determining trigger time The triggering time is set according to the current load of the power grid and the emergency of fault recovery, and a dynamic adjustment formula is used for calculatingThe formula is:
wherein, Is a constant factor which is a function of the time,Is the current load capacity of the power grid,Is the adjustment coefficient of the light source,In order to protect the threshold value,Is the trigger time;
Combining the adjusted protection threshold Trigger timeOutputting the adjusted protection parameter configuration, wherein the protection parameter configuration is applied to the relay, and the response is matched with the power grid state to recover the power grid to the normal operation state.
Determining coefficientsThis coefficient reflects the sensitivity of the adjustment of the protection threshold according to the type of fault. For example, if the fault type is a high risk such as a wire break,May be set to a higher value, such as 1.2, a low risk fault such as a short circuit of a small extent,May be set to a lower value (e.g., 0.8).
Calculating statistics of degree of influence of faults. This statistic can be obtained by analyzing the actual impact of similar faults in the past on the grid. For example, the average voltage drop amplitude or frequency fluctuation caused by the past 5 similar faults is calculated according to the historical data.
By multiplicationAnd (3) withCombining to obtain an adjusted protection threshold. This step combines the severity of the fault with its historical impact to set an appropriate protection response level.
Determining constant factorThis factor is set based on the default reaction time of the relay. Assuming that the base trigger time of the relay is1 second, thenMay be set to 1 second.
Calculating current grid loadThis can be obtained directly by a real time monitoring system of the grid, for example during peak hours, the load may be 5000 Megawatts (MW).
Using calculated protection thresholdsIn combination with the current loadTo adjust the trigger time. Here, theAs an index, the sensitivity of the load to the trigger time delay is reflected. Suppose that one wants to trigger the relay faster when the load is large, one willSet to 1.
Will beDivided byThen take the valuePower of th, multiply byObtaining the final trigger time. For example, ifIs thatIs thatIs a group consisting of-1,Is 1 second, thenCalculated asSecond.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (4)
1. Automatic point-to-point verification system based on relay protection, characterized in that the system comprises:
the waveform data acquisition module is used for monitoring the power grid in real time, collecting current and voltage waveform data of the power grid, recording a time tag and an amplitude value of each waveform, storing the data as a time sequence and generating a power grid real-time waveform record;
The waveform matching module adopts time sequence analysis, compares the real-time waveform record of the power grid with a standard waveform template stored in a database, and verifies whether the waveform is aligned or not by dynamically adjusting the expansion and contraction of a waveform time axis to generate a waveform fitness score;
the step of obtaining the waveform fitness score comprises the following steps:
extracting a stored standard waveform template from a database, and collecting real-time waveform records of a power grid;
the dynamic adjustment of the time axis is carried out on the real-time waveform data, including expansion and translation, which is aligned with the standard waveform template, and the formula is referred to:
；
wherein, Is the waveform data in real time and,AndIs an adjustment coefficient, and controls the expansion and translation respectively;
applying a contrast analysis formula:
；
Calculating differences between waveforms, generating a waveform fitness score, wherein the parameters Respectively representing template waveform data, adjusted real-time waveform data and total data points;
The fault diagnosis module determines deviation from a standard template by evaluating the waveform fitness score, classifies waveforms with the deviation exceeding a threshold value, and judges the waveforms as abnormal waveforms, so that the current state of the power grid is analyzed, potential fault types are identified, and the potential fault types are integrated into a fault type identification result;
The standard template deviation obtaining step specifically comprises the following steps:
comparing the real-time monitoring waveform with a preset standard waveform template, calculating the difference between the real-time monitoring waveform and the standard waveform template, obtaining the difference value of each point between the real-time waveform and the standard waveform by using differential operation, and setting the real-time waveform as And the standard waveform isDifference valueThe calculation formula is as follows:
；
the difference is calculated Cumulative summation is carried out to obtain total deviationThe total deviation is calculated using the sum formula:
；
wherein, As a total number of waveform data points,A value representing a waveform in real-time,The values representing the standard template waveforms are presented,Is a time-weighting factor that is used to determine,For the point in timeA difference between the real-time waveform and the standard waveform;
the classification method of the abnormal waveform specifically comprises the following steps:
setting an abnormality determination threshold Determining a threshold according to the deviation distribution of the historical data and the industry standard, and judging whether the waveform is abnormal or not by comparing the total deviationAnd (3) withThe judgment formula is as follows:
；
wherein, Is an anomaly threshold value determined based on historical data,Is an indication function according to a threshold judgment result;
When (when) Exceeding the limitIn the time-course of which the first and second contact surfaces,A value of 1 indicates abnormality, and a value of 0 indicates normal;
Using Classifying the waveforms according to the results of (a)Is marked as normal or abnormal, the abnormal waveform is analyzed, the fault type is identified, the abnormal waveform is matched with the fault model, and the model matching index is usedTo quantitatively evaluate the matching degree, the fault type of each waveform is determined by a model with the highest matching index, and the calculation formula is as follows:
；
wherein, Is the firstModel of seed fault at timeIs used for the characteristic weight of the (c),Is the abnormal waveform at timeIs used for the characteristic value of the (c),Is the first toMatching indexes of the seed fault model;
The step of obtaining the fault type identification result specifically comprises the following steps:
collecting waveforms classified as anomalies and corresponding model matching indices For each fault type, selecting the waveform with the highest matching index as a representative for analysis;
Clustering the representative waveforms according to the characteristic values Matching indexUsing a clustering algorithm to group waveform data points according to feature fitting degree and fault severity, wherein a calculation formula is as follows:
；
wherein, Is the total number of abnormal waveforms,Is a weight factor assigned according to waveform severity,Is the firstThe abnormal waveform is at timeIs used for the characteristic value of the (c),The representative model is matched with the index of the model,Is the result of cluster analysis;
Summarizing the result of each cluster into a target fault type, defining the identification result of each type by the characteristic mode of the cluster center, and outputting the identification result as Each of which isRepresenting a type of fault by cluster analysisThe obtained fault characteristic set is integrated into a fault type identification result;
And the protection parameter adjusting module adjusts the operation parameters of the relay according to the fault type identification result, wherein the operation parameters comprise the trigger time and the setting of a protection threshold, the relay is matched with the actual power grid state in response, the power grid is restored to the normal running state, and the adjusted protection parameter configuration is generated.
2. The relay protection-based automatic point-to-point verification system according to claim 1, wherein the power grid real-time waveform record comprises a time stamp, a current value and a voltage value, the waveform fitness score is specifically a matching precision, an alignment index and a fitness score, the fault type identification result comprises a fault grade, a fault reason and a fault position, and the adjusted protection parameter configuration is specifically a trigger threshold, a response time and a protection range.
3. The relay protection-based automatic point-to-point verification system according to claim 1, wherein the step of obtaining the time tag and the amplitude value specifically comprises:
In the real-time monitoring of current and voltage data, each acquired data point simultaneously records amplitude and time information, each waveform data point is provided with a time tag and a corresponding amplitude value, and the time and the amplitude of data acquisition are synchronously recorded through a timer and a sensor;
The time and the amplitude of each data point are integrated, so that a time-amplitude pairing is generated, and the formula is adopted:
；
Wherein the method comprises the steps of A time stamp representing each data point,Is the value of the corresponding amplitude value,The character string concatenation function is represented as such,For dashes, the time tag is connected with the amplitude value;
the integrated time-amplitude pairing data is stored in a database, and the time tag and the amplitude value are generated by using batch processing and transaction management functions of the database.
4. The relay protection-based automatic point-to-point verification system according to claim 1, wherein the step of obtaining the adjusted protection parameter configuration specifically comprises:
according to the fault type recognition result, analyzing the influence of the fault on the running state of the power grid, including abnormal frequency or voltage drop, and setting a preliminary value of a protection threshold according to the influence The method is characterized in that the method is initially set according to the fault severity and historical data statistics, and the calculation formula is as follows:
；
wherein, Is a coefficient that is adjusted according to the type of fault,Is a statistic of the extent of the fault impact;
Determining trigger time The triggering time is set according to the current load of the power grid and the emergency of fault recovery, and a dynamic adjustment formula is used for calculatingThe formula is:
；
wherein, Is a constant factor which is a function of the time,Is the current load capacity of the power grid,Is the adjustment coefficient of the light source,In order to protect the threshold value,Is the trigger time;
Combining the adjusted protection threshold Trigger timeOutputting the adjusted protection parameter configuration, wherein the protection parameter configuration is applied to the relay, and the response is matched with the power grid state to recover the power grid to the normal operation state.
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