CN115099141A - Method for predicting slow fusing of high-voltage fuse of generator excitation voltage transformer - Google Patents
Method for predicting slow fusing of high-voltage fuse of generator excitation voltage transformer Download PDFInfo
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Abstract
The invention relates to a method for predicting slow fusing of a high-voltage fuse of a generator excitation voltage transformer, which comprises the following steps: periodically acquiring three voltage data of two groups of excitation PT secondary voltages and silicon controlled anode voltages of the unit through a data acquisition device, calculating a voltage effective value and a negative sequence voltage value, and storing the voltage effective value and the negative sequence voltage value in a data storage device; sequentially performing feature extraction and dynamic feature analysis on data stored in a data storage device through a large data flow type algorithm model to obtain the change features of a double PT voltage difference value, a PT and silicon controlled anode voltage difference value and a negative sequence voltage value; and the early warning output device judges the threshold value of the change characteristics output by the algorithm server, logically combines the judgment results and outputs early warning information. The invention introduces a real-time big data flow type algorithm model, extracts the sensitive characteristic values of six parameters by a big data classification and aggregation method, and can monitor the tiny change of the characteristic values of the selected parameters when slow melting occurs in the early stage, thereby realizing early warning.
Description
Technical Field
The invention relates to the technical field of electrical equipment, in particular to a method for predicting slow fusing of a high-voltage fuse of a generator excitation voltage transformer.
Background
The generator excitation PT is a special voltage transformer which is arranged at the outlet of the generator and used by an excitation regulator of the generator, is used for measuring the voltage of the generator, is compared with the given value of the voltage of the generator, and is regulated and controlled by the proportion (P), the integral (I) and the differential (D) of the excitation regulator to achieve the purposes of controlling the voltage of the generator and stabilizing the voltage of the generator. If the excitation PT fails in operation, the voltage of the generator is reduced or disappears, the excitation system can be immediately excited forcibly until relay protection actions such as overvoltage of the generator trip, so that the unit is unplanned to stop operation, and even the generator unit can be damaged in severe cases. Therefore, the excitation regulator needs to have a function of protecting the excitation PT from disconnection. When a fault occurs in the PT of the excitation high-voltage PT primary side high-voltage fuse, the excitation high-voltage PT primary side high-voltage fuse can be quickly fused to cut off a power supply loop, and the purposes of protecting equipment and preventing the expansion of accidents are achieved. The action of the high-voltage fuse is realized by fusing a melt (fuse wire), and the melt has a very obvious characteristic, namely an ampere-second characteristic, also called an inverse time limit characteristic, namely that the fusing time is long when the overload current is small; when the overload current is large, the fusing time is short. According to the current technical level, a fuse wire of the fuse protector is blown out by arcing and can be blown out in 0.1 second generally; if the action time is less than 0.1 second, the operation is called rapid fusing; if the fusing time is more than 0.1 second, the fusing is called slow fusing. If the PT is quickly fused, a difference voltage larger than the PT disconnection setting value is ensured to be generated, the PT disconnection can quickly act, and the excitation system is ensured to continuously and normally operate. If the fuse is blown out slowly, the terminal voltage and the reactive power of the generator can be changed violently in a long time, and the serious condition can cause excitation error forced excitation and even tripping and shutdown.
The traditional method for judging slow fusing of the excitation high-voltage PT fuse comprises the following steps: 1. the method comprises the steps that a positive sequence component and a negative sequence component of three-phase voltage of a generator stator and a positive sequence component and a negative sequence component of three-phase current of the stator are calculated through a sampling system, when slow melting occurs to the PT high-voltage fuse, the negative sequence component of the voltage occurs, but the negative sequence component of the current does not exist, and therefore the slow melting of the high-voltage fuse is judged; the method is mainly based on the negative sequence voltage of PT disconnection for judgment, but at present, because a DSP chip is a fixed-point processor, a calculation method adopts d-q coordinate Z transformation calculation, normal operation is to calculate the negative sequence voltage to be about 5%, so that the calculation error is large, the low threshold value of the negative sequence voltage during slow melting is often difficult to achieve, and the instant early warning when the high-voltage fuse is subjected to slow melting is realized; 2. the double PT comparison method adopts two groups of PT measured values through communication, calculates through a DSP chip, can adopt 12 or 16 points in one period at equal intervals, calculates a real part and an imaginary part of alternating voltage through a Fourier algorithm, then calculates effective voltage values U1, U2 and U ═ U1-U2|, and can judge that a group of PT of min (U1, U2) is disconnected when U is larger than a certain value; the double PT voltage comparison method calculates a voltage effective value by sampling of a DSP chip and judges PT disconnection by comparing two groups of PT voltage difference values, but the method has the defects that a judgment threshold value is not easy to set, the setting is too small, an alarm is easy to trigger by mistake, and the setting is too large and the occurrence of slow melting is difficult to find in real time.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for predicting slow fusing of a high-voltage fuse of a generator excitation voltage transformer, and solves the defects of the traditional method for judging slow fusing.
The purpose of the invention is realized by the following technical scheme: a slow-fusing prediction method for a high-voltage fuse of a generator excitation voltage transformer comprises the following steps:
periodically acquiring three voltage data of two groups of excitation PT secondary voltages and silicon controlled anode voltages of the unit through a data acquisition device, calculating a voltage effective value and a negative sequence voltage value, and storing the voltage effective value and the negative sequence voltage value in a data storage device;
the algorithm server sequentially performs feature extraction and dynamic feature analysis on the data stored in the data storage device through a large data flow algorithm model to obtain the change features of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value;
and the early warning output device judges the threshold value of the change characteristics output by the algorithm server, logically combines the judgment results and outputs early warning information.
The calculating of the voltage effective value and the negative sequence voltage value includes: the real part and the imaginary part of the alternating voltage are calculated by a Fourier algorithm, then effective voltage values U1, U2 and U3 are calculated, and further Δ U1 ═ U1-U2|, Δ U2 ═ U1-U3|, Δ U3 ═ U2-U3|, and negative sequence voltage values V12, V22 and V32 are calculated.
The change characteristics of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value obtained by the characteristic extraction and dynamic characteristic analysis comprise:
extracting a difference voltage delta U and a negative sequence voltage V from the acquired data through an algorithm model and extracting characteristic values, wherein the two data are sensitive characteristic parameters reflecting slow melting;
fitting a differential voltage delta U curve and a negative sequence voltage V curve to a sample of the high-voltage insurance normal operation state data by using a fuzzy adaptive neural network algorithm to obtain a baseline model of the differential voltage delta U
Baseline model of negative sequence voltage V curve
Then, the output values of the difference voltage delta U and the negative sequence voltage V under abnormal operation are estimated by utilizing the model, wherein the delta U is used for estimating the output value of the negative sequence voltage V r A differential voltage representing actual operation; v r Negative sequence voltage, f, representing actual operation c Voltage measurement factor, f, representing the difference between the fusing degrees of the high-voltage fuse d A negative sequence voltage measurement factor representing the degree of fusing of the high voltage fuse.
The early warning output device judges the threshold value of the change characteristics output by the algorithm server, and outputs early warning information after logically combining the judgment results, wherein the early warning output device comprises:
judging whether the voltage difference values delta U1 between PT1 and PT2, the voltage difference value delta U2 between PT1 and the anode voltage of the silicon controlled rectifier and the negative voltage V12 of PT1 under abnormal output estimated by the model are not in the threshold range at the same time, if so, judging that PT1 fuse slow melting occurs and outputting early warning information, and if one is in the threshold range, not outputting the early warning information;
and judging whether the voltage difference value delta U1 between PT1 and PT2, the voltage difference value delta U3 between PT2 and the anode of the thyristor and the negative sequence voltage V22 of PT2 under abnormal output are simultaneously not in a threshold range by the model, if so, judging that PT2 fuse slow melting occurs and outputting early warning information, and if one of the PT2 fuse slow melting and the anode of the thyristor is in the threshold range, not outputting the early warning information.
The big data flow type algorithm model building comprises the following steps: establishing a thermal model Q of 0.24I according to the relation between the heat and the resistance in the fuse fusing process 2 RT, where I represents a current flowing through the fuse, R represents a resistance, and T represents time; respectively establishing corresponding models delta U and alpha according to the relation between the differential voltage delta U and the negative sequence voltage V and the resistance 1 α 2 R and V ℃ - 1 β 2 R, wherein, alpha 1 、α 2 Representing the differential voltage influence coefficient, beta 1 、β 2 Representing the negative sequence voltage influence coefficient.
The invention has the following advantages: a generator excitation voltage transformer high-voltage fuse slow-fusing prediction method selects silicon controlled rectifier anode voltage as a comparison parameter of high-voltage fuse slow-fusing, six judgment parameters including delta U1, delta U2, delta U3 and negative sequence voltage values V12, V22 and V32 are extracted, and compared with traditional comparison point selection, the fuse slow-fusing diagnosis is more accurate and reliable; a real-time big data flow type algorithm model is introduced, sensitive characteristic values of six parameters are extracted by a big data classification and aggregation method, and by adopting the method, the tiny change of the characteristic values of the selected parameters can be monitored when slow melting occurs in the early stage, so that early warning is realized.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of Δ U and V characteristic curve fitting;
FIG. 3 is a schematic diagram of early warning diagnostic logic.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided below in connection with the appended drawings is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. The invention is further described below with reference to the accompanying drawings.
As shown in figure 1, the method for predicting the slow-fusing of the high-voltage fuse of the generator excitation voltage transformer is characterized in that a large-data-flow algorithm model is used for operation and then extracting characteristic values, and the large-data-flow algorithm model consists of a physical modeling part, a characteristic extraction part, a dynamic characteristic analysis part, a model building part and a diagnosis analysis part. A mechanism model of fuse slow fusing is analyzed and used as a basis for physical modeling and feature extraction of big data, a fuse fusing process of the fuse can be regarded as a heat accumulation and conversion process, the fusing degree and the influence degree of the fuse are related to the environment temperature, the current and the material, and the fuse generates heat to cause resistance change in the slow fusing process. Practice operation shows that the slow reduction of secondary output voltage is accompanied in the fuse slow fusing process, which is caused by resistance change in the fuse slow fusing process, so that a voltage difference value and a negative sequence voltage are used for extracting characteristic parameters representing the occurrence of the fuse slow fusing; the algorithm model adopts a fuzzy self-adaptive neural network to fit a voltage difference value and a negative sequence voltage value characteristic curve to a normal sample of normal data to obtain a characteristic curve baseline model under two voltage values, and then the model is utilized to realize the characteristic output of difference voltage and negative sequence voltage under real-time big data; the method specifically comprises the following steps:
periodically acquiring three voltage data of two groups of excitation PT secondary voltages and silicon controlled anode voltages of the unit through a data acquisition device, calculating a voltage effective value and a negative sequence voltage value, and storing the voltage effective value and the negative sequence voltage value in a data storage device, wherein 16 points are sampled in one period;
the algorithm server sequentially performs feature extraction and dynamic feature analysis on the data stored in the data storage device through a large data flow algorithm model to obtain the change features of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value;
and the early warning output device judges the threshold value of the change characteristics output by the algorithm server, logically combines the judgment results and outputs early warning information.
Further, the calculating the voltage effective value and the negative sequence voltage value includes: the real part and the imaginary part of the alternating voltage are calculated by the Fourier algorithm, then the effective values of the voltage U1, U2 and U3 are calculated, and further Δ U1 ═ U1-U2|, Δ U2 ═ U1-U3|, Δ U3 ═ U2-U3|, and the negative sequence voltage values V12, V22 and V32 are calculated.
Further, the change characteristics of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value obtained by the characteristic extraction and dynamic characteristic analysis comprise:
extracting a difference voltage delta U and a negative sequence voltage V from the acquired data through an algorithm model and extracting characteristic values, wherein the two data are sensitive characteristic parameters reflecting slow melting;
as shown in FIG. 2, a fuzzy adaptive neural network algorithm is used to fit a differential voltage Δ U and a negative sequence voltage V curve to a sample of the high-voltage insurance normal operation state data to obtain a baseline model of the differential voltage Δ U
Baseline model of negative sequence voltage V curve
Then, the output values of the difference voltage delta U and the negative sequence voltage V under abnormal operation are estimated by using a model, wherein delta U r A differential voltage representing actual operation; v r Negative sequence voltage, f, representing actual operation c Voltage measurement factor, f, representing the difference between the fusing degrees of the high-voltage fuse d A negative sequence voltage measurement factor representing the degree of fusing of the high voltage fuse.
Further, as shown in fig. 3, the early warning output device performs threshold judgment on the variation characteristics output by the algorithm server, and outputs the early warning information after performing logical combination on the judgment result, including:
judging whether the voltage difference values delta U1 between PT1 and PT2, the voltage difference value delta U2 between PT1 and the anode voltage of the silicon controlled rectifier and the negative voltage V12 of PT1 under abnormal output estimated by the model are not in the threshold range at the same time, if so, judging that PT1 fuse slow melting occurs and outputting early warning information, and if one is in the threshold range, not outputting the early warning information;
and judging whether the voltage difference values delta U1 between PT1 and PT2, the voltage difference value delta U3 between PT2 and the silicon controlled anode and the negative sequence voltage V22 of PT2 under abnormal output are simultaneously not in a threshold range by the model, if so, judging that PT2 fuse slow melting occurs and outputting early warning information, and if one is in the threshold range, not outputting the early warning information.
Wherein, V32 shows the negative sequence voltage of the SCR anode voltage, and has no physical connection relation with PT slow fusing, thus not being used as the basis for judgment.
The big data flow type algorithm model building comprises the following steps: establishing a thermal model Q of 0.24I according to the relation between the heat and the resistance in the fuse fusing process 2 RT, where I represents a current flowing through the fuse, R represents a resistance, and T represents time; respectively establishing corresponding models delta U and alpha according to the relation between the differential voltage delta U and the negative sequence voltage V and the resistance 1 α 2 R and V ℃ - 1 β 2 R, wherein, alpha 1 、α 2 Representing the differential voltage influence coefficient, beta 1 、β 2 Representing the negative sequence voltage influence coefficient.
According to the method, the anode voltage of the silicon controlled rectifier is taken as a comparison parameter of the occurrence of the high-voltage fuse slow melting, the voltage of two groups of PT is generally selected as the comparison in the traditional slow melting judgment, the anode voltage of the silicon controlled rectifier is selected as a comparison parameter of the occurrence of the high-voltage fuse slow melting, six judgment parameters including delta U1, delta U2, delta U3 and negative sequence voltage values V12, V22 and V32 are extracted, and the accurate early warning of the occurrence of the slow melting is realized through the logical combination of the multiple parameter sensitive characteristic values after the multiple parameters are out of limit. A real-time big data flow type algorithm model is introduced, characteristic values of six parameters are extracted, and by adopting the method, the tiny change of the characteristic values of the selected parameters can be monitored when slow melting occurs in the early stage. Thereby realizing early warning.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A method for predicting slow fusing of a high-voltage fuse of a generator excitation voltage transformer is characterized by comprising the following steps: the slow fusing prediction method comprises the following steps:
periodically acquiring three-way voltage data of two groups of excitation PT secondary voltages and silicon controlled rectifier anode voltages of the unit through a data acquisition device, calculating a voltage effective value and a negative sequence voltage value, and storing the voltage effective value and the negative sequence voltage value in a data storage device;
the algorithm server sequentially performs feature extraction and dynamic feature analysis on the data stored in the data storage device through a large data flow algorithm model to obtain the variation features of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value;
and the early warning output device judges the threshold value of the change characteristics output by the algorithm server, logically combines the judgment results and outputs early warning information.
2. The generator excitation voltage transformer high-voltage fuse slow-fusing prediction method according to claim 1, characterized in that: the calculating of the voltage effective value and the negative sequence voltage value comprises: the real part and the imaginary part of the alternating voltage are calculated by a Fourier algorithm, then effective voltage values U1, U2 and U3 are calculated, and further Δ U1 ═ U1-U2|, Δ U2 ═ U1-U3|, Δ U3 ═ U2-U3|, and negative sequence voltage values V12, V22 and V32 are calculated.
3. The generator excitation voltage transformer high-voltage fuse slow-fusing prediction method according to claim 2, characterized in that: the characteristics of the change of the double PT voltage difference value, the PT and silicon controlled anode voltage difference value and the negative sequence voltage value obtained by the characteristic extraction and dynamic characteristic analysis comprise:
extracting a difference voltage delta U and a negative sequence voltage V from the acquired data through an algorithm model and extracting characteristic values, wherein the two data are sensitive characteristic parameters reflecting slow melting;
fitting a differential voltage delta U and a negative sequence voltage V curve to a sample of the high-voltage insurance normal operation state data by using a fuzzy self-adaptive neural network algorithm to obtain a baseline model of the differential voltage delta U
Baseline model of negative sequence voltage V curve
Then, the output values of the difference voltage delta U and the negative sequence voltage V under abnormal operation are estimated by utilizing the model, wherein the delta U is used for estimating the output value of the negative sequence voltage V r A differential voltage representing actual operation; v r Negative sequence voltage, f, representing actual operation c Voltage measurement factor, f, representing the difference between the fusing degrees of the high-voltage fuse d A negative sequence voltage measurement factor representing the degree of fusing of the high voltage fuse.
4. The method for predicting slow fuse of the high-voltage fuse of the generator excitation voltage transformer of claim 3, characterized by comprising the following steps: the early warning output device carries out threshold judgment on the change characteristics output by the algorithm server, and outputs early warning information after logically combining the judgment results, wherein the threshold judgment comprises the following steps:
judging whether the voltage difference values delta U1 between PT1 and PT2, the voltage difference value delta U2 between PT1 and the anode voltage of the silicon controlled rectifier and the negative voltage V12 of PT1 under abnormal output estimated by the model are not in the threshold range at the same time, if so, judging that PT1 fuse slow melting occurs and outputting early warning information, and if one is in the threshold range, not outputting the early warning information;
and judging whether the voltage difference values delta U1 between PT1 and PT2, the voltage difference value delta U3 between PT2 and the silicon controlled anode and the negative sequence voltage V22 of PT2 under abnormal output are simultaneously not in a threshold range by the model, if so, judging that PT2 fuse slow melting occurs and outputting early warning information, and if one is in the threshold range, not outputting the early warning information.
5. The method for predicting slow fuse of the high-voltage fuse of the generator excitation voltage transformer as claimed in any one of claims 1 to 4, wherein: the big data flow type algorithm model building comprises the following steps: establishing a thermal model Q of 0.24I according to the relation between the heat and the resistance in the fuse fusing process 2 RT, where I represents a current flowing through the fuse, R represents a resistance, and T represents time; respectively establishing corresponding models delta U and alpha according to the relation between the differential voltage delta U and the negative sequence voltage V and the resistance 1 α 2 R and V ℃ - 1 β 2 R, wherein, alpha 1 、α 2 Representing the differential voltage influence coefficient, beta 1 、β 2 Representing the negative sequence voltage influence coefficient.
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