CN112415338A - Power grid fault detection method and device and storage medium - Google Patents
Power grid fault detection method and device and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of power supply and distribution, along with the penetration of artificial intelligence in various industrial aspects, various intelligent algorithms are developed for fault diagnosis of a power grid bus, mainly a fault identification model of a power grid bus group is established by a neural network and the like, but the algorithm needs to be provided with a huge bus temperature and temperature signal library, so that the difficulty in the process of establishing a complete database is large, and the method is in an experimental stage. The invention discloses a method, a device and a storage medium for detecting a power grid fault, wherein the method comprises the following steps: establishing a data storage unit; extracting a signal X to be detected and a reference signal Y, and establishing a signal characteristic matrix A to be detected and a reference signal characteristic matrix B for the signal X to be detected and the reference signal Y respectively; respectively calculating the numerical values of the feature vectors at the same position in the A and the B based on an entropy weight method; and constructing an early warning index a and a threshold value matrix alfa, judging whether the power grid bus is normal or not according to the early warning threshold value and the early warning index, and establishing a state table Tab.
Description
Technical Field
The invention relates to the technical field of power supply and distribution, in particular to a power grid fault detection method, a device and a storage medium. The core idea is to calculate an early warning index through a signal to be detected and a reference signal, construct a threshold value matrix of the early warning index, and finally comprehensively judge whether a power grid bus has a fault or not through comparing and analyzing the early warning index and the threshold value thereof.
Background
In recent years, the research and construction of smart grids has risen globally. The intelligent power distribution network is used as an important component for connecting a main network and supplying power to users in the intelligent power grid, and whether the running state of the intelligent power distribution network is normal or not directly influences the power supply of thousands of households. Meanwhile, with the access of distributed power sources, the popularization of electric automobiles and the increase of user interaction electric power, the dynamic behavior of the power distribution network becomes complex, the operation risk is greatly increased, and once a power failure accident of the power distribution network occurs, huge influence and loss can be caused to social life. Therefore, a need exists for further research on fault early warning of the intelligent power distribution network, and guidance and help are provided for relevant management personnel to maintain the intelligent power distribution network.
At present, scholars at home and abroad propose various solutions from different angles aiming at the fault early warning of the intelligent power distribution network. Research shows that most faults of the power distribution network enter pathological operation before destructive faults occur, and the power distribution network has trend and cumulative effects. However, most of the existing solutions utilize local parameters of the power distribution network, such as harmonic current and short-circuit current, or local components, such as a transformer, or are associated with external factors, such as thunderstorm weather, to achieve the effect of early warning the fault of the intelligent power distribution network. The solutions fail to grasp the operation state of the intelligent power distribution network on the whole, and only consider a certain current factor to judge whether the early warning is needed or not in the early warning scheme, and the trend and the cumulative effect of the power distribution network faults cannot be fully utilized, so that the accuracy of fault early warning is slightly deficient in the face of more and more complex intelligent power distribution networks. Therefore, there is an urgent need for an early warning method that can realize the overall control of the operating state of the smart distribution network and can jointly determine whether a failure is about to occur from the current and past operating states.
Disclosure of Invention
The invention aims to provide a power grid fault detection method, a device and a storage medium, which take a series of practical problems into consideration, adopt signals when a power grid bus is normal, and establish threshold value matrixes of maximum early warning indexes of different voltage levels and different power grid buses when the power grid bus is normal. When the bus is diagnosed and pre-warned, the pre-warning index value of the bus is calculated, and whether the bus is pre-warned or not is indicated by comparing and analyzing the threshold value of the bus under the same voltage level. Further, an energy characteristic matrix is extracted from a normal temperature signal of a power grid bus by utilizing wavelet transformation, a threshold value matrix of a fault early warning index is established based on an entropy weight method, the early warning index of the bus to be detected obtained through calculation is compared and analyzed with a corresponding threshold value, the steps are repeated by changing the number of layers of wavelet decomposition, and finally, whether the bus to be detected carries out fault early warning or not is comprehensively analyzed.
The invention provides a power grid fault detection method, a power grid fault detection device and a storage medium.
On one hand, the invention provides a power grid fault detection method, which comprises the following specific processes:
step 1, establishing a data storage unit, wherein the storage unit periodically stores and updates all bus temperature signals of different voltage levels in a power grid bus group, which are extracted under the fault-free condition; the temperature signal comprises the actual working temperature of all buses under different voltage levels in the power grid bus group; extracting the temperature signal as a standby reference signal for early warning of faults of all buses under different voltage levels in the power grid bus group;
step 2, extracting a signal to be detected X and a reference signal Y from the power grid bus, respectively performing wavelet decomposition on the signal to be detected X and the reference signal Y, extracting respective energy coefficient characteristic attribute values, wherein each energy coefficient characteristic attribute value forms an energy coefficient characteristic attribute characteristic dimension, and further respectively constructing a signal to be detected characteristic matrix A and a reference signal characteristic matrix B;
the step 3, respectively extracting the energy coefficient characteristic attribute values at the same position of the nth column of the to-be-detected signal characteristic matrix a and the reference signal characteristic matrix B to form an m × 2 characteristic matrix Q, where n is {1,2,3, … … K }, K is the number of layers of the wavelet decomposition, and m is the number of segments of the signal division; respectively calculating the characteristic weight values of the first dimension and the second dimension of the energy coefficient characteristic dimension in the K characteristic matrixes Q by using an entropy weight method, wherein the characteristic weight values are respectively bn1And bn2；
And 4, constructing an index a of the power grid bus group fault early warning to quantitatively describe the operating temperature of the bus, wherein the formula is as follows:
constructing a threshold value matrix alfa by the power grid bus fault early warning index a;
and 5, traversing the threshold value matrix alfa according to the serial number and the temperature of the bus to be detected, and determining the early warning threshold value a of the bus to be detectedijAnd the power grid bus fault early warning index a, and establishing a power grid bus group state information table Tab;
and 6, adjusting the number K of wavelet decomposition layers and the change times thereof, recording the calculation result of each time in a bus state information table Tab in the power grid bus group, and judging the operation conditions of all buses in the power grid bus group under different voltage levels.
Further, in the step 2, the signal to be measuredX and the reference signal Y are set with a certain number of sampling points as sample lengths, defined as wlen, then the signal X to be measured and the reference signal Y are respectively divided into m sections of signals, namely
Wherein m is an integer part of a result in the formula, the number of sampling points of wlen at least comprises temperature sampling points of each voltage section of the power grid bus, and X, Y represents the signal to be measured and the reference signal.
Further, in the wavelet decomposition in the step 2, a wavelet energy formula is used to calculate energy coefficients after the decomposition of the reference signal Y and the signal to be detected X, that is, En＝∑|xn|2Wherein xnEach decomposed signal segment corresponds to a reference value, n is {1,2,3.. K }, and K is the number of layers of wavelet decomposition of the signal; the signal X to be detected and the reference signal Y are respectively divided into m sections of signals, each section of signal is respectively subjected to K-layer wavelet decomposition, the signal energy coefficient after each section of wavelet decomposition is an energy coefficient characteristic attribute value, the signal energy coefficient characteristic attribute after each section of wavelet decomposition is composed of energy coefficient characteristic dimensions, and the m sections of energy coefficient characteristic dimensions are composed of a signal characteristic matrix A to be detected and a reference signal characteristic matrix B.
Further, the step 3 is to calculate entropy weight methods adopted for feature weight values of the feature attributes of the first dimension and the second dimension in the K feature matrices Q, respectively, that is, firstly, normalization is performed according to each feature attribute value in the feature matrices Q, that is, normalization is performed
Where i ═ 1,2,3.. ·. m }, j ═ 1,2}, min (X)i) Is the minimum value, max (X), of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectioni) Is the maximum value, X, of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectionijAnd obtaining a normalized matrix Q' of the characteristic matrix Q for the energy coefficient characteristic attribute value corresponding to the temperature signal of the ith dimension of the ith section.
Further, it is characterized byConstructing the bus fault early warning index a in the step 4
Wherein b isn1And bn2Respectively representing the characteristic weight values of the energy coefficient characteristic attributes in the first dimension and the second dimension in the characteristic matrix Q, and the early warning threshold value a of the bus to be testedijComparing the fault early warning index a with the fault early warning index a of the power grid bus, and when a is less than aijIf so, the temperature of the bus to be tested is normal, otherwise, the early warning index of the power grid bus to be tested exceeds the early warning threshold value of the power grid bus, the corresponding power grid bus state information table Tab is established, and the diagnosis result is recorded.
Further, the threshold value matrix alfa in step 4 extracts temperature signals of the same voltage class and the same bus from the temperature signal storage units in three time periods, and the power grid bus fault early warning indexes a between the temperature signals are calculated according to steps 2 to 4 and respectively use a1、a2、a3If the voltage level of the bus is represented, the fault early warning index a of the bus at the voltage level is the maximum value of the three values; and (3) performing the same treatment on other buses in the power grid bus group to obtain a threshold value matrix alfa of the early warning index a:
wherein u is the total number of the bus numbers in the power grid bus group, v is the number of the buses in the power grid bus group at different temperatures, and the value range of i is [1, u]Is an integer between, j has a value in the range of [1, v]Is an integer of (1).
Further, in the step 5, the early warning threshold value a of the bus to be testedijComparing the fault early warning index a with the fault early warning index a of the power grid bus, and when a is less than aijIf so, the temperature of the bus to be tested is normal, otherwise, the early warning index of the power grid bus to be tested exceeds the early warning threshold value of the power grid bus, the corresponding power grid bus state information table Tab is established, and the diagnosis result is recorded.
Further in accordance withThe energy coefficient in the normalization matrix Q' is calculated by a formula
Wherein
i＝{1,2,3,......m}，j＝{1,2}，min(Xi) Is the minimum value, max (X), of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectioni) Is the maximum value, X, of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectionijThe characteristic attribute value of the energy coefficient corresponding to the temperature signal of the ith dimension is the ith dimension, if pijIs defined when 0
And obtaining the characteristic weight value in the normalization matrix Q'.
Further, the signal X to be detected in step 2 is a section of temperature signal generated by a power grid bus at a fixed time by collecting a certain bus in the power grid bus group; the reference signal Y is a temperature signal of the bus corresponding to the signal to be detected X under the same voltage level, and the retention time and the sample length of the signal to be detected X and the sample length of the reference signal Y are consistent.
Further, in the step 2, K layers of wavelet decomposition are respectively performed on the same temperature signal in the two signals, i.e., the signal to be detected X and the reference signal Y, where K is a constant set artificially, and a wavelet energy formula is used to extract respective energy coefficients after decomposition, so as to respectively generate a corresponding signal to be detected feature dimension X1 and a corresponding reference signal feature dimension Y1; and performing the same processing on the temperature signals in the residual sections, and respectively generating a characteristic matrix A of the signal to be detected and a characteristic matrix B of the reference signal, wherein the sizes of the characteristic matrix A and the characteristic matrix B are both mxK, m is the number of rows of the characteristic matrix, namely the number of sections of a bus, and K is the number of columns of the characteristic matrix, namely the number of layers of wavelet decomposition.
On the other hand, the invention provides a power grid fault detection device and a storage medium, and the specific process is as follows:
a grid fault detection apparatus comprising:
the acquisition module is used for acquiring a characteristic value sequence corresponding to the characteristic quantity of the power grid bus bar;
the filtering module is used for processing the characteristic value sequence to obtain a characteristic value filtering sequence, and the characteristic value filtering sequence comprises a plurality of filtering values;
a first processing module for determining difference components between each of the filtered values and a predetermined value, respectively;
the second processing module is used for respectively determining a feed forward weight factor of the corresponding filtering value according to each difference component and determining a weighted differential value of the characteristic quantity according to each feed forward weight factor;
the detection module is used for determining fault information of the power grid according to the weighted differential value;
the apparatus comprises a memory for storing a computer program and a processor;
the processor, configured to, when executing the computer program, implement the grid fault detection method according to any of claims 1 to 9.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the grid fault detection method according to any one of claims 1 to 7.
One aspect of the technical effect brought by the technical scheme of the invention is that the difficulty of establishing a complete data set in the process of establishing the whole power grid fault identification model by adopting a neural network and the like is avoided, the accuracy and the stability of power grid bus fault early warning are improved, and the method is closer to practical application.
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FIG. 1 is a flow chart of a method, apparatus and storage medium for grid fault detection in accordance with the present invention;
fig. 2 is a schematic structural diagram of a power grid fault detection apparatus according to an embodiment of the present invention.
Detailed Description
Example 1
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The specific implementation method is as follows:
as shown in fig. 1, a flowchart of a method, an apparatus and a storage medium for detecting a power grid fault according to the present invention includes the following steps:
step 1, establishing a data storage unit of a power grid bus temperature signal aiming at a power grid, wherein the acquired bus comprises temperature signals of all power grids under different voltage grade scales, the temperature signals are used as standby reference signals during power grid bus fault early warning, the storage unit needs to be completely updated at regular intervals, and the time period is measured by month;
step 2, at a fixed moment, when fault diagnosis is carried out on a power grid bus, a section of temperature signal sent by the bus temperature at the moment is collected as a signal X to be measured, a certain number of sampling points are set as sample lengths, wherein the sample lengths are defined as wlen, then the signal to be measured can be divided into m sections of temperature signals, and a specific calculation formula is as follows:
wherein m is an integer part of a result in the formula, the number of sampling points of wlen at least comprises temperature sampling points of each voltage section of the power grid bus, and X, Y represents the signal to be measured and the reference signal.
Searching a bus which is the same as the signal to be detected and a temperature signal under the same voltage level in a reference signal storage unit, wherein the time and the sample length of the reference signal are consistent with those of the signal to be detected;
performing K-layer wavelet decomposition on each temperature signal of the two signals, wherein K is a constant set artificially, and extracting an energy coefficient after decomposition by using a wavelet energy formula, wherein the calculation formula is as follows:
En＝∑|xn|2 (3)
wherein xnFor each decomposed signal, n ═ 1,2,3.. K }, where K is the number of layers in the wavelet decomposition and K ═ 1,2,3, … … K }.
Thus, each section of temperature signal can be represented by a vector generated by an energy coefficient, and energy characteristic matrixes A and B capable of representing the essence of two signals can be extracted from the signal to be measured and the reference signal through the transformation, wherein the large size of the two matrixes is m multiplied by K, namely the row number of the matrixes is the number of sections of a bus, and the column of the matrixes is the number of layers of wavelet decomposition;
step 3, respectively extracting the energy coefficient characteristic attribute values of the same position of the nth column of the to-be-detected signal characteristic matrix A and the reference signal characteristic matrix B to form an m × 2 characteristic matrix Q, wherein the total number of the characteristic matrices is K, n is {1,2,3, … … K }, K is the number of layers of the wavelet decomposition, and m is the number of segments of the signal division; respectively calculating the characteristic weight values of the first dimension and the second dimension of the energy coefficient characteristic dimension in the K characteristic matrixes Q by using an entropy weight method, wherein the characteristic weight values are respectively bn1And bn2。
Said step 3 calculates said entropy weight method used for the first and second dimension characteristic weight values in K matrices Q, i.e. normalizes according to each said energy coefficient value in said characteristic matrix Q, i.e. normalizes
Where i ═ 1,2,3.. ·. m }, j ═ 1,2}, min (X)i) Is the minimum value, max (X), of the corresponding energy coefficient of the temperature signal in the ith sectioni) Is the maximum value, X, of the energy coefficient corresponding to the temperature signal in the ith sectionijIs the ith segment jthAnd (3) normalizing the energy coefficient corresponding to the temperature signal to obtain a data normalization table so as to obtain a normalization matrix Q' of the characteristic matrix Q. According to the energy coefficient in the normalization matrix Q', a calculation formula is used
Wherein
If p isijIs defined when 0
And obtaining the characteristic weight value in the normalization matrix Q'.
And 4, constructing an index a of the power grid bus fault early warning of the power grid bus group for quantitatively describing the operating temperature of the bus, wherein the formula is as follows:
and constructing a threshold value matrix alfa by the power grid bus fault early warning index a.
Step 5, constructing a threshold value matrix alfa of the early warning index a by using a storage unit of the temperature signal,
and establishing storage units of temperature signals in three time periods, respectively taking out the temperature signals of the same bus under the same voltage level from the three storage units, calculating a indexes among the temperature signals according to the steps 2 to 4, respectively representing the values of the three indexes which can be calculated under the fixed working condition of the bus by a1, a2 and a3, and then taking the maximum value of the three indexes as the fault early warning index of the bus under the voltage level. The threshold value matrix alfa of the early warning index a can be obtained by performing the same processing on different buses:
in the formula (5), u is the total number of the serial numbers of the buses of a certain power grid, v is the number of different voltage grade scales of the power grid, the value range of i is an integer between [1 and u ], the value range of j is an integer between [1 and v ],
step 6, determining the early warning threshold value of the bus from alfa according to the serial number and the temperature condition of the bus to be detected, and when the calculated a is less than aijIf so, the bus temperature is normal, otherwise, early warning is carried out, a bus equipment manager is reminded that the power grid bus possibly has a fault, and the obtained result is recorded in a bus temperature state information table Tab;
and 7, changing the number K of wavelet decomposition layers, wherein the number of wavelet decomposition layers involved in the steps 2 to 6 must be kept consistent, the number of times of circulation of the whole process can be realized by setting the change times of the K values, each result is recorded in a bus temperature state information table Tab, if the early warning times in the table are larger than the normal times, the bus early warns the bus fault, otherwise, the bus temperature is displayed to be normal, and the fault early warning of the power grid bus is realized.
Example 2
The present embodiment further describes an implementation process and considerations of a grid fault detection method, a device and a storage medium in combination with an example of a field.
At present, a power grid bus fault early warning system needs to be established for all buses in the 5 power grid bus groups by using an entropy weight method, and the system is used for managing on-site bus equipment. The specific method comprises the following steps:
1. extracting a temperature signal Y to be measured and a reference temperature signal X under a certain voltage level, and dividing the temperature signal Y to be measured and the reference temperature signal X into 4 sections for 3-layer wavelet decomposition, so that the measured reference temperature signal Y and the measured temperature signal X are as follows:
the reference temperature signal to be measured
The reference temperature signal
2. Respectively extracting the energy coefficients of the nth columns of the signal characteristic matrix A to be detected and the reference signal characteristic matrix B, and combining the energy coefficients into an m × 2 matrix Q, wherein n is {1,2,3, … … K }, and K matrices Q are counted, where m is 4 and K is 3, so that the characteristic matrices Q are respectively:
Constructing an index a of the bus fault early warning for quantitatively describing the operating temperature of the bus, wherein a calculation formula is as follows:
a＝|b11-b12| + | b21-b22| + | b31-b32| -0.012 +0.278+0.16 | -0.45, and the power grid bus fault early warning index is 0.45.
3. And constructing a threshold value matrix alfa of the early warning index a. And establishing storage units of temperature signals in three time periods, adopting the temperature signals of the same bus under the same voltage level, calculating an early warning index a according to the steps, and calculating values of the three indexes under the fixed working condition of the bus, wherein the values are respectively represented by a1, a2 and a3, so that the fault early warning index of the bus under the voltage level is the maximum value of the three indexes. The specific process is as follows, assuming that the bus is a bus No. 1, in three time slots, namely a time slot 1, a time slot 2, a time slot 3 and the like, a temperature signal is extracted respectively under the condition that the bus has no fault and under the actual operation state, and the temperature signal is divided into 4 segments to carry out 3-layer wavelet decomposition, and a series of operations are carried out to obtain corresponding early warning indexes a1, a2 and a 3. The measured reference temperature signal Y and the measured temperature signal X are as follows:
the Q matrix corresponding to each group of temperature signals extracts the same row energy coefficient of the reference temperature signal X and the temperature signal Y to be measured respectively, and combines the Q matrix as follows:
carrying out entropy weight normalization processing on each Q matrix to obtain each normalization matrix, wherein the normalization matrixes are as follows:
according to a calculation formula
Wherein
If p isijIs defined when 0
The characteristic weight values in the normalization matrix Q' are derived, i.e. bi1, bi 2. The details are set forth in the following table:
the fault early warning index a is used for quantitatively describing the operating temperature of the bus, and the calculation formula is as follows:
wherein b isn1And bn2The characteristic weight values respectively represent the characteristic attributes of the energy coefficients in the first dimension and the second dimension of the characteristic matrix Q, so a1, a2, a3 are respectively: 0.12, 0.66, 0.5. The fault early warning index of the No. 1 bus at the voltage level is the maximum value of the three indexes, namely 0.66.
The operation is also carried out on other buses, and as the example sets that a power grid bus fault early warning system is established for 5 field buses by using an entropy weight method, the power grid bus fault early warning system is used for managing field bus equipment. Extracting a reference temperature signal Y and a temperature signal X to be detected under a certain voltage level, dividing the reference temperature signal Y and the temperature signal X to be detected into 4 sections for 3-layer wavelet decomposition, and sampling a bus in three speed sections, wherein v is 3, u is 5, and the specific steps are as follows:
| power grid bus group | Voltage class 1 | Voltage class 2 | Voltage class 3 |
| No. 1 bus | 0.66 | 0.45 | 0.42 |
| No. 2 bus | 0.64 | 0.42 | 0.27 |
| No. 3 bus | 0.78 | 0.38 | 0.56 |
| No. 4 bus | 0.45 | 0.31 | 0.69 |
| No. 5 bus | 0.38 | 0.39 | 0.36 |
Calculating a fault early warning index 0.45 of the power grid bus group according to the step 2 in the embodiment, and comparing the fault early warning actual values of the buses of the power grid bus group in the upper table under different voltage levels, namely determining an early warning threshold value of each bus from alfa according to the serial number and the temperature condition of the bus to be detected, and when a is smaller than aijAnd if not, giving an early warning to remind a bus equipment manager that the power grid bus possibly has a fault, and recording the obtained result in a bus temperature state information table Tab as follows:
finally, the adjustment of the K value and the number of times, namely the number of layers of wavelet decomposition, is tried, and each result is recorded in a bus temperature state information table Tab, as shown in the following table, so as to be used for early warning of the bus fault of the power grid bus group.
Example 3
The difference between this embodiment and embodiment 2 is that the characteristic attribute weight in the normalization matrix Q' is subjected to secondary entropy weighting by using an entropy weighting method, and the difference and the relation between the final result and embodiment 1 are compared. The following table shows the characteristic weights in the normalized matrix Q' obtained in example 2:
according to the weight calculation formula of each characteristic dimension of the characteristic attribute of the bus sample
The weights of the feature dimensions in the feature matrix Y can be obtained as shown in the following table:
formula is used for index a of fault early warning at the moment
The calculated a1, a2 and a3 are respectively: 0.05, 0.08 and 0.03, and the fault early warning index of the No. 1 bus at the voltage level is the maximum value of the three indexes, namely 0.08. The operation processing is also carried out on the bus, and as the example is set, the power grid bus fault early warning system is established by utilizing an entropy weight method for 5 field buses and is used for managing field bus equipment. Extracting a reference temperature signal Y and a temperature signal X to be detected under a certain voltage level, dividing the reference temperature signal Y and the temperature signal X to be detected into 4 sections for 3-layer wavelet decomposition, and sampling a bus in three speed sections, wherein v is 3, u is 5, and the specific steps are as follows:
| power grid bus group | Voltage class1 | Voltage class 2 | Voltage class 3 |
| No. 1 bus | 0.35 | 0.02 | 0.06 |
| No. 2 bus | 0.78 | 0.05 | 0.03 |
| No. 3 bus | 0.09 | 0.02 | 0.08 |
| No. 4 bus | 0.02 | 0.03 | 0.35 |
| No. 5 bus | 0.01 | 0.06 | 0.08 |
According to the fault early warning index 0.08 of the power grid bus group in the embodiment, fault early warning actual values of all buses of the power grid bus group in different voltage levels in the reference table are compared, namely the early warning threshold value of each bus is determined from alfa according to the serial number and the temperature condition of the bus to be detected, and when a is calculated to be less than aijIf the bus temperature is normal, otherwise, the bus temperature is early-warned to remind the busThe equipment manager may have a fault in the power grid bus, and records the result obtained this time in a bus temperature state information table Tab, which is specifically as follows:
finally, the adjustment of the K value and the number of times, namely the number of layers of wavelet decomposition, is tried, and each result is recorded in a bus temperature state information table Tab, as shown in the following table, so as to be used for early warning of the bus fault of the power grid bus group.
The power grid fault detection method, the device and the storage medium further comprise a fault early warning unit, wherein the fault early warning unit is electrically connected with a central processing unit, the central processing unit is electrically connected with a server, the server is provided with a service storage card, and the storage card is electrically connected with the data storage unit.
According to the calculation result of the embodiment, the results of extracting the secondary weights of the energy coefficient matrix characteristic dimensions of all the power grids of the power grid bus group based on the entropy weight method are consistent with those of the embodiment 2, that is, the primary fault of the power grid bus group can be early-warned and judged by solving the characteristic attribute weight of the normalized matrix or averaging the secondary weights of the characteristic dimensions of the normalized matrix by the entropy weight method.
Example 4
In this embodiment, whether the sequence of the reference temperature signal Y or the reference temperature signal X corresponding to the first characteristic dimension and the second characteristic dimension affects the final judgment result will be further described. Similarly, assuming that all power grids of a certain power grid bus group in a certain bus field total 5, performing 3-layer wavelet decomposition processing on the feature matrix extracted from all power grid buses of 5 buses, and obtaining the following result:
the temperature signal to be measured
The reference temperature signal
Respectively extracting the energy coefficients of the nth columns of the signal characteristic matrix to be detected and the reference signal characteristic matrix, and combining the energy coefficients into an m × 2 matrix Q, wherein n is {1,2,3, … … K }, K matrices Q are counted, where m is 4 and K is 3, so that the characteristic matrices Q are respectively:
at this time, the characteristic dimension of the characteristic matrix of the signal to be detected is a first characteristic dimension of the characteristic matrix Q, and the characteristic dimension of the characteristic matrix of the reference signal is a second characteristic dimension of the characteristic matrix Q. Actually, an index a of fault early warning is obtained to quantitatively describe the operating temperature of the bus, and a calculation formula is as follows:
wherein b isn1And bn2And respectively representing the characteristic weight values of the energy coefficient characteristic attributes of the first dimension and the second dimension in the characteristic matrix Q, and knowing that the sequence of the first characteristic dimension and the second characteristic dimension of the characteristic matrix Q respectively corresponding to the reference temperature signal Y or the temperature signal X to be detected has no influence on the power grid bus fault early warning index a.
As shown in fig. 2, another embodiment of the present invention provides a grid fault detection apparatus, including:
and the acquisition module is used for acquiring the characteristic value sequence corresponding to the characteristic quantity of the power grid bus bar.
And the filtering module is used for processing the characteristic value sequence to obtain a characteristic value filtering sequence, and the characteristic value filtering sequence comprises a plurality of filtering values.
And the first processing module is used for respectively determining the difference quantity between each filtering value and a preset value.
And the second processing module is used for respectively determining a feed forward weight factor of the corresponding filtering value according to each difference component and determining a weighted differential value of the characteristic quantity according to each feed forward weight factor.
And the detection module is used for determining the fault information of the power grid according to the weighted differential value.
Another embodiment of the present invention provides a grid fault detection apparatus, including a memory and a processor; the memory for storing a computer program; the processor is configured to, when executing the computer program, implement the grid fault detection method as described above.
A further embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the grid fault detection method as described above.
It is to be understood that in the description of the present specification, the terms "first", "second", and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory ROM, a random access memory RAM, or the like.
In this application, the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. The method for detecting the power grid fault is characterized by comprising the following steps of 1, establishing a data storage unit, wherein the data storage unit is used for periodically storing and updating all bus temperature signals extracted by a power grid bus group under the fault-free condition and used as standby reference signals when all bus faults are early warned; step 2, constructing a characteristic matrix A of the signal to be detected and a characteristic matrix B of the reference signal; step 3, respectively extracting the energy coefficient characteristic attribute values of the same position of the nth column of the to-be-detected signal characteristic matrix A and the reference signal characteristic matrix B to form an m × 2 characteristic matrix Q, wherein the total number of the characteristic matrices is K, n is {1,2,3, … … K }, K is the number of layers of the wavelet decomposition, and m is the number of segments of the signal division; step 4, constructing an index a of the power grid bus group fault early warning, and constructing a threshold value matrix alfa; step 5, traversing the threshold value matrix alfa, and determining the early warning threshold value a of the bus to be testedijAnd the power grid bus fault early warning index a; and 6, adjusting the size of the wavelet decomposition layer number K and the change times thereof, and judging the running state of the bus.
2. The grid fault detection method according to claim 1, wherein: in the step 2, a certain number of sampling points are set for the signal to be measured X and the reference signal Y as sample lengths, defined as wlen, and then the signal to be measured X and the reference signal Y are respectively divided into m segments of signals, that is, the signals are
Wherein m is an integer part of a result in the formula, the number of sampling points of wlen at least comprises temperature sampling points of each voltage section of the power grid bus, and X, Y represents the signal to be measured and the reference signal.
3. The grid fault detection method according to claim 1, wherein: in the step 2, the wavelet decomposition is carried out, and a wavelet energy formula is utilized to calculate energy coefficients, namely E, of the decomposed reference signal Y and the decomposed energy coefficients of the signal X to be detectedn＝∑|xn|2Wherein xnEach decomposed signal segment corresponds to a reference value, n is {1,2,3.. K }, and K is the number of layers of wavelet decomposition of the signal; the signal X to be detected and the reference signal Y are respectively divided into m sections of signals, each section of signal is respectively subjected to K-layer wavelet decomposition, the signal energy coefficient after each section of wavelet decomposition is an energy coefficient characteristic attribute value, the signal energy coefficient characteristic attribute after each section of wavelet decomposition is composed of energy coefficient characteristic dimensions, and the m sections of energy coefficient characteristic dimensions are composed of a signal characteristic matrix A to be detected and a reference signal characteristic matrix B.
4. The grid fault detection method according to claim 1, wherein: step 3 is to calculate the entropy weight method adopted by the feature weight values of the first dimension and the second dimension of the feature attributes in the K feature matrices Q respectively, namely, firstly, normalization is carried out according to each feature attribute value in the feature matrices Q, namely
Where i ═ 1,2,3,......m}，j＝{1,2}，min(Xi) Is the minimum value, max (X), of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectioni) Is the maximum value, X, of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectionijAnd obtaining a normalized matrix Q' of the characteristic matrix Q for the energy coefficient characteristic attribute value corresponding to the temperature signal of the ith dimension of the ith section.
5. The grid fault detection method according to claim 1, wherein: in the step 4, the bus fault early warning index a is constructed
Wherein b isn1And bn2Respectively representing the characteristic weight values of the energy coefficient characteristic attributes in the first dimension and the second dimension in the characteristic matrix Q, and the early warning threshold value a of the bus to be testedijComparing the fault early warning index a with the fault early warning index a of the power grid bus, and when a is less than aijIf so, the temperature of the bus to be tested is normal, otherwise, the early warning index of the power grid bus to be tested exceeds the early warning threshold value of the power grid bus, the corresponding power grid bus state information table Tab is established, and the diagnosis result is recorded.
6. The grid fault detection method according to claim 1, wherein: and step 4, extracting temperature signals of the same voltage grade of the bus from signal storage units of three time periods respectively by using the threshold value matrix alfa, calculating the power grid bus fault early warning index a by using the temperature signals of the single time period, and respectively using a1、a2、a3If the bus is represented, the fault early warning index a of the bus at the temperature is the maximum value of the three values; and (3) performing the same treatment on other buses in the power grid bus group to obtain a threshold value matrix alfa of the early warning index a:
wherein u is saidThe total number of the serial numbers of the buses in the power grid bus group, v is the number of the buses in the power grid bus group at different temperatures, and the value range of i is [1, u [ ]]Is an integer between, j has a value in the range of [1, v]Is an integer of (1).
7. The grid fault detection method according to claim 4, wherein: according to the energy coefficient in the normalization matrix Q', a calculation formula is used
Wherein
min(Xi) Is the minimum value, max (X), of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectioni) Is the maximum value, X, of the energy coefficient characteristic attribute value corresponding to the temperature signal in the ith sectionijThe characteristic attribute value of the energy coefficient corresponding to the temperature signal of the ith dimension is the ith dimension, if pijIs defined when 0
And obtaining the characteristic attribute characteristic weight value in the normalized matrix Q'.
8. The grid fault detection method according to claim 1, wherein: in the step 2, the signal X to be detected is a section of temperature signal sent by a certain bus in the power grid bus group at a fixed moment; the reference signal Y is the standby reference signal in the traversal data storage unit, a temperature signal of the bus corresponding to the signal to be detected X at the same voltage level is extracted, and the time and the sample length of the two signals of the signal to be detected X and the reference signal Y are consistent; step 2, performing K-layer wavelet decomposition on the same temperature signal in the two signals of the signal to be detected X and the reference signal Y respectively, wherein K is a constant set manually, and extracting respective energy coefficients after decomposition by using a wavelet energy formula to generate corresponding characteristic dimensions of the signal to be detected and the reference signal; and the temperature signals in the residual section are processed, the characteristic matrix A of the signals to be detected and the characteristic matrix B of the reference signals are respectively generated, the sizes of the characteristic matrix A and the characteristic matrix B are both mxK, wherein m is the row number of the characteristic matrix, namely the number of sections of a bus, and K is the number of columns of the characteristic matrix, namely the number of layers of wavelet decomposition.
9. A grid fault detection device, comprising:
the acquisition module is used for acquiring a characteristic value sequence corresponding to the characteristic quantity of the power grid bus bar;
the filtering module is used for processing the characteristic value sequence to obtain a characteristic value filtering sequence, and the characteristic value filtering sequence comprises a plurality of filtering values;
a first processing module for determining difference components between each of the filtered values and a predetermined value, respectively;
the second processing module is used for respectively determining a feed forward weight factor of the corresponding filtering value according to each difference component and determining a weighted differential value of the characteristic quantity according to each feed forward weight factor;
the detection module is used for determining fault information of the power grid according to the weighted differential value;
the apparatus comprises a memory for storing a computer program and a processor;
the processor, configured to, when executing the computer program, implement the grid fault detection method according to any of claims 1 to 9.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out a grid fault detection method according to any one of claims 1 to 7.
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