CN110095661B - Distribution transformer high-voltage side open-phase fault first-aid repair method - Google Patents
Distribution transformer high-voltage side open-phase fault first-aid repair method Download PDFInfo
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Abstract
The invention discloses a distribution transformer high-voltage side open-phase fault rush-repair method, and relates to an open-phase judgment method. At present, the problem of lack of real-time performance and insufficient intelligent degree exists in phase-missing judgment. The method comprises the steps of data acquisition, data matrixing, high-dimensional feature extraction, multi-feature fusion, fault tracing and high-voltage open-phase fault diagnosis; based on the spectrum analysis of MP-Law and the average spectrum radius index of Ring-Law, an LES statistical index system and the visualization thereof are further established, and a default phase criterion index is further established; the operation and environment information of the power distribution network is fused, fault influence factors are analyzed, the equipment state evolution process is modeled through a time sequence, and the state of a power distribution network line is evaluated by using a random matrix to form related indexes, so that guidance is provided for maintenance and arrangement of the power distribution network. The technical scheme reasonably distributes corresponding resources to each type, and realizes reasonable configuration of overhaul resources; and the distribution network overhaul resource allocation is optimized, so that emergency repair is more accurate.
Description
Technical Field
The invention relates to a phase-loss judgment method, in particular to a distribution transformer high-voltage side phase-loss fault first-aid repair method.
Background
In the normal operation process of the transformer, due to reasons such as falling of foreign matters or overload of loads of the environment, a phase circuit on a high-voltage side is disconnected or a fuse is fused, so that other two-phase currents are suddenly increased, the temperature of a transformer coil is increased, the transformer is easily burnt out, unsafe accidents are caused, and serious personnel and economic losses are caused to residents and factory users. In the most traditional transformer operation and maintenance, due to the low automation degree of equipment, problems are mostly solved in a mode of manual inspection and manual reporting, and the efficiency is extremely low. In recent years, with the popularization of automatic monitoring equipment and cloud computing, monitoring terminals periodically upload monitoring data such as real-time voltage to a cloud master station, the master station analyzes problems by analyzing the monitoring data of 2-3 continuous periods, due to the limitation of communication flow and the reduction of the burden of a cloud server, the uploading period is limited to 15 minutes at least, and therefore the fault finding time is at least half an hour and the requirements of power grid operation and power supply reliability cannot be met.
At present, in the application of a low-voltage distribution network, an emergency repair method for a phase-loss fault on a high-voltage side of a distribution transformer mainly relates to three aspects of an intelligent distribution transformer terminal (short for: distribution transformer terminal), an intelligent public transformer monitoring system (short for: master station system) and manpower, and the process includes: the method comprises four links of timed uploading of an intelligent distribution transformer terminal, timed judgment of an intelligent public transformer monitoring system, manual analysis and release and manual processing, and the specific flow is shown in figure 1.
Firstly, an intelligent distribution transformer terminal installed on site measures three-phase voltage of a low-voltage side of a distribution transformer in real time, and regularly and remotely transmits the data to an intelligent public transformer monitoring system in a task form with a period of 15 minutes; then, the intelligent public transformer monitoring system judges the phase failure of the high-voltage side by utilizing data received for three times continuously (namely at least two uploading periods), further forms alarm event information and simultaneously sends the alarm event information to related personnel in a short message form; and finally, manually analyzing information of equipment, users and the like related to the alarm event according to the content of the short message, thereby filling and publishing corresponding power failure information and sending manual work to the field for processing. The process has the following disadvantages:
first, real-time property is lacking. The intelligent distribution transformer terminal sends data to the intelligent public transformer monitoring system for 15 minutes in a remote way, the intelligent public transformer monitoring system needs at least 30 minutes (two sending periods) to judge that the high-voltage side is in phase failure, the manual link connected next has great uncertainty in time, and the whole process is far from meeting the requirement in real time;
secondly, the intelligent degree is not deep enough. In the whole process, more contents need to be participated in manually, such as analyzing the content of alarm event information, issuing power failure information, dispatching maintenance and the like, and an intelligent and informatization system adopted in the process cannot track and monitor the whole process of the problem.
Disclosure of Invention
The invention aims to solve the technical problems and provide the technical task of perfecting and improving the prior technical scheme and providing a method for rush-repairing the open-phase fault of the high-voltage side of a distribution transformer so as to achieve the aim of improving the accuracy of the rush-repairing. Therefore, the invention adopts the following technical scheme.
A distribution transformer high-voltage side open-phase fault rush-repair method comprises the following steps:
1) acquiring data:
acquiring a high-dimensional data set by using the existing communication mechanism, collecting data of a sensor related to fault detection at an intelligent distribution transformer terminal, and transferring the data from the cloud to the edge;
2) data matrixing:
sampling data to form a random matrix through a random matrix model; the method comprises the steps that a random matrix model models an equipment state evolution process based on a time sequence, and power grid state cognition is converted into a random matrix analysis problem through the random matrix model; to split the local area data that only needs to be processed;
3) high dimensional feature extraction
Extracting high-dimensional features from the preprocessed random matrix based on Ring-Law and MP-Law to obtain an average spectrum radius MSR and a linear statistical characteristic value LES, performing visualization processing, and visually embodying the high-dimensional statistics of a fault data set through a spectrum analysis chart;
4) multi-feature fusion
Performing multi-feature fusion by taking high-dimensional statistics as a main reference quantity and electric special quantity and general statistics as secondary reference quantities to construct a default phase criterion;
5) fault tracing
Tracing the source of the fault and positioning the fault phase through splicing matrix processing; recalculating indexes through data splicing, judging the influence of the spliced data on events according to the change condition of the new indexes, analyzing the statistical properties of the criteria, and judging high-voltage open-phase faults according to a set criterion threshold; the statistical properties comprise convergence and confidence; the established criterion threshold comprises a false-detection rate threshold and a false-alarm rate threshold;
since the state of the grid system depends on a plurality of influencing factors, M potential influencing factors exist by assuming that the state of a certain grid is a variable of an N-dimensional parameter, and a certain time period ti(i-1, 2, …, T), the N-dimensional vector of the state of the grid may naturally constitute the basic state matrix
The time interval can be obtained by each influence factorValue of (2), component factor vector
Two matrices (vectors) with the same length can form a new matrix through a splicing operation; the basic state matrix B and the factor vector cjSpliced to form a composite matrix Aj。
In order to analyze the influence of the influencing factors on the basic state conveniently, the influence of the influencing factors needs to be amplified; vector of selected factors cjThe factor vector is replicated K times (K can be 0.4 XN) in a certain way to form a matrix D which is matched with the state matrix sizejAs shown in formula (4):
at DjWhite noise is introduced to eliminate the internal correlation, as shown in formula (5):
Cj＝Dj+ηjR，j＝1，2，…，M (5)
in the formula (5), R is a standard Gaussian random matrix, etajAnd signal-to-noise ratio (SNR) ("p")jAnd (3) correlation:
for each factor vector c in paralleljForming a composite matrix A by matrix splicingj：
Comparison of each AjThe LES can find out different data, namely sensitive factors influencing the state;
6) high voltage phase loss fault diagnosis
And obtaining a fault tracing result, obtaining a high-voltage open-phase fault diagnosis result, and arranging a corresponding maintenance task according to the diagnosis result.
MSR and LES are essentially random matrix statistics of the matrix, and when the system is abnormal, the indexes can change significantly. By using the distributed algorithm, the overall behavior of the distribution network can be evaluated through statistics of each region, namely, the overall behavior of the distribution network can be responded to whether the open phase exists in the distribution network.
According to the technical scheme, by redesigning a local communication mechanism and utilizing edge calculation and regional data, the high-voltage open-phase alarm is researched and judged to be transferred from 'cloud' (a new generation distribution automation main station system) to 'edge' (an intelligent distribution transformer terminal) closer to a fault source, so that the real-time performance of high-voltage open-phase fault first-aid repair is greatly improved, and the real-time performance is improved to within one minute from at least half an hour before. In the function transferring process, the criterion is preliminarily set based on the overall situation, and the criterion is further revised based on the local historical data and the surrounding edge situation. The method is characterized in that an existing big data platform is combined, an alarm event big data center is deployed at the cloud end, and the flow from equipment fault detailed analysis to automatic dispatching is automatically completed through big data intelligent analysis. And finally, a feedback result of field manual processing is automatically received, and the intelligent degree of the whole emergency repair process is enriched. The technical scheme has important significance for rush repair of the phase-loss fault of the high-voltage side of the distribution transformer, and the power supply reliability of the distribution network is effectively improved.
Meanwhile, the technical scheme optimizes the distribution network maintenance resource allocation; so that emergency repair is more accurate. The operation and environment information of the power distribution network is fused, fault influence factors are analyzed, the equipment state evolution process is modeled through a time sequence, and the state of a power distribution network line is evaluated by using a random matrix to form related indexes, so that guidance is provided for maintenance and arrangement of the power distribution network. Indexes and visual images are used for evaluating the state of the line, and further guidance is provided for line maintenance and arrangement. In the distribution network, the distribution network line is divided into good sections, easy-to-send sections, fault sections and other types, corresponding resources are reasonably distributed to the types, and reasonable allocation of overhaul resources is realized.
As a preferable technical means: the method also comprises a data preprocessing step, wherein the preprocessing step is carried out before the high-dimensional feature extraction, and the data preprocessing step comprises the following steps:
translating, translating the data for a certain period of time to recover unsynchronized data;
and/or strengthening, copying a certain column of data to intensively analyze the influence of the time on the overall state of the system;
and/or augmentation, abnormal incentive excavation, so as to conveniently realize parallel computation;
and/or random wave, normalization, statistical preconditions for per-unit matrix to be satisfied.
Different observation matrices can be established for analysis by different preprocessing modes.
As a preferable technical means: the average spectrum radius MSR index is a high-dimensional statistical index, and hypothesis testing is carried out by comparing an observed value with an expected value.
As a preferable technical means: the linear statistical characteristic value LES index system is obtained by a data processing method of a random matrix theory, and the value has statistical characteristics.
Has the advantages that:
1. according to the technical scheme, the local communication mechanism is redesigned, the high-voltage open-phase alarm is researched and judged to be transferred from the cloud to the edge closer to the fault source by utilizing the edge calculation and the data in the region, the real-time performance of the high-voltage open-phase fault rush repair is greatly improved, and the real-time performance is improved to one minute from at least half an hour before. And in the function transfer process, the flow from equipment fault detailed analysis to automatic dispatching is automatically completed. And finally, a feedback result of field manual processing is automatically received, and the intelligent degree of the whole emergency repair process is enriched. The technical scheme has important significance for rush repair of the phase-loss fault of the high-voltage side of the distribution transformer, and the power supply reliability of the distribution network is effectively improved.
2. The technical scheme optimizes the distribution network maintenance resource allocation; so that emergency repair is more accurate. The operation and environment information of the power distribution network is fused, fault influence factors are analyzed, the equipment state evolution process is modeled through a time sequence, and the state of a power distribution network line is evaluated by using a random matrix to form related indexes, so that guidance is provided for maintenance and arrangement of the power distribution network. Indexes and visual images are used for evaluating the state of the line, and further guidance is provided for line maintenance and arrangement. In the distribution network, the distribution network line is divided into good sections, easy-to-send sections, fault sections and other types, corresponding resources are reasonably distributed to the types, and reasonable allocation of overhaul resources is realized.
Drawings
Fig. 1 is a prior art flow chart.
Fig. 2 is a flow chart of the present invention.
FIG. 3 is a graph of the effect of the Ring Law, MP Law, MSR/LES hypothesis test of the present invention.
FIG. 4 is a flow chart of the fault tracing portion of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 2, the present invention comprises the steps of:
the first step is as follows: obtaining data
The existing communication mechanism is utilized to obtain a high-dimensional data set, namely, data of a sensor relevant to fault detection are collected at an intelligent distribution and transformation terminal and are released to the edge from the cloud.
The second step is that: building data models for collected high dimensional data
A random matrix theory method is adopted, and a random matrix theory is constructed on the basis of the method. Stochastic matrix modeling has the advantage that spatio-temporal joint correlations (high-dimensional statistics) of the data (data set) itself in a high-dimensional space can be considered. The built model can contain certain statistical information (depending on actual scenes and data conditions), and further, the data matrix can be preprocessed to facilitate high-dimensional feature (MSR, LES) extraction; and (4) forming related indexes by using the random matrix to evaluate the state of the power grid line and provide guidance for the phase loss diagnosis of the power distribution network. A random matrix model RMM is established based on the sampled data, with rows N representing the sampling dimensions and columns T representing the samples. And (3) converting the power grid state cognition into a random matrix analysis problem through RMM. Due to the characteristic that the digital matrix can be split and combined, the modeling method can split and process data of only a local area.
The third step: extracting high-dimensional statistics of collected high-dimensional data by adopting specific analysis algorithm
MSR and LES are extracted based on Ring-Law and MP-Law, and the feature can be extracted and used as a criterion because random matrix theory is guaranteed, and the conclusion of spectrum analysis is statistical properties which can be obtained a priori, as shown in FIG. 3, as high-dimensional statistics of a fault data set.
The fourth step: multi-feature fusion
And (3) establishing a default phase criterion based on a multi-feature fusion technology by taking the high-dimensional statistics as a main reference, and detecting whether a fault occurs.
The fifth step: fault tracing
By further processing the spliced matrix, tracing the fault, namely positioning the phase of the fault, further analyzing the statistical properties (convergence, confidence) and the like of the criterion, and establishing a criterion threshold value according to the engineering requirements (omission ratio and false alarm ratio).
As shown in fig. 4, since the grid system state depends on a plurality of influencing factors, assuming that the state of a certain grid is a variable of an N-dimensional parameter, there are M potential influencing factors by a certain time period ti(i-1, 2, …, T), the N-dimensional vector of the state of the grid may naturally constitute the basic state matrix
The value of the time interval can be obtained from each influence factor, and a factor vector is formed
Two matrices (vectors) with the same length can form a new matrix through a splicing operation; the basic state matrix B and the factor vector cjSpliced to form a composite matrix Aj。
In order to analyze the influence of the influencing factors on the basic state conveniently, the influence of the influencing factors needs to be amplified; vector of selected factors cjThe factor vector is replicated K times (K can be 0.4 XN) in a manner to form a matrix of metrics matching the state matrix sizeArray DjAs shown in formula (4):
at DjWhite noise is introduced to eliminate the internal correlation, as shown in formula (5):
Cj＝Dj+ηjR，j＝1，2，…,M (5)
in the formula (5), R is a standard Gaussian random matrix, etajAnd signal-to-noise ratio (SNR) ("p")jAnd (3) correlation:
for each factor vector c in paralleljForming a composite matrix A by matrix splicingj：
Comparison of each AjThe statistical index LES of (a) can find out different data, i.e. sensitive factors affecting the state.
And a sixth step: high-voltage phase-loss fault diagnosis and first-aid repair
And obtaining a high-voltage open-phase fault diagnosis result through fault detection and fault tracing, and arranging a corresponding maintenance plan according to the diagnosis result.
In order to establish different observation matrixes for analysis and improve accuracy, preprocessing can be performed before extracting high-dimensional statistics of the observation matrixes. The pre-processing sets up the basic functions of 1. translate-translate data for some period of time to recover unsynchronized data; 2. strengthen-copy a certain column of data to focus on analyzing the impact of that time on the overall state of the system; 3. augmentation-can realize abnormal inducement excavation, can conveniently realize parallel computation; 4. random wave, normalization-a certain statistical premise to make the matrix per unit to satisfy.
The method for rush-repairing a phase-failure fault on the high-voltage side of a distribution transformer shown in fig. 2 is a specific embodiment of the invention, which already embodies the substantial features and the improvement of the invention, and can make equivalent modifications in the aspects of shape, structure and the like according to the practical use requirements and under the teaching of the invention, and the method is within the protection scope of the scheme.
Claims (4)
1. A distribution transformer high-voltage side open-phase fault rush-repair method is characterized by comprising the following steps:
1) acquiring data:
acquiring a high-dimensional data set by using the existing communication mechanism, collecting data of a sensor related to fault detection at an intelligent distribution transformer terminal, and transferring the data from the cloud to the edge;
2) data matrixing:
sampling data to form a random matrix through a random matrix model; the method comprises the steps that a random matrix model models an equipment state evolution process based on a time sequence, and power grid state cognition is converted into a random matrix analysis problem through the random matrix model; to split the local area data that only needs to be processed;
3) high dimensional feature extraction
Extracting high-dimensional features from the preprocessed random matrix based on Ring-Law and MP-Law to obtain an average spectrum radius MSR and a linear statistical characteristic value LES, performing visualization processing, and visually embodying high-dimensional statistics of a fault data set through a spectrum analysis chart;
4) multi-feature fusion
Performing multi-feature fusion by taking high-dimensional statistics as a main reference and electrical physical quantity and general statistics as secondary references to construct a default phase criterion, wherein the general statistics comprise a mean value, a variance and a low-dimensional transformation index;
5) fault tracing
Tracing the source of the fault and positioning the fault phase through splicing matrix processing; recalculating indexes through data splicing, judging the influence of the spliced data on events according to the change condition of the new indexes, analyzing the statistical properties of the criteria, and judging high-voltage open-phase faults according to a set criterion threshold; statistical properties include convergence and confidence; the set criterion threshold comprises a false-detection rate threshold and a false-alarm rate threshold;
since the state of the grid system depends on a plurality of influencing factors, M potential influencing factors exist by assuming that the state of a certain grid is a variable of an N-dimensional parameter, and a certain time period ti1,2, …, measurement of T, the N-dimensional vector of the state of the grid constituting the basic state matrix
Obtaining the value of each influence factor in the time section to form factor vector
Forming a new matrix by splicing two matrixes with the same length; the basic state matrix B and the factor vector cjSpliced to form a composite matrix Aj；
In order to facilitate the analysis of the influence of the influencing factors on the basic state, the influence of the influencing factors is amplified; vector of selected factors cjCopying the factor vector K times to form a matrix D matched with the state matrix sizejAs shown in the following formula:
at DjIntroducing white noise to eliminate internal correlation, as shown in the following formula:
Cj＝Dj+ηjR，j＝1，2，…，M (5)
wherein R is a standard Gaussian random matrix, ηjAnd the signal-to-noise ratio rhojAnd (3) correlation:
for each factor vector c in paralleljBy passingMatrix splicing to form a composite matrix Aj：
Comparison of each AjThe linear statistics of the statistical index of the characteristic value LES can find out different data, namely sensitive factors influencing the state;
6) high voltage phase loss fault diagnosis
Obtaining a fault tracing result, obtaining a high-voltage open-phase fault diagnosis result, and arranging a corresponding maintenance task according to the diagnosis result;
by utilizing edge calculation and data in the region, high-voltage open-phase alarm is researched and judged to be transferred from the cloud to the edge closer to a fault source so as to improve the real-time performance of high-voltage open-phase fault first-aid repair; the cloud is a power distribution automation master station system, and the edge of the cloud is an intelligent distribution transformer terminal close to a fault source.
2. The method for rush repair of the open-phase fault of the high-voltage side of the distribution transformer according to claim 1, characterized in that: the method also comprises a data preprocessing step, wherein the preprocessing step is carried out before the high-dimensional feature extraction, and the data preprocessing step comprises the following steps:
translating, translating the data for a certain period of time to recover unsynchronized data;
and/or strengthening, copying a certain column of data to intensively analyze the influence of the time of the corresponding time sequence on the overall state of the system;
and/or augmentation, abnormal incentive excavation, so as to conveniently realize parallel computation;
and/or random wave sum normalization, per-unit the matrix to meet statistical preconditions.
3. The method for rush repair of the open-phase fault of the high-voltage side of the distribution transformer according to claim 2, characterized in that: the average spectrum radius MSR is a high-dimensional statistical index, and hypothesis testing is carried out by comparing an observed value with an expected value.
4. The method for rush repair of the open-phase fault of the high-voltage side of the distribution transformer according to claim 3, wherein the method comprises the following steps: the linear statistical characteristic value LES is obtained by a data processing method of a random matrix theory, and the value has statistical characteristics.
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| CN110568275B (en) * | 2019-09-17 | 2022-02-01 | 国网福建省电力有限公司安溪县供电公司 | Open-phase fault studying and judging method and system based on public and private power distribution network variable data |
| CN111196201A (en) * | 2019-12-31 | 2020-05-26 | 国网北京市电力公司 | Mobile box transformer substation vehicle |
| CN112083274B (en) * | 2020-08-21 | 2023-10-31 | 深圳供电局有限公司 | Method and device for monitoring fault information of secondary equipment box of power grid |
| CN114897035B (en) * | 2021-10-09 | 2024-07-30 | 国网浙江省电力有限公司电力科学研究院 | Multi-source data feature fusion method for 10kV cable state evaluation |
| CN116008682B (en) * | 2023-03-22 | 2023-08-15 | 国网江西省电力有限公司电力科学研究院 | Real-time studying and judging method and system for distribution transformer high-voltage open-phase fault position |
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| CN105071536A (en) * | 2015-08-19 | 2015-11-18 | 江苏省电力公司扬州供电公司 | Intelligent distribution transformer area system |
| CN106571626A (en) * | 2016-08-29 | 2017-04-19 | 上海交通大学 | Power system cognitive method based on random moment theory |
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