CN110297140B - Fault prediction method and device of power distribution system - Google Patents
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Abstract
The invention relates to aA fault prediction method and a fault prediction device for a power distribution system belong to the technical field of fault prediction and solve the problem that the conventional prediction method cannot realize fault prediction based on historical data. The method comprises the following steps: collecting output voltage of power distribution system to form voltage sample set, calculating sample set mean valueAnd the variance S of the sample2Obtaining a confidence interval of the expected mu and the standard deviation sigma when the confidence level is 1-alpha; when a new voltage sample is collected, forming a new voltage sample set together with the voltage sample set at the previous moment, and calculating the confidence intervals of mu and sigma when the confidence level is 1-alpha under the new voltage sample set; repeating the above process to obtain continuous confidence intervals of μ and σ of several voltage sample sets, and taking the confidence intervals of μ and σ of the voltage sample sets if the confidence intervals are stable in the preset fluctuation rangeσ2=S2And obtaining a voltage output interval of the power distribution system in a normal working state, and realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state.
Description
Technical Field
The invention relates to the technical field of fault prediction, in particular to a fault prediction method and device of a power distribution system.
Background
The failure prediction is defined as that the electrical equipment is forecasted before failure by a technical means, and the failure prediction mechanism is that before the failure of the electrical equipment, partial internal systems or components of the electrical equipment deviate from a normal working range, although the operation of the system can be still ensured and the output in an index can be kept, as the operation time goes on, the deviation of the internal systems or components from the normal working range is more serious until the system fails. The failure prediction is to monitor the slight difference inside the system by technical means before the system failure, so as to achieve the purposes of early prediction, early preparation and early maintenance, and prevent the sudden system failure in the future.
The existing failure prediction means mostly adopt a mode of increasing the hardware arrangement of a sensor to perform data real-time interpretation on a plurality of parts which are possibly failed in a system, although the method improves the current situation that the whole electric system is used as a black box system to a certain extent, the system design and production cost is greatly increased, in addition, the local real-time data interpretation can ensure that the system fails to ensure the local failure of the system even if the system further fails in a larger range, and therefore the mode still cannot achieve the purposes of 'early prediction' and 'early maintenance'.
Through the above, the existing fault prediction has the limitation that more detection points are established in the electrical system, so that the signal acquisition range at the same moment is expanded, and the analysis and diagnosis are carried out through online monitoring essentially; the fault phenomena and factors around the events are emphasized, but the action process under the complex working condition is ignored, and the dynamic operation characteristics of the system cannot be adapted. In summary, the conventional method ignores the dynamic operation characteristic of the historical system output data to the system, and fails to realize the real fault prediction without comprehensively adopting a data processing means and the historical output data to analyze the operation state of the system.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a method and an apparatus for predicting faults of a power distribution system, so as to solve the problem that the existing prediction method does not comprehensively adopt a data processing means and historical output data to analyze the operation state of the system, and cannot realize the true fault prediction.
The purpose of the invention is mainly realized by the following technical scheme:
a method of fault prediction for an electrical distribution system, comprising the steps of:
continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) A distribution in which the expected μ and the standard deviation σ are unknowns;
calculating the voltage samplesMean value of the albumAnd variance S2Respectively obtaining confidence intervals of the expected mu and the standard deviation sigma when the confidence level is 1-alpha;
when a new voltage sample is collected, the new voltage sample set and the voltage sample set at the previous moment form a new voltage sample set, and the calculation steps are repeated to obtain a confidence interval of the expected mu and the standard deviation sigma when the confidence level of the new voltage sample set is 1-alpha;
repeating the new voltage sample collection and calculation process to obtain confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets, and taking the confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets when the confidence intervals are stable within a preset fluctuation rangeσ2=S2;
And determining a voltage output interval of the power distribution system in a normal working state according to the mu and the sigma, and realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state.
On the basis of the scheme, the invention is further improved as follows:
further, the confidence interval for the expected μ at a confidence level of 1- α is obtained by the following formula:
the confidence interval for μ at a confidence level of 1- α is then:
further, the confidence interval of the standard deviation σ at a confidence level of 1- α is obtained by the following formula:
the confidence interval for the standard deviation σ with a confidence level of 1- α is then:
further, it is determined that the confidence intervals of the expected μ and standard deviation σ for the successive sets of several voltage samples are each stable within a predetermined fluctuation range by:
confidence intervals of the expected mu and standard deviation sigma of the voltage sample sets respectively meet the condition that the deviation of the upper boundary value from the average value of the upper boundary is less than 0.1, and the deviation of the lower boundary value from the average value of the lower boundary is less than 0.1.
Further, a confidence level 1- α is set to 0.9.
Further, the voltage output interval of the power distribution system in the normal working state is determined by the following formula:
the interval of the output voltage x in the normal working state of the power distribution system is as follows: x is an element (mu-sigma. z)0.1/2,μ+σ·z0.1/2)。
Further, the fault prediction of the power distribution system is realized according to the voltage output interval in the normal working state, and the fault prediction comprises the following steps:
if the voltage output value of the power distribution system exceeds the voltage output interval in the normal working state, accumulating 100 voltage output values by taking the current voltage output value as a starting point, and calculating the average value of the 100 voltage output valuesIf it isAnd if the voltage exceeds the voltage output interval of the power distribution system in the normal working state, judging that the power distribution system has a fault.
Further, or by implementing fault prediction for the power distribution system by:
after a voltage output interval of the power distribution system in a normal working state is determined, emptying the voltage sample set;
the voltage samples are collected again, each time one voltage sample is collected, a new voltage sample set is formed together with the voltage sample set at the previous moment, and the mean value of the sample sets is calculatedSum variance S2(ii) a Mean of 100 groups per accumulationAnd the variance S of the sample2Then, respectively calculateAnd S2Average value of (2), average value of current calculationSample variance S2And respectively making differences with the average value, and if any difference value is greater than 0.2, judging that the power distribution system has faults.
The invention also discloses a fault prediction device of the power distribution system, and the system comprises: the device comprises a voltage acquisition module, a voltage processing module and a fault prediction module; wherein,
the voltage acquisition module is used for acquiring the output voltage of the power distribution system;
the voltage processing module is used for executing the following operations to obtain a voltage output interval of the power distribution system in a normal working state:
continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) A distribution wherein the desired μ and the standardThe difference sigma is an unknown quantity;
calculating a mean of the set of voltage samplesAnd variance S2Respectively obtaining confidence intervals of the expected mu and the standard deviation sigma when the confidence level is 1-alpha;
when a new voltage sample is collected, the new voltage sample set and the voltage sample set at the previous moment form a new voltage sample set, and the calculation steps are repeated to obtain a confidence interval of the expected mu and the standard deviation sigma when the confidence level of the new voltage sample set is 1-alpha;
repeating the new voltage sample collection and calculation process to obtain confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets, and taking the confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets when the confidence intervals are stable within a preset fluctuation rangeσ ═ S; determining a voltage output interval of the power distribution system in a normal working state according to the mu and the sigma;
and the fault prediction module is used for realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state.
On the basis of the scheme, the invention is further improved as follows:
further, the failure prediction module performs the following operations to implement failure prediction:
if the voltage output value of the power distribution system exceeds the voltage output interval in the normal working state, accumulating 100 voltage output values by taking the current voltage output value as a starting point, and calculating the average value of the 100 voltage output valuesIf it isBeyond the normal operation of the distribution systemJudging the power distribution system fault in a voltage output interval under the state; or,
after a voltage output interval of the power distribution system in a normal working state is determined, emptying the voltage sample set; the voltage samples are collected again, each time one voltage sample is collected, a new voltage sample set is formed together with the voltage sample set at the previous moment, and the mean value of the sample sets is calculatedSum variance S2(ii) a Mean of 100 groups per accumulationAnd the variance S of the sample2Then, respectively calculateAnd S2Average value of (2), average value of current calculationSample variance S2And respectively making differences with the average value, and if any difference value is greater than 0.2, judging that the power distribution system has faults.
The invention has the following beneficial effects:
according to the fault prediction method of the power distribution system, the distribution characteristics met by the output voltage data of the power distribution system are obtained through analysis based on the output voltage data of the power distribution system, and fault prediction is achieved according to the distribution characteristics. According to the method, a probability analysis process is introduced into the fault prediction of the power distribution system, the operation state of the system can be analyzed by adopting a data processing means and historical output data, and the problem of the real fault prediction is realized. The method has a self-learning characteristic, and as data is increased, the power distribution system presents estimation convergence and estimation is more accurate, and the algorithm is to be popularized to other similar systems meeting normal distribution. The method embodiment and the device embodiment are based on the same principle, and the related parts can be referenced mutually, and the same technical effect can be achieved.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a graph of a normal distribution;
FIG. 2 is a flow chart of a method for fault prediction in a power distribution system in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of a power distribution system voltage sampling in accordance with an embodiment of the present invention;
FIG. 4 is a diagram of the mean value of the distribution system in the embodiment of the present inventionA change curve;
FIG. 5 shows the variance S in an embodiment of the present invention2A change curve;
FIG. 6 is a graph of expected μ confidence interval variation in an embodiment of the present invention
FIG. 7 shows the variance σ in an embodiment of the present invention2Confidence interval change curve.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
First, the following description will be made of the related art used in the present embodiment:
N(μ,σ2): normal distribution with an expectation of μ and variance σ2;
Expected μ and mean of sampleThe difference of (1): one total of X ═ X1,…,XnSample expectationSince the total sample X has more elements, in practical application, one total sample (i.e. a set composed of partial total elements) is often usedWherein X1,…,XiElements which are all total X, then meanWhen the sample of the population approximates to the population X, the sample expects mu to approximate to the meanAnd isUnbiased estimation for μ;
sample variance S2And variance σ2The difference of (1): the variance of the population is the same as the expected meanIn practical applications, the total sample variance is often adoptedTo approximate sigma2When the overall sample approaches the overall X, the sample variance S2Is approximated by the variance σ2And S is2Is σ2Unbiased estimation of (2);
unbiased estimation: unbiased estimation refers to that there is no system deviation between an estimator and a true value, only random deviation exists, and the value is the true value when the average value of all estimation values of the system is obtained. I.e. as long as the sample is sufficiently large, S2=σ2,
Pivot amount: starting from a point estimate of μ, a function G (i.e., the pivot quantity in this text) is constructed with respect to μ) The distribution of this function G is known and independent of μ. Amount of pivot in textObeying t distribution, and having no relation with mu, so that the confidence interval of mu can be obtained through the setting of the pivot quantity;
confidence interval: for an unknown parameter μ, if for a given constant α, (0 < α < 1), the upper limit is determinedAnd lower limitμSo that μ is in the intervalThe probability of occurrence is 1-alpha, the intervalA confidence interval referred to as unknown parameter μ;
zα/2: represents a normal distribution N (0,1), μ ═ 0, σ2Given a confidence interval 1- α, the boundary values taken for sample X are shown in fig. 1. For a determined value of alpha, zα/2The value is a fixed value, can be obtained by table lookup, and can also be obtained by programming.
t (n): representing a t distribution with the number of samples n; chi shape2(n): χ representing number of samples n2Distributing;
t distribution, χ2The distributions are all converted to normal distributions in this patent, which is a well-known art in this field.
The embodiment of the invention discloses a fault prediction method and a fault prediction device for a power distribution system, and a flow chart is shown in fig. 2 and comprises the following steps:
a) continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) A distribution in which the expected μ and the standard deviation σ are unknowns; setting a system confidence level 1-alpha to be 0.9;
b) calculate the confidence interval for the expected μ at a confidence level of 1- α, due to the sample variance S2Is σ2Is estimated unbiased, so willToDue to the fact thatGetAs a pivot measure, get
Namely, it is
Then a confidence interval of 1-alpha for the desired μ confidence level is
T is t when n is more than 45α≈zαWherein z isαTo obey a normal distribution of N (0,1), the system calculates the system expected confidence interval from sample points N > 45, respectively, and therefore the above interval range can also be expressed as:
c) the confidence interval of the expected μ and standard deviation σ for a confidence level of 1- α is calculated, sinceGetAs a pivot amount, then
Namely, it is
The confidence interval with a confidence level of 1-alpha is then
Since n is greater than 45Wherein z isαIs positive obeying N (0,1)The state distribution, so the system calculates the system variance confidence interval after the sampling point n > 45, the calculation formula can also be expressed as:
d) collecting output voltage samples of the power distribution system in real time, repeating the steps b) and c) until 10 sample quantities are increased, respectively calculating the average values of upper and lower boundary values of a confidence interval, and when the difference between the upper boundary value corresponding to the mu confidence interval and the sigma confidence interval and the average value of the 10 upper boundary values is less than 0.1 and the difference between the lower boundary value and the average value of the 10 lower boundary values is less than 0.1, determining that the confidence interval of the mu and the sigma is stable,can be identified as the desired μ of the distribution system output voltage; s can be regarded as the standard deviation sigma of the output voltage of the power distribution system;
Out of range (probability value set to 0.9), x e (μ - σ · z)0.1/2,μ+σ·z0.1/2) Wherein z is0.1/21.65, is the range value of the system output x, μ - σ · z0.1/2、μ+σ·z0.1/2Is the self-identifying boundary value of the system.
f) Predicting system fault information according to the following criteria; and if any one of the following two criteria is met, judging that the system monitors and alarms.
Mean of 100 groups per accumulationAnd the variance S of the sample2Then, respectively calculateAnd S2Average value of (2), average value of current calculationSample variance S2And respectively making differences with the average value, if any difference value is greater than 0.2, judging that an internal system of the power distribution system has an abnormal trend, and monitoring and alarming by the system.
Monitoring the output value of the system in real time, if the output value exceeds the self-identification boundary calculated in the step e), accumulating 100 output values by taking the output value as a starting point, and calculating the average value of the 100 output valuesIf it isThe system monitors for an alarm.
If the two criteria do not meet the conditions, judging that the current state of the system is normal, otherwise, judging that the power distribution system has a fault. Repeating steps a) to f) and continuously monitoring.
According to the fault prediction method of the power distribution system, the distribution characteristics met by the output voltage data of the power distribution system are obtained through analysis based on the output voltage data of the power distribution system, and fault prediction is achieved according to the distribution characteristics. According to the method, a probability analysis process is introduced into the fault prediction of the power distribution system, the operation state of the system can be analyzed by adopting a data processing means and historical output data, and the problem of the real fault prediction is realized. The method has a self-learning characteristic, and as data is increased, the power distribution system presents estimation convergence and estimation is more accurate, and the algorithm is to be popularized to other similar systems meeting normal distribution.
In another embodiment of the present invention, there is also provided a fault prediction apparatus of a power distribution system, the apparatus including: the device comprises a voltage acquisition module, a voltage processing module and a fault prediction module; the voltage acquisition module is used for acquiring the output voltage of the power distribution system; the voltage processing module is used for executing the following operations to obtain a voltage output interval of the power distribution system in a normal working state:
continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) Distribution, wherein the desired μ and the variance σ2Is an unknown quantity;
calculating a mean of the set of voltage samplesAnd variance S2Respectively obtaining confidence intervals of the expected mu and the standard deviation sigma when the confidence level is 1-alpha;
when a new voltage sample is collected, the new voltage sample set and the voltage sample set at the previous moment form a new voltage sample set, and the calculation steps are repeated to obtain a confidence interval of the expected mu and the standard deviation sigma when the confidence level of the new voltage sample set is 1-alpha;
repeating the new voltage sample collection and calculation process to obtain confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets, and taking the confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets when the confidence intervals are stable within a preset fluctuation rangeσ2=S2(ii) a And according to the mu and the sigma2Determining a voltage output interval of the power distribution system in a normal working state; and the fault prediction module is used for realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state.
The specific implementation process of the device embodiment of the present invention may refer to the method embodiment described above, and this embodiment is not described herein again. Since the principle of the present embodiment is the same as that of the above method embodiment, the present system also has the corresponding technical effects of the above method embodiment.
The invention also carries out simulation analysis through the specific flow of monitoring the power distribution system. In the simulation results, fig. 3 shows the collection of the voltage output value of the power distribution system, and fig. 4 and 5 show the mean value of the voltage output of the power distribution system in the collection processVariance S2The real-time calculation, the change curve graph is formed after a plurality of times of cyclic calculation, and the calculation process of each point in the curve corresponds to the step a) in the section 2.
Calculating the mean value of the system in the above single stepSample variance S2And then, respectively calculating confidence intervals of the expected mu and standard deviation sigma of the system according to b) and c), and obtaining a real-time calculation confidence interval change curve after multiple times of cyclic calculation.
Respectively judging the change conditions of the confidence interval of the expected mu and standard deviation sigma of the system according to d), wherein in the simulation process, the upper limit and the lower limit of the confidence interval calculated at the 1500 th collected data point meet the fluctuation requirement of d), and as shown in the stable confidence interval marked in figures 6 and 7, the mean value of the system calculated after the 1500 th collected data point can be considered to beSample variance S2Expected μ and variance σ of data samples for system output2As indicated by the labeled "estimated stable region" in fig. 4 and 5.
After the 1500 th data point is collected, calculating the mean value of the monitoring system in real time as described in e)Sample variance S2Variation according to meanSample variance S2The power distribution system for making changes isAnd judging whether to carry out failure prediction alarm. In FIGS. 4 and 5, the system meanSample variance S2After the 1500 th sample point, the real-time calculated mean valueSample variance S2All are in stable condition, and are free from fluctuation, so that the power distribution system is in a normal state, and can not break down in a long period of time in the later period.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (8)
1. A method of fault prediction for an electrical distribution system, comprising the steps of:
continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) A distribution in which μ and the standard deviation σ are expected as unknowns;
calculating a mean of the set of voltage samplesAnd variance S2Respectively obtaining confidence intervals of the expected mu and the standard deviation sigma when the confidence level is 1-alpha;
when a new voltage sample is collected, the new voltage sample set and the voltage sample set at the previous moment form a new voltage sample set, and the calculation steps are repeated to obtain a confidence interval of the expected mu and the standard deviation sigma when the confidence level of the new voltage sample set is 1-alpha;
repeating the new voltage sample collection and calculation process to obtain confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets, and taking the confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets when the confidence intervals are stable within a preset fluctuation rangeσ2=S2;
According to the mu and the sigma2Determining a voltage output interval of the power distribution system in a normal working state, and realizing fault prediction of the power distribution system according to the voltage output interval in the normal working state;
determining a voltage output interval of the power distribution system in a normal working state by the following formula:
the interval of the output voltage X in the normal operating state of the power distribution system is: x is an element (mu-sigma. z)0.1/2,μ+σ·z0.1/2);
And realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state, wherein the fault prediction comprises the following steps:
if the voltage output value of the power distribution system exceeds the voltage output interval in the normal working state, accumulating 100 voltage output values by taking the current voltage output value as a starting point, and calculating the average value of the 100 voltage output valuesIf it isAnd if the voltage exceeds the voltage output interval of the power distribution system in the normal working state, judging that the power distribution system has a fault.
3. The method of fault prediction for an electrical distribution system of claim 1 wherein the confidence interval for a standard deviation σ at a confidence level of 1- α is obtained by the following equation:
the confidence interval for the standard deviation σ with a confidence level of 1- α is then:
n represents the number of voltage samples.
4. The method of claim 1, wherein the confidence intervals of the expected μ and standard deviation σ for the successive sets of voltage samples are determined to be stable within a predetermined fluctuation range by:
confidence intervals of the expected mu and standard deviation sigma of the voltage sample sets respectively meet the condition that the deviation of the upper boundary value from the average value of the upper boundary is less than 0.1, and the deviation of the lower boundary value from the average value of the lower boundary is less than 0.1.
5. The method of claim 1, wherein a confidence level of 1- α is set at 0.9.
6. The method of fault prediction of an electrical distribution system of claim 1, further comprising performing fault prediction of the electrical distribution system by:
after a voltage output interval of the power distribution system in a normal working state is determined, emptying the voltage sample set;
the voltage samples are collected again, each time one voltage sample is collected, a new voltage sample set is formed together with the voltage sample set at the previous moment, and the mean value of the sample sets is calculatedSum variance S2(ii) a Mean of 100 groups per accumulationAnd the variance S of the sample2Then, respectively calculateAnd S2Average value of (2), average value of current calculationSample variance S2And respectively making differences with the average value, and if any difference value is greater than 0.2, judging that the power distribution system has faults.
7. A fault prediction device for an electrical distribution system, the system comprising: the device comprises a voltage acquisition module, a voltage processing module and a fault prediction module; wherein,
the voltage acquisition module is used for acquiring the output voltage of the power distribution system;
the voltage processing module is used for executing the following operations to obtain a voltage output interval of the power distribution system in a normal working state:
continuously collecting voltage sample X output by power distribution system1,X2,...,XnTo form a set of voltage samples that satisfy N (μ, σ)2) A distribution in which μ and the standard deviation σ are expected as unknowns;
calculating a mean of the set of voltage samplesAnd variance S2Respectively obtaining confidence intervals of the expected mu and the standard deviation sigma when the confidence level is 1-alpha;
when a new voltage sample is collected, the new voltage sample set and the voltage sample set at the previous moment form a new voltage sample set, and the calculation steps are repeated to obtain a confidence interval of the expected mu and the standard deviation sigma when the confidence level of the new voltage sample set is 1-alpha;
repeating the new voltage sample collection and calculation process to obtain confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets, and taking the confidence intervals of the expected mu and the standard deviation sigma of the continuous voltage sample sets when the confidence intervals are stable within a preset fluctuation rangeσ2=S2(ii) a And according to the mu and the sigma2Determining a voltage output interval of the power distribution system in a normal working state;
the fault prediction module is used for realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state;
determining a voltage output interval of the power distribution system in a normal working state by the following formula:
the interval of the output voltage X in the normal operating state of the power distribution system is: x is an element (mu-sigma. z)0.1/2,μ+σ·z0.1/2);
And realizing the fault prediction of the power distribution system according to the voltage output interval in the normal working state, wherein the fault prediction comprises the following steps:
if the voltage output value of the power distribution system exceeds the voltage output interval in the normal working state, accumulating 100 voltage output values by taking the current voltage output value as a starting point, and calculating the average value of the 100 voltage output valuesIf it isAnd if the voltage exceeds the voltage output interval of the power distribution system in the normal working state, judging that the power distribution system has a fault.
8. The fault prediction device of the power distribution system of claim 7, wherein the fault prediction module further performs the following operations to achieve fault prediction:
after a voltage output interval of the power distribution system in a normal working state is determined, emptying the voltage sample set; the voltage samples are collected again, each time one voltage sample is collected, a new voltage sample set is formed together with the voltage sample set at the previous moment, and the mean value of the sample sets is calculatedSum variance S2(ii) a Mean of 100 groups per accumulationAnd the variance S of the sample2Then, respectively calculateAnd S2Average value of (2), average value of current calculationSample variance S2And respectively making differences with the average value, and if any difference value is greater than 0.2, judging that the power distribution system has faults.
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