Electric meter fault frequency prediction method and system
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A technology of failure times and forecasting methods, applied in forecasting, information technology support systems, data processing applications, etc.
Pending Publication Date: 2022-01-04
国网山东省电力公司营销服务中心(计量中心) +2
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[0003] The purpose of the present invention is to provide a method and system for predicting the number of electric meter faults. The present invention combines the on-site electric meter fault data to predict the number of faults in the future time period. Identify problems in advance, so as to d
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[0038] Example 1:
[0039] Based on the above explanation, see figure 1 , the invention provides a method for predicting the number of electric meter faults, the prediction method comprising:
[0040] S1: Acquire on-site timed censored fault data of electric meters; the timed censored fault data includes the installation time of each batch of electric meters, the number of installed electric meters of each batch, the observation time, the number of electric meter failures within the observation time, and the predicted time The time corresponding to the failure of each electric meter, wherein the start time of the predicted time is the end time of the observation time;
[0041] In practical applications, such as figure 2 As shown, assuming a batch of meters at time t z sometimes N z Taipower meter put into use, at the observation time (t z ,t s ] with H inside z A meter fails, and the time when each meter fails is recorded as t H1 , t H2 ,...,t Hz , if a given predic...
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[0134] Example 2:
[0135] see Figure 4 , the present invention also provides a prediction system for the number of electric meter failures, the prediction system comprising:
[0136] Timing censoring fault data acquisition unit 1, used to obtain the timing censoring fault data of the electric meter field; the timing censoring fault data includes the installation time of each batch of ammeters, the installation number of each batch of ammeters, the observation time, the observation time The number of failures of the internal electric meters, the predicted time and the corresponding time when each electric meter fails, wherein the start time of the prediction time is the end time of the observation time;
[0137] The life model construction unit 2 is used to construct the Weibull distribution life model of the electric meter according to the timing censored fault data, and utilize the maximum likelihood to estimate the parameters of the Weibull distribution life model of the ...
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Abstract
The invention combines field fault data to predict the number of faults in a future time period, improves the prediction accuracy by introducing the advantage ratio, is beneficial to solving the problem of pre-judging the faults of the electric energy meters in advance, and thus discovers the possible fault number change trend of the same batch of intelligent electric energy meters in the future interval in advance. The full-life-cycle management and control capability of a power grid company on life prediction, risk evaluation and management decision-making of the intelligent electric energy meter is improved. The possible fault frequency change trend of the same batch of intelligent electric energy meters in the future interval can be found in advance, technical support can be provided for the aspects of operation maintenance, batch rotation, hidden danger investigation and the like of the electric energy meters in the future time period, and the full-life-cycle management and control capacity of a power grid company on life prediction, risk evaluation and management decision making of the intelligent electric energy meters is improved.
Description
technical field [0001] The invention relates to the field of equipment life prediction, in particular to a method and system for predicting the number of faults of electric meters. Background technique [0002] At present, the scheme of research and analysis of electric meters based on historical fault data focuses on reliability evaluation and life prediction, and the prediction of the number of faults in the future time interval of electric meters is less. And in the frequency forecasting scheme, it focuses on forecasting methods such as neural network, time series and gray model. However, these methods are not pertinent to the prediction of the number of failures in the future time period of the electric meter, and cannot effectively solve the engineering practical problem of predicting the number of failures of the electric meter in advance, so as to discover the trend of possible failures in the future range of batches of smart energy meters in advance. It cannot effec...
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