CN115204235A - Electrical parameter noise reduction method based on time sequence analysis - Google Patents
Electrical parameter noise reduction method based on time sequence analysis Download PDFInfo
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- CN115204235A CN115204235A CN202210870153.6A CN202210870153A CN115204235A CN 115204235 A CN115204235 A CN 115204235A CN 202210870153 A CN202210870153 A CN 202210870153A CN 115204235 A CN115204235 A CN 115204235A
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Abstract
The invention relates to the technical field of data noise reduction, in particular to an electrical parameter noise reduction method based on time sequence analysis, which comprises the following steps: step 1, calling time sequence electrical parameter data from an oil field Internet of things database; step 2, carrying out Pearson correlation comparison on the latest data and the latest historical data; step 3, comparing the Pearson correlation coefficient P360 of the pumping unit in the full period of 360 degrees with the Pearson correlation coefficient P90 of the pumping unit in the previous 90 degrees of the upstroke respectively; step 4, judging whether the data change is underground working condition change or not according to the P360 and P90 results; and 5, circulating the steps, completing the data to a uniform length by adopting a linear difference method, then performing correlation analysis by Pearson, determining whether the data is noise according to a correlation result, simply and efficiently analyzing and determining the electrical parameter noise data, and effectively removing the noise data.
Description
Technical Field
The invention relates to the technical field of data noise reduction, in particular to an electrical parameter noise reduction method based on time sequence analysis.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The timing is to maintain the relative position between the data signal and its reference clock signal, and ensure that the data near the rising edge or the falling edge of the clock can be maintained stably, so that the data can be effectively read.
In the prior art, the most important pumping unit in daily production has many noises in the electrical parameter data due to various accidental factors, such as electrical parameter fluctuation, mechanical vibration, and long-term overhaul, which seriously affect the analysis and processing of the drive end data, the receive end data, and the transmission channel data, and therefore must be denoised in advance.
Disclosure of Invention
The inventor finds out through research that: the electrical parameter fluctuation of the pumping unit, common mechanical vibration such as axial vibration wave, tangential vibration wave and the like, and power loss and the like caused by long-term non-use or long-term use and non-timely repair can slowly increase the noise mixed in the electrical parameter data.
The purpose of the present disclosure is to provide an electrical parameter noise reduction method based on time sequence analysis, which solves the technical problem that the prior art cannot effectively remove electrical parameter noise data by preprocessing raw data and then analyzing the correlation between the preprocessed raw data and historical data.
According to an aspect of the present disclosure, there is provided an electrical parameter noise reduction method based on time sequence analysis, including the following steps:
wherein X: the latest data; y: historical data; : the latest data mean value; : a historical data mean value;
step 3, comparing the Pearson correlation coefficient P360 of 360 degrees in the whole cycle of the pumping unit with the Pearson correlation coefficient P90 of 90 degrees before the upstroke of the pumping unit respectively to obtain data P360 and data P90;
step 4, judging whether the data change is underground working condition change or not according to the P360 and P90 results;
and 5, circulating the steps 1-4 until the noise reduction is finished, and stopping circulation.
In some embodiments of the present disclosure, the timing electrical parameter data includes driver data, receiver data, and transmission channel data.
In some embodiments of the present disclosure, the determination criterion is: if P360 is more than or equal to 0.8 and P90 is more than or equal to 0.92, the underground working condition changes; otherwise, the data is noise data.
In some embodiments of the present disclosure, the determination criteria are: if P360<0.8 and P90<0, it belongs to underground working condition change, otherwise it is noise data.
Compared with the prior art, the method has the following advantages and beneficial effects: according to the method, the data are complemented to be uniform in length by adopting a linear difference method, then correlation analysis is carried out through Pearson, whether the data are noise or not is determined according to a correlation system result, analysis and determination of the electrical parameter noise data are simply and efficiently carried out, and the noise data can be effectively removed.
Drawings
FIG. 1 is a logic flow diagram of the present invention;
FIG. 2 is a graph of the power curve versus the normal power curve for normal production fluctuations for a pumping unit of the present invention;
FIG. 3 is a graph comparing the power curve of the pumping unit of the present invention when the pumping unit generates noise due to the power grid and mechanical vibration with the normal power curve;
fig. 4 is a power curve of the pumping unit of the present invention as compared to a normal power curve during a subterranean condition.
Illustration of the drawings: reference numerals 1, 2 in fig. 2-4 correspond to the most recent data and the historical data, respectively.
Detailed Description
Referring to fig. 1-4 together, this embodiment provides a timing analysis-based electrical parametric noise reduction method, which is already in practical testing use stage.
In the following paragraphs, different aspects of the embodiments are defined in more detail. Aspects so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature considered to be preferred or advantageous may be combined with one or more other features considered to be preferred or advantageous. The terms "first", "second", and the like in the present invention are merely for convenience of description to distinguish different constituent elements having the same name, and do not denote a sequential or primary-secondary relationship.
Examples
The present embodiment includes at least the following: an electrical parameter noise reduction method based on time sequence analysis comprises the following steps:
wherein X: the latest data; y: historical data; : the latest data mean value; : a historical data mean value; wherein the larger the absolute value of the correlation coefficient is, the stronger the correlation is; the closer the correlation coefficient is to 1, the stronger the positive correlation, the closer the correlation coefficient is to 0, the weaker the correlation, the closer the correlation coefficient is to-1, and the stronger the negative correlation.
Step 3, respectively comparing a Pearson correlation coefficient P360 of 360 degrees in the whole cycle of the pumping unit with a Pearson correlation coefficient P90 of 90 degrees before the pumping unit upstroke to obtain data P360 and data P90;
step 4, judging whether the data change is underground working condition change or not according to the P360 and P90 results;
and 5, circulating the steps 1-4 until the noise reduction is finished, and stopping circulation.
Referring to fig. 1, the timing electrical parameter data includes driver data, receiver data and transmission channel data, and the determination criteria are: if P360 is more than or equal to 0.8 and P90 is more than or equal to 0.92, the underground working condition changes; otherwise, the data is noise data; the other judgment standard is as follows: and if P360<0.8 and P90<0, the underground working condition change is considered, and otherwise, the data are noise data.
Referring to the accompanying drawings 2-4, it is to be noted that 360 degrees of the full cycle of the pumping unit is selected for comparison, mainly that the underground production condition is relatively stable in the actual production process of the pumping unit, and when the instantaneous working condition occurs in the electric parameter elimination, the fluctuation between the front and back groups of data is basically kept small; the pumping unit is selected to be compared with the pumping unit 90 degrees before the upper stroke, mainly when the pumping unit is in an instantaneous working condition, the pumping unit mainly leaks and breaks off an underground rod pump, and the balance between the underground rod pump and a balance block of the ground pumping unit is broken, so that the pumping unit generates reverse negative work within 180 degrees of the upper stroke.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. An electrical parameter noise reduction method based on time sequence analysis is characterized by comprising the following steps:
step 1, calling time sequence electrical parameter data from an oil field Internet of things database, performing linear interpolation on original data, uniformly converting all data into 360 points corresponding to 360 degrees of operation of an oil pumping unit;
step 2, carrying out Pearson correlation comparison on the latest data and the latest historical data, carrying out point-by-point comparison on the latest data and the historical data, and referring to the following formula by the comparison method:
wherein X: the latest data; y:historical data;
a latest data mean value;
historical data mean value;
step 3, respectively comparing a Pearson correlation coefficient P360 of 360 degrees in the whole cycle of the pumping unit with a Pearson correlation coefficient P90 of 90 degrees before the pumping unit upstroke to obtain data P360 and data P90;
step 4, judging whether the change of the data is the underground working condition change or not according to the results of P360 and P90;
and 5, circulating the steps 1-4 until the noise reduction is finished, and stopping circulation.
2. The method according to claim 1, wherein the time-series electrical parameter data includes driver data, receiver data and transmission channel data.
3. The electrical parameter noise reduction method based on time sequence analysis according to claim 1, wherein the judgment criteria are: if P360 is more than or equal to 0.8 and P90 is more than or equal to 0.92, the underground working condition changes; otherwise, the data is noise data.
4. The electrical parameter noise reduction method based on time series analysis according to claim 1 or 3, wherein the judgment criterion is: and if P360<0.8 and P90<0, the underground working condition change is considered, and otherwise, the data are noise data.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550700A (en) * | 2015-12-08 | 2016-05-04 | 国网山东省电力公司电力科学研究院 | Time series data cleaning method based on correlation analysis and principal component analysis |
| US20190325319A1 (en) * | 2018-04-23 | 2019-10-24 | Accenture Global Solutions Limited | Detecting correlation among sets of time series data |
| CN111444241A (en) * | 2020-03-26 | 2020-07-24 | 南京工程学院 | Data mining-based accurate positioning method for line loss abnormity associated users of distribution room |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550700A (en) * | 2015-12-08 | 2016-05-04 | 国网山东省电力公司电力科学研究院 | Time series data cleaning method based on correlation analysis and principal component analysis |
| US20190325319A1 (en) * | 2018-04-23 | 2019-10-24 | Accenture Global Solutions Limited | Detecting correlation among sets of time series data |
| CN111444241A (en) * | 2020-03-26 | 2020-07-24 | 南京工程学院 | Data mining-based accurate positioning method for line loss abnormity associated users of distribution room |
Non-Patent Citations (2)
| Title |
|---|
| 吴智力: "基于时空相关性的路网交通流缺失数据插值方法研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 10, pages 17 - 18 * |
| 宋培培: "基于时差估计的管道漏点定位方法", 《山东科技大学学报》, vol. 36, no. 3, pages 104 - 113 * |
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