CN114900190A - Multi-target fusion differential protection data compression method - Google Patents
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Abstract
The invention discloses a multi-target fusion differential protection data compression method, which comprises the following steps: collecting data, and pre-classifying the data by using a data classification module; the data classification module simultaneously transmits the pre-classified data to the data compression module and the parameter optimization module; the parameter optimization module transmits the optimal value of the calculation algorithm parameter to the data compression module; and the data compression module outputs a data compression result, and the next round of optimization is entered after loop iteration. The invention realizes the preliminary screening of differential protection data, makes a targeted strategy, compresses the differential protection data by utilizing a revolving door algorithm, establishes a multi-target comprehensive optimization target and optimizes algorithm parameters in real time, thereby realizing the target of optimal compression of the differential protection data, and meeting the requirements of the highest compression ratio and the accuracy of differential protection data storage under the conditions of high speed and real-time data transmission of differential protection.
Description
Technical Field
The invention relates to the technical field of differential protection data compression, in particular to a multi-target fusion differential protection data compression method.
Background
In recent years, with the continuous construction and development of smart power grids, smart energy sources and ubiquitous power internet of things, the requirements of large-scale wide-area data real-time acquisition and historical storage are rapidly increased, particularly under the characteristics of high speed, low time delay and the like of data in the 5G era, massive data are acquired and analyzed in real time, and the storage of related data and optimization results becomes a problem to be considered.
At present, differential protection technology based on 5G technology is more mature, relevant data is stored in a server or a disk array, but the capacity of hardware is limited, so that storage hardware has serious burden on a digital substation with massive historical data and various analysis function requirements, and an efficient data compression technology becomes a first choice for improving storage efficiency and reducing hardware pressure.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: due to the capacity limitation of hardware, the storage hardware has a serious burden on a digital substation with massive historical data and various analysis function requirements.
In order to solve the technical problems, the invention provides the following technical scheme: collecting data, and pre-classifying the data by using a data classification module; the data classification module simultaneously transmits the pre-classified data to the data compression module and the parameter optimization module; the parameter optimization module transmits the optimal value of the calculation algorithm parameter to the data compression module; and the data compression module outputs a data compression result, and the next round of optimization is entered after loop iteration.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: the pre-classified data comprises digital quantity data and analog quantity data.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: and storing the digital quantity data by adopting a displacement storage strategy, and simultaneously transmitting the analog quantity data to the data compression module and the parameter optimization module.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: setting the algorithm parameter of the revolving door as a threshold value E, and compressing the differential protection data input into the data compression module by utilizing the revolving door algorithm.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: the parameter optimization module calculates and obtains a compression Ratio, a relative compression error R and unit data block compression and decompression time T.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: obtaining the compression Ratio based on the size of the storage space occupied by the original data file and the size of the storage space occupied by the compressed file,
wherein Ratio represents a compression Ratio, m represents the size of the original data file in the storage space, and n represents the size of the compressed file in the storage space.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: the obtaining of the relative compression error R may include,
wherein R represents the relative compression error, x 1 ,x 2 ,...x n Representing data before compression, x 1 ′,x 2 ′,...,x n ' denotes compressed data.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: based on the compression Ratio, the relative compression error R, the unit block compression and decompression time T, and using weighted multi-target comparison as the target result value,
wherein obj represents the result value, Ratio ' represents the compression Ratio result of the previous round of optimization, R ' represents the relative compression error of the previous round of optimization, T ' represents the unit data block compression and decompression time of the previous round of optimization, α, β, γ represent weight coefficients, and α + β + γ is 1.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: the operating step of the parameter optimization module includes,
setting weight coefficients alpha, beta and gamma;
receiving data to be compressed input by the data classification module;
and optimizing the data to be compressed by adopting a genetic algorithm by taking the threshold value E as a parameter to be optimized and the obj as an optimized result value to obtain the minimum obj.
As a preferred scheme of the multi-target fusion differential protection data compression method of the present invention, wherein: the operating step of the parameter optimization module further comprises,
recording a threshold value E corresponding to the minimum obj, and outputting the threshold value E to the data compression module;
recording a compression Ratio ', a relative compression error R ', unit data block compression and decompression time T ' corresponding to the minimum obj;
and (6) circularly iterating and entering the next round of optimization.
The invention has the beneficial effects that: the invention realizes the preliminary screening of differential protection data, makes a targeted strategy, compresses the differential protection data by utilizing a revolving door algorithm, establishes a multi-target comprehensive optimization target and optimizes algorithm parameters in real time, thereby realizing the target of optimal compression of the differential protection data, and meeting the requirements of the highest compression ratio and the accuracy of differential protection data storage under the conditions of high speed and real-time data transmission of differential protection.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a data compression module of a multi-target fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a parameter optimization module of a multi-objective fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 3 is a comparison graph of the weight group 1 compression ratio data of the multi-target fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 4 is a graph comparing weight 1 to compression error data in a multi-target fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 5 is a data comparison diagram of compression and decompression time of a weight 1 unit data block in a multi-target fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 6 is a comparison graph of the weight group 2 compression ratio data of the multi-target fusion differential protection data compression method according to an embodiment of the present invention;
FIG. 7 is a graph comparing weight 2 with respect to compression error data in a multi-target fusion differential protection data compression method according to an embodiment of the present invention;
fig. 8 is a data comparison diagram of compression and decompression time of a weight 2 unit data block in a multi-target fusion differential protection data compression method according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 2, in an embodiment of the present invention, a multi-target fusion differential protection data compression method is provided, including: a system adopting a multi-target fusion differential protection data compression method comprises a data classification module, a data compression module and a parameter optimization module.
The main storage data of the differential protection can be divided into signals such as remote signaling, remote measurement, remote control and remote regulation, wherein the first type of remote signaling and remote control signals are state quantities and only have limited numerical value options (the conventional state quantities only have two numerical values of 0 and 1, and the double-position remote signaling has four numerical values of 0, 1, 2 and 3), a displacement storage strategy can be adopted, and only the values and time of displacement data points are stored, which means that the stored values between the n-1 point and the n-1 point are the same as the value of the n-1 point; the second type is remote measurement and remote regulation, and the data is compressed by the system and then stored.
S1: and collecting data, and performing pre-classification on the data by using a data classification module. It should be noted that:
the pre-classified data comprises digital quantity data and analog quantity data, and the data classification module shown in FIG. 1 is used for pre-classifying, then transmitting the data to be compressed to the data compression module and the parameter optimization module, and further performing processing
S2: the data classification module transmits the pre-classified data to the data compression module and the parameter optimization module at the same time. It should be noted that:
and storing the digital quantity data by adopting a displacement storage strategy, and simultaneously transmitting the analog quantity data to the data compression module and the parameter optimization module.
S3: and the parameter optimization module transmits the optimal value of the calculation algorithm parameter to the data compression module. It should be noted that:
setting the algorithm parameter of the revolving door as a threshold value E, and compressing differential protection data of the input data compression module by utilizing the revolving door algorithm.
The parameter optimization module calculates and obtains a compression Ratio, a relative compression error R and unit data block compression and decompression time T.
As shown in FIG. 1, the data to be compressed is transmitted to the data compression module and also transmitted to the parameter optimization module, in which a multi-objective comprehensive optimization objective is adopted to obtain the compression Ratio based on the size of the storage space occupied by the original data file and the size of the storage space occupied by the compressed file,
wherein Ratio represents a compression Ratio, m represents the size of the original data file in the storage space, and n represents the size of the compressed file in the storage space.
The acquisition of the relative compression error R comprises,
wherein R represents the relative compression error, x 1 ,x 2 ,...x n Representing data before compression, x 1 ′,x 2 ′,...,x n ' denotes compressed data.
The aim of establishing multiple targets is to keep the parameters of the compression algorithm at the optimal values by comparing the targets, so that the compression algorithm obtains the optimal compression result under the multiple targets.
Based on the compression Ratio, the relative compression error R, the unit block compression and decompression time T, and using weighted multi-target comparison as the target result value,
where obj denotes a result value, Ratio ' denotes a compression Ratio result of the previous round of optimization, R ' denotes a relative compression error of the previous round of optimization, T ' denotes unit data block compression and decompression time of the previous round of optimization, α, β, and γ denote weight coefficients, α + β + γ ═ 1, and a unit of the unit data block compression and decompression time T can be expressed as seconds.
As shown in fig. 2, the system needs to set specific values of the weighting coefficients in the first step, and if α is 1 and β is 0, it indicates that the user is only interested in the compression ratio of the system; if β is 1 and α is 0, it means that the user is concerned only with the relative compression error of the system, and the values of the weighting coefficients may be set according to different emphasis points.
As shown in fig. 2, while data is compressed each time, the parameter optimization module will obtain the data compressed this time at the same time for calculating the algorithm parameters of the next data compression, the optimization process is to optimize the threshold value E so that the target obj is the minimum, the optimization algorithm adopts a genetic algorithm, or may adopt other optimization algorithms.
S4: and the data compression module outputs a data compression result, and the next round of optimization is entered after loop iteration. It should be noted that:
the operating step of the parameter optimization module includes,
setting weight coefficients alpha, beta and gamma;
receiving data to be compressed input by a data classification module;
optimizing the data to be compressed by adopting a genetic algorithm by taking the threshold value E as a parameter to be optimized and obj as an optimized result value to obtain the minimum obj;
recording a threshold value E corresponding to the minimum obj, and outputting the threshold value E to a data compression module;
recording a compression Ratio ', a relative compression error R ', unit data block compression and decompression time T ' corresponding to the minimum obj;
and circularly iterating S2-S5, and entering the next round of optimization.
The invention realizes the preliminary screening of differential protection data, makes a targeted strategy, compresses the differential protection data by utilizing a revolving door algorithm, establishes a multi-target comprehensive optimization target and optimizes algorithm parameters in real time, thereby realizing the target of optimal compression of the differential protection data, and meeting the requirements of the highest compression ratio and the accuracy of differential protection data storage under the conditions of high speed and real-time data transmission of differential protection.
Example 2
Referring to fig. 3 to 8, a second embodiment of the present invention is different from the first embodiment in that a verification test of a multi-target fusion differential protection data compression method is provided, and compared with a conventional system for performing data compression with fixed parameters, the present invention can better adapt to time-varying data, and can better serve a set compression target at the same time, so as to verify that the present method has higher adaptability and better compression effect compared with the conventional method with fixed parameters. The technical effects adopted in the method are verified and explained, the traditional fixed parameters are selected, the optimized parameter conditions are implemented by adopting the method for comparison test, the test results are compared by means of scientific demonstration, and the real effect of the method is verified.
In this example, the conventional fixed parameter method selects the first optimization result as a fixed parameter, and compares the first optimization result with the dynamic parameter optimization provided by the method, and compares the compression ratio, the relative compression error, and the unit data block compression and decompression time in the two methods.
In the test environment, the one-day protection record data is derived from the scheduling master station as a test sample, and the data packets are integrated at intervals of 5 minutes to obtain 288 data packets in total. The computer processor AMD Ryzen 51400, 8G memory computer is used for testing through MATLB software programming, and simulation data shown in figures 3 to 8 are obtained according to experimental results.
As can be seen from fig. 3 to 8, 2 sets of weighting values are set, and the weighting values are shown in the following table.
Table 1: two sets of weight values.
Fig. 3 to 8 show the comparison of compression ratio, relative compression error, unit data block compression and decompression time under the conventional parameter-determining method and the present method, respectively.
As can be seen from fig. 3 to fig. 5, compared with the conventional parameter-determining method, the method can obtain a higher compression ratio, a lower relative compression error and a smaller compression and decompression time of a unit data block, but the conventional parameter-determining method is equal to or even better than the method in the presence of sporadic data packets, which is mainly the weight of group 1, in which the compression ratio is the first consideration, i.e., α is the largest, so that the relative compression error, the compression and decompression time of the unit data block are more inferior to the number of data packets of the conventional parameter-determining method.
The method in fig. 3 to 5 is not obvious enough to be inferior to the conventional parameter-fixing method, so that the weight coefficient is adjusted to the extreme, that is, α is 1, β is 0, and γ is 0, as a result, as shown in fig. 6 to 8, under such weight, the algorithm only considers that the compression ratio is guaranteed to be optimal, and completely sacrifices the performance of the relative compression error and the unit data block compression and decompression time.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A multi-target fusion differential protection data compression method is characterized in that:
collecting data, and pre-classifying the data by using a data classification module;
the data classification module simultaneously transmits the pre-classified data to the data compression module and the parameter optimization module;
the parameter optimization module transmits the optimal value of the calculation algorithm parameter to the data compression module;
and the data compression module outputs a data compression result, and the next round of optimization is entered after loop iteration.
2. The multi-objective fused differential protection data compression method of claim 1, wherein: the pre-classified data comprises digital quantity data and analog quantity data.
3. The multi-objective fused differential protection data compression method of claim 1 or 2, wherein: and storing the digital quantity data by adopting a displacement storage strategy, and simultaneously transmitting the analog quantity data to the data compression module and the parameter optimization module.
4. The multi-objective fused differential protection data compression method of claim 3, wherein: setting the algorithm parameter of the revolving door as a threshold value E, and compressing the differential protection data input into the data compression module by utilizing the revolving door algorithm.
5. The multi-target fused differential protection data compression method as claimed in any one of claims 1, 2 and 4, wherein: the parameter optimization module calculates and obtains a compression Ratio, a relative compression error R and unit data block compression and decompression time T.
6. The multi-objective fused differential protection data compression method of claim 5, wherein:
obtaining the compression Ratio based on the size of the storage space occupied by the original data file and the size of the storage space occupied by the compressed file,
wherein Ratio represents a compression Ratio, m represents the size of the original data file in the storage space, and n represents the size of the compressed file in the storage space.
7. The multi-objective fused differential protection data compression method of claim 6, wherein: the obtaining of the relative compression error R may include,
wherein R represents the relative compression error, x 1 ,x 2 ,...x n Denotes data before compression, x' 1 ,x′ 2 ,...,x′ n Representing the compressed data.
8. The multi-objective fused differential protection data compression method of claim 7, wherein: based on the compression Ratio, the relative compression error R, the unit block compression and decompression time T, and using weighted multi-target comparison as target result values,
wherein obj represents the result value, Ratio ' represents the compression Ratio result of the previous round of optimization, R ' represents the relative compression error of the previous round of optimization, T ' represents the unit data block compression and decompression time of the previous round of optimization, α, β, γ represent weight coefficients, and α + β + γ is 1.
9. The multi-objective fused differential protection data compression method of claim 8, wherein: the operating step of the parameter optimization module includes,
setting weight coefficients alpha, beta and gamma;
receiving data to be compressed input by the data classification module;
and optimizing the data to be compressed by adopting a genetic algorithm by taking the threshold value E as a parameter to be optimized and the obj as an optimized result value to obtain the minimum obj.
10. The multi-objective fused differential protection data compression method of claim 9, wherein: the operating steps of the parameter optimization module further include,
recording a threshold value E corresponding to the minimum obj, and outputting the threshold value E to the data compression module;
recording a compression Ratio ', a relative compression error R ', unit data block compression and decompression time T ' corresponding to the minimum obj;
and (6) circularly iterating and entering the next round of optimization.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756839A (en) * | 2023-08-21 | 2023-09-15 | 山东德丰重工有限公司 | Intelligent management method for data of assembled building platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140238345A1 (en) * | 2011-10-24 | 2014-08-28 | Nissan Motor Co., Ltd. | Apparatus and method for controlling rotation speed of internal combustion engine |
| CN108183463A (en) * | 2017-12-29 | 2018-06-19 | 长园深瑞继保自动化有限公司 | The method of intelligent substation transformer ratio differential protection faulty action preventing |
| CN110868221A (en) * | 2019-10-31 | 2020-03-06 | 重庆大学 | Multi-mode data automatic compression method |
| US20200302276A1 (en) * | 2019-03-20 | 2020-09-24 | Gyrfalcon Technology Inc. | Artificial intelligence semiconductor chip having weights of variable compression ratio |
| CN113094903A (en) * | 2021-04-08 | 2021-07-09 | 浙江吉利控股集团有限公司 | Engine compression ratio verification method, device, equipment and storage medium |
-
2022
- 2022-04-24 CN CN202210434847.5A patent/CN114900190B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140238345A1 (en) * | 2011-10-24 | 2014-08-28 | Nissan Motor Co., Ltd. | Apparatus and method for controlling rotation speed of internal combustion engine |
| CN108183463A (en) * | 2017-12-29 | 2018-06-19 | 长园深瑞继保自动化有限公司 | The method of intelligent substation transformer ratio differential protection faulty action preventing |
| US20200302276A1 (en) * | 2019-03-20 | 2020-09-24 | Gyrfalcon Technology Inc. | Artificial intelligence semiconductor chip having weights of variable compression ratio |
| CN110868221A (en) * | 2019-10-31 | 2020-03-06 | 重庆大学 | Multi-mode data automatic compression method |
| CN113094903A (en) * | 2021-04-08 | 2021-07-09 | 浙江吉利控股集团有限公司 | Engine compression ratio verification method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 刘劲松等: "改进的旋转门算法及其在数控机床监控领域的应用", 组合机床与自动化加工技术, no. 2, 28 February 2018 (2018-02-28), pages 81 - 83 * |
| 苏盛: "数字化电力系统若干问题研究", 中国博士学位论文全文数据库工程科技II辑, no. 11, 15 November 2009 (2009-11-15), pages 4 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756839A (en) * | 2023-08-21 | 2023-09-15 | 山东德丰重工有限公司 | Intelligent management method for data of assembled building platform |
| CN116756839B (en) * | 2023-08-21 | 2023-11-21 | 山东德丰重工有限公司 | Intelligent management method for data of assembled building platform |
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