Direct current electric energy quality evaluation method and device based on multi-index complex correlation

Abstract

The invention discloses a direct current electric energy quality evaluation method and device based on multi-index complex correlation. The method comprises the following steps: acquiring multiple groups of standard sample data and multiple groups of to-be-evaluated sample data; performing standardization processing on the multiple groups of to-be-evaluated sample data and the multiple groups of standard sample data to obtain a target index parameter matrix; obtaining a comprehensive weight value of each evaluation index; performing clustering analysis on each index parameter in the target index parameter matrix to obtain a fuzzy index parameter matrix; according to the fuzzy index parameter matrix and the comprehensive weight value of each evaluation index, determining the pasting rate of each group of to-be-evaluated sample data; and determining the DC electric energy quality grade of the to-be-evaluated sample data according to the pasting progress range of the pasting progress. According to the method, the technical problems that the evaluation result is not objective enough, the error is relatively large and the electric energy quality level is difficult to reflect objectively due to subjective factors when the direct-current electric energy quality is evaluated by using a traditional method in the related technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality monitoring, and in particular to a direct current power quality assessment method and device based on multi-index complex correlation. Background Art

[0002] Under the background of new power system construction, DC loads such as large data centers, electric vehicle charging and swapping stations, and communication equipment are growing, and high-proportion, large-capacity distributed access of DC distributed power sources such as photovoltaics is becoming more and more obvious. The characteristics of DC load, source and storage are becoming more and more obvious. Typical power quality disturbances in DC distribution networks include voltage deviation, temporary sag, temporary swell, harmonics, interruption, fluctuation, etc. DC power quality problems will increase line loss, insulation aging, and interfere with communication. In severe cases, they will also cause serious accidents such as system resonance and controller malfunction, posing a threat to the safety and high-quality operation of the distribution network. With the development of high-tech industries, users' requirements for power supply reliability and power quality are increasing.

[0003] Compared with AC distribution networks, the power quality problem of DC distribution networks presents new characteristics: on the one hand, the zero-frequency characteristics of DC voltage and the structure of the distribution network itself, the coupling characteristics between different power quality indicators are different from those on the AC side; on the other hand, DC loads are more sensitive to power quality disturbances, and short-term and large changes in indicators may affect the accuracy of grade assessment. Existing power quality assessment methods mainly evaluate AC power quality. Traditional assessment methods for DC power quality do not consider the correlation and sensitivity between DC power indicators, while artificial intelligence assessment methods require more historical data samples.

[0004] At present, most evaluation methods do not consider the correlation between power indicators. The power quality of a certain power in the DC system is very likely to be coupled with other power qualities. Simply relying on traditional weight calculation methods will result in large errors in the evaluation results and cannot objectively reflect the power quality level. The intelligent evaluation method requires a large amount of historical data, has a complex structure, and an unknown process.

[0005] In view of the problem that when using traditional methods to evaluate DC power quality in the above-mentioned related technologies, subjective factors may lead to the evaluation results being not objective and having large errors, making it difficult to objectively reflect the power quality level, no effective solution has been proposed so far. Summary of the invention

[0006] The embodiment of the present invention provides a method and device for evaluating DC power quality based on multi-indicator complex correlation, so as to at least solve the technical problem in the related art that when using traditional methods to evaluate DC power quality, subjective factors may cause the evaluation results to be not objective, the errors are large, and it is difficult to objectively reflect the power quality level.

[0007] According to one aspect of an embodiment of the present invention, a method for evaluating direct current power quality based on multi-indicator complex correlation is provided, comprising: obtaining multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate direct current power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of evaluation indicators in different sample data, the evaluation indicators are used to evaluate the direct current power quality, the multiple groups of standard sample data represent different direct current power quality levels, the sample data include the standard sample data and the sample data to be evaluated; performing standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix; obtaining a comprehensive weight value of each of the multiple evaluation indicators, wherein The comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation index in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation index in evaluating the DC power quality from a subjective perspective; cluster analysis is performed on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix; the degree of approximation of each group of sample data to be evaluated is determined according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the degree of approximation is used to determine the DC power quality level corresponding to the sample data to be evaluated; the DC power quality level corresponding to the sample data to be evaluated is determined according to the range of approximation of the degree of approximation of each group of sample data to be evaluated.

[0008] Optionally, obtaining multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality includes: determining a first quantity of the evaluation indicators when evaluating the DC power quality; obtaining multiple groups of the standard sample data, and determining a second quantity of the standard sample data; determining that the difference between the first quantity and the second quantity is a third quantity; and obtaining a corresponding multiple groups of sample data to be evaluated according to the third quantity.

[0009] Optionally, the plurality of groups of sample data to be evaluated and the plurality of groups of standard sample data are standardized to obtain a target indicator parameter matrix, including: arranging the plurality of groups of sample data to be evaluated and the plurality of groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; and standardizing each indicator parameter in the initial indicator parameter matrix using a first formula to obtain the target indicator parameter matrix, wherein the first formula is: s airepresents the initial index parameter of the i-th evaluation index in the sample data of the a-th group, s′ ai represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the smallest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0010] Optionally, obtaining the comprehensive weight value of each of the multiple evaluation indicators includes: determining the objective weight value of each of the evaluation indicators using a second formula according to the complex correlation between the multiple evaluation indicators, wherein the second formula is: i represents the number of the evaluation indicator, n represents the first number of the evaluation indicator, W i represents the objective weight value of the i-th evaluation index, R i represents the complex correlation coefficient of the i-th evaluation index, and the complex correlation coefficient is used to quantify the coupling degree between the evaluation indexes; the importance of each evaluation index in evaluating the DC power quality is evaluated by using the hierarchical analysis method, and the subjective weight value of each evaluation index is determined according to the third formula, wherein the third formula is: u ij represents the weight scale between the i-th evaluation index and the j-th evaluation index, and the weight scale is used to reflect the comparison result of the importance between the two evaluation indicators, V i represents the subjective weight value of the i-th evaluation indicator; and uses the fourth formula to fit the subjective weight value and the objective weight value of each evaluation indicator to obtain the comprehensive weight value of the evaluation indicator, wherein the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0011] Optionally, performing cluster analysis on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix includes: performing cluster analysis on each of the indicator parameters in the target indicator parameter matrix, and determining the fuzzy indicator parameters according to a fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, min s′ iIt represents the smallest indicator parameter among the i-th evaluation indicators in the target indicator parameter matrix, and the fuzzy indicator parameter is a parameter obtained after processing the indicator parameter; the fuzzy indicator parameter matrix is ​​composed based on each fuzzy indicator parameter.

[0012] Optionally, determining the progress of pasting each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator includes: determining the progress of pasting each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator using a sixth formula, wherein the sixth formula is: ρH a Indicates the posting progress of the sample data in group a.

[0013] Optionally, the DC power quality level corresponding to the sample data to be evaluated is determined according to the progress range of the progress of each group of the sample data to be evaluated, including: when the progress is greater than zero and not greater than a first progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the first level; when the progress is greater than the first progress threshold and not greater than a second progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the second level, wherein the first progress threshold is less than the second progress threshold, and the first level is lower than the second level; when the progress is greater than the second progress threshold and not greater than a third progress threshold Under the condition that the DC power quality level corresponding to the sample data to be evaluated is determined to be the third level, wherein the second progress threshold is less than the third progress threshold, and the second level is lower than the third level; when the progress is greater than the third progress threshold and not greater than the fourth progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the fourth level, wherein the third progress threshold is less than the fourth progress threshold, and the third level is lower than the fourth level; when the progress is greater than the fourth progress threshold and not greater than 1, the DC power quality level corresponding to the sample data to be evaluated is determined to be the fifth level, wherein the fourth level is lower than the fifth level.

[0014] According to another aspect of an embodiment of the present invention, a DC power quality assessment device based on multi-indicator complex correlation is also provided, including: a first acquisition unit, used to acquire multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of the evaluation indicators in different sample data, the evaluation indicators are used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include the standard sample data and the sample data to be evaluated; a second acquisition unit, used to perform standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix; a third acquisition unit, used to obtain the comprehensive weight value of each of the multiple evaluation indicators, In the embodiment, the comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation index in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation index in evaluating the DC power quality from a subjective perspective; a fourth acquisition unit is used to perform cluster analysis on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix; a first determination unit is used to determine the degree of approximation of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the degree of approximation is used to determine the DC power quality level corresponding to the sample data to be evaluated; a second determination unit is used to determine the DC power quality level corresponding to the sample data to be evaluated according to the range of approximation of the degree of approximation of each group of sample data to be evaluated.

[0015] Optionally, the first acquisition unit includes: a first determination module, used to determine a first quantity of the evaluation indicators when evaluating the DC power quality; a second determination module, used to acquire multiple groups of the standard sample data and determine a second quantity of the standard sample data; a third determination module, used to determine that the difference between the first quantity and the second quantity is a third quantity; and the first acquisition module is used to acquire corresponding multiple groups of the sample data to be evaluated according to the third quantity.

[0016] Optionally, the second acquisition unit includes: a second acquisition module, which is used to arrange multiple groups of sample data to be evaluated and multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; a third acquisition module, which is used to standardize each indicator parameter in the initial indicator parameter matrix using a first formula to obtain the target indicator parameter matrix, wherein the first formula is: sai represents the initial index parameter of the i-th evaluation index in the sample data of the a-th group, s′ ai represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the smallest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0017] Optionally, the third acquisition unit includes: a fourth determination module, configured to determine the objective weight value of each evaluation indicator by using a second formula according to the complex correlation between the plurality of evaluation indicators, wherein the second formula is: i represents the number of the evaluation indicator, n represents the first number of the evaluation indicator, W i represents the objective weight value of the i-th evaluation index, R i represents the complex correlation coefficient of the i-th evaluation index, and the complex correlation coefficient is used to quantify the coupling degree between the evaluation indicators; a fifth determination module is used to evaluate the importance of each evaluation index when evaluating the DC power quality by using the hierarchical analysis method, and determine the subjective weight value of each evaluation index according to the third formula, wherein the third formula is: u ij represents the weight scale between the i-th evaluation index and the j-th evaluation index, and the weight scale is used to reflect the comparison result of the importance between the two evaluation indicators, V i represents the subjective weight value of the i-th evaluation indicator; a fourth acquisition module, used to fit the subjective weight value and the objective weight value of each evaluation indicator by using a fourth formula to obtain the comprehensive weight value of the evaluation indicator, wherein the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0018] Optionally, the fourth acquisition unit includes: a sixth determination module, configured to perform cluster analysis on each of the indicator parameters in the target indicator parameter matrix, and determine the fuzzy indicator parameter according to a fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, min s′ iRepresents the smallest indicator parameter among the i-th evaluation indicators in the target indicator parameter matrix, and the fuzzy indicator parameter is a parameter obtained after processing the indicator parameter; a fifth acquisition module is used to form the fuzzy indicator parameter matrix based on each fuzzy indicator parameter.

[0019] Optionally, the first determination unit includes: a seventh determination module, configured to determine the pasting progress of each group of sample data to be evaluated using a sixth formula according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sixth formula is: ρH a Indicates the posting progress of the sample data in group a.

[0020] Optionally, the second determination unit includes: an eighth determination module, which is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the first level when the progress of the pasting is greater than zero and not greater than the first progress threshold; a ninth determination module, which is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the second level when the progress of the pasting is greater than the first progress threshold and not greater than the second progress threshold, wherein the first progress threshold is less than the second progress threshold, and the first level is lower than the second level; a tenth determination module, which is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the second level when the progress of the pasting is greater than the second progress threshold and not greater than the third progress threshold. The DC power quality level is the third level, wherein the second progress threshold is less than the third progress threshold, and the second level is lower than the third level; an eleventh determination module is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the fourth level when the progress is greater than the third progress threshold and not greater than the fourth progress threshold, wherein the third progress threshold is less than the fourth progress threshold, and the third level is lower than the fourth level; a twelfth determination module is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the fifth level when the progress is greater than the fourth progress threshold and not greater than 1, wherein the fourth level is lower than the fifth level.

[0021] According to another aspect of an embodiment of the present invention, a DC power quality assessment system based on multi-indicator complex correlation is also provided, and the DC power quality assessment system based on multi-indicator complex correlation uses any of the above-mentioned DC power quality assessment methods based on multi-indicator complex correlation.

[0022] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned direct current power quality assessment methods based on multi-indicator complex correlation.

[0023] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein the program, when running, executes any one of the above-mentioned direct current power quality assessment methods based on multi-index complex correlation.

[0024] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned direct current power quality assessment methods based on multi-indicator complex correlation.

[0025] In an embodiment of the present invention, multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the quality of direct current power are obtained, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple index parameters, the index parameters are specific parameter values ​​of the evaluation index in different sample data, the evaluation index is used to evaluate the quality of direct current power, the multiple groups of standard sample data represent different direct current power quality levels, and the sample data include standard sample data and sample data to be evaluated; the multiple groups of sample data to be evaluated and the multiple groups of standard sample data are standardized to obtain a target index parameter matrix; the comprehensive weight value of each evaluation index in the multiple evaluation indexes is obtained, wherein the comprehensive weight value is the comprehensive weight value of the target The objective weight value and the subjective weight value are fitted to obtain the weight value, the objective weight value reflects the importance of the evaluation index in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation index in evaluating the DC power quality from a subjective perspective; cluster analysis is performed on the indicator parameters in the target indicator parameter matrix to obtain the fuzzy indicator parameter matrix; the applicability of each group of sample data to be evaluated is determined according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the applicability is used to determine the DC power quality level corresponding to the sample data to be evaluated; the DC power quality level corresponding to the sample data to be evaluated is determined according to the applicability range of the applicability of each group of sample data to be evaluated. Through the above technical scheme, the purpose of comprehensively analyzing the weights of evaluation indicators from both subjective and objective perspectives, then combining the weights of evaluation indicators and rating standards, using matter-element analysis to perform cluster analysis, and finally obtaining the power quality evaluation results is achieved. The coupling correlation between evaluation indicators is taken into account through correlation analysis to determine the objective weights of evaluation indicators, thereby achieving the technical effect of obtaining more objective evaluation results when evaluating DC power quality, improving the accuracy of power quality evaluation, and thus solving the technical problem in related technologies that when using traditional methods to evaluate DC power quality, subjective factors may lead to insufficient objectivity of evaluation results, large errors, and difficulty in objectively reflecting the power quality level. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 It is a hardware structure block diagram of a mobile terminal of a DC power quality assessment method based on multi-index complex correlation according to an embodiment of the present invention;

[0028] Figure 2 is a flow chart of a DC power quality assessment method based on multi-index complex correlation according to an embodiment of the present invention;

[0029] Figure 3 is a flowchart of an optional DC power quality assessment method based on multi-indicator complex correlation according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of a direct current power quality assessment device based on multi-index complex correlation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] As introduced in the background technology, when using traditional methods in related technologies to evaluate DC power quality, subjective factors may lead to the evaluation results being not objective and with large errors, making it difficult to objectively reflect the power quality level. In view of the above defects, a DC power quality evaluation method and device based on multi-index complex correlation is provided in an embodiment of the present invention.

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a DC power quality assessment method based on multi-index complex correlation according to an embodiment of the present invention. Figure 1As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0036] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the DC power quality assessment method based on multi-index complex correlation in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Cont ro11er, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] According to an embodiment of the present invention, a method embodiment of a DC power quality assessment method based on multi-indicator complex correlation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 2is a flow chart of a DC power quality assessment method based on multi-index complex correlation according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0039] Step S202, obtaining multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of the evaluation indicators in different sample data, the evaluation indicators are used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include standard sample data and sample data to be evaluated.

[0040] In this embodiment, the evaluation indicators for evaluating the DC power quality can be determined first, and then multiple groups of sample data with the same number as the evaluation indicators can be obtained. The obtained sample data include standard sample data and sample data to be evaluated. The standard sample data here correspond to different DC power quality levels, and the sample data to be evaluated are data to be evaluated whose DC power quality needs to be evaluated.

[0041] According to the above embodiment of the present invention, in the above step S202, multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality are obtained, including: determining a first number of evaluation indicators when evaluating the DC power quality; obtaining multiple groups of standard sample data, and determining a second number of standard sample data; determining that the difference between the first number and the second number is a third number; and obtaining corresponding multiple groups of sample data to be evaluated according to the third number.

[0042] Combine the following Figure 3 The above embodiments of the present invention are described. Figure 3 The present invention is a flowchart of an optional method for evaluating direct current power quality based on multi-index complex correlation according to an embodiment of the present invention.

[0043] like Figure 3 As shown, the evaluation index for evaluating the DC power quality (i.e., the DC power quality index) can be determined first, and then the standard sample data can be determined. The standard sample data here may be obtained from a large number of experiments and experiences, and then the corresponding number of sample data to be evaluated is collected according to the number of evaluation indicators and the number of standard sample data, so that the sample data (including standard sample data and sample data to be evaluated) is consistent with the number of evaluation indicators.

[0044] For example, as shown in Table 1 below, 9 evaluation indicators for evaluating DC power quality are shown (in the specific evaluation process, the corresponding evaluation indicators can be selected in combination with the actual situation. This is just an example without specific restrictions). According to these 9 evaluation indicators, four DC power quality levels of excellent, good, qualified and poor can be determined according to the historical operating status (of course, in the specific evaluation process, other multiple quality levels can also be obtained in combination with the actual situation. This is not specifically limited here). In this way, four groups of standard sample data corresponding to the four quality levels can be obtained first, as shown in Table 2 below. Then, 6 groups of sample data to be evaluated can be collected. Here, the number of sample data and evaluation indicators is kept consistent in order to form the collected sample data to be evaluated and the standard sample data into a square matrix for subsequent processing. In the case where the number of sample data and evaluation indicators is inconsistent, the technical solution provided by the above embodiment of the present invention can also be used to evaluate the DC power quality of the sample data to be evaluated. This is not specifically limited here.

[0045] Table 1

[0046]

[0047] Table 2

[0048] C1 / ％ C2 / ％ C3 / s C4 C5 C6 / (times / s) C7 / ％ C8 / (V / s) C9 / ％ high quality <![CDATA[s 11 ]]> <![CDATA[s 12 ]]> <![CDATA[s 13 ]]> <![CDATA[s 14 ]]> <![CDATA[s 15 ]]> <![CDATA[s 16 ]]> <![CDATA[s 17 ]]> <![CDATA[s 18 ]]> <![CDATA[s 19 ]]> good <![CDATA[s 21 ]]> <![CDATA[s 22 ]]> <![CDATA[s 23 ]]> <![CDATA[s 24 ]]> <![CDATA[s 25 ]]> <![CDATA[s 26 ]]> <![CDATA[s 27 ]]> <![CDATA[s 28 ]]> <![CDATA[s 29 ]]> qualified <![CDATA[s 31 ]]> <![CDATA[s 32 ]]> <![CDATA[s 33 ]]> <![CDATA[s 34 ]]> <![CDATA[s 35 ]]> <![CDATA[s 36 ]]> <![CDATA[s 37 ]]> <![CDATA[s 38 ]]> <![CDATA[s 39 ]]> Poor <![CDATA[s 41 ]]> <![CDATA[s 42 ]]> <![CDATA[s 43 ]]> <![CDATA[s 44 ]]> <![CDATA[s 45 ]]> <![CDATA[s 46 ]]> <![CDATA[s 47 ]]> <![CDATA[s 48 ]]> <![CDATA[s 49 ]]>

[0049] Step S204, performing standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix.

[0050] As above Figure 3 As shown, in this embodiment, the acquired standard sample data and the sample data to be evaluated can be presented in a matrix form, and then the indicator parameters in each sample data are standardized to obtain a target indicator parameter matrix.

[0051] According to the above embodiment of the present invention, in the above step S204, the multiple groups of sample data to be evaluated and the multiple groups of standard sample data are standardized to obtain a target indicator parameter matrix, including: arranging the multiple groups of sample data to be evaluated and the multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; using a first formula to standardize each indicator parameter in the initial indicator parameter matrix to obtain a target indicator parameter matrix, wherein the first formula is: s ai represents the initial indicator parameter of the i-th evaluation indicator in the a-th group of sample data, s′ ai It represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the minimum initial indicator parameter of the i-th evaluation indicator in the initial indicator parameter matrix, max(si ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0052] Specifically, the acquired standard sample data and the sample data to be evaluated can be written into the following matrix: Wherein, a=1, 2, ..., N, a represents the number of sample data, N represents the total number of sample data, i=1, 2, ..., n (in the embodiment provided by the present invention, n is taken as 9 as an example for explanation), i represents the number of evaluation index, n represents the total number of evaluation index, s ai represents the initial indicator parameter of the i-th evaluation indicator in the a-th group of sample data; in order to avoid inaccurate evaluation results caused by dimensional differences between indicators, it is necessary to standardize the data and calculate the standardized matrix of the indicator data (i.e., the target indicator parameter matrix) S′ ai : s′ ai It represents the index parameter of the i-th evaluation index in the sample data of group a after standardization. Since the selected evaluation indicators are all positive indicators, the standardization formula is: In the formula, min(s i ) represents the minimum initial indicator parameter of the i-th evaluation indicator in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0053] Step S206, obtaining a comprehensive weight value of each evaluation indicator in the multiple evaluation indicators, wherein the comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from a subjective perspective.

[0054] In this embodiment, the comprehensive weight of each evaluation index may be comprehensively evaluated from both subjective and objective perspectives, that is, the importance of each evaluation index for the evaluation when evaluating the DC power quality.

[0055] According to the above embodiment of the present invention, in the above step S206, obtaining the comprehensive weight value of each evaluation indicator in the multiple evaluation indicators includes: determining the objective weight value of each evaluation indicator by using the second formula according to the complex correlation between the multiple evaluation indicators, wherein the second formula is: i represents the number of the evaluation index, n represents the first number of the evaluation index, and W i represents the objective weight value of the i-th evaluation indicator, R iIt represents the complex correlation coefficient of the i-th evaluation index, which is used to quantify the coupling degree between the evaluation indexes. The importance of each evaluation index in evaluating the DC power quality is evaluated by using the hierarchical analysis method, and the subjective weight value of each evaluation index is determined according to the third formula, where the third formula is: u ij Represents the weight scale between the i-th evaluation index and the j-th evaluation index. The weight scale is used to reflect the comparison result of the importance between the two evaluation indicators. V i represents the subjective weight value of the i-th evaluation indicator; the subjective weight value and the objective weight value of each evaluation indicator are fitted using the fourth formula to obtain the comprehensive weight value of the evaluation indicator, where the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0056] As above Figure 3 As shown in the figure, the objective weight value of each evaluation indicator can be calculated through the complex correlation between the evaluation indicators, and the subjective weight value of each evaluation indicator can be calculated using the hierarchical analysis method. Then, the subjective weight value and the objective weight value of each evaluation indicator are fitted to obtain the comprehensive weight value of the evaluation indicator.

[0057] Specifically, the objective weight value of each evaluation indicator can be calculated by the complex correlation between the evaluation indicators. The specific calculation process is as follows: First, use the formula: Calculate the multiple correlation coefficient R of each evaluation index i , where S′ ai The mean value of the matrix, S′ ai Matrix cross out S j The linear combination of the remaining column matrices after the corresponding columns, the correlation coefficient can better reflect the correlation between indicators and quantify the coupling relationship between indicators; then the reciprocal of the complex correlation coefficient is selected as the score: After normalization, the objective weight value is obtained as follows: W i Represents the objective weight value of the i-th evaluation indicator.

[0058] Then, the analytic hierarchy process can be used to calculate the subjective weight values ​​of each evaluation indicator. The specific process is as follows: First, relevant professionals judge the importance of each evaluation indicator and construct a judgment matrix G between indicators: In the formula, u bc is the scale value, b and c are 1, 2, ..., 9, about the scale value u bc The meaning of is explained in Table 3 below, which shows the corresponding relationship between each scale value and the degree of importance.

[0059] Table 3

[0060] Scale meaning 1 b and c are equally important 3 b is slightly more important than c 5 b is obviously more important than c 7 b is more important than c 9 b is extremely more important than c 2，4，6，8 Corresponding to the intermediate situation of the above adjacent judgments reciprocal <![CDATA[If the judgment of b compared to c is u bc , then the judgment of c compared to b is u cb = 1 / u bc >

[0061] The scale value here is used to quantify the relative importance between the two evaluation indicators. For example, in u bc When the scale value of is 1, it means that the bth evaluation indicator and the cth evaluation indicator are equally important. bc When the scale value of is 3, it means that the bth evaluation indicator is slightly more important than the cth evaluation indicator. bc The scale value is When , it means that the cth evaluation indicator is slightly more important than the bth evaluation indicator. It should be noted that when the scale value is greater than 1, the evaluation indicator with the first subscript is more important than the evaluation indicator with the second subscript. When the scale value is less than 1, the evaluation indicator with the second subscript is more important than the evaluation indicator with the first subscript. The specific degree of importance is determined by the specific size of the scale value.

[0062] Then use the formula: Calculate the consistency index value, where λ max is the maximum eigenvalue of the judgment matrix. When the number of evaluation indicators n=9, the average consistency index RI=1.45. When CR≤0.1, the consistency of the matrix is ​​considered acceptable, otherwise the judgment matrix needs to be further adjusted. After that, the square root method can be used to calculate the subjective weight of each evaluation indicator. The elements in G are multiplied row by row and then taken to the nth power to obtain a new vector. The obtained vector is normalized to obtain the subjective weight: In the formula, u ij Represents the weight scale between the i-th evaluation index and the j-th evaluation index. The weight scale is used to reflect the comparison result of the importance between the two evaluation indicators. V i Represents the subjective weight value of the i-th evaluation indicator.

[0063] Finally, after obtaining the objective weight value and subjective weight value of each evaluation indicator, the formula can be used: Calculate and determine the comprehensive weight value of each evaluation index, where w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0064] Step S208, performing cluster analysis on each indicator parameter in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix.

[0065] In this embodiment, the fuzzy matter-element model can be used to perform cluster analysis on the indicator parameters corresponding to each evaluation indicator in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix.

[0066] The fuzzy matter-element model here is an evaluation method that combines fuzzy mathematics and matter-element analysis. It comprehensively considers the fuzziness of things and the essential connection between objective things. In this embodiment, the fuzzy matter-element model can be used to convert the actual measurement value of the evaluation index into a fuzzy set to deal with the uncertainty and fuzziness in the real world, and cluster the evaluation indexes to determine the pros and cons of each sample relative to the standard sample, so as to use the processed fuzzy index parameter matrix to comprehensively evaluate the DC power quality.

[0067] According to the above embodiment of the present invention, in the above step S208, cluster analysis is performed on each indicator parameter in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix, including: cluster analysis is performed on each indicator parameter in the target indicator parameter matrix, and the fuzzy indicator parameter is determined according to the fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, min s′ i It represents the smallest indicator parameter of the i-th evaluation indicator in the target indicator parameter matrix, and the fuzzy indicator parameter is the parameter obtained after processing the indicator parameter; a fuzzy indicator parameter matrix is ​​formed based on each fuzzy indicator parameter.

[0068] Specifically, S′ can be ai The elements in are processed as follows: In the formula, μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, min s′ i It represents the smallest indicator parameter of the i-th evaluation indicator in the target indicator parameter matrix. The fuzzy indicator parameter is the parameter obtained after processing the indicator parameter. Then, it can be calculated based on μ ai Determine the difference square composite fuzzy matter-element matrix B:

[0069] Step S210, determining the progress of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the progress of each group of sample data to be evaluated is used to determine the DC power quality level corresponding to the sample data to be evaluated.

[0070] As above Figure 3 As shown, the progress of each group of sample data to be evaluated can be determined according to the fuzzy indicator parameters in the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, and the progress can be used to evaluate the DC power quality level corresponding to each sample data to be evaluated.

[0071] According to the above embodiment of the present invention, in the above step S210, the progress of pasting each group of sample data to be evaluated is determined according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, including: determining the progress of pasting each group of sample data to be evaluated by using the sixth formula according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sixth formula is: ρH a Indicates the posting progress of the a-th group of sample data.

[0072] As above Figure 3 As shown, the European posting schedule ρH can be used a As the evaluation criterion, the comprehensive weight value w i Substituting it into consideration, the calculation method is: ρH a It represents the posting progress of the a-th group of sample data, that is, the comprehensive weighted score of the a-th group of sample data.

[0073] Step S212: determining the DC power quality level corresponding to the sample data to be evaluated according to the pasting progress range of each group of sample data to be evaluated.

[0074] As above Figure 3 As shown, the DC power quality level corresponding to each sample data to be evaluated can be determined according to the progress of each group of sample data to be evaluated.

[0075] According to the above embodiment of the present invention, in the above step S212, the DC power quality level corresponding to the sample data to be evaluated is determined according to the progress range of the progress of each group of sample data to be evaluated, including: when the progress is greater than zero and not greater than the first progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the first level; when the progress is greater than the first progress threshold and not greater than the second progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the second level, wherein the first progress threshold is less than the second progress threshold, and the first level is lower than the second level; when the progress is greater than the second progress threshold and not greater than the second progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the second level. When the progress is greater than the third progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the third level, wherein the second progress threshold is less than the third progress threshold, and the second level is lower than the third level; when the progress is greater than the third progress threshold and not greater than the fourth progress threshold, the DC power quality level corresponding to the sample data to be evaluated is determined to be the fourth level, wherein the third progress threshold is less than the fourth progress threshold, and the third level is lower than the fourth level; when the progress is greater than the fourth progress threshold and not greater than 1, the DC power quality level corresponding to the sample data to be evaluated is determined to be the fifth level, wherein the fourth level is lower than the fifth level.

[0076] Specifically, the pasting progress corresponding to the several groups of standard sample data obtained in the above steps are A1, A2, A3, and A4 respectively. According to the pasting progress of the standard sample data, [0, 1] can be divided into 5 intervals, and then the DC power quality level corresponding to the sample data to be evaluated can be determined according to the interval in which the pasting progress of each sample data to be evaluated is located. The correspondence between the pasting progress interval and the DC power quality level is shown in Table 4 below.

[0077] Table 4

[0078] Excellent high quality good qualified Poor <![CDATA[pH a ]]> <![CDATA[(A1,1)]]> <![CDATA[(A2,A1]]]> <![CDATA[(A3,A2]]]> <![CDATA[(A4,A3]]]> <![CDATA[(0,A4]]]>

[0079] The five levels shown in Table 4 above: excellent, high quality, good, qualified, and poor are the fifth level, fourth level, third level, second level, and first level respectively. The higher the level, the better the DC power quality.

[0080] It should be noted that only an exemplary method of classifying DC power quality levels is provided here, and DC power quality levels may also be classified in other forms, which are not specifically limited here.

[0081] It can be seen from the above that through the technical solution provided by the above embodiment of the present invention, multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality can be obtained, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of the evaluation indicators in different sample data, the evaluation indicators are used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include standard sample data and sample data to be evaluated; the multiple groups of sample data to be evaluated and the multiple groups of standard sample data are standardized to obtain a target indicator parameter matrix; the comprehensive weight value of each evaluation indicator in the multiple evaluation indicators is obtained, wherein the comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from a subjective perspective. The importance of evaluating the DC power quality; clustering analysis is performed on each indicator parameter in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix; the sticking progress of each group of sample data to be evaluated is determined according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sticking progress is used to judge the DC power quality level corresponding to the sample data to be evaluated; the DC power quality level corresponding to the sample data to be evaluated is determined according to the sticking progress range of the sticking progress of each group of sample data to be evaluated, thereby achieving the purpose of comprehensively analyzing the weights of the evaluation indicators from both subjective and objective perspectives, and then combining the weights of the evaluation indicators and the rating standards, using the physical element analysis method to perform cluster analysis, and finally obtaining the power quality evaluation results, realizing the coupling correlation between the evaluation indicators is taken into account through correlation analysis, so as to determine the objective weight of the evaluation indicators, and then obtaining a more objective evaluation result when evaluating the DC power quality, thereby improving the accuracy of the power quality evaluation.

[0082] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem in the related art that when using traditional methods to evaluate the quality of DC power, subjective factors may lead to the evaluation results being not objective, with large errors, and it is difficult to objectively reflect the power quality level.

[0083] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0084] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0085] According to an embodiment of the present invention, a DC power quality assessment device based on multiple index complex correlation is provided for implementing the DC power quality assessment method based on multiple index complex correlation. Figure 4 is a schematic diagram of a DC power quality assessment device based on multi-index complex correlation according to an embodiment of the present invention, such as Figure 4 As shown, the device includes: a first acquisition unit 401, a second acquisition unit 403, a third acquisition unit 405, a fourth acquisition unit 407, a first determination unit 409 and a second determination unit 411. The DC power quality assessment device based on multi-index complex correlation is described in detail below.

[0086] The first acquisition unit 401 is used to acquire multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of the evaluation indicators in different sample data, the evaluation indicators are used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include standard sample data and sample data to be evaluated.

[0087] The second acquisition unit 403 is used to perform standardization processing on multiple groups of sample data to be evaluated and multiple groups of standard sample data to obtain a target indicator parameter matrix.

[0088] The third acquisition unit 405 is used to obtain the comprehensive weight value of each evaluation indicator in the multiple evaluation indicators, wherein the comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation indicator when evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation indicator when evaluating the DC power quality from a subjective perspective.

[0089] The fourth acquisition unit 407 is used to perform cluster analysis on each indicator parameter in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix.

[0090] The first determination unit 409 is used to determine the progress of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the progress of each group of sample data to be evaluated is used to determine the DC power quality level corresponding to the sample data to be evaluated.

[0091] The second determining unit 411 is used to determine the DC power quality level corresponding to the sample data to be evaluated according to the pasting progress range of each group of sample data to be evaluated.

[0092] It should be noted here that the above-mentioned first acquisition unit 401, second acquisition unit 403, third acquisition unit 405, fourth acquisition unit 407, first determination unit 409 and second determination unit 411 correspond to steps S202 to S212 in the above-mentioned embodiments, and the six units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0093] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, a first acquisition unit can be used to acquire multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to be evaluated for DC power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple index parameters, the index parameters are specific parameter values ​​of the evaluation index in different sample data, the evaluation index is used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include standard sample data and sample data to be evaluated; then the second acquisition unit is used to perform standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target index parameter matrix; then the third acquisition unit is used to obtain the comprehensive weight value of each evaluation index in the multiple evaluation indexes, wherein the comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value, the objective weight value reflects the importance of the evaluation index in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation index in evaluating the DC power quality from a subjective perspective. The importance of evaluating the DC power quality; then the fourth acquisition unit is used to perform cluster analysis on the indicator parameters in the target indicator parameter matrix to obtain the fuzzy indicator parameter matrix; then the first determination unit is used to determine the degree of approximation of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the approximation is used to judge the DC power quality level corresponding to the sample data to be evaluated; finally, the second determination unit is used to determine the DC power quality level corresponding to the sample data to be evaluated according to the approximation range of the approximation of the approximation of each group of sample data to be evaluated, thereby achieving the purpose of comprehensively analyzing the weights of the evaluation indicators from both subjective and objective perspectives, and then combining the weights of the evaluation indicators and the rating standards, using the physical element analysis method to perform cluster analysis, and finally obtaining the power quality evaluation results, realizing the coupling correlation between the evaluation indicators is taken into account through correlation analysis, so as to determine the objectivity weight of the evaluation indicators, and then obtaining a more objective evaluation result when evaluating the DC power quality, thereby improving the accuracy of the power quality evaluation.

[0094] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem in the related art that when using traditional methods to evaluate the quality of DC power, subjective factors may lead to the evaluation results being not objective, with large errors, and it is difficult to objectively reflect the power quality level.

[0095] Optionally, the first acquisition unit includes: a first determination module, used to determine a first number of evaluation indicators when evaluating the DC power quality; a second determination module, used to obtain multiple groups of standard sample data and determine a second number of standard sample data; a third determination module, used to determine that the difference between the first number and the second number is a third number; the first acquisition module is used to obtain the corresponding multiple groups of sample data to be evaluated according to the third number.

[0096] Optionally, the second acquisition unit includes: a second acquisition module, which is used to arrange multiple groups of sample data to be evaluated and multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; a third acquisition module, which is used to standardize each indicator parameter in the initial indicator parameter matrix using the first formula to obtain a target indicator parameter matrix, wherein the first formula is: s ai represents the initial indicator parameter of the i-th evaluation indicator in the a-th group of sample data, s′ ai It represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the minimum initial indicator parameter of the i-th evaluation indicator in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0097] Optionally, the third acquisition unit includes: a fourth determination module, configured to determine the objective weight value of each evaluation indicator by using a second formula according to the complex correlation between the multiple evaluation indicators, wherein the second formula is: i represents the number of the evaluation index, n represents the first number of the evaluation index, and W i represents the objective weight value of the i-th evaluation indicator, R i represents the complex correlation coefficient of the i-th evaluation index, and the complex correlation coefficient is used to quantify the coupling degree between the evaluation indexes; the fifth determination module is used to evaluate the importance of each evaluation index in evaluating the DC power quality by using the hierarchical analysis method, and determine the subjective weight value of each evaluation index according to the third formula, wherein the third formula is: u ij Represents the weight scale between the i-th evaluation index and the j-th evaluation index. The weight scale is used to reflect the comparison result of the importance between the two evaluation indicators. V i represents the subjective weight value of the i-th evaluation indicator; a fourth acquisition module is used to fit the subjective weight value and the objective weight value of each evaluation indicator using the fourth formula to obtain the comprehensive weight value of the evaluation indicator, wherein the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0098] Optionally, the third acquisition unit includes: a third acquisition module, which is used to arrange multiple groups of sample data to be evaluated and multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; a fourth acquisition module, which is used to standardize each indicator parameter in the initial indicator parameter matrix using a fourth formula to obtain a target indicator parameter matrix, wherein the fourth formula is: s ai represents the initial indicator parameter of the i-th evaluation indicator in the a-th group of sample data, s′ ai It represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the minimum initial indicator parameter of the i-th evaluation indicator in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0099] Optionally, the fourth acquisition unit includes: a sixth determination module, configured to perform cluster analysis on each indicator parameter in the target indicator parameter matrix, and determine the fuzzy indicator parameter according to a fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, It represents the smallest indicator parameter of the i-th evaluation indicator in the target indicator parameter matrix, and the fuzzy indicator parameter is the parameter obtained after processing the indicator parameter; the fifth acquisition module is used to form a fuzzy indicator parameter matrix based on each fuzzy indicator parameter.

[0100] Optionally, the first determination unit includes: a seventh determination module, which is used to determine the pasting progress of each group of sample data to be evaluated by using the sixth formula according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sixth formula is: ρH a Indicates the posting progress of the a-th group of sample data.

[0101] Optionally, the second determination unit includes: an eighth determination module, which is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the first level when the progress of pasting is greater than zero and not greater than the first progress threshold; a ninth determination module, which is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the second level when the progress of pasting is greater than the first progress threshold and not greater than the second progress threshold, wherein the first progress threshold is less than the second progress threshold, and the first level is lower than the second level; a tenth determination module, which is used to determine that the sample data to be evaluated is the second level when the progress of pasting is greater than the second progress threshold and not greater than the third progress threshold. The corresponding DC power quality level is the third level, wherein the second progress threshold is less than the third progress threshold, and the second level is lower than the third level; an eleventh determination module is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the fourth level when the progress is greater than the third progress threshold but not greater than the fourth progress threshold, wherein the third progress threshold is less than the fourth progress threshold, and the third level is lower than the fourth level; a twelfth determination module is used to determine that the DC power quality level corresponding to the sample data to be evaluated is the fifth level when the progress is greater than the fourth progress threshold but not greater than 1, wherein the fourth level is lower than the fifth level.

[0102] According to another aspect of an embodiment of the present invention, a DC power quality assessment system based on multi-indicator complex correlation is further provided. The DC power quality assessment system based on multi-indicator complex correlation uses any of the above-mentioned DC power quality assessment methods based on multi-indicator complex correlation.

[0103] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned direct current power quality assessment methods based on multi-indicator complex correlation.

[0104] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0105] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the DC power quality, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of the evaluation indicators in different sample data, the evaluation indicators are used to evaluate the DC power quality, the multiple groups of standard sample data represent different DC power quality levels, and the sample data include standard sample data and sample data to be evaluated; performing standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix; obtaining a comprehensive matrix of each evaluation indicator in the multiple evaluation indicators. The comprehensive weight value is a weight value obtained by fitting the objective weight value and the subjective weight value. The objective weight value reflects the importance of the evaluation index in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation index in evaluating the DC power quality from a subjective perspective. Cluster analysis is performed on the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix. The fitting progress of each group of sample data to be evaluated is determined according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the fitting progress is used to determine the DC power quality level corresponding to the sample data to be evaluated. The DC power quality level corresponding to the sample data to be evaluated is determined according to the fitting progress range of each group of sample data to be evaluated.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: determining a first number of evaluation indicators when evaluating DC power quality; obtaining multiple groups of standard sample data and determining a second number of standard sample data; determining the difference between the first number and the second number as a third number; and obtaining corresponding multiple groups of sample data to be evaluated according to the third number.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: arranging multiple groups of sample data to be evaluated and multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; and standardizing each indicator parameter in the initial indicator parameter matrix using a first formula to obtain a target indicator parameter matrix, wherein the first formula is: s ai represents the initial indicator parameter of the i-th evaluation indicator in the a-th group of sample data, s′ ai It represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the minimum initial indicator parameter of the i-th evaluation indicator in the initial indicator parameter matrix, max(s i) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

[0108] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining the objective weight value of each evaluation indicator by using a second formula according to the complex correlation between the multiple evaluation indicators, wherein the second formula is: i represents the number of the evaluation index, n represents the first number of the evaluation index, and W i represents the objective weight value of the i-th evaluation indicator, R i It represents the complex correlation coefficient of the i-th evaluation index, which is used to quantify the coupling degree between the evaluation indexes. The importance of each evaluation index in evaluating the DC power quality is evaluated by using the hierarchical analysis method, and the subjective weight value of each evaluation index is determined according to the third formula, where the third formula is: u ij Represents the weight scale between the i-th evaluation index and the j-th evaluation index. The weight scale is used to reflect the comparison result of the importance between the two evaluation indicators. V i represents the subjective weight value of the i-th evaluation indicator; the subjective weight value and the objective weight value of each evaluation indicator are fitted using the fourth formula to obtain the comprehensive weight value of the evaluation indicator, where the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

[0109] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: performing cluster analysis on each indicator parameter in the target indicator parameter matrix, and determining the fuzzy indicator parameter according to the fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, min s′ i It represents the smallest indicator parameter of the i-th evaluation indicator in the target indicator parameter matrix, and the fuzzy indicator parameter is the parameter obtained after processing the indicator parameter; a fuzzy indicator parameter matrix is ​​formed based on each fuzzy indicator parameter.

[0110] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining the posting progress of each group of sample data to be evaluated using the sixth formula according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sixth formula is: ρH a Indicates the posting progress of the a-th group of sample data.

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: when the pasting progress is greater than zero and not greater than a first pasting progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the first level; when the pasting progress is greater than the first pasting progress threshold and not greater than the second pasting progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the second level, wherein the first pasting progress threshold is less than the second pasting progress threshold, and the first level is lower than the second level; when the pasting progress is greater than the second pasting progress threshold and not greater than the third pasting progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the third level, wherein the second pasting progress threshold is less than the third pasting progress threshold, and the second level is lower than the third level; when the pasting progress is greater than the third pasting progress threshold and not greater than the fourth pasting progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the fourth level, wherein the third pasting progress threshold is less than the fourth pasting progress threshold, and the third level is lower than the fourth level; when the pasting progress is greater than the fourth pasting progress threshold and not greater than 1, determining that the DC power quality level corresponding to the sample data to be evaluated is the fifth level, wherein the fourth level is lower than the fifth level.

[0112] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned direct current power quality assessment methods based on multi-indicator complex correlation is executed.

[0113] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, which, when executed by a processor, execute any one of the above-mentioned direct current power quality assessment methods based on multi-indicator complex correlation.

[0114] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0115] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0118] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A DC power quality assessment method based on multi-index complex correlation, characterized in that: include: Acquire multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the quality of direct current power, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple index parameters, the index parameters are specific parameter values ​​of evaluation indicators in different sample data, the evaluation indicators are used to evaluate the quality of the direct current power, the multiple groups of standard sample data represent different direct current power quality levels, and the sample data include the standard sample data and the sample data to be evaluated; Performing standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix; Obtaining a comprehensive weight value of each of the multiple evaluation indicators, wherein the comprehensive weight value is a weight value obtained by fitting an objective weight value and a subjective weight value, the objective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation indicator in evaluating the DC power quality from a subjective perspective; Performing cluster analysis on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix; Determine the progress of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the progress of each group of sample data to be evaluated is used to determine the DC power quality level corresponding to the sample data to be evaluated; The DC power quality level corresponding to the sample data to be evaluated is determined according to the range of the degree of the degree of the degree of the degree of the sample data to be evaluated in each group.

2. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: Obtain multiple sets of standard sample data and multiple sets of sample data to be evaluated for DC power quality evaluation, including: Determining a first quantity of the evaluation indicators when evaluating the DC power quality; Acquire a plurality of sets of the standard sample data, and determine a second quantity of the standard sample data; determining a difference between the first amount and the second amount as a third amount; A corresponding plurality of groups of the sample data to be evaluated are obtained according to the third quantity.

3. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: The plurality of groups of sample data to be evaluated and the plurality of groups of standard sample data are standardized to obtain a target indicator parameter matrix, including: Arranging the multiple groups of sample data to be evaluated and the multiple groups of standard sample data in a matrix form to obtain an initial indicator parameter matrix; The target indicator parameter matrix is ​​obtained by standardizing each indicator parameter in the initial indicator parameter matrix using a first formula, wherein the first formula is: s ai represents the initial index parameter of the i-th evaluation index in the sample data of the a-th group, s′ ai represents the index parameter of the i-th evaluation index in the sample data of group a after standardization, min(s i ) represents the smallest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix, max(s i ) represents the largest initial indicator parameter among the i-th evaluation indicators in the initial indicator parameter matrix.

4. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: Obtaining a comprehensive weight value of each of the multiple evaluation indicators, including: The objective weight value of each evaluation indicator is determined by using a second formula according to the complex correlation between the multiple evaluation indicators, wherein the second formula is: i represents the number of the evaluation indicator, n represents the first number of the evaluation indicator, W i represents the objective weight value of the ith evaluation indicator, R represents the complex correlation coefficient of the ith evaluation indicator, and the complex correlation coefficient is used to quantify the coupling degree between the evaluation indicators; The importance of each evaluation index in evaluating the DC power quality is evaluated by using the hierarchical analysis method, and the subjective weight value of each evaluation index is determined according to the third formula, wherein the third formula is: u ij represents the weight scale between the i-th evaluation index and the j-th evaluation index, and the weight scale is used to reflect the comparison result of the importance between the two evaluation indicators, V i represents the subjective weight value of the i-th evaluation indicator; The subjective weight value and the objective weight value of each evaluation indicator are fitted using the fourth formula to obtain the comprehensive weight value of the evaluation indicator, wherein the fourth formula is: w i Represents the comprehensive weight value of the i-th evaluation indicator.

5. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: Performing cluster analysis on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix, including: Perform cluster analysis on each of the indicator parameters in the target indicator parameter matrix, and determine the fuzzy indicator parameters according to the fifth formula, wherein the fifth formula is: μ ai represents the fuzzy index parameter of the i-th evaluation index in the a-th group of sample data in the fuzzy index parameter matrix, mins′ i represents the smallest index parameter among the i-th evaluation index in the target index parameter matrix, and the fuzzy index parameter is a parameter obtained after processing the index parameter; The fuzzy index parameter matrix is ​​formed based on the fuzzy index parameters.

6. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: Determining the posting progress of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator includes: The pasting progress of each group of sample data to be evaluated is determined using the sixth formula according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the sixth formula is: ρH a Indicates the posting progress of the sample data in group a.

7. The DC power quality assessment method based on multi-index complex correlation according to claim 1 is characterized in that: Determining the DC power quality level corresponding to the sample data to be evaluated according to the progress range of the progress of each group of the sample data to be evaluated, includes: When the progress of the patch is greater than zero and not greater than a first progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the first level; When the progress of the patch is greater than the first progress threshold and not greater than the second progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the second level, wherein the first progress threshold is less than the second progress threshold, and the first level is lower than the second level; When the progress of the patch is greater than the second progress threshold and not greater than the third progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the third level, wherein the second progress threshold is less than the third progress threshold, and the second level is lower than the third level; In the case where the progress of the patching is greater than the third progress threshold and not greater than the fourth progress threshold, determining that the DC power quality level corresponding to the sample data to be evaluated is the fourth level, wherein the third progress threshold is less than the fourth progress threshold, and the third level is lower than the fourth level; When the progress rate is greater than the fourth progress rate threshold and is not greater than 1, it is determined that the DC power quality level corresponding to the sample data to be evaluated is the fifth level, wherein the fourth level is lower than the fifth level.

8. A DC power quality assessment device based on multi-index complex correlation, characterized in that: include: A first acquisition unit is used to acquire multiple groups of standard sample data and multiple groups of sample data to be evaluated that need to evaluate the quality of direct current power, wherein each of the multiple groups of sample data to be evaluated and the multiple groups of standard sample data includes multiple indicator parameters, the indicator parameters are specific parameter values ​​of evaluation indicators in different sample data, the evaluation indicators are used to evaluate the quality of the direct current power, the multiple groups of standard sample data represent different direct current power quality levels, and the sample data include the standard sample data and the sample data to be evaluated; A second acquisition unit is used to perform standardization processing on the multiple groups of sample data to be evaluated and the multiple groups of standard sample data to obtain a target indicator parameter matrix; A third acquisition unit is used to obtain a comprehensive weight value of each of the multiple evaluation indicators, wherein the comprehensive weight value is a weight value obtained by fitting an objective weight value and a subjective weight value, the objective weight value reflects the importance of the evaluation indicator when evaluating the DC power quality from an objective perspective, and the subjective weight value reflects the importance of the evaluation indicator when evaluating the DC power quality from a subjective perspective; A fourth acquisition unit is used to perform cluster analysis on each of the indicator parameters in the target indicator parameter matrix to obtain a fuzzy indicator parameter matrix; A first determining unit, configured to determine the progress of each group of sample data to be evaluated according to the fuzzy indicator parameter matrix and the comprehensive weight value of each evaluation indicator, wherein the progress of each group of sample data to be evaluated is used to determine the DC power quality level corresponding to the sample data to be evaluated; The second determining unit is used to determine the DC power quality level corresponding to the sample data to be evaluated according to the alignment range of the alignment progress of each group of the sample data to be evaluated.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the DC power quality assessment method based on multi-indicator complex correlation as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by the processor, the DC power quality assessment method based on multi-index complex correlation described in any one of claims 1 to 7 is executed.

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