Power supply reliability quantification method, device, computer equipment and storage medium

Abstract

The present application relates to a power supply reliability quantification method, device, computer equipment and storage medium. The method includes: obtaining multi-dimensional power supply reliability indicators taking into account voltage sag, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, and obtaining multi-dimensional economic equivalent power outage time based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time. The dimension of economic equivalent power outage time corresponds to the dimension of power supply reliability indicators taking into account voltage sag, and according to the multi-dimensional economic equivalent power outage time, combined with the preset statistical time, the index value of the multi-dimensional power supply reliability indicator taking into account voltage sag is obtained. The power supply reliability index is defined and quantified from multiple dimensions, so that the quantification result is more complete, and the power supply reliability index is solved by the process immunity time, so that the quantification result of the power supply reliability index is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of electric power grids, and in particular to a method, device, computer equipment and storage medium for quantifying power supply reliability. Background Art

[0002] Power supply reliability refers to the ability of the power supply system to continuously supply power to users. It is an important indicator for evaluating the power quality of the power supply system. It reflects the extent to which the power industry meets the national economy's power demand and has become one of the standards for measuring a country's economic development.

[0003] Traditional power supply reliability is mainly quantified based on power outage time and the number of users affected. Against the backdrop of the booming high-end manufacturing industry, the quantification of the reliability of high-quality power supply needs to be considered from multiple aspects, and the traditional power supply reliability quantification scheme can no longer fully and accurately reflect the reliability of power supply. Voltage sag has a wide range of impacts on industrial users and has a profound degree, which has attracted widespread attention from power suppliers and users. From the perspective of the scope of impact, the occurrence of voltage sags often affects the power supply reliability of an area. As a result, a power supply reliability quantification scheme based on voltage sags has emerged to further quantify high-quality power.

[0004] However, the existing methods for quantifying power supply reliability during voltage sag only take into account the structural economic losses caused by the interruption of some sensitive equipment after the voltage sag occurs, resulting in changes in the industrial process structure, interruption or abnormal operation of the industrial process. The quantification results are not perfect and cannot accurately describe the power supply reliability. Summary of the invention

[0005] Based on this, it is necessary to provide a power supply reliability quantification method, device, computer equipment and storage medium that can comprehensively and accurately quantify power supply reliability in response to the above technical problems.

[0006] A power supply reliability quantification method, the method comprising:

[0007] Obtain multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time;

[0008] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, the multi-dimensional economic equivalent power outage time is obtained. The dimension of the economic equivalent power outage time corresponds to the dimension of the power supply reliability index taking voltage sag into account.

[0009] According to the multi-dimensional economic equivalent power outage time and the preset statistical time, the index values ​​of the multi-dimensional power supply reliability index taking into account the voltage sag are obtained.

[0010] In one embodiment, the multi-dimensional voltage sag reliability index includes a structural power supply reliability index taking voltage sag into account, a functional power supply reliability index taking voltage sag into account, and a safety power supply reliability index taking voltage sag into account, and the multi-dimensional economic equivalent power outage time includes a structural economic equivalent power outage time, a functional economic equivalent power outage time, and a safety economic equivalent power outage time.

[0011] In one embodiment, based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, obtaining multi-dimensional economic equivalent power outage time includes:

[0012] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the fault tree algorithm, the structural economic equivalent power outage time is obtained;

[0013] Based on the industrial structure data of users, loss data of users and process immunity time, combined with the hierarchical and grading ideas and fuzzy matrix operation, the functional economic equivalent outage time is obtained;

[0014] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the voltage sag equipment safety area division method, the safety economic equivalent average time is obtained.

[0015] In one embodiment, based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with a fault tree algorithm, obtaining the structural economic equivalent power outage time includes:

[0016] According to the fault tree algorithm, the user's industrial structure data is decomposed to obtain a fault tree analysis model, which is used to characterize the logical relationship between industrial process data and equipment;

[0017] Based on voltage sag monitoring data, user loss data and process immunity time, combined with the probability density function based on the maximum entropy algorithm, the probability of equipment interruption is obtained;

[0018] According to the fault tree analysis model and equipment interruption probability, the structural economic equivalent power outage time is obtained.

[0019] In one embodiment, based on the user industrial structure data, user loss data and process immunity time, combined with the hierarchical and graded ideas and fuzzy matrix operations, the functional economic equivalent outage time is obtained, which includes:

[0020] Divide the industrial process data in the user's industrial structure data into layers to obtain the industrial process hierarchical results;

[0021] Perform fuzzy matrix operation on the industrial process stratification results to determine the weights of the industrial process stratification results;

[0022] Characterize the equipment characteristics according to the process immunity time, and determine the user voltage sag functional loss level according to the weight of the equipment characteristics and the industrial process stratification results;

[0023] Based on the functional loss level of user voltage sag, the functional economic equivalent power outage time is obtained.

[0024] In one embodiment, characterizing device characteristics according to process immunity time and determining the user voltage sag functional loss level according to the weight of the device characteristics and the industrial process stratification result includes:

[0025] By presetting the membership function, the process immunity time is characterized;

[0026] Characterize the equipment characteristics according to the characterized process immunity time;

[0027] The functional loss level of user voltage sag is determined by performing fuzzy operation on the weights of equipment characteristics and industrial process stratification results.

[0028] In one embodiment, the voltage sag monitoring data includes a voltage sag occurrence probability;

[0029] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the voltage sag equipment safety area division method, the safety economic equivalent average time is obtained, including:

[0030] Obtain process immunity time curves of different devices under different voltage sag effects;

[0031] The process immunity time curves of different equipment are divided into regions to obtain multiple regions, including voltage sag safety regions;

[0032] Determine the score for voltage sag safety area;

[0033] Based on the score of the voltage sag safety area and the probability of voltage sag occurrence, the voltage sag safety score is obtained;

[0034] Based on the voltage sag safety score and user loss data, the safety economic equivalent average time is obtained.

[0035] A power supply reliability quantification device, the device comprising:

[0036] Data acquisition module, used to obtain multi-dimensional power supply reliability indicators taking into account voltage sag, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time;

[0037] An equivalent power outage time determination module is used to obtain multi-dimensional economic equivalent power outage time based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time. The dimensions of the economic equivalent power outage time correspond to the dimensions of the power supply reliability index taking into account the voltage sag;

[0038] The indicator quantification module is used to obtain the indicator values ​​of the multi-dimensional power supply reliability indicators taking into account the voltage sag according to the multi-dimensional economic equivalent power outage time combined with the preset statistical time.

[0039] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0040] Obtain multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time;

[0041] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, the multi-dimensional economic equivalent power outage time is obtained. The dimension of the economic equivalent power outage time corresponds to the dimension of the power supply reliability index taking voltage sag into account.

[0042] According to the multi-dimensional economic equivalent power outage time and the preset statistical time, the index values ​​of the multi-dimensional power supply reliability index taking into account the voltage sag are obtained.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0044] Obtain multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time;

[0045] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, the multi-dimensional economic equivalent power outage time is obtained. The dimension of the economic equivalent power outage time corresponds to the dimension of the power supply reliability index taking voltage sag into account.

[0046] According to the multi-dimensional economic equivalent power outage time and the preset statistical time, the index values ​​of the multi-dimensional power supply reliability index taking into account the voltage sag are obtained.

[0047] The above-mentioned power supply reliability quantification method, device, computer equipment and storage medium define and quantify multi-dimensional power supply reliability indicators, obtain the values ​​of multi-dimensional power supply reliability indicators, and make the quantification results more complete. In addition, the power supply reliability indicator is solved by process immune time, which plays a key bridge role in the establishment and quantification of power supply reliability indicators taking voltage sag into account. It can make full use of the characteristics of process immune time, so that the power supply reliability quantified based on process immune time can fully characterize the impact of voltage sag on users, and the quantification result of the power supply reliability indicator is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a flow chart of a method for quantifying power supply reliability in one embodiment;

[0049] Figure 2 A schematic diagram of a flow chart of steps for obtaining structural economic equivalent time in one embodiment;

[0050] Figure 3 A schematic diagram of a flow chart of steps for obtaining structural economic equivalent time in one embodiment;

[0051] Figure 4-1 A schematic diagram of the structure of a fault tree analysis model in one embodiment;

[0052] Figure 4-2 A schematic diagram of the layered results of an industrial process in one embodiment;

[0053] Figure 4-3 It is a schematic diagram of the membership characterization result of the process immunity time in one embodiment;

[0054] Figure 4-4 A schematic diagram of a regional division result of a process immunity time in an embodiment;

[0055] Figure 4-5 is a schematic diagram of voltage sags in different safety zones in one embodiment;

[0056] Figure 4-6 A schematic diagram of a safe area partition of a user voltage sag device having four industrial processes in one embodiment;

[0057] Figure 4-7 is a schematic diagram of a user's industrial structure in one embodiment;

[0058] Figure 4-8 It is a schematic diagram of the membership characterization result of the process immunity time in another embodiment;

[0059] Figure 5 A schematic diagram of a flow chart of steps for obtaining structural economic equivalent time in one embodiment;

[0060] Figure 6 It is a structural block diagram of a power supply reliability quantification device in one embodiment;

[0061] Figure 7 It is a structural block diagram of a power supply reliability quantification device in another embodiment;

[0062] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] In one embodiment, Figure 1 As shown, a power supply reliability quantification method is provided. This embodiment takes the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0065] Step 202, obtaining multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time.

[0066] In this embodiment, the power supply reliability index taking into account voltage sag is represented by RS*, which is proposed in combination with the traditional power supply reliability rate RS. The specific calculation formula is as follows:

[0067]

[0068] Among them, T eco is the economic equivalent power outage time caused by voltage sag within the statistical time range, T total is the statistical time of power supply reliability rate. In this embodiment, the statistical time is in years. The multi-dimensional power supply reliability index taking voltage sag into account includes a structural power supply reliability index taking voltage sag into account, a functional power supply reliability index taking voltage sag into account, and a safety power supply reliability index taking voltage sag into account.

[0069] Specifically, the structural power supply reliability index taking voltage sag into account describes the direct economic losses of users caused by the interruption of the industrial process. During the interruption of the industrial process, no products are produced, and this part of the economic loss is the most direct. The functional power supply reliability index taking voltage sag into account includes the direct economic loss part of the structural power supply reliability index taking voltage sag into account. The functional power supply reliability index taking voltage sag into account includes the economic losses caused by general and extreme situations: in general, affected by voltage sag, sensitive equipment operates in an abnormal state, resulting in important physical parameters in the production process not meeting the rated conditions, and defective products may appear, causing certain economic losses. In extreme cases, due to voltage sag, the equipment is interrupted, and the important physical parameters required by the production process do not meet the production conditions at all, the industrial process is interrupted, and economic losses occur. The safety reliability index includes both the structural power supply reliability index taking voltage sag into account and the functional power supply reliability index taking voltage sag into account. This is because the power supply reliability index taking safety into account voltage sag is proposed to describe the losses caused by the serious impact on production or personal safety of some equipment once it is interrupted. Whether voltage sag causes equipment interruption or voltage sag causes equipment functionality degradation, it is not allowed. On this basis, even if there is no quantifiable economic loss, it is still necessary to use safety indicators to characterize the status of this type of equipment. Once the safety and reliability of the production line where the equipment is located decreases, corresponding measures need to be taken in a timely manner.

[0070] Voltage sag monitoring data refers to the voltage sag event information measured by the power quality monitoring device in units of years, including the time when the voltage sag occurred, the voltage sag amplitude and the voltage sag duration, etc. User loss data includes economic losses caused by interruptions in the production process. 1 , Economic loss C represented by voltage sag functional loss level V V , Economic loss C represented by voltage sag safety zone score X X 、Users’ annual power outage losses C eco Process Immunity Time (PIT) is a characteristic of the curve of physical parameter changes after being affected by voltage sag. It is defined as the time it takes for a process parameter to change from a rated value to a threshold value of an acceptable state when an industrial process is subjected to voltage sag. The process immunity time is different for different voltage sag amplitudes.

[0071] Step 204, based on the voltage sag monitoring data, the user industrial structure data, the user loss data and the process immunity time, obtain the multi-dimensional economic equivalent power outage time, and the dimension of the economic equivalent power outage time corresponds to the dimension of the power supply reliability index taking into account the voltage sag.

[0072] Economic equivalent power outage time T caused by voltage sag eco , is essentially a variable that describes the characteristics of user voltage sag using time by describing the relationship between voltage sag loss and traditional reliability loss. eco The proportion of voltage sag loss to economic loss in traditional reliability statistics is taken as the equivalent of the proportion of economic equivalent power outage time to traditional power outage time. In this embodiment, the economic equivalent power outage time includes structural economic equivalent power outage time, functional economic equivalent power outage time and safety economic equivalent power outage time. In specific implementation, after obtaining voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, it can be combined with fault tree algorithm, fuzzy matrix operation, etc. to solve the structural economic equivalent power outage time T 1 eco , Functional economic equivalent power outage time T 2 eco and safety economic equivalent power outage time T 3 eco .

[0073] Step 206, obtaining the index value of the multi-dimensional power supply reliability index taking into account the voltage sag according to the multi-dimensional economic equivalent power outage time in combination with the preset statistical time.

[0074] When the structural economic equivalent outage time T is solved 1 eco , Functional economic equivalent power outage time T 2 eco and safety economic equivalent power outage time T 3 eco , combined with the preset statistical time T total , using the solution formula of RS*, the index value of the power supply reliability index RS* taking into account voltage sag is solved from the three dimensions of structure, functionality and safety, that is, the index values ​​of the three power supply reliability indices are obtained to quantify the power supply reliability.

[0075] In the above power supply reliability quantification method, multi-dimensional power supply reliability indicators are defined and quantified, and the values ​​of multi-dimensional power supply reliability indicators are obtained, making the quantification results more complete. In addition, the power supply reliability indicator is solved by process immune time, which plays a key bridge role in the establishment and quantification of power supply reliability indicators taking voltage sag into account. It can make full use of the characteristics of process immune time, so that the power supply reliability quantified based on process immune time can fully characterize the impact of voltage sag on users, and the quantification result of power supply reliability indicator is more accurate.

[0076] In one embodiment, based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, obtaining multi-dimensional economic equivalent power outage time includes:

[0077] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the fault tree algorithm, the structural economic equivalent power outage time is obtained;

[0078] Based on the industrial structure data of users, loss data of users and process immunity time, combined with the hierarchical and grading ideas and fuzzy matrix operation, the functional economic equivalent outage time is obtained;

[0079] Based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the voltage sag equipment safety area division method, the safety economic equivalent average time is obtained.

[0080] In specific implementation, the structural economic equivalent power outage time T 1 eco This is to evaluate the voltage sag characteristics of economic losses caused by production process interruption after the user's production structure changes. Therefore, in this embodiment, a fault tree is used for characterization. Functional economic equivalent power outage time T 2 eco The method of layering and grading and fuzzy matrix operation is used to solve the problem. Safety and economic equivalent power outage time T 3 eco It is a more stringent feature assessment for users of equipment that threatens the safe operation of industrial processes and the personal safety of operators under the influence of voltage sag. In this embodiment, the process immunity time curve under different voltage sags can be combined to solve T using the voltage sag equipment safety area division method. 3 eco In this embodiment, the fault tree algorithm can be used to comprehensively decompose the user's industrial structure data, showing the functional logical relationship of the complex industrial process after the voltage sag, and realizing the classification of the severity of the voltage sag event; the functional economic equivalent power outage time can be systematically solved through the layered and graded method; the voltage sag equipment safety area division method introduces the concepts of steady-state safety domain, relatively safe area, and completely unsafe area, which can effectively solve the safe economic equivalent power outage time.

[0081] like Figure 2 As shown, in one embodiment, based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with a fault tree algorithm, obtaining the structural economic equivalent power outage time includes:

[0082] Step 220, decomposing the user's industrial structure data according to the fault tree algorithm to obtain a fault tree analysis model, where the fault tree analysis model is used to characterize the logical relationship between the industrial process data and the equipment;

[0083] Step 222, based on the voltage sag monitoring data, user loss data and process immunity time, combined with the probability density function based on the maximum entropy algorithm, the probability of equipment interruption is obtained;

[0084] Step 224, obtaining the structural economic equivalent power outage time according to the fault tree analysis model and the equipment outage probability.

[0085] Specifically, the fault tree algorithm is used to decompose the industrial structure data of the user into three levels: process-subprocess-equipment. The logical relationship between each level is connected according to the AND or OR logic to obtain a fault tree analysis model. The fault tree analysis model can be Figure 4-1 As shown. The fault tree analysis model can be used to analyze the different states of the user after the voltage sag, which include normal state, sub-process interruption state and process interruption state. At this time, the severity of the voltage sag event needs to be divided into three situations: normal, sub-process interruption and process interruption. Among them, normal situation: the situation where all equipment is operating normally, specifically including three possible situations: the duration of the voltage sag is less than the minimum process immunity time of all equipment; the duration of the voltage sag exceeds the minimum process immunity time of some equipment, and after calculating the interruption probability of sensitive equipment, the equipment has not been interrupted; after calculation, the equipment has been interrupted, but the backup equipment has not been interrupted. Sub-process interruption: after calculation, the equipment has been interrupted, and the sub-process has been interrupted due to the lack of backup equipment or the interruption of the backup equipment. Process interruption: the sub-process lacks backup, or the backup and sub-process are interrupted at the same time.

[0086] For a specific device, after testing, the PIT uncertainty interval under different voltage sag amplitudes can be obtained. When an industrial process is subjected to a voltage sag of a certain amplitude, if the voltage sag duration is less than the minimum value of the process immunity time PIT min , the sensitive equipment operates normally; if the voltage sag duration is greater than the maximum value of the process immunity time PIT max When the voltage sag lasts for a certain period of time, the sensitive device will be interrupted; when the voltage sag duration is between the two, the probability of the sensitive device being interrupted increases according to a certain rule. In this embodiment, the probability density function based on the maximum entropy algorithm is used to calculate the probability of the sensitive device being interrupted. Specifically, the probability density function model of the maximum entropy algorithm is:

[0087]

[0088]

[0089]

[0090]

[0091] Wherein, H is the entropy of the voltage sag duration t; f(t) is the probability density function of t; R is the value boundary of t; E1 and En are the 1st-order origin moment and nth-order central moment of the interruption sample data of sensitive equipment, respectively.

[0092] The Lagrangian operator is introduced and the classical differential method is used to obtain the analytical expression of the probability density function of sensitive user outage:

[0093]

[0094] Among them, λ n is the Lagrangian operator corresponding to the nth-order moment constraint. In addition, in the actual calculation process, N is usually taken as N=5. Therefore, it can be obtained that when the voltage sag amplitude and duration are determined, the probability of interruption of sensitive equipment is:

[0095]

[0096] The probability of interruption of the process P is calculated by the interruption probability P of the sensitive equipment and the AND and OR logical relationship of the fault tree. 1 , and then the structural economic equivalent power outage time is obtained as:

[0097]

[0098] Among them, C 1 is the economic loss caused by process interruption obtained through statistics, C eco is the annual power outage loss. Specifically, the AND logic of the fault tree is:

[0099] P a (AND)P b =P a P b ;

[0100] OR logic of fault tree:

[0101] P a (OR)P b =1-(1-P a )(1-P b )

[0102] The logical operation of the fault tree is performed from leaf to root. The interruption probability of the subprocess can be obtained through the calculated device interruption probability and logical operation, and finally the process interruption probability can be obtained. In this embodiment, the probability of device interruption is calculated more accurately using the process immune time.

[0103] like Figure 3 As shown, in one embodiment, based on the user industrial structure data, user loss data and process immunity time, combined with the hierarchical classification concept and fuzzy matrix operation, the functional economic equivalent power outage time is obtained, which includes:

[0104] Step 240, hierarchically divide the industrial process data in the user's industrial structure data to obtain an industrial process hierarchical result;

[0105] Step 242, performing fuzzy matrix operation on the industrial process stratification results to determine the weights of the industrial process stratification results;

[0106] Step 244, characterizing the equipment characteristics according to the process immunity time, and determining the user voltage sag functional loss level according to the weight of the equipment characteristics and the industrial process stratification result;

[0107] Step 246, obtaining the functional economic equivalent power outage time based on the user voltage sag functional loss level.

[0108] In specific implementation, according to functional requirements and structural division, a complete industrial process is divided into three levels of content: target layer, criterion layer, and indicator layer. The target layer is the evaluation index of the functional economic loss level of user voltage sag; the criterion layer is the evaluation basis of the target layer, which is a sub-link of functional division; the indicator layer is the refinement of the criterion layer, which is specific to a specific device. Specifically, the structural diagram of the industrial process layering result can be seen in Figure 4-2 . Figure 4-2 The hierarchical structure in is described by factor sets: U = {U 1 ,U 2 ,…,U n},U i = {U i1 ,U i2 ,…,U im}. Among them, U represents the level of division, the subscript i represents the number of layers of the criterion layer, n is the total number of layers of the criterion layer, and m is the total number of layers of the indicator layer in a certain criterion layer. In the indicator layer, the characteristics of the equipment are characterized by the process immune time. In the criterion layer, the characteristics of the function are characterized by the included equipment characteristics. Therefore, the characteristics of the evaluation indicators of the target layer are determined by the user's sensitive equipment characteristics. Then, by comparing the process immune time of the user's industrial process, the user's fuzzy judgment matrix A is determined, as shown in Table 1, and then the weight vectors in the transfer relationship of each layer are determined.

[0109] Table 1

[0110] <![CDATA[a ij ]]> <![CDATA[a ij Meaning of]]> Sorting comparison <![CDATA[a ji ]]> 0.5 Ai is equivalent to Aj σ(j)=σ(i) 0.5 0.6 Ai is slightly better than Aj 1≤σ(j)-σ(i)≤2 0.4 0.7 Ai is obviously better than Aj 2≤σ(j)-σ(i)≤3 0.3 0.8 Ai is strongly better than Aj 4≤σ(j)-σ(i)≤5 0.2 0.9 Ai is much better than Aj σ(j)-σ(i)≥6 0.1

[0111] Among them, A i and A j They represent the i-th and j-th factors of the fuzzy decision matrix A respectively; in the extreme case of the sorting comparison formula, half of the total number of factors is taken, and the others decrease in sequence; σ(i) is the serial number of factor i after the factors are sorted from small to large according to the PIT value.

[0112] Then, the fuzzy judgment matrix A is transformed into the fuzzy consistency matrix F:

[0113]

[0114]

[0115] Then, according to the fuzzy consistency matrix F, the weight vector W is calculated:

[0116]

[0117] In order to distinguish the equipment status under different voltage sag amplitudes and durations, in this embodiment, the process immune time is used to characterize the equipment characteristics, and the characterization result R is obtained. Then, according to the weight of the equipment characteristics and the industrial process stratification results, the user voltage sag functional loss level is determined. Furthermore, the comment set is introduced to characterize the characteristics of the target layer evaluation index, that is, the user voltage sag functional loss level is divided into 5 levels through the comment set: V = {v1, v2, v3, v4, v5} = {level 1, level 2, level 3, level 4, level 5}.

[0118] In another embodiment, step 244 includes: characterizing the process immunity time by presetting the membership function, characterizing the equipment characteristics according to the characterized process immunity time, performing fuzzy operation on the weights of the equipment characteristics and the industrial process stratification results, and determining the user voltage sag functional loss level.

[0119] In specific implementation, a membership function can be introduced to characterize the process immunity time, which can be as follows: Figure 4-3 As shown in the figure, the state intervals of the voltage sag sensitive equipment are divided by the process immunity time, and the intervals divided by the process immunity time are expressed by the interval number:

[0120]

[0121] Where, T i is the lower limit of the process immunity time interval, The calculation formula of membership degree is as follows:

[0122]

[0123] Among them, T Total It is the maximum time corresponding to the physical parameter dropping to the minimum in the process immunity time interval characterization. In this comment set, the risk gradually expands. The weight vector W is fuzzy operated with the characterization result R of the process immunity time characterization equipment characteristics to obtain the functional loss level of the user voltage sag, thereby determining the functional economic equivalent power outage time T 2 eco .

[0124]

[0125]

[0126] Among them, C V The economic loss represented by the voltage sag functional loss level V, C eco = is the annual power outage loss. Different reliability levels correspond to different economic losses. The greater the loss level, the smaller the reliability level, and the greater the economic loss. In this embodiment, it is more reasonable to use the process immunity time to divide the user voltage sag functional loss level after the industrial process suffers from voltage sag.

[0127] In one embodiment, the voltage sag monitoring data includes a voltage sag occurrence probability;

[0128] like Figure 5 As shown, based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, combined with the voltage sag equipment safety area division method, the safety economic equivalent average time is obtained, including:

[0129] Step 260, obtaining process immunity time curves of different devices under the influence of different voltage sags;

[0130] Step 262, dividing the process immunity time curves of different devices into regions to obtain multiple regions, wherein the regions include a voltage sag safety region;

[0131] Step 264, determining the score of the voltage sag safety area;

[0132] Step 266, obtaining a voltage sag safety score based on the voltage sag safety area score and the voltage sag occurrence probability;

[0133] Step 268, obtaining the safety economic equivalent average time according to the voltage sag safety score and the user loss data.

[0134] In order to calculate the user security economic equivalent time, this embodiment introduces the concepts of steady-state security domain, relatively safe area, and completely unsafe area. Figure 4-4 , Figure 4-4 It is the process immunity time curve of the equipment after the user is subjected to different voltage sags. Figure 4-4 As shown, P limit As the boundary, the physical parameter area is divided into two parts, the acceptable area and the unacceptable area. The equipment running in the acceptable area is normal equipment; the equipment running in the unacceptable area is abnormal equipment. When the physical parameter returns to the equipment non-operating state parameter P 0 When the device is interrupted, the device is interrupted. Among them, the steady-state safety domain is the set of all voltage sags whose physical parameters of the process immunity time curve fall into the acceptable area. Relatively safe area: the set of voltage sags whose process immunity time curve falls into the unacceptable area but does not cause device interruption. Completely unsafe area: the set of voltage sags that cause device interruption.

[0135] In specific implementation, the safety economic equivalent average time can be obtained through the voltage sag equipment safety area division method:

[0136] (1) Obtain the process immunity time curves of different devices under the influence of different voltage sags (different voltage amplitudes and durations).

[0137] (2) The process immunity time of the same device under different voltage sag conditions is integrated into one graph.

[0138] (3) Find the lowest point as P limit , P 0 The process immunity time curve is plotted and its voltage sag information (voltage sag amplitude and duration information) is marked.

[0139] (4) Find the process immunity time curves with voltage sag information of 0.1, 0.5, and 0.9 pu in the above process immunity time curves, a total of 9, and divide them into regions. The division results are as follows: Figure 4-4 shown.

[0140] (5) The above 9 groups of voltage sags are marked in the voltage amplitude-time diagram and divided into regions, including the safe area.

[0141] The voltage sags in different safety areas are shown in the voltage amplitude-duration diagram as follows: Figure 4-5 As shown in the figure. Since the user's industrial structure is complex and includes multiple devices, multiple PIT analyses are required. The voltage sags in different safety zones measured by different devices of the user are summarized on the same graph to obtain a multi-PIT safety zone voltage sag partition image. Figure 4-6 The image of voltage sag zoning in a safe area with four PIT users.

[0142] (6) Score the voltage sag safety area of ​​different devices, and superimpose the safety area scores of different devices with the same (voltage, time) voltage sag to obtain the safety area score value corresponding to any voltage sag.

[0143] From the perspective of permutation and combination, the four devices have When the number of PITs considered by the user increases, the number of safe area partitions will increase. Therefore, the voltage sag safe area corresponding to each PIT can be scored, and the number of safe area partitions of the user can be reduced by averaging the scored safe area scores. Figure 4-6 As shown in FIG. 1 , the partitioning situation when the number of security area partitions is 4 is shown.

[0144] Assuming N is the number of partitions, the voltage sag in the absolutely unsafe area is 0 points, the relatively safe area is 1 / 2N, and the absolutely safe area is 1 / N, then the regional cumulative score is X, X∈(0,1). X=1, it means that the voltage sag is in the safest area, at this time all the equipment is in a safe operating state; X=0, the voltage sag is in the dangerous area, and all equipment will be interrupted.

[0145] (7) Multiply the above-obtained arbitrary voltage sag safety area score value by the voltage sag occurrence probability to obtain the user voltage sag safety score, and finally find the corresponding loss value C in the voltage sag equipment safety loss value-safety area score table obtained from the statistical survey. X , and then the safety economic equivalent time T of voltage sag is obtained according to the following formula 3 eco :

[0146]

[0147] Among them, C X It is the economic loss represented by the voltage sag safety area partition score X. Different safety area partition scores correspond to different economic losses. The larger the score, the smaller the economic loss. It should be noted that the voltage sag equipment safety loss value-safety area score table needs to be obtained by the user through statistics. Among them, the voltage sag equipment safety loss value refers to the direct or indirect losses caused by safety problems caused by a certain equipment suffering from a voltage sag in the user's history, including but not limited to the loss of compensation for casualties caused by equipment accidents and the decline in corporate reputation caused by social opinions. In this embodiment, the process immunity time curve is used to draw the safety area of ​​the equipment, which can directly reflect the impact of voltage sags of any amplitude at any time on the industrial process, and indirectly reflects the safety performance of the equipment.

[0148] To more clearly describe the determination of the functional loss level of user voltage sag, an example is described below:

[0149] Assume that a user's industrial process consists of two sub-processes, each of which contains two devices, such as Figure 4-7 shown.

[0150] Four PIT values ​​can be measured using four devices. According to the principle of hierarchical division, a hierarchical table can be obtained, as shown in Table 2 below.

[0151] Table 2 Hierarchical table

[0152] Target layer Criteria Layer Indicator layer Physical parameters PIT Sorting Drying U1 Heating U11 temperature 70 1 Fan U12 Wind speed 110 3 Clean U2 Clean U21 Cleanliness 190 4 Pressurized U22 Pressure difference 70 1

[0153] Through fuzzy matrix operation, the target weight to be solved is determined as:

[0154]

[0155] First, calculate the weight coefficient w 11 :

[0156] 1) Find the fuzzy judgment matrix A

[0157] (1)U 11 Temperature PIT ranking σ(1)=1; U 12 Wind speed PIT ranking σ(2)=3.

[0158] σ(2)-σ(1)=2

[0159] (2) According to Table 1, a 12 =0.6, a 21 =0.4;

[0160]

[0161]

[0162] 2) Find the fuzzy consistency matrix F

[0163] f 1 =a 11 +a 12 =1.1; f 2 =a 21 +a 22 =0.9;

[0164] f 21 =0.45;

[0165]

[0166]

[0167] According to the above method, w is obtained in the same way 12 、w21 、w 22 .

[0168] Then, U 11 , U 12 The lowest PIT value in the ranking is taken as the PIT value of U1, and w is calculated according to 12 Method to find w 1 The final target weight is:

[0169]

[0170] Reference Figure 4-8 , the PIT curves of each factor layer (equipment in the sub-process) are divided into voltage sag loss levels, and the results and target weights are summarized as follows:

[0171]

[0172] Among them, the classification of loss levels refers to Figure 4-3 , and conduct.

[0173] Find: U 11 =[0.257, 0.164, 0.032, 0.418, 0.129]

[0174] Finally, the comprehensive evaluation result B of the user voltage sag functional loss level is calculated:

[0175]

[0176]

[0177]

[0178] The solution is that the maximum value 0.328 in B is located at the fourth level, V=4.

[0179] It should be understood that, although the steps in each flow chart involved in the above-described embodiment are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a portion of the steps in each flow chart involved in the above-described embodiment may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0180] In one embodiment, Figure 6 As shown, a power supply reliability quantification device is provided, including: a data acquisition module 510, an equivalent power outage time determination module 520 and an index quantification module 530, wherein:

[0181] The data acquisition module 510 is used to obtain multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time.

[0182] The equivalent power outage time determination module 520 is used to obtain multi-dimensional economic equivalent power outage time based on voltage sag monitoring data, user industrial structure data, user loss data and process immunity time. The dimensions of the economic equivalent power outage time correspond to the dimensions of the power supply reliability index taking into account voltage sag.

[0183] The index quantification module 530 is used to obtain the index value of the multi-dimensional power supply reliability index taking into account the voltage sag according to the multi-dimensional economic equivalent power outage time in combination with the preset statistical time.

[0184] In one embodiment, the equivalent power outage time determination module 520 is used to obtain the structural economic equivalent power outage time based on the voltage sag monitoring data, the user's industrial structure data, the user's loss data and the process immunity time, combined with the fault tree algorithm; based on the user's industrial structure data, the user's loss data and the process immunity time, combined with the hierarchical and graded ideas and fuzzy matrix operations, to obtain the functional economic equivalent power outage time; based on the voltage sag monitoring data, the user's industrial structure data, the user's loss data and the process immunity time, combined with the voltage sag equipment safety area division method, to obtain the safety economic equivalent average time.

[0185] like Figure 7 As shown, in one embodiment, the equivalent power outage time determination module 520 includes a structural equivalent power outage time determination unit 522, which is used to decompose the user's industrial structure data according to the fault tree algorithm to obtain a fault tree analysis model. The fault tree analysis model is used to characterize the logical relationship between industrial process data and equipment. Based on the voltage sag monitoring data, user loss data and process immunity time, combined with the probability density function based on the maximum entropy algorithm, the probability of equipment interruption is obtained. According to the fault tree analysis model and the probability of equipment interruption, the structural economic equivalent power outage time is obtained.

[0186] In one embodiment, the equivalent power outage time determination module 520 includes a functional equivalent power outage time determination unit 524, which is used to hierarchically divide the industrial process data in the user's industrial structure data to obtain industrial process stratification results, perform fuzzy matrix operations on the industrial process stratification results, determine the weights of the industrial process stratification results, characterize equipment characteristics according to process immune time, determine the user's voltage sag functional loss level according to the equipment characteristics and the weights of the industrial process stratification results, and obtain the functional economic equivalent power outage time based on the user's voltage sag functional loss level.

[0187] In one embodiment, the functional equivalent power outage time determination unit 524 is also used to characterize the process immunity time through a preset membership function, characterize the equipment characteristics based on the characterized process immunity time, perform fuzzy operations on the weights of the equipment characteristics and the industrial process stratification results, and determine the user voltage sag functional loss level.

[0188] In one embodiment, the voltage sag monitoring data includes the probability of voltage sag occurrence; the equivalent power outage time determination module 520 includes a safety equivalent power outage time determination unit 526, which is used to obtain process immunity time curves of different equipment under different voltage sag influences, divide the process immunity time curves of different equipment into regions, and obtain multiple regions, the regions include voltage sag safety regions, determine the scores of the voltage sag safety regions, and obtain the voltage sag safety scores based on the scores of the voltage sag safety regions and the probability of voltage sag occurrence; and obtain the safety economic equivalent average time based on the voltage sag safety scores and user loss data.

[0189] For the specific embodiments of the power supply reliability quantification device, please refer to the embodiments of the power supply reliability quantification method mentioned above, which will not be repeated here. Each module in the above-mentioned power supply reliability quantification device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0190] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-dimensional power supply reliability indicators taking into account voltage sags, voltage sag monitoring data, user industrial structure data, user loss data, process immunity time and other data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power supply reliability quantification method is implemented.

[0191] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0192] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned power supply reliability quantification method when executing the computer program.

[0193] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned power supply reliability quantification method are implemented.

[0194] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0195] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0196] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A power supply reliability quantification method, characterized in that: The method comprises: Acquire multi-dimensional power supply reliability indicators taking voltage sag into account, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, wherein the voltage sag monitoring data includes the probability of voltage sag occurrence, and the calculation formula of the multi-dimensional power supply reliability indicators taking voltage sag into account is determined based on the statistical time of the power supply reliability rate and the economic equivalent power outage time caused by the voltage sag within the statistical time range; According to the fault tree algorithm, the user industrial structure data is decomposed to obtain a fault tree analysis model, the fault tree analysis model is used to characterize the logical relationship between industrial process data and equipment, based on the voltage sag monitoring data, the user loss data and the process immunity time, combined with the probability density function based on the maximum entropy algorithm, the equipment interruption probability is obtained, and according to the fault tree analysis model and the equipment interruption probability, the structural economic equivalent power outage time is obtained; Performing hierarchical division on the industrial process data in the industrial structure data of the user to obtain industrial process stratification results, performing fuzzy matrix operation on the industrial process stratification results to determine the weight of the industrial process stratification results, characterizing equipment characteristics according to the process immune time, determining the user voltage sag functional loss level according to the equipment characteristics and the weight of the industrial process stratification results, and obtaining the functional economic equivalent power outage time based on the user voltage sag functional loss level; Obtain process immunity time curves of different equipment under the influence of different voltage sags, divide the process immunity time curves of the different equipment into regions to obtain multiple regions, wherein the regions include a voltage sag safety region, determine the score of the voltage sag safety region, obtain a voltage sag safety score based on the score of the voltage sag safety region and the voltage sag occurrence probability, obtain a safety economic equivalent power outage time based on the voltage sag safety score and the user loss data, and the dimensions of the structural economic equivalent power outage time, the functional economic equivalent power outage time, and the safety economic equivalent power outage time correspond to the dimensions of the power supply reliability index taking into account the voltage sag; According to the structural economic equivalent power outage time, the functional economic equivalent power outage time and the safety economic equivalent power outage time, combined with the preset statistical time, the index values ​​of the multi-dimensional power supply reliability index taking into account the voltage sag are obtained.

2. The power supply reliability quantification method according to claim 1, characterized in that: The multi-dimensional voltage sag reliability index includes a structural power supply reliability index taking voltage sag into account, a functional power supply reliability index taking voltage sag into account, and a safety power supply reliability index taking voltage sag into account.

3. The power supply reliability quantification method according to claim 1, characterized in that: Characterizing equipment characteristics according to process immunity time, and determining the user voltage sag functional loss level according to the weight of equipment characteristics and industrial process stratification results include: By presetting the membership function, the immune time of the process is characterized; Characterize the equipment characteristics according to the characterized process immunity time; A fuzzy operation is performed on the weights of the equipment characteristics and the industrial process stratification results to determine the user voltage sag functional loss level.

4. The power supply reliability quantification method according to claim 1, characterized in that: Decomposing the user industrial structure data according to the fault tree algorithm to obtain a fault tree analysis model includes: By using the fault tree algorithm, the user industrial structure data is decomposed into three levels: process, sub-process and equipment. The logical relationship between each level is connected according to the AND or OR logic to obtain the fault tree analysis model.

5. A power supply reliability quantification device, characterized in that: The device comprises: A data acquisition module, used to acquire multi-dimensional power supply reliability indicators taking into account voltage sags, voltage sag monitoring data, user industrial structure data, user loss data and process immunity time, wherein the voltage sag monitoring data includes the probability of voltage sag occurrence, and the calculation formula of the multi-dimensional power supply reliability indicators taking into account voltage sags is determined based on the statistical time of the power supply reliability rate and the economic equivalent power outage time caused by the voltage sag within the statistical time range; An equivalent power outage time determination module is used to decompose the user industrial structure data according to a fault tree algorithm to obtain a fault tree analysis model, wherein the fault tree analysis model is used to characterize the logical relationship between industrial process data and equipment, and obtain the equipment interruption probability based on the voltage sag monitoring data, the user loss data and the process immunity time in combination with a probability density function based on a maximum entropy algorithm, and obtain the structural economic equivalent power outage time according to the fault tree analysis model and the equipment interruption probability; Used to hierarchically divide the industrial process data in the user's industrial structure data to obtain industrial process stratification results, perform fuzzy matrix operations on the industrial process stratification results, determine the weights of the industrial process stratification results, characterize equipment characteristics according to the process immune time, determine the user's voltage sag functional loss level according to the equipment characteristics and the weights of the industrial process stratification results, and obtain functional economic equivalent power outage time based on the user's voltage sag functional loss level; and for obtaining process immunity time curves of different equipment under different voltage sag influences, dividing the process immunity time curves of the different equipment into regions to obtain a plurality of regions, wherein the regions include a voltage sag safety region, determining a score of the voltage sag safety region, obtaining a voltage sag safety score based on the score of the voltage sag safety region and the voltage sag occurrence probability, and obtaining a safety economic equivalent power outage time based on the voltage sag safety score and the user loss data, wherein the dimensions of the structural economic equivalent power outage time, the functional economic equivalent power outage time and the safety economic equivalent power outage time correspond to the dimensions of the power supply reliability index taking voltage sag into account; The indicator quantification module is used to obtain the indicator values ​​of the multi-dimensional power supply reliability indicator taking into account the voltage sag according to the structural economic equivalent power outage time, the functional economic equivalent power outage time and the safety economic equivalent power outage time in combination with the preset statistical time.

6. The device according to claim 5, characterized in that The multi-dimensional voltage sag reliability index includes a structural power supply reliability index taking voltage sag into account, a functional power supply reliability index taking voltage sag into account, and a safety power supply reliability index taking voltage sag into account.

7. The device according to claim 5, characterized in that The equivalent power outage time determination module is also used to characterize the process immunity time through a preset membership function, characterize the equipment characteristics based on the characterized process immunity time, perform fuzzy operations on the weights of the equipment characteristics and the industrial process stratification results, and determine the user voltage sag functional loss level.

8. The device according to claim 5, characterized in that The device is also used to use a fault tree algorithm to decompose the user industrial structure data into three levels: process, sub-process and equipment. The logical relationship between each level is connected according to the AND or OR logic to obtain the fault tree analysis model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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