CN107527131A - A kind of cluster electric system energy consumption level evaluation method and device - Google Patents

Abstract

A method and device for evaluating the energy consumption level of a cluster motor system provided by the present invention, wherein the method includes: S1, based on the established evaluation index system of the cluster motor system, the fuzzy value of the target layer of the evaluation index system is obtained by a fuzzy comprehensive evaluation method A comprehensive evaluation matrix; S2, obtaining an evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer. The invention ensures the accuracy and correctness of energy consumption level evaluation, can obtain more accurate energy consumption level evaluation results in practical applications, and provides reference assistance for energy consumption analysis and operation adjustment of cluster motor systems.

Description

Method and device for evaluating energy consumption level of cluster motor system

technical field

The invention relates to the technical field of electrical engineering, and more specifically, to a method and device for evaluating the energy consumption level of a cluster motor system.

Background technique

Research on energy consumption of motor systems includes many aspects such as energy consumption factors, energy consumption models, and energy-saving measures. Research on the energy consumption level of clustered motor systems is also a research hotspot today, but there are few research results on the evaluation of energy consumption levels. The research on the energy consumption level of the cluster motor system not only involves the analysis of the energy consumption level of each motor equipment in the system, but also involves the consideration of the system load characteristics and working conditions, and also includes the research and analysis of other factors affecting energy consumption. A reasonable analysis and evaluation of the energy consumption level of the cluster motor system can not only be familiar with and control the overall energy consumption level of the system, but also provide a reference for the energy-saving design and optimal operation of the system.

At present, the energy level consumption evaluation method of the cluster motor system includes: establishing an energy consumption calculation model of the cluster motor system according to the variable loss parameters and power balance relationship of the cluster motor system; inputting energy consumption parameters in the energy consumption calculation model Combined with the measured operation data, the energy consumption of the cluster motor system is calculated and obtained, wherein the energy consumption parameter is the variable loss of the cluster motor system; the energy consumption level of the cluster motor system is evaluated according to the energy consumption .

However, in the existing evaluation method for the energy consumption level of the cluster motor system, the energy consumption calculation model is too simple, and the energy consumption parameters input into the energy consumption calculation model are too few, resulting in the evaluation result of the energy consumption level of the cluster motor system obtained by using this method The error is large.

Contents of the invention

In view of the above technical problems, the present invention provides a method and device for evaluating the energy consumption level of a cluster motor system.

In the first aspect, the present invention provides a method for evaluating the energy consumption level of the cluster motor system, including: S1, based on the established evaluation index system of the cluster motor system, obtaining the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system through a fuzzy comprehensive evaluation method ; S2, obtain the evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer; wherein, the evaluation index system includes: a target layer, a criterion layer and an index layer; the target layer is a cluster The energy consumption level of the motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the other factors include: power grid quality, additional energy consumption ratio and energy-saving effect.

Wherein, the S1 includes: S11, obtaining the weight vector of each factor of the same layer in the evaluation index system relative to the upper layer factors; S12, based on the membership degree of each factor of the index layer in the evaluation index system relative to each element in the evaluation set , obtaining the fuzzy evaluation matrix of the criterion layer; S13, obtaining the fuzzy comprehensive evaluation matrix of the target layer based on the weight vector and the fuzzy evaluation matrix of the criterion layer;

Wherein, the evaluation set is V={excellent, good, medium, poor}={υ ₁ , υ ₂ , υ ₃ , υ ₄ }.

Wherein, the S2 includes: selecting the sentence corresponding to the maximum value in the evaluation set in the fuzzy comprehensive evaluation matrix of the target layer as the evaluation result according to the principle of the maximum degree of membership.

Wherein, the S11 includes: S111, comparing the factors of each layer in the evaluation index system two by two and assigning a scale value, establishing a judgment matrix for each layer according to the scale value; S112, obtaining the The eigenvector corresponding to the maximum eigenvalue of the judgment matrix, and normalize the eigenvector to obtain the weight vector of each factor of the same layer relative to the upper layer factor; S113, carry out consistency check on the judgment matrix, If the consistency effect of the judgment matrix is poor, the judgment matrix is corrected.

Wherein, the S12 includes: S121, obtaining the membership degree of the quantitative index in the index layer of the evaluation index system relative to each element in the evaluation set through the determined membership degree function; S122, obtaining the evaluation by means of expert evaluation The membership degree of the qualitative index in the index layer of the index system relative to each element in the evaluation set; S123, according to the membership degree of the quantitative index relative to each element in the evaluation set, and the qualitative index relative to each element in the evaluation set The membership degree of the criterion layer is obtained to obtain the fuzzy evaluation matrix of the criterion layer.

Wherein, said S13 includes: S131, selecting a sum of products type fuzzy synthesis operator, and obtaining the fuzzy comprehensive evaluation matrix of the criterion layer according to the weight vector of the index layer relative to the criterion layer and the fuzzy evaluation matrix of the criterion layer; S132 , according to the fuzzy comprehensive evaluation matrix of the criterion layer and the weight vector of the criterion layer relative to the target layer, the fuzzy comprehensive evaluation matrix of the target layer is obtained.

Wherein, the consistency check is carried out on the judgment matrix by the following formula,

If C.R.<0.1, the consistency effect of the judgment matrix is better; or, if C.R.≥0.1, the consistency effect of the judgment matrix is poor;

Among them, CI is the consistency index; RI is the average random consistency index, which is obtained by checking the average random consistency index comparison table; m is the order of the judgment matrix, and λ _max is the maximum eigenvalue of the judgment matrix.

In the second aspect, the present invention provides a device for evaluating the energy consumption level of a cluster motor system, including: an acquisition module, which is used to obtain the fuzzy value of the target layer of the evaluation index system based on the established evaluation index system of the cluster motor system through a fuzzy comprehensive evaluation method. A comprehensive evaluation matrix; an evaluation module, used to obtain the evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer; wherein, the evaluation index system includes: a target layer, a criterion layer and an index layer; The target layer is the energy consumption level of the cluster motor system; the criterion layer includes: load factor, motor equipment factor and other factors; the other factors include: power grid quality, additional energy consumption ratio and energy-saving effect.

In a third aspect, the present invention provides a device for evaluating the energy consumption level of a cluster motor system, including: at least one processor; and

at least one memory communicatively coupled to the processor, wherein:

The memory stores program instructions that can be executed by the processor, and the processor can execute the above-mentioned method by calling the program instructions.

In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the above method.

The method and device for evaluating the energy consumption level of a cluster motor system provided by the present invention can fully reflect the influence related to the energy consumption level of the cluster motor system by establishing a cluster motor system evaluation index system including a target layer, a criterion layer and an index layer factors; and based on the evaluation index system, the fuzzy comprehensive evaluation matrix of the target layer is obtained through the fuzzy comprehensive evaluation method, and the evaluation result of the energy consumption level of the cluster motor system is obtained according to the fuzzy comprehensive evaluation matrix of the target layer, which ensures the accuracy of the evaluation result The accuracy and correctness of the evaluation method make it possible to accurately evaluate the energy consumption level in practical applications, and provide reference for the energy consumption analysis and operation adjustment of cluster motor systems.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are For some embodiments of the present invention, those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a flow chart of a method for evaluating energy consumption level of a cluster motor system provided by an embodiment of the present invention;

Fig. 2 is a structural block diagram of the evaluation index system in the method for evaluating the energy consumption level of the cluster motor system in Fig. 1;

Fig. 3 is a structural block diagram of a device for evaluating the energy consumption level of a cluster motor system provided by an embodiment of the present invention;

Fig. 4 is a structural block diagram of an evaluation device for energy consumption level of a cluster motor system provided by an embodiment of the present invention.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are the Some, but not all, embodiments are invented. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Fig. 1 is a flow chart of the method for evaluating the energy consumption level of the cluster motor system provided by the embodiment of the present invention. As shown in Fig. 1, the method includes: S1, based on the established evaluation index system of the cluster motor system, obtaining all The fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system; S2, according to the fuzzy comprehensive evaluation matrix of the target layer, the evaluation result of the energy consumption level of the cluster motor system is obtained;

Wherein, the evaluation index system includes: target layer, criterion layer and index layer; the target layer is the energy consumption level of the cluster motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the other factors Including: power grid quality, additional energy consumption ratio and energy-saving effect.

Among them, the evaluation index system refers to an organic whole with an internal structure composed of multiple indicators that characterize the various aspects of the evaluation object and their interrelationships.

Among them, the fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. The comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to the membership degree theory of fuzzy mathematics, that is, uses fuzzy mathematics to make an overall evaluation of things or objects restricted by various factors. It has the characteristics of clear results and strong system, can better solve vague and difficult-to-quantify problems, and is suitable for solving various non-deterministic problems.

Specifically, the energy consumption level is the main part to measure the energy consumption of the motor system, and its level directly affects the system operation capability and economy of enterprises and parks. The design of the evaluation index system is based on the principles of scientificity, completeness and operability, and accurately and objectively reflects the energy consumption level of the cluster motor system. The universe of discourse U is a factor set including all factors of the evaluation index system, following the principle of grouping factors with similar properties into one group, where U is the highest level factor set, including l content sets, U _i is the content set, and each content set Contains n underlying factor sets, namely

U＝{U ₁ ,U ₂ ,…,U _l }

U _i ＝{u _i1 ,u _i2 ,…,u _in }

In the embodiment of the present invention, the evaluation index system is divided into three layers through research and analysis, that is, the target layer, the criterion layer and the index layer, then the factor set U={U ₁ , U ₂ , U ₃ }, and the content sets are respectively:

U ₁ ={u ₁₁ ,u ₁₂ ,u ₁₃ }

U ₂ ＝{u ₂₁ ,u ₂₂ ,u ₂₃ ,u ₂₄ }

U ₃ ={u ₃₁ ,u ₃₂ ,u ₃₃ }

The target layer of the evaluation index system is the energy consumption level of the cluster motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the index layer includes: load characteristics belonging to load factors, total load operating efficiency and load rate; belonging to motor equipment factors The total constant loss, the total rated variable loss, the operating efficiency of the cluster motor system and the duty and rating of the motor; and the quality of the power grid, the ratio of additional energy consumption, and the energy-saving effect of other factors, as shown in Figure 2. Then, the fuzzy comprehensive evaluation matrix of the target layer in the evaluation index system is obtained through the fuzzy comprehensive evaluation method, and the evaluation result of the energy consumption level of the cluster motor system is obtained according to the fuzzy comprehensive evaluation matrix of the target layer.

In the embodiment of the present invention, by establishing the cluster motor system evaluation index system including the target layer, the criterion layer and the index layer, the influencing factors related to the energy consumption level of the cluster motor system can be fully reflected; and based on the evaluation index system, the fuzzy The comprehensive evaluation method obtains the fuzzy comprehensive evaluation matrix of the target layer, and obtains the evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer, which ensures the accuracy and correctness of the evaluation result, and makes the evaluation method in the In practical application, the energy consumption level can be accurately evaluated, and it can provide reference for the energy consumption analysis and operation adjustment of the cluster motor system.

On the basis of the above-mentioned embodiments, the S1 includes: S11, obtaining the weight vector of each factor of the same layer in the evaluation index system relative to the upper layer factors; S12, based on the weight vector of each factor of the index layer in the evaluation index system relative to the evaluation Concentrate the degree of membership of each element to obtain the fuzzy evaluation matrix of the criterion layer; S13, obtain the fuzzy comprehensive evaluation matrix of the target layer based on the weight vector and the fuzzy evaluation matrix of the criterion layer; wherein, the evaluation set is V={ Excellent, Good, Medium, Poor}={υ ₁ ,υ ₂ ,υ ₃ ,υ ₄ }.

Among them, weight is a relative concept for a certain indicator. The weight of an indicator refers to the relative importance of the indicator in the overall evaluation. The weight is to distinguish the importance from several evaluation indicators, and a group of corresponding weights of the evaluation index system constitutes the weight system.

Among them, if there is a number A(x)∈[0,1] corresponding to any element x in the domain of discourse (the scope of research) U, then A is called a fuzzy set on U, and A(x ) is called the membership degree of x to A. When x changes in U, A(x) is a function called the membership function of A. The closer the degree of membership A(x) is to 1, the higher the degree that x belongs to A, and the closer A(x) is to 0, the lower the degree that x belongs to A. The degree to which x belongs to A is represented by the membership function A(x) whose value is in the interval (0, 1). The degree of membership belongs to the concept in the fuzzy evaluation function: fuzzy comprehensive evaluation is a very effective multi-factor decision-making method for making a comprehensive evaluation of things affected by multiple factors. Its characteristic is that the evaluation result is not absolutely positive or negative, but rather is represented by a fuzzy set.

Specifically, when evaluating the energy consumption level of the cluster motor system, obtaining the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system through the fuzzy comprehensive evaluation method includes: obtaining the weight vector of each factor in the same layer relative to the upper layer factors in the evaluation index system , and then obtain the membership degree of each factor in the index layer in the evaluation index system relative to each element in the evaluation set. The i-th factor u _i in the index layer of the evaluation index system can be expressed as r _ij with respect to the membership degree of the j-th element v _j in the evaluation set, and 0<r _ij <1, so that each element in the evaluation index system index layer The degree of membership of the factors, and according to the degree of membership, the fuzzy evaluation matrix of the criterion layer can be established.

Then according to the fuzzy evaluation matrix of the criterion layer and the weight vector of the index layer relative to the criterion layer, the fuzzy comprehensive evaluation matrix of the criterion layer is obtained, and then the fuzzy evaluation matrix of the target layer is obtained according to the fuzzy comprehensive assessment matrix of the criterion layer, and then according to the fuzzy comprehensive evaluation matrix of the target layer The fuzzy comprehensive evaluation matrix of the target layer is obtained from the evaluation matrix and the weight vector of the criterion layer relative to the target layer. Finally, according to the fuzzy comprehensive evaluation matrix of the target layer, the evaluation result of the energy consumption level of the cluster motor system can be obtained.

In the embodiment of the present invention, by obtaining the weight vector of each factor of the same layer in the evaluation index system relative to the upper layer factors, the importance of each factor affecting the energy consumption level of the cluster motor system in the evaluation index system can be intuitively reflected; Obtain the fuzzy evaluation matrix of the criterion layer based on the membership degree of each factor in the middle and bottom layers relative to each element in the evaluation set; and obtain the fuzzy comprehensive evaluation matrix of the target layer based on the weight vector and the fuzzy evaluation matrix of the criterion layer, and obtain the fuzzy comprehensive evaluation matrix according to the fuzzy comprehensive evaluation of the target layer The evaluation result of the energy consumption level of the cluster motor system is obtained by matrix. The accuracy and correctness of energy consumption level evaluation are guaranteed, and more accurate energy consumption level evaluation results can be obtained in practical applications, providing reference for energy consumption analysis and operation adjustment of cluster motor systems.

On the basis of the above embodiments, the S2 includes: selecting the sentence corresponding to the maximum value in the evaluation set in the fuzzy comprehensive evaluation matrix of the target layer as the evaluation result according to the principle of maximum membership degree.

Among them, the maximum membership principle (maximum membership principle), one of the basic principles of fuzzy mathematics, is a direct method for model identification using fuzzy set theory.

Specifically, after obtaining the fuzzy comprehensive evaluation matrix of the target layer through the fuzzy comprehensive evaluation method, according to the principle of maximum membership degree, select the sentence corresponding to the maximum value in the evaluation set of the fuzzy comprehensive evaluation matrix of the target layer as the evaluation result, for example, If the maximum value in the fuzzy comprehensive evaluation matrix of the target layer is the second element, then the sentence corresponding to the second element in the evaluation set is good, that is, the evaluation result of the energy consumption level of the cluster motor system is good.

In the embodiment of the present invention, through the principle of the maximum degree of membership, the sentence corresponding to the maximum value in the fuzzy comprehensive evaluation matrix of the target layer in the evaluation set is selected as the evaluation result, which can make the evaluation result of the energy consumption level of the cluster motor system It is more accurate and provides reference help for the energy consumption analysis and operation adjustment of the cluster motor system.

On the basis of the above embodiments, the S11 includes: S111, comparing the factors of each layer in the evaluation index system two by two and assigning a scale value, and establishing a judgment matrix for each layer according to the scale value; S112. Obtain the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of each layer, and perform normalization processing on the eigenvector to obtain a weight vector of each factor of the same layer relative to the upper layer factor; S113, the The judgment matrix is checked for consistency, and if the consistency effect of the judgment matrix is poor, the judgment matrix is corrected.

Specifically, the weight calculation of each factor in the evaluation index system of the energy consumption level of the cluster motor system is carried out by using the Analytic Hierarchy Process (AHP). , programs and other levels, on this basis to conduct qualitative and quantitative analysis of decision-making methods. First, based on the 1-9 scale proposed by Satty, compare the pairwise factors in each layer of the evaluation index system and assign scale values, and then assign scale values according to the formation criteria of the judgment matrix in Table 1, and the scale values satisfy a _ij = 1/a _ji .

Table 1 Judgment matrix formation criteria

Construct the judgment matrix of the criterion layer and the index layer respectively according to the scale value, for example, the judgment matrix of the criterion layer is A=(a _ij ) _n×n , namely

Then obtain the eigenvector corresponding to the maximum eigenvalue of the criterion layer judgment matrix, and normalize the eigenvector, calculate and obtain the weight vector of the criterion layer relative to the target layer; and obtain the maximum eigenvalue of the index layer judgment matrix The corresponding eigenvector, and normalize the eigenvector, calculate and obtain the weight vector of the index layer relative to the criterion layer. And the vector composed of weights becomes a hierarchical single sort vector. The weight vector is usually obtained by summation method and square root method.

Then, the consistency check is carried out on the judgment matrix of the criterion layer and the index layer. Through the consistency check, the rationality of the judgment matrix can be effectively evaluated. For the judgment matrix with better consistency, it can be used to obtain the corresponding weight vector; for the consistency A poor judgment matrix needs to be corrected and perfected, and it can be used to obtain the corresponding weight vector after the consistency test is passed. By choosing a judgment matrix with good consistency as the basis for obtaining the weight vector, the evaluation of the energy consumption level of the cluster motor system is more reasonable and accurate.

In the embodiment of the present invention, by comparing the factors of each layer in the evaluation index system and assigning a scale value, the judgment matrix of each layer is established according to the scale value, so that the judgment matrix can more fully reflect the evaluation index The degree of influence of each factor in the system on the energy consumption level, and the consistency of the judgment matrix is checked, so that the consistency of the judgment matrix is corrected and improved, and the weight value in the weight vector obtained based on the judgment matrix can be intuitively reflected The importance of each energy consumption level influencing factor of cluster motor system.

On the basis of the above-mentioned embodiments, the S12 includes: S121, obtaining the membership degree of the quantitative index in the index layer of the evaluation index system relative to each element in the evaluation set through the determined membership degree function; Obtain the membership degree of the qualitative index in the index layer of the evaluation index system relative to each element in the evaluation set by means of evaluation; S123, according to the membership degree of the quantitative index relative to each element in the evaluation set, and the qualitative index The index is relative to the membership degree of each element in the evaluation set to obtain the fuzzy evaluation matrix of the criterion layer.

Among them, quantitative indicators are assessment indicators that can be defined accurately, measured accurately, and can set performance goals. In the quantitative evaluation index system, the evaluation reference value of each index is the evaluation reference to measure whether the index meets the basic requirements of production.

Among them, qualitative indicators refer to indicators that cannot be directly calculated and analyzed through data to evaluate the evaluation content, and require objective description and analysis of the evaluation object to reflect the evaluation results.

Specifically, after obtaining the weight vector of each factor in the same layer relative to the factors in the upper layer, firstly, select the quantitative index and qualitative index in the index layer of the evaluation index system, that is, the quantitative index is: the total operating efficiency of the load, the load rate, Motor system operating efficiency, total rated variable loss, total constant loss, power grid quality and additional energy consumption; qualitative indicators are: load characteristics, motor duty system and quota, and energy-saving effect. Then determine the membership function according to the data such as the number of indicators in the index layer of the evaluation index system. For example, the membership function is:

Among them, a is the minimum selection of the index; b is the maximum value of the index; x ₁ and x ₂ are the equivalent points inserted into the interval [a, b], and x ₁ < x ₂ ; x _v ⁽¹⁾ , x _v ⁽²⁾ , x _v ⁽³⁾ and x _v ⁽⁴⁾ respectively represent the membership functions of "excellent", "good", "medium" and poor in the evaluation set; u is the current data of quantitative indicators. Then get the membership degree of each index in the bottom layer, for example, the membership degree of the i-th factor in the quantitative index is r _ij (j=1, 2, ..., s), and 0<r _ij <1, where j is the evaluation The jth element in the set, s is the number of quantitative indicators. Then, according to the determined membership function, the membership degree of the quantitative index in the index layer relative to each element in the evaluation set is obtained.

Qualitative indicators need to be evaluated through quantitative processing. Through fuzzy statistical methods, multiple experts are allowed to judge each qualitative indicator. The content of the qualitative index evaluation and the qualitative index evaluation criteria shown in Table 3 are used to judge, but it is not limited to this. In this way, the membership degree of the qualitative index relative to each factor in the evaluation set can be obtained.

Table 2 Qualitative index evaluation content

Table 3 Qualitative index evaluation criteria

According to the above calculation and evaluation, the membership degree of each index in the index layer relative to each element in the evaluation set is obtained, and then the fuzzy evaluation matrix of the criterion layer is established according to the obtained membership degree. For example, the established fuzzy evaluation matrix is:

The fuzzy evaluation matrix contains all the information obtained by the evaluation set V on the evaluation index layer in the evaluation index system, and from the fuzzy evaluation matrix R, it can be seen that the i-th row of R reflects the i-th factor that affects the evaluation object's membership The degree to each evaluation set; the jth column of R reflects the degree to which all factors affect the evaluation object's belonging to the jth evaluation set element.

Then according to the fuzzy evaluation matrix of the criterion layer and the weight vector of the index layer relative to the criterion layer, the fuzzy comprehensive evaluation matrix of the criterion layer can be obtained, and then the fuzzy comprehensive evaluation matrix of the target layer can be obtained, and then the target layer is selected according to the principle of maximum membership The sentence corresponding to the maximum value in the fuzzy comprehensive evaluation matrix is in the evaluation set, and the sentence is used as the evaluation result of the energy consumption level of the cluster motor system.

In the embodiment of the present invention, the fuzzy evaluation matrix of the criterion layer is established based on the obtained membership degree of each index in the index layer of the evaluation index system, so that the fuzzy evaluation matrix can fully reflect the impact of each index in the index layer of the evaluation index system on the energy consumption level. Therefore, the fuzzy comprehensive evaluation matrix of the target layer obtained according to the fuzzy evaluation matrix of the criterion layer can more accurately reflect the energy consumption level of the cluster motor system.

Among them, the calculation method of each index in the quantitative index includes: the total operating efficiency of the load, the calculation of the total operating efficiency of each load in the cluster motor system, that is, the ratio of the output power of all loads in the cluster motor system to the motor shaft power, that is, through the following formula Find the total operating efficiency of the load,

In the above formula, η _L is the total operating efficiency of the system load; n is the number of motors in the cluster system; P _Li is the output power of the i-th load; P _2l is the output power of the i-th motor, that is, the shaft power.

The load rate of the system, the evaluation of the load rate in the cluster motor system, mainly considers whether the load distribution in the cluster motor system is reasonable, avoids the phenomenon of large horses and small carts and overloading, and ensures that each motor equipment operates at a higher efficiency. Calculate the load rate of all motor equipment in the cluster motor system. It is stipulated that the load rate is 60%-100% as qualified, and the qualified data volume of the system load rate is counted to calculate the load rate, that is, the system load rate is obtained by the following formula,

In the above formula, α is the qualified ratio of load rate; n _qualified is the qualified number of motor equipment load rate in the system; n is the total number of motors in the system.

For the operating efficiency of the motor system, select the operating data in the steady state and calculate the system efficiency, that is, obtain the operating efficiency of the motor system through the following formula,

In the above formula, n is the number of motors in the cluster system; P _2i is the output power of the i-th motor; P ₁ is the total input power of the cluster motor system.

The total rated variable loss of the system and the total constant loss of the system are based on the energy consumption model combined with the recent operating data of the cluster motor system. The data content includes the total input power of the system and the output power of each motor equipment. Variable loss and total constant loss calculations. Among them, the formula of the energy consumption model is as follows,

In the above formula, n is the number of motors in the system; P _T is the total input power of the system; β _j is the load rate of the jth motor equipment; P _j2 is the output power of the jth motor equipment, ΔP _jaN is the jth motor The rated variable loss of the equipment; ΔP _{b always} is the total constant loss of the system; P ₂ ′ is the additional energy consumption power in the system; ΔP _aN is the total rated variable loss of the system.

Power grid quality, measure or calculate the degree of system voltage deviation, and obtain the power grid quality through the following formula,

Additional energy consumption ratio, measure and calculate the additional energy consumption ratio of the system, and obtain the additional energy consumption ratio by the following formula,

In the above formula, λ is the additional energy consumption ratio of the system.

On the basis of the above-mentioned embodiments, said S13 includes: S131, selecting a product sum type fuzzy synthesis operator, and obtaining a criterion according to the weight vector of the index layer relative to the criterion layer and the fuzzy evaluation matrix of the criterion layer. The fuzzy comprehensive evaluation matrix of the layer; S132, according to the fuzzy comprehensive evaluation matrix of the criterion layer and the weight vector of the criterion layer relative to the target layer, obtain the fuzzy comprehensive evaluation matrix of the target layer.

Among them, the synthetic operation of fuzzy relation "." is called fuzzy operator, and fuzzy operator is defined artificially, so "." can also be given different definitions in order to compare which fuzzy operator controls in the process of fuzzy reasoning. The effect is optimal and more reasonable. Common fuzzy operators include: "Min-Max" fuzzy operator, "product-sum" fuzzy operator, "Min/product-sum" fuzzy operator, "Min-product" fuzzy operator, "Min-sum" fuzzy operator.

Specifically, after obtaining the fuzzy evaluation matrix of the criterion layer and the weight vector of each factor in the same layer relative to the upper layer, by selecting an appropriate fuzzy synthesis operator, the fuzzy evaluation matrix of the criterion layer and the weight vector of the index layer relative to the criterion layer Carry out fuzzy synthesis to obtain the fuzzy comprehensive evaluation matrix of the criterion layer. In the embodiment of the present invention, the product-sum type fuzzy synthesis operator is selected, and the fuzzy evaluation matrix of the criterion layer and the weight vector of the index layer relative to the criterion layer are fuzzy synthesized, thereby obtaining the fuzzy comprehensive evaluation matrix of the criterion layer.

Then, based on the fuzzy comprehensive evaluation matrix of the criterion layer, the fuzzy evaluation matrix of the target layer is obtained, and then the fuzzy evaluation matrix of the target layer and the weight vector of the criterion layer relative to the target layer are fuzzy synthesized by using the product sum type fuzzy synthesis operator , to obtain the fuzzy comprehensive evaluation matrix of the target layer, for example, the obtained fuzzy comprehensive evaluation matrix of the target layer is:

B _H = [0.3488 0.4257 0.2226 0.0079],

Finally, according to the principle of maximum membership degree, the sentence corresponding to the maximum value in the target layer fuzzy comprehensive evaluation matrix in the evaluation set is taken as the evaluation result. For example, the maximum value in the above B _H matrix is 0.4257, and the sentence corresponding to it in the evaluation set Therefore, the evaluation of the energy consumption level of the cluster motor system is "good".

In the embodiment of the present invention, by selecting the product summation type fuzzy synthesis operator, the weight vector of the index layer relative to the criterion layer and the fuzzy evaluation matrix of the criterion layer are fuzzy synthesized to obtain the fuzzy comprehensive evaluation matrix of the criterion layer; and similarly According to the fuzzy comprehensive evaluation matrix of the criterion layer and the weight vector of the criterion layer relative to the target layer, the fuzzy comprehensive evaluation matrix of the target layer is obtained, so that the fuzzy comprehensive evaluation matrix is obtained layer by layer, so that the fuzzy comprehensive evaluation matrix of the target layer finally obtained The comprehensive evaluation matrix can accurately reflect the energy consumption level of the cluster motor system.

On the basis of the above-mentioned embodiments, the consistency check of the judgment matrix is carried out by the following formula,

If C.R.<0.1, the consistency effect of the judgment matrix is better; or, if C.R.≥0.1, the consistency effect of the judgment matrix is poor;

Among them, CI is the consistency index; RI is the average random consistency index, which is obtained by checking the average random consistency index comparison table; m is the order of the judgment matrix, and λ _max is the maximum eigenvalue of the judgment matrix.

Specifically, after the judgment matrix of each layer is correspondingly established according to the scale value, it is necessary to check the consistency of the judgment matrix of each layer. For example, to check the judgment matrix of the criterion layer, first calculate the maximum Eigenvalue λ _max , then according to the formula Calculate the consistency index, and then get the average random consistency index RI by looking up Table 4, and then according to the formula Calculate the consistency of the criterion layer judgment matrix.

If C.R.<0.1, it indicates that the consistency of the judgment matrix of the criterion layer is good; or, if C.R.≥0.1, it indicates that the consistency of the judgment matrix of the criterion layer is poor, and the judgment matrix of the criterion layer needs to be corrected and improved. The rationality of the judgment matrix can be effectively evaluated through the consistency test, and the judgment matrix with good consistency can be used to obtain the corresponding weight vector; for the judgment matrix with poor consistency, it needs to be corrected and perfected until it reaches its final value. After the consistency check is passed, it can be used to obtain the corresponding weight vector. The weight vector obtained through the judgment matrix with good consistency can more accurately reflect the importance of each factor in the evaluation index system affecting the energy consumption level of the cluster motor system, making the evaluation of the energy consumption level of the cluster motor system more reasonable and accurate.

Matrix order 1 2 3 4 5 6 7 8 9 R.I. 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

Table 4 Average Random Consistency Index Comparison Table

The following examples illustrate the embodiments of the present invention, but do not limit the protection scope of the present invention. The evaluation index system of the energy consumption level of the cluster motor system is shown in Table 5. Based on the Analytic Hierarchy Process (AHP), use the 1-9 scale method to compare and assign scale values to the two factors in the criterion layer and the index layer. The basis for assigning scale values is the importance of factors relative to the target layer. Taking the criterion layer as an example, experts compare and assign scale values to the two factors in the criterion layer and the index layer by understanding the structure of the cluster motor system and the model of the motor equipment, as shown in Table 6.

Table 5 Evaluation Index System

h A B C A 1 1/2 3 B 2 1 4 C 1/3 1/4 1

Table 6 Comparison of factors at the criterion level

Then establish the judgment matrix of the criterion layer, namely Then the weight vector of the criterion layer relative to the target layer is have The maximum eigenvalue λ _max =3.0191 can be obtained. Consistency test is carried out on the judgment matrix of the criterion layer,

Pass the consistency check.

Similarly, the judgment matrix of the index layer can be obtained as follows:

The corresponding weight vector calculated by the judgment matrix is w _A =[0.164 0.297 0.539] ^T ,

w _B =[0.142 0.671 0.132 0.055] ^T , w _C =[0.633 0.106 0.261] ^T .

The consistency check of the above weight vectors all meet the requirements. By obtaining the product of the weight vector of the criterion layer and the weight vector of the index layer, the weights of the factors of each layer of the cluster motor system are obtained, as shown in Table 7. From Table 7, it can be seen that the factors that have the greatest impact on the energy consumption level of the cluster motor system The factor is "total rated variable loss of the system", followed by "load rate", and the factors with relatively small influence are "extra energy consumption" and "motor duty system and quota".

Table 7 Factor weights of each layer of cluster motor system

Then, the membership degree of each factor in the index layer in the evaluation index system relative to each element in the evaluation set is obtained. For quantitative indicators, organize or calculate the operation data of the cluster motor system in the past year, the data interval is one month, a total of 12 sets of data, the data volume includes: the load rate of each motor equipment, the total load operation efficiency, the system operation efficiency, the system total rating Variable loss, total system constant loss, voltage value and additional energy consumption ratio, construct the scope of the evaluation set based on the data, and then collect or calculate the above-mentioned data under the current operating status of the system, and calculate the membership degree of each index in combination with the membership degree function; For qualitative indicators, select 10 as experts to judge each indicator, and obtain the membership degree of each factor in the indicator layer, as shown in Table 8.

Table 8 Index membership degree

Then, according to the degree of membership, the fuzzy evaluation matrix is constructed, and the fuzzy evaluation matrix of the criterion layer is obtained as follows:

In the fuzzy evaluation matrix of the criterion layer, the row vector represents the membership degree of the index to each factor in the evaluation set.

Finally, according to the obtained weight vectors of each layer and the fuzzy evaluation matrix of the criterion layer, the fuzzy comprehensive evaluation matrix of the criterion layer is calculated based on the sum-of-products algorithm as follows:

In the above formula, B _A , B _B , and B _C represent the fuzzy comprehensive evaluation matrix of "load factors", "motor equipment factors" and "other factors" in the criterion layer. According to the principle of the maximum degree of membership, the comprehensive evaluation of the energy consumption level of the cluster motor system by each factor in the criterion layer of the evaluation index system can be obtained, as shown in Table 9.

Table 9 Comprehensive evaluation at the standard level

According to the fuzzy comprehensive evaluation matrix of the criterion layer, the fuzzy evaluation matrix of the target layer of the cluster motor system is obtained, that is,

Calculate the fuzzy comprehensive evaluation matrix of the target layer of the cluster motor system, namely,

The comprehensive evaluation of the target layer is shown in Table 10.

Table 10 Comprehensive evaluation of system target layer

According to the principle of the maximum degree of membership, the comprehensive evaluation of the energy consumption level of the system can be obtained as "good".

FIG. 3 is a structural block diagram of a device for evaluating the energy consumption level of a cluster motor system provided by an embodiment of the present invention. As shown in FIG. 3 , the device includes: an acquisition module 301 and an evaluation module 302 . The acquisition module 301 is used to obtain the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system through the fuzzy comprehensive evaluation method based on the established cluster motor system evaluation index system; the evaluation module 302 is used to obtain the fuzzy comprehensive evaluation matrix according to the target layer. The evaluation result of the energy consumption level of the cluster motor system; wherein, the evaluation index system includes: a target layer, a criterion layer and an index layer; the target layer is the energy consumption level of the cluster motor system; the criterion layer includes: a load factor , motor equipment factors and other factors; the other factors include: power grid quality, additional energy consumption ratio and energy-saving effect.

Specifically, the design of the evaluation index system is based on the principles of scientificity, completeness and operability, and accurately and objectively reflect the energy consumption level of the cluster motor system. The universe of discourse U is a factor set including all factors of the evaluation index system, following the principle of grouping factors with similar properties into one group, where U is the highest-level factor set, including l content sets, U _i is a content set, and each content set Contains n underlying factor sets, namely

U＝{U ₁ ,U ₂ ,…,U _l }

U _i ＝{u _i1 ,u _i2 ,…,u _in }

In the embodiment of the present invention, the evaluation index system is divided into three layers through research and analysis, that is, the target layer, the criterion layer and the index layer, then the factor set U={U ₁ , U ₂ , U ₃ }, and the content sets are respectively:

U ₁ ={u ₁₁ ,u ₁₂ ,u ₁₃ }

U ₂ ＝{u ₂₁ ,u ₂₂ ,u ₂₃ ,u ₂₄ }

U ₃ ={u ₃₁ ,u ₃₂ ,u ₃₃ }

The target layer of the evaluation index system is the energy consumption level of the cluster motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the index layer includes: load characteristics belonging to load factors, total load operating efficiency and load rate; belonging to motor equipment factors The total constant loss, the total rated variable loss, the operating efficiency of the cluster motor system and the duty and rating of the motor; and the quality of the power grid, the ratio of additional energy consumption, and the energy-saving effect of other factors, as shown in Figure 2. Then the obtaining module 301 obtains the fuzzy comprehensive evaluation matrix of the target layer in the evaluation index system through the fuzzy comprehensive evaluation method, and the evaluation module 302 obtains the evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer.

In the embodiment of the present invention, by establishing the cluster motor system evaluation index system including the target layer, the criterion layer and the index layer, the influencing factors related to the energy consumption level of the cluster motor system can be fully reflected; and the acquisition module is based on the evaluation index system The fuzzy comprehensive evaluation matrix of the target layer is obtained through the fuzzy comprehensive evaluation method, and the evaluation module obtains the evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer, which ensures the accuracy and correctness of the evaluation result, making the The evaluation method can accurately evaluate the energy consumption level in practical application, and provide reference help for the energy consumption analysis and operation adjustment of the cluster motor system.

Fig. 4 is a structural block diagram of a cluster motor system energy consumption level evaluation device provided by an embodiment of the present invention. As shown in Fig. 4, the device includes: a processor (processor) 401, a memory (memory) 402 and a bus 403;

Wherein, the processor 401 and the memory 402 complete mutual communication through the bus 403;

The processor 401 is used to call the program instructions in the memory 402 to execute the methods provided by the above method embodiments, for example, including: based on the established cluster motor system evaluation index system, the fuzzy comprehensive evaluation method is used to obtain the The fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system; the evaluation result of the energy consumption level of the cluster motor system is obtained according to the fuzzy comprehensive evaluation matrix of the target layer.

This embodiment provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the methods provided in the above method embodiments, for example, including : Based on the established cluster motor system evaluation index system, the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system is obtained through the fuzzy comprehensive evaluation method; the energy consumption level of the cluster motor system is obtained according to the fuzzy comprehensive evaluation matrix of the target layer Evaluation results.

Those of ordinary skill in the art can understand that all or part of the steps for realizing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the It includes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (10)

1. A method for evaluating the energy consumption level of a cluster motor system, characterized in that it comprises: S1, based on the established cluster motor system evaluation index system, obtain the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system through the fuzzy comprehensive evaluation method; S2. Obtain an evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer; Wherein, the evaluation index system includes: target layer, criterion layer and index layer; the target layer is the energy consumption level of the cluster motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the other factors Including: power grid quality, additional energy consumption ratio and energy-saving effect. 2. The method according to claim 1, wherein said S1 comprises: S11. Obtain the weight vector of each factor of the same layer relative to the upper layer factor in the evaluation index system; S12. Obtain a fuzzy evaluation matrix of the criterion layer based on the degree of membership of each factor in the index layer in the evaluation index system relative to each element in the evaluation set; S13. Obtain a fuzzy comprehensive evaluation matrix of the target layer based on the weight vector and the fuzzy evaluation matrix of the criterion layer; Wherein, the evaluation set is V={excellent, good, medium, poor}={v ₁ , v ₂ , v ₃ , v ₄ }. 3. The method according to claim 1, wherein said S2 comprises: According to the principle of maximum membership degree, the sentence corresponding to the maximum value in the evaluation set in the fuzzy comprehensive evaluation matrix of the target layer is selected as the evaluation result. 4. The method according to claim 2, wherein said S11 comprises: S111, comparing the factors of each layer in the evaluation index system in pairs and assigning a scale value, and establishing a judgment matrix for each layer according to the scale value; S112. Obtain an eigenvector corresponding to the maximum eigenvalue of the judgment matrix of each layer, and perform normalization processing on the eigenvector to obtain a weight vector of each factor of the same layer relative to the upper layer factor; S113, performing a consistency check on the judgment matrix, and correcting the judgment matrix if the consistency effect of the judgment matrix is poor. 5. The method according to claim 2, wherein said S12 comprises: S121. Obtain the membership degree of the quantitative index in the index layer of the evaluation index system relative to each element in the evaluation set through the determined membership degree function; S122. Obtain the degree of membership of the qualitative indicators in the index layer of the evaluation index system relative to each element in the evaluation set through expert evaluation; S123. Acquire a fuzzy evaluation matrix of the criterion layer according to the membership degree of the quantitative index with respect to each element in the evaluation set and the membership degree of the qualitative index with respect to each element in the evaluation set. 6. The method according to any one of claims 2-5, wherein said S13 comprises: S131, selecting a product summation type fuzzy synthesis operator, and obtaining the fuzzy comprehensive evaluation matrix of the criterion layer according to the weight vector of the index layer relative to the criterion layer and the fuzzy evaluation matrix of the criterion layer; S132. Acquire a fuzzy comprehensive evaluation matrix of the target layer according to the fuzzy comprehensive evaluation matrix of the criterion layer and the weight vector of the criterion layer relative to the target layer. 7. method according to claim 4, is characterized in that, described judgment matrix is carried out consistency test by following formula, <mrow><mi>C</mi><mo>.</mo><mi>R</mi><mo>.</mo><mo>=</mo><mfrac><mrow><mi>C</mi><mo>.</mo><mi>I</mi><mo>.</mo></mrow><mrow><mi>R</mi><mo>.</mo><mi>I</mi><mo>.</mo></mrow></mfrac><mo>,</mo><mi>C</mi><mo>.</mo>mo><mi>I</mi><mo>.</mo><mo>=</mo><mfrac><mrow><msub><mi>&amp;lambda;</mi><mi>max</mi></msub><mo>-</mo><mi>m</mi></mrow><mrow><mi>m</mi><mo>-</mo><mn>1</mn></mrow></mfrac></mrow> If C.R.<0.1, the consistency effect of the judgment matrix is better; or, if C.R.≥0.1, the consistency effect of the judgment matrix is poor; Among them, CI is the consistency index; RI is the average random consistency index, which is obtained by checking the average random consistency index comparison table; m is the order of the judgment matrix, and λ _max is the maximum eigenvalue of the judgment matrix. 8. A device for evaluating the energy consumption level of a cluster motor system, characterized in that it comprises: The acquisition module is used to obtain the fuzzy comprehensive evaluation matrix of the target layer of the evaluation index system through the fuzzy comprehensive evaluation method based on the established cluster motor system evaluation index system; An evaluation module, configured to obtain an evaluation result of the energy consumption level of the cluster motor system according to the fuzzy comprehensive evaluation matrix of the target layer; Wherein, the evaluation index system includes: target layer, criterion layer and index layer; the target layer is the energy consumption level of the cluster motor system; the criterion layer includes: load factors, motor equipment factors and other factors; the other factors Including: power grid quality, additional energy consumption ratio and energy-saving effect. 9. An evaluation device for the energy consumption level of a cluster motor system, characterized in that it comprises: at least one processor; and at least one memory communicatively coupled to the processor, wherein: The memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 7. 10. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the computer according to any one of claims 1 to 7. Methods.