CN112836958A - Wind turbine generator system running state evaluation system based on fuzzy comprehensive evaluation - Google Patents
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Abstract
The invention relates to a wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation, which comprises a data acquisition module, a quantification module, a weight calculation module, a fuzzy evaluation module and a result analysis module, wherein the data acquisition module acquires monitoring data of running state evaluation indexes of each wind turbine generator and carries out quantification processing on the monitoring data to obtain the degradation degree of each evaluation index; dividing the evaluation indexes into a plurality of sub items, and respectively determining the item weight of each sub item and the index weight of each evaluation index based on the degradation degree; obtaining the evaluation vector of each sub-item in a fuzzy evaluation module; and determining a final wind turbine generator evaluation result according to the project weight and the maximum membership degree of the sub-projects. Compared with the prior art, the wind turbine state evaluation method based on the multi-layer fuzzy reasoning improves the analysis result from the evaluation vector of the sub-item to the wind turbine state, and selects the evaluation result of the sub-item with the largest weight as the final wind turbine evaluation result.
Description
Technical Field
The invention relates to the field of wind turbine generator running state evaluation, in particular to a wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation.
Background
In recent years, equipment state monitoring and state evaluation methods are gradually applied to wind power generation equipment, and although the research on monitoring of the running state of important parts of a wind turbine generator is mature at present, the research on evaluating the overall state and health degree of the wind turbine generator is little. The large-scale wind power grid-connected development in China is late, the operation data and experience of a unit are relatively deficient, and excessive reliability test data accumulation and analysis are not available temporarily; although the remote monitoring system of the wind power plant can collect the operation data of the wind turbine generator, an effective evaluation algorithm is lacked, and the comprehensive state of the wind turbine generator cannot be evaluated in time.
Common evaluation methods at present include a probability statistical method, an intelligent method based on a neural network and the like. The probability statistical method is suitable for the condition that the evaluation index factors are subjected to independent and same distribution, but the evaluation effect is not good under the condition that the relevance of each index factor representing the running state of the wind turbine system is strong; the evaluation accuracy of the intelligent method based on the neural network is high only by a large number of training samples, but the existing offshore direct-drive wind turbine generator is just started, the operation data amount is small, and obviously the method is not a good choice. Therefore, the method for evaluating the running state of the wind turbine generator based on the on-line monitoring information without depending on the test data of the wind turbine generator is important in academic value and application prospect.
Chinese patent CN201010187808.7 discloses a real-time running state evaluation system and evaluation method for a wind turbine generator system, the evaluation system mainly includes a monitoring module, a quantization module, a weight determination module, and an evaluation module, data monitored in real time by a wind turbine generator system control system is used as input of the real-time running state evaluation system, the data is quantized again to obtain the degradation degree of each real-time evaluation index, and finally, when the deviation between the degradation degree of a single evaluation index and an allowable value is large, the evaluation result is directly given as "serious"; otherwise, a weight module and an evaluation module are adopted to calculate the evaluation result of the running state of the unit, namely the evaluation result is used as the output of the system. The evaluation system and the evaluation method divide the evaluation index of the running state of the fan into different sub-items by using a multi-level fuzzy evaluation method, however, the method evaluates the final evaluation vector by adopting a maximum membership principle, and the deterioration phenomenon is covered at the final layer after partial parameters of the fan are transmitted by multi-level weights by multi-level fuzzy reasoning.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation, the wind turbine generator state evaluation is obtained through multilayer fuzzy reasoning, the analysis result from the evaluation vector of the sub-item to the wind turbine generator state is improved, and the evaluation result of the sub-item with the largest weight is selected as the final wind turbine generator evaluation result.
The purpose of the invention can be realized by the following technical scheme:
a wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation comprises a data acquisition module, a quantization module, a weight calculation module, a fuzzy evaluation module and a result analysis module,
the data acquisition module is used for acquiring monitoring data of the evaluation indexes of the running states of the wind turbine generators;
the quantification module is used for quantifying the monitoring data of the wind turbine generator running state evaluation indexes to obtain the degradation degree of the wind turbine generator running state evaluation indexes;
the weight determination module divides the wind turbine generator running state evaluation indexes into a plurality of sub-items, and determines the item weight of each sub-item and the index weight of each wind turbine generator running state evaluation index under each sub-item based on the degradation degree;
the fuzzy evaluation module is used for forming a fuzzy evaluation matrix by using the evaluation indexes of the running state of the wind generating set in each sub-project and combining the fuzzy evaluation matrix with the evaluation indexesAnd obtaining the evaluation vector of each sub item by the index weight of the evaluation index of the running state of each wind generating set: s1、S2、…Si…、Sn(n>1)，Si＝{Si1、Si2、…Sij…、Sim}(m>1)，SiAn evaluation vector representing the ith item, SijRepresenting the membership degree of the ith sub-project to the wind turbine generator operation state evaluation result j;
the result analysis module is used for acquiring the project weight of each sub-project and obtaining a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project;
the result analysis module obtains a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project, and the final wind turbine generator operation state evaluation result is specifically as follows: and taking the evaluation vector of the sub item with the largest project weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the largest membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result.
Further, the wind turbine generator running state evaluation index comprises a generator bearing temperature, a generator winding temperature, a generator cooling water temperature, a phase voltage, a phase current, an active power, a reactive power, a cabin position, a cabin temperature, a highest Topbox, a highest control cabinet, a variable pitch motor, a variable pitch capacitor cabinet, a variable pitch cabinet body and a variable pitch inverter temperature.
Further, the generator bearing temperature, the generator winding temperature and the generator cooling water temperature are divided into generator sub-items; the phase voltage, the phase current, the active power and the reactive power are divided into power grid system sub-items; the cabin position, cabin temperature, maximum Topbox and maximum control cabinet are divided into cabin and control cabinet items; the temperature of the variable pitch motor, the variable pitch capacitor cabinet, the variable pitch cabinet body and the variable pitch inverter is divided into variable pitch system sub-items.
Further, the evaluation result of the running state of the wind turbine generator is divided into 4 conditions of 'good', 'normal', 'warning' and 'serious'.
Further, the membership function of the fuzzy evaluation matrix in the fuzzy evaluation module is a distribution function of a combination of a triangle and a half trapezoid.
Furthermore, the deterioration degree characterizes the relative deterioration degree of the current actual state of the wind turbine generator compared with the fault state, and the value range of the deterioration degree is [0,1 ].
Further, the result analysis module obtains the maximum membership degree S of each sub-item according to the evaluation vector of each sub-item1_max、S2_max、…Si_max…、Sn_maxAcquiring the project weight of each sub-project, and obtaining a final wind turbine generator operation state evaluation result based on the maximum membership and the project weight of each sub-project, wherein the method specifically comprises the following steps: obtaining an evaluation result of the running state of the wind turbine generator based on the maximum membership and the project weight of each sub-project, which specifically comprises the following steps: maximum membership S from all sub-items1_max、S2_max、…Si_max…、Sn_maxThe maximum value is selected to obtain the maximum item membership degree Smax；
λ (λ) if the maximum item membership is the maximum membership of the remaining sub-items>1) Multiplying, and if the maximum membership degree of the sub-item with the maximum item weight is less than alpha, then the maximum item membership degree SmaxThe evaluation vector of the sub item is used as the wind turbine running state evaluation vector, and the wind turbine running state evaluation result with the maximum membership degree in the wind turbine running state evaluation vector is selected as the final wind turbine running state evaluation result;
if the maximum item membership degree is lambda (lambda is more than 1) times of the maximum membership degree of other sub items, and the maximum membership degree of the sub item with the maximum item weight is larger than beta, taking the evaluation vector of the sub item with the maximum item weight as a wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as a final wind turbine generator running state evaluation result;
if the difference value between the maximum membership degree of k or more than k (n > k >1) sub items and the maximum item membership degree is less than epsilon, the evaluation vector of the sub item with the maximum comprehensive value of the weight and the maximum membership degree is used as the wind turbine running state evaluation vector, and the wind turbine running state evaluation result with the maximum membership degree in the wind turbine running state evaluation vector is selected as the final wind turbine running state evaluation result;
if the difference values of the maximum membership degrees of all the sub items and the maximum membership degree of the item are less than rho, taking the evaluation vector of the sub item with the maximum item weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result;
wherein, λ, α, β, k, ε, and ρ are parameters preset according to the number of sub items, the evaluation index, and the normal operating state of the wind turbine.
Further, α is 0.1.
Further, β is 0.1.
Further, k has a value of 2.
Compared with the prior art, the invention has the following beneficial effects:
(1) the wind turbine state evaluation is obtained through multilayer fuzzy reasoning, the analysis result from the evaluation vector of the sub item to the wind turbine state is improved, and the evaluation result of the sub item with the largest weight is selected as the final wind turbine evaluation result.
(2) And acquiring the weight and the maximum membership degree of the sub-item, and obtaining a final wind turbine evaluation result by using different methods according to the weight and the maximum membership degree respectively, so that the degradation phenomenon is prevented from being covered at a final layer.
Drawings
FIG. 1 is a schematic structural diagram of a wind turbine generator operation state evaluation system;
FIG. 2 is a schematic diagram of an evaluation index of an operating state of a wind turbine generator system;
FIG. 3 is a graph of the distribution of the membership functions for half trapezoids and triangles.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1:
a wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation is shown in figure 1 and comprises a data acquisition module, a quantification module, a weight calculation module, a fuzzy evaluation module and a result analysis module,
the data acquisition module is used for acquiring monitoring data of the evaluation indexes of the running states of the wind turbine generators;
the quantification module is used for quantifying the monitoring data of the wind turbine generator running state evaluation indexes to obtain the degradation degree of the wind turbine generator running state evaluation indexes;
the weight determination module divides the wind turbine generator running state evaluation indexes into a plurality of sub-items, and determines the item weight of each sub-item and the index weight of each wind turbine generator running state evaluation index under each sub-item based on the degradation degree;
the fuzzy evaluation module is used for forming a fuzzy evaluation matrix by the evaluation indexes of the running states of the wind generating sets in each sub-project, and obtaining the evaluation vector of each sub-project by combining the index weight of the evaluation index of the running state of each wind generating set: s1、S2、…Si…、Sn(n>1)，Si＝{Si1、Si2、…Sij…、Sim}(m>1)，SiAn evaluation vector representing the ith item, SijRepresenting the membership degree of the ith sub-project to the wind turbine generator operation state evaluation result j;
the result analysis module is used for acquiring the project weight of each sub-project and obtaining a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project;
the result analysis module obtains a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project, and the final wind turbine generator operation state evaluation result is specifically as follows: and taking the evaluation vector of the sub item with the largest project weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the largest membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result.
The method comprises the steps that after partial parameters of the fan are transmitted through multiple layers of weights, the deterioration phenomenon is covered at a final layer, after evaluation vectors of all sub-items are obtained, according to the item weights of all the sub-items, the evaluation vector of the sub-item with the largest item weight is used as a wind turbine running state evaluation vector, and a wind turbine running state evaluation result with the largest membership degree in the wind turbine running state evaluation vector is selected as a final wind turbine running state evaluation result.
The wind turbine generator running state evaluation result determined directly according to the project weight of the sub-project can be combined with the maximum membership degree and the project weight to obtain the wind turbine generator running state evaluation result.
The result analysis module obtains the maximum membership S of each sub-item according to the evaluation vector of each sub-item1_max、S2_max、…Si_max…、Sn_maxAcquiring the project weight of each sub-project, and obtaining a final wind turbine generator operation state evaluation result based on the maximum membership and the project weight of each sub-project, wherein the method specifically comprises the following steps: obtaining an evaluation result of the running state of the wind turbine generator based on the maximum membership and the project weight of each sub-project, which specifically comprises the following steps: maximum membership S from all sub-items1_max、S2_max、…Si_max…、Sn_maxThe maximum value is selected to obtain the maximum item membership degree Smax；
(1) λ (λ) if the maximum item membership is the maximum membership of the remaining sub-items>1) Multiplying, and if the maximum membership degree of the sub-item with the maximum item weight is less than alpha, then the maximum item membership degree SmaxThe evaluation vectors of the sub items are used as wind turbine generator running state evaluation vectors, and the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vectors is selected as the final wind turbine generator running stateEvaluating the result;
(2) if the maximum item membership degree is lambda (lambda is more than 1) times of the maximum membership degree of other sub items, and the maximum membership degree of the sub item with the maximum item weight is larger than beta, taking the evaluation vector of the sub item with the maximum item weight as a wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as a final wind turbine generator running state evaluation result;
(3) if the difference value between the maximum membership degree of k or more than k (n > k >1) sub items and the maximum item membership degree is less than epsilon, the evaluation vector of the sub item with the maximum comprehensive value of the weight and the maximum membership degree is used as the wind turbine running state evaluation vector, and the wind turbine running state evaluation result with the maximum membership degree in the wind turbine running state evaluation vector is selected as the final wind turbine running state evaluation result;
(4) if the difference values of the maximum membership degrees of all the sub items and the maximum membership degree of the item are less than rho, taking the evaluation vector of the sub item with the maximum item weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result;
wherein, λ, α, β, k, ε, and ρ are parameters preset according to the number of sub items, the evaluation index, and the normal operating state of the wind turbine. In this embodiment, α is 0.1, β is 0.1, and k is 2.
The wind turbine generator running state evaluation indexes comprise generator bearing temperature, generator winding temperature, generator cooling water temperature, phase voltage, phase current, active power, reactive power, engine room position, engine room temperature, highest Topbox, highest control cabinet, variable pitch motor, variable pitch capacitor cabinet, variable pitch cabinet body and variable pitch inverter temperature.
As shown in fig. 2, the generator bearing temperature, generator winding temperature and generator cooling water temperature are divided into generator sub-items; phase voltage, phase current, active power and reactive power are divided into power grid system sub-items; the cabin position, cabin temperature, maximum Topbox and maximum control cabinet are divided into cabin and control cabinet items; the temperature of the variable pitch motor, the variable pitch capacitor cabinet, the variable pitch cabinet body and the variable pitch inverter is divided into variable pitch system sub-items.
The evaluation result of the running state of the wind turbine generator is divided into 4 conditions of 'good', 'normal', 'warning' and 'serious'.
As shown in FIG. 3, the membership function of the fuzzy evaluation matrix in the fuzzy evaluation module is a distribution function of a combination of triangles and half trapezoids.
The deterioration degree characterizes the relative deterioration degree of the current actual state of the wind turbine generator compared with the fault state, and the value range of the deterioration degree is [0,1 ].
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A wind turbine generator running state evaluation system based on fuzzy comprehensive evaluation comprises a data acquisition module, a quantization module, a weight calculation module, a fuzzy evaluation module and a result analysis module,
the data acquisition module is used for acquiring monitoring data of the evaluation indexes of the running states of the wind turbine generators;
the quantification module is used for quantifying the monitoring data of the wind turbine generator running state evaluation indexes to obtain the degradation degree of the wind turbine generator running state evaluation indexes;
the weight determination module divides the wind turbine generator running state evaluation indexes into a plurality of sub-items, and determines the item weight of each sub-item and the index weight of each wind turbine generator running state evaluation index under each sub-item based on the degradation degree;
fuzzy commentAnd the estimation module is used for forming a fuzzy estimation matrix by the wind generating set operation state estimation indexes in each sub-item, and obtaining the evaluation vector of each sub-item by combining the index weight of each wind generating set operation state estimation index: s1、S2、…Si…、Sn(n>1)，Si＝{Si1、Si2、…Sij…、Sim}(m>1)，SiAn evaluation vector representing the ith item, SijRepresenting the membership degree of the ith sub-project to the wind turbine generator operation state evaluation result j;
the result analysis module is used for acquiring the project weight of each sub-project and obtaining a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project;
the result analysis module obtains a final wind turbine generator operation state evaluation result based on the project weight and the evaluation vector of each sub-project, and the final wind turbine generator operation state evaluation result is specifically as follows: and taking the evaluation vector of the sub item with the largest project weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the largest membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result.
2. The system according to claim 1, wherein the wind turbine generator operation state evaluation index comprises a generator bearing temperature, a generator winding temperature, a generator cooling water temperature, a phase voltage, a phase current, an active power, a reactive power, a cabin position, a cabin temperature, a maximum Topbox, a maximum control cabinet, a variable pitch motor, a variable pitch capacitor cabinet, a variable pitch cabinet body and a variable pitch inverter temperature.
3. The wind turbine generator system running state evaluation system based on fuzzy comprehensive evaluation is characterized in that the generator bearing temperature, the generator winding temperature and the generator cooling water temperature are divided into generator sub-items; the phase voltage, the phase current, the active power and the reactive power are divided into power grid system sub-items; the cabin position, cabin temperature, maximum Topbox and maximum control cabinet are divided into cabin and control cabinet items; the temperature of the variable pitch motor, the variable pitch capacitor cabinet, the variable pitch cabinet body and the variable pitch inverter is divided into variable pitch system sub-items.
4. The wind turbine generator running state evaluation system based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the wind turbine generator running state evaluation result is divided into 4 cases of "good", "general", "caution" and "serious".
5. The system for evaluating the running state of the wind turbine generator based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the membership function of the fuzzy evaluation matrix in the fuzzy evaluation module is a distribution function combining a triangle and a half trapezoid.
6. The wind turbine generator running state evaluation system based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the degradation degree characterizes a relative degradation degree of a current actual state of the wind turbine generator compared with a fault state, and the value range is [0,1 ].
7. The system according to claim 1, wherein the result analysis module obtains the maximum membership degree S of each sub-item according to the evaluation vector of each sub-item1_max、S2_max、…Si_max…、Sn_maxAcquiring the project weight of each sub-project, and obtaining a final wind turbine generator operation state evaluation result based on the maximum membership and the project weight of each sub-project, wherein the method specifically comprises the following steps: obtaining an evaluation result of the running state of the wind turbine generator based on the maximum membership and the project weight of each sub-project, which specifically comprises the following steps: maximum membership S from all sub-items1_max、S2_max、…Si_max…、Sn_maxThe maximum value is selected to obtain the maximum item membership degree Smax；
λ (λ) if the maximum item membership is the maximum membership of the remaining sub-items>1) Multiplying, and if the maximum membership degree of the sub-item with the maximum item weight is less than alpha, then the maximum item membership degree SmaxThe evaluation vector of the sub item is used as the wind turbine running state evaluation vector, and the wind turbine running state evaluation result with the maximum membership degree in the wind turbine running state evaluation vector is selected as the final wind turbine running state evaluation result;
if the maximum item membership degree is lambda (lambda is more than 1) times of the maximum membership degree of other sub items, and the maximum membership degree of the sub item with the maximum item weight is larger than beta, taking the evaluation vector of the sub item with the maximum item weight as a wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as a final wind turbine generator running state evaluation result;
if the difference value between the maximum membership degree of k or more than k (n > k >1) sub items and the maximum item membership degree is less than epsilon, the evaluation vector of the sub item with the maximum comprehensive value of the weight and the maximum membership degree is used as the wind turbine running state evaluation vector, and the wind turbine running state evaluation result with the maximum membership degree in the wind turbine running state evaluation vector is selected as the final wind turbine running state evaluation result;
if the difference values of the maximum membership degrees of all the sub items and the maximum membership degree of the item are less than rho, taking the evaluation vector of the sub item with the maximum item weight as the wind turbine generator running state evaluation vector, and selecting the wind turbine generator running state evaluation result with the maximum membership degree in the wind turbine generator running state evaluation vector as the final wind turbine generator running state evaluation result;
wherein, λ, α, β, k, ε, and ρ are parameters preset according to the number of sub items, the evaluation index, and the normal operating state of the wind turbine.
8. The wind turbine generator running state evaluation system based on the fuzzy comprehensive evaluation as claimed in claim 7, wherein a value of α is 0.1.
9. The wind turbine generator running state evaluation system based on the fuzzy comprehensive evaluation as claimed in claim 7, wherein a value of β is 0.1.
10. The wind turbine generator running state evaluation system based on the fuzzy comprehensive evaluation as claimed in claim 7, wherein the value of k is 2.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101858312A (en) * | 2010-05-31 | 2010-10-13 | 重庆大学 | System and method thereof for evaluating real-time running state of wind generating set |
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| Title |
|---|
| 黄必清等: "基于模糊评价的海上直驱风电机组运行状态评估", 《清华大学学报（自然科学版）》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116027730A (en) * | 2023-03-24 | 2023-04-28 | 承德泰宇热控工程技术有限公司 | PLC switch board remote control system |
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