CN112990710A - DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method - Google Patents

DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method Download PDF

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CN112990710A
CN112990710A CN202110290058.4A CN202110290058A CN112990710A CN 112990710 A CN112990710 A CN 112990710A CN 202110290058 A CN202110290058 A CN 202110290058A CN 112990710 A CN112990710 A CN 112990710A
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颜全椿
顾文
袁超
刘亚南
范立新
唐一铭
杨宏宇
孙平平
喻建
闫涛
杨春
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Jiangsu Frontier Electric Power Technology Co Ltd
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Abstract

The invention discloses a DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method, which is characterized in that a comprehensive evaluation index system is established according to the regulations in the national standard of electric energy quality, multi-level data of evaluation indexes are utilized, wind power plant electric energy quality control related measures and electric energy quality indexes are taken as input and output data, the technical efficiency and scale efficiency of a wind power plant are evaluated according to the input and output data based on a data envelope analysis theory, and harmonic waves can be reduced, three-phase imbalance is reduced, power factors are improved, voltage is stabilized, and voltage fluctuation and flicker are reduced.

Description

DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method
Technical Field
The invention belongs to the technical field of new energy grid-connected operation analysis and control, and particularly relates to a DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method.
Background
The requirements of large-scale wind power access to an electric power system and electric power users on the electric energy quality are stricter and stricter, and the method has important significance for comprehensive evaluation of the electric energy quality accurately and comprehensively. On one hand, because wind power generation has great uncertainty, a great amount of power electronic equipment interface devices are needed to realize energy conversion in the grid connection process, and severe examination is brought to grid connection; on the other hand, the quality-based pricing is the development trend of the future power market, and the comprehensive evaluation of the power quality is an important basis of the quality-based pricing.
The conventional electric energy quality comprehensive evaluation method comprises the following steps: a method based on a probability statistics principle, a method based on a fuzzy mathematics theory, an evaluation method based on an artificial intelligence algorithm and the like. In fact, the method mainly focuses on performing quality ranking analysis on the power quality, and lacks discussion of comprehensive evaluation significance and guidance of a power supply side of a power generation system on power quality control.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method aiming at the defects of the prior art, firstly, a comprehensive evaluation system is established aiming at the regulations in the national standard of electric energy quality, and multi-level detailed data such as wind power plant power division intervals, higher harmonics and the like are fully utilized. Then, the wind power plant electric energy quality control related measures and electric energy quality indexes are used as input and output data, and the technical efficiency and the scale efficiency of the wind power plant are evaluated on the input and output data based on a Data Envelope Analysis (DEA) theory. Finally, actual measurement tests are carried out on a plurality of main wind power plants of a certain actual provincial grid, and the results show that the method provided by the patent has a good engineering application prospect.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
a DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method comprises the following steps:
a comprehensive evaluation index system is established according to the regulations in the national standard of electric energy quality, multilevel data of evaluation indexes are utilized, wind power plant grid-connected capacity and reactive compensation device capacity are used as input data, electric energy quality indexes (voltage deviation, frequency deviation, three-phase imbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage fluctuation, flicker and the like) are used as output data, and the technical efficiency and the scale efficiency of the wind power plant are evaluated on the input and output data based on a data envelope analysis theory.
In order to optimize the technical scheme, the specific measures adopted further comprise:
the method comprises the following steps:
step 1: constructing a comprehensive evaluation index system, and acquiring actually measured data of the wind power plant according to evaluation indexes in the comprehensive evaluation index system;
step 2: calculating index values of power intervals of actual measurement data of the wind power plant, selecting 5 data of each power interval, obtaining weights of each interval by using an entropy method, and obtaining a weighting index;
step 3: dividing the evaluation index into a forward index and a reverse index according to the action of the evaluation index, and processing the reverse index as follows:
yij＝1/yij (6)
step 4: building a DEA (dead reckoning) evaluation model of the power quality of the wind power plant, determining an input-output matrix, and performing linear programming solution on the DEA evaluation model to obtain efficiency values of each wind power plant;
step 5: comprehensively evaluating the efficiency value of each wind power plant, and dividing the wind power plant into non-DEA effective value, weak DEA effective value and strong DEA effective value;
step 6: counting the number k of the wind power plants with effective weak DEA or effective strong DEA in the comprehensive evaluation result, if k is 1, displaying each weight and efficiency value, and quitting the operation; if k >1, execute Step 7;
step 7: and re-evaluating by utilizing an SE-DEA model, namely solving by utilizing linear programming to obtain the comprehensive sequencing of the electric energy quality and the input-output efficiency of the wind power plant, outputting an evaluation result, and finishing the evaluation.
The comprehensive evaluation index system described in Step1 above comprises the following evaluation indexes: voltage deviation, frequency deviation, three-phase imbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage ripple and flicker.
Respectively testing 0-100% of 10 power intervals for 5 continuous 10 minutes at 10% intervals by Step2, and deriving reports of 10 power intervals;
calculating each power interval index according to the following method:
Figure BDA0002982052920000021
in the formula, wkThe weight of each power interval can be determined by an entropy method; x is the number ofijIs an index of the quality of the electric energy.
In Step3, because wind power plants with different voltage levels have different assessment indexes and the short plate effect is considered, the input data are subjected to 3-stage function mapping conversion, the equation is as shown in formula (7), wherein d1＝0.2/yir； d2＝0.8/yir，yirIs a threshold value;
Figure BDA0002982052920000031
in Step4, it is assumed that n wind farms represent n DMUs, each DMU having m inputs and p outputs, xijRepresenting the input amount of the jth DMU to the ith type of input; y ispjRepresenting the output of the jth DMU to the p-type output, then DMUjIs input IjAnd aOutput OjRespectively as follows:
Ij＝v1x1j+v2x2j+…+vmxmj (1)
Oj＝u1y1j+u2y2j+…+upypj (2)
the total input I can be found by the analysis of the formulas (1) and (2)jSmaller and total output OjThe larger the wind power field effect rate is, the higher the corresponding evaluation index is, and the total input I is definedjAnd total output OjThe ratio of (a) to (b) is taken as an efficiency evaluation index, namely:
Figure BDA0002982052920000032
reasonable weight vectors V and U are selected to enable hjLess than or equal to 1, and V and U are vectors more than 0;
for this purpose, the index h is evaluated as the efficiency of the kth DMUjThe maximum is an objective function, the efficiency indexes of all decision units are constraint conditions, and a DEA evaluation model is obtained:
Figure BDA0002982052920000033
in Step4, the wind farm grid connection capacity and the reactive power compensator capacity are used as input data, and the electric energy quality index is used as output data.
Comprehensively evaluating the efficiency value of each wind power plant at Step5, and if theta is larger than thetai<1 is not DEA effective, if θi1 and em TS—+ep TS+>0 is effective as weak DEA, and theta isi1 and em TS—+ep TS+When 0, it is effective as strong DEA.
The SE-DEA model described in Step7 above refers to the maximum ratio of DMU to DMU that can be increased to maintain relative effectiveness, referred to as the "over-efficiency" value of DMU, while reflecting the redundancy of inputs and insufficient output of non-DEA active units.
The invention has the following beneficial effects:
the invention utilizes an analytic hierarchy process to establish a hierarchical hierarchy structure for detailed data and treatment measures of different power quality indexes, and utilizes an entropy method to weight data of each power interval. Meanwhile, envelope analysis is carried out on multi-level indexes, a super-efficiency improvement model is provided for the problem that the DEA effective wind power plant cannot be sequenced, the technical efficiency and scale efficiency of each wind power plant are obtained, and harmonic waves can be reduced, three-phase imbalance can be reduced, power factors can be improved, voltage can be stabilized, and voltage fluctuation and flicker can be reduced.
Drawings
FIG. 1 is a wind power plant power quality multi-level evaluation system provided by the invention;
FIG. 2 is a wild goose-shaped graph for evaluating the power quality of a wind power plant at multiple levels according to the evaluation method.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
The invention relates to a DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method, which comprises the following steps:
the method is characterized in that a comprehensive evaluation index system is established according to regulations in national standards of electric energy quality, a wind power plant sub-power interval, higher harmonics and other multi-layer times of evaluation indexes are utilized, wind power plant grid-connected capacity and reactive compensation device capacity serve as input data, electric energy quality indexes (voltage deviation, frequency deviation, three-phase imbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage fluctuation, flicker and the like) serve as output data, and the technical efficiency and the scale efficiency of the wind power plant are evaluated on the input and output data based on a Data Envelope Analysis (DEA) theory, and specifically the method comprises the following steps:
step 1: constructing a comprehensive evaluation index system, and acquiring actually measured data of the wind power plant according to evaluation indexes in the comprehensive evaluation index system;
step 2: calculating index values of power intervals of actual measurement data of the wind power plant, selecting 5 data of each power interval, obtaining weights of each interval by using an entropy method, and obtaining a weighting index;
step 3: dividing the evaluation index into a forward index and a reverse index according to the action of the evaluation index, and processing the reverse index as follows:
yij＝1/yij (6)
step 4: building a DEA (dead reckoning) evaluation model of the power quality of the wind power plant, determining an input-output matrix, and performing linear programming solution on the DEA evaluation model to obtain efficiency values of each wind power plant;
step 5: comprehensively evaluating the efficiency value of each wind power plant, and dividing the wind power plant into non-DEA effective value, weak DEA effective value and strong DEA effective value;
step 6: counting the number k of the wind power plants with effective weak DEA or effective strong DEA in the comprehensive evaluation result, if k is 1, displaying each weight and efficiency value, and quitting the operation; if k >1, execute Step 7;
step 7: and re-evaluating by utilizing an SE-DEA model, namely solving by utilizing linear programming to obtain the comprehensive sequencing of the electric energy quality and the input-output efficiency of the wind power plant, outputting an evaluation result, and finishing the evaluation.
At present, the national standard mainly comprises the following steady-state power quality indexes: voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage fluctuation and flicker 6 indexes. The latest standards for wind power plants are GB/T19963 plus 2011 technical specification for accessing a wind power plant to a power system and an industry standard NB/T31005 plus 2011 method for testing the power quality of the wind power plant.
The assessment of the power quality of the wind power plant still follows the conventional method, and the particularity of a plurality of random variables of the wind power plant is not considered. Meanwhile, in the conventional electric energy quality comprehensive evaluation method, a correlation algorithm is mostly adopted to perform weighting processing on each index to obtain a comprehensive index. Because wind power output is influenced by uncertain wind speed, the general variation range is large, which is also one of the key difficulties influencing wind power integration. Therefore, testing according to sub-power intervals is provided for testing the power quality of the wind power plant in the latest national standard, namely, 0-100% of 10 power intervals are tested for 5 continuous 10 minutes at 10% intervals respectively, and reports of 10 sub-power intervals are derived. Meanwhile, the conventional method only examines the total harmonic distortion rate for harmonic voltage/current and inter-harmonic voltage/current, does not pay attention to each harmonic value, obviously easily causes information loss, and particularly when the higher harmonic exceeds the standard. In order to fully utilize measured data of a wind power plant, a total 4-level multi-level comprehensive evaluation method for the power quality is provided, and a comprehensive evaluation index system is shown in figure 1.
For multi-level data of each index, a 95% probability maximum value or a maximum value is taken according to the requirements of the current standard, but regarding the sub-power interval test of the wind power plant, the standard only requires that the sub-power interval index value is given in a report, and in the comprehensive evaluation of the power quality, only certain interval data is obviously insufficient.
Therefore, the patent calculates each power interval index according to the following method:
Figure BDA0002982052920000051
in the formula, wkThe weight of each power interval can be determined by an entropy method; x is the number ofijIs an index of the quality of the electric energy.
The invention can simultaneously reduce harmonic waves, reduce three-phase unbalance, improve power factor, stabilize voltage and reduce voltage fluctuation and flicker.
The reactive power compensation device that wind-powered electricity generation field adopted includes at present: static Var Compensator (SVC), Static Var Generator (SVG), etc., wherein SVG has the advantages of short response time, large capacity, etc., and becomes the most widely used reactive power compensator in recent years, and has a significant effect on improving low voltage ride through capability. Therefore, the wind power plant rated power and the SVG capacity are used as input data.
Therefore, in the examples, Step1 describes a comprehensive evaluation index system including the following evaluation indexes: voltage deviation, frequency deviation, three-phase imbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage ripple and flicker.
In the embodiment, Step2 tests 10 power intervals of 0-100% for 5 continuous 10 minutes at 10% intervals respectively, and derives a report of 10 sub-power intervals;
calculating each power interval index according to the following method:
Figure BDA0002982052920000061
in the formula, wkThe weight of each power interval can be determined by an entropy method; x is the number ofijIs an index of the quality of the electric energy.
In the embodiment, in Step3, because wind power plant assessment indexes of different voltage levels are different, and the short-plate effect is considered at the same time, the input data is subjected to 3-stage function mapping conversion, wherein the equation is as shown in formula (7), wherein d is1＝0.2/yir；d2＝0.8/yir，yirIs a threshold value;
Figure BDA0002982052920000062
in the embodiment, in Step4, it is assumed that n wind farms have n decision units (DMUs), and each DMU has m inputs and p outputs, xijRepresenting the input amount of the jth DMU to the ith type of input; y ispjRepresenting the output of the jth DMU to the p-type output, then DMUjIs input IjAnd a total output OjRespectively as follows:
Ij＝v1x1j+v2x2j+…+vmxmj (1)
Oj＝u1y1j+u2y2j+…+upypj (2)
the total input I can be found by the analysis of the formulas (1) and (2)jSmaller and total output OjThe larger the wind power field effect rate is, the higher the corresponding evaluation index is, and the total input I is definedjAnd total output OjThe ratio of (a) to (b) is taken as an efficiency evaluation index, namely:
Figure BDA0002982052920000063
reasonable weight vectors V and U are selected to enable hjLess than or equal to 1, and V and U are vectors more than 0;
for this purpose, the index h is evaluated as the efficiency of the kth DMUjThe maximum is an objective function, the efficiency indexes of all decision units are constraint conditions, and a DEA evaluation model is obtained:
Figure BDA0002982052920000071
in the embodiment, in Step4, the wind farm grid-connected capacity and the reactive power compensation device capacity are used as input data, and the power quality index is used as output data.
In the embodiment, the Step5 comprehensively evaluates the efficiency value of each wind farm, and if theta is determinedi<1 is not DEA effective, if θi1 and em TS—+ep TS+>0 is effective as weak DEA, and theta isi1 and em TS—+ep TS+When 0, it is effective as strong DEA.
In the embodiment, the SE-DEA model described in Step7 is a super-efficiency DEA model, and can solve the problem that ordering cannot be compared when a plurality of θ is 1 in DEA validity, and in the SE-DEA, a maximum proportion value that a certain DMU can increase its input to maintain relative validity is called as a super-efficiency value of the DMU, and at the same time, the input redundancy and output insufficiency of non-DEA valid units can be reflected.
In order to verify the effectiveness and feasibility of the method, 5 wind power plants in a certain province are selected for actual measurement, and the model is solved based on the Lingo 9.0.
Firstly, weighting data of each power division interval by using an entropy method, taking rated power and SVG capacity of a wind power plant as input data, and taking a weighting index as output data, wherein the weighting index is shown in table 1.
The indexes include the rated power of an electric field, static var generator capacity of SVG, voltage deviation of delta U, frequency deviation of delta f, unbalanced three-phase voltage of unbalances of unbU, long-time flicker of Plt, short-time flicker of Pst, total harmonic voltage distortion rate of THDu and total harmonic current distortion rate of THDi, and are calculated according to national standards and weighted by using a formula (8).
Because the voltage grades of the 5 wind power plant booster station power quality test points in the meter are different, the voltage grades are uniformly converted to 110kV by using the following formula:
Figure BDA0002982052920000072
in the formula, r110Is an index ykAt a threshold value of 110kV voltage class.
TABLE 1 input/output data
Figure BDA0002982052920000081
When the method is implemented and verified, the evaluation result of the traditional DEA is adopted to know that the DMU efficiency value theta in the comprehensive evaluation result of the power quality of 5 wind power plants is (1,1,1,0.9567, 0.6544). DMU1、DMU2、DMU3The evaluation result is qualified and the scale is optimal; DMU4And DMU5And if the evaluation result is not weak and effective, the evaluation result of the power quality is unqualified, namely the non-scale optimization is realized. It should be noted that the evaluation result here is not weak and effective, and does not represent that the power quality is not qualified, but does not yet exert the best performance under the current SVG configuration. Since evaluation points 1, 2, and 3 are weak and effective, no ranking comparison can be performed. The re-evaluation was performed using the SE-DEA model, and the relaxation variables and the remaining variables are shown in Table 2.
TABLE 2 relaxation variables and residual variables
Figure BDA0002982052920000082
As can be seen from Table 2, the power quality data is reevaluated by using the SE-DEA model, and the point DMU is measured4And DMU5Still not weakly effective, while the DMU1、DMU2、DMU3Due to the limitation being lifted, the efficiency rate may be greater than 1, i.e. (4.065,1.3840,10.2901, 0.9567,0.6544), when the final ranking is: DMU3>DMU1>DMU2>DMU4>DMU5. It can be seen that the grid-connected feasibility of 5 wind power plants is evaluated according to the quality of electric energy, the wind power plant 3 is the most feasible, and the wind power plant 5 is the worst feasible. Meanwhile, although 5 wind power plants are all provided with SVG reactive power compensation equipment, on the technical scale, the performance of the wind farm 3 is the best, and the wind farm 5 has a larger improvement space. The goose-shape graph of the multi-level comprehensive evaluation result is shown in figure 2, namely, the comprehensive ranking participating in evaluation is given.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that various modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention and are intended to be within the scope of the present invention.

Claims (9)

1. A DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method is characterized by comprising the following steps:
a comprehensive evaluation index system is established according to the regulations in the national standard of power quality, multilevel data of evaluation indexes are utilized, wind power plant grid-connected capacity and reactive compensation device capacity are used as input data, power quality indexes are used as output data, and the technical efficiency and scale efficiency of the wind power plant are evaluated on the input and output data based on a data envelope analysis theory.
2. The DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method according to claim 1, characterized by comprising the following steps:
step 1: constructing a comprehensive evaluation index system, and acquiring actual measurement data of the wind power plant according to evaluation indexes in the comprehensive evaluation index system;
step 2: calculating index values of power intervals of actual measurement data of the wind power plant, selecting 5 data of each power interval, obtaining weights of each interval by using an entropy method, and obtaining a weighting index;
step 3: dividing the evaluation index into a forward index and a reverse index according to the action of the evaluation index, and dividing the reverse index into a forward index and a reverse indexijThe following treatments are carried out:
yij＝1/yij (6)
step 4: building a DEA (dead reckoning) evaluation model of the power quality of the wind power plant, determining an input-output matrix, and performing linear programming solution on the DEA evaluation model to obtain efficiency values of each wind power plant;
step 5: comprehensively evaluating the efficiency value of each wind power plant, and dividing the wind power plant into non-DEA effective value, weak DEA effective value and strong DEA effective value;
step 6: counting the number k of the wind power plants with effective weak DEA or effective strong DEA in the comprehensive evaluation result, if k is 1, displaying each weight and efficiency value, and quitting the operation; if k >1, execute Step 7;
step 7: and re-evaluating by utilizing an SE-DEA model, namely solving by utilizing linear programming to obtain the comprehensive sequencing of the electric energy quality and the input-output efficiency of the wind power plant, outputting an evaluation result, and finishing the evaluation.
3. The DEA-based wind power plant power quality multi-level comprehensive evaluation method according to claim 2, characterized in that the comprehensive evaluation index system Step1 comprises the following evaluation indexes: voltage deviation, frequency deviation, three-phase imbalance, harmonic voltage/current, inter-harmonic voltage/current, voltage ripple and flicker.
4. The DEA-based wind power plant electric energy quality multi-level comprehensive evaluation method according to claim 1, characterized in that Step2 tests 10 power intervals of 0-100% for 5 consecutive 10 minutes at 10% intervals respectively, and derives a report of 10 divided power intervals;
calculating each power interval index according to the following method:
Figure FDA0002982052910000021
in the formula, wkThe weight of each power interval can be determined by an entropy method; x is the number ofijIs an index of the quality of the electric energy.
5. The DEA-based wind power plant power quality multi-level comprehensive assessment method according to claim 1, characterized in that in Step3, due to the fact that wind power plant assessment indexes of different voltage levels are different, and the short plate effect is considered at the same time, input data are subjected to '3-stage' function mapping conversion, the equation is as shown in formula (7), wherein d is1＝0.2/yir；d2＝0.8/yir，yirIs a threshold value;
Figure FDA0002982052910000022
6. the DEA-based wind farm power quality multi-level comprehensive assessment method according to claim 5, wherein in Step4, assuming that n wind farms have n DMUs, each DMU has m inputs and p outputs, xijRepresenting the input amount of the jth DMU to the ith type of input; y ispjRepresenting the output of the jth DMU to the p-type output, then DMUjIs input IjAnd a total output OjRespectively as follows:
Ij＝v1x1j+v2x2j+…+vmxmj (1)
Oj＝u1y1j+u2y2j+…+upypj (2)
the total input I can be found by the analysis of the formulas (1) and (2)jThe smaller and the totalOutput OjThe larger the wind power plant efficiency is, the larger the corresponding evaluation index is, and the total input I is definedjAnd total output OjThe ratio of (a) to (b) is used as an efficiency evaluation index, namely:
Figure FDA0002982052910000023
reasonable weight vectors V and U are selected to enable hjLess than or equal to 1, and V and U are vectors more than 0;
for this purpose, the index h is evaluated as the efficiency of the kth DMUjThe maximum is an objective function, the efficiency indexes of all decision units are constraint conditions, and a DEA evaluation model is obtained:
Figure FDA0002982052910000024
7. the DEA-based wind power plant power quality multi-level comprehensive assessment method according to claim 6, wherein in Step4, wind power plant grid-connected capacity and reactive power compensation device capacity are used as input data, and power quality indexes are used as output data.
8. The DEA-based wind power plant power quality multi-level comprehensive evaluation method according to claim 7, characterized in that Step5 comprehensively evaluates efficiency values of each wind power plant if theta is determinedi<1 is not DEA effective, if θi1 and em TS—+ep TS+>0 is effective as weak DEA, and theta isi1 and em TS—+ep TS+When 0, it is effective as strong DEA.
9. The DEA-based wind power plant power quality multi-level comprehensive evaluation method according to claim 1, characterized in that the SE-DEA model in Step7 is used for setting a maximum proportion value of a DMU which can increase the input of the DMU and keep the relative effectiveness, namely an over-efficiency value of the DMU, and reflecting the input redundancy and output insufficiency of non-DEA effective units.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114113870A (en) * 2022-01-28 2022-03-01 西安德纳检验检测有限公司 New energy station power grid adaptability detection method, device and system
CN114677031A (en) * 2022-04-01 2022-06-28 四川大学 Harmonic wave treatment demand evaluation method based on data envelope analysis

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114113870A (en) * 2022-01-28 2022-03-01 西安德纳检验检测有限公司 New energy station power grid adaptability detection method, device and system
CN114113870B (en) * 2022-01-28 2022-04-26 西安德纳检验检测有限公司 New energy station power grid adaptability detection method, device and system
CN114677031A (en) * 2022-04-01 2022-06-28 四川大学 Harmonic wave treatment demand evaluation method based on data envelope analysis
CN114677031B (en) * 2022-04-01 2022-11-25 四川大学 Harmonic wave treatment demand evaluation method based on data envelope analysis

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