CN103235563B - Industrial field equipment energy efficiency evaluation method - Google Patents

Abstract

The invention discloses a method for evaluating the energy efficiency of industrial site equipment. The method is based on the collection of equipment operation data at the industrial site, and considering the limitations of online data collection at the industrial site, four online calculation methods for the energy efficiency of equipment applicable to different situations are proposed. The method is mainly Based on the equipment efficiency test method in the national standard, the physical model of the equipment is identified through the artificial neural network, and the error analysis of the results is carried out by using the mathematical statistics theory to realize the online evaluation of the energy efficiency of industrial field equipment. Compared with the existing methods, this method takes into account the limitations of industrial field data collection, and the method is more practical; compared with the commonly used empirical methods and estimation methods, this method is more scientific and ensures the accuracy of the evaluation results. Accuracy, so as to help industrial enterprises grasp the energy efficiency of equipment in real time, discover the key links of energy saving and consumption reduction, optimize electricity consumption, reduce the electricity consumption cost of enterprises, and promote the implementation of national energy conservation and emission reduction policies.

Description

A Method for Energy Efficiency Evaluation of Industrial Site Equipment

technical field

The invention relates to a method for evaluating the energy efficiency of industrial field equipment, which belongs to the fields of energy technology and electrical engineering.

Background technique

In order to further strengthen power demand-side management and implement the national energy-saving and emission-reduction strategy, six ministries and commissions including the National Development and Reform Commission and the Electricity Regulatory Commission jointly issued the "Power Demand Side Management Measures". In order to better complete the evaluation indicators of the method, promote the transformation of my country's demand side management from load management to energy efficiency management, and promote the development of contract energy management and energy-saving service companies in my country, scientific and accurate assessment of equipment energy efficiency is an important link. . The state has successively promulgated standards for energy efficiency test methods, energy efficiency limit values and energy efficiency grades for numerous equipment.

Due to the specific equipment energy efficiency calculation method stipulated by the national standard has high requirements for equipment data collection, there are many non-electrical parameters, such as: pressure, light, flow, etc., and there are also certain requirements for the test conditions of the equipment. The feasibility of this evaluation method in the laboratory in the industrial field puts forward high requirements on the professional level of the equipment energy efficiency evaluators and the company; and because most of the industrial field equipment is assembled in complete sets, it is not easy to disassemble, and the enterprise has production tasks. In order to meet the requirements of other aspects, it is necessary to explore a more operable method for evaluating the energy efficiency of industrial field equipment.

At present, most enterprises in my country still only understand the energy efficiency of their own equipment at the level of energy balance. They can only grasp the power consumption of equipment, but cannot grasp its conversion efficiency; Audits often use equipment nameplate identification, self-experience identification, energy efficiency estimation, or measurement of some parameters to calculate energy efficiency. They often pay more attention to the energy-saving potential of the enterprise, while ignoring the scientific evaluation of the actual operating efficiency of the equipment.

Contents of the invention

Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for evaluating the energy efficiency of industrial field equipment to solve the problem of online energy efficiency calculation of industrial field equipment.

Technical scheme: in order to achieve the above object, the technical scheme adopted in the present invention is:

A method for evaluating energy efficiency of industrial field equipment, comprising the following steps:

(1) Observing energy efficiency: check the equipment nameplate, check the national standard energy efficiency limit value and energy efficiency grade standard of this type of equipment according to the equipment model and factory rated power η _N , and judge whether the equipment belongs to the high-efficiency equipment stipulated by the state;

If it is judged to be high-efficiency equipment, proceed to step (2);

If it is judged as non-efficient equipment, the economical evaluation of the energy-saving transformation or replacement update of the equipment will be carried out according to the current market price, and the simple investment recovery period t _r of the equipment will be determined. If t _r >t _l , the energy-saving transformation or replacement of the equipment will be carried out Update to form the energy efficiency assessment report of the equipment; where t _r is the simple payback period for equipment energy-saving renovation or replacement update, t _l is the average life of the equipment, and the units of t _r and t _l are both years;

(2) Measuring energy efficiency: According to the difference of specific equipment, the difference of measurement ability and measurement environment, select one of step (3), step (4), step (5) or step (6) for the equipment according to the following method The energy efficiency evaluation method arranges the energy efficiency measurement points of the equipment, collects data, and preprocesses the data to eliminate bad data;

If the _industrial site can directly measure the output power P _out of the equipment online, or can accurately calculate the parameters λ ₁ , λ 2 ... λ of the output power P _out = f ₁ (λ ₁ ,λ ₂ ...λ _n ) through measurement _n , then use the accurate energy efficiency evaluation method in step (3) to evaluate the energy efficiency of the equipment, where the subscript n of λ is the number of parameters required for calculating the output power P _{out of} the equipment;

If the output power of the equipment cannot be measured on the industrial site, but all the parameters α ₁ , α ₂ for calculating the efficiency η=f ₂ (α ₁ ,α ₂ ...α _n ) of this type of equipment specified in the national standard can be measured online. ..α _n , then use the accurate energy efficiency evaluation method in step (4) to evaluate the energy efficiency of the equipment, where the subscript n of α is the number of parameters required to calculate the equipment efficiency η, that is, use the accurate energy efficiency evaluation method to evaluate the equipment The number of parameters required for energy efficiency assessment;

If the output power of the equipment can be measured but cannot be measured online due to a series of environmental reasons such as production requirements, equipment working conditions, and economics, or all parameters that characterize equipment efficiency can be measured but cannot be measured online, then use step (5) The probabilistic energy efficiency assessment method in the equipment energy efficiency assessment;

If the output power of the equipment cannot be measured, and the parameters representing the efficiency of the equipment cannot be measured completely or cannot be measured, then the energy efficiency estimation method in step (6) is used to evaluate the energy efficiency of the equipment;

(3) Accurate energy efficiency assessment: First, directly measure the output power P _out of the equipment online, or accurately calculate the parameters λ ₁ , λ ₂ ... λ _n of the output power by measuring, and calculate the equipment in combination with the input electric power P _{in of} the online measurement equipment The online energy efficiency η:

Then, according to the online energy efficiency η, an energy efficiency evaluation report of the equipment is formed;

(4) Accurate energy efficiency assessment: According to the type of equipment, query the test methods on the energy efficiency of this type of equipment in the national standard, obtain the calculation formula of equipment energy efficiency, and the measurement method of the required parameters α ₁ , α ₂ ... α _n , online Measure the parameters, and calculate the online energy efficiency η of the equipment according to the formula in the national standard:

η=f ₂ (α ₁ ,α ₂ ...α _n )

Then, according to the online energy efficiency η, an energy efficiency evaluation report of the equipment is formed;

(5) Probabilistic energy efficiency evaluation: through field testing, collect parameters λ ₁ , λ ₂ ... λ _n that can calculate output power P _out , or collect all parameters α ₁ , α ₂ ... α _n that characterize equipment efficiency , the parameters are uniformly expressed as η=f ₃ (ξ ₁ , ξ ₂ ... ξ _n ), where the subscript n of ξ is the number of parameters required for unified parameter representation; according to step (3) or step (4 ) to calculate the equipment efficiency η _i corresponding to the parameters (ξ _1i , ξ _2i ... ξ _ni ), and form a data source for neural network training (ξ _1i , ξ _2i ... ξ _ni , η _i ); according to the ability of the equipment to collect parameters on site, exclude the (nm) parameters (ξ _m+1 ... ξ _n ) that cannot be measured online, and form k sets of data pairs {(ξ ₁₁ , ξ ₂₁ ...ξ _m1 ,η ₁ ),(ξ ₁₂ ,ξ ₂₂ ...ξ _m2 ,η ₂ ),...(ξ _1k ,ξ _2k ...ξ _mk ,η _k )}; carry out BP The training of the artificial neural network determines the weights and thresholds of the neural network and the error e of the neural network model; through online measurement (ξ ₁ , ξ ₂ ... ξ _n ), input it into the trained artificial neural network model, and get Obtain the energy efficiency η of the equipment and the corresponding error e, and form the equipment energy efficiency evaluation report;

(6) Energy efficiency estimation: According to the factory rated power η _N on the equipment nameplate, on-site observation or measurement of a certain characteristic quantity of the equipment, such as illuminance, rotational speed, etc., divide the range of this characteristic quantity into five levels, from high to low in order : very good file, score λ=1; normal file, score λ=0.8; average file, score λ=0.6; poor file, score λ=0.4; poor file, score λ=0.2; according to the equipment The current state, determine the λ value, and finally calculate the estimated energy efficiency η of the equipment:

η=λ·η _N

Then, according to the estimated energy efficiency η, an energy efficiency assessment report of the equipment is formed.

The present invention adopts the BP artificial neural network to identify the physical model of the equipment when the parameters required for energy efficiency calculation on the industrial site cannot be measured online, so that the energy efficiency of the equipment can be measured online, and the error e of the model is used to represent the accuracy of the equipment energy efficiency evaluation results The energy efficiency estimation method adopted when the parameters required for energy efficiency calculation on the industrial site cannot be measured, divides the selected feature quantity range into five levels, and the product of the score of the corresponding level and the rated efficiency is used as the result of the equipment energy efficiency evaluation.

The invention mainly aims at the online energy efficiency evaluation problem of the equipment in the industrial field. There are certain differences between the online energy efficiency evaluation of commercial and residential equipment and industrial field equipment, but the principle is the same, so the method proposed by the present invention can also be referred to.

Beneficial effects: The method for evaluating the energy efficiency of industrial field equipment provided by the present invention is based on the collection of equipment operation data in the industrial field, considering the limitations of online data collection in the industrial field, and proposes four online calculation methods for energy efficiency of equipment suitable for different situations; compared with the current Commonly used empirical methods and estimation methods, this method is more scientific and ensures the accuracy of the evaluation results, thus helping industrial enterprises to grasp the operating energy efficiency of equipment in real time, discover the key links of energy saving and consumption reduction, optimize power consumption, and reduce enterprise cost of electricity, and promote the implementation of national energy conservation and emission reduction policies.

Description of drawings

Fig. 1 is the general flowchart of the inventive method of this method;

Figure 2 is a flow chart of the probabilistic energy efficiency assessment method;

Figure 3 is an artificial neural network diagram for probabilistic energy efficiency evaluation of three-phase asynchronous motors.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings.

An energy efficiency evaluation method for industrial field equipment, which uses the artificial neural network-based probabilistic energy efficiency evaluation method for the evaluation of the energy efficiency of three-phase asynchronous motors with limited industrial field parameter measurement, as follows.

(1) Observing energy efficiency: check the equipment nameplate, check the national standard energy efficiency limit value and energy efficiency grade standard of this type of equipment according to the equipment model and factory rated power η _N , and judge whether the equipment belongs to the high-efficiency equipment stipulated by the state; for non-high-efficiency equipment Economic evaluation of energy-saving transformation and replacement transformation of equipment.

For example: the nameplate parameters of the three-phase asynchronous motor are: model Y112M-4, rated efficiency 0.845. Check the national standard "GB18613-2006 Energy Efficiency Limits and Energy Efficiency Grades of Small and Medium-Sized Three-phase Asynchronous Motors" to know that the motor has an energy efficiency grade of three, which is a non-high-efficiency equipment; use the same type of high-efficiency motor on the market for replacement and transformation, and the model of the replacement high-efficiency motor is YX3-112M-4; The rated efficiency of YX3-112M-4 high-efficiency motor is _0.883 , and the market price is ￥739. The electricity price is 0.7, and the simple investment recovery period t _r of the equipment is 10.6 years, which is longer than its life span of 10 years, so the energy efficiency is measured.

(2) Selection of measurement energy efficiency method: According to the difference of specific equipment, the difference of measurement ability and measurement environment, according to the following method, select the equipment in step (3), step (4), step (5) or step (6) An energy efficiency evaluation method arranges the energy efficiency measurement points of equipment, collects data, preprocesses the data, eliminates bad data, and performs online energy efficiency measurement of equipment according to the corresponding method.

If the _industrial site can directly measure the output power P _out of the equipment online, or can accurately calculate the parameters λ ₁ , λ 2 ... λ of the output power P _out = f ₁ (λ ₁ ,λ ₂ ...λ _n ) through measurement _n , then use the accurate energy efficiency evaluation method in step (3) to evaluate the energy efficiency of the equipment, where the subscript n of λ is the number of parameters required to calculate the output power P _{out of} the equipment.

If the output power of the equipment cannot be measured on the industrial site, but all the parameters α ₁ , α ₂ for calculating the efficiency η=f ₂ (α ₁ ,α ₂ ...α _n ) of this type of equipment specified in the national standard can be measured online. ..α _n , then use the accurate energy efficiency evaluation method in step (4) to evaluate the energy efficiency of the equipment, where the subscript n of α is the number of parameters required to calculate the equipment efficiency η, that is, use the accurate energy efficiency evaluation method to evaluate the equipment The number of parameters required for energy efficiency evaluation.

If the output power of the equipment can be measured but cannot be measured online due to a series of environmental reasons such as production requirements, equipment working conditions, and economics, or all parameters that characterize equipment efficiency can be measured but cannot be measured online, then use step (5) The probabilistic energy efficiency assessment method in the equipment energy efficiency assessment.

If the output power of the equipment cannot be measured, and the parameters representing the efficiency of the equipment cannot be measured completely or cannot be measured, then the energy efficiency estimation method in step (6) is used to evaluate the energy efficiency of the equipment.

For example: Since the motor is a complete set of equipment, the torque cannot be measured. Only the tachometer can measure the speed of the motor on site, but it does not support online data recording and transmission. Due to the output power of the motor The torque T cannot be measured; according to the energy efficiency calculation formula that characterizes the asynchronous motor where the parameters are Among them, n ₁ is the synchronous speed, which can be calculated from the number of pole pairs or the rated speed of the motor. The number of pole pairs of this motor is 2, so n ₁ =1500r/min; since the speed n cannot be measured online, the probabilistic energy efficiency evaluation is selected for this The motor conducts online energy efficiency evaluation.

(3) Accurate energy efficiency evaluation: Firstly, directly measure the output power P _out of the equipment online, or accurately calculate the parameters λ ₁ , λ ₂ ... λ _n of the output power by measuring, and calculate the equipment in combination with the input electric power P _{in of} the online measurement equipment The online energy efficiency η:

Among them, the power unit is kW, and then according to the online energy efficiency η, the energy efficiency evaluation report of the equipment is formed.

Example: Energy efficiency of three-phase asynchronous motors It can be evaluated by online measurement of torque, rotational speed and input electric power.

(4) Accurate energy efficiency assessment: According to the type of equipment, query the test methods on the energy efficiency of this type of equipment in the national standard, obtain the calculation formula of equipment energy efficiency, and the measurement method of the required parameters α ₁ , α ₂ ... α _n , online Measure the parameters, and calculate the online energy efficiency η of the equipment according to the formula in the national standard:

η=f ₂ (α ₁ ,α ₂ ...α _n )

Then, according to the online energy efficiency η, an energy efficiency evaluation report of the equipment is formed.

Example: Energy efficiency of three-phase asynchronous motors It can be evaluated by online measurement of input electric power, frequency, speed, power factor, and voltage reference nameplate parameters.

(5) Probabilistic energy efficiency evaluation: through field testing, collect parameters λ ₁ , λ ₂ ... λ _n that can calculate output power P _out , or collect all parameters α ₁ , α ₂ ... α _n that characterize equipment efficiency , the parameters are uniformly expressed as η=f ₃ (ξ ₁ , ξ ₂ ... ξ _n ), where the subscript n of ξ is the number of parameters required for unified parameter representation; according to step (3) or step (4 ) to calculate the equipment efficiency η _i corresponding to the parameters (ξ _1i , ξ _2i ... ξ _ni ), and form a data source for neural network training (ξ _1i , ξ _2i ... ξ _ni , η _i ); according to the ability of the equipment to collect parameters on site, exclude the (nm) parameters (ξ _m+1 ... ξ _n ) that cannot be measured online, and form k sets of data pairs {(ξ ₁₁ , ξ ₂₁ ...ξ _m1 ,η ₁ ),(ξ ₁₂ ,ξ ₂₂ ...ξ _m2 ,η ₂ ),...(ξ _1k ,ξ _2k ...ξ _mk ,η _k )}; carry out BP The training of the artificial neural network determines the weights and thresholds of the neural network and the error e of the neural network model; through online measurement (ξ ₁ , ξ ₂ ... ξ _n ), input it into the trained artificial neural network model, and get Get the energy efficiency η of the equipment and the corresponding error e, and form the equipment energy efficiency evaluation report.

For example: the three-phase asynchronous motor in step (2) cannot be measured online due to the speed, so probabilistic energy efficiency evaluation is required. Comparing with the flow chart of probabilistic energy efficiency evaluation in Figure 2, first, according to the calculation formula of motor energy efficiency in step (4), calculate the 82 groups obtained by data collection including For the 82 energy efficiency values corresponding to the parameter data, since only the speed n cannot be measured, one parameter is eliminated to form 82 groups containing The data pair of parameters; then, establish a single hidden layer BP artificial neural network model, as shown in Figure 3. Use 41 of the 82 sets of data pairs to train the artificial neural network, identify the physical model of the motor, and obtain the average error e of the model as ±1%; take 8 parameters of the 82 sets of data except energy efficiency as input, and use the identification The solution error e of the obtained model is ±1.04%.

(6) Energy efficiency estimation: According to the factory rated power η _N on the equipment nameplate, on-site observation or measurement of a certain characteristic quantity of the equipment, such as illuminance, rotational speed, etc., divide the range of this characteristic quantity into five levels, from high to low in order : very good file, score λ=1; normal file, score λ=0.8; average file, score λ=0.6; poor file, score λ=0.4; poor file, score λ=0.2; according to the equipment The current state, determine the λ value, and finally calculate the estimated energy efficiency η of the equipment:

η=λ·η _N

Then, according to the estimated energy efficiency η, an energy efficiency assessment report of the equipment is formed.

For example: the rated luminous efficacy of energy-saving lamps is 60lm/w, the first 2000 hours, the luminous effect is very good, and its luminous efficacy is estimated to be 60lm/w; after 1000 hours, the light is slightly dimmed, and the subjective evaluation is normal, so its luminous efficacy Estimated to be 48lm/w.

At present, the error of energy efficiency estimation of equipment equipped with measuring points is about 5% to 10%, the error of direct estimation without measuring points is >10%, and the error of the evaluation result in step (5) is kept at 1% ~2% It can be seen that the method for evaluating the energy efficiency of industrial field equipment of the present invention can well guarantee the accuracy of the evaluation results in the case of limited industrial field measurement.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also possible. It should be regarded as the protection scope of the present invention.

Claims (1)

1. an industrial field equipment energy efficiency evaluation, is characterized in that: comprise the steps:

(1) efficiency is observed: check equipment nameplate, according to model and the rated power η that dispatches from the factory of equipment _n, check national standard energy efficiency market and the efficiency classification standard of this classification equipment, judge whether this equipment belongs to the high-efficiency appliance of national regulation;

If be judged as high-efficiency appliance, then enter step (2);

If be judged as non-efficient equipment, then according to Vehicles Collected from Market price, economic evaluation is carried out to the reducing energy consumption of equipment or alternative renewal, determine the simple and easy investment payback time t of equipment _rif, t _r>t _l, then carry out reducing energy consumption or substitute upgrading to equipment, the energy efficiency evaluation report of forming device; Wherein t _rfor device energy conservation transformation or the simple and easy investment payback time substituting renewal, t _lfor the mean lifetime of equipment, t _rand t _lunit be year;

(2) efficiency is measured: according to the difference of concrete equipment, the difference of measurement capability and measurement environment, according to a kind of energy efficiency evaluating method that following method is in equipment choice step (3), step (4), step (5) or step (6), the efficiency measuring point of equipment is arranged, image data, and pre-service is carried out to data, reject bad data;

If industry spot can the output power P of direct measuring equipment online _out, or can by measuring accurate Calculation output power P _out=f ₁(λ ₁, λ ₂... λ _n) parameter lambda ₁, λ ₂... λ _n, then adopt the accurate energy efficiency evaluating method in step (3) to carry out the energy efficiency evaluation of equipment, wherein the subscript n of λ is computing equipment output power P _outtime required parameter number;

If industry spot can not measure the output power of equipment, but calculating such the plant efficiency η=f that can specify in on-line measurement national standard ₂(α ₁, α ₂... α _n) whole parameter alpha ₁, α ₂... α _nthe accurate energy efficiency evaluating method in step (4) is then adopted to carry out the energy efficiency evaluation of equipment, the number of required parameter when wherein the subscript n of α is computing equipment efficiency eta, namely adopts accurate energy efficiency evaluating method to carry out the number of the parameter required for the energy efficiency evaluation of equipment;

If but the output power of equipment can be measured can not on-line measurement, or the parameter of characterization device efficiency all can measure but can not on-line measurement, then adopt the probability energy efficiency evaluating method in step (5) to carry out the energy efficiency evaluation of equipment;

If the output power immeasurability of equipment, the parameter of characterization device efficiency can not all be measured or immeasurability, then adopt the efficiency evaluation method in step (6) to carry out the energy efficiency evaluation of equipment;

(3) accurate energy efficiency evaluation: first by the output power P of online directly measuring equipment _out, or by measuring the parameter lambda of accurate Calculation output power ₁, λ ₂... λ _n, in conjunction with the input electric power P of in-situ measurement equipment _inthe online efficiency η of computing equipment:

Then according to online efficiency η, the energy efficiency evaluation report of forming device;

(4) accurate energy efficiency evaluation: according to device class, about the test method of such energy efficiency of equipment in inquiry national standard, obtains the computing formula of energy efficiency of equipment, and desired parameters α ₁, α ₂... α _nmeasuring method, parameter described in on-line measurement, the online efficiency η according to the formulae discovery equipment in national standard:

η＝f ₂(α ₁,α ₂...α _n)

Then according to online efficiency η, the energy efficiency evaluation report of forming device;

(5) probability energy efficiency evaluation: by on-the-spot test, collection can calculate output power P _outparameter lambda ₁, λ ₂... λ _n, or gather the whole parameter alpha characterizing plant efficiency ₁, α ₂... α _n, improve parameter unification is expressed as η=f ₃(ξ ₁, ξ ₂... ξ _n), wherein the subscript n of ξ is the number of unified parameters required parameter when representing; Corresponding parameter (ξ is calculated according to the method in step (3) or step (4) _1i, ξ _2i... ξ _ni) time plant efficiency η _i, form the data source (ξ being used for neural metwork training _1i, ξ _2i... ξ _ni, η _i); According to the ability of device context acquisition parameter, getting rid of can not (n-m) individual parameter (ξ of on-line measurement _m+1... ξ _n), formation is used for the k group data of neural metwork training to { (ξ ₁₁, ξ ₂₁... ξ _m1, η ₁), (ξ ₁₂, ξ ₂₂... ξ _m2, η ₂) ... (ξ _1k, ξ _2k... ξ _mk, η _k); Carry out the training of BP artificial neural network, determine the error e of the weights of neural network, threshold value and neural network model; By on-line measurement (ξ ₁, ξ ₂... ξ _n), be input to the artificial nerve network model of having trained, draw the efficiency η of equipment and the error e of correspondence, forming device energy efficiency evaluation is reported;

(6) efficiency estimation: according to the rated power η that dispatches from the factory on equipment nameplate _n, a certain characteristic quantity of field observation or measuring equipment, is five grades by this characteristic quantity interval division, is followed successively by from high to low: shelves very well, score value λ=1; Normal shelves, score value λ=0.8; General shelves, score value λ=0.6; Difference shelves, score value λ=0.4; Poor shelves, score value λ=0.2; State belonging to current according to equipment, determine λ value, finally calculate the estimation efficiency η of equipment:

η＝λ·η _N

Then according to estimation efficiency η, the energy efficiency evaluation report of forming device.