阅读说明：本技术 一种预测空气源热泵最佳除霜控制点的模型及其建立方法 (Model for predicting optimal defrosting control point of air source heat pump and establishing method thereof ) 是由 王伟 李昭阳 孙育英 王世权 吴旭 于 2020-05-22 设计创作，主要内容包括：一种预测空气源热泵最佳除霜控制点的模型及其建立方法,属于空气源热泵除霜控制领域。以空气源热泵名义制热量为基准,进行衡量空气源热泵在结霜工况运行时机组制热量损失大小；利用广义人工神经网络GRNN方法,确定结霜工况、机组结霜运行时间与评价指标之间的影响关系,模拟机组在不同结霜工况下采用不同除霜控制点的制热量损失大小；基于模拟结果,将同一结霜工况下最小制热量损失对应的除霜控制点确认为空气源热泵在此结霜工况下的最佳除霜控制点,依此方法建立全工况下空气源热泵最佳除霜控制点数据库；利用多元非线性方程回归方法,构建最佳除霜控制点与结霜工况间的函数关系,建立空气源热泵最佳除霜控制点预测模型。(A model for predicting an optimal defrosting control point of an air source heat pump and an establishing method thereof belong to the field of defrosting control of the air source heat pump. Measuring the heating capacity loss of the unit when the air source heat pump operates under the frosting working condition by taking the nominal heating capacity of the air source heat pump as a reference; determining an influence relation among a frosting working condition, a unit frosting operation time and an evaluation index by utilizing a generalized artificial neural network GRNN method, and simulating the heating loss of the unit under different frosting working conditions by adopting different defrosting control points; based on the simulation result, confirming the defrosting control point corresponding to the minimum heating loss under the same frosting working condition as the optimal defrosting control point of the air source heat pump under the frosting working condition, and establishing an optimal defrosting control point database of the air source heat pump under the full working condition according to the method; and (3) constructing a function relation between the optimal defrosting control point and the frosting working condition by utilizing a multiple nonlinear equation regression method, and establishing an optimal defrosting control point prediction model of the air source heat pump.)

1. A method for establishing a model for predicting an optimal defrosting control point of an air source heat pump is characterized by comprising the following steps:

(1) taking the nominal heating capacity of the air source heat pump as a reference, providing a performance evaluation index of the air source heat pump in the defrosting operation process, and measuring the heating capacity loss of the unit when the air source heat pump operates under the frosting working condition by using the index;

(2) determining the influence relationship among the frosting working condition, the unit frosting operation time and the evaluation index provided in the step (1) by utilizing a generalized artificial neural network (GRNN) method, establishing a GRNN model, and simulating the heating loss of the unit adopting different defrosting control points under different frosting working conditions;

(3) based on the GRNN model simulation result, confirming the defrosting control point corresponding to the minimum heating loss under the same frosting working condition as the optimal defrosting control point of the air source heat pump under the frosting working condition, and establishing an optimal defrosting control point database of the air source heat pump under the full working condition according to the method;

(4) and constructing a function relation between the optimal defrosting control point and the frosting working condition by utilizing a multiple nonlinear equation regression method based on the optimal defrosting control point database, thereby establishing an optimal defrosting control point prediction model of the air source heat pump.

2. The method for building a model for predicting the optimal defrosting control point of an air source heat pump according to claim 1, wherein in the step (1), the performance evaluation index of the defrosting operation process of the air source heat pump junction is a nominal heating loss coefficient,_NLthe physical meaning of the method is the ratio of the sum of the frosting heating loss and the defrosting heating loss to the total nominal heating capacity of the unit in the single defrosting circulation process of the air source heat pump.

3. The method of modeling a predicted optimal defrost control point for an air source heat pump as recited in claim 2 wherein Q is_L1-nominal frost loss (kJ); q_L2-nominal defrost loss (kJ); t is t_i-a frosting run time or defrosting control point(s); t is t_n-defrost end time(s); q. q.s_hc-nominal heating capacity (kW); q. q.s_hc2-actual heat production (kW);

nominal heating loss coefficient_NLThe calculation is as follows:

nominal frost loss:

nominal defrost loss:

nominal heating loss coefficient:。

4. the method for building a model for predicting the optimal defrosting control point of an air source heat pump according to claim 1, wherein in the step (2), the GRNN model is built by training the operation data of the air source heat pump unit in the perennial heating season, and the relative error of the simulation data is within ± 10%.

5. The method for establishing the model for predicting the optimal defrosting control point of the air source heat pump according to the claim 1, wherein in the step (3), the optimal defrosting control point database comprises the optimal defrosting control points of the air source heat pump under the working conditions that the ambient temperature is-15 to 6 ℃ and the relative humidity is 50 to 100%, the temperature interval is 1 ℃ and the humidity interval is 1%.

6. The method for building a model for predicting the optimal defrosting control point of an air source heat pump according to claim 1, wherein in the step (4), the built optimal defrosting control point prediction model only inputs the frosting condition parameters, namely the ambient temperature and the ambient humidity, so as to output the optimal defrosting control point of the air source heat pump under the condition.

7. A model for predicting an optimal defrost control point for an air-source heat pump prepared according to the method of any of claims 1 to 6.

Technical Field

The invention relates to a model for predicting an optimal defrosting control point of an air source heat pump and an establishing method thereof, belonging to the field of defrosting control of the air source heat pump.

Technical Field

Frosting is a key problem affecting the operating efficiency of the air source heat pump unit. Due to the existence and growth of the frost layer, the heat transfer resistance of the outdoor heat exchanger of the air source heat pump unit is increased, the heat transfer coefficient is reduced, the air flow resistance is increased, and the heating capacity of the unit is reduced, so that the defrosting control must be carried out on the unit. The ideal defrost is a "defrost on demand" process that includes: sensing existence of frost layer; monitoring the growth of the frost layer; judging the optimal defrosting control point; and fourthly, defrosting. However, frosting is a complex heat and mass transfer process, the existing defrosting control method can only sense the existence of a frost layer or monitor the growth of the frost layer, systematic research on an optimal defrosting control point is lacked, and the setting of the defrosting control point only depends on experience or experimental judgment, so that the unit is difficult to defrost at a proper defrosting time in actual operation, thereby causing frequent 'wrong defrosting' accidents, and further deteriorating the operation efficiency of the air source heat pump unit.

The optimal defrosting control point refers to the optimal defrosting time of the air source heat pump in the defrosting operation process, so that the heating loss of the unit in the periodic defrosting cycle is minimum. In the periodic defrosting process, if the defrosting time of the unit is earlier, the defrosting times in unit time are increased, and the defrosting loss of the unit is increased; on the other hand, if the defrosting is late, the frost loss of the unit is increased due to the accumulation of a large amount of frost as the frost running time is increased. Therefore, whether the optimal defrosting control point of the air source heat pump can be accurately judged is the key for avoiding the occurrence of the 'wrong defrosting' accident of the air source heat pump and improving the actual operation performance of the unit.

Disclosure of Invention

The invention aims to: the model is used for predicting the optimal defrosting control point of the air source heat pump, and the establishment method is provided, so that the optimal defrosting control point of the air source heat pump running under different frosting working conditions can be effectively predicted through the model, and theoretical support is provided for the efficient defrosting control of the air source heat pump.

In order to achieve the purpose, the technical scheme of the invention is as follows: a method for establishing a model for predicting an optimal defrosting control point of an air source heat pump comprises the following steps:

(1) taking the nominal heating capacity of the air source heat pump as a reference, providing a performance evaluation index of the air source heat pump in the defrosting operation process, and measuring the heating capacity loss of the unit when the air source heat pump operates under the frosting working condition by using the index;

(2) determining the influence relationship among the frosting working condition, the unit frosting operation time and the evaluation index provided in the step (1) by utilizing a generalized artificial neural network (GRNN) method, establishing a GRNN model, and simulating the heating loss of the unit adopting different defrosting control points under different frosting working conditions;

(3) based on the GRNN model simulation result, confirming the defrosting control point corresponding to the minimum heating loss under the same frosting working condition as the optimal defrosting control point of the air source heat pump under the frosting working condition, and establishing an optimal defrosting control point database of the air source heat pump under the full working condition according to the method;

(4) and constructing a function relation between the optimal defrosting control point and the frosting working condition by utilizing a multiple nonlinear equation regression method based on the optimal defrosting control point database, thereby establishing an optimal defrosting control point prediction model of the air source heat pump.

Furthermore, in the step (1), the performance evaluation index of the air source heat pump junction defrosting operation process is a nominal heating loss coefficient,_NLthe physical meaning of the method is the ratio of the sum of the frosting heating loss and the defrosting heating loss to the total nominal heating capacity of the unit in the single defrosting circulation process of the air source heat pump.

In the step (2), the GRNN model is established by training the operation data of the air source heat pump unit in the heating season of the year, and the relative error of the simulation data is within +/-10%.

In the step (3), the optimal defrosting control point database comprises the optimal defrosting control points of the air source heat pump under the working conditions that the environmental temperature is-15-6 ℃ (the interval is 1 ℃) and the relative humidity is 50-100% (the interval is 1%).

In the step (4), the optimal defrosting control point prediction model established only inputs frosting condition parameters (environment temperature and environment humidity), and can output the optimal defrosting control point of the air source heat pump under the condition.

The invention has the beneficial effects that: (1) the optimal defrosting control point of the air source heat pump under different frosting working conditions can be judged quickly and effectively; (2) the model has good applicability and is not limited by regions and meteorological conditions; and (3) guidance is provided for improvement and development of a defrosting control method, and the occurrence of 'wrong defrosting' accidents is avoided.

Drawings

FIG. 1 is a schematic diagram of the nominal heating loss coefficient of the air source heat pump according to the present invention, wherein Q_L1-nominal frost loss (kJ); q_L2-nominal defrost loss (kJ); t is t_i-a frosting run time or defrosting control point(s); t is t_n-defrost end time(s); q. q.s_hc-nominal heating capacity (kW); q. q.s_hc2-actual heat production (kW);

FIG. 2 is a generalized artificial neural network (GRNN) model structure diagram established by the present invention, the structure diagram is composed of four links of an input layer, a mode layer, a summation layer and an output layer, the environmental temperature, the relative humidity and the frosting operation time of the unit are used as input parameters, and the nominal heating loss coefficient is used as an output parameter;

fig. 3 is a prediction model of the optimal defrosting control point of the air source heat pump established by the invention, and the model can determine the optimal defrosting control point of the air source heat pump through the ambient temperature and the relative humidity, namely, the unit performs defrosting control after the frosting operation reaches the time point under the working condition.

Detailed Description

The following describes a model for predicting an optimal defrost control point of an air source heat pump and a method for establishing the same according to the present invention with reference to the accompanying drawings, but the present invention is not limited to the following embodiments.