CN115935604A - Energy-saving control method and terminal for heating and ventilation system of converter station - Google Patents
Energy-saving control method and terminal for heating and ventilation system of converter station Download PDFInfo
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Abstract
The invention discloses an energy-saving control method and a terminal for a converter station heating and ventilation system, which are used for processing input historical environment parameters among converter station equipment according to a prediction model to obtain an environment parameter predicted value, introducing a weight coefficient to process power grid operation data to obtain a weight coefficient group under different operation conditions of a power grid, further combining the environment parameter predicted value and the weight coefficient group to obtain a relation function, optimizing and calculating the relation function through a particle swarm algorithm, and finally outputting an optimal fan operation parameter to carry out optimization control on a fan, so that the energy-saving maximization of the heating and ventilation system can be realized under the condition that the environment parameters among the converter station equipment meet the normal operation conditions of all power equipment.
Description
Technical Field
The invention relates to the technical field of converter station control, in particular to an energy-saving control method and terminal for a converter station heating and ventilation system.
Background
The converter station is a station established in a high-voltage direct-current transmission system for converting alternating current into direct current or converting direct current into alternating current and meeting the requirements of a power system on safety, stability and power quality. An equipment room is arranged in the converter station, and the equipment room is a closed space. Because the equipment in the equipment room can generate heat when in operation, the special heating and ventilating equipment is required to be used for ventilation in the equipment room so as to keep the temperature in the equipment room within a reasonable range.
At present, a heating and ventilation combined machine used in a station runs continuously for twenty-four hours and does not have energy-saving control capability. The equipment room is a closed building, and a heating and ventilating system of the equipment room comprises an air supply part and an air exhaust part. The heating and ventilation combined machine is an air supply system integrating a fan, a filter, a heater, a humidifier and a refrigeration coil, and can supply air with specific temperature, humidity and air volume to a device room. The air exhaust system mainly comprises an exhaust fan positioned on the top or side wall of the equipment room, and the requirement of micro-positive pressure between the equipment rooms is kept through air supply and exhaust linkage control. When equipment load is low or outdoor temperature is low, less air volume can satisfy the requirement, consequently can be through carrying out frequency conversion control to the combination machine motor, realize energy-conserving target under the prerequisite of guaranteeing the operation environment between equipment. The common control variables are temperature, humidity, etc., i.e. the working frequency of the motor of the combined machine is adjusted according to the temperature and humidity conditions. However, the adjustment method is simple and is hysteresis adjustment.
In the prior art, as the patent with application number CN201720346355.5 discloses a new trend and exhaust linkage control system, including control processor, the input of air inlet device is connected to control processor's output electricity, and exhaust device's input is connected to control processor's output electricity, and air inlet device includes first gas response module, and exhaust device includes second gas response module, and control processor's input electricity respectively connects the output of first gas response module and the output of second gas response module. The air inlet device and the air exhaust device are controlled by sensing the flowing condition of air in the air inlet device and the air exhaust device, the air exhaust device is controlled by the control processor to operate when the air inlet device is sensed to operate, otherwise, the air inlet device is controlled by the control processor to operate, the advantage of linkage is achieved, and therefore the problem that the indoor air circulation is poor due to the fact that the existing fresh air device and the air exhaust device cannot be started simultaneously is effectively solved. But the proposal can only realize the linkage control of air supply and air exhaust.
Still as patent application No. CN 2016212009381.5 discloses a linkage economizer system of freezer air conditioning system and wind drench tunnel exhaust system, including freezer air conditioning system, wind drench tunnel exhaust system, air quality detector and multi-functional integrated control ware, freezer air conditioning system includes the air conditioner, the air conditioner has air conditioner return air end and air conditioner air supply end, wind drenches tunnel exhaust system and includes the exhaust fan, the exhaust fan has air inlet end of airing exhaust and air outlet end of airing exhaust, multi-functional integrated control ware difference signal connection air quality detector, air conditioner and exhaust fan. Preferably, freezer air conditioning system still includes new fan. Various regulating valves and the like are also included. The refrigeration house air conditioning system also comprises a dehumidifier. The linkage of the air conditioning system of the refrigeration house and the air spraying tunnel exhaust system is realized, so that the indoor air quality is adjusted, the service life of the system can be prolonged, the minimum operation energy consumption is realized, the optimal use effect is achieved, the design is ingenious, the structure is simple, the use is convenient, and the air spraying tunnel exhaust system is suitable for large-scale popularization and application. However, in the scheme, different devices of the system are controlled correspondingly mainly by detecting parameters such as indoor temperature, humidity, cleanliness, dust degree and the like, and a specific energy-saving strategy and an energy-saving method are not disclosed.
And the patent with application number CN202220829526.0 discloses a quick balanced control system of clean room pressure differential variable working condition, belong to the technical field of automated control, including multiple clean rooms, equip with the constant air volume valve, clean room pressure sensor and variable air volume valve on multiple said clean rooms separately, the said constant air volume valve is assembled in the air intake section of the clean room, the said clean room pressure sensor is assembled in the arbitrary position of the clean room, the said variable air volume valve is assembled in the air exhaust section of the clean room; through the application automation technology, make the controller can real time monitoring toilet's constant air volume valve and variable air volume valve, according to the record under the different operating mode aperture after constant air volume valve and the variable air volume valve of toilet stabilize, in order to reach the later stage after the start at every turn, can give the optimal aperture of controller memory storage, come to carry out initial positioning to the aperture of toilet's constant air volume valve and variable air volume valve, stabilize the regulation based on this aperture again, just so can make the pressure differential of toilet fast stable, thereby can establish the pressure differential gradient of clean district very fast. According to the scheme, the initial opening degree of the air valve between the devices under different working conditions is preset, the rapid adjustment of the pressure difference is realized, and although the air quantity can be controlled in an air valve adjusting mode, the energy-saving effect is poor.
Therefore, the prior art scheme mainly researches how to realize linkage control of the air supply and exhaust system and quickly realize the control target. But does not consider maximizing the energy savings of the hvac system while maintaining the operating requirements between the devices.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the energy-saving control method and the terminal for the heating and ventilation system of the converter station are provided, so that the normal operation of equipment is ensured, and the energy-saving maximization of the heating and ventilation system is realized.
In order to solve the technical problems, the invention adopts the technical scheme that:
a converter station heating and ventilation system energy-saving control method comprises the following steps:
acquiring historical environmental parameters, power grid operation data and initial parameters of a fan between convertor station equipment;
processing the historical environmental parameters between the convertor station equipment according to a prediction model to obtain an environmental parameter prediction value;
processing the power grid operation data according to a weight coefficient model to obtain a weight coefficient group;
obtaining a relation function according to the environment parameter predicted value, the weight coefficient group and the initial fan parameter;
optimizing and calculating the relation function through a particle swarm algorithm, and outputting optimal fan operation parameters;
and adjusting the working state of the fan according to the optimal fan operation parameter.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a converter station heating and ventilating system energy-saving control terminal comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor executes the computer program to realize the steps of the converter station heating and ventilating system energy-saving control method.
The invention has the beneficial effects that: the method comprises the steps of processing input historical environmental parameters among convertor station equipment according to a prediction model to obtain an environmental parameter prediction value, introducing a weight coefficient to process power grid operation data to obtain a weight coefficient group under different operation conditions of a power grid, further combining the environmental parameter prediction value and the weight coefficient group to obtain a relation function, optimizing and calculating the relation function through a particle swarm algorithm, and finally outputting optimal fan operation parameters to carry out optimization control on a fan, so that the energy conservation maximization of a heating and ventilation system can be realized under the condition that the environmental parameters among the convertor station equipment meet the normal operation conditions of all power equipment.
Drawings
Fig. 1 is a flowchart illustrating steps of a method for controlling energy saving of a heating and ventilation system of a converter station according to an embodiment of the present invention;
fig. 2 is a flowchart of control steps of an energy saving control method for a heating and ventilation system of a converter station in an embodiment of the present invention;
fig. 3 is a flowchart of a step of solving a relation function of a method for controlling energy saving of a heating and ventilation system of a converter station in an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an energy-saving control terminal of a heating and ventilation system of a converter station in an embodiment of the present invention.
Detailed Description
In order to explain the technical contents, the objects and the effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1, a converter station heating and ventilation system energy-saving control method includes the steps:
acquiring historical environment parameters, power grid operation data and initial fan parameters of equipment rooms of the converter station;
processing the historical environmental parameters between the converter station equipment according to a prediction model to obtain an environmental parameter prediction value;
processing the power grid operation data according to a weight coefficient model to obtain a weight coefficient group;
obtaining a relation function according to the environment parameter predicted value, the weight coefficient group and the initial fan parameter;
optimizing and calculating the relation function through a particle swarm algorithm, and outputting optimal fan operation parameters;
and adjusting the working state of the fan according to the optimal fan operation parameter.
As can be seen from the above description, the beneficial effects of the present invention are: the method comprises the steps of processing input historical environmental parameters among convertor station equipment according to a prediction model to obtain an environmental parameter prediction value, introducing a weight coefficient to process power grid operation data to obtain a weight coefficient group under different operation conditions of a power grid, further combining the environmental parameter prediction value and the weight coefficient group to obtain a relation function, optimizing and calculating the relation function through a particle swarm algorithm, and finally outputting optimal fan operation parameters to carry out optimization control on a fan, so that the energy conservation maximization of a heating and ventilation system can be realized under the condition that the environmental parameters among the convertor station equipment meet the normal operation conditions of all power equipment.
Further, the historical environmental parameters comprise a temperature parameter, a humidity parameter, a pressure parameter and a fan operation parameter;
the processing the historical environmental parameters between the convertor station equipment according to the prediction model to obtain the environmental parameter prediction value comprises the following steps:
respectively constructing prediction models between the temperature parameter, the humidity parameter and the pressure parameter and the fan operation parameter to obtain a temperature prediction model, a humidity prediction model and a pressure prediction model;
processing the temperature parameter according to the temperature prediction model to obtain a temperature parameter prediction value;
processing the humidity parameter according to the humidity prediction model to obtain a humidity parameter prediction value;
processing the pressure parameter according to the pressure prediction model to obtain a pressure parameter prediction value;
and obtaining the environmental parameter predicted value according to the temperature parameter predicted value, the temperature parameter predicted value and the pressure parameter predicted value.
According to the above description, the prediction models between the temperature parameter, the humidity parameter and the pressure parameter and the fan operation parameter are constructed respectively, the temperature parameter, the humidity parameter and the pressure parameter are processed, the processed temperature parameter prediction value, the processed humidity parameter prediction value and the processed pressure parameter prediction value are synthesized to obtain the environment parameter prediction side, and the temperature condition, the humidity condition and the pressure condition of the equipment room are fully considered, so that the normal operation of the equipment room is considered, and meanwhile, the energy conservation maximization of the heating and ventilation system can be realized by reasonably controlling the equipment such as the air supply and exhaust system motor and the air valve.
Further, the temperature prediction model includes:
T(k+1)＝f[x T (k),…,x T (k-j),…,x T (k-d+1),
T(k),…,T(k-j),…,T(k-d+1)]；
t (k + 1) is a predicted value of the temperature parameter of the predicted output between the converter station devices at the next moment; d is the set delay step number; t (k-j) is the temperature value between the converter station devices output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of T (k) Inputting a temperature parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency.
According to the description, the temperature prediction model is obtained according to the historical temperature value and the temperature parameter matrix input by the fan from the outside, and the delay step number parameter is set, so that the calculation accuracy of the temperature prediction model can be adjusted by adjusting the lead-in quantity of the delay step number, and different accuracy requirements are met.
Further, the humidity prediction model includes:
h (k + 1) is a humidity parameter predicted value of predicted output between the converter station devices at the next moment; d is the set delay step number; h (k-j) is the humidity value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of H (k) Inputting a humidity parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
And (k-j) is the air valve opening and the air fan frequency output at the previous d moments.
According to the description, the humidity prediction model is obtained according to the historical humidity value and the humidity parameter matrix input from the outside by the fan, and the delay step number parameter is set, so that the calculation accuracy of the humidity prediction model can be adjusted by adjusting the lead-in quantity of the delay step number, and different accuracy requirements are met.
Further, the pressure prediction model includes:
q (k + 1) is a pressure parameter predicted value of predicted output between the converter station devices at the next moment; d is the set delay step number; q (k-j) is the pressure value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of Q (k) Inputting a pressure parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency.
According to the description, the pressure prediction model is obtained according to the historical pressure value and the pressure parameter matrix input from the outside by the fan, and the delay step number parameter is set, so that the calculation accuracy of the pressure prediction model can be adjusted by adjusting the lead-in quantity of the delay step number, and different accuracy requirements are met.
Further, the obtaining the environmental parameter predicted value according to the temperature parameter predicted value, the temperature parameter predicted value and the pressure parameter predicted value includes:
F 1 ＝k 1 (T * -T(k+i)) 2 +k 2 (H * -T(k+i)) 2 +k 3 (Q * -Q(k+i)) 2 ；
wherein k1, k2 and k3 are weight coefficients of temperature, humidity and pressure intensity among equipment respectively; t is a temperature reference value, H is a humidity reference value, and Q is a pressure reference value; q (k + i) is a pressure parameter predicted value of predicted output between the converter station devices at the next moment; h (k + i) is a humidity parameter predicted value of predicted output between the converter station devices at the next moment; and T (k + i) is a predicted value of the temperature parameter of the predicted output between the converter station equipment at the next moment.
As can be seen from the above description, the predicted values of the environmental parameters are output more accurately by setting different weight coefficients for the temperature, the humidity, and the pressure, and by limiting and weight-distributing the predicted values of the environmental parameters output according to the temperature parameter values, the humidity parameter values, and the pressure reference values.
Further, the method also comprises the following steps of constructing the weight coefficient model:
acquiring an environment factor and an initial weight coefficient;
and generating a weight coefficient model according to the environment factor and the initial weight coefficient.
From the above description, the weight coefficient model is obtained from the environmental factors and the initial weight coefficients, so that the influence of different environmental factors on the equipment is considered, and the prediction accuracy of the prediction model is improved.
Further, the weight coefficient model includes:
wherein A is j0 (j =1 to 3) are initial values of the weight coefficients, which are the same, i.e., a 10 ＝A 20 ＝A 30 ；A 1 (t) is a weight value corresponding to the predicted value of the environmental parameter; a. The 2 (t) and A 3 (t) is a weight value corresponding to the initial parameter of the fan; k (t) is an environmental factor;
the obtaining of the relation function according to the environment parameter predicted value, the weight coefficient group and the fan initial parameter comprises the following steps:
C＝A 1 F 1 +A 2 F 2 +A 3 F 3 ；
wherein A is 1 、A 2 And A 3 Weight coefficients which are respectively relation functions; f1 is an environmental parameter predicted value; f 2 And F 3 Is the initial parameter of the fan.
As can be seen from the above description, the expression A 1 (t)、A 2 (t) and A 3 And (t) three groups of weight coefficients are formed to respectively carry out weight control on the environment parameter predicted value and the fan initial parameter, so that weight distribution of different parameters is realized, and the precision of the prediction model is improved.
Further, the environmental factors include:
wherein, ki (t) is a corresponding deviation value of the power equipment under different operating environments, and gi (t) is a weight of each deviation value; t is an environmental factor independent variable.
Another embodiment of the present invention provides an energy saving control terminal for a converter station heating and ventilation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps in the energy saving control method for a converter station heating and ventilation system.
The energy-saving control method for the heating and ventilation system of the converter station can be suitable for controlling the environment between equipment of the converter station in a power transmission system, and realizes the energy-saving maximization of the heating and ventilation system under the condition that the environment parameters between the equipment of the converter station meet the normal operation condition of each power equipment, and the following description is given by a specific implementation mode:
example one
Referring to fig. 1, a converter station heating and ventilation system energy-saving control method includes the steps:
s1, obtaining historical environmental parameters, power grid operation data and initial parameters of a fan between convertor station equipment; the historical environment parameters comprise a temperature parameter, a humidity parameter, a pressure parameter and a fan operation parameter; such as temperature values, humidity values and pressure values corresponding to all the moments between equipment before the moment to be predicted, and fan operation parameters such as air supply temperature, air supply humidity, air supply quantity, air valve opening and fan frequency;
s2, processing the historical environmental parameters between the convertor station equipment according to the prediction model to obtain an environmental parameter prediction value, specifically:
referring to fig. 2, respectively constructing a temperature parameter, a humidity parameter, and a relationship prediction model between a pressure parameter and the fan operation parameter to obtain a temperature prediction model, a humidity prediction model, and a pressure prediction model; in a specific embodiment, the temperature, the humidity and the pressure between the devices are predicted through a temperature multilayer sensor, a humidity multilayer sensor and a pressure multilayer sensor respectively; the multilayer perceptron is a neural network structure, and temperature, humidity and pressure sensors are distributed in the equipment room of the convertor station; a large amount of training can be performed in advance through data obtained by the sensors to obtain a corresponding multilayer perceptron model;
s21, processing the temperature parameter according to the temperature prediction model to obtain a temperature parameter prediction value; wherein the temperature prediction model comprises:
T(k+1)＝f[x T (k),…,x T (k-j),…,x T (k-d+1),
T(k),…,T(k-j),…,T(k-d+1)]；
t (k + 1) is a predicted value of the temperature parameter of the predicted output between the converter station devices at the next moment; d is a set delay step number, the adjustment of the calculation accuracy of the temperature prediction model can be realized by adjusting the lead-in quantity of the delay step number, and different accuracy requirements are met, for example, the larger the value of the delay step number d is, the more data parameters are led in at the past moment, the more accurate the prediction result is, but the calculation speed is reduced, and the more complex the model is; t (k-j) is the temperature value between the converter station devices output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of T (k) Inputting a temperature parameter matrix for the outside;
for the blowing-in temperature>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency;
s22, processing the humidity parameter according to the humidity prediction model to obtain a humidity parameter prediction value; wherein the humidity prediction model comprises:
h (k + 1) is a humidity parameter predicted value of predicted output between the converter station devices at the next moment; d is the set delay step number; h (k-j) is the humidity value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is a radical of a fluorine atom H (k) Inputting a humidity parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency;
s23, processing the pressure parameter according to the pressure prediction model to obtain a pressure parameter prediction value; wherein the pressure prediction model comprises:
q (k + 1) is a pressure parameter predicted value of predicted output between the converter station devices at the next moment; d is the set delay step number; q (k-j) is the pressure value among the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of Q (k) Inputting a pressure parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency; the f () expression in the temperature prediction model, the humidity prediction model and the pressure prediction model is a prediction model, and the f () is model input; applying the prediction model to particle swarm optimization, searching the air valve opening and frequency under the optimal solution, and then outputting and controlling;
obtaining the environmental parameter predicted value according to the temperature parameter predicted value, the temperature parameter predicted value and the pressure parameter predicted value comprises:
F 1 ＝k 1 (T * -T(k+i)) 2 +k 2 (H * -T(k+i)) 2 +k 3 (Q * -Q(k+i)) 2 ；
wherein k1, k2 and k3 are weight coefficients of temperature, humidity and pressure intensity among equipment respectively; t is a temperature reference value, H is a humidity reference value, and Q is a pressure reference value; q (k + i) is a pressure parameter predicted value of predicted output between the converter station equipment at the next moment; h (k + i) is a humidity parameter predicted value of predicted output between the converter station devices at the next moment; t (k + i) is a predicted value of the temperature parameter of the predicted output between the converter station devices at the next moment;
s3, processing the power grid operation data according to a weight coefficient model to obtain a weight coefficient group; in an optional embodiment, the weight coefficient set is generated according to date, time, equipment load and power conservation, and the weight coefficient set is obtained as follows: a. The 1 、A 2 And A 3 ；
S4, obtaining a relation function according to the environment parameter predicted value, the weight coefficient group and the fan initial parameter, specifically:
obtaining a relation function:
C＝A 1 F 1 +A 2 F 2 +A 3 F 3 ；
F 2 ＝P(k)；
F 3 ＝f(k)；
wherein A is 1 、A 2 And A 3 Weight coefficients which are respectively relation functions; f 1 Predicting the environmental parameter; f 2 And F 3 Setting the initial parameters of the fan; p (k) is the opening degree of the air valve; f (k) is the fan frequency;
s5, optimizing and calculating the relation function through a particle swarm algorithm, and outputting optimal fan operation parameters; obtaining an optimal solution through the relation function to obtain the fan frequency and the air valve opening under the optimal solution;
s6, adjusting the working state of the fan according to the optimal fan operation parameters; the optimum fan frequency and the air valve opening obtained in step S5 are applied to the actual combined machine control.
Example two
The difference between the present embodiment and the first embodiment is that a weight calculation model is defined, specifically:
constructing the weight coefficient set in step S3 as a variable weight coefficient; wherein A1 in the weight coefficient group reflects the control precision of the environmental parameter predicted value, and the air valve opening and the fan frequency corresponding to A2 and A3 reflect the energy-saving control of the combined machine; if the current power equipment operation condition requires the control reliability, the weight coefficient of A1 is more important; on the contrary, on the basis of considering certain control precision, the weight coefficients of A1, A2 and A3 are balanced, so that more energy-saving effects can be realized; therefore, a dynamic variable weight calculation model is constructed based on the actual operation condition of the power grid, and the specific calculation process is as follows:
acquiring an environment factor and an initial weight coefficient, and generating a weight coefficient model according to the environment factor and the initial weight coefficient;
the weight coefficient model includes:
wherein A is j0 (j =1 to 3) is an initial value of the weight coefficient, and the initial values are the same, i.e., a 10 ＝A 20 ＝A 30 ；A 1 (t) is andthe weight value corresponding to the predicted value of the environmental parameter; a. The 2 (t) and A 3 (t) is a weight value corresponding to the initial parameter of the fan; k (t) is an environmental factor;
the environmental factors include:
wherein k is i (t) is a corresponding deviation value of the power equipment under different operating environments, and gi (t) is the weight of each deviation value; t is an environmental factor independent variable; wherein, k is i (t) and g i (t) set as follows:
1) Setting different operating environment deviation values of the power equipment:
wherein, when t =1, k i (1) Taking a date variable, wherein the corresponding deviation value is 3; e.g. t =2, k i (2) Taking a time variable, wherein the corresponding deviation value is 5, and so on;
2) Weight value setting for each bias value
In an optional embodiment, different date weights are set according to the temperature conditions of months in different regions, and if the temperature is the lowest in 12-2 months every year, the peak-to-peak degree and summer period is 6-10 months, and the power grid operation load is large in winter and summer, the date weights are set as follows:
setting a time weight value according to different day and night electricity consumption, if the requirement on the control precision of equipment operation can be reduced when the electricity consumption is smaller at night time, setting the time weight value as follows:
The load weight is set according to the ratio of the actual load to the rated load as follows, wherein P rate Is the ratio of the current actual load to the rated load, namely t is 0-1:
according to the power conservation situation, a power conservation situation weighted value is set, wherein B =1 means in a power conservation period, B =0 means in a non-power conservation period, and the following steps are carried out:
referring to fig. 3, the optimizing and calculating the relationship function through the particle swarm algorithm, and outputting the optimal fan operation parameter specifically include:
s1, randomly setting a search step length and an initial position; wherein the elements of which the initial position is a position vector include: the fan frequency and the damper opening; the fan frequency searching range is 0-50Hz, the searching range of the air valve opening is 0-100%, and the corresponding searching range is used as a termination condition;
s2, substituting the initial position, namely the randomly set fan frequency and the air valve opening degree, into the relation function; calculating the objective function value of each point, finding out the current each body extreme value Pb, and finding out the current global optimal solution Gb;
s3, updating the position and the speed of each point, and updating the speed and the position of each point according to the following formula:
D＝X*D+s 1 *t 1 *(P b -W)+s 2 *t 2 *(G b -W)；
W＝W+D；
wherein D is the velocity; x is the inertial weight; w is the current position (i.e., current fan frequency and fan valve opening); s1 and s2 are learning factors; t1 and t2 are random numbers distributed in the interval of [0,1 ];
s4, calculating a target function value after updating the position, judging the relation between the current function value and an individual extreme value, if the current function value is more optimal, updating the position (the frequency of the fan and the opening degree of the air valve) and the individual extreme value and a group extreme value, otherwise, not updating;
s5, judging whether a termination condition is met, if so, terminating the calculation, and if not, returning to S3 to continue updating, iterating and optimizing; namely, the optimal fan frequency and the air valve opening degree are finally output.
EXAMPLE III
Referring to fig. 4, a converter station heating and ventilation system energy saving control terminal includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement each step in a converter station heating and ventilation system energy saving control method as described in the first, second, or third embodiment.
In summary, the invention provides an energy-saving control method and a terminal for a converter station heating and ventilation system, wherein the fan operation parameters and the temperature are established by combining the input temperature generation, humidity parameters and pressure parameters with the air valve opening and the fan frequency according to the temperature prediction model, the humidity prediction model and the pressure prediction model. And outputting a corresponding predicted value of the environmental parameter according to the relationship between the humidity and the pressure, generating a weight coefficient according to the date, the time, the load and the power supply protection condition, further combining the predicted value of the environmental parameter and the weight coefficient set to obtain a relationship function, optimizing and calculating the relationship function through a particle swarm algorithm, and finally outputting an optimal fan operation parameter to carry out optimization control on the fan, so that the energy-saving maximization of the heating and ventilation system can be realized under the condition that the environmental parameter between the converter station equipment meets the normal operation condition of each power equipment.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A converter station heating and ventilation system energy-saving control method is characterized by comprising the following steps:
acquiring historical environmental parameters, power grid operation data and initial parameters of a fan between convertor station equipment;
processing the historical environmental parameters between the converter station equipment according to a prediction model to obtain an environmental parameter prediction value;
processing the power grid operation data according to a weight coefficient model to obtain a weight coefficient group;
obtaining a relation function according to the environment parameter predicted value, the weight coefficient group and the fan initial parameter;
optimizing and calculating the relation function through a particle swarm algorithm, and outputting optimal fan operation parameters;
and adjusting the working state of the fan according to the optimal fan operation parameter.
2. The energy-saving control method for the heating and ventilation system of the converter station according to claim 1, wherein the historical environmental parameters comprise a temperature parameter, a humidity parameter, a pressure parameter and a fan operation parameter;
the processing the historical environmental parameters between the convertor station equipment according to the prediction model to obtain the environmental parameter prediction value comprises the following steps:
respectively constructing prediction models among temperature parameters, humidity parameters, pressure parameters and fan operation parameters to obtain a temperature prediction model, a humidity prediction model and a pressure prediction model;
processing the temperature parameter according to the temperature prediction model to obtain a temperature parameter prediction value;
processing the humidity parameters according to the humidity prediction model to obtain humidity parameter prediction values;
processing the pressure parameter according to the pressure prediction model to obtain a pressure parameter prediction value;
and obtaining the environmental parameter predicted value according to the temperature parameter predicted value, the temperature parameter predicted value and the pressure parameter predicted value.
3. The energy-saving control method for the heating and ventilation system of the converter station according to claim 2, wherein the temperature prediction model comprises:
T(k+1)＝f[x T (k),…,x T (k-j),…,x T (k-d+1),
T(k),…,T(k-j),…,T(k-d+1)]；
t (k + 1) is a predicted value of the temperature parameter of the predicted output between the converter station devices at the next moment; d is the set delay step number; t (k-j) is the temperature value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is a radical of a fluorine atom T (k) Inputting a temperature parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is combined with the air valve>
Is the fan frequency.
4. The energy-saving control method for the heating and ventilation system of the converter station as claimed in claim 2, wherein the humidity prediction model comprises:
wherein H (k + 1) is the next momentPredicting the humidity parameter predicted value of the predicted output between the converter station equipment; d is the set delay step number; h (k-j) is the humidity value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of H (k) Inputting a humidity parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency.
5. The energy-saving control method for the heating and ventilation system of the converter station as claimed in claim 2, wherein the pressure prediction model comprises:
q (k + 1) is a pressure parameter predicted value of predicted output between the converter station devices at the next moment; d is the set delay step number; q (k-j) is the pressure value between the converter station equipment output at the previous d moments, and j is more than or equal to 0 and less than or equal to d-1; x is the number of Q (k) Inputting a pressure parameter matrix for the outside;
for the temperature of the air supply>
Is the opening degree of the air valve and is greater or less than>
Is the fan frequency.
6. The energy-saving control method for the heating and ventilation system of the converter station according to claim 2, wherein the obtaining the predicted value of the environmental parameter according to the predicted value of the temperature parameter, the predicted value of the temperature parameter and the predicted value of the pressure parameter comprises:
F 1 ＝k 1 (T * -T(k+i)) 2 +k 2 (H * -T(k+i)) 2 +k 3 (Q * -Q(k+i)) 2 ；
wherein k1, k2 and k3 are weight coefficients of temperature, humidity and pressure intensity among equipment respectively; t is a temperature reference value, H is a humidity reference value, and Q is a pressure reference value; q (k + i) is a pressure parameter predicted value of predicted output between the converter station equipment at the next moment; h (k + i) is a humidity parameter predicted value of predicted output between the converter station devices at the next moment; and T (k + i) is a predicted value of the temperature parameter of the predicted output between the converter station equipment at the next moment.
7. The energy-saving control method for the heating and ventilation system of the converter station according to claim 1, further comprising constructing the weight coefficient model:
acquiring an environment factor and an initial weight coefficient;
and generating a weight coefficient model according to the environment factor and the initial weight coefficient.
8. The converter station heating and ventilating system energy saving control method according to claim 7, wherein the weight coefficient model comprises:
wherein, A j0 (j =1 to 3) are initial values of the weight coefficients, which are the same, i.e., a 10 ＝A 20 ＝A 30 ；A 1 (t) is with the ringA weighted value corresponding to the environmental parameter predicted value; a. The 2 (t) and A 3 (t) is a weight value corresponding to the initial parameter of the fan; k (t) is an environmental factor;
the obtaining of the relation function according to the environment parameter predicted value, the weight coefficient group and the fan initial parameter comprises:
C＝A 1 F 1 +A 2 F 2 +A 3 F 3 ；
wherein, A 1 、A 2 And A 3 Weight coefficients which are respectively relation functions; f1 is an environmental parameter predicted value; f 2 And F 3 Is the initial parameter of the fan.
9. The energy-saving control method for the heating and ventilation system of the converter station as claimed in claim 7, wherein the environmental factor comprises:
wherein, ki (t) is a corresponding deviation value of the power equipment under different operating environments, and gi (t) is a weight of each deviation value; t is an environmental factor independent variable.
10. A converter station heating and ventilation system energy-saving control terminal, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program to implement the steps of the converter station heating and ventilation system energy-saving control method according to claims 1-9.
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