CN113534703A - Heating and ventilation combined machine energy-saving system and control method thereof - Google Patents

Heating and ventilation combined machine energy-saving system and control method thereof Download PDF

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CN113534703A
CN113534703A CN202110768459.6A CN202110768459A CN113534703A CN 113534703 A CN113534703 A CN 113534703A CN 202110768459 A CN202110768459 A CN 202110768459A CN 113534703 A CN113534703 A CN 113534703A
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equipment
combined machine
frequency
reference value
humidity
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CN113534703B (en
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范彦琨
姚国华
薛嵩凌
何志甘
李冠颖
许卉
熊旭
付胜宪
陈德兴
黄东方
张锦吉
徐显烨
李升晖
张舒雅
陈灼
傅哲宁
殷立庚
严欣
林杰
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Maintenance Branch of State Grid Fujian Electric Power Co Ltd
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/04Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423Input/output
    • HELECTRICITY
    • H05ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05KPRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00Constructional details common to different types of electric apparatus
    • H05K7/20Modifications to facilitate cooling, ventilating, or heating
    • H05K7/2089Modifications to facilitate cooling, ventilating, or heating for power electronics, e.g. for inverters for controlling motor
    • HELECTRICITY
    • H05ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05KPRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00Constructional details common to different types of electric apparatus
    • H05K7/20Modifications to facilitate cooling, ventilating, or heating
    • H05K7/2089Modifications to facilitate cooling, ventilating, or heating for power electronics, e.g. for inverters for controlling motor
    • H05K7/20909Forced ventilation, e.g. on heat dissipaters coupled to components
    • HELECTRICITY
    • H05ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05KPRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00Constructional details common to different types of electric apparatus
    • H05K7/20Modifications to facilitate cooling, ventilating, or heating
    • H05K7/2089Modifications to facilitate cooling, ventilating, or heating for power electronics, e.g. for inverters for controlling motor
    • H05K7/20945Thermal management, e.g. inverter temperature control
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/20Pc systems
    • G05B2219/25Pc structure of the system
    • G05B2219/25257Microcontroller

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Abstract

本发明提出一种暖通组合机节能系统及其控制方法,所述暖通组合机用于对设备间通风,所述节能系统包括中央处理模块和与之通信连接环境量采集模块、现场运行工况采集模块;所述环境量采集模块用于采集设备间及设备间室外环境的温度数据和湿度数据;所述现场运行工况采集模块用于采集设备间的主设备负荷数据;所述中央处理模块通过对温度数据、湿度数据和主设备负荷数据的评估,对暖通组合机的电机进行控制,以节能降耗并使设备间环境符合设备运行需要;本发明能综合设备间环境参数、室外环境参数、日期、时段、负荷、保供电情况下等条件,制定最优节能策略,对组合机电机进行变频控制,在满足设备运行环境的前提下实现组合机节能运行。

Figure 202110768459

The present invention provides an energy-saving system for an HVAC combined machine and a control method thereof. The HVAC combination machine is used for ventilation between equipments. condition collection module; the environmental quantity collection module is used to collect temperature data and humidity data of the equipment room and the outdoor environment of the equipment room; the on-site operating condition collection module is used to collect the main equipment load data between the equipment; the central processing Through the evaluation of temperature data, humidity data and main equipment load data, the module controls the motor of the HVAC combined machine to save energy and reduce consumption and make the equipment room environment meet the needs of equipment operation; the invention can integrate the equipment room environmental parameters, outdoor According to the conditions of environmental parameters, date, time period, load, and power supply, the optimal energy-saving strategy is formulated, and the motor of the combined machine is controlled by frequency conversion to realize the energy-saving operation of the combined machine under the premise of meeting the equipment operating environment.

Figure 202110768459

Description

Heating and ventilation combined machine energy-saving system and control method thereof
Technical Field
The invention relates to the technical field of power grid operation, in particular to an energy-saving system of a heating and ventilation combined machine and a control method thereof.
Background
At present, equipment in an equipment room of a converter station generates heat when running, and the equipment room is a closed space and needs to be ventilated by using special heating and ventilation equipment so as to keep the temperature in the equipment room within a reasonable range. The heating and ventilation combined machine used in the existing station runs continuously for twenty-four hours and does not have energy-saving control capability.
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. The regulation method is single, and the temperature fluctuation in the equipment room is large due to the lag regulation. And the load quantity is added as feed-forward regulation, so that the response speed can be improved to a certain extent.
The prior art scheme mainly researches how to realize energy conservation maximization on the premise of ensuring the temperature in the equipment room, or how to regulate and control the temperature to a preset value more accurately, stably and quickly. But the requirements for equipment in different seasons, time periods and load situations in the power system are different. For example, the operation environment between the devices should be ensured as much as possible during the peak-meeting summer and the heavy load period of the devices, so as to ensure the safe and stable operation of the devices; the energy saving can be maximized in winter or at night when the equipment is lightly loaded.
The existing variable weight multi-model comprehensive prediction method mainly carries out single selection or weighted selection by judging a model with a prediction result closest to a target value in a plurality of models, and excludes a model with a prediction result which has a larger difference with the target value, wherein a main strategy is also the preferred selection of the multi-model.
Disclosure of Invention
The invention provides an energy-saving system of a heating and ventilation combined machine and a control method thereof, which can synthesize the conditions of environmental parameters, outdoor environmental parameters, date, time period, load, power supply protection and the like among equipment, formulate an optimal energy-saving strategy, perform variable frequency control on a combined machine motor, and realize the energy-saving operation of the combined machine on the premise of meeting the operating environment of the equipment.
The invention adopts the following technical scheme.
The heating and ventilation combined machine energy-saving system is used for ventilating equipment rooms and comprises a central processing module, an environment quantity acquisition module and a field operation working condition acquisition module, wherein the environment quantity acquisition module is in communication connection with the central processing module; the environment quantity acquisition module is used for acquiring temperature data and humidity data of the equipment room and the outdoor environment of the equipment room; the field operation condition acquisition module is used for acquiring main equipment load data among equipment; the central processing module controls a motor of the heating and ventilation combined machine through evaluation of temperature data, humidity data and main equipment load data so as to save energy, reduce consumption and enable the environment between equipment to meet the running requirements of the equipment.
The energy-saving system also comprises a display module and an output module used for controlling the motor of the heating and ventilation combined machine.
A control method applicable to the heating and ventilation combined machine energy-saving system comprises the following steps;
step S1, collecting outdoor environment quantity parameters between equipment and between equipment, wherein the environment quantity parameters comprise temperature data and humidity data;
step S2, collecting a main equipment load value between equipment;
step S3, based on the variable weight prediction model, obtaining a maximum value T1 of a temperature reference value, a minimum value T2 of the temperature reference value, an inter-equipment humidity reference value H1 and an inter-equipment outdoor environment humidity reference value H2 according to the date, time, equipment load and power supply protection condition;
step S4, judging whether the inter-device temperature T is greater than a reference value T1; if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the motor frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S5, if the inter-equipment temperature is less than T1, judging whether the inter-equipment humidity is greater than a reference value H1 and the outdoor humidity is less than a reference value H2; if so, calculating the humidity difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S6, if the temperature between the devices is less than T1, the humidity between the devices is less than a reference value H1 or the outdoor humidity is greater than a reference value H2, further judging whether the temperature between the devices is less than T2, if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to decrease, and if the minimum frequency is reached, keeping the minimum frequency unchanged;
the PI calculation described above uses a proportional integral control algorithm. The maximum frequency refers to the maximum operating frequency of the combined machine, and the minimum frequency refers to the minimum operating frequency of the combined machine.
In step S3, the calculation expression of the reference value is
Ti(t)=Ti+k(t)
Hi(t)=Hi+k(t)
Wherein Ti is a temperature reference value, Hi is a humidity reference value, and k (t) is a deviation value; ti (t) is the corresponding temperature reference value under different conditions, and Hi (t) is the corresponding humidity reference value under different conditions.
The variable weight prediction model expression is as follows:
Figure BDA0003152834170000031
wherein k isi(t) deviation values corresponding to different conditions, gi(t) is the weight of each bias value, e (t) is the adaptive learning coefficient;
Figure BDA0003152834170000032
k is a compensation coefficient and is preset according to different field operation conditions; f (t) is the frequency of the combiner at the current moment, f (t-1) is the frequency of the combiner at the last moment, and fm is the rated frequency of the combiner.
The deviation values corresponding to different conditions are set as follows:
Figure BDA0003152834170000033
the weight values corresponding to different conditions are set as follows:
condition 1 is date: the environment temperature is lowest in 12-2 months every year, and 6-10 months are in the peak-facing summer stage, so the weight values are as follows:
Figure BDA0003152834170000034
wherein Month is Month. Because the difference of the environmental temperature in the same month is not large, the month is taken as the date cycle, the regulation and control requirements can be met, the calculation amount can be reduced to a great extent, and the control efficiency is improved.
Condition 2 is time:
Figure BDA0003152834170000041
wherein, Hour is time.
Condition 3 is inter-device master load:
Figure BDA0003152834170000042
wherein Prate is the load factor;
condition 4 is a power conservation condition:
Figure BDA0003152834170000043
wherein, B is equal to 1 for the power-saving stage, and B is equal to 0 for the power-non-saving stage.
The invention discloses a comprehensive prediction of environmental parameter target values under multiple conditions, which is based on the premise of ensuring safe and stable operation of power equipment, calculates the deviation value of the environmental parameters according to different condition weights at the current moment, and then obtains an environmental quantity reference value which is most suitable for the current working condition, and synthesizes environmental parameters, outdoor environmental parameters, dates, time periods, loads, power supply protection conditions and other conditions among equipment to formulate an optimal energy-saving strategy, thereby ensuring the operation environment of the equipment and saving electric energy to the maximum extent.
The invention fully considers the condition of low equipment load or low outdoor temperature, can carry out variable frequency control on the combined machine motor, and realizes energy-saving operation of the combined machine on the premise of meeting the requirement of equipment operation environment.
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The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of the control method of the present invention.
Detailed Description
As shown in the figure, the heating and ventilation combined machine energy-saving system is used for ventilating equipment rooms and comprises a central processing module, an environment quantity acquisition module and a field operation working condition acquisition module, wherein the environment quantity acquisition module is in communication connection with the central processing module; the environment quantity acquisition module is used for acquiring temperature data and humidity data of the equipment room and the outdoor environment of the equipment room; the field operation condition acquisition module is used for acquiring main equipment load data among equipment; the central processing module controls a motor of the heating and ventilation combined machine through evaluation of temperature data, humidity data and main equipment load data so as to save energy, reduce consumption and enable the environment between equipment to meet the running requirements of the equipment.
The energy-saving system also comprises a display module and an output module used for controlling the motor of the heating and ventilation combined machine.
A control method applicable to the heating and ventilation combined machine energy-saving system comprises the following steps;
step S1, collecting outdoor environment quantity parameters between equipment and between equipment, wherein the environment quantity parameters comprise temperature data and humidity data;
step S2, collecting a main equipment load value between equipment;
step S3, based on the variable weight prediction model, obtaining a maximum value T1 of a temperature reference value, a minimum value T2 of the temperature reference value, an inter-equipment humidity reference value H1 and an inter-equipment outdoor environment humidity reference value H2 according to the date, time, equipment load and power supply protection condition;
step S4, judging whether the inter-device temperature T is greater than a reference value T1; if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the motor frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S5, if the inter-equipment temperature is less than T1, judging whether the inter-equipment humidity is greater than a reference value H1 and the outdoor humidity is less than a reference value H2; if so, calculating the humidity difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S6, if the temperature between the devices is less than T1, the humidity between the devices is less than a reference value H1 or the outdoor humidity is greater than a reference value H2, further judging whether the temperature between the devices is less than T2, if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to decrease, and if the minimum frequency is reached, keeping the minimum frequency unchanged;
the PI calculation described above uses a proportional integral control algorithm. The maximum frequency refers to the maximum operating frequency of the combined machine, and the minimum frequency refers to the minimum operating frequency of the combined machine.
In step S3, the calculation expression of the reference value is
Ti(t)=Ti+k(t)
Hi(t)=Hi+k(t)
Wherein Ti is a temperature reference value, Hi is a humidity reference value, and k (t) is a deviation value; ti (t) is the corresponding temperature reference value under different conditions, and Hi (t) is the corresponding humidity reference value under different conditions.
The variable weight prediction model expression is as follows:
Figure BDA0003152834170000061
wherein k isi(t) deviation values corresponding to different conditions, gi(t) is the weight of each bias value, e (t) is the adaptive learning coefficient;
Figure BDA0003152834170000062
k is a compensation coefficient and is preset according to different field operation conditions; f (t) is the frequency of the combiner at the current moment, f (t-1) is the frequency of the combiner at the last moment, and fm is the rated frequency of the combiner;
the deviation values corresponding to different conditions are set as follows:
Figure BDA0003152834170000063
the weight values corresponding to different conditions are set as follows:
condition 1 is date: the temperature is lowest in 12-2 months every year, and 6-10 months are in the peak-facing summer stage, so the weight values are as follows:
Figure BDA0003152834170000064
wherein Month is Month; because the environmental temperature difference is not great in the same month, the month is taken as the date cycle, the regulation and control requirements can be met, the calculation amount can be reduced to a great extent, and the control efficiency is improved;
condition 2 is time:
Figure BDA0003152834170000071
wherein, Hour is time;
condition 3 is inter-device master load:
Figure BDA0003152834170000072
wherein Prate is the load factor;
condition 4 is a power conservation condition:
Figure BDA0003152834170000073
b is equal to 1 and is a power supply protection stage, and B is equal to 0 and is a power supply non-protection stage;
the comprehensive prediction of the environmental parameter target value under the multiple conditions of the embodiment is based on the premise of ensuring the safe and stable operation of the power equipment, and the deviation value of the environmental parameter is calculated according to the different weights of the different conditions at the current moment, so as to obtain the environmental quantity reference value which is most suitable for the current working condition.

Claims (5)

1. An energy-saving system of a heating and ventilation combined machine, wherein the heating and ventilation combined machine is used for ventilating an equipment room, and is characterized in that: the energy-saving system comprises a central processing module, and an environment quantity acquisition module and a field operation working condition acquisition module which are in communication connection with the central processing module; the environment quantity acquisition module is used for acquiring temperature data and humidity data of the equipment room and the outdoor environment of the equipment room; the field operation condition acquisition module is used for acquiring main equipment load data among equipment; the central processing module controls a motor of the heating and ventilation combined machine through evaluation of temperature data, humidity data and main equipment load data so as to save energy, reduce consumption and enable the environment between equipment to meet the running requirements of the equipment.
2. The heating and ventilation combined machine energy-saving system as claimed in claim 1, wherein: the energy-saving system also comprises a display module and an output module used for controlling the motor of the heating and ventilation combined machine.
3. The control method of the heating and ventilation combined machine energy-saving system is characterized in that: the method comprises the following steps;
step S1, collecting outdoor environment quantity parameters between equipment and between equipment, wherein the environment quantity parameters comprise temperature data and humidity data;
step S2, collecting a main equipment load value between equipment;
step S3, based on the variable weight prediction model, obtaining a maximum value T1 of a temperature reference value, a minimum value T2 of the temperature reference value, an inter-equipment humidity reference value H1 and an inter-equipment outdoor environment humidity reference value H2 according to the date, time, equipment load and power supply protection condition;
step S4, judging whether the inter-device temperature T is greater than a reference value T1; if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the motor frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S5, if the inter-equipment temperature is less than T1, judging whether the inter-equipment humidity is greater than a reference value H1 and the outdoor humidity is less than a reference value H2; if so, calculating the humidity difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to increase, and keeping the maximum frequency unchanged if the maximum frequency is reached;
step S6, if the temperature between the devices is less than T1, the humidity between the devices is less than a reference value H1 or the outdoor humidity is greater than a reference value H2, further judging whether the temperature between the devices is less than T2, if so, calculating the temperature difference, obtaining a frequency variation delta f after PI calculation, controlling the frequency of the combined machine to decrease, and if the minimum frequency is reached, keeping the minimum frequency unchanged;
the PI calculation described above uses a proportional integral control algorithm. The maximum frequency refers to the maximum operating frequency of the combined machine, and the minimum frequency refers to the minimum operating frequency of the combined machine.
4. The control method of the heating and ventilation combined machine energy saving system according to claim 3, characterized in that: in step S3, the calculation expression of the reference value is
Ti(t)=Ti+k(t)
Hi(t)=Hi+k(t)
Wherein Ti is a temperature reference value, Hi is a humidity reference value, and k (t) is a deviation value; ti (t) is the corresponding temperature reference value under different conditions, and Hi (t) is the corresponding humidity reference value under different conditions.
5. The control method of the heating and ventilation combined machine energy saving system according to claim 4, characterized in that: the variable weight prediction model expression is as follows:
Figure FDA0003152834160000021
wherein k isi(t) deviation values corresponding to different conditions, gi(t) is the weight of each bias value, e (t) is the adaptive learning coefficient;
Figure FDA0003152834160000022
k is a compensation coefficient and is preset according to different field operation conditions; f (t) is the frequency of the combiner at the current moment, f (t-1) is the frequency of the combiner at the last moment, and fm is the rated frequency of the combiner;
the deviation values corresponding to different conditions are set as follows:
Figure FDA0003152834160000023
the weight values corresponding to different conditions are set as follows:
condition 1 is date:
Figure FDA0003152834160000031
wherein Month is Month;
condition 2 is time:
Figure FDA0003152834160000032
wherein, Hour is time;
condition 3 is inter-device master load:
Figure FDA0003152834160000033
wherein Prate is the load factor;
condition 4 is a power conservation condition:
Figure FDA0003152834160000034
wherein, B is equal to 1 for the power-saving stage, and B is equal to 0 for the power-non-saving stage.
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CN116736780A (en) * 2023-08-15 2023-09-12 贵州汇通华城股份有限公司 Startup and shutdown control optimization method and system for regional energy station
CN116736780B (en) * 2023-08-15 2023-11-24 贵州汇通华城股份有限公司 Startup and shutdown control optimization method and system for regional energy station

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