Heat supply network balancing system and method based on heating station load characteristic difference weighting algorithm

Abstract

The invention discloses a heat supply network balancing system and method based on a heating station load characteristic difference weighting algorithm. The system comprises a data acquisition module, a characteristic analysis module, a weighting balancing module, an execution control module and a wireless transmission module. The data acquisition module is deployed at each heating station and is used for acquiring temperature, pressure, flow and user load data of each heating station in real time; the characteristic analysis module is connected with the data acquisition module, and the characteristic analysis module is used for generating load characteristic parameters of each heating station according to the historical data of each heating station and the real-time state of each heating station, and when the system is implemented, the running state of each heating station can be mastered in real time by arranging the data acquisition module; according to different loads of each heating station, the loads are redistributed so as to ensure that each heating station can operate more safely and efficiently, and timely response can be made after weather changes, so that the comfort of users is further ensured.

Description

Technical Field

[0001] This application relates to the technical field of urban central heating, and specifically relates to a heat network balancing system and method based on a weighted algorithm for the load characteristics difference of heat substations. Background Art

[0002] In the cold regions of the north, central heating is carried out in winter. However, there are some problems in the traditional urban heating system. For example: 1. In the prior art, when heating, heat is mostly distributed relying on manual experience or fixed ratios, which will lead to overloading of some heat substations and inefficient operation of some heat substations, and further lead to poor uniformity of heating; 2. When the weather changes, it is often necessary to adjust the heating temperature, and the heat substation is difficult to make a timely response.

[0003] Therefore, this application proposes a heat network balancing system and method based on a weighted algorithm for the load characteristics difference of heat substations. Summary of the Invention

[0004] Therefore, this application provides a heat network balancing system and method based on a weighted algorithm for the load characteristics difference of heat substations to solve the problems of uneven load distribution of heat substations and difficulty for heat substations to make timely responses according to the weather existing in the prior art.

[0005] To achieve the above object, this application provides the following technical solutions:

[0006] In a first aspect, a heat network balancing system based on a weighted algorithm for the load characteristics difference of heat substations includes: a data acquisition module, a feature analysis module, a weighted balancing module, an execution control module, and a wireless transmission module;

[0007] The data acquisition module is deployed at each heat substation, and is used for real-time acquisition of temperature, pressure, flow rate, and user load data of each heat substation;

[0008] The feature analysis module is connected to the data acquisition module, and is used for generating load characteristic parameters of each heat substation according to the historical data and real-time status of each heat substation;

[0009] The weighted balancing module is connected to the feature analysis module, and can dynamically allocate weight coefficients based on the load characteristic parameters to generate a heat distribution strategy for the whole network;

[0010] The execution control module is connected to the weighted balancing module, and adjusts the heat source output, pump valve opening, and heat exchange station flow rate according to the distribution strategy;

[0011] The wireless communication module is connected to the data acquisition module and is used for transmitting the data collected by the data acquisition module.

[0012] Preferably, the characteristic analysis module includes a heat generation characteristic database, a real-time status evaluation unit, and a priority ranking unit.

[0013] The heat generation characteristic database is used to store the historical load curves, pipe network topologies, and equipment parameters of each heat station.

[0014] The real-time status evaluation unit is used to calculate the efficiency index and health score of each heat station based on the data collected by the current data acquisition module.

[0015] The priority ranking unit is used to generate a heat station regulation priority list according to the efficiency index of the heat station and the user density. The smaller the efficiency index, the higher the regulation priority of the corresponding heat station.

[0016] Preferably, mark the maximum value in the historical heat generation curve of the heat station and denote it as Q1, mark the current heat generation of the heat station and denote it as Q0. The efficiency index of the heat station is Q0 / Q1.

[0017] According to the historical heat generation and historical equipment parameters of the heat station, draw the historical health curve of the heat station, record the equipment parameters of the heat station at this time, and mark the heat generation of the heat station at this time as Q2. According to the equipment parameters at this time, obtain the corresponding heat generation of the heat station in the historical health curve of the heat station and mark the heat generation as Q3. The score of the health score is Q2 / Q3.

[0018] Preferably, the weighted balancing module includes a dynamic weight generation unit and a heat distribution engine. The dynamic weight generation unit is used to generate a weight coefficient according to the efficiency index of the heat station, the urgency of user demand, and the pipe network loss rate. The heat distribution engine is used to distribute the total heat to each heat station according to the weight coefficient, and the heat distribution engine also supports real-time dynamic adjustment.

[0019] The weighted balancing unit also includes a conflict resolution unit. When the data acquisition module collects data such as insufficient hydraulic supply or equipment damage in the heat station, the weighted balancing unit can re-distribute the load assigned to the heat station to other heat stations.

[0020] Preferably, the execution control module includes an adaptive PID controller, a redundant execution unit, and a safety limit unit. The adaptive PID controller includes dynamically adjusting the parameters assigned to each heat station according to the distribution strategy. The redundant execution unit is used to execute a dual-loop regulation mechanism. When one loop fails, the redundant execution unit can switch to the other loop to control the heat station. The safety limit unit is used to restrict the execution instructions within the safety range of each equipment parameter.

[0021] Preferably, it further includes a log generation module, which can generate logs of the temperature, pressure, flow rate, and user load data of each heat substation on a daily, weekly, and monthly basis.

[0022] To achieve the above object, the present application also provides the following technical solutions:

[0023] A heat network balancing method based on a weighted algorithm for load characteristic differences of heat substations

[0024] Step S1: The data acquisition module obtains the operation data of each heat substation in real time through the wireless communication module;

[0025] Step S2: The characteristic analysis module evaluates the load characteristics of the heat substations and generates a priority list;

[0026] Step S3: The weighted balancing module dynamically calculates the weight coefficients and generates a heat distribution strategy;

[0027] Step S4: The execution control module adjusts the heat source and pipe network parameters to achieve balanced heating of the whole network.

[0028] Compared with the prior art, the present application has at least the following beneficial effects:

[0029] When implementing this system, by setting up a data acquisition module, the operation status of each heat substation can be grasped in real time, and the load can be redistributed according to the different loads of each heat substation to ensure that each heat substation can operate more safely and efficiently. Moreover, it can also make a timely response after the weather changes, further ensuring the comfort of users;

[0030] By calculating the efficiency index and health score, the current situation of the heat substation can be inferred. And when the efficiency index and health score are less than the predetermined values, it indicates that there are certain potential safety hazards or maintenance is required for the heat substation, thereby helping the staff to master the status of the heat substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To more intuitively illustrate the prior art and the present application, exemplary drawings are given below. It should be understood that the specific shapes and structures shown in the drawings are generally not regarded as limiting conditions when implementing the present application; for example, those skilled in the art are capable of making routine adjustments or further optimizations to the addition / deletion / attribution division of certain units (components), specific shapes, positional relationships, connection methods, dimensional proportional relationships, etc. based on the technical concept disclosed in the present application and the exemplary drawings.

[0032] Figure 1 It is a module diagram of the heat network balancing system based on the weighted algorithm for load characteristic differences of heat substations of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The present application will be further described in detail below with reference to the accompanying drawings and through specific embodiments.

[0034] As Figure 1 shown, a heat network balancing system based on a weighted algorithm for the load characteristic differences of heat supply stations includes: a data acquisition module, a feature analysis module, a weighted balancing module, an execution control module, and a wireless transmission module;

[0035] The data acquisition module is deployed at each heat supply station. The data acquisition module is used to collect the temperature, pressure, flow rate, and user load data of each heat supply station in real time. The data acquisition module includes temperature sensors, pressure sensors, flow sensors, etc.;

[0036] The feature analysis module is connected to the data acquisition module, and the feature analysis module is used to generate the load characteristic parameters of each heat supply station according to the historical data and the real-time status of each heat supply station. Through the feature analysis module, the load characteristic data of the entire network can be analyzed to facilitate the distribution of the total heat production to different heat supply stations;

[0037] The weighted balancing module is connected to the feature analysis module and can dynamically allocate weight coefficients based on the load characteristic parameters to generate a heat distribution strategy for the entire network. By introducing weight coefficients, heat can be more efficiently distributed to each heat supply station;

[0038] The execution control module is connected to the weighted balancing module and adjusts the heat source output, pump valve opening, and heat exchange station flow rate according to the distribution strategy. The execution control module controls each heat supply station to efficiently control the heat production performance of the heat supply station;

[0039] The wireless communication module is connected to the data acquisition module and is used to transmit the data collected by the data acquisition module.

[0040] When implementing this system, by setting up a data acquisition module, the operating status of each heat supply station can be grasped in real time, and the load can be redistributed according to the different loads of each heat supply station to ensure that each heat supply station can operate more safely and efficiently. Moreover, it can also make timely responses after weather changes, further ensuring the comfort of users.

[0041] The feature analysis module includes a heat production feature database, a real-time status evaluation unit, and a priority ranking unit.

[0042] The heat production feature database is used to store the historical load curves, historical heat production curves, pipe network topology structures, and equipment parameters of each heat supply station. The historical load curve of the heat supply station corresponds to the historical heat production curve of the heat supply station, and according to the topology structure of the pipe network, the heat loss of the heat supply station can also be calculated.

[0043] The real-time status evaluation unit is used to calculate the efficiency index and health score of each heat substation based on the data collected by the current data acquisition module. The performance of the heat substation can be evaluated through the efficiency index and health score.

[0044] The priority ranking unit is used to generate a heat substation regulation priority list according to the efficiency index of the heat substation and the user density. The smaller the efficiency index, the higher the regulation priority of the corresponding heat substation.

[0045] Mark the maximum value in the historical heat production curve of the heat substation and denote it as Q1. Mark the current heat production of the heat substation and denote it as Q0. The efficiency index of the heat substation is Q0 / Q1.

[0046] According to the historical heat production and historical equipment parameters of the heat substation, draw the historical health curve of the heat substation. Record the equipment parameters of the heat substation at this time and mark the heat production of the heat substation at this time, denoted as Q2. According to the equipment parameters at this time, obtain the corresponding heat production of the heat substation in the historical health curve of the heat substation and mark the heat production, denoted as Q3. The score of the health score is Q2 / Q3.

[0047] By calculating the efficiency index and health score, the condition of the heat substation at this time can be inferred. When the efficiency index and health score are less than the predetermined values, it indicates that there are certain potential safety hazards or maintenance is required for the heat substation, which helps the staff to master the status of the heat substation.

[0048] The weighted balancing module includes a dynamic weight generation unit and a heat distribution engine. The dynamic weight generation unit is used to generate a weight coefficient according to the efficiency index of the heat substation, the urgency of user demand, and the pipeline network loss rate. The heat distribution engine is used to distribute the total heat to each heat substation according to the weight coefficient, and the heat distribution engine also supports real-time dynamic adjustment.

[0049] Through the weight system, the distribution strategies of different heat substations are adjusted, so that when a certain parameter changes, each heat substation can respond in a timely manner.

[0050] The weighted balancing unit also includes a conflict resolution unit. When the data acquisition module collects data on insufficient water supply or equipment damage in the heat substation, the weighted balancing unit can re-distribute the load assigned to the heat substation to other heat substations, so that when a special situation occurs in a certain heat substation, other heat substations can replace it to ensure the normal operation of the heat network.

[0051] The execution control module includes an adaptive PID controller, a redundant execution unit, and a safety limiter. The adaptive PID controller includes dynamically adjusting the parameters allocated to each heat sub-station according to the allocation strategy. The redundant execution unit is used to execute a dual-loop regulation mechanism. When one loop fails, the redundant execution unit can switch to the other loop to control the heat sub-station. The safety limiter is used to restrict the execution instructions within the safe range of the parameters of each device.

[0052] It also includes a log generation module, which can generate logs of the temperature, pressure, flow rate, and user load data of each heat sub-station on a daily, weekly, and monthly basis.

[0053] To achieve the above object, the present application also provides the following technical solutions:

[0054] A heat network balancing method based on a load characteristic difference weighting algorithm for heat sub-stations

[0055] Step S1: The data acquisition module continuously obtains the operation data of each heat sub-station and transmits the obtained data through the wireless communication module.

[0056] Step S2: The characteristic analysis module evaluates the load characteristics of the heat sub-stations and generates a priority list.

[0057] Step S3: The weighted balancing module dynamically calculates the weight coefficients and generates a heat allocation strategy.

[0058] Step S4: The execution control module adjusts the parameters of the heat source and the pipe network to achieve balanced heat supply across the network.

[0059] The technical features of the above embodiments can be combined arbitrarily (as long as there is no contradiction in the combination of these technical features). For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described; these embodiments that are not explicitly written out should also be considered to be within the scope described in this specification.

Claims

1. A heat network balancing system based on a weighted algorithm for heat station load characteristics difference, characterized in that: include: Data acquisition module, feature analysis module, weighted balancing module, execution control module, wireless transmission module; The data acquisition module is deployed in each thermal power station, and is used to collect temperature, pressure, flow and user load data of each thermal power station in real time; The characteristic analysis module is connected to the data acquisition module, and the characteristic analysis module is used to generate the load characteristic parameters of each thermal power station according to the historical data of each thermal power station and the real-time status of each thermal power station; The weighted balancing module is connected to the characteristic analysis module and can dynamically allocate weight coefficients based on load characteristic parameters to generate a heat distribution strategy for the entire network; The execution control module is connected to the weighted balance module and adjusts the heat source output, pump valve opening and heat exchange station flow according to the allocation strategy; The wireless communication module is connected to the data acquisition module and is used to transmit the data collected by the data acquisition module.

2. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 1 is characterized in that: The characteristic analysis module includes a heat generation characteristic database, a real-time state evaluation unit and a priority sorting unit. The heat generation characteristic database is used to store the historical load curves, historical heat generation curves, pipe network topology and equipment parameters of each thermal power station. The real-time status evaluation unit is used to calculate the efficiency index and health score of each thermal power station based on the data collected by the current data collection module. The priority sorting unit is used to generate a thermal power station control priority list according to the efficiency index and user density of the thermal power station. The smaller the efficiency index, the higher the control priority of the corresponding thermal power station.

3. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 2 is characterized in that: Mark the maximum value in the historical heat production curve of the heat station and record it as Q1, mark the current heat production of the heat station and record it as Q0, and the efficiency index of the heat station is Q0 / Q1; According to the historical heat production and historical equipment parameters of the thermal power station, a historical health curve of the thermal power station is drawn, the equipment parameters of the thermal power station at this time are recorded, and the heat production of the thermal power station at this time is marked as Q2. According to the equipment parameters at this time, the corresponding heat production of the thermal power station is obtained in the historical health curve of the thermal power station, and the heat production is marked as Q3. The health score is Q2 / Q3.

4. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 2 is characterized in that: The weighted balancing module includes a dynamic weight generation unit and a heat distribution engine. The dynamic weight generation unit is used to generate a weight coefficient according to the efficiency index of the thermal power station, the urgency of user demand and the pipeline loss rate. The heat distribution engine is used to distribute the total heat to each thermal power station according to the weight coefficient, and the heat distribution engine also supports real-time dynamic adjustment.

5. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 4 is characterized in that: The weighted balancing unit also includes a conflict resolution unit. When the data acquisition module collects data on insufficient hydraulic supply or damaged equipment at the thermal power station, the weighted balancing unit can reallocate the load allocated to the thermal power station to other thermal power stations.

6. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 1 is characterized in that: The execution control module includes an adaptive PID controller, a redundant execution unit and a safety limiter unit. The adaptive PID controller dynamically adjusts the parameters allocated to each thermal power station according to the allocation strategy. The redundant execution unit is used to execute a dual-loop regulation mechanism. When one of the loops fails, the redundant execution unit can switch to another loop to control the thermal power station. The safety limiter unit is used to constrain the execution instructions to be within the safety range of each device parameter.

7. The heat network balancing system based on the heat station load characteristic difference weighted algorithm according to claim 1 is characterized in that: It also includes a log generation module, which can generate logs of temperature, pressure, flow and user load data of each thermal power station on a daily, weekly and monthly basis.

8. A heating network balancing method based on a weighted algorithm for heat station load characteristics difference, characterized in that: Step S1: The data acquisition module acquires the operating data of each thermal power station in real time, and transmits the acquired data through the wireless communication module; Step S2: the characteristic analysis module evaluates the load characteristics of the thermal power station and generates a priority list; Step S3: The weighted balancing module dynamically calculates the weight coefficient and generates a heat distribution strategy; Step S4: The execution control module adjusts the heat source and pipe network parameters to achieve balanced heating for the entire network.

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