CN116933455A - Kalman filtering-based heating system pipeline network state estimation method - Google Patents
Kalman filtering-based heating system pipeline network state estimation method Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
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- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
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Abstract
The invention discloses a heating system pipeline network state estimation method based on Kalman filtering, which comprises the following steps: 1) The temperature sensors arranged at the outlet end of the pipeline acquire new temperature data, each temperature sensor corresponds to one local Kalman filter, the temperature data is input into the local Kalman filter, and the temperature data are exchanged between the local Kalman filters corresponding to the temperature sensors on the connected pipelines; 2) Fusing the exchanged temperature data in each local Kalman filter to obtain a fused state estimation value; 3) Substituting the fused state estimation value into an improved pipeline state model for calculation to obtain the state estimation value of each pipeline at the next moment; 4) And 1) circulating the steps 1) to 3) until the temperature sensor does not acquire new temperature data any more, and obtaining a final pipeline state estimation result sequence by using the series state estimation values. The invention can obtain more accurate pipeline temperature state estimation value, reduce the data communication requirement and improve the data quality and reliability of the sensor.
Description
Technical Field
The invention relates to the technical field of heat supply system operation analysis, in particular to a heat supply system pipeline network state estimation method based on Kalman filtering.
Background
The operational analysis tasks of heating systems typically rely on sensors to collect and transmit data. However, in practical applications, the data obtained by the sensors cannot perfectly represent the true state of the variables in the system. The sensor data is generally affected by noise, external disturbance, self-fault and communication packet loss, if the sensor data is directly used, the accuracy of analysis and calculation is possibly poor, or the effect is not ideal because of data missing.
At present, a state estimation method of a heating system pipeline network exists for the problem, but the existing method requires collecting all sensor data in a system to a core computing unit, a large amount of data needs to be exchanged, the requirement on communication capacity is high, the calculated amount is large, the existing method emphasizes state estimation at a certain moment, and the tracking effect on a dynamic change process needs to be improved.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a heating system pipeline network state estimation method based on Kalman filtering, which can combine temperature sensor data with a state model by using a local Kalman filter to obtain a more accurate pipeline temperature state estimation value, reduce the requirement of data communication and improve the quality and reliability of sensor data.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows: a heating system pipeline network state estimation method based on Kalman filtering comprises the following steps:
1) The temperature sensors arranged at the outlet end of the pipeline acquire new temperature data, each temperature sensor corresponds to one local Kalman filter, the temperature data of the temperature sensors are input into the local Kalman filters, and then the temperature data are exchanged between the local Kalman filters corresponding to the temperature sensors on the pipelines connected in the pipeline network;
2) Fusing the temperature data obtained by the exchange in the step 1) in each local Kalman filter to obtain a fused state estimation value;
3) Substituting the fused state estimation value into an improved pipeline state model for calculation to obtain the state estimation value of each pipeline at the next moment; wherein the improvement of the pipeline state model is to consider the thermal inertia of the temperature sensor;
4) And (3) circulating the steps 1) to 3) until the temperature sensor does not acquire new temperature data any more, and connecting the pipeline state estimated values at all moments in series to obtain a final pipeline state estimated result sequence.
Further, in step 1), the processing procedure of the temperature data acquired by the temperature sensor is as follows: at time k, the temperature sensors at the outlet ends of the pipes 1,2,3, n in the heating system pipe network acquire new temperature data, respectively denoted as z 1,k ,z 2,k ,z 3,k ,...,z n,k Where n is the total number of pipes in the network;
the temperature sensor on each pipeline corresponds to a local Kalman filter, and the temperature data z is obtained 1,k ,z 2,k ,z 3,k ,...,z n,k Respectively sending the signals into corresponding local Kalman filters; then, for each pipe, the pipe that it meets is found out, and temperature data is exchanged between the pipe and the local kalman filter corresponding to the temperature sensor of the connected pipe.
Further, in step 2), the exchanged temperature data is fused in each local kalman filter, and the implementation steps of the local kalman filter are as follows:
2.1 Using exchanged temperature data z j,k Calculating fusion sensor data y i,k ：
Wherein k represents the time, i represents the number of pipes, J i A set of connected pipes indicating the pipe i, j indicating the number of connected pipes, H j,k The Kalman gain coefficient of the local Kalman filter is calculated by the inherent attribute of the pipeline j and the sensor characteristic,represents H j,k Transpose of R j,k An observation noise excitation matrix of the temperature sensor at the outlet end of the pipeline j is represented;
2.2 Calculating a fused inverse covariance S i,k ：
Wherein C is j,k Is an observation matrix of the pipeline j state model, the dimension is 1× (ζ+1), ζ is the number of segments of the pipeline j state model during calculation, and C j,k The expression of (2) is:
in the method, in the process of the invention,is C j,k Is a transpose of (2);
2.3 Calculating an intermediate coefficient M i,k ：
In the method, in the process of the invention,the covariance matrix is estimated optimally, which is obtained by iteration, and the expression is as follows:
wherein A is k A state transition matrix for a pipeline i state model, B k A is a state control matrix of a pipeline i state model k And B k Given by the pipe characteristics, Q k Noise covariance is transferred for the state of pipe i;
2.4 Using intermediate coefficients M i,k Based on the estimated value, the sensor data y is fused i,k And state estimation valueCompensating the error of (2) to obtain a corrected state estimate +.>
In the formula, the state estimation valueThe method is obtained by the last iteration;
2.5 Exchanging the corrected state estimation value between the pipeline i and the local Kalman filter corresponding to the temperature sensor of the connected pipeline, and performing fusion calculation to obtain a fused state estimation value
Where μ is the fusion coefficient.
Further, in step 3), the fused state estimation value is used forSubstituting the improved pipeline state model to calculate to obtain the state estimated value of the temperature at the next moment +.>The computational expression is as follows:
wherein B is i,k A state control matrix for a pipeline i state model, given by pipeline characteristics, u i,k A is calculated from the water temperature at the inlet of the pipeline and is the state control variable of the state model of the pipeline i i,k The dimension is (ζ+1) x (ζ+1) for the state control variable of the pipeline i state model, wherein ζ is the number of segments when the pipeline j state model is calculated; in the improved pipeline state model, the state control variable A is controlled by i,k Modified to incorporate the thermal inertia coefficient eta, A of the state control variable i,k The expression is:
where e is the heat loss coefficient, calculated from the pipe properties, η is the thermal inertia coefficient, calculated from the sensor's thermal conductivity properties.
Further, in step 4), steps 1) to 3) are cycled, assuming that the temperature sensor no longer acquires new temperature data up to time k, at which time the pipe state estimate at all times will beSeries connection to obtain final state estimation result sequence of each pipeline>
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the method combines the local Kalman filter with the pipeline state model of the heating system for the first time, and carries out state estimation on the pipeline network of the heating system.
2. Compared with other heat supply system pipeline network state estimation methods, the method reduces the data quantity to be exchanged, thereby reducing the communication requirement and being easier to realize.
3. Compared with other heating system pipeline network state estimation methods, the method provided by the invention has the advantages that the known information and redundant data are fully utilized, the utilization rate of the data is improved, and the data volume and the calculation workload are reduced.
4. The method improves the pipeline state model of the heating system, introduces the thermal inertia coefficient, thereby describing the temperature dynamic change process more accurately and realizing more accurate state estimation effect.
Drawings
FIG. 1 is a schematic diagram of a logic flow of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
As shown in fig. 1, the embodiment discloses a heating system pipeline network state estimation method based on kalman filtering, which comprises the following steps:
1) For a heating system piping network with 29 pipes, at time k, the temperature sensors at the outlet end of the pipes 1,2,3, 29 acquire new temperature data, denoted as z, respectively 1,k ,z 2,k ,z 3,k ,...,z 29,k ；
The temperature sensor on each pipeline corresponds to one local Kalman filter, and the total of 29 local Kalman filters are used for obtaining temperature data z 1,k ,z 2,k ,z 3,k ,...,z 29,k Respectively sending the signals into corresponding local Kalman filters; along with itAnd then, for each pipeline, finding out the pipeline connected with the pipeline, and exchanging temperature data between the pipeline and a local Kalman filter corresponding to a temperature sensor of the connected pipeline.
2) The exchanged temperature data are respectively fused in 29 local Kalman filters, and the temperature data are calculated according to the following steps:
2.1 Using exchanged temperature data z j,k Calculating fusion sensor data y i,k ：
Wherein k represents the time, i represents the number of pipes, J i A set of connected pipes indicating the pipe i, j indicating the number of connected pipes, H j,k The Kalman gain coefficient of the local Kalman filter is calculated by the inherent attribute of the pipeline j and the sensor characteristic,represents H j,k Transpose of R j,k The observation noise excitation matrix of the temperature sensor at the outlet end of the pipeline j is given by people;
2.2 Calculating a fused inverse covariance S i,k ：
Wherein C is j,k Is an observation matrix of the pipeline j state model, the dimension is 1× (ζ+1), ζ is the number of segments calculated by the pipeline j state model, and is given by human beings, C j,k The expression of (2) is:
C j,k ＝[0,0,...,0,1,0]
in the method, in the process of the invention,is C j,k Is a transpose of (2);
2.3 Calculating an intermediate coefficient M i,k ：
In the method, in the process of the invention,the covariance matrix is estimated optimally, which is obtained by iteration, and the expression is as follows:
wherein A is k A state transition matrix for a pipeline i state model, B k A is a state control matrix of a pipeline i state model k And B k Given by the pipe characteristics, Q k The state transition noise covariance for pipeline i is artificially given;
2.4 Using intermediate coefficients M i,k Based on the estimated value, the sensor data y is fused i,k And state estimation valueCompensating for errors in (a) and updating the corrected state estimate +.>
In the formula, the state estimation valueThe initial value is given by human body;
2.5 Exchanging the corrected state estimation value between the pipeline i and the local Kalman filter corresponding to the temperature sensor of the connected pipeline, and performing fusion calculation to obtain a fused state estimation value
Wherein mu is a fusion coefficient and is set according to actual requirements.
3) The fused state estimation valueSubstituting the improved pipeline state model to calculate to obtain the state estimated value of the temperature at the next moment +.>
Wherein B is i,k A state control matrix for a pipeline i state model, given by pipeline characteristics, u i,k A is calculated from the water temperature at the inlet of the pipeline and is the state control variable of the state model of the pipeline i i,k The dimension is (ζ+1) x (ζ+1), and ζ is the number of segments calculated by the pipeline j state model and is manually given; in the improved pipeline state model, the state control variable A is controlled by i,k Modified to incorporate the thermal inertia coefficient eta, A of the state control variable i,k The expression is:
where e is the heat loss coefficient, calculated from the pipe properties, η is the thermal inertia coefficient, calculated from the sensor's thermal conductivity properties.
4) Cycling through steps 1) through 3), assuming that until time k, the temperature sensor no longer acquires new temperature data,at this time, the state estimation values of all pipelines at all momentsIn series of->Finally obtaining the final state estimation result sequence of 29 pipelines
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (5)
1. The heating system pipeline network state estimation method based on Kalman filtering is characterized by comprising the following steps of:
1) The temperature sensors arranged at the outlet end of the pipeline acquire new temperature data, each temperature sensor corresponds to one local Kalman filter, the temperature data of the temperature sensors are input into the local Kalman filters, and then the temperature data are exchanged between the local Kalman filters corresponding to the temperature sensors on the pipelines connected in the pipeline network;
2) Fusing the temperature data obtained by the exchange in the step 1) in each local Kalman filter to obtain a fused state estimation value;
3) Substituting the fused state estimation value into an improved pipeline state model for calculation to obtain the state estimation value of each pipeline at the next moment; wherein the improvement of the pipeline state model is to consider the thermal inertia of the temperature sensor;
4) And (3) circulating the steps 1) to 3) until the temperature sensor does not acquire new temperature data any more, and connecting the pipeline state estimated values at all moments in series to obtain a final pipeline state estimated result sequence.
2. The kalman filter-based heating system pipeline network state estimation method according to claim 1, wherein in step 1), the processing procedure of the temperature data acquired by the temperature sensor is: at time k, the temperature sensors at the outlet ends of the pipes 1,2,3, n in the heating system pipe network acquire new temperature data, respectively denoted as z 1,k ,z 2,k ,z 3,k ,...,z n,k Where n is the total number of pipes in the network;
the temperature sensor on each pipeline corresponds to a local Kalman filter, and the temperature data z is obtained 1,k ,z 2,k ,z 3,k ,...,z n,k Respectively sending the signals into corresponding local Kalman filters; then, for each pipe, the pipe that it meets is found out, and temperature data is exchanged between the pipe and the local kalman filter corresponding to the temperature sensor of the connected pipe.
3. The kalman filter-based heating system pipeline network state estimation method according to claim 2, wherein in step 2), the exchanged temperature data is fused in each local kalman filter, and the implementation steps of the local kalman filter are as follows:
2.1 Using exchanged temperature data z j,k Calculating fusion sensor data y i,k ：
Wherein k represents the time, i represents the number of pipes, J i A set of connected pipes indicating the pipe i, j indicating the number of connected pipes, H j,k The Kalman gain coefficient of the local Kalman filter is calculated by the inherent attribute of the pipeline j and the sensor characteristic,represents H j,k Transpose of R j,k An observation noise excitation matrix of the temperature sensor at the outlet end of the pipeline j is represented;
2.2 Calculating a fused inverse covariance S i,k ：
Wherein C is j,k Is an observation matrix of the pipeline j state model, the dimension is 1× (ζ+1), ζ is the number of segments of the pipeline j state model during calculation, and C j,k The expression of (2) is:
C j,k ＝[0,0,...,0,1,0]
in the method, in the process of the invention,is C j,k Is a transpose of (2);
2.3 Calculating an intermediate coefficient M i,k ：
In the method, in the process of the invention,the covariance matrix is estimated optimally, which is obtained by iteration, and the expression is as follows:
wherein A is k A state transition matrix for a pipeline i state model, B k A is a state control matrix of a pipeline i state model k And B k Given by the pipe characteristics, Q k Noise covariance is transferred for the state of pipe i;
2.4 Using intermediate coefficients M i,k Based on the estimated value, the sensor data y is fused i,k And state estimation valueCompensating the error of (2) to obtain a corrected state estimate +.>
In the formula, the state estimation valueThe method is obtained by the last iteration;
2.5 Exchanging the corrected state estimation value between the pipeline i and the local Kalman filter corresponding to the temperature sensor of the connected pipeline, and performing fusion calculation to obtain a fused state estimation value
Where μ is the fusion coefficient.
4. A method for estimating the state of a heating system piping network based on kalman filtering according to claim 3, wherein in step 3), the fused state estimation values are usedSubstituting the improved pipeline state model to calculate to obtain the state estimated value of the temperature at the next moment +.>The computational expression is as follows:
wherein B is i,k A state control matrix for a pipeline i state model, given by pipeline characteristics, u i,k A is calculated from the water temperature at the inlet of the pipeline and is the state control variable of the state model of the pipeline i i,k The dimension is (ζ+1) x (ζ+1) for the state control variable of the pipeline i state model, wherein ζ is the number of segments when the pipeline j state model is calculated; in the improved pipeline state model, the state control variable A is controlled by i,k Modified to incorporate the thermal inertia coefficient eta, A of the state control variable i,k The expression is:
where e is the heat loss coefficient, calculated from the pipe properties, η is the thermal inertia coefficient, calculated from the sensor's thermal conductivity properties.
5. The Kalman filtering-based heating system pipeline network state estimation method according to claim 4, wherein in step 4), the steps 1) to 3) are circulated, and the temperature sensor is not used for acquiring new temperature data until the k moment, and the pipeline state estimation values at all the moments are obtainedSeries connection to obtain final state estimation result sequence of each pipeline>
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