CN111274668A - Method for improving quality of time sequence data of centralized heat supply pipe network - Google Patents
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Abstract
The invention discloses a method for improving the quality of time sequence data of a centralized heat supply pipe network, which comprises the following steps: establishing a heat supply network model; the method specifically comprises the steps of representing a complex network by using graph theory, establishing a network equation of a system in a matrix form, establishing a static model of the heat supply network and establishing a dynamic model of the heat supply network. Estimating the state of the heat supply network; the method specifically comprises the steps of selecting state quantity, establishing a heat supply network state estimation iteration equation, simplifying calculation of heat supply network state estimation and detection and correction of bad data of the heat supply network state estimation. Example verification was performed.
Description
Technical Field
The invention relates to the field of data quality improvement of a centralized heat supply pipe network, in particular to a method for improving the time sequence data quality of the centralized heat supply pipe network.
Background
Along with the improvement of the informatization level of the centralized heat supply pipe network, a large amount of operation monitoring data are collected and stored, and the safety management and the economic operation of the system are facilitated. The quality of the collected data is not high, so that the deviation of a system operation strategy is caused, the phenomena of energy waste, hydraulic inequality and the like are caused, and the economic operation of the heat supply network is not facilitated. Only with data with higher quality, the subsequent research can reflect the actual situation better, and the simulation and simulation results can help solve the actual problem better. For the work using the actual operation data, such as data mining and the like, high-quality data is also needed, so that the obtained conclusion can be more accurate, and reference is provided for the operation of the heat supply network. The expansion of the scale of the heat supply network causes the rapid increase of the types and the quantity of data, and the measurement error problem of the thermal parameters causes the low data quality, so that the monitoring data of the system can not meet the node quality conservation and the system energy conservation. Therefore, the data quality is improved, the control precision of the heat supply network is improved, the system control strategy is perfected, and the optimization design and operation of the heat supply network are facilitated.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method for improving the quality of time sequence data of a centralized heat supply pipe network
The purpose of the invention is realized by the following technical scheme:
a method for improving the quality of time sequence data of a centralized heat supply pipe network comprises the following steps:
establishing a heat supply network model;
the method specifically comprises the steps of representing a complex network by using graph theory, establishing a network equation of a system in a matrix form, establishing a static model of the heat supply network and establishing a dynamic model of the heat supply network; the heat supply network static model comprises a hydraulic model and a thermal model, and a simplification method is provided for the heat supply network dynamic model;
estimating the state of the heat supply network;
selecting state quantity, establishing a heat supply network state estimation iteration equation, simplifying calculation of heat supply network state estimation and detection and correction of bad data of heat supply network state estimation;
example verification was performed.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention aims at the research of the method for improving the data quality of the existing operation data, processes the original data to obtain the data closer to the real operation state, and can provide high-quality data for the research of the system simulation technology, the operation scheduling and the like which are developing. The method has the advantages that the quality of the operation data is improved, the error influence of the sensor is eliminated, and the data closer to the real operation condition is obtained, so that the method has important research significance.
2. The problem of measurement errors in the heat network, which are caused on the one hand by faults or installation problems of the sensors, etc., and on the other hand by random errors of the sensors, is always present and the data quality is not high. Due to the existence of the errors, the system monitoring data often cannot meet the node quality conservation and the system energy conservation. Through the state estimation of the heat supply network, the distribution of the sensors and the measurement time interval of the quantity measurement can be guided, and a foundation is laid for the practical engineering application.
3. By adopting a state estimation calculation method, data errors are eliminated, the overall quality of data is improved, the data can meet a heat supply network mathematical model, basic mass conservation and energy conservation are met, and data closer to the real operation condition is provided for subsequent research.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a diagram of a heat supply network according to the present invention;
FIG. 3 is a basic circuit diagram of a heat supply network according to the present invention;
FIG. 4 is a graph of temperature delay profile of the present invention;
FIG. 5 is a flow chart of bad data diagnosis in the present invention;
FIG. 6 is a flow chart of a programming process for estimating the state of a heat supply network according to the present invention;
FIG. 7 is a diagram of the identification steps of the basic correlation matrix of the pipe network according to the present invention;
FIG. 8 is a topology diagram of a regional heat network according to the present invention;
FIG. 9-1 and FIG. 9-2 show the heat exchanger efficiency of 1000 groups of data and the heat loss coefficient of 1000 groups of data pipe network in the present invention, respectively;
FIGS. 10-1 and 10-2 are graphs of temperature differences between 1000 and 24 sets of raw data, respectively, in accordance with the present invention;
FIG. 11 illustrates the temperature difference before and after estimation of the heat exchange station in the present invention;
FIG. 12 illustrates the manifold outlet and heat exchange station inlet temperatures after state estimation in accordance with the present invention;
FIG. 13-1 is a graph of 1000 sets of estimated data temperature differences;
FIG. 13-2 is a graph of estimated front and rear manifold outlet and heat exchange station inlet temperature differences;
FIG. 14 is a temperature difference frequency statistic of data before and after estimation in the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses a method for improving the quality of time sequence data of a centralized heat supply pipe network, the specific process is shown in the attached figure 1, and the method comprises the following steps:
(1) the establishment of the heat supply network model comprises the following steps:
(1-1) representing a complex network by using a graph theory, and establishing a network equation of the system in a matrix form:
(1-1-1) correlation matrix and basic correlation matrix:
if a heating network has J nodes and N branches, and all the nodes and the branches can represent a matrix b (g), which is a correlation matrix of graph theory and has the elements:
in the associative matrix b (g), the columns of the matrix represent branches, the serial numbers on the columns are branch numbers, the rows of the matrix represent nodes, and the serial numbers on the rows are node numbers, there is always one 1 and one-1 in each column, and the other numbers in the matrix are 0, because 1 branch corresponds to only 2 nodes, 1 starting point number 1 and 1 ending point number-1, v represents a node, and e represents a pipeline, see fig. 2:
according to the definition of the graph theory, the following can be obtained:
V＝{v1，v2，v3，v4，v5，u6}
E＝{e1，e2，e3，e4，e5，e6，e7}
the incidence matrix of the heat supply pipe network diagram can be expressed as:
the J × N order matrix B (G) can represent the connection state of N branches and J nodes in the network, but the (J-1) × N order matrix can express the connection state of the two clearly, namely the row of the incidence matrix can be reduced by 1, and the row of the node k is removed to form the (J-1) × N order basic incidence matrix B of the reference node kk(G) It can be expressed as:
by means of a basic incidence matrix Bk(G) And (4) obtaining the relation between the inflow flow and the outflow flow of each node.
(1-1-2) basic loop matrix and independent loop matrix:
aiming at the existing heat supply pipe network diagram, the number of basic loops is often more than 1, so that the association relationship between each basic loop and a branch in the heat supply pipe network diagram is conveniently determined, a basic loop matrix can be obtained, in the upper section of the heat supply pipe network diagram, three basic loops are provided, each basic loop capable of being drawn is given a direction, when the direction of the branch is the same as the given direction, the direction is forward, otherwise, the direction is reverse. The elements of the basic loop matrix c (g) are as follows:
in the basic loop matrix c (g), each row of the matrix represents a basic loop of the diagram, each column represents a direction in which a branch is located, and all elements of the ith row in the matrix, which are not 0, constitute all branches of the basic loop. The loop is shown in the attached figure 3:
the fundamental loop matrix c (g) can be obtained as follows:
the independent loop matrix of fig. 3 can be represented as:
CI-the row vector for loop I; cII-the row vector for loop II; cIII-the row vector for loop III;
a spanning tree can be obtained in a heat supply pipe network diagram, a loop can be formed by adding one branch every time, all the rest branches are added to form an independent loop group, and an independent loop matrix is formed, so that the number of independent loops is the same as that of the rest branches, the rest branches can be placed in front of the matrix, and the branch parts are placed behind to obtain two block matrixes:
Cf(G)＝[Cf11Cf12]＝[I Cf12]
i is an identity matrix
The simplified processing of the independent loop matrix of the above heat supply pipe network can form the following matrix:
the outdoor meteorological data are real-time meteorological data observed by a meteorological station, and comprise outdoor temperature, solar radiation intensity and outdoor wind speed, and the time scale of the outdoor meteorological data is consistent with the heat supply historical data. Outdoor weather data is data obtained from weather stations and can be considered as accurate data and does not need to be processed.
(1-1-3) for any one node, two matrices satisfy orthogonality, namely:
Bk-a basic incidence matrix of the pipe network a;
-loop matrix Cf(G) The corresponding transpose matrix;
(1-2) establishing a static model of the heat supply network:
(1-2-1) establishing a hydraulic model:
(1-2-1-1) node flow equation:
Bkq+Q＝0
Bk-a basic incidence matrix of the pipe network a; q-line flow, m3H; q-node traffic, m3/h
(1-2-1-2) Loop pressure balance equation:
C(ΔP-HP)＝0
c-independent loop matrix of pipe network; Δ P-loss of resistance, Pa; hp-full pressure power, Pa
(1-2-1-3) pressure drop equation:
ΔP＝S·q2
s-a resistance characteristic coefficient matrix; q-line flow, m3/h
K-equivalent absolute roughness of the inner surface of the pipe, K being 0.0005m for a normal heat supply pipe; d-inner diameter of pipe, m
l、ldEquivalent lengths of effective and local resistances of the pipeline, m
The water power model of the heat supply network can be obtained by combining the equations.
(1-2-2) establishment of a thermodynamic model:
the thermal model of the heat supply network mainly means that heat dissipation loss exists in a pipeline, so that the temperature of a working medium can be gradually reduced in the transportation process, and the thermal resistance in the transportation process is mainly divided into four types: thermal resistance of the working medium to the inner wall of the pipeline; thermal resistance of the tube wall; thermal resistance of the insulating layer; the thermal resistance between the outer surface of the pipeline insulating layer and the surrounding environment. Among the four thermal resistances, the thermal resistance from the working medium to the inner wall of the pipeline and the thermal resistance of the pipeline wall are smaller than the other two thermal resistances, and the numerical values are ignored in the calculation.
The thermal resistance of the insulation layer can be expressed as:
λbthermal conductivity of the insulation layer material, W/m.K
dzDiameter of the outer surface of the insulation layer, m
dwOuter diameter of pipe, m
The thermal resistance of the outer surface of the pipe insulation and the atmospheric conditions outside the pipe can be expressed as:
dzdiameter of the outer surface of the insulation layer, m
αWHeat transfer coefficient of the outer surface of the insulation with respect to the environment, W/m2·K
Omega-flow velocity of air near the outer surface of the insulation layer, m/s
The heat loss per unit length of the pipe can be expressed as:
t-temperature of working medium in pipe, K
t0-ambient (air) temperature of the pipe, K
During the transportation process of the working medium in the pipeline of the heat supply system, heat can be spontaneously transmitted to the external environment of the pipeline with lower temperature, so that the temperature of the working medium can be continuously reduced during the transportation process. The temperature drop of the working fluid can be expressed as:
delta t-temperature drop along the pipeline, K
l-pipe length, m
Mass flow of heating medium in G-tube section, kg/s
c-mass specific heat of working medium, kJ/kg. K
β -local loss additive coefficient of various parts of the pipeline, 0.25 in the overhead space, 0.20 in the trench and 0.15 in the direct burial.
The computational model of the heating power can be expressed as:
Bkbasic incidence matrix of pipe network A
q-line flow, m3/h
Q-node traffic, m3/h
T-node temperature, K
Delta T-temperature drop of pipe section, K
Diagonal matrix of order Y-m
qjHeat loss per unit length of pipe section, W/m
ljLength of pipe section j, m
c-mass specific heat of working medium, kJ/kg. K
The relationship between temperature and flow can be obtained by formulating the equation:
the thermodynamic model of the pipe network is mainly the relation between the flow and the temperature in the pipe network, and together with the hydraulic model, the mathematical relation between the pressure, the flow and the temperature in the pipe network can be obtained.
(1-3) establishing a heat supply network dynamic model:
(1-3-1) establishing a dynamic model:
(1-3-1-1) continuity equation:
qv＝uA
a-cross sectional area
u-velocity of fluid in axial direction of tube
qvVolume flow in the pipe
Can obtain
qv＝C
(1-3-1-2) momentum equation: the rate of change of momentum of the fluid within the control body is equal to the sum of the surface and mass forces acting on the control body from the outside.
The surface force is mainly divided into two parts: positive pressure and shear stress. Positive pressure being pA and pA acting on the control body
Shear stress of τ0。
Gravity caused by mass force when the pipeline is not horizontal has component force in the flowing direction, namely rho gAdxsin theta, and according to the definition of a momentum equation, the following can be obtained:
shear stress is related to flow rate as follows:
wherein lambda is the on-way resistance coefficient of the pipeline, and the shear stress is always opposite to the flowing direction of the fluid in the pipeline.
The formula is arranged to obtain:
substituting the continuity equation with
It is possible to obtain:
(1-3-1-3) energy equation: the sum of the heat quantity increased by the system in unit time and the work done by the mass force and the surface force in unit time is equal to the change rate of the total energy of the control body fluid. The total energy of the system comprises two parts of internal energy and kinetic energy.
The internal energy per unit mass of fluid can be expressed as e, and the total energy rate of change can be expressed as follows:
can be simplified into:
the work is divided into surface force and mass force work, wherein the mass force work is rho gAdxsin theta.u, and the positive pressure
Does work as
Shear stress acting as-tau0Pi Ddx · u, the total work of surface force consisting of positive and shear stress is:
the amount of heat per unit surface area per unit time in the control body is represented by q, and the energy equation obtained by the processing can be expressed as follows:
comprises the following components in the formula,
And definition of enthalpy
It is possible to obtain:
due to the continuity equation, the pressure of the fluid varies only with position, independent of time, and then
So the above formula can be simplified to obtain:
expression by momentum equation and enthalpy in thermodynamics
And
further simplification of the above formula can lead to:
the formula can be arranged to obtain:
the dynamic model describes the change condition of the temperature in time scale, and can be a steady-state model when the temperature does not change along with the time.
(1-3-2) simplification of dynamic model: the dynamic model of the above formula is simplified, the length of the pipe section is used as an abscissa, the time is used as an ordinate, and an analysis chart of the temperature time lag effect can be made, see the attached figure 4
For small time differences and distance differences, the dynamic model can be simplified, and can be written as:
in the above formula get
And from the continuity equation, the above equation can be simplified to obtain the following formula:
according to the formula, the temperature data T at a certain point and a certain moment is known1，1Data along the line of FIG. 4, e.g. T2，2According to the preceding data T1，1And calculating, so that for the temperature hysteresis phenomenon existing in the heat supply network, the temperature data is selected according to the slope when being selected, and the temperature is selected in a delayed manner according to the distance position of the heat exchange station and the flow velocity of the fluid, so that the selected data are ensured to be in the same working state.
(2) And (3) state estimation of the heat supply network:
(2-1) selecting the state quantity:
state quantity: and representing the running state of the whole system, and calculating the working state of each node of the system through the state quantity. And for the heat supply network, selecting flow data of all tail end branches, inlet and outlet temperature data of the main pipe and inlet and outlet pressure data of the main pipe as state quantities.
The state quantity x is selected as follows based on the above:
x＝[Tz，q1，q2…qn，Pz]
state quantity of x-heat supply network
TzOutlet temperature of the header pipe, K
q1，q2…qnFlow of each branch, m3/h
PzOutlet pressure of the manifold, Pa.
(2-2) iterative equation for heat supply network state estimation:
after the state quantity is selected, calculating state estimation, and obtaining the system running state by using a measurement equation on the basis of the known state quantity as follows:
z＝h(x)+v
value of z-quantity measurement
h (x) -measuring a mathematical expression expressed by a state quantity x
v-residual vector
The idea of state estimation is to find out the data most meeting the system running state according to the incidence relation among the data, i.e. to solve the optimal solution of the whole heat supply network system, so that the integral residual vector is minimum, i.e.:
minJ(x)＝[z-h(x)]T[z-h(x)]
the minimum value of this equation is required, subject to the requirement that
Substitution can result in:
is provided with
H is called the Jacobian matrix, assuming a function f (x):
f(x)＝HT[z-h(x)]＝0
the optimal solution of the formula on the Newton method is applied for processing, and the final solved iteration form is as follows:
Δx(k)＝[HT(x(k))H(x(k))]-1HT(x(k))[z-h(x(k))]
x(k+1)＝x(k)+Δx(k)
by the above formula, programming calculation can be performed, and after the initial state quantity is input, iterative operation is performed to continuously obtain the state quantity after iteration.
(2-3) means for solving the problem of heat supply network:
(2-3-1) temperature hysteresis effect: the temperature has serious lag, the temperature difference of adjacent nodes needs to consider the time lag effect, and then the temperature difference of the i node and the j node can be expressed as:
at is the skew time of two nodes.
(2-3-2) supply and return water correlation:
the equations of the relationship are as follows:
in the formula:
q2secondary side flow, m3/h
T2cOutlet temperature data of the secondary side of the heat exchange station, K
T2rInlet temperature data of the secondary side of the heat exchange station, K
q1Flow data on the primary side of the heat exchange station, m3/h
T1rInlet of the primary side of the heat exchange stationTemperature data, K
T1cOutlet temperature data of the primary side of the heat exchange station, K
The outlet temperature at the primary side is expressed by the inlet temperature, and it is possible to obtain:
three quantities are fused together as one state quantity to solve for:
K＝q2(T2c-T2r)
the return water temperature of each heat exchange station can be effectively estimated, and return water temperature data after state estimation is obtained.
(2-4) the simplified calculation method of the heat supply network state estimation comprises the following steps:
for the Jacobian matrix
The measurement of the pressure and the measurement of the temperature are divided into an upper part and a lower part and arranged, and a matrix can be obtained as follows:
wherein:
Pnpressure of n node
TnTemperature of-n node
(2-5) bad data detection and correction of heat supply network state estimation:
the invention adopts a detection and residual error method before state estimation calculation, and a flow chart is shown in figure 5.
(3) And (3) verifying the heat supply network state estimation process and the example:
(3-1) heat supply network state estimation process:
the calculation needs to continuously perform iterative operation according to a heat supply network state estimation iterative equation, so as to generate a new state quantity, and when the state quantity change is within a certain error range, the optimal solution of the state quantity is obtained. And substituting the numerical value of the state quantity into the heat supply network model to obtain the estimated value of each measured data. The iterative method is compiled by matlab, and the programming steps are shown in the attached figure 6:
the flow of the heat supply network state estimation mainly comprises the following points:
1) for data of a heat supply network, the topological relation of the centralized heat supply network needs to be read, and the basic incidence matrix is input to obtain the connection relation of each heat exchange station of the network. And reading data detected by the system, wherein the read data comprises the temperature, flow and pressure data of a header pipe, the temperature, flow and pressure data of the primary side of each heat exchange station and the temperature, flow and pressure data of the secondary side of each heat exchange station into matlab, recording the EXCEL data in a matrix mode, and performing subsequent calculation.
2) And (3) processing the temperature time lag effect on the read temperature data, solving the temperature time lag time of each heat exchange station by using a pipe network mathematical model under the condition that pipe length and pipe diameter data exist, and solving the time lag time by using original measured temperature data when the pipe length and pipe diameter data do not exist. And solving by using a correlation coefficient method, comparing the temperature curve of the heat exchange station with the temperature curve of the header pipe, solving the correlation coefficients of the temperature curve and the header pipe, pushing the temperature curve of the heat exchange station backwards along a certain time step, continuously calculating the correlation coefficient of the temperature curve and the header pipe, pushing backwards for enough time, finding the maximum value of the correlation coefficient, obtaining the pushing time corresponding to the maximum value of the correlation coefficient, namely the time lag time of the heat exchange station, carrying out the same treatment on each heat exchange station, and obtaining the time lag matrix of the primary side inlet temperature of each heat exchange station relative to the header pipe temperature.
3) According to a matrix formed by the read temperature, flow and pressure data of the header pipe and the heat exchange stations, a pipe network model is used for calculation, heat loss and resistance coefficients obtained by each group of data are calculated, a heat loss coefficient curve and a resistance coefficient curve can be formed by calculating multiple groups of data, the heat loss coefficient and the resistance coefficient are obtained by using an averaging method, and then the same treatment is carried out on each heat exchange station, so that an integral heat loss matrix and a resistance coefficient matrix are obtained.
The method comprises the steps of calculating heat exchanger efficiency by using the temperature and the flow of supply and return water at the secondary side of a heat exchange station, the temperature and the flow of supply and return water at the primary side, calculating multiple groups of data to form a heat exchanger efficiency curve, calculating the heat exchanger efficiency by using an averaging method, calculating the heat exchanger efficiency as a parameter of state estimation, establishing a correlation equation of supply and return water temperature at the primary side, and obtaining an estimated value of return water temperature.
4) After all the parameters are read, various parameters, matrixes and the like required to be obtained by state estimation calculation are solved one by one, and the connection relation of the pipelines is required to be identified according to the basic incidence matrix. The heat supply network model is established by adopting a graph theory method, the relationship between the nodes and each branch is identified by using a basic incidence matrix, no matter how complex heat supply network models are, the relationships can be represented by using the corresponding basic incidence matrix, the basic incidence matrix obtained in the heat supply network effectively inputs the information contained in the basic incidence matrix into a calculation program, the basic incidence matrix needs to be identified, and the identification steps are shown in the attached figure 7
5) And calculating state estimation of each group of temperature, flow and pressure data, and solving an over-determined equation by using the redundancy of the equation to obtain the optimal solution of the state quantity. Inputting initial values of all state quantities in software, solving the change quantity in an iterative mode, forming a new state quantity, stopping calculation when the change quantity of the state quantity is within a certain error range, and solving the optimal calculation result.
(3-2) example verification:
the application analysis of state estimation is carried out on the basis of data of a certain heat supply pipe network in Tianjin City, the total area of the area reaches 35.1 square kilometers, the heating of most of the districts uses a means of regional central heating, and the heat comes from the same thermal power plant. For this network, a thermal power plant has a total of 13 heat exchange stations. The topological diagram of the whole heat supply network is shown in FIG. 8, wherein a point 1 represents a boiler of a thermal power plant, points 2 to 14 represent 13 heat exchange stations respectively, and the arrow direction represents the flowing direction of working media in a pipeline. All historical operating data can be obtained through the monitoring platform. All historical data is stored in EXCEL form.
(3-2-1) problems with the raw data:
firstly, the original data of the heat supply network are integrally analyzed, and the data cannot well meet the mass conservation and the energy conservation, and the thermal model and the hydraulic model have deviation. The quality of the heat supply network data has the following problems:
1) 1000 groups of continuously operated measurement data are selected to obtain the heat exchanger efficiency of the heat exchange station and the heat loss coefficient of the pipe network, and a statistical chart is shown in figure 9.
The calculation formula is as follows:
in the formula:
q2Secondary side flow, m3/h
T2cOutlet temperature data of the secondary side of the heat exchange station, K
T2rInlet temperature data of the secondary side of the heat exchange station, K
q1Flow data on the primary side of the heat exchange station, m3/h
T1rInlet temperature data of the primary side of the heat exchange station, K
T1cOutlet temperature data of the primary side of the heat exchange station, K
In the formula:
c-coefficient of heat loss of pipe network
TzOutlet temperature data of the header pipe, K
T1rInlet temperature data of the primary side of the heat exchange station, K
q1Flow data on the primary side of the heat exchange station, m3/h
Theoretically, a group of heat exchangers exist in each heat exchange station to transmit heat of the primary network and the secondary network, the efficiency of the heat exchangers in the heat exchange stations is basically kept unchanged during system operation, and the situation of large fluctuation as shown in the attached figure 9-1 does not occur. For an established thermodynamic system, the change of heat loss of each pipeline is small, the internal parameter conditions, construction and the like of the pipelines are finished, the influence factors of the heat loss are basically kept unchanged, the heat loss coefficient of a pipe network is basically kept constant in a short time, as shown in the attached figure 9-2, the heat loss coefficient has large fluctuation and negative values, which indicates that obvious errors exist in data, and the basic mathematical model and the theoretical equation of a heat supply network cannot be satisfied.
It can be seen that the heat exchanger efficiency and the heat loss coefficient obtained from the measured data do not meet the conditions, so that the measured data can be judged to have errors, and in the resistance coefficient diagram, abnormal points exist, which indicates that the data deviation of the part exists is large.
2) For the temperatures of the header pipe and the heat exchange stations, theoretically, the temperature data of the header pipe in the same working state after the original data are processed according to the processing method of the temperature time lag effect (2-3-1) is higher than the temperature data of each heat exchange station, the temperature difference basically keeps unchanged, the actually measured data have opposite conditions, the inlet temperature of the heat exchange stations is higher than the outlet water temperature of the boiler, and the measured data have obvious errors, so that the data quality can be improved by using state estimation. The fluctuation of the difference between the main pipe temperature and the inlet temperature of each heat exchange station is large, as shown in the attached drawing 10-1, twenty-four points are taken as shown in the attached drawing 10-2, the fluctuation of the temperature difference is large, and the heat conservation relation is not satisfied.
(3-2-2) analysis of the estimation result of the heat supply network state:
and performing state estimation calculation on water supply temperature and water return temperature of a boiler main pipe, primary side inlet water temperature, water return temperature and flow of 13 heat exchange stations, and secondary side outlet water temperature, water return temperature and flow data by adopting matlab programming, and performing state estimation calculation on 1000 groups of continuously-operated data. For fig. 9-1 and 9-2, because the measured data has errors, which results in unstable heat exchanger efficiency and heat loss coefficient and fluctuation, the heat exchanger efficiency and heat loss coefficient averaged from 1000 sets of operating data can be used to obtain a value for subsequent calculation. Setting the measured temperature as TyEstimated post temperature of Tg. Calculating the temperature difference between the heat exchange stations before and after estimation as Ty-TgThe statistics are shown in FIG. 11.
FIG. 11 shows the temperature difference T before and after the estimation of 1000 sets of datay-TgHas an average value of-0.00069.
Counting the temperature difference T of 13 heat exchange stationsy-TgThe mean, calculated as shown in Table 3-1:
TABLE 3-113 heat-exchange stations 1000 groups of data temperature difference Ty-TgMean value
From the perspective of satisfying the thermal and hydraulic models of the pipe network, the result after state estimation can better satisfy the mathematical model of the whole pipe network, and the effect of eliminating the random error of sensor measurement can be achieved. Since the original measurement data has random errors, the mean value of the difference between the measurement value and the true value should be 0 as the measurement data increases. The difference between the estimated temperature and the temperature before estimation is the eliminated random error, theoretically, as the number of groups increases, the calculated mean value should be 0, and the T counted in the table abovey-TgAll mean values of (A) are close to 0The original measurement data is verified to be corrected.
The state estimation calculation is carried out on the water inlet temperature and the header pipe water outlet temperature of 13 heat exchange stations, the result is shown in figure 12, the integral trend of the temperature curves of each heat exchange station and the header pipe is kept consistent, the temperature of the header pipe is the highest, the heat loss of each heat exchange station is different due to different transportation distances, and the inlet temperature of each heat exchange station is inconsistent when the heat exchange station arrives. Overall, the estimated data can meet the heat supply network mathematical model, and the data quality is improved.
The difference between the boiler outlet water temperature and the heat exchange station inlet temperature is required to be kept stable in continuous operation in a short time, the difference between the boiler outlet water temperature and the heat exchange station inlet temperature is calculated by using 1000 groups of continuous operation data after estimation and is shown in the attached figure 13-1, and the result of taking 24 groups of data is shown in the attached figure 13-2.
The result of the statistical estimation of the temperature difference between the outlet of the front header pipe and the outlet of the heat exchange station and the inlet of the heat exchange station is shown in the attached figure 14:
as can be seen from fig. 13-1 and 13-2, the temperature difference of the original measured data cannot be kept stable, the variation range is large, the actual operation situation cannot be met, the data quality is not high, and the moment when the inlet temperature of the heat exchange station is higher than the outlet temperature of the header pipe exists, the actual operation situation is seriously deviated, and an obvious error exists. Fig. 14 can visually reflect the stability of the estimated temperature difference. Compared with measured data, the estimated value obtained after state estimation calculation can better meet the actual condition, the difference between the outlet temperature of the main pipe and the inlet temperature of the heat exchange station is basically kept stable, the variation amplitude is small, the basic equation of a thermodynamic model can be met, and the capability of reducing the error of the measured data of state estimation is verified.
The method comprises the steps of firstly establishing a hydraulic model and a thermal model of the heat supply network by using a graph theory method, then providing a calculation method for estimating the state of the heat supply network aiming at the characteristics of the heat supply network, providing the state quantity of the heat supply network, solving the problem of temperature lag existing in the heat supply network, providing a set of feasible methods for forming a heat loss matrix and a resistance coefficient matrix, analyzing return water and water supply, and establishing an equation of the return water temperature and the water supply temperature by using the efficiency of a heat exchanger in a heat exchange station. Theoretical analysis is carried out on the characteristics of the heat supply network, a set of simplified calculation method is provided, the state estimation calculation speed can be improved, and the influence on the calculation precision is small. A set of new detection method is provided for the bad data existing in the heat supply network, the detection before the state estimation and the detection after the state estimation are combined for judgment, the advantages of the respective methods can be exerted, the judgment time of the bad data is shortened, and the misjudgment of the bad data is avoided to a certain extent.
The calculation of the state estimation is carried out by depending on a programming means, the whole calculation program is written, and the feasibility of the state estimation is verified by utilizing a Monte Carlo simulation experiment mode.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make many changes and modifications to the invention without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (2)
1. A method for improving the quality of time sequence data of a centralized heat supply pipe network is characterized by comprising the following steps:
establishing a heat supply network model;
the method specifically comprises the steps of representing a complex network by using graph theory, establishing a network equation of a system, establishing a static model of a heat supply network and establishing a dynamic model of the heat supply network in a matrix form, and simplifying the dynamic model of the heat supply network;
estimating the state of the heat supply network;
selecting state quantity, establishing a heat supply network state estimation iteration equation, simplifying calculation of heat supply network state estimation and detection and correction of bad data of heat supply network state estimation;
example verification was performed.
2. The method for improving the quality of the time series data of the district heating pipe network according to claim 1, wherein the static model of the heat supply network comprises a hydraulic model and a thermal model.
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