A Method for Judging Observable States of a Heating System Considering the Quasi-Dynamics of Pipeline Temperature

Abstract

The present invention discloses a method for judging observable states of a heating system considering the quasi-dynamics of pipeline temperature, including: S1, determining input parameters of the observable state judgment model of the heating system; S2, establishing an observable state judgment model of the heating system according to the input parameters; S3, solving the established observable state judgment model of the heating system to obtain the observable states of the system. When considering the quasi-dynamics of pipeline temperature, the propagation speed of temperature depends on the magnitude of water flow, and there is a time-delay characteristic in the propagation process of temperature. At this time, the thermal dynamic equation of the heating system and the estimation result of flow rate influence each other, which makes it difficult to apply the traditional observable state judgment method to the heating system. In view of this problem, the present invention proposes an observable state judgment method based on multi-time-delay scenarios, which can ensure that the obtained observable states are effective in these scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of observable analysis of heating systems, and in particular to a method for judging observable states of a heating system considering the quasi-dynamics of pipeline temperature. Background Art

[0002] In practical applications, it is necessary to perform state estimation on the heating system to monitor the operating state. Before performing state estimation, it is necessary to analyze the observability of the system to obtain the input measurements for state estimation. The problem of judging observable states is the primary problem to be dealt with in observable analysis, which determines the set of states that the system can observe under the collected measurements. The existing methods for judging observable states mainly adopt numerical methods, which require determining the specific form of the measurement equation and cannot directly handle the problem of judging observable states of the heating system. In the heating system, temperature is transmitted in the pipeline with water as the carrier. Since it takes time for water to flow from the head end of the pipeline to the end of the pipeline, the temperature transmission in the pipeline has a time-delay characteristic. When considering the quasi-dynamic characteristics of the pipeline temperature in the heating system, the thermal dynamic equation of the heating system is related to the time-delay scenario, and the time-delay scenario is determined by the estimation result of the water flow rate. Therefore, the thermal dynamic equation and the estimation result influence each other, and the exact form of the thermal dynamic equation cannot be determined when judging observable states, and the existing numerical methods cannot be directly applied to the heating system. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and propose a method for judging observable states of a heating system considering the quasi-dynamics of pipeline temperature. This method considers the problem of judging observable states under multiple time-delay scenarios and can ensure that the obtained observable states are valid in these scenarios. It addresses the problem that the thermal dynamic equation of the heating system and the estimation result influence each other and can provide reliable observable analysis results for the heating system.

[0004] To achieve the above purpose, the technical solution provided by the present invention is: A method for judging observable states of a heating system considering the quasi-dynamics of pipeline temperature, the heating system is composed of a heat source, a heat load, a supply pipeline and a return pipeline, and includes the following steps:

[0005] S1. Determine the input parameters of the observable state judgment model of the heating system, including the set of measurement equations collected The set of states of the heating system The set of time-delay scenarios considered The correlation coefficient a between the measurement equation j and the system state i under the time-delay scenario (γ, φ) ij，(γ，φ) and the measurement equation j in The union of the states involved in each scenario The number n of nodes in the heating system Node The set of time periods considered Set of flow continuity equations of the heating system at time period t

[0006] S2. Establish the objective function and constraint conditions of the observable state judgment model of the heating system according to the input parameters, and linearize the non-linear terms in the constraint conditions;

[0007] S3. Use the solver to solve the processed observable state judgment model of the heating system to obtain the observable state of the system.

[0008] Furthermore, the step S1 includes:

[0009] Determine the set of measurement equations collected It is derived from the real-time measurements obtained by the measuring instruments and the state equations of the system; the real-time measurements include pipeline flow measurements, node temperature measurements, heat source heat supply measurements and heat load heat consumption measurements; the state equations of the system include flow continuity equations, loop pressure drop equations and pipeline temperature quasi-dynamic equations;

[0010] Determine the state set of the heating system It includes the temperatures of all nodes and the flows of all pipelines in the heating system;

[0011] Determine the set of time-delay scenarios considered It can be calculated through the pseudo-measurement of pipeline flow and the flow estimation results in historical time periods;

[0012] Determine the correlation coefficient a between the measurement equation j and the system state i under the time-delay scenario (γ, φ) ij，(γ，φ) ., the measurement equation j at Union of states involved in each scenario Use to represent the set of system states involved in the measurement equation j under the time-delay scenario (γ, φ); if the system state i belongs to then assign the correlation coefficient a ij，(γ，φ) = 1; if the system state i does not belong to then assign the correlation coefficient a ij，(γ，φ) = 0; Can be calculated through Calculated as:

[0013] Determine the number n of nodes in the heating system Node ., the set of time periods considered Set of flow continuity equations of the heating system at time period t They can be obtained by analyzing the system structure.

[0014] Furthermore, the step S2 includes the following steps:

[0015] S201. Establish the objective function of the observable state judgment model for the heating system based on the input parameters, which maximizes the number of observable states:

[0016]

[0017] In the formula, n i is a 0-1 variable. If state i is observable, the variable is 1; otherwise, the variable is 0.

[0018] S202. Establish the constraint conditions of the observable state judgment model for the heating system based on the input parameters, which ensure that in each scenario, a set of basic measurement equations composed of relevant measurement equations can be found. The relevant measurement equations refer to the measurement equations that only involve observable states in

[0019] The established constraint conditions specifically include: The observability constraint of the states involved in the relevant measurement equations, which ensures that the states involved in the relevant measurement equations are observable in

[0020]

[0021] In the formula, w j is a 0-1 variable. If measurement equation j is relevant, the variable is 1; otherwise, the variable is 0.

[0022] The source constraint of the basic measurement equations, which ensures that all basic measurement equations are relevant measurement equations:

[0023]

[0024] In the formula, v j，(γ，φ) is a 0-1 variable. If measurement equation j is a basic measurement equation in the time-delay scenario (γ, φ), the variable is 1; otherwise, the variable is 0.

[0025] The independence constraint of the basic measurement equations, which ensures that all basic measurement equations involving multiple states are independent of each other:

[0026]

[0027] The mapping constraint between the observable states and the basic measurement equations, which ensures that there is a one-to-one mapping relationship between the observable states and the basic measurement equations. This is the condition that the basic measurement equations need to satisfy to solve the observable states:

[0028]

[0029]

[0030] In the formula, y ij，(γ，φ) is a 0-1 variable. If there is a one-to-one correspondence between state i and measurement equation j in the time-delay scenario (γ, φ), the variable is 1; otherwise, the variable is 0.

[0031] S203. Linearize the non-linear terms in the constraint conditions; there are non-linear terms with multiple decision variables multiplied in constraint conditions (2) and (6). Use to represent the k-th non-linear term:

[0032]

[0033] In the formula, is the set of decision variables involved in the k-th non-linear term; x l is the l-th decision variable;

[0034] After linearizing formula (7), we get:

[0035]

[0036]

[0037] In the formula, is the number of decision variables involved in the k-th non-linear term.

[0038] Furthermore, step S3 includes the following steps:

[0039] S301. Use a solver to solve the processed observable state judgment model of the heating system;

[0040] S302. Obtain the observable state of the system; the solution result of the observable state judgment model of the heating system contains the value of variable n i whose value represents the observability of state i. If n i = 1, it indicates that state i is observable.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] 1. The method of the present invention considers the quasi-dynamic characteristics of the pipeline temperature in the heating system, and proposes an observable state judgment method based on multiple time-delay scenarios, ensuring that the obtained observable states are effective in these scenarios. The method of the present invention can handle the problem that the thermal dynamic equation and the estimation result in the heating system affect each other, and provides reliable observable state results while considering the dynamic characteristics of the heating system.

[0043] 2. The traditional method for judging observable states requires specifying the specific form of the measurement equation and can only obtain the observable state results for one time-delay scenario each time. Compared with the method of judging observable states for each time-delay scenario one by one, the method of the present invention simultaneously considers the observable judgment problems for multiple time-delay scenarios, with fewer decision variables and constraints in the established model, a smaller calculation scale, and higher solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of the method of the present invention.

[0045] Figure 2 It is a classification diagram of measurement equations in the observable state judgment problem.

[0046] Figure 3 It is a schematic diagram of real-time measurements collected in each time period in Case 1.

[0047] Figure 4 It is a schematic diagram of real-time measurements collected in each time period in Case 2.

[0048] Figure 5 It is a schematic diagram of real-time measurements collected in each time period in Case 3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The present invention will be further described in detail below in conjunction with embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0050] As Figure 1 shown, this embodiment provides a method for judging the observable state of a heating system considering the quasi-dynamic pipeline temperature. The heating system consists of a heat source, a heat load, a supply pipeline, and a return pipeline. The method for judging the observable state specifically includes the following steps:

[0051] S1. Determine the input parameters of the observable state judgment model for the heating system, including the set of measurement equations collected the set of states of the heating system the set of time-delay scenarios considered the correlation coefficient a between the measurement equation j and the system state i in the time-delay scenario (γ, φ) ij，(γ，φ) ., the measurement equation j in the union of the states involved in each scenario the number n of nodes in the heating system Node the set of time periods considered the set of flow continuity equations of the heating system at time period t Specifically as follows:

[0052] Determine the set of measurement equations collected It is derived from the real-time measurements obtained by the measuring instruments and the state equations of the system; the real-time measurements include pipeline flow measurements, node temperature measurements, heat source heat supply measurements, and heat load heat consumption measurements; the state equations of the system include the flow continuity equation, the loop pressure drop equation, and the pipeline temperature quasi-dynamic equation;

[0053] Determine the state set of the heating system It includes the temperatures of all nodes and the flows of all pipelines in the heating system;

[0054] Determine the set of considered time-delay scenarios It can be calculated from the pseudo-measurements of pipeline flow and the flow estimation results in historical periods;

[0055] Determine the correlation coefficient \(a\) between the measurement equation \(j\) and the system state \(i\) under the time-delay scenario \((\gamma,\varphi)\) ij，(γ，φ) and the measurement equation \(j\) in the union of the states involved in each scenario Use to represent the set of system states involved in the measurement equation \(j\) under the time-delay scenario \((\gamma,\varphi)\); if the system state \(i\) belongs to then assign the correlation coefficient \(a\) ij，(γ，φ) = 1; if the system state \(i\) does not belong to then assign the correlation coefficient \(a\) ij，(γ，φ) = 0; It can be calculated through as follows:

[0056] Determine the number \(n\) of nodes in the heating system Node and the set of considered time periods the set of flow continuity equations of the heating system at time period \(t\) They can be obtained by analyzing the system structure.

[0057] S2. Establish the objective function and constraint conditions of the observable state judgment model of the heating system, and linearize the non-linear terms in the constraint conditions. The specific steps are as follows:

[0058] S201. Establish the objective function of the observable state judgment model of the heating system, which maximizes the number of observable states:

[0059]

[0060] In the formula, \(n\) i is a 0-1 variable. If the state \(i\) is observable, the variable is 1; otherwise, the variable is 0;

[0061] S202. Establish the constraint conditions of the observable state judgment model of the heating system, which ensure that at In each scenario, a set of basic measurement equations consisting of relevant measurement equations can be found. Here, relevant measurement equations refer to measurement equations that only involve observable states. Basic measurement equations refer to a set of independent equations that can solve for observable states (as shown in Figure 2 ); The established constraint conditions specifically include:

[0062] The observability constraint of the states involved in the relevant measurement equations, which ensures that the states involved in the relevant measurement equations are observable in each scenario:

[0063]

[0064] In the formula, w j is a 0-1 variable. If measurement equation j is relevant, the variable is 1; otherwise, the variable is 0.

[0065] The source constraint of the basic measurement equations, which ensures that all basic measurement equations are relevant measurement equations:

[0066]

[0067] In the formula, v j，(γ，φ) is a 0-1 variable. If measurement equation j is a basic measurement equation in the time-delay scenario (γ, φ), the variable is 1; otherwise, the variable is 0.

[0068] The independence constraint of the basic measurement equations, which ensures that all basic measurement equations involving multiple states are independent of each other:

[0069]

[0070] The mapping constraint between the observable states and the basic measurement equations, which ensures that there is a one-to-one mapping relationship between the observable states and the basic measurement equations. This is the condition that the basic measurement equations need to satisfy to solve for the observable states:

[0071]

[0072]

[0073] In the formula, y ij，(γ，φ) is a 0-1 variable. If there is a one-to-one correspondence between state i and measurement equation j in the time-delay scenario (γ, φ), the variable is 1; otherwise, the variable is 0.

[0074] S203. Linearize the non-linear terms in the constraint conditions; There are non-linear terms in the constraint conditions (2) and (6) where multiple decision variables are multiplied. Use to represent the k-th non-linear term among them:

[0075]

[0076] In the formula, is the set of decision variables involved in the k-th non-linear term; x l is the l-th decision variable;

[0077] Linearizing Equation (7) gives:

[0078]

[0079]

[0080] where is the number of decision variables involved in the k-th non-linear term.

[0081] S3. Use a solver to solve the processed observable state judgment model of the heating system to obtain the observable state of the system. The specific steps are as follows:

[0082] S301. Use a solver to solve the observable state judgment model of the heating system;

[0083] S302. Obtain the observable state of the system; the solution result of the observable state judgment model of the heating system contains the value of the variable n i whose value represents the observability of state i. If n i = 1, it means that state i is observable.

[0084] To enable those skilled in the art to better understand the effectiveness of the present invention and to understand the advantages of the present invention over the prior art, the applicant further elaborates with specific cases.

[0085] It is set to test the performance of the observable state judgment model of the heating system using different cases. All observable state judgment models are solved using the Gurobi solver.

[0086] 1. Validity of the observable state judgment result

[0087] The following verifies the effectiveness of the present invention through the observable state judgment result of Case 1. Case 1 is a 4-node heating system considering 3 time periods. The real-time measurements collected by the system in each time period are as Figure 3 shown. The time-delay coefficients of each pipeline in Case 1 are the same, and the set of time-delay coefficients considered is {(0, 1), (1, 1), (0, 2), (1, 2), (2, 2)}. The inventor illustrates the effectiveness of the present invention by comparing the unobservable state results of the present invention with those in under each scenario. The detailed comparison results are shown in Table 1. The last row in the table is the result obtained by the present invention, T3，t-2 Indicates the temperature state of node 3 at time period t - 2, T 3，t-1 Indicates the temperature state of node 3 at time period t - 1, and 'x' indicates an unobservable state.

[0088] Table 1 Results of unobservable states under different time-delay scenarios

[0089] Time-delay scenario (γ, φ) <![CDATA[T 3，t-2 > <![CDATA[T 3，t-1 > (0，1) x (1，1) x (0，2) x x (1，2) x x (2，2) x Considering all of the above scenarios (the present invention) x x

[0090] As can be seen from Table 1, the unobservable states obtained by the present invention are the union of unobservable states in each scenario. It can be deduced therefrom that the observable states obtained by the present invention are the intersection of observable states in each scenario. Therefore, the observable states obtained by the present invention are observable in each scenario. In addition, for an unobservable state obtained by the present invention, a scenario can always be found in such that the state is unobservable. Therefore, the number of unobservable states obtained by the present invention is the least, and the number of observable states obtained is the most. These results illustrate the effectiveness of the present invention.

[0091] 2. Comparison with existing methods

[0092] The performance of the present invention is illustrated below by the observable state judgment results of Case 2 and Case 3. Both Case 2 and Case 3 are 12-node heating systems. In Case 2, the number of time periods considered is 3, and the real-time measurements collected in each time period are as Figure 4 shown. Use to represent the set of time-delay coefficients considered for pipeline b at time period t. Case 2 assumes that the symmetric supply pipeline and return pipeline have the same time-delay coefficient. Therefore, the set of time-delay coefficients is the Cartesian product of 5 groups of time-delay coefficients involving time period t and 5 supply pipelines , and they are all taken as {(1, 1), (1, 2), (2, 2)}. contains a total of 243 time-delay scenarios. In Case 3, the number of time periods considered is 6. The measurement configuration of the heating system is complete from time period t - 5 to time period t - 3, and the system states are all observable; the real-time measurements collected by the system from time period t - 2 to time period t are as Figure 5 shown. Case 3 assumes that the time-delay coefficients of the symmetric supply pipeline and return pipeline are the same. Therefore, the set of time-delay coefficients is the Cartesian product of 15 groups of time-delay coefficients involving time periods t - 2 to t and 5 supply pipelines , and their settings are shown in Table 2. contains a total of 216 time-delay scenarios.

[0093] Table 2 Set of time delay coefficients considered for the water supply pipeline in Case 3

[0094]

[0095] Existing observable state judgment methods need to clarify the specific form of the measurement equation. Therefore, the observable state results can only be obtained for one time delay scenario each time. Under the solution strategy based on multiple time delay scenarios, the existing methods can only analyze one by one the observable state judgment problems in each scenario, and take the intersection of the observable states obtained in each scenario as the final result. The inventor illustrates the advantages of the present invention by comparing the results of the present invention and the existing methods in Case 2 and Case 3. The specific comparison results are shown in Table 3. T 8，t-2 represents the temperature state of Node 8 at time period t - 2, T 9，t-2 represents the temperature state of Node 9 at time period t - 2, T 8，t-1 represents the temperature state of Node 8 at time period t - 1, T 9，t-1 represents the temperature state of Node 9 at time period t - 1, T 8，t represents the temperature state of Node 8 at time period t.

[0096] Table 3 Result comparison of different solution methods

[0097]

[0098] As can be seen from Table 3, the present invention can obtain the same observable state judgment results as the existing methods, and the solution time is shorter. The existing methods can only analyze the observable state judgment problems in different time delay scenarios one by one, while the present invention simultaneously considers the observable state judgment problems in multiple time delay scenarios. The decision variables and constraints for establishing the model are fewer, and the scale of the established model is smaller. Therefore, it has a faster calculation efficiency than the existing methods.

[0099] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and shall be included in the protection scope of the present invention.

Claims

1. A method for judging the observable state of a heating system considering the quasi-dynamics of pipeline temperature. The heating system is composed of a heat source, a heat load, a supply pipeline and a return pipeline. Characterized in that, It includes the following steps: S1. Determine the input parameters of the observable state judgment model for the heating system, including the set of measurement equations collected Set of states of the heating system Set of time-delay scenarios considered Correlation coefficient a between measurement equation j and system state i under time-delay scenario (γ, φ) ij,(γ,φ) , measurement equation j at Union of states involved in each scenario Number n of nodes in the heating system Node , set of time periods considered Set of flow continuity equations of the heating system at time period t S2. Establish the objective function and constraint conditions of the observable state judgment model of the heating system according to the input parameters, and linearize the non-linear terms in the constraint conditions, including the following steps: S201. Establish the objective function of the observable state judgment model of the heating system according to the input parameters, which maximizes the number of observable states: where n i is a 0-1 variable, which is 1 if state i is observable and 0 otherwise; S202. Establish the constraint conditions for the observable state judgment model of the heating system according to the input parameters, which ensure that in each scenario, a set of basic measurement equations composed of relevant measurement equations can be found, where the relevant measurement equations refer to measurement equations that only involve observable states, and the basic measurement equations refer to a set of independent equations that can solve for observable states; the established constraint conditions specifically include: The associated measurement equation involves the observability constraint of the state, which ensures that the state involved in the associated measurement equation is observable in each scenario: where w j is a 0-1 variable, which is 1 if the measurement equation j is relevant, and 0 otherwise; The source constraint of the basic measurement equation, which ensures that all basic measurement equations are relevant measurement equations: where, v j,(γ,φ) is a 0-1 variable, which is 1 if the measurement equation j is a basic measurement equation in the time-delay scenario (γ, φ), and 0 otherwise; The independence constraint of the basic measurement equation, which ensures that all basic measurement equations involving multiple states are independent of each other: The mapping constraint between the observable state and the basic measurement equation, which ensures that there is a one-to-one mapping relationship between the observable state and the basic measurement equation, which is the condition that the basic measurement equation needs to meet to solve the observable state: where y ij,(γ,φ) is a 0-1 variable. If there is a one-to-one correspondence between state i and measurement equation j in the time-delay scenario (γ, φ), the variable is 1; otherwise, the variable is 0. S203. Linearize the non-linear terms in the constraint conditions; there are non-linear terms with multiple decision variables multiplied in the constraint conditions (2) and (6), and use to represent the k-th non-linear term therein: wherein, is the set of decision variables involved in the k-th non-linear term; x l is the l-th decision variable; Linearize Equation (7) to obtain: wherein, is the number of decision variables involved in the k-th non-linear term; S3. Use a solver to solve the processed observable state judgment model of the heating system to obtain the observable state of the system.

2. A method for judging the observable state of a heating system considering the quasi-dynamics of pipeline temperature according to claim 1. Characterized in that, The step S1 includes: Determine the set of collected measurement equations It is derived from the real-time measurements obtained by the measuring instruments and the state equations of the system; the real-time measurements include pipeline flow measurements, node temperature measurements, heat supply measurements of heat sources, and heat consumption measurements of heat loads; the state equations of the system include flow continuity equations, loop pressure drop equations, and quasi-dynamic equations of pipeline temperature; Determine the state set of the heating system It includes the temperatures of all nodes in the heating system and the flow rates of all pipes; Determine the set of time-delay scenarios to be considered It can be calculated through the pseudo-measurement of pipeline flow rate and the flow rate estimation results in historical periods; Determine the correlation coefficient \(a\) between the measurement equation \(j\) and the system state \(i\) in the time-delay scenario \((\gamma,\varphi)\). ij,(γ,φ) , the measurement equation \(j\) in The union of the states involved in each scenario Use To represent the set of system states involved in the measurement equation \(j\) in the time-delay scenario \((\gamma,\varphi)\); if the system state \(i\) belongs to Then assign the correlation coefficient \(a\) ij,(γ,φ) = 1; if the system state \(i\) does not belong to Then assign the correlation coefficient \(a\) ij,(γ,φ) = 0; Can be obtained by Calculated as: Determine the number n of nodes in the heating system Node and the set of time periods considered the set of flow continuity equations of the heating system for time period t They can be obtained by analyzing the system structure.

3. A method for judging the observable state of a heating system considering the quasi-dynamics of pipeline temperature according to claim 1. Characterized in that, The step S3 includes the following steps: S301. Use a solver to solve the processed observable state judgment model of the heating system; S302. Obtain the observable state of the system; the solution result of the observable state judgment model of the heating system contains the value of variable n i which represents the observability of state i. If n i = 1, it indicates that state i is observable.

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