Method for calculating communication rate of power system

Abstract

The invention discloses an electric power system communication rate calculation method, which belongs to the technical field of electric power communication, and comprises the following steps: decomposing an electric power system into a plurality of electric power subsystems, and designing a predictive event trigger mechanism for the electric power subsystems; performing reduction and periodic input design on the observation value of the power subsystem based on the predictive event trigger mechanism to obtain an unknown parameter estimation value of the power system; performing error convergence analysis on the unknown parameter estimation value of the power system, and proving error convergence of the unknown parameter estimation value of the power system; and an optimization model is designed, so that the average communication rate of the power system is minimized under the condition that the unknown parameter estimation value error of the power system is converged, and the optimal communication rate of the power system is obtained. According to the invention, the use degree of communication resources and the possibility of data transmission errors can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power communication, and in particular to a method for calculating a communication rate of a power system. Background Art

[0002] The communication rate is the ratio of the cumulative number of communication moments to the total number of moments, reflecting the degree of communication resource utilization in the power system. A higher communication rate means that the power system is communicating more frequently, indicating that communication resources are being used more frequently. A lower communication rate, on the other hand, indicates less communication activity in the power system and lower resource utilization. For application scenarios such as control systems or sensor networks that require high transmission stability, a lower communication rate is more appropriate. A lower communication rate reduces the possibility of data transmission errors and improves data transmission reliability.

[0003] However, existing power system communication mechanisms often rely on frequent data transmission, requiring high communication rates. This leads to wasted communication resources and excessive system load. In power systems where transmission stability is crucial, high communication rates impose excessive demands on data flow and transmission bandwidth, potentially undermining the system's transmission stability. Summary of the Invention

[0004] The present invention aims to provide a method for calculating the communication rate of a power system, which obtains the minimum average communication rate based on the convergence of the error of the estimated value of the unknown parameter of the power system, thereby achieving the effect of lowering the utilization of communication resources and reducing the possibility of data transmission errors in power systems with high transmission stability requirements. The present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for calculating the communication rate of a power system, comprising:

[0005] Decomposing the power system into multiple power subsystems and designing a predictive event triggering mechanism for the power subsystems;

[0006] Based on the predictive event trigger mechanism, the observed values ​​of the power subsystem are restored and periodic input is designed to obtain estimated values ​​of unknown parameters of the power system;

[0007] Performing error convergence analysis on the estimated values ​​of the unknown parameters of the power system to prove that the errors of the estimated values ​​of the unknown parameters of the power system converge;

[0008] An optimization model is designed so that the average communication rate of the power system is minimized when the error of the estimated value of the unknown parameter of the power system converges, thereby obtaining the optimal communication rate of the power system.

[0009] Optionally, the design of the predictive event triggering mechanism includes the following:

[0010] when =1, the event trigger is triggered at time k, Sent to the estimation center via the communication network; =0, then no transmission is made;

[0011] Where i is the power subsystem number, is the trigger mechanism expression of the predictive event of the i-th power subsystem at time k, is the observed value of the i-th power subsystem at time k, The remote estimation center at time k is based on the unknown parameters of the i-th power subsystem The predicted value of is the true value of the unknown parameter of the i-th power subsystem at time k-1 estimated value of;

[0012] The predictive event trigger mechanism expression The details are as follows:

[0013] .

[0014] The present invention adopts a prediction-based event triggering scheme to decide whether to send the measured output value of the power subsystem to the remote estimation center. With the current observation At different times, =1, the event trigger is triggered at time k, at which time the observation value Send to the estimation center via the communication network; select Send to the estimated center; when the predicted value When it is the same as the current observation value c, =0, then do not send .

[0015] Optionally, based on the predictive event triggering mechanism, the observed values ​​of the power subsystem are restored and periodic input is designed to obtain estimated values ​​of unknown parameters of the power system; including: based on the predictive event triggering mechanism, the observed values ​​of the power subsystem are restored to obtain restored observed values; based on the predictive event triggering mechanism, periodic input is designed for the restored observed values ​​to obtain a circulant matrix composed of the restored observed values; based on the circulant matrix, estimated values ​​of unknown parameters of the power system are obtained.

[0016] Optionally, the restored observation values ​​of the power subsystem obtained by restoring the observation values ​​based on the predictive event triggering mechanism include the following:

[0017] Depend on It can be seen that the remote estimation center can only obtain whether communication occurs at a certain moment and the data received at that moment, that is, and The value of is known. And the event trigger There are only two possible values, namely =1 or 0, and satisfies =0 , so there is the following identity relation: ,

[0018] Where, is the restored observation value, and the symbol := indicates that it is defined as.

[0019] Optionally, performing periodic input design on the restored observation values ​​to obtain a circulant matrix composed of the restored observation values ​​includes:

[0020] The restored observations As input value of the power subsystem, set the input value is a periodic sequence with period N, then = , and let ,in , is the input value of the i-th power subsystem at time k The regression vector is composed of the input value The circulant matrix is:

[0021] ,

[0022] Where, is the circulant matrix of the i-th power subsystem, which is composed of the regression vectors of multiple input signals, and n is the circulant matrix The number of rows and columns.

[0023] Optionally, the estimated value of the unknown parameter of the power system is obtained by calculating using the following formula:

[0024] ,

[0025] ,

[0026] ,

[0027] , ,

[0028] Where, is the unknown parameter of the i-th power subsystem at time k The estimated value of The jth corrected estimation error, is the jth recursive correction factor, j is the number of the corrected estimation error and recursive correction factor, and its value range is 1-b. Represents an integer less than or equal to k / g, where g is the base number used for modular operations, which determines whether the remainders of k and j divided by g are equal. is the threshold of the binary sensor, is the inverse matrix of the power system distribution function F, is an indicator function, when the condition When it was established, The value of is 1, otherwise The value of is 0, is the parameter estimation value of the i-th power subsystem at time k−1 and the regression vector C i，k The linear transformation performed.

[0029] Optionally, an expression for performing error convergence analysis on the estimated values ​​of unknown parameters of the power system is as follows:

[0030] ,

[0031] in, , , ,

[0032] Where, Indicates convergence to the distribution, represents the error between the estimated value and the true value of the i-th power subsystem at time k, The mean is 0 and the variance is The normal distribution of describes the asymptotic convergence behavior of the error when k→∞, n ’ represents the size of the sample, is a circulant matrix The inverse matrix of is the diagonal matrix of the ith power subsystem, represents the error term of the mth observation value of the i-th power subsystem, represents the function value associated with the mth observation value of the i-th power subsystem.

[0033] Optionally, the optimization model is expressed as follows:

[0034] ,

[0035] Where A is the optimization model, is the average communication rate of the ith power subsystem, a is the number of subsystems in the power system, is the optimized communication parameter of the i-th power subsystem in the j-th cycle;

[0036] The constraints on the expression A of the optimization model are:

[0037]

[0038] Where, represents the transmission error, represents the tolerance of the ith power subsystem to the convergence performance.

[0039] Optionally, the method of minimizing the average communication rate of the power system when the error of the estimated value of the unknown parameter of the power system converges includes recording the optimal solution of the optimization model A as , the optimal solution is obtained by using the particle swarm algorithm , through the optimal solution Optimizing communication parameters , so that the average communication rate Minimum.

[0040] Optionally, the optimal solution is obtained by using a particle swarm algorithm Including using the particle swarm algorithm to solve the particle position that maximizes the fitness of the objective function of the optimization model A;

[0041] Among them, the objective function of the optimization model A is for:

[0042] ,

[0043] Where, For penalty items;

[0044] The particle swarm algorithm comprises the following steps:

[0045] Initialize the particle's speed, position, historical optimal position of the individual particle, historical optimal position of the group particle, number of iterations G, and stopping condition t; update the speed and position of each particle in each iteration, and calculate the objective function based on the updated particle speed and position , thereby updating the objective function The position of the particle with greater fitness; the speed of the particle is updated according to the iterative process and location , and continuously record the particles in the group that make the objective function The position of the particle with a larger fitness is obtained until the number of iterations G or the stopping condition t is met, so that the objective function The optimal solution with the highest fitness ;

[0046] The iterative process of the particles is:

[0047] ,

[0048] ,

[0049] Where, represents the velocity of the i-th particle at the d+1th iteration, w is the inertia coefficient, and Represent the self-learning factor and group learning factor respectively, is the solution of the optimal target value of the i-th particle in the first d iterations, is the solution where all particles obtain the optimal target value in the first d iterations, and are two random numbers in the range [0,1].

[0050] Each particle continuously adjusts its speed and position to take advantage of the optimal position of a single particle. and the optimal position of all particles To search for the best solution. The iterative process can help the algorithm find the solution that minimizes the objective function Δ, thereby optimizing the communication rate. Beneficial effects

[0051] (1) The predictive event triggering mechanism designed in this invention triggers communication only when a key event occurs. It dynamically triggers communication events based on changes in the power system state, thereby effectively reducing unnecessary communication operations, reducing the power system's communication overhead, and improving the efficiency of network resource utilization. Especially for scenarios such as smart power systems that require high-frequency data interaction and low latency, the predictive event triggering mechanism can significantly improve communication efficiency and ensure the stability and response speed of the power system in complex environments.

[0052] (2) The present invention uses a particle swarm algorithm to simulate the group behavior of particles in nature and continuously optimize the power system parameters. Ultimately, the average communication rate of the power system is minimized when the error of the estimated value of the unknown power system parameters converges, thereby obtaining the optimal power system communication rate, that is, the optimal communication resource allocation. This reduces the possibility of data transmission errors and improves the reliability of data transmission in power systems with high transmission stability requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Shown is a flow chart of the method for calculating the communication rate of the power system according to the present invention. DETAILED DESCRIPTION

[0054] The following is a further description with reference to the accompanying drawings and specific embodiments. It should be understood that in the description of the present invention, the terms "first," "second," etc. are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Therefore, a feature designated "first," "second," etc. may explicitly or implicitly include one or more of the features.

[0055] Example 1

[0056] This embodiment introduces a method for calculating the communication rate of a power system, including the following contents:

[0057] Decomposing the power system into multiple power subsystems and designing a predictive event triggering mechanism for the power subsystems;

[0058] Based on the predictive event trigger mechanism, the observed values ​​of the power subsystem are restored and periodic input is designed to obtain estimated values ​​of unknown parameters of the power system;

[0059] Performing error convergence analysis on the estimated values ​​of the unknown parameters of the power system to prove that the errors of the estimated values ​​of the unknown parameters of the power system converge;

[0060] An optimization model is designed so that the average communication rate of the power system is minimized when the error of the estimated value of the unknown parameter of the power system converges, thereby obtaining the optimal communication rate of the power system.

[0061] Example 2

[0062] Based on Example 1, this example introduces a specific implementation process of a method for calculating the communication rate of a power system, such as Figure 1 As shown, specifically including the following:

[0063] The design of the predictive event triggering mechanism includes the following contents:

[0064] when =1, the event trigger is triggered at time k, Sent to the estimation center via the communication network; =0, then no transmission is made;

[0065] Where i is the power subsystem number, is the trigger mechanism expression of the predictive event of the i-th power subsystem at time k, is the observed value of the i-th power subsystem at time k, The remote estimation center at time k is based on the unknown parameters of the i-th power subsystem The predicted value of is the true value of the unknown parameter of the i-th power subsystem at time k-1 estimated value of;

[0066] The predictive event trigger mechanism expression The details are as follows:

[0067] .

[0068] Based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are restored and periodically input designed to obtain estimated values ​​of unknown parameters of the power system; including: based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are restored to obtain restored observation values; based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are periodically input designed to obtain a circulant matrix composed of the restored observation values; based on the circulant matrix, estimated values ​​of the unknown parameters of the power system are obtained.

[0069] The observation values ​​of the power subsystem are restored based on the predictive event triggering mechanism to obtain the restored observation values ​​including the following:

[0070] Depend on Knowable Satisfaction =0 , so there is the following identity relation: ,

[0071] Where, is the restored observation value, and the symbol := indicates that it is defined as.

[0072] Performing periodic input design on the restored observation values ​​to obtain a circulant matrix composed of the restored observation values, including:

[0073] The restored observations As input value of the power subsystem, set the input value is a periodic sequence with period N, then = , and let ,in , is the input value of the i-th power subsystem at time k The regression vector is composed of the input value The circulant matrix is:

[0074] ,

[0075] Where, is the circulant matrix of the i-th power subsystem, which is composed of the regression vectors of multiple input signals, and n is the circulant matrix The number of rows and columns.

[0076] The estimated values ​​of the unknown parameters of the power system are calculated using the following formula:

[0077] ,

[0078] ,

[0079] ,

[0080] , ,

[0081] Where, is the unknown parameter of the i-th power subsystem at time k The estimated value of The jth corrected estimation error, is the jth recursive correction factor, j is the number of the corrected estimation error and recursive correction factor, and its value range is 1-b. Represents an integer less than or equal to k / g, where g is the base number used for modular operations. is the threshold of the binary sensor, is the inverse matrix of the power system distribution function F, is the indicator function, is the parameter estimation value of the i-th power subsystem at time k−1 and the regression vector C i，k The linear transformation performed.

[0082] The expression for error convergence analysis of the unknown parameter estimation values ​​of the power system is as follows:

[0083] ,

[0084] in, , , ,

[0085] Where, Indicates convergence to the distribution, represents the error between the estimated value and the true value of the i-th power subsystem at time k, The mean is 0 and the variance is The normal distribution of describes the asymptotic convergence behavior of the error when k→∞, n ’ represents the size of the sample, is a circulant matrix The inverse matrix of is the diagonal matrix of the ith power subsystem, represents the error term of the mth observation value of the i-th power subsystem, represents the function value associated with the mth observation value of the i-th power subsystem.

[0086] The expression of the optimization model is as follows:

[0087] ,

[0088] Where A is the optimization model, is the average communication rate of the ith power subsystem, a is the number of subsystems in the power system, is the optimized communication parameter of the i-th power subsystem in the j-th cycle;

[0089] The constraints on the expression A of the optimization model are:

[0090]

[0091] Where, represents the transmission error, represents the tolerance of the ith power subsystem to the convergence performance.

[0092] The method of minimizing the average communication rate of the power system under the condition that the error of the estimated value of the unknown parameter of the power system converges includes recording the optimal solution of the optimization model A as , the optimal solution is obtained by using the particle swarm algorithm , through the optimal solution Optimizing communication parameters , so that the average communication rate Minimum.

[0093] The optimal solution is obtained by using the particle swarm algorithm Including using the particle swarm algorithm to solve the particle position that maximizes the fitness of the objective function of the optimization model A;

[0094] Among them, the objective function of the optimization model A is for:

[0095] ,

[0096] Where, For penalty items;

[0097] The particle swarm algorithm comprises the following steps:

[0098] Initialize the particle's speed, position, historical optimal position of the individual particle, historical optimal position of the group particle, number of iterations G, and stopping condition t; update the speed and position of each particle in each iteration, and calculate the objective function based on the updated particle speed and position , thereby updating the objective function The position of the particle with greater fitness; update the speed and position of the particle according to the iterative process, and continuously record the particles in the group that make the objective function The position of the particle with a larger fitness is obtained until the number of iterations G or the stopping condition t is met, so that the objective function The optimal solution with the highest fitness ;

[0099] The iterative process of the particles is:

[0100] ,

[0101] ,

[0102] Where, represents the velocity of the i-th particle at the d+1th iteration, w is the inertia coefficient, and Represent the self-learning factor and group learning factor respectively, is the solution of the optimal target value of the i-th particle in the first d iterations, is the solution where all particles obtain the optimal target value in the first d iterations, and are two random numbers in the range [0,1].

[0103] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for calculating a power system communication rate, characterized in that: include: Decomposing the power system into multiple power subsystems and designing a predictive event triggering mechanism for the power subsystems; Based on the predictive event trigger mechanism, the observed values ​​of the power subsystem are restored and periodic input is designed to obtain estimated values ​​of unknown parameters of the power system; Performing error convergence analysis on the estimated values ​​of the unknown parameters of the power system to prove that the errors of the estimated values ​​of the unknown parameters of the power system converge; An optimization model is designed so that the average communication rate of the power system is minimized when the error of the estimated value of the unknown parameter of the power system converges, thereby obtaining the optimal communication rate of the power system.

2. The method for calculating the power system communication rate according to claim 1, wherein: The design of the predictive event triggering mechanism includes the following contents: when =1, the event trigger is triggered at time k, Sent to the estimation center via the communication network; =0, then no transmission is made; Where i is the power subsystem number, is the trigger mechanism expression of the predictive event of the i-th power subsystem at time k, is the observed value of the i-th power subsystem at time k, The remote estimation center at time k is based on the unknown parameters of the i-th power subsystem The predicted value of is the true value of the unknown parameter of the i-th power subsystem at time k-1 estimated value of; The predictive event trigger mechanism expression The details are as follows: 。 3. The method for calculating the power system communication rate according to claim 2, wherein: Based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are restored and periodically input designed to obtain estimated values ​​of unknown parameters of the power system; including: based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are restored to obtain restored observation values; based on the predictive event trigger mechanism, the observation values ​​of the power subsystem are periodically input designed to obtain a circulant matrix composed of the restored observation values; based on the circulant matrix, estimated values ​​of the unknown parameters of the power system are obtained.

4. The method for calculating the power system communication rate according to claim 3, wherein: The observation values ​​of the power subsystem are restored based on the predictive event triggering mechanism to obtain the restored observation values ​​including the following: Depend on Knowable Satisfaction =0 , so there is the following identity relation: ， Where, is the restored observation value, and the symbol := indicates that it is defined as.

5. The method for calculating the power system communication rate according to claim 4, wherein: Performing periodic input design on the restored observation values ​​to obtain a circulant matrix composed of the restored observation values, including: The restored observations As input value of the power subsystem, set the input value is a periodic sequence with period N, then = , and let ,in , is the input value of the i-th power subsystem at time k The regression vector is composed of the input value The circulant matrix is: ， Where, is the circulant matrix of the i-th power subsystem, which is composed of the regression vectors of multiple input signals, and n is the circulant matrix The number of rows and columns.

6. The method for calculating the power system communication rate according to claim 5, wherein: The estimated values ​​of the unknown parameters of the power system are calculated using the following formula: ， ， ， ， ， Where, is the unknown parameter of the i-th power subsystem at time k The estimated value of The jth corrected estimation error, is the jth recursive correction factor, j is the number of the corrected estimation error and recursive correction factor, and its value range is 1-b. Represents an integer less than or equal to k / g, where g is the base number used for modular operations. is the threshold of the binary sensor, is the inverse matrix of the power system distribution function F, is the indicator function, is the parameter estimation value of the i-th power subsystem at time k−1 and the regression vector C i，k The linear transformation performed.

7. The method for calculating the communication rate of a power system according to claim 6, wherein: The expression for error convergence analysis of the unknown parameter estimation values ​​of the power system is as follows: ， in, , , , Where, Indicates convergence to the distribution, represents the error between the estimated value and the true value of the i-th power subsystem at time k, The mean is 0 and the variance is The normal distribution of describes the asymptotic convergence behavior of the error when k→∞, n ’ represents the size of the sample, is a circulant matrix The inverse matrix of is the diagonal matrix of the ith power subsystem, represents the error term of the mth observation value of the i-th power subsystem, represents the function value associated with the mth observation value of the i-th power subsystem.

8. The method for designing the communication rate of a power system according to claim 7, wherein: The expression of the optimization model is as follows: ， Where A is the optimization model, is the average communication rate of the ith power subsystem, a is the number of subsystems in the power system, is the optimized communication parameter of the i-th power subsystem in the j-th cycle; The constraints on the expression A of the optimization model are: ， Where, represents the transmission error, represents the tolerance of the ith power subsystem to the convergence performance.

9. The method for calculating the power system communication rate according to claim 8, wherein: The method of making the average communication rate of the power system reach the minimum when the error of the estimated value of the unknown parameter of the power system converges includes recording the optimal solution of the optimization model A as , the optimal solution is obtained by using the particle swarm algorithm , through the optimal solution Optimizing communication parameters , so that the average communication rate Minimum.

10. The method for designing the communication rate of a power system according to claim 9, wherein: The optimal solution is obtained by using the particle swarm algorithm Including using the particle swarm algorithm to solve the particle position that maximizes the fitness of the objective function of the optimization model A; Among them, the objective function of the optimization model A is for: ， Where, For penalty items; The particle swarm algorithm comprises the following steps: Initialize the particle's speed, position, the historical optimal position of the individual particle, the historical optimal position of the group particle, the number of iterations G, and the stopping condition t; update the speed and position of each particle in each round of iteration, and calculate the objective function based on the updated particle speed and position , thereby updating the objective function The position of the particle with greater fitness; update the speed and position of the particle according to the iterative process, and continuously record the particles in the group that make the objective function The position of the particle with a larger fitness is obtained until the number of iterations G or the stopping condition t is met, so that the objective function The optimal solution with the highest fitness ; The iterative process of the particles is: ， ， Where, represents the velocity of the i-th particle at the d+1th iteration, w is the inertia coefficient, and Represent the self-learning factor and group learning factor, is the solution of the optimal target value of the i-th particle in the first d iterations, is the solution where all particles obtain the optimal target value in the first d iterations, and are two random numbers in the range [0,1].

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