Power system pi sampling frequency control method and device based on multi-modal sampling and continuous packet loss

Abstract

This invention discloses a power system PI sampling frequency control method and apparatus based on multimodal sampling and continuous packet loss. The method includes: establishing a power system model; establishing a multimodal switching sampling model and a continuous packet loss model for the power system; the multimodal switching sampling model is used to characterize the aperiodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission; based on the multimodal switching sampling model and the continuous packet loss model, a PI sampling control strategy is used to dynamically adjust the frequency deviation of the power system, and a boosting technique is used to transform the continuous-time power system into an equivalent discrete-time system; and sufficient conditions for the stochastic stability of the equivalent discrete-time system are established. This invention can achieve frequency control in non-ideal network environments with multimodal switching sampling and continuous packet loss, ensuring the stable operation of the power system.

Description

Technical Field

[0001] This invention relates to the technical field of power system modeling and control, and in particular to a power system PI sampling frequency control method, device, terminal equipment, and computer-readable storage medium based on multimodal sampling and continuous packet loss. Background Technology

[0002] In recent years, with the rapid popularization of IoT, cloud computing, and artificial intelligence technologies in power systems, modern power grids are developing towards digitalization and intelligence. Leveraging these new technologies, power systems can achieve more accurate load frequency control and more flexible resource dispatch, maintaining strong stability and adaptability even as the scale of new energy integration continues to expand and load characteristics become increasingly complex.

[0003] In traditional power system frequency regulation, primary and secondary frequency regulation commonly employ proportional-integral (PI) control methods based on continuous-time models. However, in networked control scenarios, both control and measurement signals need to be transmitted through communication networks. The system's operating mode gradually shifts from continuous control to sampled control, resulting in discrete characteristics in the signals received by the controller. These signals may be affected by time delays, variations in the sampling period, and data loss, leading to insufficient adaptability of continuous-time PI control in engineering applications.

[0004] On the other hand, in real-world networked environments, the sampling period is often difficult to maintain due to factors such as network congestion, scheduling strategies, and equipment performance, exhibiting aperiodic sampling characteristics. A common modeling approach treats aperiodic sampling as a random switching between different sampling periods, i.e., multimodal switching sampling. This multimodal characteristic makes the sampling process more unpredictable and makes it more difficult for the controller to accurately grasp system state information. Furthermore, unreliable communication links may cause multiple consecutive data packets to fail to transmit successfully, i.e., continuous packet loss. When the controller cannot obtain valid state data for an extended period, frequency regulation performance will significantly degrade, and in severe cases, may even threaten the stable operation of the power system.

[0005] Existing research has proposed various analytical tools for the analysis and design of sampled-sample control systems, including boosting techniques, input time-delay modeling methods, and ring functional methods. However, most of these methods are based on the assumption of a constant sampling period or do not consider the impact of continuous packet loss. Research on scenarios involving simultaneous multi-mode switching sampling and data loss in complex networks remains relatively limited. Therefore, it is necessary to develop a PI sampling frequency control strategy for aperiodic sampling and continuous packet loss environments to ensure stable power system operation while improving frequency regulation performance, thus providing strong support for frequency regulation and safe, stable operation of smart power systems under complex communication conditions. Summary of the Invention

[0006] To overcome the shortcomings of fixed sampling assumptions and limited control performance in existing technologies, this invention provides a power system PI sampling frequency control method, device, terminal equipment, and computer-readable storage medium based on multimodal sampling and continuous packet loss. It can achieve frequency control in non-ideal network environments with multimodal switching sampling and continuous packet loss, ensuring the stable operation of the power system.

[0007] The first objective of this invention is to provide a power system PI sampling frequency control method based on multimodal sampling and continuous packet loss.

[0008] The second objective of this invention is to provide a power system PI sampling frequency control device based on multimodal sampling and continuous packet loss.

[0009] The third objective of this invention is to provide a terminal device.

[0010] A fourth objective of this invention is to provide a computer-readable storage medium.

[0011] The fifth objective of this invention is to provide a computer program product.

[0012] The first objective of this invention can be achieved by adopting the following technical solution:

[0013] A power system PI sampling frequency control method based on multimodal sampling and continuous packet loss, the method comprising:

[0014] Establish a power system model;

[0015] For power systems, a multi-mode switching sampling model and a continuous packet loss model are established. The multi-mode switching sampling model is used to characterize the non-periodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission.

[0016] Based on the multimodal switching sampling model and the continuous packet loss model, a PI sampling control strategy is adopted to dynamically adjust the frequency deviation of the power system, and the continuous-time power system is transformed into an equivalent discrete-time system through the boosting technique.

[0017] Establish sufficient conditions for the stochastic stability of an equivalent discrete-time system;

[0018] The method of dynamically adjusting the frequency deviation of the power system using a PI sampling control strategy includes:

[0019] The PI sampling controller is designed as follows:

[0020] ;

[0021] In the formula, The control input signal at time t, , These are the proportional gain matrix and the integral gain matrix, respectively. and For a moment , The output vector, where k is the sequence number of successfully received data. For the first The sampling interval for each successful data reception; and The actual sampling time between two consecutive successful data receptions;

[0022] Will Substituting into the power system model, the closed-loop system is:

[0023] ;

[0024] In the formula, Let be the state vector at time t. for The first derivative with respect to t, B and These are the system matrix, output matrix, and disturbance matrix, respectively. Let t be the system disturbance at time t.

[0025] The second objective of this invention can be achieved by adopting the following technical solution:

[0026] A power system PI sampling frequency control device based on multimodal sampling and continuous packet loss, the device comprising:

[0027] The first module is used to build the power system model;

[0028] The second module is used to establish a multi-mode switching sampling model and a continuous packet loss model for the power system. The multi-mode switching sampling model is used to characterize the non-periodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission.

[0029] The adjustment and conversion module is used to dynamically adjust the frequency deviation of the power system based on the multi-mode switching sampling model and the continuous packet loss model, using a PI sampling control strategy, and to convert the continuous-time power system into an equivalent discrete-time system through boosting technology.

[0030] The third module is used to establish sufficient conditions for the stochastic stability of the equivalent discrete-time system.

[0031] The method of dynamically adjusting the frequency deviation of the power system using a PI sampling control strategy includes:

[0032] The PI sampling controller is designed as follows:

[0033] ;

[0034] in, The control input signal at time t, , These are the proportional gain matrix and the integral gain matrix, respectively. and For a moment , The output vector, where k is the sequence number of successfully received data. For the first The sampling interval for each successful data reception; and The actual sampling time between two consecutive successful data receptions;

[0035] Will Substituting into the power system model, the closed-loop system is:

[0036] ;

[0037] in, Let be the state vector at time t. for The first derivative with respect to t, B and These are the system matrix, output matrix, and disturbance matrix, respectively. Let t be the system disturbance at time t.

[0038] The third objective of this invention can be achieved by adopting the following technical solution:

[0039] A terminal device includes a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the above-described power system PI sampling frequency control method based on multimodal sampling and continuous packet loss.

[0040] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0041] A computer-readable storage medium storing a program that, when executed by a processor, implements the above-described power system PI sampling frequency control method based on multimodal sampling and continuous packet loss.

[0042] The fifth objective of this invention can be achieved by adopting the following technical solution:

[0043] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned power system PI sampling frequency control method for a motor based on multimodal sampling and continuous packet loss.

[0044] The present invention has the following advantages over the prior art:

[0045] (1) The PI sampling control strategy proposed in this invention retains the basic structure of proportional-integral control. It does not require an additional observer or a complex state estimator. It can achieve control updates by relying only on successfully received sampling data. The control law is simple, the parameters are adjustable, and it is easy to deploy and implement directly in existing generator set control systems and networked execution units.

[0046] (2) By simultaneously considering network uncertainties such as multimodal switching sampling and continuous data loss, this invention can maintain stable system operation under conditions such as limited communication bandwidth, fluctuating network load, or potential network attacks. Compared with traditional methods, this invention significantly improves the robustness and reliability of the system under complex network conditions.

[0047] (3) The improvement technology method adopted in this invention is not only applicable to the load frequency control problem of power systems, but can also be extended to distributed power grids, microgrids and other networked control systems. This invention takes into account both theoretical rigor and engineering feasibility, and provides new control design ideas and technical means for the stable operation of power systems under complex communication conditions. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the power system PI sampling frequency control method based on multimodal sampling and continuous packet loss according to Embodiment 1 of the present invention;

[0050] Figure 2 This is a model diagram of a single-area power system with load frequency control according to Embodiment 1 of the present invention;

[0051] Figure 3 In Embodiment 1 of the present invention, under the conditions of multimodal sampling and continuous packet loss, the frequency deviation is... Regulating valve deviation and mechanical power deviation The dynamic response curve;

[0052] Figure 4 This is the dynamic response curve of the region control error signal ACE(t) under multimodal sampling and continuous packet loss conditions in Embodiment 1 of the present invention;

[0053] Figure 5 The sampling period variation and the corresponding number of consecutive packet losses are shown in Embodiment 1 of the present invention;

[0054] Figure 6 In Embodiment 1 of the present invention, under ideal communication conditions, the sampling period is respectively taken as... frequency deviation Regulating valve deviation and mechanical power deviation The dynamic response curve;

[0055] Figure 7 In Embodiment 1 of the present invention, under ideal communication conditions, the sampling period is respectively taken as The dynamic response curve of the regional control error signal ACE(t);

[0056] Figure 8 This is a structural block diagram of the power system PI sampling frequency control device based on multimodal sampling and continuous packet loss according to Embodiment 2 of the present invention;

[0057] Figure 9 This is a structural block diagram of the terminal device according to Embodiment 3 of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be understood that the specific embodiments described are merely used to explain this application and are not intended to limit this application.

[0059] Example 1:

[0060] like Figure 1 As shown, this embodiment provides a power system PI sampling frequency control method based on multimodal sampling and continuous packet loss, including the following steps:

[0061] S101. Establish a power system model to describe the relationship between generator speed deviation and power dynamics.

[0062] refer to Figure 2 Taking into account the dynamic characteristics of the governor, turbine, and system load, the dynamic equation of a single-region power system can be expressed as:

[0063]

[0064] in, This is the generator damping coefficient. For rotational inertia, The frequency deviation at time t, express The first derivative with respect to t, Let be the mechanical output deviation at time t. express The first derivative with respect to t, Let be the load disturbance deviation at time t. R is the governor time constant, and R is the speed reduction coefficient. Let be the position deviation of the regulating valve at time t. express The first derivative with respect to t, The control input signal at time t, The time constant of the steam turbine. For frequency deviation factor, Let t be the region control error signal at time t.

[0065] Furthermore, define Let be the state vector at time t. Let be the output vector at time t. Let be the system disturbance at time t, then the state-space expression of the power system is:

[0066]

[0067] in, express The first derivative with respect to t, the system matrix Output matrix B, Output matrix Perturbation matrix They are defined as follows:

[0068] .

[0069] S102. For power systems, establish a multi-mode switching sampling model and a continuous packet loss model.

[0070] The sampling period is randomly switched between multiple finite values. The non-periodic sampling characteristics of the communication network are characterized by a multimodal switching sampling model, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission.

[0071] (1) Multimodal switching sampling model.

[0072] In real-world network environments, the sampling period is difficult to maintain constant, and its variation is affected by factors such as communication load, scheduling strategies, and external interference. Let... Let i be the sampling time sequence and i be the sampling number. Then the interval between two adjacent sampling times is defined as: To characterize the non-periodic sampling properties, this embodiment uses the sampling period... The model is a discrete random variable that switches randomly among a finite number of values, i.e.:

[0073]

[0074] in, Indicates all sampling periods The set of possible values, where N is the number of sampling period switches. Indicates the first There are several possible sampling period values, and their probabilities satisfy:

[0075]

[0076] in, The corresponding probability, and satisfying .

[0077] (2) Continuous packet loss model.

[0078] Considering the limitations of communication network bandwidth and the impact of potential network attacks, packet loss is inevitable during transmission. For simplicity, we assume that packet loss only occurs in the communication link from the sensor to the controller. The time series of successfully received sampled data by the controller, where k is the sequence number of successfully received data, satisfies... In any two adjacent successful reception time intervals The number of consecutive packet losses that occur within the specified time is recorded as follows: And assume that the number of consecutive packet losses has an upper bound. Assuming each data packet transmission is independent, the packet loss rate is... Then the number of consecutive packet losses The probability distribution satisfies the following relationship:

[0079]

[0080] Furthermore, to describe the actual signal reception interval, the actual sampling period between two consecutive successful receptions of sampled data is defined as . ,in, By random sampling period Number of consecutive packet losses Jointly decided. Set within the interval. Within, sampling period The number of times it appears is In the event of continuous packet loss In this case, the actual sampling period is ; where, set Defined as .

[0081] Therefore, the probability distribution of the actual sampling period can be expressed as:

[0082]

[0083] in, Indicates the actual sampling period is The probability of.

[0084] S103. Based on the multi-mode switching sampling model and the continuous packet loss model, a PI sampling control strategy is adopted to dynamically adjust the frequency deviation of the power system and transform the continuous-time power system into an equivalent discrete-time system.

[0085] This step employs a PI sampling control strategy to dynamically adjust the frequency deviation of the power system, and uses boosting technology to transform the continuous-time power system into an equivalent discrete-time system.

[0086] Under conditions of continuous packet loss and multi-modal switching sampling, a practical PI sampling controller can be designed as follows:

[0087]

[0088] in, The summation index is used to represent the sequence number of historically successfully received data. and These are the proportional gain matrix and the integral gain matrix, respectively. For the first The sampling interval corresponding to each successful data reception.

[0089] Substituting the above control law into the controlled power system model, the closed-loop system can be expressed as:

[0090]

[0091] To facilitate analysis, augmented vectors are introduced:

[0092]

[0093] in, This represents the state vector of the augmented system at time t;

[0094] Therefore, the augmented closed-loop power system can be represented as:

[0095]

[0096] in, express The first derivative with respect to the time variable t, This indicates that the augmented system is at time [time]. State vector, matrix , , , , and They are respectively:

[0097] .

[0098] To accurately characterize the dynamics of the controlled object, the combined effects of the sampled data PI controller, and continuous packet loss, this embodiment employs a boosting technique to discretize the augmented system. Furthermore, this embodiment focuses on the small-signal stability problem under power system load frequency control, i.e., under external disturbances... The stability of the system.

[0099] make Using enhancement techniques to augment continuous-time systems in intervals Integrating over the time step, we can obtain the equivalent discrete-time system:

[0100]

[0101] in, This indicates that the augmented system is at time [time]. The state vector, For the interval [0, Integral variable on ] Let be the state transition matrix.

[0102] S104. Establish sufficient conditions for the stochastic stability of an equivalent discrete-time system.

[0103] This step uses existing methods to establish sufficient conditions for the stochastic stability of the equivalent discrete-time system: combining Lyapunov stability theory and probabilistic analysis methods, sufficient conditions for the stochastic stability of the power system are derived, specifically including:

[0104] Given scalar Maximum number of consecutive packet losses If there exists a symmetric positive definite matrix If the following matrix inequality (LMI) holds, then the discrete-time power system is stochastically stable under multimode switching sampling and continuous packet loss:

[0105]

[0106] In this context, each block item in the first row of the matrix is ​​uniformly represented as: , , , This indicates that when there are v consecutive packet losses, the actual sampling period is... The probability of; Indicates the corresponding actual sampling period The state transition matrix below, with superscript This represents the transpose of a matrix. In a matrix... and These correspond to the minimum and maximum actual sampling periods, respectively; the omitted terms represent all other possible combinations of sampling periods.

[0107] When constructing sufficiency conditions using the existing methods described above, it is typically necessary to construct and stack state transition matrices for each of the actual sampling period combinations corresponding to multimodal switching sampling and continuous packet loss, resulting in large-scale linear matrix inequalities. Since the actual sampling period is determined by both multimodal switching sampling and the number of continuous packet losses, the number of combinations increases rapidly when the number of sampling period switching or the maximum number of continuous packet losses increases, leading to a significant increase in the dimension of the linear matrix inequalities and a heavy computational burden. Therefore, this embodiment designs a dimension reduction computation method based on matrix decomposition. This method decomposes the expected matrix, extracting a small number of equivalent matrices to uniformly represent all combinations, eliminating the need to set independent matrix blocks for each combination of continuous packet loss and sampling period, thus significantly reducing the solution dimension and computational complexity of the linear matrix inequalities. The expected matrix is ​​obtained by weighting the state transition matrices corresponding to each actual sampling period according to their respective probabilities.

[0108] Specifically, a sufficiency condition for the stochastic stability of an equivalent discrete-time system is established using a dimensionality reduction computation method, including:

[0109] (1) Dimensionality reduction calculation method based on matrix decomposition.

[0110] For any symmetric positive definite matrix ,matrix It can be represented in the following decomposition form:

[0111]

[0112] in, , , Let be the indices of the non-zero eigenvalues ​​of the rearranged matrix and their corresponding eigenvectors, with symbols _____. Represent the vectorization operator and the inverse vectorization operator, respectively; define Let be a rearranged matrix, and denote its rank as . Its form is , yes The transpose of the matrix can be subjected to the following spectral decomposition: , These are the non-zero eigenvalues ​​of the rearranged matrix. For the corresponding eigenvectors, the correlation matrix is ​​defined as follows: , , , , , , , It is an identity matrix.

[0113] (2) Sufficient stability conditions based on Lyapunov theory.

[0114] Given scalar , Maximum number of consecutive packet losses and packet loss rate If a positive definite matrix exists If the following matrix inequality (LMI) holds, then the discrete-time power system is stochastically stable under multimode switching sampling and continuous packet loss:

[0115]

[0116] in, For PI control gain matrix, For dimension is The identity matrix; for eigenvalues ​​and eigenvectors , , , , , , , , , , , , , .

[0117] The proof is as follows:

[0118] (1) Define the Lyapunov function as:

[0119]

[0120] Its increment is:

[0121]

[0122] (2) Based on the dimensionality reduction calculation method of the matrix decomposition above, we have:

[0123]

[0124]

[0125] in, .

[0126] Therefore, there is ,in .

[0127] Applying the conditional Schul complement lemma, we can obtain Therefore, This indicates that the discrete-time power system can converge to zero in the expected sense, that is, the discrete-time power system is stochastically stable.

[0128] Furthermore, based on establishing the sufficiency conditions for the stochastic stability of the equivalent discrete-time system using a dimensionality reduction calculation method, this embodiment designs a hybrid optimization algorithm combining a genetic algorithm and a binary search method to determine the PI control gain parameters and the maximum number of consecutive packet losses the system can tolerate, including:

[0129] (1) Parameter initialization.

[0130] Input system matrix Sampling period and its probability Packet loss rate PI gain search range, genetic algorithm parameters (population size) Maximum number of iterations Crossover probability Probability of mutation Maximum number of packet losses and precision threshold .

[0131] (2) Setting the binary search interval.

[0132] Initialize search range: lower bound of search Search upper bound .

[0133] (3) Outer layer binary search.

[0134] Calculate the intermediate value And perform the following two steps:

[0135] (3-1) Matrix construction.

[0136] According to the rearrangement matrix Calculate its non-zero characteristic pairs And construct a matrix ,in And then calculate .

[0137] (3-2) Call the inner genetic algorithm.

[0138] PI gain matrix Encoded as chromosome and initialize the population. , Indicates the population iteration number. .

[0139] In each iteration, the control gain matrix corresponding to the current individual is obtained by decoding each individual. ;Will Substitute the LMI stability condition and solve for the corresponding matrix. If the conditions are feasible, then calculate the fitness function. ,in = Otherwise, assign a penalty value to the individual; generate a new generation of population through selection, crossover, and mutation operations. Repeat the above process until the number of iterations reaches [number missing]. Output the optimal solution .

[0140] (4) Feasibility assessment and interval update.

[0141] If the current If the corresponding LMI stability condition is feasible, then update the lower bound. ;

[0142] Otherwise update the upper bound. .

[0143] (5) Iteration termination and output of optimal result.

[0144] when When the optimal result is obtained, output the following: ,in, This indicates the system's optimal PI control gain ( The maximum number of consecutive packet losses that can be tolerated under the given conditions.

[0145] The control method provided in this embodiment is simulated and analyzed. The PI controller gain and the maximum tolerable number of packet losses are solved by writing a MATLAB program, thereby verifying the effectiveness of the proposed PI sampling frequency control method.

[0146] Considering a single-area power system, the following system parameters are selected: moment of inertia. Steam turbine time constant Generator damping coefficient governor time constant 1. Speed ​​reduction coefficient Frequency deviation factor To characterize the non-ideal nature of the network, a sampling period is assumed. Switching between three modes, namely , and Their corresponding probabilities are respectively , , The packet loss rate is .

[0147] To verify the effectiveness of the matrix factorization-based dimensionality reduction method, the existing methods and the dimensionality reduction method proposed in this embodiment are compared under different sampling period switching numbers N and maximum consecutive packet loss counts. The dimensions of the linear matrix inequalities were compared, and the results are shown in Table 1.

[0148] Table 1 Comparison of results between existing methods and dimensionality reduction calculation methods

[0149]

[0150] As shown in Table 1, when using the existing method, the dimension of the linear matrix inequality increases rapidly with the number of sampling period switches and the maximum number of consecutive packet losses, from 60×60 to 1716×1716. However, by using the dimension reduction method in this embodiment, by decomposing the expected matrix and extracting r=5 (the rank of the rearranged matrix) equivalent decomposition matrices to uniformly represent all combinations, the dimension of the linear matrix inequality remains constant at 36×36, unchanged with the increase of the number of sampling period switches and the number of consecutive packet losses. These results demonstrate that the dimension reduction method can significantly reduce the solution scale of the linear matrix inequality, effectively reducing computational complexity and storage overhead.

[0151] In the hybrid optimization algorithm, the parameters are set as follows: population size Maximum number of iterations Crossover probability Probability of mutation Control gain search range And the upper bound of packet loss. Precision threshold By running a hybrid optimization algorithm, the maximum number of consecutive packet losses that the system can tolerate is obtained as follows: And the corresponding optimal PI controller parameters are: .

[0152] Simulation results are as follows Figures 3-5 As shown in the figure, the frequency deviations are given respectively. Regulating valve deviation and mechanical power deviation and area control error signal The dynamic response of the system is shown, along with the changes in the sampling period and the corresponding number of consecutive packet losses. As can be seen from the simulation curves, under non-ideal network conditions of random sampling switching and continuous packet loss, the power system's state trajectory remains stable, and the various state variables and regional control error signals remain consistent. All values ​​gradually converge to zero, indicating that the proposed PI sampling frequency control method can effectively maintain the frequency stability of the power system.

[0153] To further verify the superiority of the employed boosting technique, its maximum allowable sampling period is compared with that of the input delay method and the ring functional method under ideal communication conditions (i.e., no random switching sampling and no continuous packet loss). The maximum allowable sampling periods for the three methods are shown in Table 2. The results show that the maximum sampling period obtained by the boosting technique used in this embodiment under ideal conditions is... It is significantly greater than that of the input time delay method. and the ring functional method 6. Therefore, under the premise of ensuring power system stability, improving the technical method to tolerate longer sampling periods effectively reduces the conservatism of the results. Figures 6-7 The system state and regional control error signals under different sampling periods are shown. The dynamic response shows that as the sampling period increases, the system convergence speed decreases slightly, but the system as a whole remains stable.

[0154] Table 2. Maximum permissible sampling period for the three methods under ideal communication conditions.

[0155]

[0156] In summary, the PI sampling frequency control method proposed in this embodiment can effectively guarantee the stability of the power system under non-ideal communication environments such as multi-mode switching sampling and continuous packet loss. At the same time, it can achieve a larger allowable sampling period under ideal communication conditions (no multi-mode sampling and no continuous packet loss), further verifying the effectiveness and feasibility of this implementation scheme.

[0157] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0158] It should be noted that although the method operations of the above embodiments are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the order of execution of the described steps may be changed. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0159] Example 2:

[0160] like Figure 8 As shown, this embodiment provides a power system PI sampling frequency control device based on multimodal sampling and continuous packet loss. The device includes a first establishment module 801, a second establishment module 802, an adjustment and conversion module 803, and a third establishment module 804, wherein:

[0161] The first module 801 is used to establish a power system model;

[0162] The second module 802 is used to establish a multi-mode switching sampling model and a continuous packet loss model for the power system; the multi-mode switching sampling model is used to characterize the non-periodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission.

[0163] The adjustment and conversion module 803 is used to dynamically adjust the frequency deviation of the power system based on the multi-mode switching sampling model and the continuous packet loss model, using a PI sampling control strategy, and to convert the continuous-time power system into an equivalent discrete-time system through boosting technology.

[0164] The third module 804 is used to establish sufficient conditions for the stochastic stability of the equivalent discrete-time system.

[0165] The method of dynamically adjusting the frequency deviation of the power system using a PI sampling control strategy includes:

[0166] The PI sampling controller is designed as follows:

[0167]

[0168] in, The control input signal at time t, , These are the proportional gain matrix and the integral gain matrix, respectively. and For a moment , The output vector, where k is the sequence number of successfully received data. For the first The sampling interval for each successful data reception; and The actual sampling time between two consecutive successful data receptions;

[0169] Will Substituting into the power system model, the closed-loop system is:

[0170]

[0171] in, Let be the state vector at time t. for The first derivative with respect to t, B and These are the system matrix, output matrix, and disturbance matrix, respectively. Let t be the system disturbance at time t.

[0172] The specific implementation of each module in this embodiment can be found in Embodiment 1 above, and will not be repeated here. It should be noted that the device provided in this embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0173] Example 3:

[0174] This embodiment provides a terminal device, which can be a computer, such as... Figure 9 As shown, the processor 902, memory, input device 903, display 904, and network interface 905 are connected via system bus 901. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 906 and an internal memory 907. The non-volatile storage medium 906 stores the operating system, computer programs, and database. The internal memory 907 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 902 executes the computer programs stored in the memory, it implements the power system PI sampling frequency control method based on multimodal sampling and continuous packet loss in Embodiment 1 described above.

[0175] Example 4:

[0176] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the power system PI sampling frequency control method based on multimodal sampling and continuous packet loss described in Embodiment 1 above.

[0177] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0178] Example 5:

[0179] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the power system PI sampling frequency control method based on multimodal sampling and continuous packet loss described in Embodiment 1 above.

[0180] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A power system PI sampling frequency control method based on multimodal sampling and continuous packet loss, characterized in that, The method includes: Establish a power system model; For power systems, a multi-mode switching sampling model and a continuous packet loss model are established. The multi-mode switching sampling model is used to characterize the non-periodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission. Based on the multimodal switching sampling model and the continuous packet loss model, a PI sampling control strategy is adopted to dynamically adjust the frequency deviation of the power system, and the continuous-time power system is transformed into an equivalent discrete-time system through the boosting technique. Establish sufficient conditions for the stochastic stability of an equivalent discrete-time system; The method of dynamically adjusting the frequency deviation of the power system using a PI sampling control strategy includes: The PI sampling controller is designed as follows: ； In the formula, The control input signal at time t, , These are the proportional gain matrix and the integral gain matrix, respectively. and For a moment , The output vector, where k is the sequence number of successfully received data. For the first The sampling interval for each successful data reception; and The actual sampling time between two consecutive successful data receptions; Will Substituting into the power system model, the closed-loop system is: ； In the formula, Let be the state vector at time t. for The first derivative with respect to t, B and These are the system matrix, output matrix, and disturbance matrix, respectively. Let t be the system disturbance at time t.

2. The power system PI sampling frequency control method according to claim 1, characterized in that, The method of transforming a continuous-time power system into an equivalent discrete-time system through enhancement techniques includes: Introducing augmented vectors: ； In the formula, To augment the system's state vector at time t, and They are time points , The state vector; The augmented closed-loop power system is then represented as: ； In the formula, for The first derivative with respect to t, To enhance the system at time The state vector, To output the matrix, the matrix , , , , and They are respectively: ； External disturbances season For continuous-time augmented closed-loop power systems in intervals Integrating on the above, we obtain the equivalent discrete-time system: ； In the formula, This is the actual sampling interval between two consecutive successful data receptions, and Let be the state transition matrix.

3. The power system PI sampling frequency control method according to claim 1, characterized in that, The process of establishing the multimodal switching sampling model is as follows: set up Given a sampling time sequence, where i is the sampling number, then the interval between two adjacent sampling times is... ; To characterize the characteristics of aperiodic sampling, the sampling period is... The model is a discrete random variable that switches randomly among a finite number of values, i.e.: ； In the formula, For all sampling periods The set of possible values; For the first There are several possible sampling periods, the probability of which satisfies , The corresponding probability, and satisfying .

4. The power system PI sampling frequency control method according to claim 3, characterized in that, The process of establishing the continuous packet loss model is as follows: set up The time series of successfully received sampled data by the controller, where k is the sequence number of successfully received data, satisfies... ; Between any two adjacent successful reception time intervals The number of consecutive packet losses that occur within the specified time is recorded as follows: And assume that the number of consecutive packet losses has an upper bound. ; Assuming each data packet transmission is independent, the packet loss rate is... Then the number of consecutive packet losses The probability distribution satisfies the following relationship: ； The actual sampling period between two consecutive successful receptions of sampled data is defined as: ; Let in the interval Within, sampling period The number of times it appears is In the event of continuous packet loss In this case, the actual sampling period is ; where, set ; The probability distribution of the actual sampling period is then expressed as: 。 5. The power system PI sampling frequency control method according to claim 1, characterized in that, A dimensionality reduction computation method based on matrix factorization is used to establish sufficient conditions for the stochastic stability of the equivalent discrete-time system, including: Given , Maximum number of consecutive packet losses and packet loss rate ; For the first The probability of taking a value in a sampling period is: ; ; If a positive definite matrix exists If the following matrix inequality holds, then the discrete-time power system is stochastically stable under multimode switching sampling and continuous packet loss: ； Among them, for positive definite matrices ,matrix It can be represented in the following decomposition form: ； definition For rearranged matrices, the form is: , They represent the vectorization operator and the inverse vectorization operator, respectively. yes The transpose of the rearranged matrix is ​​used for spectral decomposition: , These are the non-zero eigenvalues ​​of the rearranged matrix. for The corresponding feature vector, Let be the rank of the rearranged matrix. The index of the non-zero eigenvalues ​​of the rearranged matrix is ​​given by the following: , , , , , , , It is the identity matrix. The actual sampling period between two consecutive successful receptions of sampled data; in, , , for The transpose of the matrix, For PI control gain matrix, , , , , For dimension is The identity matrix; matrix , This is the output matrix.

6. The power system PI sampling frequency control method according to claim 5, characterized in that, The method also includes a hybrid optimization algorithm combining genetic algorithms and binary search to determine the PI control gain parameters and the maximum number of consecutive packet losses the system can tolerate, including: Parameter initialization: Initialize the PI gain search range, genetic algorithm parameters, and maximum number of packet loss attempts. and precision threshold Genetic algorithm parameters include population size. Maximum number of iterations Crossover probability and mutation probability ; Binary search interval setting: Set the lower bound of the search. and the upper bound of the search ; Outer binary search includes: Calculate the intermediate value and execute: According to the rearrangement matrix Calculate its non-zero characteristic pairs And construct a matrix , and calculate ; PI gain matrix Encoded as chromosome and initialize the population. ; , These are the proportional and integral gain matrices, respectively. The population iteration number; In each iteration, the control gain matrix corresponding to the current individual is obtained by decoding each chromosome individual. ;Will Substitute into the matrix inequality and solve for the corresponding matrix. If the conditions are feasible, then calculate the fitness function. , = Otherwise, assign a penalty value to the current individual; generate a new generation of population through selection, crossover, and mutation operations; repeat the above process until the number of iterations reaches [number missing]. Output the optimal solution ; Feasibility assessment and interval update: If If the corresponding matrix inequality holds, then update the lower bound. Otherwise, update the upper bound. ; Iteration Termination and Optimal Result Output: When When the optimal result is obtained, output the following: ,in, This indicates the system at the optimal PI control gain. The maximum number of consecutive packet losses that can be tolerated.

7. The power system PI sampling frequency control method according to any one of claims 1 to 6, characterized in that, The process of establishing the power system model is as follows: Taking into account the dynamic characteristics of the governor, turbine, and system load, the dynamic equation of a single-area power system is expressed as: ； In the formula, This is the generator damping coefficient. For rotational inertia, The frequency deviation at time t, express The first derivative with respect to t, Let be the mechanical output deviation at time t. express The first derivative with respect to t, Let be the load disturbance deviation at time t. R is the governor time constant, and R is the speed reduction coefficient. Let be the position deviation of the regulating valve at time t. express The first derivative with respect to t, The control input signal at time t, The time constant of the steam turbine. For frequency deviation factor, The region control error signal at time t; Define the state vector of the system at time t. Output vector System disturbances The state-space expression of the power system is then: ； In the formula, express The first derivative with respect to t, For the system matrix, B, All are output matrices. Let be the perturbation matrix.

8. A power system PI sampling frequency control device based on multimodal sampling and continuous packet loss, characterized in that, The device includes: The first module is used to build the power system model; The second module is used to establish a multi-mode switching sampling model and a continuous packet loss model for the power system. The multi-mode switching sampling model is used to characterize the non-periodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission. The adjustment and conversion module is used to dynamically adjust the frequency deviation of the power system based on the multi-mode switching sampling model and the continuous packet loss model, using a PI sampling control strategy, and to convert the continuous-time power system into an equivalent discrete-time system through boosting technology. The third module is used to establish sufficient conditions for the stochastic stability of the equivalent discrete-time system. The method of dynamically adjusting the frequency deviation of the power system using a PI sampling control strategy includes: The PI sampling controller is designed as follows: ； in, The control input signal at time t, , These are the proportional gain matrix and the integral gain matrix, respectively. and For a moment , The output vector, where k is the sequence number of successfully received data. For the first The sampling interval for each successful data reception; and The actual sampling time between two consecutive successful data receptions; Will Substituting into the power system model, the closed-loop system is: ； in, Let be the state vector at time t. for The first derivative with respect to t, B and These are the system matrix, output matrix, and disturbance matrix, respectively. Let t be the system disturbance at time t.

9. A terminal device, comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the power system PI sampling frequency control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power system PI sampling frequency control method according to any one of claims 1 to 7.

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