Optimization control method considering safety and stability of power grid

Abstract

The invention provides an optimization control method considering the safety and stability of a power grid, and relates to the technical field of power system dispatching operation control, and the method comprises the steps: building a voltage stability prediction model based on a time sequence convolution residual network, inputting the voltage time sequence data of a sensitive node of the power grid into the voltage stability prediction model, and outputting a voltage stability evaluation quantitative index; based on the voltage stability evaluation quantitative index, taking the minimum total capacity of generator tripping and load shedding as an objective function, and constructing a voltage stability control model containing voltage stability constraint and power balance constraint; solving the voltage stability control model by adopting a pelican optimization algorithm, and outputting an optimal control strategy; according to the optimization control method considering the safety and stability of the power grid, the prediction precision and control efficiency of the power grid in a complex scene can be improved, and safe and stable operation of a power grid system is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation control technology, and more specifically, to an optimized control method that takes into account the safety and stability of the power grid. Background Technology

[0002] With a high proportion of new energy sources (wind power, photovoltaic, etc.) connected to the grid, their random fluctuation characteristics lead to a decrease in system inertia and a weakening of voltage regulation capability. Traditional safe and stable control methods face severe challenges: (1) Relying on manual comparison or fixed strategy tables, it is difficult to cover all operating states when facing complex fault scenarios after new energy grid connection, such as three-phase short circuits and cascading faults. Decision delays or misjudgments are likely to occur, leading to an increased risk of system instability; (2) Voltage stability prediction based on mechanism modeling takes a long time and is difficult to meet real-time requirements; (3) There are problems such as insufficient extraction of time-series features and gradient vanishing, resulting in a prediction accuracy of less than 90%; (4) Insufficient optimization algorithm efficiency: Traditional optimization algorithms (such as particle swarm optimization and genetic algorithms) are prone to getting trapped in local optima under high-dimensional control variables, have slow convergence speed, and cannot quickly output the minimum control cost strategy. Summary of the Invention

[0003] The purpose of this invention is to provide an optimized control method that takes into account the safety and stability of the power grid, which can improve the prediction accuracy and control efficiency of the power grid in complex scenarios and ensure the safe and stable operation of the power grid system.

[0004] The technical solution of this invention is as follows:

[0005] In a first aspect, this application provides an optimized control method considering the safety and stability of the power grid, which includes the following steps:

[0006] S1. Construct a voltage stability prediction model based on a temporal convolutional residual network, and input the voltage time series data of sensitive nodes in the power grid into the voltage stability prediction model to output a quantitative index for voltage stability assessment.

[0007] S2. Based on the quantitative indicators of voltage stability assessment, and with the minimum total capacity of load shedding during power outage as the objective function, a voltage stability control model including voltage stability constraints and power balance constraints is constructed.

[0008] S3. The Pelican optimization algorithm is used to solve the voltage stability control model and output the optimal control strategy.

[0009] Further, step S1 includes: constructing a voltage stability prediction model based on a temporal convolutional residual network, taking the voltage time series data of sensitive nodes in the power grid as input, extracting multi-dimensional features of the time axis and feature axis through a temporal convolutional layer, and combining residual connections, modified linear activation units and batch normalization layers to output a voltage stability evaluation quantitative index that maps the relationship between the node voltage time series features and system stability.

[0010] Furthermore, the calculation formula for the voltage stability prediction model constructed based on the temporal convolutional residual network includes: ,

[0011] In the formula, For the network input of layer L, Let be the output of the Lth layer of the network, concise be the result of multiple convolutions combined along the feature axis, f be the activation function, K be the kernel parameters, b be the bias, and s be the distance moved along the time axis for each convolution with a specific stride. Let i be a temporal convolution matrix, where i represents the time axis and j represents the channel.

[0012] Furthermore, the aforementioned voltage stability assessment quantification index is calculated using a binary table, and its calculation formula is as follows: ,

[0013] In the formula, ξ m τ is a quantitative indicator for voltage stability assessment. crm Let u be the time threshold value of the m-th binary table. crm Let u be the voltage threshold value of the m-th binary table, t0 be the time when the voltage decreases, and u N Let u(t) be the rated voltage, and u(t) be the value of the bus voltage at time t.

[0014] Furthermore, in step S2, the calculation formula for constructing the voltage stability control model, which includes voltage stability constraints and power balance constraints, with the objective function of minimizing the total capacity of load shedding during power outages, includes:

[0015] The objective function is to minimize the total load shearing capacity of the shearing machine. ,

[0016] Voltage stability constraints: , ,

[0017] Power balance constraints: ,

[0018] In the formula, u i u j These are the shedding factor and the load shedding factor, ΔP. G,i Let ΔP be the generator disconnection capacity of the i-th node. L,j Let N be the load shedding capacity of the j-th node. G N L These represent the number of generators and the number of load shedding nodes, respectively. k，0The voltage stability evaluation index for the k-th node after a large disturbance fault, without implementing stability control measures, is λ. k，i Let ξ be the sensitivity of the k-th node to the i-th generator switching. k Δξ is the voltage stability evaluation index for the k-th node after implementing load shedding / load shedding measures. k ΔP represents the change in voltage stability index. i To adjust the amount of control measures, S b,min S is the minimum power imbalance. b,max This represents the maximum power imbalance.

[0019] Furthermore, in step S3, the calculation process of solving the voltage stability control model using the pelican optimization algorithm includes: , , ,

[0020] In the formula, x ij Let α be the j-th position of the i-th pelican, and l be a random number. j v is the minimum value obtained by solving the j-th dimension model. j Let m be the maximum value obtained from the solution of the j-th dimension model, where m is the spatial dimension and n is the number of pelicans; Let F be the j-th position of the i-th pelican after the first stage. p σ is the target value for the prey, I is a random number, and F is an integer (taking the value 1 or 2). i Let P be the objective function value for the i-th pelican. j Let be the location of the prey in the j-th dimension; Let β be the j-th position of the i-th pelican after the second stage, R be a random number, t be the current iteration number, and T be the maximum set iteration number.

[0021] Secondly, this application provides an electronic device, comprising:

[0022] Memory, used to store one or more programs;

[0023] processor;

[0024] When one or more of the above programs are executed by the above processor, an optimized control method considering power grid security and stability is implemented as described in any of the first aspects above.

[0025] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an optimization control method considering power grid security and stability as described in any of the first aspects above.

[0026] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0027] (1) The present invention provides an optimized control method that considers the safety and stability of the power grid. It adopts a time-series convolutional residual network and effectively extracts voltage time-series features through bidirectional time-series convolution and residual connection, thereby improving the voltage stability prediction accuracy of the power grid system.

[0028] (2) This invention aims to minimize the total capacity of load shedding and combine the global search capability of the Pelican optimization algorithm to significantly improve the prediction accuracy and control efficiency of the power grid in complex scenarios, thus ensuring the safe and stable operation of the power grid system. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the steps of an optimized control method for power grid safety and stability according to the present invention.

[0031] Figure 2 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0032] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0035] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0036] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0037] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0038] Example 1

[0039] Please see Figure 1 , Figure 1 The diagram shows the steps of an optimized control method that takes into account the safety and stability of the power grid, as provided in an embodiment of this application.

[0040] In a first aspect, this application provides an optimized control method considering the safety and stability of the power grid, which includes the following steps:

[0041] S1. Construct a voltage stability prediction model based on a temporal convolutional residual network, and input the voltage time series data of sensitive nodes in the power grid into the voltage stability prediction model to output a quantitative index for voltage stability assessment.

[0042] S2. Based on the quantitative indicators of voltage stability assessment, and with the minimum total capacity of load shedding during power outage as the objective function, a voltage stability control model including voltage stability constraints and power balance constraints is constructed.

[0043] S3. The Pelican optimization algorithm is used to solve the voltage stability control model and output the optimal control strategy.

[0044] As a preferred implementation, step S1 includes: constructing a voltage stability prediction model based on a temporal convolutional residual network, taking the voltage time series data of sensitive nodes in the power grid as input, extracting multi-dimensional features of the time axis and feature axis through a temporal convolutional layer, and combining residual connections, modified linear activation units and batch normalization layers to output a voltage stability evaluation quantitative index that maps the relationship between the node voltage time series features and system stability.

[0045] As a preferred implementation, the calculation formula for constructing a voltage stability prediction model based on a temporal convolutional residual network includes: ,

[0046] In the formula, For the network input of layer L, Let be the output of the Lth layer of the network, concise be the result of multiple convolutions combined along the feature axis, f be the activation function, K be the kernel parameters, b be the bias, and s be the distance moved along the time axis for each convolution with a specific stride. Let i be a temporal convolution matrix, where i represents the time axis and j represents the channel.

[0047] As a preferred implementation method, the voltage stability assessment quantification index is calculated using a binary table, and the calculation formula is as follows: ,

[0048] In the formula, ξ m τ is a quantitative indicator for voltage stability assessment. crm Let u be the time threshold value of the m-th binary table. crm Let u be the voltage threshold value of the m-th binary table, t0 be the time when the voltage decreases, and u N Let u(t) be the rated voltage, and u(t) be the value of the bus voltage at time t.

[0049] In a preferred embodiment, step S2, with the objective function being the minimum total load shedding capacity, includes the following calculation formulas for constructing a voltage stability control model that incorporates voltage stability constraints and power balance constraints:

[0050] The objective function is to minimize the total load shearing capacity of the shearing machine. ,

[0051] Voltage stability constraints: , ,

[0052] Power balance constraints: ,

[0053] In the formula, u i u j These are the shedding factor and the load shedding factor, ΔP. G,i Let ΔP be the generator disconnection capacity of the i-th node. L,j Let N be the load shedding capacity of the j-th node. G N L These represent the number of generators and the number of load shedding nodes, respectively. k，0 The voltage stability evaluation index for the k-th node after a large disturbance fault, without implementing stability control measures, is λ. k，iLet ξ be the sensitivity of the k-th node to the i-th generator switching. k Δξ is the voltage stability evaluation index for the k-th node after implementing load shedding / load shedding measures. k ΔP represents the change in voltage stability index. i To adjust the amount of control measures, S b,min S is the minimum power imbalance. b,max This represents the maximum power imbalance.

[0054] As a preferred implementation, step S3, the calculation process of solving the voltage stability control model using the Pelican optimization algorithm, includes: , , ,

[0055] In the formula, x ij Let α be the j-th position of the i-th pelican, and l be a random number. j v is the minimum value obtained by solving the j-th dimension model. j Let m be the maximum value obtained from the solution of the j-th dimension model, where m is the spatial dimension and n is the number of pelicans; Let F be the j-th position of the i-th pelican after the first stage. p σ is the target value for the prey, I is a random number, and F is an integer (taking the value 1 or 2). i Let P be the objective function value for the i-th pelican. j Let be the location of the prey in the j-th dimension; Let β be the j-th position of the i-th pelican after the second stage, R be a random number, t be the current iteration number, and T be the maximum set iteration number.

[0056] Example 2

[0057] Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0058] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0059] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0060] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0061] It is understood that the structure shown in the figure is for illustrative purposes only. An optimized control method considering power grid security and stability may include more or fewer components than those shown in the figure, or have a different configuration. The components shown in the figure can be implemented using hardware, software, or a combination thereof.

[0062] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0063] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0064] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0066] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An optimized control method considering the safety and stability of the power grid, characterized in that, Includes the following steps: S1. Construct a voltage stability prediction model based on a temporal convolutional residual network, and input the voltage time series data of sensitive nodes in the power grid into the voltage stability prediction model to output a quantitative index for voltage stability assessment. S2. Based on the quantitative indicators of voltage stability assessment, and with the minimum total capacity of load shedding during power outage as the objective function, a voltage stability control model including voltage stability constraints and power balance constraints is constructed. S3. The Pelican optimization algorithm is used to solve the voltage stability control model and output the optimal control strategy.

2. The optimization control method considering power grid safety and stability as described in claim 1, characterized in that, Step S1 includes: constructing a voltage stability prediction model based on a temporal convolutional residual network, taking the voltage time series data of sensitive nodes in the power grid as input, extracting multi-dimensional features of the time axis and feature axis through a temporal convolutional layer, and combining residual connections, modified linear activation units and batch normalization layers to output a voltage stability evaluation quantitative index that maps the relationship between the node voltage time series features and system stability.

3. The optimization control method considering power grid safety and stability as described in claim 2, characterized in that, The calculation formula for the voltage stability prediction model constructed based on the temporal convolutional residual network includes: , In the formula, For the network input of layer L, Let be the output of the Lth layer of the network, concise be the result of multiple convolutions combined along the feature axis, f be the activation function, K be the kernel parameters, b be the bias, and s be the distance moved along the time axis for each convolution with a specific stride. Let i be a temporal convolution matrix, where i represents the time axis and j represents the channel.

4. The optimization control method considering power grid safety and stability as described in claim 3, characterized in that, The voltage stability assessment quantification index is calculated using a binary table, and its calculation formula is as follows: , In the formula, ξ m τ is a quantitative indicator for voltage stability assessment. crm Let u be the time threshold value of the m-th binary table. crm Let u be the voltage threshold value of the m-th binary table, t0 be the time when the voltage decreases, and u N Let u(t) be the rated voltage, and u(t) be the value of the bus voltage at time t.

5. The optimization control method considering power grid safety and stability as described in claim 1, characterized in that, In step S2, the calculation formula for constructing a voltage stability control model that includes voltage stability constraints and power balance constraints, with the objective function of minimizing the total load shedding capacity, includes: The objective function is to minimize the total load shearing capacity of the shearing machine. , Voltage stability constraints: , , Power balance constraints: , In the formula, u i u j These are the shedding factor and the load shedding factor, ΔP. G,i Let ΔP be the generator disconnection capacity of the i-th node. L,j Let N be the load shedding capacity of the j-th node. G N L These represent the number of generators and the number of load shedding nodes, respectively. k，0 The voltage stability evaluation index for the k-th node after a large disturbance fault, without implementing stability control measures, is λ. k，i Let ξ be the sensitivity of the k-th node to the i-th generator switching. k Δξ is the voltage stability evaluation index for the k-th node after implementing load shedding / load shedding measures. k ΔP represents the change in voltage stability index. i To adjust the amount of control measures, S b,min S is the minimum power imbalance. b,max This represents the maximum power imbalance.

6. The optimization control method considering power grid safety and stability as described in claim 1, characterized in that, In step S3, the calculation process of solving the voltage stability control model using the Pelican optimization algorithm includes: , , , In the formula, x ij Let α be the j-th position of the i-th pelican, and l be a random number. j The minimum value of the j-th dimension model is obtained by solving v. j Let m be the maximum value obtained from the solution of the j-th dimension model, where m is the spatial dimension and n is the number of pelicans; Let F be the j-th position of the i-th pelican after the first stage. p σ is the target value for the prey, I is a random number, and F is an integer (taking the value 1 or 2). i Let P be the objective function value for the i-th pelican. j Let be the position of the prey in the j-th dimension; Let β be the j-th position of the i-th pelican after the second stage, R be a random number, t be the current iteration number, and T be the maximum set iteration number.

7. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the processor executes the one or more programs, it implements an optimized control method that takes into account the safety and stability of the power grid as described in any one of claims 1-6.

8. 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 an optimized control method that takes into account the safety and stability of the power grid as described in any one of claims 1-6.

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