State estimation method, device and equipment of power distribution network and medium
By establishing dynamic models and sliding mode observer models in the distribution network, combined with Kalman filters, the state acquisition problem caused by the operation of distribution network equipment in complex environments is solved, the accuracy and reliability of state estimation are improved, and the power supply reliability and power quality are enhanced.
Patent Information
- Application Number
- CN202411939766.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-22
AI Technical Summary
Distribution network equipment operates in complex and harsh environments, resulting in a decline in equipment life and performance, and the accuracy of measurement data is affected, especially with the access of distributed power supplies, data stability and accuracy are further disturbed. The limitations of communication system and insufficient measurement equipment lead to the missing or insufficient real-time data, which makes the acquisition of distribution network status a thorny problem.
By establishing a dynamic model of the distribution network and establishing a sliding mode observer model based on the dynamic model, combining the Kalman filter, considering the adjustment parameters of the sliding mode observer, thereby improving the accuracy and reliability of the determination of the state estimation value.
It improves the accuracy and accuracy of distribution network state estimation, can effectively deal with the complexity of equipment operating environment in the distribution network and the randomness and fluctuation of distributed power supplies, and enhances the guarantee of power supply reliability and power quality.
Smart Images
Figure CN119864798A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of distribution network state estimation, and in particular to a distribution network state estimation method, device, equipment and medium. Background Art
[0002] As an important part of the power system directly facing users, accurate knowledge of the operating status of the distribution network is crucial to ensuring power supply reliability and power quality. However, the current distribution network faces many unfavorable factors. Distribution network equipment is often in a complex and harsh operating environment, such as high temperature, humidity, strong electromagnetic interference, etc., which not only affects the life and performance of the equipment itself, but also poses a hidden danger to the accuracy of the measurement data.
[0003] As various types of distributed power sources are continuously connected to the distribution network, their randomness and volatility further disrupt the stability and accuracy of the measurement data. In addition, the limitations of the communication system and the insufficient number of measuring devices often lead to missing or insufficient real-time data, making the acquisition of distribution network status an extremely difficult problem. In this context, it is of great practical significance to study an algorithm that can effectively deal with these problems and improve the accuracy of distribution network status estimation. Summary of the invention
[0004] The present invention provides a method, device, equipment and medium for estimating the state of a distribution network, so as to improve the precision and accuracy of the state estimation of the distribution network.
[0005] In a first aspect, the present invention provides a method for estimating a state of a distribution network, comprising:
[0006] Establishing a dynamic model of the distribution network;
[0007] According to the dynamic model, a sliding mode observer model of the distribution network is established;
[0008] A state estimation value of the distribution network is determined according to the dynamic model and the sliding mode observer model.
[0009] Optionally, the dynamic model is:
[0010]
[0011] Among them, x k is an n-dimensional state vector, and x k =[U k ,θ k ]∈R, where U k is the voltage amplitude of each node in the system, θ k is the phase angle of each node in the system: k is an m-dimensional measurement vector, and y k =[U k, P k , Q k , P i , Q i ]∈R m , P k and Q k are respectively the active power and reactive power of each node in the system, P i and Q i are respectively the active power and reactive power of each branch in the system, f is the state transfer equation; h is the measurement equation; w k and v k They are the n-dimensional systematic error and the m-dimensional measurement error respectively.
[0012] Optionally, the state transfer equation is calculated using Holt's two-exponential smoothing method:
[0013]
[0014] Among them, x k|k-1 is the estimated state of the distribution network at time K, s k is the horizontal component, b k is the tilt component, is the state value at time k, α k and β H is a smoothing parameter between 0 and 1.
[0015] Optionally, the measurement equation includes:
[0016]
[0017] in, is the p-phase active power of node i, is the p-phase reactive power of node i, is the conductivity, is the susceptance, and n is the total number of nodes.
[0018] Optionally, the measurement equation also includes:
[0019]
[0020] in, is the active power of the branch, is the reactive power of the branch.
[0021] Optionally, the dynamic model (1) is linearly processed to obtain the following formula:
[0022]
[0023] According to formula (5), the sliding mode observer model is established as follows:
[0024]
[0025] Among them, y is the actual measured value, Calculated value for measurement.
[0026] Optionally, determining a state estimation value of the distribution network according to the dynamic model and the sliding mode observer model includes:
[0027] According to formula (5) and formula (6), the Kalman filter gain and sliding mode observer adjustment parameters are determined;
[0028] The state estimation value is determined according to the Kalman filter gain and the sliding mode observer adjustment parameter.
[0029] In a second aspect, the present invention provides a state estimation device for a power distribution network, comprising:
[0030] A dynamic model building module, used to build a dynamic model of the distribution network;
[0031] An observer model building module, used to build a sliding mode observer model of the distribution network according to the dynamic model;
[0032] The state estimation value determination module is used to determine the state estimation value of the distribution network according to the dynamic model and the sliding mode observer model.
[0033] In a third aspect, the present invention provides an electronic device, the electronic device comprising:
[0034] at least one processor; and
[0035] a memory communicatively connected to the at least one processor; wherein,
[0036] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the state estimation method of the distribution network described in the first aspect.
[0037] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the distribution network state estimation method described in the first aspect when executed by a processor.
[0038] The technical solution provided by the present invention establishes a dynamic model of the distribution network, and establishes a sliding mode observer model of the distribution network based on the dynamic model, so as to take the adjustment parameters of the sliding mode observer into account on the dynamic model of the Kalman filter, thereby improving the accuracy and reliability of determining the state estimation value. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Figure 1 A flow chart of a method for estimating a state of a distribution network provided by an embodiment of the present invention;
[0040] Figure 2 A schematic diagram of the structure of a sliding mode observer provided by an embodiment of the present invention;
[0041] Figure 3 A schematic diagram of the structure of a state estimation device for a power distribution network provided by an embodiment of the present invention;
[0042] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.
[0044] Figure 1 The flowchart of a method for estimating the state of a distribution network provided by an embodiment of the present invention is applicable to the case of estimating the state parameters of a distribution network. The method can be executed by a device for estimating the state of a distribution network provided by an embodiment of the present invention. The device for estimating the state of a distribution network can be implemented in the form of hardware and / or software. Figure 1 As shown, the state estimation method of the distribution network includes:
[0045] S101. Establish a dynamic model of the distribution network.
[0046] Specifically, the extended Kalman filter is the application of the Kalman filter in a nonlinear system. Since there are many nonlinear devices in the distribution network, the extended Kalman filter can be used to construct a dynamic model of the distribution network. Optionally, the dynamic model is:
[0047]
[0048] Among them, x k is an n-dimensional state vector, and x k =[U k ,θ k ]∈R, where U k is the voltage amplitude of each node in the system, θ k is the phase angle of each node in the system: k is an m-dimensional measurement vector, and y k =[U k , P k , Qk , P i , Q i ]∈R m , P k and Q k are respectively the active power and reactive power of each node in the system, P i and Q i are respectively the active power and reactive power of each branch in the system, f is the state transfer equation; h is the measurement equation; w k and v k They are the n-dimensional systematic error and the m-dimensional measurement error respectively.
[0049] Among them, the state transfer equation is calculated using Holt's two-exponential smoothing method:
[0050]
[0051] Among them, x k|k-1 is the estimated state of the distribution network at time K, s k is the horizontal component, b k is the tilt component, is the state value at time k, α k and β H is a smoothing parameter between 0 and 1. By setting different smoothing parameters, the two factors in the original state time series are smoothed.
[0052] Optionally, the measurement equation includes:
[0053]
[0054] in, is the p-phase active power of node i, is the p-phase reactive power of node i, is the conductivity, is the susceptance, and n is the total number of nodes.
[0055] Optionally, the measurement equation also includes:
[0056]
[0057] in, is the active power of the branch, is the reactive power of the branch.
[0058] The voltage amplitude U and phase θ determined by the state transfer equation can be substituted into the measurement equation for calculation to obtain the quantity measurement.
[0059] Specifically, the state transfer equation f and the measurement equation h are both nonlinear functions. After performing first-order Taylor expansion of the state transfer equation f and the measurement equation h near time k, combined with the recursive formula of the linear Kalman filter, the following formula can be obtained:
[0060]
[0061] Then, according to formula (5), the covariance matrix is estimated in one step:
[0062] P k|k-1 =AP k-1 A T +Q; (11)
[0063] Where Q is the system observation noise covariance matrix.
[0064] Kalman gain update:
[0065] H k =P k|k-1 C T [CP k|k-1 C T +R] -1 ; (12)
[0066] Covariance update:
[0067] P k =(IH k C)P k|k -1; (13)
[0068] S102. Establish a sliding mode observer model of the distribution network based on the dynamic model.
[0069] Specifically, by establishing a dynamic model of the extended Kalman filter of the distribution network, the system error w can be eliminated by using this model. k and measurement error v k , but it cannot eliminate the errors caused by the parameters A, B and C in the dynamic model. Because, in order to reduce the errors caused by the parameters A, B and C to the state estimation, the sliding mode observer model can be established based on formula (5) as follows:
[0070]
[0071] Among them, y is the actual measured value, Calculated value for measurement.
[0072] The core of the state observer is to reconstruct the state equivalent to the system frame state under certain indicators by reconstructing the system input and output. Its structure is as follows: Figure 2 As shown, 21 is the actual state of the system, and 22 is the sliding mode observer model constructed based on the output and the output. Figure 2 The G in is the n×m real matrix of output deviation feedback gain. Figure 2 The state equation is as follows:
[0073]
[0074] The differential equation satisfied by the observation error is as follows:
[0075]
[0076] The above formula shows that by selecting a suitable output deviation feedback gain matrix G, all eigenvalues of A-GC can be located in the left half plane of the complex plane, and the sliding mode observer It will gradually approach the actual state x(t) of the system with a certain accuracy and speed, and this process is independent of x(0), that is, it allows x(0) and There are differences. The eigenvalues of the sliding mode observer poles A-GC will determine the convergence speed of the observation error. If matrices A, B, and C are observable, the pole eigenvalues of the sliding mode observer can adjust the performance of the sliding mode observer by selecting the output deviation feedback gain real matrix.
[0077] S103. Determine a state estimation value of the distribution network according to the dynamic model and the sliding mode observer model.
[0078] Specifically, the estimated state value of the distribution network at time K can be determined based on the dynamic model, and then the Kalman filter gain in the dynamic model and the adjustment parameters in the sliding mode observer are combined, and the above parameters are substituted into the calculation formula of the state estimation value. The result of the calculation is the state estimation value.
[0079] Optionally, the state estimation value of the distribution network is determined according to the dynamic model and the sliding mode observer model, including: determining the Kalman filter gain and the sliding mode observer adjustment parameter according to formula (5) and formula (6); determining the state estimation value according to the Kalman filter gain and the sliding mode observer adjustment parameter.
[0080] Specifically, according to formula (5), the Kalman filter gain can be determined as shown in formula (12), and according to formula (6), the adjustment parameter D can be determined as k According to the sliding mode observer formula (7) and formula (8), the eigenvector of the matrix A-GC is located in the unit circle of the Z plane. Then the algorithm meets the sliding mode stability condition and has the advantages of the sliding mode observer. The adjustment parameter D can be obtained based on this. k The gradient descent method can be used to adjust the parameter D k Fine-tune, that is, parameter D k Each time, fine-tune along the direction of gradient descent to ensure that the adjustment parameter D is always k The setting is reasonable. The algorithm error can be defined as:
[0081]
[0082] Solve D for formula (14) k The partial derivative of can be obtained:
[0083]
[0084] And in each cycle, D k To make corrections:
[0085]
[0086] Among them, α is the correction coefficient, which is between 0 and 1. α is used to adjust the degree of each adjustment. If α is too large, the oscillation result may diverge, and if α is too small, the adjustment result may drop. Therefore, this application sets α to 0.04 to improve the adjustment parameter D. k The correction reference can be used to improve the reliability of the state estimation value.
[0087] According to formula (5) and formula (6), the updated state estimate of the sliding mode observer of the joint Kalman filter is:
[0088]
[0089] in, is the estimated state value at time k determined based on the state value at time k-1 based on the state transition equation in formula (5), k is the Kalman filter gain, D k is the adjustment parameter of the sliding mode observer, y is the actual measured value, is the measurement value calculated based on the measurement equation in formula (5). In this way, the Kalman filter gain in the Kalman filter and the adjustment parameter in the sliding mode observer can be taken into account on the basis of the state estimation value determined according to the dynamic model of the extended Kalman filter to improve the accuracy and precision of the determination of the state estimation value.
[0090] It should be noted that:
[0091]
[0092] in,
[0093] The sliding surface can be defined based on the Lyapunov function as follows:
[0094]
[0095] According to the arrival condition of the sliding mode observer, it can be converted into:
[0096] s 2 (k+1)-s 2 (k) = ((A-GC)s(k)) 2 -s 2 (k) = ((A-GC)s(k)) 2 -△x 2 (k)<0;
[0097] It can be seen from the above formula that the eigenvector of the matrix (A-GC) is located within the unit circle of the Z plane, which can make the state estimation value have the advantages of a sliding mode observer, thereby making the state estimation value closer to the actual value and improving the reliability of the determination of the state estimation value.
[0098] The technical solution provided by the present invention establishes a dynamic model of the distribution network, and establishes a sliding mode observer model of the distribution network based on the dynamic model, so as to take the adjustment parameters of the sliding mode observer into account on the dynamic model of the Kalman filter, thereby improving the accuracy and reliability of determining the state estimation value.
[0099] The present invention uses the IEEE33 node test system as an example for simulation testing. On the basis that the test system contains Gaussian noise, the maximum and mean values of the absolute error of the voltage amplitude and the absolute error of the voltage phase angle obtained by the state estimation method provided in the above embodiment are shown in Table 1:
[0100] Table 1
[0101]
[0102] Taking the IEEE33 node test system as an example, a simulation test is carried out. By artificially reducing the model accuracy, the low accuracy of the actual distribution network model is simulated. Under this test condition, the maximum and mean values of the absolute error of the voltage amplitude and the absolute error of the voltage phase angle obtained by the state estimation method provided in the above embodiment are shown in Table 2:
[0103] Table 2
[0104]
[0105] Among them, EKF in Table 1 and Table 2 represents an extended Kalman filter, SMO represents a sliding mode state observer, and ESO represents a state estimation model combining an extended Kalman filter and a sliding mode state observer, that is, the algorithm model used for the state estimation provided by the present invention. It can be seen from Table 1 and Table 2 that the state estimation method provided by the present invention can achieve lower errors and improve the accuracy and reliability of state estimation. The state estimation method provided by the present invention can simultaneously integrate the strong anti-noise disturbance capability of the Kalman algorithm and the strong anti-parameter interference capability of the sliding mode observer, and further improve the state estimation accuracy of the power grid parameters.
[0106] Figure 3 The present invention provides a schematic diagram of the structure of a state estimation device for a power distribution network, which can be implemented in the form of hardware and / or software. Figure 3 As shown, the state estimation device of the distribution network includes:
[0107] A dynamic model building module 10 is used to build a dynamic model of the distribution network;
[0108] An observer model building module 20 is used to build a sliding mode observer model of the distribution network according to the dynamic model;
[0109] The state estimation value determination module 30 is used to determine the state estimation value of the distribution network according to the dynamic model and the sliding mode observer model.
[0110] The state estimation device of the distribution network provided in the embodiment of the present invention can execute the state estimation method of the distribution network provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. The similarities can be referred to the above description.
[0111] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.
[0112] like Figure 4 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In RAM13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, ROM12 and RAM13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0113] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0114] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a state estimation method for a distribution network.
[0115] In some embodiments, the state estimation method of the distribution network may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 12 and / or a communication unit 19. When the computer program is loaded into RAM 13 and executed by the processor 11, one or more steps of the state estimation method of the distribution network described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the state estimation method of the distribution network in any other appropriate manner (e.g., by means of firmware).
[0116] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0117] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0118] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer instructions. The computer instructions are used to enable a processor to implement the state estimation method of the distribution network provided by the embodiment of the present invention when executed. The method has corresponding functional modules and beneficial effects of the execution method, which will not be repeated here.
[0119] Computer readable storage medium can be a tangible medium that can contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. Computer readable storage medium can include but is not limited to electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, computer readable storage medium can be a machine readable signal medium. More specific examples of machine readable storage medium can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.
[0120] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, combinations and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims
1. A method for estimating the state of a distribution network, characterized in that: include: Establishing a dynamic model of the distribution network; According to the dynamic model, a sliding mode observer model of the distribution network is established; A state estimation value of the distribution network is determined according to the dynamic model and the sliding mode observer model.
2. The state estimation method according to claim 1, characterized in that: The dynamic model is: Among them, x k is an n-dimensional state vector, and x k = [ U k ,θ k] ∈R, where U k is the voltage amplitude of each node in the system, θ k is the phase angle of each node in the system: k is an m-dimensional measurement vector, and y k =[U k , P k , Q k , P i , Q i ]∈R m , P k and Q k are respectively the active power and reactive power of each node in the system, P i and Q i are respectively the active power and reactive power of each branch in the system, f is the state transfer equation; h is the measurement equation; w k and v k They are the n-dimensional systematic error and the m-dimensional measurement error respectively.
3. The state estimation method according to claim 2, characterized in that: The state transfer equation is calculated using Holt's two-exponential smoothing method: Among them, x k|k-1 is the estimated state of the distribution network at time K, s k is the horizontal component, b k is the tilt component, is the state value at time k, α k and β H is a smoothing parameter between 0 and 1.
4. The state estimation method according to claim 2, characterized in that: The measurement equation includes: in, is the p-phase active power of node i, is the p-phase reactive power of node i, is the conductivity, is the susceptance, and n is the total number of nodes.
5. The state estimation method according to claim 4, characterized in that: The measurement equation also includes: in, is the active power of the branch, is the reactive power of the branch.
6. The state estimation method according to claim 2, characterized in that: The dynamic model (1) is linearly processed to obtain the following formula: According to formula (5), the sliding mode observer model is established as follows: Among them, y is the actual measured value, Calculated value for measurement.
7. The state estimation method according to claim 6, characterized in that: Determining a state estimation value of the distribution network according to the dynamic model and the sliding mode observer model includes: According to formula (5) and formula (6), the Kalman filter gain and sliding mode observer adjustment parameters are determined; The state estimation value is determined according to the Kalman filter gain and the sliding mode observer adjustment parameter.
8. A state estimation device for a distribution network, characterized in that: include: A dynamic model building module, used to build a dynamic model of the distribution network; An observer model building module, used to build a sliding mode observer model of the distribution network according to the dynamic model; The state estimation value determination module is used to determine the state estimation value of the distribution network according to the dynamic model and the sliding mode observer model.
9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the state estimation method for the power distribution network according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the state estimation method of a power distribution network according to any one of claims 1 to 7 when executed.