Intelligent substation dynamic regulation and control method and system based on RIS and medium

Abstract

The invention provides an intelligent substation dynamic regulation and control method and system based on RIS and a medium, and belongs to the technical field of substation communication regulation and control. The method comprises the following steps: giving a risk weight according to the type of equipment, constructing a substation equipment risk grading quantitative model based on the risk weight, and obtaining a risk index of the equipment based on the substation equipment risk grading quantitative model and real-time data; constructing a space electromagnetic fingerprint map of the intelligent substation based on the electromagnetic field data; acquiring a propagation shielding scene in the intelligent substation according to the signal propagation path, acquiring a corresponding signal transmission compensation mode according to the propagation shielding scene, adjusting the RIS unit according to the signal transmission compensation mode and the risk index, and generating an initial optimization phase matrix; and optimizing the initial optimization phase matrix based on a power grid state constraint condition, and generating a working phase matrix of the RIS unit. The communication quality is improved through the signal transmission compensation mode and the risk index, and meanwhile the power grid safety is guaranteed by meeting the unit constraint condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of substation communication control, and in particular relates to a RIS-based intelligent substation dynamic control method, system and medium. Background Art

[0002] RIS, or Reconfigurable Intelligent Surface, is an emerging wireless communication technology. In traditional wireless networks, the electromagnetic environment is not controlled by the network. In intelligent wireless environments, RIS transforms the environment into an intelligent, reconfigurable electromagnetic space, bringing a paradigm shift in information transmission and processing.

[0003] With the rise of RIS, RIS-based phased array communication systems have been applied in smart substations. However, the power grid imposes security constraints on the operation of smart substations. The power consumption generated by the use of RIS phased array communication systems can easily have an adverse impact on grid security. In addition, the surrounding environment of smart substations is relatively complex, and most of them have devices such as cleaning robots that affect communication transmission quality. This reduces the signal transmission quality of the RIS phased array communication system, thereby affecting the communication quality and security of the entire smart substation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to improve the communication quality of smart substations while ensuring the security of the power grid. In view of the shortcomings of the existing technology, a dynamic control method, system and medium for smart substations based on RIS are provided.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a dynamic control method for a smart substation based on RIS, comprising:

[0007] S1. Collect real-time data of equipment in a smart substation, assign risk weights based on the types of the equipment, construct a risk grading and quantitative model for substation equipment based on the risk weights, and obtain a risk index for the equipment based on the risk grading and quantitative model and the real-time data.

[0008] S2. Collect electromagnetic field data in the smart substation, and construct a spatial electromagnetic fingerprint of the smart substation based on the electromagnetic field data;

[0009] S3. Predicting a signal propagation path according to the spatial electromagnetic fingerprint, obtaining a propagation shielding scenario in the smart substation according to the signal propagation path, obtaining a corresponding signal transmission compensation method according to the propagation shielding scenario, adjusting a RIS unit according to the signal transmission compensation method and the risk index, and generating an initial optimized phase matrix;

[0010] S4. Obtain grid state constraints, optimize the initial optimized phase matrix based on the grid state constraints, and generate a working phase matrix of the RIS unit.

[0011] Compared with the prior art, the beneficial effects of the RIS-based smart substation dynamic control method of the present invention include: first, different risk weights are assigned according to different types of equipment, and then a substation equipment risk grading and quantitative model is constructed based on the risk weights of different equipment to achieve refined grading of equipment risks. At the same time, real-time data of equipment in the smart substation is collected, and the risk index of the equipment is obtained based on the substation equipment risk grading and quantitative model and real-time data, thereby avoiding the problem that traditional risk monitoring relies on a single threshold alarm, cannot quantify the dynamic risk level of the equipment, and has a delayed fault warning, ensuring the accuracy of risk identification, and facilitating subsequent precise dynamic control according to the risk index; then, electromagnetic field data in the smart substation is collected, and then a spatial electromagnetic fingerprint map of the smart substation is constructed based on the electromagnetic field data. Since the metal structure in the substation causes serious signal multipath effect, the use of the spatial electromagnetic fingerprint map can effectively reflect real-time electromagnetic environment changes, which is conducive to the subsequent improvement of signal transmission. The proposed method provides stable support for the prediction accuracy of the propagation path; then, the signal propagation path is predicted according to the spatial electromagnetic fingerprint, and the propagation shielding scenario in the smart substation is obtained based on the signal propagation path. In this way, different signal transmission compensation methods can be selected according to different propagation shielding scenarios. Finally, the RIS unit is adjusted according to the signal transmission compensation method and the risk index to generate an initial optimized phase matrix. This can not only improve the risk failure of the equipment, but also improve the communication delay under dynamic shielding scenarios, effectively improving the communication quality of the smart substation; finally, the initial optimized phase matrix is ​​optimized by the obtained grid state constraint conditions, and then the working phase matrix of the RIS unit is generated. Subsequently, the RIS unit is adjusted according to the working phase matrix of the RIS unit. In this way, while ensuring the communication quality of the smart substation, the grid state constraint conditions can be met, thereby avoiding the problem of reduced substation safety factor caused by grid voltage fluctuations due to high RIS power consumption, effectively ensuring grid security.

[0012] Optionally, the S1 includes:

[0013] S11, collecting real-time temperature gradient data and partial discharge data of the device to form the real-time data;

[0014] S12. Assign risk weights according to the types of the equipment, and construct a risk grading and quantitative model for substation equipment based on the risk weights as shown in the following formula:

[0015]

[0016] Among them, the ω i is the device type weight, the α i is the thermal fault sensitivity coefficient, the β i is the discharge fault sensitivity coefficient, is the temperature gradient on the surface of the device, T max The maximum allowable temperature of the device, the PD rms，i is the effective value of partial discharge;

[0017] S13, according to the maintenance record of the equipment, the ω i Make dynamic adjustments:

[0018]

[0019] Wherein, μ is the fault impact factor, N fault is the number of faults within the preset time;

[0020] S14. Obtaining a risk index of the equipment based on the substation equipment risk grading quantitative model and the real-time data.

[0021] Optionally, the S2 includes:

[0022] S21. Arrange multiple radio frequency probes in the smart substation space, and collect the electromagnetic field data through the radio frequency probes;

[0023] S22. Based on the electromagnetic field data, construct the spatial electromagnetic fingerprint using the following formula:

[0024]

[0025] Wherein, (x, y) is the spatial coordinate, and ψ k is the probe weight, which is positively correlated with the signal-to-noise ratio. k are the real part, imaginary part, modulus and phase of the electric field intensity, and the H k are the real part, imaginary part, modulus and phase of the magnetic field intensity.

[0026] Optionally, the S3 includes:

[0027] S31, predicting the signal propagation path according to the spatial electromagnetic fingerprint, distinguishing three scenarios of direct radiation, fixed occlusion, and moving occlusion according to the signal propagation path, and obtaining the propagation occlusion scenario;

[0028] S32: If the propagation shielding scenario is the direct radiation, obtaining the signal transmission compensation mode as reference phase compensation;

[0029] S33: If the propagation obstruction scenario is the fixed obstruction, obtaining the signal transmission compensation mode as diffraction enhancement compensation;

[0030] S34: If the transmission obstruction scenario is the motion obstruction, obtaining the signal transmission compensation mode as dynamic avoidance compensation;

[0031] S35. Adjust the RIS unit according to the signal transmission compensation method, the risk index, and the following compensation formula to generate an initial optimized phase matrix:

[0032]

[0033] Among them, the θ base is the reference phase, and is generated by the spatial electromagnetic fingerprint, the F path is the path attenuation factor, and is obtained from the spatial electromagnetic fingerprint according to the signal transmission compensation method, the R device is the risk index, the d m is the distance from the terminal to the RIS unit.

[0034] Optionally, the F in S35 path Extraction methods include:

[0035] S351: If the propagation occlusion scene is the fixed occlusion, obtain the F according to the following formula: path :

[0036]

[0037] Wherein, L is the number of sampling points of the occlusion path, and F ref The path attenuation factor obtained from the spatial electromagnetic fingerprint when the propagation shielding scene is the direct radiation;

[0038] S352: If the propagation occlusion scene is the fixed occlusion, obtain the F according to the following formula: path :

[0039]

[0040] Wherein, κ is the velocity sensitivity coefficient, and v obj (t) is the real-time speed of the moving object.

[0041] Optionally, the S4 includes:

[0042] S41, obtaining the objective function of the RIS unit as shown in the following formula:

[0043]

[0044] The Θ is the phase control matrix of the RIS unit, the SINR sum is the total signal to interference and noise ratio of the system, the SINR req is the minimum required signal to interference and noise ratio, λ is the energy efficiency weight coefficient, and P RIS is the total power consumption of the RIS unit;

[0045] S42. Obtain the power grid state constraint condition as shown in the following formula:

[0046]

[0047] Among them, the is the total system power consumption of the RIS unit to which the power grid belongs, and the P grid is the power supply capacity of the power grid, 0.3 is the maximum energy consumption ratio threshold, and f dev is the equipment failure risk value, the R th is the risk threshold;

[0048] S43. Optimize the initial optimized phase matrix based on the grid state constraint and the objective function to generate the working phase matrix.

[0049] Optionally, the f in S42 dev Calculated by the following formula:

[0050]

[0051] Wherein, σ is the risk change rate weight.

[0052] Optionally, in S43, the initial optimized phase matrix is ​​optimized by a constrained particle swarm algorithm, and the particle position update formula is:

[0053] Θ k+1 =Θ k +v k+1 ,

[0054] v k+1 =ωv k +c1r1(P best -Θ k )+c2r2(G best -Θ k ),

[0055] Wherein, ω is the inertia weight, c1 and c2 are both learning factors, and r1 and r2 are both random numbers of [0.1].

[0056] In a second aspect, the present invention further provides a RIS-based smart substation dynamic control system, comprising a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement the RIS-based smart substation dynamic control method as described above.

[0057] In a third aspect, the present invention further provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the above-mentioned RIS-based smart substation dynamic control method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be described in further detail below with reference to the accompanying drawings.

[0059] Figure 1 : Flow chart of the dynamic control method of the intelligent substation based on RIS in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to better understand the present invention, the content of the present invention is further clearly set forth below in conjunction with the examples, but the protection content of the present invention is not limited to the following examples. In the following description, a large number of specific details are provided in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.

[0061] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0062] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0063] In a first aspect, an embodiment of the present invention provides a dynamic control method for a smart substation based on RIS, including: S1, collecting real-time data of equipment in the smart substation, assigning risk weights according to the type of equipment, constructing a substation equipment risk grading and quantitative model based on the risk weights, and obtaining a risk index of the equipment based on the substation equipment risk grading and quantitative model and real-time data; S2, collecting electromagnetic field data in the smart substation, and constructing a spatial electromagnetic fingerprint map of the smart substation based on the electromagnetic field data; S3, predicting a signal propagation path according to the spatial electromagnetic fingerprint map, obtaining a propagation obstruction scenario in the smart substation according to the signal propagation path, obtaining a corresponding signal transmission compensation method according to the propagation obstruction scenario, adjusting the RIS unit according to the signal transmission compensation method and the risk index, and generating an initial optimized phase matrix; S4, obtaining grid state constraints, optimizing the initial optimized phase matrix based on the grid state constraints, and generating a working phase matrix of the RIS unit.

[0064] In this embodiment, first, Figure 1 As shown in S1 in , different risk weights are assigned according to different types of equipment, and then it is convenient to build a substation equipment risk grading quantitative model based on the risk weights of different equipment to achieve refined grading of equipment risks. At the same time, real-time data of equipment in the smart substation is collected, and the risk index of the equipment is obtained based on the substation equipment risk grading quantitative model and real-time data, thereby avoiding the problem that traditional risk monitoring relies on a single threshold alarm, cannot quantify the dynamic risk level of the equipment, and has a delayed fault warning, ensuring the accuracy of risk identification, which is conducive to subsequent precise dynamic regulation according to the risk index; then, as Figure 1 As shown in S2 in the figure, the electromagnetic field data in the smart substation is collected, and then the spatial electromagnetic fingerprint of the smart substation is constructed based on the electromagnetic field data. Since the metal structure in the substation causes serious signal multipath effect, the use of the spatial electromagnetic fingerprint can effectively reflect the real-time electromagnetic environment changes, providing a stable support for the subsequent improvement of the signal propagation path prediction accuracy; then, as shown in Figure 1 As shown in S3 in

[15] , the signal propagation path is predicted according to the spatial electromagnetic fingerprint, and then the propagation shielding scenario in the smart substation is obtained according to the signal propagation path. In this way, different signal transmission compensation methods can be selected according to different propagation shielding scenarios. Finally, the RIS unit is adjusted according to the signal transmission compensation method and the risk index to generate the initial optimized phase matrix. This can not only improve the risk failure of the equipment, but also improve the communication delay under dynamic shielding scenarios, effectively improving the communication quality of the smart substation. Finally, as shown in Figure 1As shown in S4, the initial optimized phase matrix is ​​optimized by the obtained grid state constraints, and then the working phase matrix of the RIS unit is generated. Subsequently, the RIS unit is adjusted according to the working phase matrix of the RIS unit. In this way, while ensuring the communication quality of the smart substation, the grid state constraints can be met, thereby avoiding the problem of grid voltage fluctuations caused by high RIS power consumption and reducing the substation safety factor, effectively ensuring the safety of the grid.

[0065] Optionally, S1 includes: S11, collecting real-time temperature gradient data and partial discharge data of the equipment to form real-time data; S12, assigning risk weights according to the type of equipment, and constructing a substation equipment risk grading quantitative model based on the risk weights as shown in the following formula:

[0066]

[0067] Among them, ω i is the device type weight, α i is the thermal fault sensitivity coefficient, β i is the discharge fault sensitivity coefficient, is the surface temperature gradient of the equipment, T max is the maximum allowable temperature of the device, PD rms，i is the effective value of partial discharge; S13, according to the maintenance record of the equipment, based on the following formula i Make dynamic adjustments:

[0068]

[0069] Where μ is the fault impact factor, N fault is the number of failures within a preset time; S14, obtaining the risk index of the equipment based on the substation equipment risk grading quantitative model and real-time data.

[0070] Specifically, for the equipment type weight, in this embodiment, the main transformer takes 0.4, the circuit breaker takes 0.3, and the mutual inductor takes 0.2; the thermal fault sensitivity coefficient and discharge fault sensitivity coefficient of the main transformer are 0.7 and 0.3 respectively; and the fault impact factor of the main transformer is 0.1.

[0071] In this optional embodiment, in the process of risk grading and quantifying each device in the smart substation, the real-time temperature gradient data and partial discharge data of the device are collected to form real-time data, so as to facilitate the dynamic adjustment of the weight according to the real-time changes of the real-time data. At the same time, risk weights are assigned according to the type of equipment, and a substation equipment risk grading and quantification model as shown in formula (1.1) is constructed based on the risk weights. Thus, the real-time change value of the real-time data is used to obtain the real-time risk index of different devices according to the substation equipment risk grading and quantification model, thereby realizing risk prediction. On this basis, according to the maintenance record of the equipment, ω is calculated based on formula (1.2). i Dynamic adjustments are made to avoid the problem that static weights cannot adapt to changes in failure probability caused by equipment aging, so that risk weights can be adaptively modified according to the number of failures, thereby further improving the risk prediction accuracy of the substation equipment risk grading quantitative model.

[0072] Optionally, S2 includes: S21, arranging multiple radio frequency probes in the smart substation space to collect electromagnetic field data through the radio frequency probes; S22, constructing a spatial electromagnetic fingerprint based on the electromagnetic field data using the following formula:

[0073]

[0074] Among them, (x, y) is the spatial coordinate, ψ k is the probe weight, which is positively correlated with the signal-to-noise ratio. k are the real part, imaginary part, modulus and phase of the electric field intensity, H k are the real part, imaginary part, modulus and phase of the magnetic field intensity.

[0075] In this optional embodiment, in order to realize the construction of the spatial electromagnetic fingerprint, multiple radio frequency probes are arranged in the space of the smart substation, and based on the electromagnetic field data, the spatial electromagnetic fingerprint is constructed by formula (2.1), where the weight of the radio frequency probe ψ k It is positively correlated with the signal-to-noise ratio. This can not only improve the completeness of the subsequent spatial electromagnetic fingerprint map construction through multiple RF probes, but also ensure the high signal-to-noise ratio of the RF probe by increasing the weight of the RF probe, effectively improve the spatial resolution of the spatial electromagnetic fingerprint map, avoid the contamination of the overall spatial electromagnetic fingerprint map by low signal-to-noise ratio probe data, reduce positioning errors, and effectively ensure the subsequent path prediction accuracy.

[0076] Optionally, S3 includes: S31, predicting the signal propagation path according to the spatial electromagnetic fingerprint, distinguishing three scenarios of direct radiation, fixed occlusion and mobile occlusion according to the signal propagation path, and obtaining the propagation occlusion scenario; S32, if the propagation occlusion scenario is direct radiation, obtaining the signal transmission compensation method as reference phase compensation; S33, if the propagation occlusion scenario is fixed occlusion, obtaining the signal transmission compensation method as diffraction enhancement compensation; S34, if the propagation occlusion scenario is mobile occlusion, obtaining the signal transmission compensation method as dynamic avoidance compensation; S35, adjusting the RIS unit according to the signal transmission compensation method, the risk index and the following compensation formula to generate an initial optimized phase matrix:

[0077]

[0078] Among them, θ base is the reference phase, and is generated by the spatial electromagnetic fingerprint, F path is the path attenuation factor, and is obtained from the spatial electromagnetic fingerprint according to the signal transmission compensation method, R device is the risk index, d m is the distance from the terminal to the RIS unit.

[0079] In this optional embodiment, during the phase compensation process, the signal propagation path is first predicted based on the spatial electromagnetic fingerprint map, and the propagation obstruction scenario on the signal propagation path is distinguished according to different signal propagation paths to determine whether it belongs to the three types of direct, fixed and mobile obstructions. If the propagation obstruction scenario is direct, the signal transmission compensation method is reference phase compensation, and if the propagation obstruction scenario is fixed, the signal transmission compensation method is diffraction enhancement compensation, and if the propagation obstruction scenario is mobile, the signal transmission compensation method is dynamic avoidance compensation. In this way, different compensation methods are selected according to different propagation obstruction scenarios. Finally, the RIS unit is adjusted according to the signal transmission compensation method, the risk index and formula (3.1) to generate an initial optimized phase matrix. By dynamically adjusting the RIS unit to the initial optimized phase matrix, problems such as high propagation signal attenuation caused by fixed obstruction and instantaneous communication interruption caused by mobile obstruction can be avoided, while reducing the dynamic avoidance response time and effectively ensuring the signal transmission quality.

[0080] Optionally, S35 F path The extraction method includes: S351, if the propagation occlusion scene is a fixed occlusion, obtain F according to the following formula path :

[0081]

[0082] Among them, L is the number of sampling points of the occlusion path, F refis the path attenuation factor obtained from the space electromagnetic fingerprint when the propagation occlusion scene is direct; S352, if the propagation occlusion scene is fixed occlusion, obtain F according to the following formula path :

[0083]

[0084] Among them, κ is the velocity sensitivity coefficient, v obj (t) is the real-time speed of the moving object.

[0085] Specifically, the speed sensitivity coefficient is set to 0.05.

[0086] In this optional embodiment, during the adjustment of the RIS unit according to formula (3.1), the corresponding path attenuation factor is calculated according to different propagation obstruction scenarios, thereby ensuring dynamic adjustment of the RIS unit and ensuring the quality of signal transmission; wherein, if the propagation obstruction scenario is fixed obstruction, the corresponding path attenuation factor can be calculated according to formula (4.1); correspondingly, if the propagation obstruction scenario is dynamic obstruction, the corresponding path attenuation factor can be calculated according to formula (4.2).

[0087] Optionally, S4 includes: S41, obtaining an objective function of the RIS unit as shown in the following formula:

[0088]

[0089] Θ is the phase control matrix of the RIS unit, SINR sum is the total signal-to-interference-and-noise ratio of the system, SINR req is the minimum required signal-to-interference-and-noise ratio, λ is the energy efficiency weight coefficient, P RIS is the total power consumption of the RIS unit; S42, obtain the grid state constraint conditions shown in the following formula:

[0090]

[0091] in, is the total system power consumption of the RIS unit to which the power grid belongs, P grid is the power supply capacity of the power grid, 0.3 is the maximum energy consumption ratio threshold, f dev is the equipment failure risk value, R th is the risk threshold;

[0092] S43. Optimize the initial optimized phase matrix based on the grid state constraints and the objective function to generate a working phase matrix.

[0093] In this optional embodiment, during the generation of the working phase matrix, the initial optimized phase matrix is ​​optimized based on the grid state constraints and the objective function to generate the working phase matrix, thereby improving the signal transmission quality while ensuring the safety of the grid. The objective function of the RIS unit is calculated according to formula (5.1), and then the initial optimized phase matrix is ​​optimized to control the total power consumption of the RIS unit. On this basis, the initial optimized phase matrix is ​​optimized according to the grid state constraints of formula (5.2), so that the total power consumption of the RIS unit meets the conditions for safe operation of the grid, avoiding the RIS operating at full power causing the voltage of the smart substation to exceed the limit, effectively reducing voltage fluctuations, and ensuring the safety of grid operation.

[0094] Optionally, f in S42 dev Calculated by the following formula:

[0095]

[0096] Among them, σ is the risk change rate weight.

[0097] Specifically, the risk change rate weight is 0.2.

[0098] In this optional embodiment, the equipment failure risk value is calculated using Formula 6.1, and the risk change rate weight is used to achieve dynamic prediction of the equipment failure risk value, avoiding the problem of relying solely on the instantaneous risk value and being unable to warn of sudden failures. Early warning is achieved when the risk change rate accelerates, effectively improving the success rate of fault interception.

[0099] Optionally, in S43, the initial optimized phase matrix is ​​optimized by a constrained particle swarm algorithm, and the particle position update formula is:

[0100] Θ k+1 =Θ k +v k+1 (7.1),

[0101] v k+1 =ωv k +c1r1(P best -Θ k )+c2r2(G best -Θ k ) (7.2),

[0102] Where ω is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers between [0.1].

[0103] In this optional embodiment, the initial optimized phase matrix is ​​optimized by the constrained particle swarm algorithm, wherein, Equations (7.1) and (7.2) are used to update the particle positions, effectively improving the convergence speed, improving the optimization gain of the system's total signal-to-interference-noise ratio, and then improving the signal transmission quality, ensuring the comprehensiveness of the optimization, and avoiding the situation where traditional gradient optimization falls into local optimality.

[0104] In a second aspect, an embodiment of the present invention provides a RIS-based smart substation dynamic control system, comprising a memory and a processor; the memory is used to store programs; the processor is used to execute programs to implement the RIS-based smart substation dynamic control method as claimed in any one of claims 1 to 8.

[0105] The technical effects of the RIS-based smart substation dynamic control system in this embodiment are similar to the technical effects of the above-mentioned RIS-based smart substation dynamic control method, and will not be repeated here.

[0106] In a third aspect, an embodiment of the present invention provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned RIS-based smart substation dynamic control method is implemented.

[0107] The technical effect of the computer storage medium in this embodiment is similar to the technical effect of the above-mentioned RIS-based smart substation dynamic control method, and will not be repeated here.

[0108] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A dynamic control method for intelligent substation based on RIS, characterized in that: include: S1. Collect real-time data of equipment in a smart substation, assign risk weights based on the types of the equipment, construct a risk grading and quantitative model for substation equipment based on the risk weights, and obtain a risk index for the equipment based on the risk grading and quantitative model and the real-time data. S2. Collect electromagnetic field data in the smart substation, and construct a spatial electromagnetic fingerprint of the smart substation based on the electromagnetic field data; S3. Predicting a signal propagation path according to the spatial electromagnetic fingerprint, obtaining a propagation shielding scenario in the smart substation according to the signal propagation path, obtaining a corresponding signal transmission compensation method according to the propagation shielding scenario, adjusting a RIS unit according to the signal transmission compensation method and the risk index, and generating an initial optimized phase matrix; S4. Obtain grid state constraints, optimize the initial optimized phase matrix based on the grid state constraints, and generate a working phase matrix of the RIS unit.

2. The RIS-based smart substation dynamic control method according to claim 1, characterized in that: Said S1 comprises: S11, collecting real-time temperature gradient data and partial discharge data of the device to form the real-time data; S12. Assign risk weights according to the types of the equipment, and construct a risk grading and quantitative model for substation equipment based on the risk weights as shown in the following formula: Among them, the ω i is the device type weight, the α i is the thermal fault sensitivity coefficient, the β i is the discharge fault sensitivity coefficient, is the temperature gradient on the surface of the device, T max The maximum allowable temperature of the device, the PD rms，i is the effective value of partial discharge; S13, according to the maintenance record of the equipment, the ω i Make dynamic adjustments: Wherein, μ is the fault impact factor, and N fault is the number of faults within the preset time; S14. Obtaining a risk index of the equipment based on the substation equipment risk grading quantitative model and the real-time data.

3. The RIS-based smart substation dynamic control method according to claim 1, characterized in that: The S2 includes: S21. Arrange multiple radio frequency probes in the smart substation space, and collect the electromagnetic field data through the radio frequency probes; S22. Based on the electromagnetic field data, construct the spatial electromagnetic fingerprint using the following formula: Wherein, (x, y) is the spatial coordinate, and ψ k is the probe weight, which is positively correlated with the signal-to-noise ratio. k are the real part, imaginary part, modulus and phase of the electric field intensity, and the H k are the real part, imaginary part, modulus and phase of the magnetic field intensity.

4. The RIS-based smart substation dynamic control method according to claim 1, characterized in that: The S3 includes: S31, predicting the signal propagation path according to the spatial electromagnetic fingerprint, distinguishing three scenarios of direct radiation, fixed occlusion, and moving occlusion according to the signal propagation path, and obtaining the propagation occlusion scenario; S32: If the propagation blocking scenario is the direct radiation, obtaining the signal transmission compensation mode as reference phase compensation; S33: If the propagation obstruction scenario is the fixed obstruction, obtaining the signal transmission compensation mode as diffraction enhancement compensation; S34: If the transmission obstruction scenario is the motion obstruction, obtaining the signal transmission compensation mode as dynamic avoidance compensation; S35. Adjust the RIS unit according to the signal transmission compensation method, the risk index, and the following compensation formula to generate an initial optimized phase matrix: Among them, the θ base is the reference phase, and is generated by the spatial electromagnetic fingerprint, the F path is the path attenuation factor, and is obtained from the spatial electromagnetic fingerprint according to the signal transmission compensation method, the R device is the risk index, the d m is the distance from the terminal to the RIS unit.

5. The RIS-based smart substation dynamic control method according to claim 4, characterized in that: The S35 F path Extraction methods include: S351: If the propagation occlusion scene is the fixed occlusion, obtain the F according to the following formula: path : Wherein, L is the number of sampling points of the occlusion path, and F ref The path attenuation factor obtained from the spatial electromagnetic fingerprint when the propagation shielding scene is the direct radiation; S352: If the propagation occlusion scene is the fixed occlusion, obtain the F according to the following formula: path : Wherein, κ is the velocity sensitivity coefficient, and v obj (t) is the real-time speed of the moving object.

6. The RIS-based smart substation dynamic control method according to claim 4, characterized in that: The S4 includes: S41, obtaining the objective function of the RIS unit as shown in the following formula: The Θ is the phase control matrix of the RIS unit, the SINR sum is the total signal to interference and noise ratio of the system, the SINR req is the minimum required signal to interference and noise ratio, λ is the energy efficiency weight coefficient, and P RIS is the total power consumption of the RIS unit; S42. Obtain the power grid state constraint condition as shown in the following formula: Among them, the is the total system power consumption of the RIS unit to which the power grid belongs, and the P grid is the power supply capacity of the power grid, 0.3 is the maximum energy consumption ratio threshold, and f dev is the equipment failure risk value, the R th is the risk threshold; S43. Optimize the initial optimized phase matrix based on the grid state constraint and the objective function to generate the working phase matrix.

7. The RIS-based smart substation dynamic control method according to claim 6, characterized in that: The f in S42 dev Calculated by the following formula: Wherein, σ is the risk change rate weight.

8. The RIS-based smart substation dynamic control method according to claim 6, characterized in that: In the step S43, the initial optimized phase matrix is ​​optimized by using the constrained particle swarm algorithm, and the particle position update formula is: I k+1 =Θ k +n k+1 , v k+1 =ωv k +c1r1(P best -I k )+c2r2(G best -I k ), Wherein, ω is the inertia weight, c1 and c2 are both learning factors, and r1 and r2 are both random numbers of [0.1].

9. A RIS-based intelligent substation dynamic control system, characterized in that: The invention comprises a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement the RIS-based smart substation dynamic control method according to any one of claims 1 to 8.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the RIS-based smart substation dynamic control method according to any one of claims 1 to 8 is implemented.

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