Power grid disturbance early warning method and device and storage medium

Abstract

The invention discloses a disturbance early warning method and device for a power grid and a storage medium. The method comprises the steps that original disturbance signals are collected from a power grid micro-area at multiple time points; preprocessing the original disturbance signal to obtain a target disturbance signal; dividing the target disturbance signal into a fuzzy set; fractal clustering is carried out on the target disturbance signal in the fuzzy set so as to construct disturbance characteristics in each clustering level; recognizing the disturbance type of the disturbance characteristic according to the clustering level; generating a disturbance identification result for the power grid microcell according to the disturbance type; and performing early warning operation on the power grid micro-area according to the disturbance identification result. According to the embodiment, on the basis of the fuzzy topology theory and the fractal algorithm, overall and detailed capturing of the power grid micro-area disturbance signals is taken into consideration, high-resolution disturbance analysis is provided in a short time, particularly when the disturbance signals have randomness or localization characteristics, the disturbance signals can be accurately recognized, the disturbance monitoring capacity of a power grid is effectively improved, and the disturbance monitoring accuracy of the power grid is improved. And the overall safety and reliability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a disturbance early warning method, device and storage medium for a power grid. Background Art

[0002] There are many disturbances in the micro-areas of the power grid. In order to ensure the stable and safe operation of the power grid, threshold methods, deep learning and other methods are currently used to detect disturbances in the micro-areas of the power grid.

[0003] However, the power grid environment is complex and highly uncertain. Disturbance signals within a small range are often easily masked by larger signal fluctuations. It is difficult to accurately capture disturbances in micro-areas of the power grid, resulting in low accuracy in identifying disturbances. Summary of the invention

[0004] In view of this, the present invention provides a disturbance early warning method, device and storage medium for a power grid, so as to improve the accuracy of identifying disturbance signals in a micro-area of ​​the power grid.

[0005] A first aspect of the present invention provides a disturbance early warning method for a power grid, comprising:

[0006] Collecting original disturbance signals from power grid micro-areas at multiple time points;

[0007] Preprocessing the original disturbance signal to obtain a target disturbance signal;

[0008] dividing the target disturbance signal into fuzzy sets;

[0009] fractal clustering of the target disturbance signal in the fuzzy set to construct disturbance features in each clustering level;

[0010] Identifying disturbance types of the disturbance features according to the clustering level;

[0011] Generating a disturbance identification result for the power grid micro-area according to the disturbance type;

[0012] An early warning operation is performed on the power grid micro-area according to the disturbance identification result.

[0013] A second aspect of the present invention provides a disturbance warning device for a power grid, comprising:

[0014] A disturbance signal acquisition module is used to collect original disturbance signals from a power grid micro-area at multiple time points;

[0015] A preprocessing module, used for preprocessing the original disturbance signal to obtain a target disturbance signal;

[0016] A fuzzy set partitioning module, used for partitioning the target disturbance signal into fuzzy sets;

[0017] A disturbance feature construction module, used for fractal clustering the target disturbance signal in the fuzzy set to construct disturbance features in each clustering level;

[0018] A disturbance type identification module, used for identifying the disturbance type of the disturbance feature according to the clustering level;

[0019] A disturbance identification result generating module, used for generating a disturbance identification result for the power grid micro-area according to the disturbance type;

[0020] The early warning operation execution module is used to execute the early warning operation on the power grid micro area according to the disturbance identification result.

[0021] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0022] at least one processor; and

[0023] a memory communicatively connected to the at least one processor; wherein,

[0024] 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 disturbance warning method for the power grid as described in the first aspect above.

[0025] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the disturbance warning method for a power grid as described in the first aspect above.

[0026] A fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the disturbance warning method for a power grid as described in the first aspect above is implemented.

[0027] In this embodiment, the original disturbance signal is collected from the power grid micro-area at multiple time points; the original disturbance signal is preprocessed to obtain the target disturbance signal; the target disturbance signal is divided into fuzzy sets; the target disturbance signal is fractally clustered in the fuzzy set to construct disturbance features in the middle layer of each clustering level; the disturbance type is identified based on the clustering level for the disturbance feature; the disturbance identification result is generated for the power grid micro-area based on the disturbance type; and the early warning operation is performed on the power grid micro-area based on the disturbance identification result. This embodiment is based on fuzzy topology theory and fractal algorithm to capture both the overall and detailed disturbance signals of the power grid micro-area, and provide high-resolution disturbance analysis in a short time, especially when the disturbance signal has random or localized characteristics, the disturbance signal can be accurately identified, effectively improving the disturbance monitoring capability of the power grid, and improving the overall safety and reliability of the power grid.

[0028] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 This is a flow chart of a disturbance warning method for a power grid provided in Embodiment 1 of the present invention.

[0031] Figure 2 It is a structural diagram of a disturbance warning device for a power grid provided in Embodiment 2 of the present invention.

[0032] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] See also Figure 1, shows a flow chart of a disturbance warning method for a power grid provided by a first embodiment of the present invention, the method can be executed by a disturbance warning device for a power grid, the disturbance warning device for a power grid can be implemented in the form of hardware and / or software, and the disturbance warning device for a power grid can be configured in an electronic device. Figure 1 As shown, the method includes:

[0037] Step 101: Collect original disturbance signals from a power grid micro-area at multiple time points.

[0038] In this embodiment, monitoring equipment of a power grid micro-area (also called a microgrid) may be called at multiple time points to collect original disturbance signals generated when disturbances occur in the power grid micro-area.

[0039] In a specific implementation, on one hand, the current sensor in the power grid micro-area can be called at multiple time points to collect the current disturbance signal I(t) for the power grid micro-area, wherein t is the sampling time point.

[0040] On the other hand, the voltage sensor in the power grid micro-area can be called at multiple time points to collect the voltage disturbance signal V(t) for the power grid micro-area, wherein t is the sampling time point.

[0041] The current disturbance signal I(t) and the voltage disturbance signal V(t) are aligned to obtain a combined disturbance signal D(t)={I(t), V(t)}; the combined disturbance signal D(t)={I(t), V(t)} includes the current disturbance signal I(t) and the voltage disturbance signal V(t) collected at the same time point.

[0042] The multiple combined disturbance signals are sorted and processed according to the time points to obtain a sequence disturbance signal, which represents the change characteristics of the disturbance signal on the time axis.

[0043] The sequence disturbance signal is expressed as D(T)={D(t 1 ),D(t 2 ),...,D(t n )}, where T is the time period, t 1 ,t 2 ,...,t n Represents each time point in the acquisition time series.

[0044] The sequence disturbance signal D(T) is structured to obtain the original disturbance signal D final .

[0045] Step 102: pre-process the original disturbance signal to obtain a target disturbance signal.

[0046] In this embodiment, one or more preprocessing steps may be performed on the original disturbance signal to obtain a standardized target disturbance signal to improve signal quality.

[0047] In the specific implementation, the original disturbance signal D final Perform abnormal filtering and record it as the original disturbance signal D clean ; Among them, the abnormal filtering process is the filtering threshold range [D min ,D max ] outside the original disturbance signal, retaining the threshold range [D min ,D max ], where D min is the lower limit of the threshold range, D max is the upper limit of the threshold range, which can be an empirical value or set using the 3σ principle.

[0048] If the abnormal filtering is completed, a low-pass filter is used to filter the original disturbance signal D clean Perform noise filtering to eliminate high-frequency noise, which is recorded as the original disturbance signal D filtered .

[0049] If the noise filtering process is completed, the original disturbance signal is normalized to obtain the target disturbance signal D normalized ; Among them, the normalization process is to linearly map the original disturbance signal to the standardized range [D norm_min ,D norm_max ], D norm_min is the lower limit of the standardized range, D norm_max is the upper limit of the standardized range.

[0050] Step 103: Divide the target disturbance signal into fuzzy sets.

[0051] In this embodiment, a fuzzy topology model can be constructed based on fuzzy topology theory, the target disturbance signal is input into the fuzzy topology model, the uncertainty of the target disturbance signal in the power grid micro-area is quantified using the fuzzy membership function, and the target disturbance signal data is divided into different fuzzy sets.

[0052] In a specific implementation, a fuzzy topology model constructed based on fuzzy topology theory may be determined, wherein the fuzzy topology model has multiple fuzzy sets, and each fuzzy set has a central point.

[0053] The target disturbance signal is input into the fuzzy topological model, the fuzzy membership function is constructed, and the membership degree of each target disturbance signal to each fuzzy set is calculated using the fuzzy membership function.

[0054] Among them, the membership degree is:

[0055]

[0056] In the formula, μ(d,C i) is the fuzzy set C to which the target disturbance signal d belongs i The membership degree of ∥dc i ∥ is the target disturbance signal d and the fuzzy set C i The center point c i The distance between (such as Euclidean distance, etc.), σ i is the broadband parameter of the fuzzy set, reflecting the degree of fuzziness of the fuzzy set, exp is the natural exponential function, i = {1, 2, …, m}, m is the number of fuzzy sets.

[0057] Use Min-Max (minimum-maximum) and other methods to calculate the membership of the same target disturbance signal μ(d) = [μ(d, C 1 ),μ(d,C 2 ),…,μ(d,C m )] is normalized so that the sum of the memberships of the same target disturbance signal is 1, that is,

[0058] If the normalization process is completed, the target disturbance signal is divided into the fuzzy set with the largest membership, so that each fuzzy set reflects the uncertainty and fuzziness of the disturbance signal in the power grid micro-area.

[0059] The partitioning process can be expressed as: j ={d∈D normalized |argmax i μ(d,C i )=j}, where j={1,2,……,m}, m is the number of fuzzy sets, indicating that the target disturbance signal d is assigned to the fuzzy set C with the largest membership. j .

[0060] Step 104: fractal clustering is performed on the target disturbance signal in the fuzzy set to construct disturbance features in the middle layer of each clustering level.

[0061] Fractal clustering analysis is introduced in each fuzzy set. The fractal dimension of the target disturbance signal is extracted based on the fractal characteristics of the target disturbance signal in the power grid micro-area. The target disturbance signals in different fuzzy sets are hierarchically analyzed through the fractal clustering algorithm. The similarity of the target disturbance signals is distinguished based on the fractal dimension, and the disturbance feature categories are further divided and the disturbance features with higher similarity are identified.

[0062] In one embodiment of the present invention, step 104 may include the following steps:

[0063] Step 1041: Calculate the fractal dimension of the fuzzy set according to the target disturbance signal.

[0064] For each fuzzy set C j Perform fractal characteristic analysis and calculate the fuzzy set Cj The fractal dimension D of the target disturbance signal f (C j ), fractal dimension characterizes fuzzy set C j Complexity of spatial distribution of internal target disturbance signals.

[0065] Exemplarily, the fractal dimension is:

[0066]

[0067] Among them, D f is the fuzzy set C j The fractal dimension, N ∈ is the fuzzy set C under the scale parameter ∈ j The number of target disturbance signals in , μ(d i ,c j ) is the target disturbance signal d i Fuzzy set C j The membership degree of i -c j ∥ is the target disturbance signal d i With fuzzy set C j The center point c j The distance between them (such as Euclidean distance), α is the fractal scaling coefficient, which is used to adjust the self-similarity characteristics of the target disturbance signal.

[0068] Step 1042: Perform fractal clustering on the target disturbance signal at each clustering level according to the fractal dimension to obtain a fractal clustering model.

[0069] Based on fractal dimension D f (C j ) For each fuzzy set C j The target disturbance signal in is fractal clustered, and the fractal clustering model P of each fuzzy set is constructed by minimizing the square error of the fractal dimension and other objectives. j , distinguish target disturbance signals with different similarities.

[0070] Exemplarily, the fractal clustering model is:

[0071]

[0072] Among them, P j is the fuzzy set C j fractal clustering model, K is the number of clusters, k∈K, |G k | is the clustering level G k The number of target disturbance signals in D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C jThe fractal dimension of ∥dc j ∥ is the target disturbance signal d and the fuzzy set C j The center point c j The distance between j is the fuzzy set C j The bandwidth parameter of argmin is the minimum value.

[0073] Step 1043: In the clustering model, the target disturbance signal closest to the fuzzy set is divided into the same clustering level according to the fractal dimension.

[0074] In the fractal clustering model P j In the above example, the fuzzy set C j The target disturbance signals in are layered according to fractal similarity, and the target disturbance signals with fractal dimensions similar to the fuzzy set are divided into the same clustering level G k , forming a multi-level disturbance feature category structure.

[0075] Exemplarily, the clustering hierarchy is:

[0076]

[0077] Where d∈C j Indicates that the target disturbance signal d belongs to the fuzzy set C j , d'∈G k Indicates that the target disturbance signal d' is not divided into the clustering level G k , D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension, D f (d') is the fractal dimension of the target disturbance signal d', |G k | is the clustering level G k , δ is the stratification threshold, which is used to control the similarity range of the fractal dimension.

[0078] Step 1044: classify the target disturbance signal in the same clustering level to obtain disturbance features.

[0079] For clustering level G k The disturbance feature categories are calibrated, and the features of the target disturbance signal in each clustering level are divided into different disturbance feature categories according to factors such as fractal dimension and similarity, and a subset S of disturbance features with higher similarity is identified within the clustering level. k .

[0080] Exemplarily, the disturbance characteristics are:

[0081]

[0082] Among them, S k is the disturbance feature, d∈G k Indicates that the target disturbance signal d is divided into clustering levels G k , d'∈G k Indicates that the target disturbance signal d' is not divided into the clustering level G k ,μ(d,C j ) is the target disturbance signal d belonging to the fuzzy set C j The membership degree, μ(d',C j ) is the target disturbance signal d' belongs to the fuzzy set C j The membership degree, D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j fractal dimension, β is the fractal sensitivity coefficient, which is used to adjust the influence of fractal dimension on similarity, θ is the similarity threshold, which is used to judge the aggregation degree of disturbance features, and max is the maximum value.

[0083] Step 105: Identify disturbance types based on disturbance features according to the clustering level.

[0084] In this embodiment, the results of the fractal cluster analysis are summarized, and different disturbance features are classified according to a comprehensive disturbance feature model constructed by combining factors such as fractal dimension and membership degree to obtain disturbance types.

[0085] In the specific implementation, each fuzzy set C can be summarized j The fractal clustering analysis results are used to extract each clustering level G k The perturbation feature category in and the subset S of perturbation features with higher similarity k , forming the feature vector:

[0086] F(G k )=[D f (G k ),μ(G k ),σ(G k )]

[0087] Among them, D f (G k ) is the clustering level G k The fractal dimension, μ(G k ) is the membership degree, σ(G k ) is the clustering level G k The similarity threshold.

[0088] Constructing a comprehensive disturbance characteristic model M composite , the feature vector F(G k) are combined to form a comprehensive disturbance feature matrix:

[0089]

[0090] Where n represents the number of clustering levels, M composite It is a comprehensive disturbance feature model, which is used to comprehensively reflect the fractal dimension, membership degree and similarity threshold of each disturbance feature.

[0091] Query the preset disturbance type; the disturbance type is configured with classification dimension and membership degree.

[0092] Match the comprehensive disturbance characteristic model to the disturbance type.

[0093] On the one hand, the first similarity between the fractal dimension corresponding to the clustering level and the fractal dimension corresponding to the disturbance type is calculated, and on the other hand, the second similarity between the membership corresponding to the clustering level and the membership corresponding to the disturbance type is calculated.

[0094] If the first similarity is greater than or equal to the first disturbance threshold, and the second similarity is greater than or equal to the second disturbance threshold, the clustering level is divided into disturbance types.

[0095] The process of classifying disturbance types can be expressed as:

[0096] T p = {G k |sim(D f (G k ),D f (T ref ))≥λ and sim(μ(G k ),μ(T ref ))≥γ}

[0097] Among them, T p is the clustering level G k The disturbance type, T ref is the reference disturbance type, sim is the calculated similarity, D f is the fractal dimension, D f is the membership degree, λ is the first perturbation threshold corresponding to the fractal dimension, and γ is the second perturbation threshold corresponding to the membership degree. The two are combined to identify perturbation categories with higher similarity.

[0098] Step 106: Generate a disturbance identification result for the power grid micro-area according to the disturbance type.

[0099] In this embodiment, the disturbance type of the power grid micro-area is classified and identified using a comprehensive disturbance characteristic model to obtain the disturbance type, and a structured disturbance identification result is constructed based on the disturbance type.

[0100] In the specific implementation, the area covered by each disturbance type is quantified, its spatial distribution range is analyzed, the main impact area and boundary of the disturbance signal are determined, it is clear which areas are mainly affected by the disturbance signal under a specific disturbance type, and the boundary characteristics of the covered area are identified, so as to determine the impact range of the disturbance type in the power grid micro-area.

[0101] The propagation characteristics of the disturbance signal under each disturbance type are analyzed, and the propagation trend of the disturbance type in the time dimension is evaluated based on the diffusion direction of the disturbance type in the affected range to obtain the potential propagation path.

[0102] The disturbance type, impact range and propagation path are used to generate structured disturbance identification results.

[0103] Step 107: Perform early warning operations on the power grid micro-area according to the disturbance identification result.

[0104] In this embodiment, a corresponding warning signal is generated for the disturbance identification result, and the disturbance identification result and the warning signal are fed back to the control system of the power grid to perform a warning operation, so that the power grid management personnel can respond and intervene in real time.

[0105] In this embodiment, the original disturbance signal is collected from the power grid micro-area at multiple time points; the original disturbance signal is preprocessed to obtain the target disturbance signal; the target disturbance signal is divided into fuzzy sets; the target disturbance signal is fractally clustered in the fuzzy set to construct disturbance features in the middle layer of each clustering level; the disturbance type is identified based on the clustering level for the disturbance feature; the disturbance identification result is generated for the power grid micro-area based on the disturbance type; and the early warning operation is performed on the power grid micro-area based on the disturbance identification result. This embodiment is based on fuzzy topology theory and fractal algorithm to capture both the overall and detailed disturbance signals of the power grid micro-area, and provide high-resolution disturbance analysis in a short time, especially when the disturbance signal has random or localized characteristics, the disturbance signal can be accurately identified, effectively improving the disturbance monitoring capability of the power grid, and improving the overall safety and reliability of the power grid.

[0106] Embodiment 2

[0107] See also Figure 2 , shows a schematic diagram of the structure of a disturbance warning device for a power grid provided by Embodiment 2 of the present invention. Figure 2 As shown, the device comprises:

[0108] The disturbance signal acquisition module 201 is used to collect the original disturbance signal of the power grid micro-area at multiple time points;

[0109] A preprocessing module 202 is used to preprocess the original disturbance signal to obtain a target disturbance signal;

[0110] A fuzzy set partitioning module 203 is used to partition the target disturbance signal into fuzzy sets;

[0111] A disturbance feature construction module 204 is used to perform fractal clustering on the target disturbance signal in the fuzzy set to construct disturbance features in each clustering level;

[0112] A disturbance type identification module 205, used for identifying the disturbance type of the disturbance feature according to the clustering level;

[0113] A disturbance identification result generating module 206, configured to generate a disturbance identification result for the power grid micro-area according to the disturbance type;

[0114] The early warning operation execution module 207 is used to execute an early warning operation on the power grid micro-area according to the disturbance identification result.

[0115] In one embodiment of the present invention, the disturbance signal acquisition module 201 is further used for:

[0116] Collect current disturbance signals from power grid micro-areas at multiple time points;

[0117] Collect voltage disturbance signals from power grid micro-areas at multiple time points;

[0118] Aligning the current disturbance signal and the voltage disturbance signal to obtain a combined disturbance signal; the combined disturbance signal includes the current disturbance signal and the voltage disturbance signal collected at the same time point;

[0119] Sorting the plurality of combined disturbance signals according to the time points to obtain a sequence disturbance signal;

[0120] The sequence disturbance signal is subjected to structured processing to obtain an original disturbance signal.

[0121] In one embodiment of the present invention, the preprocessing module 202 is further used for:

[0122] Performing an abnormal filtering process on the original disturbance signal; the abnormal filtering process is filtering out the original disturbance signal outside a threshold range and retaining the original disturbance signal within the threshold range;

[0123] If the abnormal filtering process is completed, performing noise filtering process on the original disturbance signal;

[0124] If the noise filtering process is completed, a normalization process is performed on the original disturbance signal to obtain a target disturbance signal; the normalization process is to map the original disturbance signal to a standardized range.

[0125] In one embodiment of the present invention, the fuzzy set partitioning module 203 is further used for:

[0126] Determine multiple fuzzy sets;

[0127] Calculating the membership degree of each of the target disturbance signals to each of the fuzzy sets;

[0128] Normalizing the membership degree;

[0129] If the normalization process is completed, the target disturbance signal is divided into the fuzzy set with the largest membership degree;

[0130] Wherein, the membership degree is:

[0131]

[0132] Where μ is the membership degree, ∥dc i ∥ is the target disturbance signal d and the center point c of the fuzzy set i The distance between i is the broadband parameter of the fuzzy set.

[0133] In one embodiment of the present invention, the disturbance feature construction module 204 is further used for:

[0134] Calculating the fractal dimension of the fuzzy set according to the target disturbance signal;

[0135] Performing fractal clustering on the target disturbance signal at each clustering level according to the fractal dimension to obtain a fractal clustering model;

[0136] In the clustering model, the target disturbance signal closest to the fuzzy set is divided into the same clustering level according to the fractal dimension;

[0137] The target disturbance signals in the same clustering level are classified to obtain disturbance features.

[0138] In one embodiment of the present invention, the fractal dimension is:

[0139]

[0140] Among them, D f is the fuzzy set C j The fractal dimension, N ∈ is the fuzzy set C under the scale parameter ∈ j The number of target disturbance signals in , μ(d i ,c j ) is the target disturbance signal d i Fuzzy set C j The membership degree of i -c j ∥ is the target disturbance signal di With fuzzy set C j The center point c j The distance between them, α is the fractal scaling factor;

[0141] The fractal clustering model is:

[0142]

[0143] Among them, P j is the fractal clustering model, K is the number of clusters, |G k | is the clustering level G k The number of target disturbance signals in D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension of ∥dc j ∥ is the target disturbance signal d and the fuzzy set C j The center point c j The distance between j is the fuzzy set C j Bandwidth parameters;

[0144] The clustering level is:

[0145]

[0146] Where d∈C j Indicates that the target disturbance signal d belongs to the fuzzy set C j , d'∈G k Indicates that the target disturbance signal d' is not divided into the clustering level G k , D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension, D f (d') is the fractal dimension of the target disturbance signal d', |G k | is the clustering level G k The size of , δ is the stratification threshold;

[0147] The disturbance characteristics are:

[0148]

[0149] Among them, S k is the disturbance characteristic, d∈G k Indicates that the target disturbance signal d is divided into clustering levels G k , d'∈G k Indicates that the target disturbance signal d' is not divided into the clustering level Gk ,μ(d,C j ) is the target disturbance signal d belonging to the fuzzy set C j The membership degree, μ(d',C j ) is the target disturbance signal d' belongs to the fuzzy set C j The membership degree, D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension of is, β is the fractal sensitivity coefficient, and θ is the similarity threshold.

[0150] In one embodiment of the present invention, the disturbance type identification module 205 is further used to:

[0151] Query the preset disturbance type; the disturbance type is configured with classification dimension and membership degree;

[0152] Calculating a first similarity between the fractal dimension corresponding to the clustering level and the fractal dimension corresponding to the disturbance type;

[0153] Calculating a second similarity between the degree of membership corresponding to the clustering level and the degree of membership corresponding to the disturbance type;

[0154] If the first similarity is greater than or equal to a first disturbance threshold, and the second similarity is greater than or equal to a second disturbance threshold, the clustering level is divided into the disturbance type.

[0155] In one embodiment of the present invention, the disturbance identification result generating module 206 is further used for:

[0156] Determining the impact range of the disturbance type in the power grid micro-area;

[0157] In the impact range, based on the diffusion direction of the disturbance type, the propagation trend of the disturbance type in the time dimension is evaluated to obtain a potential propagation path;

[0158] The disturbance type, the impact range and the propagation path are used to generate a structured disturbance identification result.

[0159] The disturbance warning device for a power grid provided in the embodiment of the present invention can execute the disturbance warning method for a power grid provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the disturbance warning method for a power grid.

[0160] Embodiment 3

[0161] See also Figure 3, showing a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), 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 required herein.

[0162] like Figure 3 As shown, the electronic device 10 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 the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0163] A number of components in the electronic device 10 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 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0164] 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 special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a disturbance warning method for a power grid.

[0165] In some embodiments, the disturbance warning method of the power grid 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 the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the disturbance warning method of the power grid described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the disturbance warning method of the power grid in any other appropriate manner (e.g., by means of firmware).

[0166] 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.

[0167] 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.

[0168] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0170] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0171] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0172] Embodiment 4

[0173] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the disturbance warning method for a power grid provided in any embodiment of the present invention is implemented.

[0174] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0175] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0176] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A disturbance early warning method for a power grid, characterized in that: include: Collecting original disturbance signals from power grid micro-areas at multiple time points; Preprocessing the original disturbance signal to obtain a target disturbance signal; dividing the target disturbance signal into fuzzy sets; fractal clustering of the target disturbance signal in the fuzzy set to construct disturbance features in each clustering level; Identifying disturbance types of the disturbance features according to the clustering level; Generating a disturbance identification result for the power grid micro-area according to the disturbance type; An early warning operation is performed on the power grid micro-area according to the disturbance identification result.

2. The method according to claim 1, characterized in that The collecting of original disturbance signals from the power grid micro-area at multiple time points includes: Collect current disturbance signals from power grid micro-areas at multiple time points; Collect voltage disturbance signals from power grid micro-areas at multiple time points; Aligning the current disturbance signal and the voltage disturbance signal to obtain a combined disturbance signal; the combined disturbance signal includes the current disturbance signal and the voltage disturbance signal collected at the same time point; Sorting the plurality of combined disturbance signals according to the time points to obtain a sequence disturbance signal; The sequence disturbance signal is subjected to structured processing to obtain an original disturbance signal.

3. The method according to claim 1, characterized in that The preprocessing of the original disturbance signal to obtain a target disturbance signal includes: Performing an abnormal filtering process on the original disturbance signal; the abnormal filtering process is filtering out the original disturbance signal outside a threshold range and retaining the original disturbance signal within the threshold range; If the abnormal filtering process is completed, performing noise filtering process on the original disturbance signal; If the noise filtering process is completed, a normalization process is performed on the original disturbance signal to obtain a target disturbance signal; the normalization process is to map the original disturbance signal to a standardized range.

4. The method according to claim 1, characterized in that The dividing the target disturbance signal into fuzzy sets comprises: Determine multiple fuzzy sets; Calculating the membership degree of each of the target disturbance signals to each of the fuzzy sets; Normalizing the membership degree; If the normalization process is completed, the target disturbance signal is divided into the fuzzy set with the largest membership degree; Wherein, the membership degree is: Where μ is the membership degree, ∥dc i ∥ is the target disturbance signal d and the center point c of the fuzzy set i The distance between i is the broadband parameter of the fuzzy set.

5. The method according to claim 4, characterized in that The fractal clustering of the target disturbance signal in the fuzzy set to construct disturbance features in each clustering level includes: Calculating the fractal dimension of the fuzzy set according to the target disturbance signal; Performing fractal clustering on the target disturbance signal at each clustering level according to the fractal dimension to obtain a fractal clustering model; In the clustering model, the target disturbance signal closest to the fuzzy set is divided into the same clustering level according to the fractal dimension; The target disturbance signals in the same clustering level are classified to obtain disturbance features.

6. The method according to claim 5, characterized in that The fractal dimension is: Among them, D f is the fuzzy set C j The fractal dimension, N ∈ is the fuzzy set C under the scale parameter ∈ j The number of target disturbance signals in , μ(d i , c j ) is the target disturbance signal d i Fuzzy set C j The membership degree, ||d i -c j || is the target disturbance signal d i With fuzzy set C j The center point c j The distance between them, α is the fractal scaling factor; The fractal clustering model is: Among them, P j is the fractal clustering model, K is the number of clusters, |G k | is the clustering level G k The number of target disturbance signals in D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension of ||dc j || is the target disturbance signal d and the fuzzy set C j The center point c j The distance between j is the fuzzy set C j Bandwidth parameters; The clustering level is: Where d∈C j Indicates that the target disturbance signal d belongs to the fuzzy set C j , d′∈G k Indicates that the target disturbance signal d′ is not classified into the clustering level G k , D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension, D f (d′) is the fractal dimension of the target disturbance signal d′, |G k | is the clustering level G k The size of , δ is the stratification threshold; The disturbance characteristics are: Among them, S k is the disturbance characteristic, d∈G k Indicates that the target disturbance signal d is divided into clustering levels G k , d′∈G k Indicates that the target disturbance signal d′ is not classified into the clustering level G k , μ(d, C j ) is the target disturbance signal d belonging to the fuzzy set C j The membership degree, μ(d′, C j ) is the target disturbance signal d′ belonging to the fuzzy set C j The membership degree, D f (d) is the fractal dimension of the target disturbance signal d, D f (C j ) is a fuzzy set C j The fractal dimension of is, β is the fractal sensitivity coefficient, and θ is the similarity threshold.

7. The method according to claim 5, characterized in that The classifying the disturbance types at the cluster level according to the disturbance characteristics includes: Query the preset disturbance type; the disturbance type is configured with classification dimension and membership degree; Calculating a first similarity between the fractal dimension corresponding to the clustering level and the fractal dimension corresponding to the disturbance type; Calculating a second similarity between the degree of membership corresponding to the clustering level and the degree of membership corresponding to the disturbance type; If the first similarity is greater than or equal to a first disturbance threshold, and the second similarity is greater than or equal to a second disturbance threshold, the clustering level is divided into the disturbance type.

8. The method according to any one of claims 1 to 7, characterized in that Generating a disturbance identification result for the power grid micro-area according to the disturbance type includes: Determining the impact range of the disturbance type in the power grid micro-area; In the impact range, based on the diffusion direction of the disturbance type, the propagation trend of the disturbance type in the time dimension is evaluated to obtain a potential propagation path; The disturbance type, the impact range and the propagation path are used to generate a structured disturbance identification result.

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 disturbance warning method for a power grid according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the disturbance warning method for a power grid according to any one of claims 1 to 8 is implemented.

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