A safe power utilization system based on short circuit and harmonic suppression in wet environment

Abstract

The present application belongs to the technical field of power management, and discloses a safe power utilization system based on short circuit and harmonic treatment in a humid environment; comprising: collecting environmental data and power data of a power system, and preprocessing the environmental data and the power data to obtain a plurality of pieces of processed environmental data and processed power data; calculating a plurality of short circuit risk indexes and harmonic content indexes according to the processed environmental data and the processed power data; constructing a multi-objective optimization model according to the short circuit risk indexes and the harmonic content indexes, and solving a plurality of corresponding optimal power parameters based on the constructed multi-objective optimization model; and applying the solved optimal power parameters to the power system, which can not only significantly improve power utilization safety and reliability, but also reduce energy consumption and economic losses.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity management, and more particularly to a safe electricity system based on short-circuit and harmonic control in a humid environment. Background Art

[0002] The patent application publication number CN115133532A discloses a method, device, equipment, storage medium and storage medium for the management and control of an electric power system; including: obtaining object data of an entity object of an equipment in an electric power system, and obtaining business data in an electric power business system corresponding to the electric power system; constructing a digital twin electric power system model based on the object data and the business data; obtaining system optimization and reconstruction requirements, and generating optimization parameters based on the system optimization and reconstruction requirements, and constructing an optimization simulation environment for the digital twin electric power system model based on the optimization parameters; simulating the digital twin electric power system model based on the optimization simulation environment and obtaining simulation results; feeding back the optimal parameters in the simulation results to the electric power system and the electric power business system to control and process the electric power system and the electric power business system, thereby enabling the management and control of the electric power system.

[0003] However, the existing power consumption system still has significant shortcomings in fully ensuring safety and power quality, especially when dealing with short-circuit risks and harmonic interference in humid environments. For example, in complex industrial environments, equipment aging and environmental factors often lead to short-circuit accidents and harmonic pollution, affecting production efficiency and safety. At the same time, it is difficult to maintain stable operation in a changing environment. For example, the high humidity and high salt spray environment of coastal chemical plants increase safety hazards. Traditional data processing methods are relatively crude and lack the ability to accurately capture data characteristics and changing trends, resulting in the inability to timely detect potential faults in places such as large data centers. In addition, due to the lack of segmented differentiated analysis capabilities, existing systems find it difficult to achieve accurate load forecasting and scheduling in smart grids, nor can they provide personalized power consumption strategies for large complexes. Existing technologies generally lack multi-objective optimization and adaptive capabilities, making it difficult to achieve a balance under multiple factors. These problems have led to energy waste, increased operating costs, and limited the overall performance and safety and reliability of the power system.

[0004] In view of this, the present invention proposes a safe power system based on short circuit and harmonic control in humid environments to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: a safe power system based on short circuit and harmonic control in humid environments, comprising: a data acquisition and processing module, an exponential fitting module, and a strategy optimization module; each module is connected to another by wired and / or wireless means;

[0006] The data acquisition and processing module includes an acquisition unit and a processing unit; the acquisition unit is used to collect environmental data and power data of the power system, and the processing unit is used to pre-process the environmental data and power data to obtain several segments of processed environmental data and processed power data;

[0007] The index fitting module is used to calculate several short circuit risk indices and harmonic content indices based on the processed environmental data and the processed power data;

[0008] The strategy optimization module includes a model construction unit and an optimization solution unit; the model construction unit is used to construct a multi-objective optimization model based on the short-circuit risk index and the harmonic content index; the optimization solution unit solves several corresponding optimal setting power parameters based on the constructed multi-objective optimization model; and the solved optimal setting power parameters are applied to the power system.

[0009] Furthermore, the environmental data includes ambient temperature, ambient humidity and ambient dust concentration; for a seaside environment, the environmental data also includes ambient salt spray concentration; the power data includes voltage, current, insulation resistance, leakage current and load type.

[0010] Furthermore, the method of pre-processing the environmental data and the power data includes:

[0011] The environmental data and power data are organized into several time series in chronological order, and each time series contains all the environmental data and power data at a specific time point; the several time series are combined side by side by columns to construct a two-dimensional data matrix, in which the rows of the two-dimensional data matrix represent time points and the columns represent different environmental data and power data; a column is added to the two-dimensional data matrix to represent the timestamp corresponding to each row, and several columns are added to the two-dimensional data matrix to represent the spatial coordinates corresponding to each row of data to obtain a multi-dimensional data matrix; the multi-dimensional data matrix is ​​standardized to obtain a standard multi-dimensional data matrix; the standard multi-dimensional data matrix is ​​preliminarily analyzed to obtain several multi-dimensional data clusters; the time distribution characteristics and spatial distribution characteristics of the data objects within each multi-dimensional data cluster are obtained; according to the time distribution characteristics and spatial distribution characteristics, each multi-dimensional data cluster is subdivided into several segments; according to the determined segment boundaries, the data objects of each segment are extracted from the standard multi-dimensional data matrix; several segments of processed environmental data and several groups of processed power data are obtained.

[0012] Furthermore, the method of performing preliminary analysis on the standard multi-dimensional data matrix includes:

[0013] Expanding a standard multi-dimensional data matrix into several data vectors by row, treating each data vector as a waveform signal, and constructing several waveform data; convolving the waveform data with Coiflet wavelet basis functions at different scales to obtain wavelet decomposition coefficients of corresponding scales; quantizing the wavelet decomposition coefficients to obtain quantized wavelet decomposition coefficients; and packing all the quantized wavelet decomposition coefficients in a packing order to obtain conversion domain coefficients;

[0014] Extract eigenvalues ​​from the conversion domain coefficients, including energy, entropy, and peak value; construct the extracted eigenvalues ​​into new eigenvectors in the feature space, and the eigenvectors corresponding to all data objects form a feature point cloud in the feature space;

[0015] In the feature space, density estimation is performed on the neighborhood of each feature point to obtain the density estimation value corresponding to each feature point; the density estimation value corresponding to each feature point is normalized to obtain the corresponding density value; a density threshold is preset, and the feature point with a density value greater than the density threshold is used as the center point of the initial multidimensional data cluster; for feature points that are not center points, they are assigned to the multidimensional data cluster where the nearest center point is located to obtain preliminary multidimensional data clusters; all preliminary multidimensional data clusters are post-processed to obtain the final multidimensional data clusters.

[0016] Furthermore, the method of quantizing the wavelet decomposition coefficients includes:

[0017] Define the sparse representation of wavelet decomposition coefficients, that is, wavelet decomposition coefficients in, is the wavelet basis matrix, and α is the sparse coefficient vector to be determined. The sparse representation is reconstructed to obtain the measurement process function. The formula of the measurement process function is: Among them, θ is the measurement matrix and y is the observation value;

[0018] Define the optimization model including the objective function FH and constraint functions;

[0019] FH=min∑w_i×|α(i)|0; the constraint function formula is: Where w_i is the weight of the i-th sparse coefficient vector, α(i) is the i-th sparse coefficient vector; |α(i)|0 is the L0 norm of α(i);

[0020] Solve the optimization model to obtain the optimal sparse coefficients; retain the non-zero elements in the optimal sparse coefficients, and quantize the remaining elements to 0 to obtain the quantized sparse coefficient vector as the quantized wavelet decomposition coefficients;

[0021] The method of solving the optimization model includes:

[0022] Initialize a sparse solution α_0, which takes a full 0 vector or a random vector; calculate the initial residual

[0023] For the kth iteration; calculate the gradient vector in, is the transpose of the wavelet basis matrix, θ T is the transpose of the measurement matrix; r_(k-1) is the residual of the k-1th iteration;

[0024] Threshold the gradient vector to obtain the intermediate solution α'_k. The formula for threshold processing is: α'_k=α_(k-1)+g_k; where α_(k-1) is the sparse solution obtained at the k-1th iteration;

[0025] Keep the k elements with the largest absolute values ​​in α'_k and set the rest to 0 to get a new sparse solution α_k; calculate the new residual based on the new sparse solution α_k The iterative process continues until the preset number of iterations is reached, and the sparse solution obtained last time is output as the optimal sparse coefficient.

[0026] Furthermore, the method of post-processing all preliminary multi-dimensional data groups includes:

[0027] For each preliminary multidimensional data cluster, a corresponding weighted undirected graph G = (V, E) is constructed; V is a set of nodes, and E is a set of edges; each feature point in the preliminary multidimensional data cluster is used as a node of the weighted undirected graph G; the cosine similarity of any two feature points is calculated, and a similarity threshold is preset. If the cosine similarity is greater than the similarity threshold, an edge is connected between the nodes corresponding to the two feature points; the weight of the edge is the cosine similarity between the two feature points;

[0028] Calculate the degree matrix D and adjacency matrix A of the weighted undirected graph G corresponding to the preliminary multi-dimensional data cluster, and calculate the Laplace matrix L = DA;

[0029] Calculate the vectors corresponding to the first K smallest non-zero eigenvalues ​​of the Laplace matrix L, map each feature point in the preliminary multidimensional data cluster to the K-dimensional space composed of these K vectors, and perform K-Means clustering on the feature points in the preliminary multidimensional data cluster in the K-dimensional space, that is, split the preliminary multidimensional data cluster into several subclusters, and all the obtained subclusters are multidimensional data clusters.

[0030] Furthermore, the temporal distribution characteristics and spatial distribution characteristics are obtained by:

[0031] For each data object in a multidimensional data cluster, its timestamp information is extracted and a data time series is constructed; for each data object in a multidimensional data cluster, its spatial coordinate information is extracted; and the spatial distance between data objects is calculated to construct a distance matrix;

[0032] Perform discrete Fourier transform on the data time series to obtain the amplitude spectrum in the frequency domain, and calculate the power spectrum density based on the amplitude spectrum Where N1 is the length of the data time series, X(e) is the complex coefficient of the e-th frequency component obtained by discrete Fourier transform of the data time series, that is, the amplitude spectrum;

[0033] The calculated power spectrum density constitutes a power spectrum density graph. The peak value is found in the power spectrum density graph, and the frequency corresponding to the peak value is regarded as the periodic component, which is the time distribution feature.

[0034] Take any two data objects in the distance matrix as data pairs, and count the distances between all data pairs as the weight parameters between the data pairs;

[0035] Initialize each data object in the distance matrix as a tree containing only itself to form a forest F. Start from the data pair with the smallest weight parameter and traverse all data pairs. If the current data pair belongs to different trees in F, add this data pair to F and merge the two trees into one tree. If the current data pair belongs to the same tree in F, skip this data pair. Repeat until only one tree is left in F. At this time, this tree is recorded as a greedy tree.

[0036] Calculate the topological index of the greedy tree as the spatial distribution feature. The topological index includes the degree and clustering coefficient of each data object.

[0037] The method of subdividing each multi-dimensional data group into a plurality of segments includes:

[0038] The degree threshold and clustering coefficient threshold are preset, and data objects with a degree lower than the degree threshold are marked as potential outliers, and data objects with a clustering coefficient lower than the clustering coefficient threshold are marked as potential outliers; potential outliers are used as segmentation boundaries; the boundaries of the periodic components of the data time series are used as segmentation boundaries; the segmentation boundaries are the dividing lines between different segments, that is, several segments are obtained.

[0039] Furthermore, the calculation method of the short circuit risk index and the harmonic content index includes:

[0040] The standardized data corresponding to the insulation resistance and leakage current in each section of processed power data are input into a pre-built neural network-based assessment model, and a short circuit risk index is output. The short circuit risk index has a value range between 0 and 1.

[0041] The voltage and current in the processed power data are formed into segmented waveforms, namely, voltage waveforms and current waveforms. Fast Fourier transform is performed on the voltage waveforms and current waveforms to obtain spectral components. Based on the spectral components, the ratio of the effective value of the fundamental component to the effective value of all components is calculated, which is the total harmonic content rate. The fundamental component is a special component in the spectral components, which corresponds to the fundamental frequency of the waveform, namely, the lowest frequency component.

[0042] Collect voltage and current waveforms under different load types and working environments within a fixed historical timeframe; manually label each set of voltage and current waveforms with the expected harmonic impact level.

[0043] The total harmonic content rate is extracted from the voltage and current waveforms, and the relevant feature vectors of the environmental data corresponding to the load type and the working environment are extracted at the same time, which are recorded as the load type feature vector and the working environment feature vector. The total harmonic content rate, the load type feature vector, and the working environment feature vector are used as inputs, and the manually labeled expected harmonic impact level is used as the output target. A machine learning model is used to train and obtain a mapping function f; the machine learning model can be a neural network, a decision tree, or a support vector machine. The output of the mapping function f is a harmonic content index in the range of 0-1.

[0044] When the load type is a nonlinear load, during the training process, the loss weight of high-order harmonics is increased, and based on the environmental data of the working environment, it is judged whether the working environment is a harsh working environment; the various types of environmental data are weighted and calculated to obtain the environmental value, and the environmental threshold is preset. The working environment corresponding to the environmental data with an environmental value greater than the environmental threshold is recorded as a harsh working environment; for harsh working environments, during the training process, the threshold value of the expected harmonic impact level is lowered.

[0045] Furthermore, the method of constructing the multi-objective optimization model includes:

[0046] The multi-objective optimization model is defined to consist of the optimization objective function FD(a) and constraints;

[0047] Optimization objective function FD(a)=min[f1(a),f2(a),f3(a)]; where f1(a) represents the short-circuit risk function, and the calculated short-circuit risk index is used as the function value of the short-circuit risk function; f2(a) represents the harmonic content function, and the calculated harmonic content index is used as the function value of the harmonic content function; f3(a) represents the energy consumption function; a is the decision variable vector; the decision variable vector includes insulation level, filter parameters and reactive power compensation; filter parameters include filter resistance and filter inductance;

[0048]

[0049] Among them, k1, k2 and k3 are the corresponding item weights; U is the operating voltage; Rins(a) is the insulation resistance; δ(E) is the environmental correction coefficient, E is the environmental data; I is the effective value of current, R(a) is the filter resistance, ω is the angular frequency; L(a) is the filter inductance, Q(a) is the reactive power compensation amount; H(M) is the compensation method correction coefficient, M is the compensation type;

[0050] The constraints are that the insulation level, filter parameters and reactive power compensation amount are all within the preset corresponding ranges.

[0051] Furthermore, the method for solving the optimal setting of power parameters includes:

[0052] The optimization objective function is recombined into a weighted sum of a single objective function to obtain the single objective optimization function FH(a) = p1×f1(a)+p2×f2(a)+p3×f3(a); where p1, p2, and p3 are the corresponding objective weights;

[0053] Randomly generate m1 initial solutions in the solution space to form an initial population, where m1 is a positive integer greater than 1. Each solution corresponds to a mass point and contains a decision variable vector (insulation level, filter parameters, and reactive compensation amount). Define a gravity function as a single-objective optimization function and calculate the function value of the gravity function for each mass point as the mass of the corresponding mass point.

[0054] Iteration is performed based on the mass of the particle. The iterative process includes:

[0055] The preset black hole threshold is used to treat particles with a mass less than the black hole threshold as black holes; the gravitational force FL(l,j) between any two particles l and j in the initial population is calculated;

[0056] Wherein, GP(t) is the gravitational constant of the tth iteration, R(l,j) is the Euclidean distance between two particles l and j, τ is a constant; m(l) is the mass of particle l, m(j) is the mass of particle j; β1 and β2 are mass indexes, γ1 is distance index;

[0057] In the formula, GP_in is the initial preset gravitational constant, GP_nal is the preset final gravitational constant, and Max_iter is the preset maximum iteration number;

[0058] When there is a black hole among any two particles l and j participating in the calculation, the gravitational constant is replaced by the black hole gravitational constant GBH when the gravitational force FL(l,j) is calculated; the black hole gravitational constant GBH is initially set to several times the gravitational constant; when iteration is performed, Wherein, α1 is an adjustment coefficient, and m_bh is the mass of the black hole;

[0059] In each iteration, a fixed proportion of particles are randomly selected as thrust objects, and for each thrust object J, the size of the thrust acting on it is defined as Wherein, m(J) is the mass of the thrust object J; for each thrust object J, the direction of the thrust acting on it is a random direction; the calculated thrust is superimposed with the gravitational force; and the resultant force FU(J) after superimposing the thrust is obtained.

[0060] The acceleration of the thrust object J is calculated based on the resultant force FU(J) The direction of the acceleration a(J) is the direction of the resultant force; for particles that are not thrust objects, the acceleration is the gravitational force divided by the corresponding mass;

[0061] The speed and position of each particle are updated according to the calculated acceleration;

[0062] The update formula of the speed is:

[0063] v(j,t+1) = rand_j * v(j,t) + a(j,t), wherein rand_j is a random number in the interval [0, 1], v(j,t+1) is the speed of particle j at the (t+1)th iteration, v(j,t) is the speed of particle j at the tth iteration, and a(j,t) is the acceleration of particle j at the tth iteration.

[0064] The update formula of the position is:

[0065] X(j,t+1) = X(j,t) + v(j,t+1); wherein X(j,t+1) is the position of particle j at the (t+1)th iteration, and X(j,t) is the position of particle j at the tth iteration.

[0066] Check whether the updated position exceeds the value range of the decision variable vector, if it exceeds, the corresponding particle is projected back to the solution space;When the preset maximum iteration number Max_iter is reached, the particle with the highest mass at this time is output as the optimal solution;The combination of the insulation level, filter parameter and reactive power compensation amount corresponding to the optimal solution is the optimal power parameter setting.

[0067] The technical effects and advantages of the safety power utilization system based on short circuit and harmonic management in a humid environment of the present application are as follows:

[0068] The present application can comprehensively improve the safety of power utilization and power quality, significantly reduce the risk of short circuit and harmonic interference, has excellent environmental adaptability, can maintain stable and reliable operation in various complex and changeable environments;Through intelligent data processing and analysis, the essence characteristics and change trend of data are captured more accurately, the accuracy and efficiency of data analysis are greatly improved, the system can more sensitively identify potential risks and abnormalities, can more deeply mine data value, provide more accurate risk assessment and decision support;The data of different time periods and spatial characteristics are processed differently, thereby providing more personalized and accurate safety power utilization solutions;The multi-objective optimization capability of the present application enables the system to achieve the best balance among safety, power quality and energy efficiency, and can automatically adjust parameters with environmental changes, always maintain optimal performance, not only can significantly improve the safety and reliability of power utilization, but also can reduce energy consumption and economic losses, create greater economic and social benefits for users. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 A safety power utilization system based on short circuit and harmonic management in a humid environment of the present application is shown in the figure;

[0070] Figure 2 A safety power utilization method based on short circuit and harmonic management in a humid environment of the present application is shown in the figure. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0072] Embodiment 1

[0073] Please refer to Figure 1As shown, the safe power consumption system based on short circuit and harmonic control in humid environments described in this embodiment includes: a data acquisition and processing module, an exponential fitting module and a strategy optimization module; each module is connected by wired and / or wireless means to realize data transmission between modules.

[0074] The data acquisition and processing module includes an acquisition unit and a processing unit; the acquisition unit is used to collect environmental data and power data of the power system, and the processing unit is used to pre-process the environmental data and power data to obtain several segments of processed environmental data and processed power data;

[0075] The index fitting module is used to calculate several short circuit risk indices and harmonic content indices based on the processed environmental data and the processed power data;

[0076] The strategy optimization module includes a model construction unit and an optimization solution unit; the model construction unit is used to construct a multi-objective optimization model based on the short-circuit risk index and the harmonic content index; the optimization solution unit solves several corresponding optimal setting power parameters based on the constructed multi-objective optimization model; and the solved optimal setting power parameters are applied to the power system.

[0077] Furthermore, the environmental data includes ambient temperature, ambient humidity and ambient dust concentration (dust in the environment may be deposited on the insulating parts of electrical equipment, reducing insulation performance and increasing the risk of flashover or insulation breakdown of the equipment); for coastal environments, the environmental data also includes ambient salt spray concentration; power data includes voltage, current, insulation resistance, leakage current and load type; load types include resistive loads (such as electric heaters, heaters, lighting fixtures), inductive loads (equipment containing windings), nonlinear loads (equipment containing power electronic devices), etc.

[0078] It should be noted that the ambient dust concentration is obtained using a laser scattering dust monitor, the insulation resistance is obtained using an insulation resistance tester, and the leakage current is obtained using a clamp-type leakage current sensor.

[0079] Furthermore, the methods for pre-processing the environmental data and the power data include:

[0080] The environmental data and power data are organized into several time series in chronological order, each time series contains all the environmental data and power data at a specific time point; several time series are combined side by side by columns to construct a two-dimensional data matrix, in which the rows of the two-dimensional data matrix represent time points and the columns represent different environmental data and power data; a column is added to the two-dimensional data matrix to represent the timestamp corresponding to each row, and several more columns are added to the two-dimensional data matrix to represent the spatial coordinates corresponding to each row of data to obtain a multi-dimensional data matrix; the multi-dimensional data matrix is ​​standardized to eliminate the dimensional influence of data of different dimensions to obtain a standard multi-dimensional data matrix.

[0081] A preliminary analysis is performed on the standard multi-dimensional data matrix to obtain several multi-dimensional data clusters; specifically, the standard multi-dimensional data matrix is ​​expanded into several data vectors by row, each data vector is regarded as a waveform signal, and several waveform data are constructed.

[0082] Perform waveform conversion on each waveform data to obtain the conversion domain coefficient of each waveform data; specifically, convolve the waveform data with Coifl et wavelet basis functions at different scales to obtain wavelet decomposition coefficients of corresponding scales; quantize the wavelet decomposition coefficients to obtain quantized wavelet decomposition coefficients, and pack all the quantized wavelet decomposition coefficients in a packing order to obtain the conversion domain coefficients; the packing order is level-by-level or tree-based.

[0083] Ways to quantize wavelet decomposition coefficients include:

[0084] Define the sparse representation of wavelet decomposition coefficients, that is, wavelet decomposition coefficients in, is the wavelet basis matrix (a matrix form of a set of basis functions used for wavelet transform), α is the sparse coefficient vector to be determined. Since most wavelet decomposition coefficients are close to zero, α is a sparse coefficient vector. Reconstruct the sparse representation to obtain the measurement process function. The formula of the measurement process function is: Among them, θ is the measurement matrix (the matrix used to measure or observe the original signal), and y is the observation value (the compressed observation data obtained by measuring the wavelet decomposition coefficients through the measurement matrix).

[0085] Define the optimization model including the objective function FH and constraint functions;

[0086] FH=minΣw_i×|α(i)|0; the constraint function formula is: Where w_i is the weight of the i-th sparse coefficient vector, α(i) is the i-th sparse coefficient vector; |α(i)|0 is the L0 norm of α(i).

[0087] Solve the optimization model to obtain the optimal sparse coefficients; retain the non-zero elements in the optimal sparse coefficients, and quantize the remaining elements to 0 to obtain the quantized sparse coefficient vector as the quantized wavelet decomposition coefficients.

[0088] Ways to solve the optimization model include:

[0089] Initialize a sparse solution α_0, which takes a full 0 vector or a random vector; calculate the initial residual For the kth iteration, k is a positive integer greater than 1; calculate the gradient vector in, is the transpose of the wavelet basis matrix, θ T is the transpose of the measurement matrix; r_(k-1) is the residual of the k-1th iteration.

[0090] The gradient vector is thresholded to obtain the intermediate solution α'_k. The formula for thresholding is: α'_k = α_(k-1) + g_k; where α_(k-1) is the sparse solution obtained at the k-1th iteration.

[0091] Keep the k elements with the largest absolute values ​​in α'_k and set the rest to 0 to get the new sparse solution α_k (the sparse solution of the kth iteration); calculate the new residual based on the new sparse solution α_k The iterative process continues until the preset number of iterations is reached, and the sparse solution obtained last time is output as the optimal sparse coefficient.

[0092] Eigenvalues ​​are extracted from the conversion domain coefficients, including energy, entropy and peak value; the extracted eigenvalues ​​are constructed into new eigenvectors in the feature space, and the eigenvectors corresponding to all data objects (each data point or data sample in environmental data and power data) form a feature point cloud (composed of several feature points) in the feature space.

[0093] In the feature space, density estimation is performed on the neighborhood of each feature point (calculated using the eigenvector) to obtain the density estimation value corresponding to each feature point; the density estimation value corresponding to each feature point is normalized to obtain the corresponding density value; a density threshold is preset, and the feature point with a density value greater than the density threshold is used as the center point of the initial multidimensional data cluster; for feature points that are not center points, they are assigned to the multidimensional data cluster where the nearest center point is located to obtain preliminary multidimensional data clusters; all preliminary multidimensional data clusters are post-processed to obtain several final multidimensional data clusters.

[0094] Post-processing methods include:

[0095] For each preliminary multidimensional data cluster, a corresponding weighted undirected graph G = (V, E) is constructed; V is a set consisting of nodes, and E is a set consisting of edges; each feature point in the preliminary multidimensional data cluster is used as a node of the weighted undirected graph G; the cosine similarity of any two feature points is calculated (based on the feature vector), and a similarity threshold is preset. If the cosine similarity is greater than the similarity threshold, an edge is connected between the nodes corresponding to the two feature points; the weight of the edge is the cosine similarity between the two feature points.

[0096] Calculate the degree matrix D and adjacency matrix A of the weighted undirected graph G corresponding to the preliminary multi-dimensional data group, and calculate the Laplace matrix L=DA.

[0097] Calculate the vectors corresponding to the first K smallest non-zero eigenvalues ​​of the Laplace matrix L, map each feature point in the preliminary multidimensional data cluster to the K-dimensional space composed of these K vectors, and perform K-Means clustering on the feature points in the preliminary multidimensional data cluster in the K-dimensional space, that is, split the preliminary multidimensional data cluster into several subclusters, and all the obtained subclusters are multidimensional data clusters.

[0098] Obtain the temporal and spatial distribution characteristics of the data objects within each multidimensional data cluster. Specifically, for each data object within a multidimensional data cluster, extract its timestamp information and construct a data time series. For each data object within a multidimensional data cluster, extract its spatial coordinate information. Calculate the spatial distance between data objects and construct a distance matrix.

[0099] Perform discrete Fourier transform (DFT) on the data time series to obtain the amplitude spectrum in the frequency domain, and calculate the power spectrum density based on the amplitude spectrum Where N1 is the length of the data time series, and X(e) is the complex coefficient of the e-th frequency component obtained by discrete Fourier transform of the data time series, that is, the amplitude spectrum.

[0100] The calculated power spectrum density constitutes a power spectrum density graph. The peak value is found in the power spectrum density graph, and the frequency corresponding to the peak value is regarded as the periodic component, which is the time distribution feature.

[0101] Take any two data objects in the distance matrix as data pairs, count the distances between all data pairs, and use them as weight parameters between the data pairs.

[0102] Initialize each data object in the distance matrix as a tree containing only itself to form a forest F. Start from the data pair with the smallest weight parameter and traverse all data pairs. If the current data pair belongs to different trees in F, add this data pair to F and merge the two trees into one tree. If the current data pair belongs to the same tree in F, skip the data pair. Repeat until only one tree is left in F. At this time, this tree is recorded as a greedy tree.

[0103] The topological index of the greedy tree is calculated as the spatial distribution feature. The topological index includes the degree of each data object (the number of connected edges) and the clustering coefficient (the connection degree value between neighboring data objects).

[0104] According to the temporal distribution characteristics and spatial distribution characteristics, each multidimensional data cluster is subdivided into several segments; specifically, a degree threshold and a clustering coefficient threshold are preset (set by multiple experimental fittings), and data objects with a degree lower than the degree threshold are marked as potential outliers, and data objects with a clustering coefficient lower than the clustering coefficient threshold are marked as potential outliers; potential outliers are used as segment boundaries; the boundaries of the periodic components of the data time series are used as segment boundaries; the segment boundaries are the dividing lines between different segments, that is, several segments are obtained.

[0105] According to the determined segment boundaries, the data objects of each segment are extracted from the standard multi-dimensional data matrix; and several segments of processed environmental data and several groups of processed power data are obtained.

[0106] Segmentation can better capture the essential characteristics of data and provide high-quality segmented data input for subsequent risk assessment and optimization decisions.

[0107] Furthermore, the calculation methods of the short circuit risk index and the harmonic content index include:

[0108] The standardized data corresponding to the insulation resistance and leakage current in each section of processed power data are obtained; each section of processed environmental data and the extracted standardized data corresponding to the insulation resistance and leakage current are input into a pre-built neural network-based assessment model to output a short-circuit risk index. The value range of the short-circuit risk index is between 0 and 1, and the larger the value, the higher the short-circuit risk.

[0109] The evaluation model is constructed by:

[0110] An artificial neural network is used as the skeleton structure of the evaluation model. The artificial neural network includes an input layer, a hidden layer and an output layer. The input layer has 5 neurons, corresponding to the standardized data corresponding to the insulation resistance and leakage current and the data type corresponding to the processed environmental data respectively. The hidden layer has 10 neurons, and the activation function adopts the Sigmod function. The output layer has 1 neuron, corresponding to the short-circuit risk index Risk, and its value range is [0, 1]. The collected training data includes known environmental data, power data and corresponding short-circuit risk labels. The back-propagation algorithm is used to train the artificial neural network to minimize the mean square error between the output and the short-circuit risk label. The constructed evaluation model is obtained.

[0111] After processing, the voltage and current in the power data are formed into segmented waveforms; namely, voltage waveforms and current waveforms; fast Fourier transform (FFT) is performed on the voltage waveforms and current waveforms to obtain spectral components; and based on the spectral components, the ratio of the effective value of the fundamental component to the effective value of all components is calculated, which is the total harmonic content rate; the fundamental component is a special component in the spectral components, which corresponds to the fundamental frequency of the waveform, that is, the lowest frequency component.

[0112] At a fixed historical time, voltage and current waveforms are collected under different load types (resistive, inductive, nonlinear) and working environments (temperature, humidity, dust, salt spray, etc.). The expected harmonic impact level (e.g., 1-5) corresponding to each set of voltage and current waveforms is manually labeled.

[0113] The total harmonic content rate is extracted from the voltage waveform and the current waveform, and the relevant feature vectors of the environmental data corresponding to the load type and the working environment are extracted at the same time, and recorded as the load type feature vector and the working environment feature vector.

[0114] The total harmonic content rate, load type feature vector, and working environment feature vector are taken as input, and the manually labeled expected harmonic impact level is taken as the output target. A machine learning model is used for training to obtain the mapping function f; the machine learning model is a neural network, a decision tree, or a support vector machine; the output of the mapping function f is the harmonic content index in the range of 0-1.

[0115] When the load type is a nonlinear load, during the training process, the loss weight of high-order harmonics is increased to make the mapping function more sensitive to high-order harmonics; based on the environmental data of the working environment, it is judged whether the working environment is a harsh working environment; specifically, the various types of environmental data are weighted and calculated to obtain the environmental value, and the environmental threshold is preset. The working environment corresponding to the environmental data with an environmental value greater than the environmental threshold is recorded as a harsh working environment.

[0116] For harsh working environments, during the training process, the threshold value of the expected harmonic impact level (the dividing value used when mapping the total harmonic content rate to the harmonic impact level) is lowered. The reduction strategy is to reduce the multiple, and the multiple is determined according to the actual situation, so that the mapping function outputs a higher harmonic content index at the same total harmonic content rate.

[0117] Ways to build a multi-objective optimization model include:

[0118] The multi-objective optimization model is defined to consist of the optimization objective function FD(a) and constraints;

[0119] The optimization objective function FD(a)=min[f1(a),f2(a),f3(a)]; wherein, f1(a) represents the short-circuit risk function, and the calculated short-circuit risk index is used as the function value of the short-circuit risk function; f2(a) represents the harmonic content function, and the calculated harmonic content index is used as the function value of the harmonic content function; f3(a) represents the energy consumption function; a is the decision variable vector; the decision variable vector includes insulation level, filter parameters and reactive power compensation; the filter parameters include filter resistance and filter inductance.

[0120]

[0121] Among them, k1, k2 and k3 are the corresponding item weights, all of which are constants; U is the operating voltage; Rins(a) is the insulation resistance, which depends on the insulation grade; δ(E) is the environmental correction factor, and E is the environmental data; I is the effective value of the current, R(a) is the filter resistance, and ω is the angular frequency; L(a) is the filter inductance, and Q(a) is the reactive power compensation amount; H(M) is the compensation method correction factor, and M is the compensation type (such as capacitor compensation, reactive power shunt compensator, synchronous motor reactive compensation, reactive generator compensation, etc.).

[0122] The environmental correction coefficient δ(E) is obtained by weighted summing the various types of environmental data using an exponential function. The exponential function can better characterize the nonlinear effect of environmental factors on insulation loss.

[0123] The compensation method correction coefficient H(M) is obtained by weighted summing the indicator functions of different compensation methods. The indicator function takes a value of 1 when the condition is met and takes a value of 0 otherwise.

[0124] The constraints are that the insulation level, filter parameters and reactive power compensation amount are all within the preset corresponding ranges.

[0125] Furthermore, the solution for optimal power parameter setting includes:

[0126] The optimization objective function is recombined into a single objective function weighted sum, obtaining a single objective optimization function FH(a)=p1xf1(a)+p2xf2(a)+p3xf3(a); wherein p1, p2 and p3 are corresponding target weights; f1(a), f2(a) and f3(a) are taken as a single objective function.

[0127] m1 initial solutions are randomly generated in the solution space (data space where insulation levels, filter parameters and reactive power compensation amounts are located) to form an initial population, and m1 is a positive integer greater than 1.

[0128] Each solution corresponds to a particle; contains a decision variable vector (insulation level, filter parameter and reactive compensation amount); defines a gravitational function, defines the gravitational function as a single objective optimization function, and calculates the function value of the gravitational function of each particle as the mass of the corresponding particle.

[0129] Iterate based on the mass of the particle, and the iteration process includes:

[0130] The preset black hole threshold value is used to regard the particle with a mass less than the black hole threshold value as a black hole; and the gravitational force FL(l,j) between any two particles l and j in the initial population is calculated.

[0131] Wherein, GP(t) is the gravitational constant of the tth iteration, R(l,j) is the Euclidean distance between the two particles l and j, τ is a very small constant to avoid the denominator being 0; m(l) is the mass of the particle l, m(j) is the mass of the particle j; β1 and β2 are mass indexes for adjusting the influence degree of mass on gravity, γ1 is a distance index for adjusting the influence degree of distance on gravity.

[0132] In the formula, GP_in is the initial preset gravitational constant, GP_nal is the preset final gravitational constant, and Max_iter is the preset maximum iteration number.

[0133] When there is a particle acting as a black hole in any two particles l and j participating in the calculation, the gravitational constant is replaced by the black hole gravitational constant GBH when calculating the gravitational force FL(l,j); the black hole gravitational constant GBH is initially set to several times (for example, 10 times) of the gravitational constant; when iterating, Wherein, α1 is an adjustment coefficient, and m_bh is the mass of the black hole.

[0134] In each iteration, a fixed proportion of particles are randomly selected as thrust objects, and for each thrust object J, the size of the thrust acting on it is defined as Where m(J) is the mass of the thrust object J. For each thrust object J, the thrust direction acting on it is defined as a random direction. The calculated thrust is superimposed on the gravity to obtain the resultant force FU(J).

[0135] Calculate the acceleration of the thrust object J based on the resultant force FU(J) The direction of acceleration a(J) is the direction of the resultant force; for a point mass that is not the object of the thrust, its acceleration is the gravitational force divided by the corresponding mass;

[0136] Update the velocity and position of each particle based on the calculated acceleration.

[0137] The update formula of speed is:

[0138] v(j,t+1)=rand_j×v(j,t)+a(j,t), where rand_j is a random number in the interval [0,1], v(j,t+1) is the velocity of particle j at the t+1th iteration, v(j,t) is the velocity of particle j at the tth iteration, and a(j,t) is the acceleration of particle j at the tth iteration;

[0139] The update formula for position is:

[0140] X(j,t+1)=X(j,t)+v(j,t+1); where X(j,t+1) is the position of particle j at the t+1th iteration, and X(j,t) is the position of particle j at the tth iteration.

[0141] Check whether the updated position exceeds the value range of the decision variable vector. If so, project the corresponding particle back to the solution space. When the preset maximum number of iterations Max_iter is reached, output the particle with the highest mass at this time as the optimal solution. The combination of insulation level, filter parameters and reactive power compensation corresponding to the optimal solution is the optimal setting power parameter.

[0142] This embodiment can comprehensively improve electricity safety and power quality, significantly reduce short-circuit risks and harmonic interference, has excellent environmental adaptability, and can maintain stable and reliable operation in various complex and changing environments; through intelligent data processing and analysis, it can more accurately capture the essential characteristics and changing trends of data, and segmentation greatly improves the accuracy and efficiency of data analysis, enabling the system to more keenly identify potential risks and anomalies, and to more deeply explore the value of data, providing more accurate risk assessment and decision support; differentiated processing is performed on data with different time periods and spatial characteristics, thereby providing more personalized and accurate safe electricity solutions; its multi-objective optimization capability enables the system to achieve the best balance between safety, power quality and energy efficiency, and can automatically adjust parameters as the environment changes to always maintain optimal performance, which can not only significantly improve electricity safety and reliability, but also reduce energy consumption, reduce economic losses, and create greater economic and social benefits for users.

[0143] Example 2

[0144] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A safe electricity use method based on short circuit and harmonic control in a humid environment is provided, including:

[0145] S1. Collect environmental data and power data of the power system, pre-process the environmental data and power data to obtain several segments of processed environmental data and processed power data;

[0146] S2. Calculate several short circuit risk indices and harmonic content indices based on the processed environmental data and the processed power data;

[0147] S3. Construct a multi-objective optimization model based on the short-circuit risk index and the harmonic content index. Based on the constructed multi-objective optimization model, solve the corresponding optimal power setting parameters; apply the solved optimal power setting parameters to the power system to achieve safe electricity use.

[0148] Example 3

[0149] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for safe electricity use based on short-circuit and harmonic control in a humid environment is realized.

[0150] Since the electronic device introduced in this embodiment is an electronic device adopted by the embodiment of this application to implement a method for safe use of electricity based on short circuit and harmonic control in a humid environment, based on the method for safe use of electricity based on short circuit and harmonic control in a humid environment introduced in the embodiment of this application, those skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as those skilled in the art implement the electronic device adopted by the method for safe use of electricity based on short circuit and harmonic control in a humid environment in the embodiment of this application, they are all within the scope of protection to be provided by this application.

[0151] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0152] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A safe power system based on short circuit and harmonic control in humid environment, characterized by: include: Data acquisition and processing module, index fitting module and strategy optimization module; The data acquisition and processing module includes an acquisition unit and a processing unit; The acquisition unit is used to collect environmental data and power data of the power system. Environmental data includes ambient temperature, ambient humidity, and ambient dust concentration. For coastal environments, environmental data also includes ambient salt spray concentration. Power data includes voltage, current, insulation resistance, leakage current, and load type. The processing unit is used to pre-process the environmental data and power data, including: The environmental data and power data are organized into several time series in chronological order; the several time series are combined side by side by columns to construct a two-dimensional data matrix; a column is added to the two-dimensional data matrix to represent the timestamp corresponding to each row, and several more columns are added to the two-dimensional data matrix to represent the spatial coordinates corresponding to each row of data to obtain a multi-dimensional data matrix; the multi-dimensional data matrix is ​​standardized to obtain a standard multi-dimensional data matrix; the standard multi-dimensional data matrix is ​​preliminarily analyzed, including: Expand the standard multi-dimensional data matrix into several data vectors by row to construct several waveform data; convolve the waveform data with the Coiflet wavelet basis function at different scales and quantize them to obtain quantized wavelet decomposition coefficients; pack the quantized wavelet decomposition coefficients in a packing order to obtain conversion domain coefficients; Extract eigenvalues ​​from the conversion domain coefficients; construct the eigenvalues ​​into new eigenvectors in the feature space, and the eigenvectors corresponding to all data objects form a feature point cloud in the feature space; In the feature space, density estimation is performed on the neighborhood of each feature point to obtain the density estimation value corresponding to each feature point; and normalization is performed to obtain the corresponding density value; a density threshold is preset, and feature points with density values ​​greater than the density threshold are used as the center points of the initial multidimensional data cluster; for feature points that are not center points, they are assigned to the multidimensional data cluster where the nearest center point is located to obtain preliminary multidimensional data clusters; all preliminary multidimensional data clusters are post-processed to obtain several multidimensional data clusters; Obtaining the temporal and spatial distribution characteristics of the data objects within each multidimensional data cluster; subdividing each multidimensional data cluster into a number of segments based on the temporal and spatial distribution characteristics; extracting the data objects of each segment from the standard multidimensional data matrix based on the determined segment boundaries; and obtaining several segments of processed environmental data and several groups of processed power data; The index fitting module is used to calculate several short circuit risk indices and harmonic content indices based on the processed environmental data and the processed power data; The strategy optimization module includes a model construction unit and an optimization solution unit; the model construction unit is used to construct a multi-objective optimization model based on the short-circuit risk index and the harmonic content index; the optimization solution unit solves several corresponding optimal setting power parameters based on the constructed multi-objective optimization model; and the solved optimal setting power parameters are applied to the power system.

2. A safe power system based on short circuit and harmonic control in humid environments according to claim 1, characterized in that: The method of quantizing the wavelet decomposition coefficients includes: Define the sparse representation of wavelet decomposition coefficients, that is, wavelet decomposition coefficients in, is the wavelet basis matrix, and α is the sparse coefficient vector to be determined. The sparse representation is reconstructed to obtain the measurement process function. The formula of the measurement process function is: Among them, θ is the measurement matrix and y is the observation value; Define the optimization model including the objective function FH and constraint functions; FH=min∑w_i×|α(i)|0; the constraint function formula is: Where w_i is the weight of the i-th sparse coefficient vector, α(i) is the i-th sparse coefficient vector; |α(i)|0 is the L0 norm of α(i); Solve the optimization model to obtain the optimal sparse coefficients; retain the non-zero elements in the optimal sparse coefficients, and quantize the remaining elements to 0 to obtain the quantized sparse coefficient vector as the quantized wavelet decomposition coefficients; The method of solving the optimization model includes: Initialize a sparse solution α_0, which takes a full 0 vector or a random vector; calculate the initial residual For the kth iteration; calculate the gradient vector in, is the transpose of the wavelet basis matrix, θ T is the transpose of the measurement matrix; r_(k-1) is the residual of the k-1th iteration; Threshold the gradient vector to obtain the intermediate solution α'_k. The formula for threshold processing is: α'_k=α_(k-1)+g_k; where α_(k-1) is the sparse solution obtained at the k-1th iteration; Keep the k elements with the largest absolute values ​​in α'_k and set the rest to 0 to get a new sparse solution α_k; calculate the new residual based on the new sparse solution α_k The iterative process continues until the preset number of iterations is reached, and the sparse solution obtained last time is output as the optimal sparse coefficient.

3. A safe power system based on short circuit and harmonic control in humid environment according to claim 2, characterized in that: The method of post-processing all preliminary multi-dimensional data groups includes: For each preliminary multidimensional data cluster, a corresponding weighted undirected graph G = (V, E) is constructed; V is a set of nodes, and E is a set of edges; each feature point in the preliminary multidimensional data cluster is used as a node of the weighted undirected graph G; the cosine similarity of any two feature points is calculated, and a similarity threshold is preset. If the cosine similarity is greater than the similarity threshold, an edge is connected between the nodes corresponding to the two feature points; the weight of the edge is the cosine similarity between the two feature points; Calculate the degree matrix D and adjacency matrix A of the weighted undirected graph G corresponding to the preliminary multi-dimensional data cluster, and calculate the Laplace matrix L = DA; Calculate the vectors corresponding to the first K smallest non-zero eigenvalues ​​of the Laplace matrix L, map each feature point in the preliminary multidimensional data cluster to the K-dimensional space composed of these K vectors, and perform K-Means clustering on the feature points in the preliminary multidimensional data cluster in the K-dimensional space, that is, split the preliminary multidimensional data cluster into several subclusters, and all the obtained subclusters are multidimensional data clusters.

4. A safe power system based on short circuit and harmonic control in humid environments according to claim 3, characterized in that: The acquisition method of the temporal distribution characteristics and the spatial distribution characteristics includes: For each data object in a multidimensional data cluster, its timestamp information is extracted and a data time series is constructed; for each data object in a multidimensional data cluster, its spatial coordinate information is extracted; and the spatial distance between data objects is calculated to construct a distance matrix; Perform discrete Fourier transform on the data time series to obtain the amplitude spectrum in the frequency domain, and calculate the power spectrum density based on the amplitude spectrum Where N1 is the length of the data time series, X(e) is the complex coefficient of the e-th frequency component obtained by discrete Fourier transform of the data time series, that is, the amplitude spectrum; The calculated power spectrum density constitutes a power spectrum density graph. The peak value is found in the power spectrum density graph, and the frequency corresponding to the peak value is regarded as the periodic component, which is the time distribution feature. Take any two data objects in the distance matrix as data pairs, and count the distances between all data pairs as the weight parameters between the data pairs; Initialize each data object in the distance matrix as a tree containing only itself to form a forest F. Start from the data pair with the smallest weight parameter and traverse all data pairs. If the current data pair belongs to different trees in F, add this data pair to F and merge the two trees into one tree. If the current data pair belongs to the same tree in F, skip this data pair. Repeat until only one tree is left in F. At this time, this tree is recorded as a greedy tree. Calculate the topological index of the greedy tree as the spatial distribution feature. The topological index includes the degree and clustering coefficient of each data object. The method of subdividing each multi-dimensional data group into a plurality of segments includes: The degree threshold and clustering coefficient threshold are preset, and data objects with a degree lower than the degree threshold are marked as potential outliers, and data objects with a clustering coefficient lower than the clustering coefficient threshold are marked as potential outliers; potential outliers are used as segmentation boundaries; the boundaries of the periodic components of the data time series are used as segmentation boundaries; the segmentation boundaries are the dividing lines between different segments, that is, several segments are obtained.

5. A safe power system based on short circuit and harmonic control in humid environments according to claim 4, characterized in that: The calculation method of the short circuit risk index and the harmonic content index includes: The standardized data corresponding to the insulation resistance and leakage current in each section of processed power data are input into a pre-built neural network-based assessment model, and a short circuit risk index is output. The short circuit risk index has a value range between 0 and 1. The voltage and current in the processed power data are formed into segmented waveforms, namely, voltage waveforms and current waveforms. Fast Fourier transform is performed on the voltage waveforms and current waveforms to obtain spectral components. Based on the spectral components, the ratio of the effective value of the fundamental component to the effective value of all components is calculated, which is the total harmonic content rate. The fundamental component is a special component in the spectral components, which corresponds to the fundamental frequency of the waveform, namely, the lowest frequency component. Collect voltage and current waveforms under different load types and working environments within a fixed historical timeframe; manually label each set of voltage and current waveforms with the expected harmonic impact level. The total harmonic content rate is extracted from the voltage and current waveforms, and the relevant feature vectors of the environmental data corresponding to the load type and the working environment are extracted at the same time, which are recorded as the load type feature vector and the working environment feature vector. The total harmonic content rate, the load type feature vector, and the working environment feature vector are used as inputs, and the manually labeled expected harmonic impact level is used as the output target. A machine learning model is used to train and obtain a mapping function f; the machine learning model can be a neural network, a decision tree, or a support vector machine. The output of the mapping function f is a harmonic content index in the range of 0-1. When the load type is a nonlinear load, during the training process, the loss weight of high-order harmonics is increased, and based on the environmental data of the working environment, it is judged whether the working environment is a harsh working environment; the various types of environmental data are weighted and calculated to obtain the environmental value, and the environmental threshold is preset. The working environment corresponding to the environmental data with an environmental value greater than the environmental threshold is recorded as a harsh working environment; for harsh working environments, during the training process, the threshold value of the expected harmonic impact level is lowered.

6. A safe power system based on short circuit and harmonic control in humid environments according to claim 5, characterized in that: The method of constructing the multi-objective optimization model includes: The multi-objective optimization model is defined to consist of the optimization objective function FD(a) and constraints; Optimization objective function FD(a)=min[f1(a),f2(a),f3(a)]; where f1(a) represents the short-circuit risk function, and the calculated short-circuit risk index is used as the function value of the short-circuit risk function; f2(a) represents the harmonic content function, and the calculated harmonic content index is used as the function value of the harmonic content function; f3(a) represents the energy consumption function; a is the decision variable vector; the decision variable vector includes insulation level, filter parameters and reactive power compensation; filter parameters include filter resistance and filter inductance; Among them, k1, k2 and k3 are the corresponding item weights; U is the operating voltage; Rins(a) is the insulation resistance; δ(E) is the environmental correction coefficient, E is the environmental data; I is the effective value of current, R(a) is the filter resistance, ω is the angular frequency; L(a) is the filter inductance, Q(a) is the reactive power compensation amount; H(M) is the compensation method correction coefficient, M is the compensation type; The constraints are that the insulation level, filter parameters and reactive power compensation amount are all within the preset corresponding ranges.

7. A safe power system based on short circuit and harmonic control in humid environments according to claim 6, characterized in that: The method for solving the optimal power parameter setting includes: The optimization objective function is recombined into a weighted sum of a single objective function to obtain the single objective optimization function FH(a) = p1×f1(a)+p2×f2(a)+p3×f3(a); where p1, p2, and p3 are the corresponding objective weights; Randomly generate m1 initial solutions in the solution space to form an initial population, where m1 is a positive integer greater than 1. Each solution corresponds to a mass point and contains a decision variable vector (insulation level, filter parameters, and reactive compensation amount). Define a gravity function as a single-objective optimization function and calculate the function value of the gravity function for each mass point as the mass of the corresponding mass point. Iteration is performed based on the mass of the particle. The iterative process includes: The preset black hole threshold is used to treat particles with a mass less than the black hole threshold as black holes; the gravitational force FL(l,j) between any two particles l and j in the initial population is calculated; Where GP(t) is the gravitational constant of the t-th iteration, R(l,j) is the Euclidean distance between two particles l and j, τ is a constant; m(l) is the mass of particle l, m(j) is the mass of particle j; β1 and β2 are mass exponents, and γ1 is the distance exponent; Where GP_in is the initial preset gravitational constant, GP_nal is the preset final gravitational constant, and Max_iter is the preset maximum number of iterations; When there is a black hole particle among any two particles l and j involved in the calculation, the gravitational constant is replaced by the black hole gravitational constant GBH when calculating the gravitational force FL(l,j); the black hole gravitational constant GBH is initially set to several times the gravitational constant; when iterating, Among them, α1 is the adjustment coefficient, m_bh is the mass of the black hole; In each iteration, a fixed proportion of mass points are randomly selected as thrust objects. For each thrust object J, the magnitude of the thrust acting on it is defined as Where m(J) is the mass of the thrust object J. For each thrust object J, the thrust direction acting on it is defined as a random direction. The calculated thrust is superimposed with the gravity to obtain the resultant force FU(J). Calculate the acceleration of the thrust object J based on the resultant force FU(J) The direction of acceleration a(J) is the direction of the resultant force; for a point mass that is not the object of the thrust, its acceleration is the gravitational force divided by the corresponding mass; Update the velocity and position of each particle based on the calculated acceleration; The speed update formula is: v(j,t+1)=rand_j×v(j,t)+a(j,t), where rand_j is a random number in the interval [0,1], v(j,t+1) is the velocity of particle j at the t+1th iteration, v(j,t) is the velocity of particle j at the tth iteration, and a(j,t) is the acceleration of particle j at the tth iteration; The update formula for position is: X(j,t+1)=X(j,t)+v(j,t+1); where X(j,t+1) is the position of particle j at iteration t+1, and X(j,t) is the position of particle j at iteration t; Check whether the updated position exceeds the value range of the decision variable vector. If so, project the corresponding particle back to the solution space. When the preset maximum number of iterations Max_iter is reached, output the particle with the highest mass at this time as the optimal solution. The combination of insulation level, filter parameters and reactive power compensation corresponding to the optimal solution is the optimal setting power parameter.

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