Distribution network carrying capacity assessment method and system for large-scale vehicle charging loads

Abstract

A method and system for evaluating the carrying capacity of a distribution network for large-scale automobile charging loads. First, by combining a variational autoencoder and a polynomial fitting method, efficient data expansion is achieved, effectively increasing the number of training samples and reducing the risk of overfitting. Then, an adaptive biological influence optimization method is used to improve stability in complex training environments, improve the accuracy of feature extraction and training efficiency. Then, an autoencoder neural network with adaptive feature refinement is used to finely capture and reconstruct data, optimize information flow, reduce dimensionality while retaining important information. Then, an adaptive error compensation mechanism of a kernel extreme learning machine is used to adjust the error learning rate and weight update in real time, thereby improving the accuracy and robustness of the model. Through the above optimization training, the model can fully grasp the characteristics of the charging load data and realize accurate evaluation of the carrying capacity of the distribution network through the final category of the output. Therefore, the accuracy of the present invention is relatively high.

Description

Technical Field

[0001] The present invention relates to a means for evaluating the carrying capacity of a distribution network, and in particular to a method and system for evaluating the carrying capacity of a distribution network for large-scale automobile charging loads. Background Art

[0002] The uncertainty and temporal variability of electric vehicle charging behavior increase the complexity of distribution network operations. Furthermore, with the increasing popularity of electric vehicles and the rapid development of charging infrastructure, distribution networks are facing unprecedented load pressure, placing higher demands on their stability and reliability. Therefore, accurately assessing the carrying capacity of distribution networks under the influence of large-scale electric vehicle charging is crucial for ensuring safe grid operation and optimizing grid resource allocation.

[0003] When dealing with such problems, existing technologies usually rely on traditional load forecasting models and power grid simulation technologies. Although these methods can provide certain support, they often lack sufficient flexibility and accuracy and cannot effectively cope with highly dynamic charging loads. In addition, existing technologies still have certain limitations in data processing and model training. For example, when expanding, extracting, reducing dimensions and classifying data, there are problems such as incomplete data collection, inaccurate feature extraction, and unstable classification performance. These all limit the effectiveness and application scope of distribution network carrying capacity assessment, resulting in low accuracy of the final assessment results. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems in the prior art and to provide a method and system for evaluating the carrying capacity of a distribution network for large-scale automobile charging loads with high accuracy.

[0005] To achieve the above objectives, the technical solution of the present invention is: a distribution network carrying capacity assessment method for large-scale vehicle charging loads, comprising:

[0006] S1. Obtain the data generated by the interaction between the electric vehicle and the distribution network during the charging process of the electric vehicle, and annotate it to obtain the original distribution network data training set;

[0007] The data includes grid load, voltage, frequency, EV usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type;

[0008] The marked categories include normal charging state, high load state, low load state and fault state;

[0009] S2. A composite data augmentation method based on variational sparse matrix decomposition and polynomial fitting is used to augment the original distribution network data training set, and a data augmentation model is trained to obtain an augmented distribution network data training set.

[0010] S3. Using a neural network algorithm based on adaptive biological influence optimization, perform feature extraction on the expanded distribution network data training set, and train a feature extraction model to obtain a feature distribution network data training set;

[0011] S4. Using an autoencoding neural network algorithm based on feature adaptive refinement, perform feature dimensionality reduction on the feature distribution network data training set, and train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set;

[0012] S5. Using the kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, classify the reduced-dimensional distribution network data training set, and train the classifier model to obtain the distribution network status classification category;

[0013] S6. Input the new distribution network data set samples into the feature extraction model and the feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

[0014] The step S2 specifically includes:

[0015] S21. Initialize the parameters of the variational autoencoder; the parameters include the weight and bias of the variational autoencoder; the expression of the initialization is as follows:

[0016]

[0017] in: is the initial weight matrix of the variational autoencoder, is the initial bias vector of the variational autoencoder, is the variance of the initialization of the variational autoencoder;

[0018] S22. Input the original distribution network data training set into the variational autoencoder, map the data into the potential feature space through nonlinear transformation, and use nonlinear activation function to extract key features; the expression of the transformation is as follows:

[0019]

[0020] Where: Z c is the potential feature after encoding, Res() is based on the steep ReLU activation function, are the weights and biases of the variational autoencoder, X cis the vector of the input original distribution network data training set;

[0021] The steep ReLU activation function uses adaptive parameters to control the activation threshold, and its calculation expression is as follows:

[0022]

[0023] Where: X is the input of the ReLU activation function, σ is the adaptive weight, and β is the parameter that controls the steepness of the Sigmoid curve;

[0024] S23. Use L1 regularization to implement sparsity constraints in the latent feature space, optimize the key feature representation of the latent feature space, and calculate the total loss function;

[0025] The expression of the sparsity constraint is as follows:

[0026]

[0027] in: is the sparsity loss function, λ c is the sparsity regularization coefficient, z ci is the i-th element in the encoded latent feature;

[0028] The expression of the total loss function is as follows:

[0029]

[0030] in: is the total loss of the variational autoencoder, is the reconstruction loss of the variational autoencoder;

[0031] The sparsity regularization coefficient is set in an adaptive adjustment manner, and its expression is as follows:

[0032]

[0033] Where: γ c is the adjustment coefficient, ρ c is the target sparsity, is the average sparsity of the current iteration;

[0034] S24. Performing polynomial fitting on the key features in the latent feature space, and dynamically adjusting the coefficients of the polynomial according to the distribution characteristics of the key features;

[0035] The expression of the polynomial fitting is as follows:

[0036]

[0037] in: is the key feature after polynomial fitting, a cn are the coefficients of the nth-order polynomial, is the power of the potential distribution network data feature, Nf is the total number of samples in the current input batch;

[0038] The ∩th order polynomial coefficient a cn The calculation expression of the optimization process is as follows:

[0039]

[0040] Where: α ck is the basic weight of the polynomial coefficient, Kf is the highest order of the fitting polynomial;

[0041] S25, mapping the key features after polynomial fitting back to the original data space through the decoder to generate new distribution network data points;

[0042] The expression of the new distribution network data point is as follows:

[0043]

[0044] in: is a new distribution network data point, Sig( ) is the Sigmoid activation function, are the weight and bias of the decoder respectively;

[0045] S26. Dynamically adjust the learning rate of the variational autoencoder according to the loss function of the variational autoencoder;

[0046] The expression of the learning rate is as follows:

[0047]

[0048] in: are the learning rates for iterations t+1 and t, respectively, and γ c is the learning rate decay coefficient, is the gradient of total loss with respect to learning rate;

[0049] S27. Repeat steps S21-S26 until the preset stop iteration condition is met, obtaining the expanded distribution network data training set and completing the data expansion model training.

[0050] The step S3 specifically includes:

[0051] S31. Initialize the parameters of the neural network and define the weights and biases of the neural network; the expressions of the weights and biases are as follows:

[0052]

[0053] Where: WNN 、b NN are the weights and biases of the neural network, ~ means obeying a specific distribution, σ sn is the initialization standard deviation, are the initial weights and initial biases of the neural network, is a normal distribution;

[0054] S32. At the beginning of each iteration, the loss function under the current parameter configuration is calculated, and the environmental fitness is estimated, and then resources are allocated based on the contribution of each parameter;

[0055] The expression of the environmental adaptability is as follows:

[0056] E NN =exp(-β NN ·(W NN , b NN ));

[0057] Where: E NN is the environmental adaptability, β NN is the sensitivity parameter;

[0058] The loss function is calculated using cross entropy loss, and its calculation expression is as follows:

[0059]

[0060] Where: L NN (W NN , b NN ) is the loss function, C is the number of categories, y c is the one-hot encoding of the true label, is the predicted probability,

[0061] The predicted probability is calculated by the softmax function, and its calculation expression is as follows:

[0062]

[0063] Where: z c is the logistic regression probability output by the neural network, z k is the logistic regression probability of the kth category output by the network;

[0064] The resource calculation expression for each parameter competition allocation is as follows:

[0065]

[0066] Where: R ij The resources allocated for the parameters of the jth neuron in the i-th neural network layer, is the weight parameter of the jth neuron in the i-th neural network layer, is the weight parameter of the lth neuron in the kth neural network layer, k and 1 are both indexes, γ NN is a hyperparameter, is the loss function L NN About parameters The partial derivative of is the loss function L NN About parameters The partial derivative of

[0067] S33. Based on the resource competition results, update the weights and biases, and use the momentum term to smooth the learning process of the weight parameters;

[0068] The updated expression is as follows:

[0069]

[0070] in: is the weight parameter update of the jth neuron in the i-th neural network layer, is the update amount of the bias parameter of the neurons in the i-th layer, is the bias of the neuron in layer i, R i The resources allocated to the parameters of the i-th neural network layer, α NN is the learning rate;

[0071] The momentum term is used to smooth the learning process of the weight parameters as follows:

[0072]

[0073] in: is the momentum term of the jth neuron in the i-th neural network layer at the t-th iteration, is the momentum term of the jth neuron in the i-th neural network layer at the t-1 iteration, β xm is the momentum decay parameter, is the weight parameter of the jth neuron in the i-th neural network layer at the t+1th iteration, is the weight parameter of the jth neuron in the i-th neural network layer at the t-th iteration,

[0074] S34. Adjust the sensitivity parameter and the learning rate parameter according to the result of the environmental perception evaluation; the result of the environmental perception evaluation refers to the difference between the environmental fitness of the current iteration and the previous iteration;

[0075] The expression for updating the learning rate is as follows:

[0076]

[0077] in: is the updated learning rate parameter, is the updated sensitivity parameter, η NN 、k NN are adjustment factors, ΔE NN is the difference in environmental fitness between this iteration and the previous iteration;

[0078] S35. Based on the results of the environment perception evaluation, dynamically adjust the hyperparameters controlling the sensitivity of resource allocation between the global search and the local search; the expression for the adjustment is as follows:

[0079]

[0080] in: is the hyperparameter for the sensitivity of the adjusted control resource allocation, ρ NN is a balanced hyperparameter;

[0081] S36. Repeat steps S31-S35 until the preset iteration stop condition is reached, obtain the characteristic distribution network data training set and complete the feature extraction model training.

[0082] The step S4 specifically includes:

[0083] S41. Initialize the network parameters of the autoencoder, which include the weights and biases of the autoencoder; the expressions are as follows:

[0084]

[0085] in: is the initial weight of the autoencoder, W r is the weight parameter of the autoencoder, The mean of the table is 0 and the variance is The normal distribution of is the number of neurons in the input layer of the autoencoder, is the initial bias of the autoencoder, b r is the bias parameter of the autoencoder;

[0086] S42. The distribution network data after feature extraction is propagated through the encoder layer sequence and converted into a low-dimensional representation; the expression of the low-dimensional representation is as follows:

[0087]

[0088] Among them: H r is the low-dimensional representation of the encoder output, Re() is the ReLU activation function, are the weight and bias of the encoder respectively, is the reconstructed output of the distribution network data, are the weight and bias of the decoder respectively;

[0089] S43. In the hidden layer of the autoencoder, the weights are adjusted through the dynamic weight adjustment layer, and the gradient of the loss function with respect to the weights is solved by the chain rule.

[0090] The expression of the adjustment weight is as follows:

[0091]

[0092] Where: ΔW r is the weight update amount, tanh() is the hyperbolic tangent function, λ r is the learning rate, γ r is the adjustment factor, is the loss function L r About weight W r The gradient of L r is the reconstruction error of the loss function,

[0093] The chain rule solves the gradient of the loss function with respect to the weight as follows:

[0094]

[0095] in: is the partial derivative of the loss function with respect to the reconstruction error output;

[0096] The calculation expression of the mean square error of the loss function is as follows:

[0097]

[0098] Among them: |||| is the L2 norm, α r is the regularization coefficient, is the L1 norm of the encoder weight;

[0099] S44. Based on the adjusted weights, the weight parameters and bias parameters of the autoencoder are updated using the gradient descent method; the expressions of the weight parameters and bias parameters are as follows:

[0100]

[0101] in: is the weight parameter of the autoencoder at the t+1th iteration, is the weight parameter of the autoencoder at the tth iteration, η r is the learning rate, is the bias parameter of the autoencoder at the t+1th iteration, is the bias parameter of the autoencoder at the tth iteration, t is the number of iterations, are the gradients of the loss function with respect to weights and biases respectively;

[0102] The learning rate is set by adaptive adjustment, and its expression is as follows:

[0103]

[0104] in: is the learning rate for the t+1th iteration, is the learning rate of the tth iteration, δ is the sensitivity parameter for adjusting the learning rate;

[0105] S45. Repeat steps S41-S44 until the iteration stop condition is met, obtain the reduced-dimensionality distribution network data training set and complete the feature-dimensionality reduction model training.

[0106] The step S5 specifically includes:

[0107] S51, initializing the parameters of the kernel function and the weights and biases of the network, and dynamically adjusting the width parameter of the kernel function according to the error distribution of each iteration;

[0108] The initialization expression is as follows:

[0109] K u (x u , x′ u )=exp(-γ u ||x u -x′ u || 2 );

[0110]

[0111] Among them: K u is the kernel function, x u is the distribution network data point, x′ u To distinguish from x u Another distribution network data point, γ u is the width parameter of the kernel function, are the initialized weights and biases, is the initialization variance;

[0112] The expression for the width parameter is as follows:

[0113]

[0114] Where: γ u (t+1) is the width parameter of the kernel function of the t+1th iteration, γ u (t) is the width parameter of the kernel function at the tth iteration, ||e u(t)||2 is the L2 norm of the error of the current iteration, ||x u (t)||2 is the L2 norm of the dimensionality-reduced distribution network data training set, ∈ ug A small constant to prevent division by zero, η γ The learning rate for kernel parameters adjustment;

[0115] S52. Before each training iteration begins, calculate the current error compensation factor based on the error of the previous iteration; the expression of the error compensation factor is as follows:

[0116]

[0117] Where: u (t) is the error compensation factor, || || is the L2 norm, λ u is the adjustment coefficient of error compensation, e u (t-1) is the error vector of the t-1th iteration, e u (t-2) is the error vector of the t-2th iteration, e u (t) is the error vector of the tth iteration, y u is the true label vector, is the prediction of the tth iteration;

[0118] S53, dynamically adjust the weights of the kernel extreme learning machine based on the error compensation factor and kernel technology;

[0119] The expression of the weight of the kernel extreme learning machine is as follows:

[0120]

[0121] in: is the weight of the kernel extreme learning machine at the t+1th iteration, is the weight of the kernel extreme learning machine at the tth iteration, η u is the learning rate of the kernel extreme learning machine, is the gradient of the loss function of the kernel extreme learning machine with respect to the weight, L u ( ) is the loss function of the kernel extreme learning machine, nE is the number of samples in the current batch, and I is the identity matrix;

[0122] The loss function of the kernel extreme learning machine is expressed as follows:

[0123]

[0124] in: is the predicted value calculated by kernel technique and weights, is the true label, λ fg is the regularization parameter;

[0125] S54: After the weight adjustment is completed, check the gradient value and determine whether the gradient value exceeds the stage threshold;

[0126] If the gradient value exceeds the preset cutoff threshold, Then perform truncation processing; the expression of the truncation processing is as follows:

[0127]

[0128] Where: θ u is the gradient cutoff threshold;

[0129] S55. Repeat steps S51-S54 until the iteration stop condition is met, obtain the distribution network status classification category and complete the classifier model training.

[0130] The classification result includes any one of a normal charging state, a high load state, a low load state, and a fault state.

[0131] According to the classification results, the carrying capacity of the distribution network can be obtained as follows:

[0132] If the classification result is a normal charging state, it means that the current carrying capacity of the distribution network is normal, which means that the electric vehicle is charging under standard conditions and the impact on the distribution network is within an acceptable range;

[0133] If the classification result is a high load state, it means that the current carrying capacity of the distribution network is close to the upper limit, which means that during peak power consumption hours or when multiple electric vehicles are charging at the same time, the distribution network needs to handle a larger load;

[0134] If the classification result is a low load state, it means that the current carrying capacity of the distribution network still has redundant capacity, which means that there are fewer charging electric vehicles or the charging demand is low, and the distribution network can bear the additional load;

[0135] If the classification result is a fault state, it means that the distribution network does not have the carrying capacity, which means that the distribution network or the charging facility has a fault due to overload or other abnormal conditions.

[0136] A distribution network carrying capacity assessment system for large-scale vehicle charging loads, which is applied to the above method, includes:

[0137] The training set acquisition module is used to obtain the data generated by the interaction between the electric vehicle and the distribution network during the charging process of the electric vehicle, and annotate it to obtain the original distribution network data training set;

[0138] A data expansion module is used to expand the original distribution network data training set based on a composite data expansion method of variational sparse matrix decomposition and polynomial fitting, and to train a data expansion model to obtain an expanded distribution network data training set;

[0139] A feature extraction module is used to extract features from the expanded distribution network data training set using a neural network algorithm based on adaptive biological influence optimization, and to train a feature extraction model to obtain a feature distribution network data training set;

[0140] A feature dimensionality reduction module is used to perform feature dimensionality reduction on the feature distribution network data training set using an autoencoding neural network algorithm based on feature adaptive refinement, and to train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set;

[0141] The data classification module is used to classify the reduced-dimensional distribution network data training set using the kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, and train the classifier model to obtain the distribution network status classification category;

[0142] The carrying capacity assessment module is used to input the new distribution network data set samples into the feature extraction model and feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

[0143] A distribution network carrying capacity assessment device for large-scale vehicle charging loads, the device comprising a processor and a memory;

[0144] The memory is used to store computer program code and transmit the computer program code to the processor;

[0145] The processor is used to execute the above-mentioned distribution network carrying capacity assessment method for large-scale automobile charging loads according to the instructions in the computer program code.

[0146] A storage medium for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads stores a computer program, which, when executed by a processor, implements the above-mentioned method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads.

[0147] Compared with the prior art, the present invention has the following beneficial effects:

[0148] The present invention provides a distribution network carrying capacity assessment method and system for large-scale automobile charging loads. The method first obtains and labels the interactive data of the charging process to obtain a training set; then, a composite data expansion method based on variational sparse matrix decomposition and polynomial fitting is used to expand the data; then, a neural network algorithm based on adaptive biological influence optimization is used to extract features; then, an autoencoder neural network algorithm based on feature adaptive refinement is used to reduce the feature dimension; then, a kernel extreme learning machine algorithm based on adaptive error compensation is used as a classifier for classification to obtain the distribution network state classification category; finally, a new distribution network data set sample is used as input for classification, and the carrying capacity of the distribution network can be obtained according to the classification result; in the application of the present invention, first, by combining variational autoencoder and multi- The term fitting method realizes efficient data expansion, effectively increases the number of training samples, and reduces the risk of overfitting. Then, the adaptive biological impact optimization method is used to improve the stability of the model in a complex training environment, improve the accuracy of feature extraction and training efficiency, and then adopt the feature adaptive refinement autoencoding neural network to finely capture and reconstruct data, optimize information flow, reduce dimensions while retaining important information, and then adopt the adaptive error compensation mechanism of the kernel extreme learning machine to adjust the error learning rate and weight update in real time, effectively improving the accuracy and robustness of the classification model; through the above optimization training of the model, the model can fully grasp the characteristics of the charging load data, and through the final category of the output, accurately evaluate the carrying capacity of the distribution network. Therefore, the accuracy of the present invention is relatively high. BRIEF DESCRIPTION OF THE DRAWINGS

[0149] Figure 1 It is a flow chart of the method steps of the present invention.

[0150] Figure 2 It is a training flow chart of feature extraction of the present invention.

[0151] Figure 3 It is a training flow chart of feature dimensionality reduction of the present invention.

[0152] Figure 4 It is a schematic diagram of the system structure of the present invention.

[0153] Figure 5 It is a schematic diagram of the device structure of the present invention.

[0154] In the figure: training set acquisition module 1, data expansion module 2, feature extraction module 3, feature dimension reduction module 4, data classification module 5, carrying capacity assessment module 6, processor 7, memory 8, computer program code 81. DETAILED DESCRIPTION

[0155] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0156] Example 1:

[0157] See also Figure 1-Figure 3 , a distribution network carrying capacity assessment method for large-scale vehicle charging loads, including:

[0158] S1. Obtain the data generated by the interaction between the electric vehicle and the distribution network during the charging process of the electric vehicle, and annotate it to obtain the original distribution network data training set;

[0159] Furthermore, this embodiment collects multi-source, heterogeneous data from the operation of electric vehicles and distribution networks during the charging process. This data can be collected through real-time monitoring systems for the distribution network and electric vehicles. The data generated by the interaction between electric vehicles and the distribution network includes, but is not limited to, grid load, voltage, frequency, electric vehicle usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type. This collected data is statistically analyzed and stored in a structured JSON format, forming a training set of raw distribution network data. In actual collection, the types or attributes of the data generated during interaction can reach dozens or even hundreds. This embodiment only lists a few representative ones.

[0160] After the data is collected, it needs to be manually labeled. The labeled categories include normal charging state, high load state, low load state and fault state. These categories are the "real" labels before model training and are used to guide the model to learn how to identify and classify data.

[0161] During the charging process, data is exchanged between the electric vehicle, the charging station, and the distribution network using communication protocols. When an electric vehicle needs to charge, it sends a charging request to the charging station. Upon receiving the request, the charging station first performs protocol matching to ensure consistency in the communication protocols between the two parties, enabling accurate data exchange. Simultaneously, the charging station performs authentication, verifying the electric vehicle's identity and other relevant information to ensure a safe and compliant charging process. After completing these preliminary steps, the charging station transmits the electric vehicle's charging request to the distribution network. Upon receiving the request, the distribution network provides power to the electric vehicle based on its current carrying capacity, including power capacity and line load. Furthermore, the distribution network features real-time coordination capabilities, dynamically adjusting the charging status of each electric vehicle based on the overall system's operating status, such as overall load conditions and power supply stability. This allows for the coordinated management of all charging electric vehicles within a large coverage area.

[0162] S2. A composite data augmentation method based on variational sparse matrix decomposition and polynomial fitting is used to augment the original distribution network data training set, and a data augmentation model is trained to obtain an augmented distribution network data training set.

[0163] In this embodiment, the acquisition, labeling, and preprocessing of training data are time-consuming and labor-intensive. Insufficient training samples can easily lead to poor model generalization and affect model accuracy. Therefore, this solution employs a composite data distribution network augmentation method based on variational sparse matrix factorization and polynomial fitting. Using the variational autoencoder framework, the data distribution network data is mapped into a latent space. Sparsity constraints are employed to increase the expressive power of the data distribution network, thereby capturing its core features.

[0164] Furthermore, the step S2 specifically includes:

[0165] S21. Initialize the parameters of the variational autoencoder; the parameters include the weight and bias of the variational autoencoder; the expression of the initialization is as follows:

[0166]

[0167] in: is the initial weight matrix of the variational autoencoder, is the initial bias vector of the variational autoencoder, is the variance of the initialization of the variational autoencoder;

[0168] S22. Input the original distribution network data training set into the variational autoencoder, map the data into the potential feature space through nonlinear transformation, and use nonlinear activation function to extract key features; the expression of the transformation is as follows:

[0169]

[0170] Where: z c is the potential feature after encoding, Res( ) is based on the steep ReLU activation function, are the weights and biases of the variational autoencoder, X c is the vector of the input original distribution network data training set; the vector includes attributes such as grid load, voltage, frequency, electric vehicle usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type.

[0171] The steep ReLU activation function uses adaptive parameters to control the activation threshold, and its calculation expression is as follows:

[0172]

[0173] Where: X is the input of the ReLU activation function, σ is the adaptive weight (preferably set to 0.3), and β is the parameter that controls the steepness of the Sigmoid curve (preferably set to 3);

[0174] S23. Use L1 regularization to implement sparsity constraints in the latent feature space, optimize the key feature representation of the latent feature space, and calculate the total loss function;

[0175] In this scheme, sparsity constraints are adopted in the latent space to force the activity of some latent variables to be reduced, so as to achieve the purpose of extracting key features.

[0176] The expression of the sparsity constraint is as follows:

[0177]

[0178] in: is the sparsity loss function, λ c is the sparsity regularization coefficient, z ci is the i-th element in the encoded latent feature;

[0179] The expression of the total loss function is as follows:

[0180]

[0181] in: is the total loss of the variational autoencoder, is the reconstruction loss of the variational autoencoder;

[0182] The sparsity regularization coefficient is set in an adaptive adjustment manner and changes dynamically according to the sparsity of the data distribution network data to optimize the training effect (preferably set to 0.3). Its expression is as follows:

[0183]

[0184] Where: γ c is the adjustment coefficient (preferably set to 2), ρ c is the target sparsity, is the average sparsity of the current iteration, where the sparsity is represented by the number of connection weights between neurons;

[0185] S24, performing polynomial fitting on key features in the latent feature space, and dynamically adjusting the coefficients of the polynomial according to the distribution characteristics of the key features to enhance the naturalness and coherence of the data expansion of the data distribution network;

[0186] The expression of the polynomial fitting is as follows:

[0187]

[0188] in: is the key feature after polynomial fitting, a cn are the coefficients of the nth-order polynomial, is the nth power of the potential distribution network data feature, Nf is the total number of samples in the current input batch;

[0189] The nth order polynomial coefficient a cn The calculation expression of the optimization process is as follows:

[0190]

[0191] Where: α ck is the basic weight of the polynomial coefficient, Kf is the highest order of the fitting polynomial;

[0192] S25, mapping the key features after polynomial fitting back to the original data space through the decoder to generate new distribution network data points;

[0193] The expression of the new distribution network data point is as follows:

[0194]

[0195] in: is a new distribution network data point, Sig() is the Sigmoid activation function, are the weight and bias of the decoder respectively;

[0196] S26. Dynamically adjust the learning rate of the variational autoencoder according to the loss function of the variational autoencoder;

[0197] In this scheme, the learning rate of the variational autoencoder is dynamically adjusted based on the error feedback of the decoding result. The learning rate of the variational autoencoder is used to control the step size when adjusting the parameters of the variational autoencoder using gradient descent to optimize the training process and improve the accuracy of data distribution network data generation; (preferably set to 0.3)

[0198] The expression of the learning rate is as follows:

[0199]

[0200] in: are the learning rates for iterations t+1 and t, respectively, and γ c is the learning rate decay coefficient (preferably set to 0.95), is the gradient of total loss with respect to learning rate;

[0201] S27. Repeat steps S21-S26 until the preset stop iteration condition is met, obtaining the expanded distribution network data training set and completing the data expansion model training.

[0202] In this embodiment, the preset condition for stopping iteration is: reaching a preset maximum number of iterations; the preset maximum number of iterations is set to 1000 times.

[0203] S3. Using a neural network algorithm based on adaptive biological influence optimization, perform feature extraction on the expanded distribution network data training set, and train a feature extraction model to obtain a feature distribution network data training set;

[0204] The expanded distribution network data training set is input into the distribution network data feature extraction model to train the distribution network data feature extraction model. In this scheme, a three-layer fully connected neural network is used to extract distribution network data features.

[0205] In the prior art, some solutions use neural networks for feature extraction. However, in certain neural network structures, they may encounter problems such as vanishing gradients, exploding gradients, or being stuck in local optimal solutions, affecting training stability and model performance. This paper adopts a neural network algorithm based on adaptive biological influence optimization as a distribution network data feature extraction model. Inspired by the adaptive behavior of organisms in nature, particularly their ability to adjust strategies based on resource changes in the environment, this method optimizes the parameters of the neural network by simulating the survival competition and resource adaptation processes of organisms, thereby avoiding problems such as vanishing gradients, exploding gradients, and being stuck in local optimal solutions, thereby improving the distribution network data feature extraction capability.

[0206] Furthermore, the step S3 specifically includes:

[0207] S31. Initialize the parameters of the neural network and define the weights and biases of the neural network; the expressions of the weights and biases are as follows:

[0208]

[0209] Where: W NN 、b NN are the weights and biases of the neural network, ~ means obeying a specific distribution, σ sn is the initialization standard deviation (preferably set to 0.01), are the initial weights and initial biases of the neural network, is a normal distribution;

[0210] S32. At the beginning of each iteration, evaluate the performance of the current network parameters in a specific training environment, calculate the loss function under the current parameter configuration, and estimate the environmental fitness as a basis for subsequent adjustments. Then, allocate resources based on the contribution of each parameter.

[0211] The expression of the environmental adaptability is as follows:

[0212] E NN =exp(-β NN ·(W NN , b NN ));

[0213] Where: E NN is the environmental adaptability, β NN is the sensitivity parameter;

[0214] The loss function is calculated using cross entropy loss, and its calculation expression is as follows:

[0215]

[0216] Where: L NN (W NN , b NN ) is the loss function, C is the number of categories, y c is the one-hot encoding of the true label, is the predicted probability,

[0217] The predicted probability is calculated by the softmax function, and its calculation expression is as follows:

[0218]

[0219] Where: z c is the logistic regression probability output by the neural network, z k is the logistic regression probability of the kth category output by the network;

[0220] In the resource competition behavior in nature, each parameter is regarded as an individual, competing to expand the resources in the distribution network dataset, and the resource allocation is determined by the contribution of the parameter;

[0221] The resource calculation expression for each parameter competition allocation is as follows:

[0222]

[0223] Where: R ij The resources allocated for the parameters of the jth neuron in the i-th neural network layer, is the weight parameter of the jth neuron in the i-th neural network layer, is the weight parameter of the first neuron in the kth neural network layer, k and l are both indices, γ NN is a hyperparameter, is the loss function L NN About parameters The partial derivative of is the loss function L NN About parameters The partial derivative of

[0224] S33. Based on the resource competition results, update the weights and biases, and use the momentum term to smooth the learning process of the weight parameters;

[0225] The updated expression is as follows:

[0226]

[0227] in: is the weight parameter update of the jth neuron in the i-th neural network layer, is the update amount of the bias parameter of the neurons in the i-th layer, is the bias of the neuron in layer i, R i The resources allocated to the parameters of the i-th neural network layer, α NN is the learning rate;

[0228] The momentum term is used to smooth the learning process of the weight parameters as follows:

[0229]

[0230] in: is the momentum term of the jth neuron in the i-th neural network layer at the t-th iteration, is the momentum term of the jth neuron in the i-th neural network layer at the t-1 iteration, β xm is the momentum decay parameter, is the weight parameter of the jth neuron in the i-th neural network layer at the t+1th iteration, is the weight parameter of the jth neuron in the i-th neural network layer at the t-th iteration,

[0231] S34. Adjust the sensitivity parameter and the learning rate parameter according to the result of the environmental perception evaluation; the result of the environmental perception evaluation refers to the difference between the environmental fitness of the current iteration and the previous iteration, which is used as the change in environmental fitness. This allows the system to perceive environmental changes during parameter tuning and improve the accuracy of parameter tuning.

[0232] The expression for updating the learning rate is as follows:

[0233]

[0234] in: is the updated learning rate parameter, is the updated sensitivity parameter, η NN , K NNare adjustment factors, ΔE NN is the difference in environmental fitness between this iteration and the previous iteration;

[0235] S35. Based on the results of the environmental perception evaluation, dynamically adjust the hyperparameters controlling the sensitivity of resource allocation between the global search and the local search to avoid falling into the local optimal solution; the expression for the adjustment is as follows:

[0236]

[0237] in: is the hyperparameter for the sensitivity of the adjusted control resource allocation, ρ NN is a balanced hyperparameter (preferably set to 0.1), | | is the absolute value of compliance;

[0238] S36. Repeat steps S31-S35 until the preset iteration stop condition is reached, obtain the characteristic distribution network data training set and complete the feature extraction model training.

[0239] In this embodiment, the preset condition for stopping iteration is: reaching a preset maximum number of iterations; the preset maximum number of iterations is set to 1000 times.

[0240] S4. Using an autoencoding neural network algorithm based on feature adaptive refinement, perform feature dimensionality reduction on the feature distribution network data training set, and train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set;

[0241] In this embodiment, the feature distribution network dataset after feature extraction is input into a feature dimensionality reduction model for training. This solution uses an autoencoder neural network algorithm based on feature adaptive refinement for feature dimensionality reduction. The autoencoder neural network algorithm includes two parts: an encoder and a decoder. The encoder is used to convert the extracted features of the high-dimensional input into a low-dimensional hidden representation, and the decoder is used to reconstruct the low-dimensional hidden representation back into the original high-dimensional space to form a reconstructed output. This solution uses a feature coupling mechanism, which enables the autoencoder to not only reduce the dimensionality of the data distribution network data, but also more finely capture and reconstruct the inherent correlations and structural features of the input data distribution network data, thereby optimizing the information flow of the network.

[0242] Furthermore, S41, initialize the network parameters of the autoencoder, wherein the network includes the weights and biases of the autoencoder; the expressions are as follows:

[0243]

[0244] in: is the initial weight of the autoencoder, W r is the weight parameter of the autoencoder, The mean of the table is 0 and the variance is The normal distribution of is the number of neurons in the input layer of the autoencoder, is the initial bias of the autoencoder, b r is the bias parameter of the autoencoder;

[0245] S42. The distribution network data after feature extraction is propagated through the encoder layer sequence and converted into a low-dimensional representation; the expression of the low-dimensional representation is as follows:

[0246]

[0247] Among them: H r is the low-dimensional representation of the encoder output, Re( ) is the ReLU activation function, are the weight and bias of the encoder respectively, is the reconstructed output of the distribution network data, are the weight and bias of the decoder respectively;

[0248] S43. In the hidden layer of the autoencoder, the weights are adjusted through the dynamic weight adjustment layer, and the gradient of the loss function with respect to the weights is solved by the chain rule.

[0249] The expression of the adjustment weight is as follows:

[0250]

[0251] Where: ΔW r is the weight update amount, tanh( ) is the hyperbolic tangent function, λ r is the learning rate (preferably set to 0.01), γ r is the adjustment factor (preferably set to 3.14), is the loss function L r About weight W r The gradient of L r is the reconstruction error of the loss function,

[0252] The chain rule solves the gradient of the loss function with respect to the weight as follows:

[0253]

[0254] in: is the partial derivative of the loss function with respect to the reconstruction error output;

[0255] The calculation expression of the mean square error of the loss function is as follows:

[0256]

[0257] Among them: |||| is the L2 norm, αr is the regularization coefficient (preferably set to 0.3), is the L1 norm of the encoder weight;

[0258] S44. Based on the adjusted weights, the weight parameters and bias parameters of the autoencoder are updated using the gradient descent method; the expressions of the weight parameters and bias parameters are as follows:

[0259]

[0260] in: is the weight parameter of the autoencoder at the t+1th iteration, is the weight parameter of the autoencoder at the tth iteration, η r is the learning rate, is the bias parameter of the autoencoder at the t+1th iteration, is the bias parameter of the autoencoder at the tth iteration, t is the number of iterations, are the gradients of the loss function with respect to weights and biases respectively;

[0261] The learning rate is set in an adaptive manner to dynamically adjust according to the different stages of network training. Its expression is as follows:

[0262]

[0263] in: is the learning rate for the t+1th iteration, is the learning rate of the tth iteration, δ is the sensitivity parameter for adjusting the learning rate (preferably set to 0.5);

[0264] S45. Repeat steps S41-S44 until the iteration stop condition is met, obtain the reduced-dimensionality distribution network data training set and complete the feature-dimensionality reduction model training.

[0265] In this embodiment, the preset condition for stopping iteration is: reaching a preset maximum number of iterations; the preset maximum number of iterations is set to 1000 times.

[0266] S5. Using the kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, classify the reduced-dimensional distribution network data training set, and train the classifier model to obtain the distribution network status classification category;

[0267] In this embodiment, the reduced-dimensional distribution network dataset is input into the classifier to train the classifier model. This solution adopts a kernel extreme learning machine algorithm based on adaptive error compensation as the classifier, and adopts a dynamic error compensation factor, which is automatically adjusted according to the classification error in each training iteration, thereby optimizing the weight update process of subsequent iterations and improving the classification accuracy.

[0268] Furthermore, the step S5 specifically includes:

[0269] S51, initializing the parameters of the kernel function and the weights and biases of the network, and dynamically adjusting the width parameter of the kernel function according to the error distribution of each iteration;

[0270] The initialization expression is as follows:

[0271] K u (x u , x′ u )=exp(-γ u ||x u -x′ u || 2 );

[0272]

[0273] Among them: K u is the kernel function, x u is the distribution network data point, x′ u To distinguish from x u Another distribution network data point, γ u is the width parameter of the kernel function, are the initialized weights and biases, is the initialization variance (preferably set to 0.01);

[0274] The expression for the width parameter is as follows:

[0275]

[0276] Where: γ u (t+1) is the width parameter of the kernel function of the t+1th iteration, γ u (t) is the width parameter of the kernel function at the tth iteration, ||e u (t)||2 is the L2 norm of the error of the current iteration, ||x u (t)||2 is the L2 norm of the dimensionality-reduced distribution network data training set, ∈ ug A small constant (preferably set to 0.01) to prevent division by zero, η γ The learning rate for kernel parameters adjustment;

[0277] S52. Before each training iteration begins, calculate the current error compensation factor based on the error of the previous iteration to adjust the learning rate and weight update rule of this iteration to reduce the overall training error; the expression of the error compensation factor is as follows:

[0278]

[0279] Where: u (t) is the error compensation factor, || || is the L2 norm, λ u is the error compensation adjustment coefficient (preferably set to 3), e u (t-1) is the error vector of the t-1th iteration, e u (t-2) is the error vector of the t-2th iteration, eu ( t) is the error vector of the tth iteration, y u is the true label vector, is the prediction of the tth iteration;

[0280] S53, dynamically adjust the weights of the kernel extreme learning machine based on the error compensation factor and kernel technology;

[0281] In this scheme, kernel technology is used to map the input distribution network data into a high-dimensional space, linear regression is performed in the high-dimensional space, the weight update formula is adjusted according to the calculated error compensation factor, and the rate and direction of change of the weight are dynamically adjusted. Taking the weight parameters of the kernel extreme learning machine as an example, the update rule adopts kernel technology and error compensation factor adjustment.

[0282] The expression of the weight of the kernel extreme learning machine is as follows:

[0283]

[0284] in: is the weight of the kernel extreme learning machine at the t+1th iteration, is the weight of the kernel extreme learning machine at the tth iteration, η u is the learning rate of the kernel extreme learning machine, is the gradient of the loss function of the kernel extreme learning machine with respect to the weight, L u ( ) is the loss function of the kernel extreme learning machine, nE is the number of samples in the current batch, and I is the identity matrix;

[0285] The loss function of the kernel extreme learning machine is expressed as follows:

[0286]

[0287] in: is the predicted value calculated by kernel technique and weights, is the true label, λ fg is the regularization parameter (preferably set to 0.3);

[0288] S54: After the weight adjustment is completed, check the gradient value and determine whether the gradient value exceeds the stage threshold;

[0289] If the gradient value exceeds the preset cutoff threshold, Then truncation is performed to prevent the gradient explosion problem during training and ensure the stability of the training process; the expression of the truncation process is as follows:

[0290]

[0291] Where: θ u is the gradient cutoff threshold;

[0292] S55. Repeat steps S51-S54 until the iteration stop condition is met, obtain the distribution network status classification category and complete the classifier model training.

[0293] In this embodiment, the preset condition for stopping iteration is: reaching a preset maximum number of iterations; the preset maximum number of iterations is set to 1000 times.

[0294] S6. Input the new distribution network data set samples into the feature extraction model and the feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

[0295] Furthermore, the classification result includes any one of a normal charging state, a high load state, a low load state and a fault state.

[0296] If the classification result is a normal charging state, it means that the current carrying capacity of the distribution network is normal. This state means that the electric vehicle is charging under standard conditions and the impact on the distribution network is within an acceptable range. This state can be used as a basis for judging the current carrying capacity of the distribution network.

[0297] If the classification result is a high load state, it means that the current carrying capacity of the distribution network is close to its upper limit. This state means that during peak power consumption periods or when multiple electric vehicles are charging simultaneously, the distribution network needs to handle a larger load. If the high load state occurs frequently and lasts for a long time, it means that the current distribution network's carrying capacity is close to its upper limit and infrastructure strengthening or load management may be necessary.

[0298] If the classification result is a low load state, it means that the current carrying capacity of the distribution network still has redundant capacity. This state means that there are fewer charging electric vehicles or the charging demand is low, and the distribution network can bear the additional load. The redundancy in this state can be used as a basis for optimizing the scheduling strategy.

[0299] If the classification result is a fault state, it means that the distribution network does not have the carrying capacity. This state means that the distribution network or charging facilities have failed due to load overload or other abnormal conditions. This state directly reflects the weakness of the distribution network in responding to load fluctuations. By analyzing the frequency and causes of fault states, weak links in the distribution network can be identified and improved.

[0300] Furthermore, based on the classification results, the load management strategy of the distribution network can be dynamically adjusted. For example, after detecting multiple high-load or fault conditions, the operating parameters of the distribution equipment can be automatically adjusted to reduce the load pressure; or a peak-shaving and valley-filling strategy can be used to shift some of the load to low-load periods.

[0301] In addition, the load status of the distribution network can be monitored and predicted in real time, and the stability of the distribution network can be maintained through intelligent control measures (such as scheduling electric vehicle charging time, enabling backup power supply, etc.).

[0302] Example 2:

[0303] See also Figure 4 A distribution network carrying capacity assessment system for large-scale vehicle charging loads is provided. The system is applied to the method described in Example 1. The system includes:

[0304] The training set acquisition module 1 is used to acquire the data generated by the interaction between the electric vehicle and the distribution network during the charging process of the electric vehicle, and to annotate it to obtain the original distribution network data training set;

[0305] Furthermore, the data collected by the training set acquisition module 1 include: grid load, voltage, frequency, electric vehicle usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type;

[0306] The marked categories include: normal charging state, high load state, low load state and fault state.

[0307] Data expansion module 2 is used to expand the original distribution network data training set based on a composite data expansion method of variational sparse matrix decomposition and polynomial fitting, and train a data expansion model to obtain an expanded distribution network data training set;

[0308] Furthermore, the data expansion module 2 performs data expansion according to steps S21-S27 in Example 1;

[0309] Feature extraction module 3, used to extract features from the expanded distribution network data training set using a neural network algorithm based on adaptive biological influence optimization, and train a feature extraction model to obtain a feature distribution network data training set;

[0310] Furthermore, the feature extraction module 3 performs feature extraction according to steps S31-S36 in Example 1;

[0311] The feature dimensionality reduction module 4 is used to perform feature dimensionality reduction on the feature distribution network data training set using an autoencoding neural network algorithm based on feature adaptive refinement, and train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set;

[0312] Furthermore, the feature dimension reduction module 4 performs feature dimension reduction according to steps S41-S45 in Example 1;

[0313] The data classification module 5 is used to classify the reduced-dimensional distribution network data training set using a kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, and to train a classifier model to obtain a distribution network state classification category;

[0314] Furthermore, the data classification module 5 performs classification according to steps S51-S55 in Example 1;

[0315] The carrying capacity assessment module 6 is used to input the new distribution network data set samples into the feature extraction model and the feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

[0316] Furthermore, the classification result of the carrying capacity evaluation module 6 includes any one of: a normal charging state, a high load state, a low load state and a fault state.

[0317] The specific content of the carrying capacity of the distribution network can be obtained according to the classification result. Please refer to step S6 in Example 1.

[0318] Example 3:

[0319] See also Figure 5 , a distribution network carrying capacity assessment device for large-scale vehicle charging loads, the device comprising a processor 7 and a memory 8;

[0320] The memory 8 is used to store computer program code 81 and transmit the computer program code 81 to the processor 7;

[0321] The processor 7 is configured to execute the method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads described in Example 1 according to the instructions in the computer program code 81 .

[0322] This embodiment also includes a distribution network carrying capacity assessment storage medium for large-scale automobile charging loads, which stores a computer program. When the computer program is executed by the processor, it implements the distribution network carrying capacity assessment method for large-scale automobile charging loads described in Example 1.

[0323] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0324] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM 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 thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0325] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. 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 to an external computer (for example, using an Internet service provider to connect through the Internet).

[0326] The above-mentioned device and non-transitory computer-readable storage medium can be referred to the detailed description of a distribution network carrying capacity assessment method for large-scale automobile charging loads and its beneficial effects, which will not be repeated here.

[0327] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads, characterized in that: include: S1. Obtain the data generated by the interaction between the electric vehicle and the distribution network during the charging process of the electric vehicle, and annotate it to obtain the original distribution network data training set; The data includes grid load, voltage, frequency, EV usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type; The marked categories include normal charging state, high load state, low load state and fault state; S2. A composite data augmentation method based on variational sparse matrix decomposition and polynomial fitting is used to augment the original distribution network data training set, and a data augmentation model is trained to obtain an augmented distribution network data training set. S3. Using a neural network algorithm based on adaptive biological influence optimization, perform feature extraction on the expanded distribution network data training set, and train a feature extraction model to obtain a feature distribution network data training set; S4. Using an autoencoding neural network algorithm based on feature adaptive refinement, perform feature dimensionality reduction on the feature distribution network data training set, and train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set; S5. Using the kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, classify the reduced-dimensional distribution network data training set, and train the classifier model to obtain the distribution network status classification category; S6. Input the new distribution network data set samples into the feature extraction model and the feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

2. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 1 is characterized in that: The step S2 specifically includes: S21. Initialize the parameters of the variational autoencoder; the parameters include the weight and bias of the variational autoencoder; the expression of the initialization is as follows: in: is the initial weight matrix of the variational autoencoder, is the initial bias vector of the variational autoencoder, is the variance of the initialization of the variational autoencoder; S22. Input the original distribution network data training set into the variational autoencoder, map the data into the potential feature space through nonlinear transformation, and use nonlinear activation function to extract key features; the expression of the transformation is as follows: Where: z c is the potential feature after encoding, Res( ) is based on the steep ReLU activation function, are the weights and biases of the variational autoencoder, X c is the vector of the input original distribution network data training set; The steep ReLU activation function uses adaptive parameters to control the activation threshold, and its calculation expression is as follows: Where: X is the input of the ReLU activation function, σ is the adaptive weight, and β is the parameter that controls the steepness of the Sigmoid curve; S23. Based on the encoded latent features, L1 regularization is used to implement sparsity constraints in the latent feature space, optimize the key feature representation of the latent feature space, and calculate the total loss function; The expression of the sparsity constraint is as follows: in: is the sparsity loss function, λ c is the sparsity regularization coefficient, z ci is the i-th element in the encoded latent feature; The expression of the total loss function is as follows: in: is the total loss of the variational autoencoder, is the reconstruction loss of the variational autoencoder; The sparsity regularization coefficient is set in an adaptive adjustment manner, and its expression is as follows: Where: γ c is the adjustment coefficient, ρ c is the target sparsity, is the average sparsity of the current iteration; S24. Performing polynomial fitting on the key features in the latent feature space, and dynamically adjusting the coefficients of the polynomial according to the distribution characteristics of the key features; The expression of the polynomial fitting is as follows: in: is the key feature after polynomial fitting, a cn are the coefficients of the nth-order polynomial, is the nth power of the potential distribution network data feature, Nf is the total number of samples in the current input batch; The nth order polynomial coefficient a cn The calculation expression of the optimization process is as follows: Among them: a ck is the basic weight of the polynomial coefficient, Kf is the highest order of the fitting polynomial; S25, mapping the key features after polynomial fitting back to the original data space through the decoder to generate new distribution network data points; The expression of the new distribution network data point is as follows: in: is a new distribution network data point, Sig() is the Sigmoid activation function, are the weight and bias of the decoder respectively; S26. Dynamically adjust the learning rate of the variational autoencoder according to the loss function of the variational autoencoder; The expression of the learning rate is as follows: in: are the learning rates for iterations t+1 and t, respectively, and γ c is the learning rate decay coefficient, is the gradient of total loss with respect to learning rate; S27. Repeat steps S21-S26 until the preset stop iteration condition is met, obtaining the expanded distribution network data training set and completing the data expansion model training.

3. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 1 is characterized in that: The step S3 specifically includes: S31. Initialize the parameters of the neural network and define the weights and biases of the neural network; the expressions of the weights and biases are as follows: Where: W NN 、b NN are the weights and biases of the neural network, ~ means obeying a specific distribution, σ sn is the initialization standard deviation, are the initial weights and initial biases of the neural network, is a normal distribution; S32. At the beginning of each iteration, the loss function under the current parameter configuration is calculated, and the environmental fitness is estimated, and then resources are allocated based on the contribution of each parameter; The expression of the environmental adaptability is as follows: E NN Nexp(-β). NN ·(W NN ,b NN )) Where: E NN is the environmental adaptability, β NN is the sensitivity parameter; The loss function is calculated using cross entropy loss, and its calculation expression is as follows: Where: L NN (W NN , b NN ) is the loss function, C is the number of categories, y c is the one-hot encoding of the true label, is the predicted probability, The predicted probability is calculated by the softmax function, and its calculation expression is as follows: Where: z c is the logistic regression probability output by the neural network, z k is the logistic regression probability of the kth category output by the network; The resource calculation expression for each parameter competition allocation is as follows: Where: R ij The resources allocated for the parameters of the jth neuron in the i-th neural network layer, is the weight parameter of the jth neuron in the i-th neural network layer, is the weight parameter of the lth neuron in the kth neural network layer, k and l are both indexes, γ NN is a hyperparameter, is the loss function L NN About parameters The partial derivative of is the loss function L NN About parameters The partial derivative of S33. Based on the resource competition results, update the weights and biases, and use the momentum term to smooth the learning process of the weight parameters; The updated expression is as follows: in: is the weight parameter update of the jth neuron in the i-th neural network layer, is the update amount of the bias parameter of the neurons in the i-th layer, is the bias of the neuron in layer i, R i The resources allocated to the parameters of the i-th neural network layer, α NN is the learning rate; The momentum term is used to smooth the learning process of the weight parameters as follows: in: is the momentum term of the jth neuron in the first neural network layer at the tth iteration, is the momentum term of the jth neuron in the i-th neural network layer at the t-1 iteration, β xm is the momentum decay parameter, is the weight parameter of the jth neuron in the i-th neural network layer at the t+1th iteration, is the weight parameter of the jth neuron in the i-th neural network layer at the t-th iteration, S34. Adjust the sensitivity parameter and the learning rate parameter according to the result of the environmental perception evaluation; the result of the environmental perception evaluation refers to the difference between the environmental fitness of the current iteration and the previous iteration; The expression for updating the learning rate is as follows: in: is the updated learning rate parameter, is the updated sensitivity parameter, η NN , K NN are adjustment factors, ΔE NN is the difference in environmental fitness between this iteration and the previous iteration; S35. Based on the results of the environment perception evaluation, dynamically adjust the hyperparameters controlling the sensitivity of resource allocation between the global search and the local search; the expression for the adjustment is as follows: in: is the hyperparameter for the sensitivity of the adjusted control resource allocation, ρ NN is a balanced hyperparameter; S36. Repeat steps S31-S35 until the preset iteration stop condition is reached, obtain the characteristic distribution network data training set and complete the feature extraction model training.

4. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 1 is characterized in that: The step S4 specifically includes: S41. Initialize the network parameters of the autoencoder, which include the weights and biases of the autoencoder; the expressions are as follows: in: is the initial weight of the self-encoder, Wr, is the weight parameter of the self-encoder, The mean of the table is 0 and the variance is The normal distribution of is the number of neurons in the input layer of the autoencoder, is the initial bias of the autoencoder, b r is the bias parameter of the autoencoder; S42. The characteristic distribution network data training set is propagated through the encoder layer sequence and converted into a low-dimensional representation; the expression of the low-dimensional representation is as follows: Among them: H r is the low-dimensional representation of the encoder output, Re( ) is the ReLU activation function, are the weight and bias of the encoder respectively, To reconstruct the output, are the weight and bias of the decoder respectively; S43. In the hidden layer of the autoencoder, the weights are adjusted through the dynamic weight adjustment layer, and the gradient of the loss function with respect to the weights is solved by the chain rule. The expression of the adjustment weight is as follows: Where: ΔW r is the weight update amount, tanh( ) is the hyperbolic tangent function, λ r is the learning rate, γ r is the adjustment factor, is the loss function L r About weight W r The gradient of L r is the reconstruction error of the loss function, The chain rule solves the gradient of the loss function with respect to the weight as follows: in: is the partial derivative of the loss function with respect to the reconstruction error output; The calculation expression of the mean square error of the loss function is as follows: Among them: || || is the L2 norm, α r is the regularization coefficient, is the L1 norm of the encoder weight, X r is the data input to the autoencoder; S44. Based on the adjusted weights, the weight parameters and bias parameters of the autoencoder are updated using the gradient descent method; the expressions of the weight parameters and bias parameters are as follows: in: is the weight parameter of the autoencoder at the t+1th iteration, is the parameter of the autoencoder weight at the tth iteration, η r is the learning rate, is the parameter of the autoencoder bias at the t+1th iteration, is the parameter of the autoencoder bias at the tth iteration, t is the number of iterations, are the gradients of the loss function with respect to weights and biases respectively; The learning rate is set by adaptive adjustment, and its expression is as follows: in: is the learning rate for the t+1th iteration, is the learning rate of the tth iteration, δ is the sensitivity parameter for adjusting the learning rate; S45. Repeat steps S41-S44 until the iteration stop condition is met, obtain the reduced-dimensionality distribution network data training set and complete the feature-dimensionality reduction model training.

5. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 1 is characterized in that: The step S5 specifically includes: S51, initializing the parameters of the kernel function and the weights and biases of the neural network, and dynamically adjusting the width parameter of the kernel function according to the error distribution of each iteration; The initialization expression is as follows: K u (x u ,x′ u )=exp(-γ u ||x u -x′ u || 2 ); Among them: K u is the kernel function, x u is the distribution network data point, x′ u To distinguish from X u Another distribution network data point, γ u is the width parameter of the kernel function, To initialize the weights and biases of the neural network, is the initialization variance; The expression for the width parameter is as follows: Where: γ u (t+1) is the width parameter of the kernel function of the t+1th iteration, γ u (t) is the width parameter of the kernel function at the tth iteration, ||e u (t)||2 is the L2 norm of the error of the current iteration, ||x u (t)||2 is the L2 norm of the dimensionality-reduced distribution network data training set, ∈ ug A small constant to prevent division by zero, η γ The learning rate for kernel parameters adjustment; S52. Before each training iteration begins, calculate the current error compensation factor based on the error of the previous iteration; the expression of the error compensation factor is as follows: Where: u (t) is the error compensation factor, || || is the L2 norm, λ u is the adjustment coefficient of error compensation, e u (t-1) is the error vector of the t-1th iteration, e u (t-2) is the error vector of the t-2th iteration, e u (t) is the error vector of the tth iteration, y u is the true label vector, is the prediction of the tth iteration; S53, dynamically adjust the weights of the kernel extreme learning machine based on the error compensation factor and kernel technology; The expression of the weight of the kernel extreme learning machine is as follows: in: is the weight of the kernel extreme learning machine at the t+1th iteration, is the weight of the kernel extreme learning machine at the tth iteration, η u is the learning rate of the kernel extreme learning machine, is the gradient of the loss function of the kernel extreme learning machine with respect to the weight, L u ( ) is the loss function of the kernel extreme learning machine, nE is the number of samples in the current batch, and I is the identity matrix; The loss function of the kernel extreme learning machine is expressed as follows: in: is the predicted value calculated by kernel technique and weights, is the true label, λ fg is the regularization parameter; S54: After the weight adjustment is completed, check the gradient value and determine whether the gradient value exceeds the stage threshold; If the gradient value exceeds the preset cutoff threshold, Then perform truncation processing; the expression of the truncation processing is as follows: Where: θ u is the gradient cutoff threshold; S55. Repeat steps S51-S54 until the iteration stop condition is met, obtain the distribution network status classification category and complete the classifier model training.

6. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 1 is characterized in that: The classification result includes any one of a normal charging state, a high load state, a low load state, and a fault state.

7. The method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to claim 6 is characterized in that: According to the classification results, the carrying capacity of the distribution network can be obtained as follows: If the classification result is a normal charging state, it means that the current carrying capacity of the distribution network is normal, which means that the electric vehicle is charging under standard conditions and the impact on the distribution network is within an acceptable range; If the classification result is a high load state, it means that the current carrying capacity of the distribution network is close to the upper limit, which means that during peak power consumption hours or when multiple electric vehicles are charging at the same time, the distribution network needs to handle a larger load; If the classification result is a low load state, it means that the current carrying capacity of the distribution network still has redundant capacity, which means that there are fewer charging electric vehicles or the charging demand is low, and the distribution network can bear the additional load; If the classification result is a fault state, it means that the distribution network does not have the carrying capacity, which means that the distribution network or the charging facility has a fault due to overload or other abnormal conditions.

8. A distribution network carrying capacity assessment system for large-scale vehicle charging loads, characterized by: The system is applied to the method according to any one of claims 1 to 7, and the system comprises: The training set acquisition module (1) is used to acquire data generated when the electric vehicle interacts with the distribution network during the charging process of the electric vehicle, and to mark the data to obtain the original distribution network data training set; The data includes grid load, voltage, frequency, EV usage frequency, mileage, charging habits, charging time, charging station location, charging type, and vehicle type; The marked categories include normal charging state, high load state, low load state and fault state; A data expansion module (2) is used to expand the original distribution network data training set based on a composite data expansion method of variational sparse matrix decomposition and polynomial fitting, and to train a data expansion model to obtain an expanded distribution network data training set; A feature extraction module (3) is used to extract features from the expanded distribution network data training set using a neural network algorithm based on adaptive biological impact optimization, and to train a feature extraction model to obtain a feature distribution network data training set; A feature dimensionality reduction module (4) is used to perform feature dimensionality reduction on a feature distribution network data training set using an autoencoding neural network algorithm based on feature adaptive refinement, and to train a feature dimensionality reduction model to obtain a reduced-dimensional distribution network data training set; A data classification module (5) is used to classify the reduced-dimensional distribution network data training set using a kernel extreme learning machine algorithm based on adaptive error compensation as a classifier, and to train a classifier model to obtain a distribution network state classification category; The carrying capacity assessment module (6) is used to input the new distribution network data set samples into the feature extraction model and the feature dimensionality reduction model in sequence for feature processing, and then input them into the classifier model for classification to obtain the classification results. According to the classification results, the carrying capacity of the distribution network can be obtained.

9. A distribution network carrying capacity assessment device for large-scale vehicle charging loads, characterized by: The device comprises a processor (7) and a memory (8); The memory (8) is used to store computer program code (81) and transmit the computer program code (81) to the processor (7); The processor (7) is configured to execute the distribution network carrying capacity assessment method for large-scale vehicle charging loads according to any one of claims 1 to 7 according to instructions in the computer program code (81).

10. A storage medium for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads, having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the carrying capacity of a distribution network for large-scale vehicle charging loads according to any one of claims 1 to 7 is implemented.

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