1140 results about "Modal decomposition" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention provides a high-temperature chemical pipeline defect detection system and method based on magnetostrictive ultrasonic guided waves, and relates to the technical field of high-temperature chemical pipeline defect detection.The method comprises the steps that magnetostrictive excitation signals are applied, the propagation process of the guided waves in a pipeline is calculated through numerical simulation, and the amplitude and phase information of guided wave signals is obtained; performing time-frequency domain analysis on the guided wave signal, extracting group velocities and phase velocities of guided waves in different modes, determining frequency dispersion characteristics of the guided waves, and evaluating the influence of a high-temperature environment on guided wave propagation; processing the received guided wave signal by adopting a modal decomposition and reconstruction algorithm to obtain a corrected guided wave signal; and carrying out feature extraction and parameter estimation on the corrected guided wave signal to obtain the position, size and type information of the pipeline defect. According to the invention, the problem of multilayer heterogeneous pipeline guided wave detection in a high-temperature environment is solved, the accuracy and reliability of defect detection are improved, and an effective method is provided for pipeline health monitoring and safety assessment.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention discloses an insurance intelligent decision engine system based on a multi-modal user portrait, and relates to the technical field of insurance industry, the system comprises a semantic representation space construction unit used for performing feature decoupling on collected multi-modal data by using variational modal decomposition, extracting modal features, and constructing a semantic representation space based on each modal feature; a user portrait generation unit; a user classification result acquisition unit; and the insurance decision-making unit is used for analyzing the multi-source health data in the user portrait by using a cross-modal alignment technology, constructing a layered health risk assessment model, and generating an insurance recommendation strategy and a risk avoidance scheme in combination with a user classification result. According to the method, resource waste and efficiency loss caused by scattered storage and repeated development of data are avoided through multi-modal data fusion and construction of a unified semantic representation space; and in combination with a hierarchical label system, the user portrait can be automatically generated, and the intelligent level of insurance business is enhanced.

The invention relates to the technical field of side slope deformation prediction, and discloses a side slope three-dimensional deformation prediction method, which comprises the following steps of: 1, carrying out InSAR three-dimensional time sequence deformation inversion to generate three-dimensional time sequence deformation of a side slope high-coherence point; 2, decomposing the three-dimensional time sequence deformation by using a grey wolf optimization algorithm and a variational mode decomposition model to obtain a trend term, a periodic term and a residual error; 3, establishing a deep learning network prediction model considering the environmental impact factors of the region where the slope is located; and step 4, generating a slope time sequence deformation prediction value. The method comprises the following steps: resolving a three-dimensional deformation sequence by using a synthetic aperture radar time sequence image; constructing a variational mode signal decomposition model based on a grey wolf optimization algorithm; and establishing a deep learning network prediction model considering the environmental impact factors. By means of the method, the difficult problem of the hydropower station dam area slope three-dimensional real-time deformation prediction technology can be solved, environmental physical factors such as the reservoir water level, the gradient and the direction are fully considered, and rapid, real-time and accurate three-dimensional deformation prediction of the slope can be achieved.

A deep learning model based method for forecasting online ride-hailing short-term demand is provided. The deep learning model based method includes S1: collecting online ride-hailing demand data in a large transportation hub, and preprocessing original data, to form a data set; S2: performing time series decomposition, specifically, decomposing time series data processed in S1 through a variational modal decomposition (VMD) method, to obtain the certain number of intrinsic mode functions; S3: forecasting a decomposed model by means of a deep learning model Transformer; S4: performing sub-series integration, specifically, accumulating forecast results in S3, to obtain an integrated forecast result; and S5: performing forecast error correction, specifically, correcting a forecast error by using a time series forecast model, that is, an autoregressive integrated moving average model (ARIMA).

The invention relates to the technical field of performance detection, and discloses a blade structure performance detection device and method, and the method comprises the steps: carrying out the laser ultrasonic scanning of a titanium alloy engine blade, and obtaining multi-mode spectrum feature data; performing parameter inversion and iterative optimization calculation according to the multi-modal spectrum characteristic data to obtain material parameter distribution data of a local area of the blade; performing annular scanning analysis on the titanium alloy engine blade based on the material parameter distribution data to obtain local structure feature data; performing modal decomposition on the local structure feature data to obtain fatigue damage accumulation data; and inputting the accumulated fatigue damage data into a dual decoupling diagnosis model for comparative learning and antagonistic feature separation to obtain a target diagnosis result, the method overcomes the limitation of a traditional linear superposition method, accurately predicts the fatigue damage evolution law under a complex load, effectively solves the problem of working condition interference, and improves the diagnosis accuracy. And the robustness and generalization of fault diagnosis are enhanced.

The invention relates to a hydroelectric power generation prediction method and system based on a VMD-LSTM-KAN model. The method comprises the following steps that historical power generation data and historical meteorological data of a target hydropower station are collected and preprocessed; carrying out VMD variational mode decomposition on the preprocessed historical power generation data, and decomposing the historical power generation data into a plurality of mode components from time sequence data; combining the plurality of modal components and historical power generation data into a multi-dimensional output matrix; fusing the preprocessed historical meteorological data with the multi-dimensional output matrix to generate an input data set; constructing a prediction model based on the LSTM network, performing nonlinear optimization on an output layer of the LSTM network in combination with a KAN network, and training the constructed prediction model through an input data set; and predicting a modal component prediction value and a power generation prediction value of the target hydropower station in a future time period through the trained prediction model.

The invention belongs to the technical field of bridge health monitoring, and aims at solving the problem that a traditional real-time cable force calculation method is low in recognition precision. The invention provides an inhaul cable real-time cable force identification method and device based on deep learning. The method comprises the following steps that vibration image information of a target inhaul cable is acquired; obtaining a target image sequence based on the vibration image information; adopting an optical flow estimation algorithm based on deep learning to obtain vibration displacement of a target inhaul cable in the target image sequence; variational mode decomposition transformation is conducted on the vibration displacement, and the mode component and the real-time frequency of the target inhaul cable are obtained; performing fast Fourier transform on the modal component, and determining the inherent frequency of each order and the corresponding order of the target inhaul cable; and calculating the real-time cable force of the target cable based on the real-time frequency and the order of the inherent frequency corresponding to the real-time frequency. According to the method, the vibration displacement identification precision of the target inhaul cable is improved, and the robustness of real-time cable force identification is improved.

The invention discloses a harbor shore power system oscillation damping suppression method based on heuristic probability optimization. According to the method, sliding window preprocessing is carried out on an alternating current bus side current time domain oscillation signal, modal decomposition is carried out based on a Prony algorithm, and modal energy is calculated to judge an oscillation mode; the discriminated oscillation modes are aggregated and mapped into equivalent circuit elements, a corresponding three-order circuit state matrix is constructed, and the damping ratio of each mode is determined according to the characteristic value of the matrix; taking a damping ratio as an evaluation index, introducing an influence factor to construct a multi-objective optimization function, parameterizing the parallel adjustable impedance, and limiting an adjustable range; and searching the control parameters by adopting a heuristic probability optimization strategy to obtain final damping control parameters, and downloading the final damping control parameters to the parallel adjustable impedance device to realize online dynamic adjustment. According to the method, multi-modal identification, physical magnitude mapping and online adaptive optimization can be realized, and the suppression capability and the operation stability of the shore power system on sub / super synchronous oscillation are improved.

The invention belongs to the technical field of time series prediction, and more specifically relates to a time series data prediction method based on a deep integrated learning model and high and low frequency separation. The method comprises the following steps: collecting time sequence data in a system, and preprocessing the obtained data; vMD variational mode decomposition is carried out on original time sequence data, and a time sequence is decomposed into K mode components with limited bandwidth; combining a maximum information coefficient method and a reconstruction error analysis method to determine an optimal decomposition mode number K; dividing high and low frequency components for all decomposed modes by using a zero-crossing rate and a center frequency; appropriate prediction models are established for the high-frequency component and the low-frequency component respectively; and superposing all modal prediction results to obtain a final time sequence prediction result. According to the method, multiple deep learning models are combined, so that the prediction method has higher flexibility and adaptability, and proper models can be selected for components with different characteristics for prediction, thereby improving the overall performance.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention relates to the technical field of energy storage frequency modulation, in particular to a prediction method and system for an energy storage frequency modulation instruction, and the method comprises the steps: decomposing a frequency modulation instruction sequence Pt through VMD, and obtaining a plurality of IMF subsequences; according to the total area Sii of the current IMF sub-sequence and the test group error Wi, judging whether the current IMF sub-sequence is a false sequence or not by using an activation function; and improving the IMF sub-sequence of which the judgment result is false, and performing prediction output of a frequency modulation instruction by utilizing the improved IMF sub-sequence. The method has the advantages that false sequences possibly generated in the variational mode decomposition process can be effectively recognized and improved. According to the method, the false sequence can be accurately found out and processed by calculating the area defined by the adjacent data points in the subsequences and the X axis, the prediction error is reduced, and the prediction precision is improved. In addition, a false sequence is optimized by means of generating a random sequence, performing mirror image transformation and the like, and the response speed and the frequency modulation efficiency of the system are further improved.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a primary equipment state monitoring method and system in a new energy power system, and the method comprises the following steps: deploying a multi-mode sensor array on primary equipment, and synchronously collecting a voltage signal, a current signal, a temperature signal, a vibration signal and an environment parameter; inputting the collected data into an edge computing node for preprocessing to obtain a multi-modal signal sequence; variational mode decomposition is carried out to form a multi-dimensional feature vector; inputting a long-short-term memory neural network model containing an attention mechanism, performing training and reasoning by using an AdamW optimizer and a cosine annealing learning rate strategy, and outputting the health degree of equipment; determining the weight of each monitoring index based on an analytic hierarchy process, and dividing the equipment into a plurality of state grades; the fault probability is obtained through fuzzy Petri net reasoning, and a corresponding early warning mechanism is triggered according to a preset threshold value. According to the invention, through multi-dimensional data fusion and intelligent analysis, the accuracy and real-time performance of state monitoring are significantly improved.

The invention discloses a rolling bearing fault diagnosis method based on a WSETCNNLBKALSSVM, and the method comprises the steps: S1, carrying out the modal decomposition of a rolling bearing vibration signal collected by a sensor through employing the high time-frequency resolution characteristic of wavelet synchronous extraction transformation, obtaining a decomposed time-frequency image, and carrying out the lossless compression processing of the time-frequency image; s2, dividing the time-frequency image data after lossless compression processing into a training set and a test set according to a set proportion; s3, inputting the generated time-frequency image data in the training set into a two-dimensional convolutional neural network, and performing adaptive fault feature extraction; and S4, taking an output result of a full connection layer of the two-dimensional convolutional neural network as an input of a least square support vector machine, optimizing hyper-parameters of the LSSVM by adopting an improved black-wing optimization algorithm, and completing classification of fault types through the LSSVM. The rolling bearing fault diagnosis method has higher classification precision and diagnosis accuracy in rolling bearing fault diagnosis.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

The invention discloses a power grid broadband oscillation tracing method and system based on a modal dynamic modulation graph network, and the method comprises the steps: inputting an oscillation data sequence of a power grid and a power system graph into the modal dynamic modulation graph network to obtain an oscillation source positioning result based on the power system graph; the processing of each stage of modal dynamic modulation graph module in the modal dynamic modulation graph network comprises the following steps of: modulating an oscillation data sequence through a modal modulator and then performing characteristic modal decomposition; key features are extracted through a convolution layer and a pooling layer; dynamic modulation is carried out based on a Prony dynamic modulator, and feature blocks are segmented; key features are extracted through a convolution layer and a pooling layer; extracting node features through a graph network; and realizing power grid broadband oscillation traceability by using a classifier according to node features. The method aims at reducing interference of other information on broadband oscillation information, data are dynamically analyzed from the two dimensions of time and space, and broadband oscillation source positioning is rapidly and accurately achieved.

The invention discloses a multi-source observation data dam automatic monitoring and early warning method fused with deep learning, and relates to the technical field of hydraulic engineering, a grey wolf optimization algorithm is used to optimize variational mode decomposition parameters, and an envelope entropy is used as a fitness function to obtain an optimal parameter combination; introducing a multi-scale permutation entropy as a standard for screening signals, determining an effective modal component, and reconstructing the effective modal component to obtain an effective signal; and according to the optimal parameter combination and the effective signal, fusing a deep learning algorithm long-short term neural network to carry out multi-source observation data dam deformation feature learning, taking a mean absolute error and a root-mean-square error as evaluation indexes of GWO-VMD-LSTM prediction accuracy, introducing a decision coefficient to judge the performance of a prediction model, and carrying out multi-source observation data dam deformation feature prediction. And accurate prediction of dam deformation displacement is realized. The prediction result has higher accuracy and precision, and a reliable basis is provided for automatic monitoring and early warning of the dam.

The invention belongs to the technical field of radar signal processing, and discloses a sea surface small target detection method based on optimized characteristic mode decomposition, which comprises the following steps: S1, acquiring to-be-detected signal data; s2, decomposing an original signal into a plurality of modal components by using FMD, and selecting an envelope spectrum entropy as a fitness function; s3, performing global optimization on the fitness function in the FMD by using an SOS algorithm; s4, introducing a PSO algorithm to carry out local optimization on key parameters of the FMD; s5, components with low envelope spectrum entropy values and correlation coefficients larger than a threshold value are reserved; s6, extracting an envelope spectrum entropy and frequency band energy ratio feature from the screened modal components, introducing a Gini coefficient as a weighting factor, and constructing a GSEBE joint feature; and S7, inputting the entropy value of the envelope spectrum into a DELM classifier with a controllable false alarm, and realizing target detection based on comparison between a predicted value and a judgment threshold. According to the invention, the capability of distinguishing sea clutters and target echoes is enhanced, and more accurate classification detection is realized.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention discloses a traveling wave head fault positioning method and system based on modal decomposition and a neural network, and the method comprises the steps: obtaining a first traveling wave signal on a power transmission line, and extracting a waveform feature corresponding to the first traveling wave signal; inputting the waveform characteristics into a preset parameter optimization model, determining a corresponding optimal decomposition parameter, performing multi-scale variational mode decomposition on the first traveling wave signal by using the optimal decomposition parameter to obtain a plurality of initial modes, and determining a corresponding key mode based on each initial mode; inputting the key mode into a preset distortion elimination model, determining a distortion result of each sampling point in the key mode, and filtering a distortion section of the first traveling wave signal based on the distortion result and the key mode to obtain a second traveling wave signal; and determining a traveling wave head abrupt change point based on the second traveling wave signal, and determining a corresponding fault position based on the traveling wave head abrupt change point. According to the invention, the accuracy of traveling wave head fault positioning can be improved.

The invention discloses a power system load prediction method and system based on modal decomposition and an independent attention mechanism. The method comprises the steps of obtaining load historical data used for load prediction; performing empirical mode decomposition on the load historical data to obtain a first intrinsic mode function component, and obtaining a second intrinsic mode function component based on the first intrinsic mode function component; the method comprises the following steps of: constructing an i Transform model containing an independent attention mechanism; and inputting the second intrinsic mode function component into the i Transform model, and outputting a load prediction value. Compared with a current mainstream prediction model, the model provided by the invention has higher prediction performance, and when the model is applied to the power grid, the precision and efficiency of load sequence data analysis and prediction can be remarkably improved, the stability and robustness of the power grid are enhanced, the efficiency of power grid management and operation is improved, and powerful support is provided for the development of the smart power grid.

The invention relates to the technical field of power quality monitoring, and discloses a method for identifying power quality disturbance characteristics of a distributed photovoltaic interference power supply, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power quality signal of a photovoltaic system and carrying out denoising, normalization and time alignment; and S2, carrying out disturbance signal adaptive decomposition based on improved variational mode decomposition, and automatically selecting a mode number by utilizing information geometric optimization. An improved variational mode decomposition method is adopted, information geometric optimization and Bayesian optimization are combined, self-adaptive mode decomposition of disturbance signals is achieved, the optimal mode number and penalty factors can be automatically determined through improved variational mode decomposition, decomposition errors caused by manual parameter setting in traditional variational mode decomposition are avoided, and the method is suitable for large-scale popularization and application. Compared with a fixed parameter variational mode decomposition method in the prior art, the method has the advantages that the analysis capability of complex disturbance signals is improved, and the disturbance mode in the photovoltaic system is accurately decomposed.

The invention discloses a short-term load prediction method based on variational mode decomposition and a recurrent neural network, and the method specifically comprises the following steps: firstly, obtaining the load data of each charging station through processing the operation order data of the charging station; then, by analyzing the load data of the charging station, constructing a time sequence data set of historical load data of the charging station; secondly, decomposing the load flow data of the charging station based on a variational mode decomposition algorithm; thirdly, training and fitting the load flow data of the charging station based on a recurrent neural network; secondly, adopting a zebra optimization algorithm to adjust and obtain optimal parameters of the variational mode decomposition model and the recurrent neural network; and finally, performing load prediction on each modal component according to the trained recurrent neural network to obtain a load prediction result of the charging station. Noise separation and smooth decomposition processing are carried out on the load data, the load prediction precision of the charging station is improved, and effective services are provided for operation and maintenance of the charging station.