87 results about "Permutation entropy" patented technology

Provided is a rolling bearing fault diagnosis method based on FFT coding and L-CNN, including: obtaining original bearing fault vibration data, extracting intrinsic mode components of different frequency bands in the original bearing fault vibration data, calculating a permutation entropy value corresponding to each of the intrinsic mode components, and performing wavelet threshold denoising according to the permutation entropy value to obtain a denoised reconstructed time domain signal; performing fast Fourier transform to the denoised reconstructed time domain signal to obtain a frequency domain signal and a phase angle corresponding to the time domain signal, reconstructing the frequency domain signal according to a preset rule, retaining frequency domain data of features of the phase angle, and drawing FFT-x heat maps of different fault type data according to an amplitude range; and constructing an model L-CNN, and inputting coded data into the L-CNN model, and obtaining fault diagnosis results.

The invention discloses a transformer substation on-line monitoring system based on big data analysis, and relates to the technical field of power system state monitoring, the system obtains data through a data acquisition module, and after the data is processed by a signal preprocessing and feature window extraction module, a system response entropy calculation module calculates a multi-scale permutation entropy, a multi-scale fuzzy entropy and a transfer entropy; the method comprises the following steps of: constructing a composite entropy feature vector, establishing a working condition self-adaptive health entropy baseline by an entropy feature baseline management module by utilizing a machine learning algorithm, comparing a current entropy feature with the health baseline by a degradation evaluation and critical early warning module, performing analysis by combining various abnormal judgment rules and indexes based on a critical moderation theory, and outputting an evaluation and early warning result; according to the method, the functional out-of-order of the equipment can be sensed in advance, the dynamic interaction health degree is evaluated in a non-intrusive mode, effective early warning is provided for critical transformation such as system instability, and the reliability and safety of operation of the transformer substation are remarkably improved.

The invention relates to the technical field of wind speed prediction, in particular to a wind speed prediction method and system based on entropy clustering, and the method comprises the following steps: carrying out the multi-scale decomposition of a wind speed sequence through a permutation entropy algorithm, generating subsequences, carrying out the sliding window probability distribution calculation of a plurality of subsequences, setting a dynamic classification threshold value based on a normalized entropy value, and carrying out the calculation of the sliding window probability distribution. And dividing the sub-sequence set into an entropy classification result set. According to the method, a classification threshold value is dynamically set through sliding window probability distribution, the sensitivity of fixed entropy division to data complexity change is overcome, nonlinear mapping between an original sequence and entropy components is established through GRU training, the feature extraction depth and low component error transmission are enhanced, high, medium and low entropy components are modeled through Bi-RNN, LSTM and SVR, feature extraction with different complexities is matched, and the accuracy of feature extraction is improved. Chaotic mapping optimization fusion weight, multi-target optimization balance component complementarity and dynamic error reference are combined with feedback to adjust a confidence interval, error distribution adaptive tracking is realized, and prediction reliability is enhanced.

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 discloses a financial traceability risk measurement and analysis method and system based on dynamic entropy transfer, and the method comprises the steps: constructing a dynamic entropy transfer graph through combining with various types of structure information, such as local guiding entropy, path exploration entropy and behavior collaborative entropy; the method can effectively describe the propagation path and the structure level of the risk factor in the complex financial network while keeping the calculation controllable. According to the method, the edge behavior sequence is constructed through the Gaussian kernel function on the basis of the node behavior time sequence, and the multi-scale permutation entropy is combined to model the inter-edge time sequence collaborative change, so that the expression ability of the system to the microscopic interaction behavior is remarkably improved, and the method has good adaptability and interpretability. The method can achieve the positioning of a potential high-influence risk source and the reconstruction of a propagation chain, is suitable for a plurality of scenes, such as financial supervision, risk early warning and abnormal path tracking, is high in processing efficiency, and is stable and reliable in result.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention relates to the technical field of power system energy storage equipment, in particular to an energy storage frequency modulation instruction prediction method and system based on chaotic phase injection. The method comprises the steps of selecting a candidate period to segment an original frequency modulation instruction to generate a sub-sequence set, calculating the dynamic time warping distance of all extracted sequence pairs and calculating and obtaining the similarity integral of the candidate period, screening a real period through standardized saliency test, selecting an optimal period with the maximum saliency value, and obtaining the optimal frequency modulation instruction. And calculating a consistency index in combination with the permutation entropy of the optimal period sub-sequence set, reconstructing an original frequency modulation instruction based on chaotic phase injection, and inputting a reconstructed sequence into the pre-trained GRU network to output a predicted value. According to the method, the nonlinear time sequence alignment problem is solved through the dynamic time warping distance, the chaotic intensity and the periodic disturbance are controlled through the PCI, anti-noise periodic detection and chaotic feature decoupling are achieved, finally, the prediction precision is improved, and response lag is eliminated.

The invention discloses a migration working condition bearing remaining service life prediction method based on a contrast double-confrontation mechanism, and the method comprises the steps: obtaining a bearing vibration signal of a single time step and a corresponding label, decomposing the bearing vibration signal of the single time step, obtaining an intrinsic mode IMF, reconstructing the bearing vibration signal of the single time step, and obtaining a prediction result of the bearing remaining service life. Fault point detection is carried out; constructing a sample containing time sequence information; constructing a residual service life prediction model, inputting the sample into the residual service life prediction model, and outputting the residual service life corresponding to the sample; the residual service life prediction model comprises a dynamic convolution long-short memory neural network feature extractor and a predictor; and constructing a migration training framework of a contrast double-confrontation mechanism. According to the method, noise reduction processing is carried out on an original vibration signal in combination with multi-scale permutation entropy, and fault occurrence point detection in the bearing degradation process is carried out by using hidden Markov based on Bayesian reasoning. Through the technology, the influence of non-degradation data information on the residual service life after degradation is reduced, and the workload and prediction difficulty of a prediction task are reduced.

The invention relates to the technical field of nonlinear dynamics and chaotic systems, in particular to a chaotic system model construction method based on state variables and uncertain variables. Starting from the angle of combination of a state variable and an uncertain variable, a construction method with a unified form is proposed in combination with an amplitude undetermined matrix, the feasibility of the construction method is verified by using a lyapunov index definition, and the dynamic behavior change of the system under the condition that the system is influenced by unknown parameters is explored. The permutation entropy is used to explore system chaotic behavior changes caused by random natural numerical values and mathematical functions in random amplitudes, a three-dimensional continuous chaotic system model structure is used as a basis to expand into a four-dimensional state, and different complexities are used to display system complex characteristics in the four-dimensional state. A three-dimensional continuous system and a four-dimensional continuous system are applied to a classical chaotic system DNA color image encryption system. The method provided by the invention not only has theoretical innovation in the aspect of constructing a chaotic system, but also shows a relatively high practical value in practical application.

The invention relates to the field of water environment pollution detection and analysis, in particular to a water environment pollution detection and analysis system and method based on the Internet of Things, and solves the problems of sampling redundancy, missing detection, high-dimensional data over-fitting, difficulty in traceability and early warning lag in traditional detection. The system comprises a monitoring acquisition module, a water quality anomaly detection module, a pollution tracing module, a pollution early warning module and a database. The monitoring acquisition module adopts chaotic adaptive sampling to adjust sensor frequency, and entropy optimization water quality data is obtained by combining permutation entropy and collaborative filtering denoising; the water quality anomaly detection comprises the following steps: standardizing data into high-dimensional point clouds, calculating a Betti number and a non-equilibrium state entropy, and fusing to generate a comprehensive pollution topological entropy index; according to pollution traceability, a dynamic graph and a diffusion kernel matrix are constructed by using GNN, and the optimal source intensity is solved through quantum annealing; the range is predicted through a diffusion equation for early warning, and information is integrated to generate a report to be pushed to users. The method corresponds to a module flow and provides support for pollution prevention and control.

The invention belongs to the technical field of power grid load prediction, and particularly provides a micro-grid load prediction method and system based on big data, and the method comprises the steps: collecting micro-grid multi-source data of a preset service area through employing an Internet of Things sensor, carrying out the denoising cleaning of the micro-grid multi-source data, and obtaining the processed multi-source data; performing data-level fusion on the processed multi-source data to obtain fused data; performing multi-scale decomposition on the fused data based on an empirical mode decomposition method combined with multi-scale permutation entropy to obtain a multi-scale sub-data sequence; based on the multi-scale sub-data sequence, constructing a micro-grid load prediction model by adopting a time convolution and time sequence mode attention mechanism combined algorithm; and on the basis of the micro-grid load prediction model, predicting a micro-grid load to be predicted to obtain a load prediction result. According to the technical scheme, the accuracy, robustness and calculation efficiency of load prediction can be remarkably improved.

The invention belongs to the technical field of wind power prediction, and particularly discloses an ultra-short-term wind power prediction method, which comprises the following steps: S1, obtaining an original wind power sequence; s2, decomposing the wind power signal into a series of sub-signals by adopting a CEEMDAN signal decomposition algorithm; s3, denoising and reconstructing the IMF1 component by adopting an improved singular spectrum analysis (SSA) algorithm; s4, the permutation entropy of each component is calculated, the components are divided into high frequency, medium frequency and low frequency according to the permutation entropy, the high frequency is combined together, and the medium frequency and the low frequency are combined together; and S5, sending the data into the established TCN-GRU prediction network, and superposing the results of the two prediction networks to obtain a final prediction result. According to the ultra-short-term wind power prediction method, the prediction performance of the model is better than that of a traditional model and other models based on TCN, high-precision and high-stability prediction of the wind power plant power is achieved, and decision support is provided for operation management of the wind power plant.

The invention relates to a sea surface target recognition method applying time-frequency domain multi-scale permutation entropy features, and belongs to the technical field of radar signal processing and feature recognition. The method comprises the following steps: step 1, caching target echo data; 2, carrying out time-frequency domain analysis on the target echo data; step 3, target time-frequency domain multi-scale permutation entropy feature extraction; and step 4, target identification is carried out by using the time-frequency domain multi-scale permutation entropy features. The objective of the invention is to identify a sea surface target under the condition that target identification is difficult to be effectively performed by traditional target features.

The invention relates to the technical field of intelligent operation and maintenance supervision, and discloses an industry intelligent operation and maintenance supervision method and system based on a large model. The method comprises the steps of collecting multi-source time sequence data of an industry operation and maintenance system; performing time-frequency conversion on the data to obtain time-frequency feature representation, and configuring a decomposition basis function and hierarchical parameters of an adaptive decomposition model according to the time-frequency feature representation; the model is used for carrying out multi-scale analysis on data to obtain a multi-resolution characteristic component, and permutation entropy of the multi-resolution characteristic component is calculated to serve as complexity measurement. And based on the feature component and complexity, selecting a core feature set through importance evaluation, and generating a customized monitoring strategy in combination with a system operation baseline portrait, a real-time working mode and large model reasoning. Constructing an operation and maintenance event feature sequence according to the core features and strategies, and performing variational mode decomposition on the operation and maintenance event feature sequence to extract steady-state and transient components; and a machine learning classifier is adopted to carry out mode recognition on the components, event categories are output, and intelligent operation and maintenance decision and accurate supervision are realized.

The invention provides a data-driven saline screw compressor unit dynamic modeling method, and belongs to the technical field of industrial intelligence and predictive maintenance. According to the method, adaptive deep denoising of original data is realized through a composite signal processing flow of fusing variational mode decomposition, permutation entropy criterion and wavelet packet transformation optimal threshold denoising; meanwhile, a sectional sampling strategy is introduced to enhance the diversity of training data. Then, a wavelet multi-scale energy entropy extraction layer is used for constructing a high-information-density feature vector; furthermore, a prediction model formed by multiple layers of stacked long and short-term memory network units is adopted, and the complex time sequence dependency relationship of the system is deeply captured. According to the method, pure and stable system dynamic representation can be extracted from high-noise industrial data, the dynamic characteristics of the system in the full working condition range are accurately described, and it is ensured that the prediction result is self-consistent physically and reliable in engineering, so that the prediction precision and generalization performance of the model are remarkably improved.

The invention discloses a similarity query method and system for any long-time sequence data, and the method comprises the steps: firstly, extracting permutation entropy spectrum features of subsequences from segmented feature representation; then, through data index construction, extracting a prefix feature vector represented by segmented entropy, and constructing a spatial index based on an Euclidean distance; finally, through query processing, the candidate set is filtered based on lower bound distance query, and accurate similarity query is achieved. According to the segmented entropy representation, the influence of data standardization on distance measurement can be effectively avoided, unified and efficient query processing is achieved for query requirements of any length, the space overhead of indexes is remarkably reduced, the query efficiency is improved, and the method has high robustness and is suitable for large-scale popularization and application. The method can play an important role in the fields of big data and data mining, artificial intelligence, databases and the like.

The invention discloses a current transformer error prediction method based on improved adaptive composite mode decomposition. The method comprises the following steps: preprocessing current transformer measurement data based on improved fully adaptive noise ensemble empirical mode decomposition (ICEEMDAN); performing feature selection and reconstruction on the complex components decomposed by the ICEEMDAN based on a multi-scale permutation entropy and a probability density function; further decomposing the reconstructed complex component based on variational mode decomposition (VMD); and constructing a depth prediction model based on CNN-BiGRU, optimizing sub-model network parameters by using a target optimization method, and finally superposing sub-results to obtain a final prediction result of the current transformer. According to the method, ICEEMDAN is comprehensively utilized for signal decomposition, MPE and PDF are utilized for feature selection and reconstruction, VMD is utilized for further decomposition of complex components, and CNN-BiGRU-MHA deep learning architecture is utilized for feature learning and prediction, so that nonlinear and non-stationary signals can be effectively processed, deep-level features can be extracted, a complex dynamic relationship in time sequence data can be captured, and the time sequence data can be obtained. And the prediction accuracy and the generalization ability of the model are improved.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

The invention relates to a daily runoff prediction method based on adaptive signal decomposition and deep learning fusion. The method comprises the following steps: acquiring daily runoff sequence data of a historical time period; adopting an ICEEMDAN algorithm to decompose the standardized runoff sequence into a plurality of intrinsic mode function components and one residual component; calculating the permutation entropy of each IMF component and screening noise dominant components; carrying out wavelet hard threshold noise reduction processing on the screened noise dominant component; carrying out superposition reconstruction on the noise dominant component after noise reduction, the effective characteristic component and the residual component; an ICEEMDAN-CNN-LSTM hybrid prediction model is constructed, and an ICEEMDAN- Constructing a sample set; training a hybrid prediction model; and performing prediction based on the daily runoff prediction model. According to the method, the prediction precision of the non-stationary runoff sequence is remarkably improved, the dependence on hydrological observation conditions is reduced, the model adaptability is high, the application threshold is low, and the method can be widely popularized to flood control and disaster reduction, water resource optimal configuration and other scenes.

The invention discloses a cable fault feature extraction method and device based on an improved GAN algorithm, and a medium, and the method comprises the steps: firstly collecting the three-phase current of a cable, and carrying out the optimization of an input potential variable of a training generator based on permutation entropy and approximate entropy; importing the potential variables into a training generator to obtain synthetic data, and expanding the small sample fault data set; importing the real data and the synthetic data into a training discriminator for discrimination, and correcting network parameters of the discriminator and the generator according to a discrimination result; when the discriminator cannot distinguish the synthetic data from the real data, outputting sample data sets with the same quantity in different states; feature values of the three-phase current of the cable in the time domain, the frequency domain and the time-frequency domain under the healthy and fault working conditions are taken, and a feature matrix is established; according to the technical scheme, the problem that the number of samples is unbalanced in existing cable fault diagnosis can be solved, the stability and accuracy of synthetic data are greatly improved, the sample expansion process is accelerated, and convenience is provided for extraction of fault features.

The invention discloses a ship equipment degradation state identification method based on a multi-source fusion feature and a series DBN model, and belongs to the technical field of health state management, and the method comprises the steps: extracting a center frequency value of each component of a signal feature; carrying out signal feature analysis by adopting an improved multi-scale permutation entropy, and obtaining a stable permutation entropy value according to a scale factor; obtaining a center frequency corresponding to the maximum kurtosis value of the signal, screening a plurality of BIMF component layer-by-layer data sets, and constructing a feature vector set Q through permutation entropy; performing dimension reduction processing on the feature vector set Q to obtain a feature vector set T; training the DBN1 by adopting the feature vector set T, and training the DBN2 by combining the working condition identification result with the feature vector set T to realize ship equipment degradation state identification; and establishing an observer model, obtaining a health baseline of the ship equipment, and evaluating the degradation state of the identified ship equipment by calculating a mahalanobis distance. According to the invention, the degradation state of the key equipment can be accurately and rapidly evaluated.

The invention relates to the technical field of fluid mechanical fault diagnosis, and discloses an emulsion pump fault intelligent diagnosis method and system based on multi-dimensional entropy feature fusion, and the method comprises the steps: constructing a multi-dimensional physical field state monitoring space, and synchronously collecting vibration acceleration, outlet pressure and flow time sequence signals; the method comprises the following steps: performing adaptive variational mode decomposition and effective sensitive component screening reconstruction on a vibration signal, calculating a normalized vibration quantile permutation entropy through phase-space reconstruction and quantile mapping, extracting a pressure fluctuation entropy and a flow pulsation variance, fusing the pressure fluctuation entropy and the flow pulsation variance into a multidimensional fault feature vector, inputting the multidimensional fault feature vector into a multi-classification support vector machine, and outputting an operation state label; executing hierarchical closed-loop control; according to the method, through quantile mapping and multi-physics field fusion, the problems of entropy value feature distortion and liquid-machine coupling weak fault feature masking caused by non-Gaussian impact noise are effectively solved, and the fault diagnosis robustness and accuracy of the emulsion pump under complex working conditions are improved.

The invention provides a wind power probability prediction method, which comprises the following steps: calculating a Moran index value of an area where a wind power plant cluster is located, extracting spatial features, and forming a final input matrix; decomposing the wind power time sequence data to obtain an initial component, and decomposing the initial component to obtain a plurality of components; calculating an entropy value by adopting a fine composite multi-scale weighted permutation entropy, and performing comprehensive measurement and analysis by adopting an averaging method to obtain a plurality of comprehensive components; according to the FAAM-Stacking model, a final deterministic prediction result is obtained, and the performance is verified; and according to the error of the final deterministic prediction result, a distributed kernel density estimation method is adopted to obtain a wind power interval prediction result, and the wind power interval prediction result is verified. According to the method, the problems of poor decomposition and influence of different bandwidths of each component on an estimation error are effectively avoided, and the prediction precision of the wind power can be effectively improved.

The invention discloses an ultra-short-term wind power prediction method and system for a long-short-term memory network of permutation entropy, and the method comprises the steps: collecting historical wind power data, and repairing the missing data and abnormal values in the historical wind power data; performing normalization processing on the preprocessed historical wind power data; inputting the normalized data, optimizing and solving the number K of decomposition modes and a penalty factor alpha by using a multi-target grey wolf algorithm, ensuring that fitness functions RMSE and PE are globally minimum, obtaining solved parameters, and further performing decomposition to obtain decomposed subsequences; the decomposed subsequences are used as input, and then prediction model training of the subsequences is carried out; and carrying out rolling decomposition, inputting a prediction model of the subsequences, superposing predicted values of the subsequences, and carrying out reverse normalization to obtain a prediction result, thereby realizing rolling prediction. According to the method, the problem that the prediction precision of data with strong volatility is not high in the existing prediction technology is solved, and the problem that the wind power plant faces assessment due to inaccurate prediction is reduced.

The invention provides a fetal brain age prediction network training method, an application method and electronic equipment, and belongs to the technical field of medical image processing, and the training method comprises the steps: carrying out the similarity sorting conversion of a fetal brain magnetic resonance image and a corresponding brain age label, and obtaining a similarity sorting matrix, performing vector embedding reconstruction on the similarity sorting matrix to obtain an embedded vector, and determining an initial permutation entropy; performing median absolute deviation weighting on the initial permutation entropy to obtain a weighted permutation entropy, performing multi-scale ordinal number information reconstruction on the weighted permutation entropy to obtain a multi-scale permutation entropy, performing time sequence enhancement reconstruction on the multi-scale permutation entropy to obtain a time sequence multi-scale permutation entropy, and constructing permutation entropy regularization loss according to the time sequence multi-scale permutation entropy; and obtaining a fetal brain age prediction network according to permutation entropy regularization loss iterative training. According to the method, the loss function is constructed through the permutation entropy regularizer, so that the continuous ordered relation in regression can be captured, and the distinguishing ability and generalization ability of the fetal brain age prediction network are remarkably improved.

The embodiment of the invention provides a multi-mode collaborative gas pipe network hidden danger identification method and system, and relates to the technical field of gas pipe networks. The identification method comprises the following steps: acquiring data of a pressure sensor, a temperature sensor and a gas concentration sensor at key nodes of the gas pipe network; performing wavelet packet decomposition on the data to obtain sub-band signals of the signals; performing feature extraction on the sub-band signals to obtain an energy entropy, a sample entropy and a permutation entropy; normalizing the energy entropy, the sample entropy and the permutation entropy to obtain a corresponding normalized entropy value; inputting the corresponding normalized entropy value into the intelligent prediction model, and outputting a probability distribution diagram of the pipe network hidden danger position; and leakage point positioning and corrosion degree grading identification are carried out according to the probability distribution diagram. According to the method, data of pressure, temperature and gas concentration sensors in the gas pipe network are effectively integrated, through wavelet packet decomposition and deep feature learning, pipe network leakage, corrosion and other hidden dangers are accurately recognized, and stable operation of a gas supply system is ensured.

The invention relates to a short-term power load prediction method and system based on modal aggregation and optimal integration. The method specifically comprises the following steps: S1, acquiring power load data in a power system; step S2, carrying out preliminary decomposition on the load sequence based on ICEEMDAN; s3, quantifying the complexity of the subsequences by adopting permutation entropy, and reconstructing the subsequences with high entropy similarity through K-medoids clustering to obtain an aggregation mode component; step S4, carrying out further noise reduction on the high-frequency noise component in the aggregation mode component by adopting a Savitzky-Golay filter; s5, adopting BiGRU as a predictor, and respectively training and predicting the aggregation mode components; s6, constructing a weight optimization model by taking prediction precision and stability as targets; step S7, adopting NSDBO to solve the optimization model, and obtaining a Pareto leading edge; s8, TOPSIS is used as a decision-making method, the relative close degree of each Pareto optimal solution is calculated, and an optimal weight scheme is determined; and S9, performing weighted stacking on the aggregation modal prediction sequence based on a decision scheme to obtain a final short-term power load prediction result.

The invention discloses a phase modifier static eccentricity fault diagnosis method based on an improved deep belief network, particularly relates to the technical field of electrical fault diagnosis, and solves the technical problems of low accuracy and high sample dependence degree of a phase modifier fault diagnosis method in the prior art. According to the technical scheme, electromagnetic torque, stator current and rotor current signals of the phase modifier in different fault states are obtained, signal decomposition is carried out on collected multi-source signals through fast fourier transform to obtain pre-features, feature extraction is carried out on the pre-features through ensemble empirical mode decomposition and permutation entropy, and the pre-features are extracted through the permutation entropy; fusing the extracted characteristic parameters to construct a fault sample set; according to the method, the operation state of the phase modifier can be reflected more comprehensively, the defect that single signal diagnosis information is insufficient is overcome, and the accuracy and reliability of fault diagnosis are remarkably improved.

The invention belongs to the technical field of power system operation and planning, and discloses a power grid net load fluctuation scene generation method, system and device based on an ARIMA and Copula combined model and a medium, so as to solve the problem of poor scene generation accuracy. The method comprises the following steps: decomposing and reconstructing an original time sequence of the net load of the power grid by using discrete wavelet transform; taking permutation entropy minimization as an optimization target, adopting a variable chromosome length hybridization genetic algorithm to divide time segments for the low-frequency linear subsequences, and respectively establishing ARIMA models to generate linear trend scenes; establishing a joint probability distribution model of the high-frequency fluctuation subsequences at adjacent moments based on a Copula function, and deducing conditional probability distribution in combination with a Bayesian formula to generate a fluctuation scene; and the linear trend scene and the fluctuation scene are superposed to form an initial net load scene set, and a k-means clustering algorithm is adopted to reduce the initial net load scene set to obtain a representative scene set.

The present invention relates to the field of wind speed prediction technology, specifically to a wind speed prediction method and system based on entropy clustering, comprising the following steps: performing multi-scale decomposition of a wind speed sequence to generate subsequences using a permutation entropy algorithm, performing sliding window probability distribution calculation on the multiple subsequences, setting a dynamic classification threshold based on a normalized entropy value, and dividing a subsequence set into an entropy classification result set. The present invention dynamically sets the classification threshold through a sliding window probability distribution, overcomes the sensitivity of fixed entropy value division to changes in data complexity, establishes a nonlinear mapping between the original sequence and the entropy component through GRU training, enhances feature extraction depth, propagates low-component error, and uses Bi-RNN, LSTM, and SVR to model high, medium, and low entropy components, respectively, to match feature extraction of different complexities, optimizes fusion weights using chaotic mapping, balances component complementarity through multi-objective optimization, and adjusts confidence intervals with dynamic error benchmarks and feedback to achieve adaptive tracking of error distribution, thereby enhancing prediction reliability.