59 results about "Permutation entropy" patented technology

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 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 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 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 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 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 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 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.

The invention provides a charging anomaly detection method and system based on kernel regression, and belongs to the technical field of data processing. The method comprises the following steps: acquiring charging power data in a charging process as original power data; smoothing the original power data to generate smooth data; calculating the difference between the original power data and the smooth data as a first-dimensional feature; based on the first-order gradient and the second-order gradient of the original power data, second and three-dimensional features are calculated respectively; performing Fourier transform on the original power data and the smooth data to obtain fourth-dimensional and fifth-dimensional features; calculating the original power data, the first-order gradient and the second-order gradient of the original power data and the permutation entropy of the first-dimensional feature as sixth-dimensional, seventh-dimensional, eighth-dimensional and ninth-dimensional features respectively; and constructing extended feature data based on the nine features. According to the method, feature information in the charging process can be extracted from multiple dimensions, and accurate and efficient anomaly detection is realized through the kernel regression model.

The invention discloses a high-reliability planetary gearbox fault diagnosis method, system, medium and equipment, and the method comprises the steps: carrying out the feature extraction and feature selection based on a random forest algorithm, and carrying out the nonlinear feature extraction based on multi-scale permutation entropy, performing multi-scale coarse graining processing on each vibration signal, calculating permutation entropy values of coarse graining sequences under different scales, forming a multi-dimensional phase space, and obtaining a feature matrix of permutation entropy; performing feature dimension reduction and feature embedding based on kernel principal component analysis; according to the fault diagnosis based on the bidirectional long-short term memory neural network, a classifier of the bidirectional long-short term memory neural network is used, a feature set is divided into a training set and a test set to serve as input layers, and then a classification result is output through two LSTM units of a hidden layer, a full connection layer and Softmax function mapping. And high-reliability planetary gearbox fault diagnosis is realized.

The application relates to the technical field of intelligent monitoring and protection of power systems, and discloses a grounding fault diagnosis method based on intelligent time sequence feature extraction. In the feature extraction stage, a multi-scale convolutional neural network is adopted, combined with multi-scale permutation entropy, to perform coarse-grained processing on an original signal, calculate permutation entropy values under different scales, quantify the spatial distribution characteristics of signal complexity, and inhibit noise interference. After feature extraction, the features extracted by the multi-scale convolutional neural network and the multi-scale permutation entropy two channels are spliced through a feature fusion layer, and the fused features are input into a BiGRU module. In view of the problems that the traditional fault diagnosis method has insufficient multi-scale feature extraction, weak anti-noise ability and low time sequence modeling precision under complex working conditions, the application fuses multi-scale feature extraction and dynamic time sequence features, realizes high-precision diagnosis of grounding faults, and significantly improves the precision and robustness of grounding fault diagnosis.

The invention discloses a window optimization-based intra-day prospective dispatching method and device for a power grid, and relates to the technical field of power dispatching, and the method comprises the steps: obtaining a power grid data feature prediction model through employing historical power grid data features and an adaptive differential evolution algorithm; obtaining predicted values of intra-day power grid data features based on a power grid data feature prediction model, thereby generating a plurality of operation scenes; calculating corresponding net load power based on each operation scene, determining permutation entropy, sample entropy and fuzzy entropy based on all the net load power, and establishing a combined entropy calculation formula by using a multi-target equalization method; constructing a look-ahead window optimization model based on a combined entropy calculation formula, and solving the look-ahead window optimization model by adopting a carbon black stingless bee optimization algorithm to obtain a window optimization result; based on a window optimization result, establishing an intra-day look-ahead optimization scheduling model; and solving the intra-day look-ahead optimization scheduling model by adopting a state optimization algorithm to obtain a power grid intra-day look-ahead scheduling plan. The flexibility of the power grid dispatching plan is improved.

The present invention relates to a method for predicting short-term satellite clock errors that integrates time series decomposition and deep learning, and belongs to the technical field of satellite clock error prediction. The method comprises: performing first-order difference processing on the historical satellite clock error sequence, and filtering out the outliers in the data according to the Raida criterion, performing time series decomposition on the first-order clock error data, firstly using the adaptive noise complete set empirical mode decomposition algorithm to decompose the first-order clock error sequence to obtain multiple modal components, introducing the permutation entropy algorithm to sort the modal components, and using the t-test algorithm to classify the random terms and periodic terms of the above components and reconstruct them into trend terms, periodic terms and random terms. The reconstructed terms are input into the Transformer model in a channel-independent mode for training, and the required predicted clock error is obtained after the first-order difference processing and superposition of the prediction results of each component. While improving the accuracy and stability of the satellite short-term clock error forecast, the present invention also improves the interpretability of the forecast results.

The application discloses a modeling method and device for a vehicle-network interaction system and a nonvolatile storage medium. The method comprises the following steps: acquiring time series signals in the vehicle-network interaction system; performing modal decomposition on the time series signals to obtain first-layer intrinsic modal components; calculating target permutation entropy of the first-layer intrinsic modal components; in the case that the target permutation entropy of the first-layer intrinsic modal components is greater than a preset entropy threshold value, determining that the first-layer intrinsic modal components are abnormal modal components; repeating the above steps to obtain an abnormal component set and normal modal components; performing denoising processing on the abnormal component set, and performing weighted aggregation on the processed abnormal component set and the normal modal components to obtain a reconstructed signal; and performing federated learning on multiple subjects based on the reconstructed signal to obtain a target global model. The application solves the technical problem that the current modal decomposition technology is prone to modal aliasing and affects the charging and discharging rule analysis in the vehicle-network interaction.

The invention provides an equipment life prediction method and device, electronic equipment and a storage medium, and relates to the technical field of neural networks. Comprising the steps that a feature sequence of to-be-predicted equipment is collected based on an electrical life test result, and the feature sequence comprises feature parameter sets under continuous time steps; decomposing and reconstructing the feature sequence according to the feature sequence of the to-be-predicted equipment and a plurality of preset scale factors to obtain at least one feature subsequence of the to-be-predicted equipment under each preset scale factor; determining the permutation entropy of the to-be-predicted equipment under each preset scale factor according to the sequence permutation mode of each feature subsequence of the to-be-predicted equipment under each preset scale factor, and determining a multi-scale feature matrix corresponding to the to-be-predicted equipment according to the permutation entropy under each preset scale factor; and according to the multi-scale feature matrix corresponding to the to-be-predicted device, using a pre-trained life prediction model to predict life information of the to-be-predicted device. The method improves the accuracy of the life prediction result.

The invention relates to the technical field of intelligent monitoring and protection of a power system, and discloses a ground fault diagnosis method based on intelligent time sequence feature extraction, in a feature extraction stage, a multi-scale convolutional neural network is adopted, multi-scale permutation entropy is combined, coarse graining processing is carried out on an original signal, permutation entropy values under different scales are calculated, and a ground fault diagnosis result is obtained; after spatial distribution features of signal complexity are quantized, noise interference is suppressed and features are extracted, features extracted by two channels of a multi-scale convolutional neural network and a multi-scale permutation entropy are spliced through a feature fusion layer, and the fused features are input into a BiGRU module. Aiming at the problems of insufficient multi-scale feature extraction, weak anti-noise capability, low time sequence modeling precision and the like of a traditional fault diagnosis method under a complex working condition, the method integrates the features of multi-scale feature extraction and a dynamic time sequence, realizes high-precision diagnosis of a grounding fault, and improves the fault diagnosis accuracy. And the accuracy and robustness of grounding fault diagnosis are obviously improved.

The invention relates to the technical field of smart power grids, and discloses a flexible straight valve capacitor fault diagnosis method and system, and the method comprises the steps: obtaining an original signal data set; wavelet packet decomposition is combined with the Leimeh-Tiangers complexity, empirical mode decomposition is integrated with permutation entropy, feature extraction, fusion and dimensionality quantitative optimization are performed on the original signal data set, and the optimal fault feature dimensionality is obtained; a deep belief network model with the visible layer node number equal to the optimal fault feature dimension is constructed, and a target fault diagnosis model is obtained after optimization; in the diagnosis process, the target fault diagnosis model outputs each fault probability, the D-S evidence theory is adopted to fuse the basic probability distribution values obtained by weighting and adjusting the fault probabilities according to the global credibility and the local credibility, and the fused basic probability distribution value is obtained and used for fault diagnosis. The method provided by the invention improves the precision of fault diagnosis, does not need complex diagnosis equipment, and has higher adaptability.

The present application relates to a kind of electromagnetic reversing valve life prediction method based on flow signal, belong to hydraulic component life prediction field.The present application utilizes improved lumped average modal empirical decomposition method, by adding positive and negative pairs of noise to reduce the degree of modal aliasing in modal decomposition, using permutation entropy to detect abnormal component, realize the accurate adaptive modal decomposition of nonlinear measured signal;Application kernel principal component method, introduce nonlinear function as kernel function, based on the principle of mapping, convert original space into high-dimensional space, form new data set, using principal component analysis for data dimension reduction in high-dimensional data space, form feature vector, get performance degradation fusion index;Through cubic exponential smoothing processing;Finally based on the trained adaptive neural network model, establish the life prediction model of electromagnetic reversing valve, calculate the life of electromagnetic reversing valve.This method can effectively predict the pressure drop trend and life of electromagnetic reversing valve.

The present invention discloses an online monitoring system for substations based on big data analysis, which relates to the technical field of power system status monitoring. The system acquires data through a data acquisition module. After being processed by a signal preprocessing and feature window extraction module, a system response entropy calculation module calculates multi-scale permutation entropy, multi-scale fuzzy entropy and transfer entropy to construct a composite entropy feature vector. An entropy feature baseline management module uses a machine learning algorithm to establish a healthy entropy baseline that is adaptive to working conditions. A degradation assessment and critical warning module compares the current entropy feature with the healthy baseline, analyzes it in combination with multiple abnormality judgment rules and indicators based on critical slowing down theory, and outputs assessment and warning results. The present invention can proactively perceive functional disorder of equipment, non-invasively evaluate the health of dynamic interaction, and provide effective warnings for critical transitions such as system instability, thereby significantly improving the reliability and safety of substation operation.

The invention discloses an electric energy quality signal noise reduction and optimization detection method and system and application thereof, and the method specifically comprises the following steps: S1, dynamically optimizing a penalty factor delta and a mode number beta of variational mode decomposition (VMD) through a particle swarm optimization (PSO) algorithm, constructing a fitness function with a composite index (permutation entropy and mutual information) as a core, and carrying out the dynamic optimization of the penalty factor delta and the mode number beta of the variational mode decomposition (VMD) through the particle swarm optimization (PSO) algorithm; and screening an optimal decomposition parameter. S2, performing VMD decomposition on the power quality signal according to the optimized decomposition parameters delta and beta to obtain a plurality of intrinsic mode function components, namely IMF components; s3, screening an optimal signal component from the IMF components based on the composite index to realize signal noise reduction; according to the method, the parameters of VMD decomposition can be optimized for the electric energy quality signal, so that noise interference in the signal is overcome, the problem of low signal-to-noise ratio of the signal is solved, finally, the detection precision of the electric energy quality is effectively improved, and the measurement error is below 2%.