121 results about "Lyapunov exponent" patented technology

Aspects of the present disclosure involve methods, which may be to manage control of a battery such as through charging, comprising obtaining a value indicative of at least one of a dynamic state of equilibrium, periodic behavior, quasi-periodic behavior, chaotic behavior and random behavior of a battery, which may involve electrodynamic parameters of Lyapunov Exponent, Correlation Dimension, Sample Entropy and Hurst Exponent, among others, the value obtained from a voltage measurement or a current measurement from the battery, and based on the value, operating the battery to maintain the battery within one of the dynamic states.

The invention discloses a multi-dimensional risk supervision method for bridge and underground pipe gallery engineering, and particularly relates to the field of underground pipe gallery engineering supervision, and the method comprises the steps: an underground structure slippage risk identification and prediction process based on pressure data driving: firstly, collecting an original pressure signal and screening effective data; establishing a standardized two-dimensional pressure field matrix by using the variation function; local gradient mutation features are recognized through Sobe l convolution, evaluation units are divided to calculate entropy distribution, entropy change abnormity is extracted, and a multi-dimensional phase space trajectory is constructed; the maximum Lyapunov index is further applied to estimate and recognize the stability state, gradient and entropy features are combined to construct a parameter inversion observation set, and a constrained optimization algorithm is adopted to iteratively solve an equivalent friction angle; constructing the time sequence into a time sequence, inputting the time sequence into an ARI MA model, executing slip rolling prediction, finally generating a risk thermodynamic diagram with confidence, and achieving the dynamic evaluation and early warning of the slip trend of the underground pipe gallery.

The invention discloses a power capacitor bank dielectric loss on-line monitoring method, particularly relates to the technical field of power system equipment insulation state monitoring, and is used for solving the problems of insufficient signal synchronization precision and large dielectric loss calculation error under complex electromagnetic interference in the existing method. The method comprises the following steps: acquiring voltage and current signals in real time and dynamically tracking fundamental frequency; intercepting a main period waveform, constructing phase space trajectory analysis complexity, and generating distortion features in combination with symbol sequence transfer entropy; dynamically adjusting filtering parameters based on the distortion characteristics and the Lyapunov exponent gradient; calculating a high-precision phase difference through zero crossing point interpolation correction and sliding window filtering, and finally determining a dielectric loss value; through dynamic frequency tracking, nonlinear feature collaborative analysis and an adaptive filtering strategy, noise interference is effectively suppressed, phase synchronization precision and dielectric loss calculation reliability are improved, and accurate and real-time monitoring support is provided for insulation performance evaluation of the power capacitor bank.

The invention discloses a real-time fatigue monitoring system based on an EEG signal, and relates to the technical field of neural regulation and control, and the system comprises a chaos reconstruction module which collects an original EEG signal, carries out the phase-space reconstruction of the original EEG signal through a chaos attractor processor, generates a chaos trajectory tensor, and carries out the phase-space reconstruction of the original EEG signal when the Lyapunov index of the chaos trajectory tensor exceeds a preset index threshold value, outputting a low-speed frequency band oscillation component and a high-speed frequency band oscillation component; and the closed-loop termination module is used for terminating the physical light field projection when the continuous detection periods of the updated alertness drop probability value, cognitive overload probability value and physiological fatigue probability value are all lower than a preset safety threshold value. Through multi-scale recursion phase space reconstruction, an original EEG signal is mapped to a three-dimensional chaotic trajectory tensor space, and dynamic frequency band decoupling is triggered by using a Lyapunov exponent threshold.

The invention discloses a high-precision dynamic error real-time compensation method based on five-axis linkage, and relates to the technical field of machine manufacturing, and the method comprises the following steps: multi-source dynamic error perception: collecting geometric errors of a machine tool through a grating ruler, collecting temperature field distribution through an infrared thermal imager, and collecting cutting force disturbance through a piezoelectric force sensor; the method comprises the steps of constructing a grating ruler, constructing a thermal infrared imager, constructing a multi-source data acquisition system, carrying out nonlinear coupling modeling, and establishing a dynamic error model based on improved Volterra series, and has the advantages that the multi-source data acquisition system is constructed through the grating ruler, the thermal infrared imager and the piezoelectric force sensor, geometric, thermal and force multi-source dynamic errors are sensed synchronously, and the dynamic error detection accuracy is improved. The nonlinear coupling characteristic of the thermal-force-geometric error is accurately quantified by combining an improved Volterra series model and Lyapunov exponent analysis, the problem that a traditional single-source error model cannot accurately describe a complex error field is solved, and the accuracy of error prediction is remarkably improved.

The invention discloses a direct-current voltage self-balancing method in the high-low voltage ride-through process of a high-voltage frequency converter, and the method comprises the steps: obtaining an electric signal of the high-voltage frequency converter, calculating the Lyapunov index of a system in real time based on the electric signal, so as to quantify the chaotic state of the system, and generating a real-time Lyapunov index sequence; according to the real-time Lyapunov index sequence, the gradient of the real-time Lyapunov index sequence relative to a preset system state variable is determined, and a Lyapunov gradient field is constructed; using the Lyapunov gradient field to generate a basic control quantity through nonlinear mapping; and converting the basic control quantity into a pulse width modulation parameter to form a PWM control signal set to be applied to the high-voltage frequency converter. According to the method, the Lyapunov gradient field is constructed to provide prospective direction guidance for control, so that the chaotic oscillation is quickly suppressed, and the dynamic stability of the system under power grid disturbance is remarkably improved.

The invention discloses a zero-carbon park inter-industry information fusion operation and maintenance system, particularly relates to the technical field of carbon emission dynamic management and control, and is used for solving the problem of carbon accounting distortion caused by state deviation of an existing static carbon emission model and a physical system. The method comprises the following steps: establishing an initial carbon emission model containing energy efficiency reference parameters in a design stage, adopting multi-working-condition dynamic calibration in a construction stage to obtain an operation boundary characteristic spectrum and synchronously acquiring a thermodynamic trace, and identifying topological fragile nodes based on Lyapunov exponential stability analysis and optical flow method thermal gradient analysis; quantizing a carbon emission cascade conduction effect by combining an equipment energy topology network, generating a dynamic carbon emission model correction instruction to update an energy efficiency parameter, and finally executing double-instruction collaborative management and control in an operation and maintenance stage; a complete-cycle carbon data flow closed loop is realized, and through multi-source information fusion and conduction chain modeling, the effect of continuously reducing the carbon emission intensity of the park is achieved.

The invention discloses a distribution line abnormity monitoring and early warning method and system, and relates to the technical field of distribution line intelligent monitoring. The method comprises the following steps: acquiring operation data acquired by multiple types of sensors, and performing time alignment, normalization and fusion processing to form a comprehensive data set; inputting the comprehensive data set into a generative denoising model based on noise and abnormal signal distribution separability learning, and outputting a clean data sequence through noise suppression and abnormal feature fidelity joint optimization; identifying an abnormal evolution trend with a nonlinear amplification characteristic by using a Lyapunov index and a Hurst index, and generating a risk assessment result; and constructing and dynamically adjusting a self-adaptive early warning threshold set according to a risk assessment result, and outputting abnormal early warning information when a risk index exceeds a threshold. The method realizes parallel noise suppression and abnormal feature fidelity, has dynamic identification and self-learning capabilities, and significantly improves the accuracy of distribution line anomaly detection and the stability of an early warning system.

The invention discloses a 5G base station optical storage integrated power supply control system, and relates to the field of 5G base station optical storage integrated power supply control, and the system comprises a detection module which detects the output voltage of a photovoltaic array, the SOC of an energy storage battery and the impedance parameter of a DC bus, and obtains a detection result; and the 5G wireless sensing module monitors reference signal receiving power and channel state information by initializing a 5GNR physical layer chip based on a detection result, transmits the reference signal receiving power to the micro-control unit through an SPI interface, and reads the maximum power point voltage and current of the photovoltaic module to obtain a time sequence and a photovoltaic array. According to the method, the Lyapunov exponent is solved through phase-space reconstruction and the Wolf algorithm, the chaos state flag bit and the load impedance are generated, the parameters of the LLC resonant converter are dynamically matched, the nonlinear dynamics theory is applied to the field of power supply control, the working modes can be adaptively switched through intelligent recognition and utilization of chaos features, and the power supply efficiency is improved. And optimal scheduling of energy is realized while the ZVS state is ensured.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention relates to the technical field of signal correction, in particular to a nonlinear distortion correction method for a vibration monitoring signal, and provides the following scheme: carrying out broadening and sparse reconstruction on a vibration signal through dispersion Fourier transform and compressed sensing, and extracting a high-frequency local structure; on the basis of phase-space reconstruction and a Lyapunov exponent, nonlinear distortion segments are identified; further performing orbit guiding modeling and degradation path reasoning on the orbit cluster corresponding to the segment, and selecting a reference orbit matched with the structure from the residual linear segments; and constructing an affine mapping relation between orbits and back-projecting the affine mapping relation to a time domain to generate a correction signal. According to the invention, high-precision identification and low-error correction of millisecond-level local nonlinear distortion are realized, and the stability and accuracy of vibration signal analysis are improved.

The invention discloses an intelligent fault diagnosis system for a cold and heat source center, and belongs to the technical field of fault judgment of heating, ventilation and air conditioning equipment. The system collects an operation feature vector X (t) in real time, and a state signal database stores data; a signal processing module of the upper computer subsystem adopts empirical mode decomposition X (t) to generate an IMF sequence and a residual sequence rn (t), a characteristic analysis module judges chaotic characteristics by calculating the maximum Lyapunov index of each IMF, and a neural network modeling module selects a modeling path according to chaotic / non-chaotic marks to generate a predicted value. The result integration module fuses the multi-path prediction results, compares the multi-path prediction results with a preset threshold interval, and outputs normal, early warning or fault state marks; and the result display module dynamically visualizes the state curve and the maintenance suggestion. According to the system, real-time judgment and early warning of abnormal operation of the cold and heat source equipment are realized, and decision support is provided for preventive maintenance.

The invention provides a smoke analysis AI system based on a quantum field theory tensor network, the algorithm is provided with a multi-camera fusion module, a quantum state coding module, a dynamic Hamiltonian construction module, a tensor network evolution module and a chaos detection module, and by monitoring a smoke diffusion Lyapunov index in real time, the sudden change from smoke to fire behavior is warned in advance; according to the algorithm, a quantum multi-body system theory and computer vision are combined, smoke diffusion is modeled into a phase change process of a quantum spin chain, high-dimensional quantum state information is compressed by using a tensor network, and a mutation behavior of a chaotic edge is detected in combination with a Lyapunov exponent. The method solves the problem of early fire hazard false alarm under the complex operation condition of the transformer substation in a traditional method, improves the initiative and accuracy of monitoring and prevention of the extremely early smoke fire hazard of the transformer substation, provides more reliable guarantee for the safe operation of the transformer substation, and has wide application prospects and important practical significance.

The invention relates to the technical field of data processing, in particular to an electric energy monitoring method and system for a remote electric energy meter, and the method comprises the steps: constructing a time sequence factor graph model with voltage and current phasors as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence, generating a time-frequency energy distribution diagram, and obtaining a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a depth auto-encoder pre-trained on normal power utilization condition data to calculate a reconstruction error, calculating a Lyapunov index of the sequence in a preset time window, and calculating the reconstruction error according to a pre-established Gaussian mixture model for describing index distribution under a normal condition. Obtaining a negative logarithm likelihood probability; and carrying out weighted fusion on the reconstruction error and the negative logarithm likelihood probability to generate a comprehensive abnormal index which is used for judging a power utilization event. According to the invention, high-precision and low-false-alarm-rate detection of power consumption events can be realized.

The invention discloses a transformer winding loose fault feature extraction method based on chaos theory and an ephemeral optimization K-means algorithm. The method comprises the following steps: step 1: setting measuring points on a transformer box, obtaining vibration signals of each measuring point, and selecting a measuring point D with the largest vibration amplitude as the optimal measuring point; step 2: calculating the maximum Lyapunov exponent of the vibration signal collected by the measuring point D, and judging its chaotic characteristics; step 3: calculating the correlation dimension and Kolmogorov entropy of the vibration signal with chaotic characteristics as the chaotic characteristics of the transformer winding loose fault; step 4: reconstructing the vibration signal in phase space to obtain a phase space trajectory; step 5: optimizing the initial cluster centers of the K-means algorithm by using the ephemeral optimization algorithm, clustering the phase space trajectory, and finally calculating the sum of the cluster center moments and the radial vector offset of the obtained K cluster centers as the geometric characteristics of the transformer winding loose fault; and step 6: comparing the chaotic characteristics and geometric characteristics of the abnormal state of the transformer winding with reference values ​​to achieve accurate identification.

The invention discloses a multi-degree-of-freedom variant wing aeroelastic coupling test measurement method. The method comprises the following steps: S1, arranging a sensor network; s2, a PIV flow field is measured, laser sheet light is adopted to irradiate the surface of the wing, a high-speed camera captures tracer particle displacement, and a flow field velocity field is calculated; s3, constructing an aeroelastic inverse problem model, and inverting the unsteady aerodynamic force by combining the structural deformation, the vibration speed and the flow field speed; s4, correcting a model error by using a deep learning network; s5, constructing a Kalman filtering-data driven hybrid model; and S6, self-adaptive threshold flutter early warning is carried out, and the system stability is judged based on a Lyapunov index. According to the measurement method based on distributed optical fiber sensing, high-frequency PIV and laser Doppler vibration measurement fusion, high-precision and full-dimension synchronous acquisition of aeroelastic parameters can be realized; in addition, in combination with PIV flow field data, structural deformation and vibration data, the unsteady aerodynamic force is inverted through an aeroelastic inverse problem model.

The invention relates to the field of information security and random number generation, and discloses a pseudo-random sequence generation system based on a four-dimensional hyperchaotic system, and the system comprises a chaotic signal generation module which is used for generating a chaotic state track composed of four state variables; the numerical solution module is used for carrying out multi-step numerical integral iteration on the state variable; the signal optimization module is used for performing cross recombination, disturbance arrangement and nonlinear quantization on the state variables; the control management module is used for controlling initialization, operation time sequence and state synchronization of each module; and the output interface module is used for outputting the pseudorandom sequence. According to the method, a four-dimensional hyper-chaotic system and a double positive Lyapunov index structure are adopted as a core dynamics basis, the technical effect of improving the unpredictability of the pseudo-random sequence and the trajectory complexity is achieved, and the potential safety hazards that the dynamic behavior is single, the dependence on initial conditions is too weak and the like are effectively overcome.

The invention discloses a lithium battery first-order RC-ECM fractional order chaotic system parameter identification method. The method comprises the following steps of defining a lithium battery first-order RC-ECM mathematical model by adopting Captop's fractional order differential; selecting a first-order RC-ECM model to describe the dynamic characteristics of the lithium battery in the working process, and establishing a first-order RC-ECM model fractional order mathematical model by adopting Captop's fractional order differential; a charge-controlled memristor, a resistor, an inductor and a first-order RC-ECM are adopted to establish a four-dimensional fractional order chaotic system; carrying out balance point analysis, dissipative and attractor existence analysis, Lyapunov exponential spectrum analysis and Poincare cross section analysis on a first-order RC-ECM fractional order chaotic system; a, B and D unknown parameters in a first-order RC-ECM fractional order chaotic system are identified by adopting a state observer, and then unknown parameter values in the first-order RC-ECM are further derived by utilizing a formula. Meanwhile, the influence of environment temperature, driving road conditions, load conditions and battery material conditions is avoided.

The invention discloses a grid-connected inverter fusion control method based on voltage and current source time sharing, and relates to the technical field of electric power automation, and the method comprises the steps: carrying out the two-phase rotating coordinate system conversion of power grid coupling data, and generating a voltage-current joint component; performing power error analysis on the voltage-current joint component, outputting a power grid state vector, performing modal decomposition on the power grid state vector, obtaining a dominant oscillation mode matrix, performing Lyapunov exponent calculation on the dominant oscillation mode matrix, forming a stability pre-judgment mark, and inputting the stability pre-judgment mark into a time-sharing fusion decision network model to obtain a time-sharing fusion decision network model. The characteristic coupling layer carries out cross physical quantity correlation, and the stability evaluation layer carries out stability boundary calculation and outputs a voltage-current source control signal. Through the intrinsic orthogonal decomposition algorithm and the time-sharing fusion decision network model, the response speed and the fusion control capability of the grid-connected inverter are improved.

The present invention discloses a servo motor energy-saving control system and method, which relates to the technical field of servo motor energy saving. It includes: based on the magnetic field intensity matrix and the temperature distribution matrix, obtaining the magnetothermal coupling coefficient, identifying the temperature anomaly area, and generating a hot spot marker; based on the magnetothermal coupling coefficient and the hot spot marker, encoding the magnetothermal coupling coefficient into a pulse sequence through a pulsed neural network, and using the synaptic weight update mechanism of the hidden layer neurons to generate an energy recovery threshold and an efficiency threshold; based on the energy recovery threshold and the efficiency threshold, performing dynamic energy recovery on the servo motor, collecting the vibration signal after energy recovery, calculating the maximum Lyapunov exponent, and updating the pulsed neural network. The present invention realizes the precise correlation mapping between magnetic field distortion and local temperature rise through the calculation of the magnetothermal coupling coefficient and the hot spot marker, breaks through the limitations of traditional independent analysis, and provides a physical basis for energy recovery.

The invention relates to the technical field of defect detection, in particular to a copper wire internal defect detection method and system combined with eddy current. The method comprises the following steps: applying composite excitation formed by superposing multi-frequency harmonic signals, collecting eddy current response signals, and obtaining an initial time-frequency spectrum matrix through short-time Fourier transform; singular value decomposition is carried out on the matrix, and after main singular value components related to the lift-off effect are removed, a lift-off correction spectrum matrix is reconstructed; calculating the maximum Lyapunov exponent and the spectrum kurtosis value of the correction matrix as a first defect characteristic quantity and a second defect characteristic quantity respectively; and constructing a two-dimensional feature plane based on the two feature quantities, calculating an angle correction quantity by utilizing energy of a rejected component so as to adjust a defect judgment area, and finally judging the defect by combining a Manhattan distance and a corrected angle interval. According to the scheme, the lift-off effect interference can be effectively inhibited, the dynamic criterion is constructed, and high-precision and high-reliability detection of the weak defects in the copper wire is realized.

A method and system for optimizing the charging process of lithium-ion batteries by utilizing a determined parameter (e.g., Lyapunov Exponent) as a cell degradation (e.g., anode overpotential) indicator are disclosed. The system and method enable intelligent charging, which may involve integrating a controller that adjusts the charging current in real-time (or near real time or otherwise) based on the cell degradation indicator, calculated from probing the battery. This approach has various possible benefits to which the method and system may be tuned including extending battery lifespan, charge timing, and safety.

The invention relates to the technical field of control and adjustment, in particular to a civil engineering construction equipment multi-parameter adjustment control system, which comprises a data acquisition module for capturing multi-dimensional physical states and geological environment data of key nodes of equipment; the data analysis module adopts a quantum annealing optimizer to analyze a multi-parameter coupling relationship to generate a decoupling scheme, and combines a multi-target attention controller to allocate resource priorities of an inner ring, a middle ring and an outer ring to form a coding control strategy; and the optimization module identifies chaotic features based on the Lyapunov exponent for executing the feedback data, quantifies performance indexes through a three-dimensional evaluation space, updates a parameter library by using a quantum migration engine and outputs a correction instruction to form a closed-loop optimization path. According to the invention, the problem of insufficient cooperative control precision under coupling interference of multiple execution mechanisms is solved.

The invention provides an automatic test method and system for an intelligent power distribution automation terminal, and relates to the technical field of automatic test, and the method comprises the steps: obtaining the model information and environment information of a to-be-tested terminal; generating test data of the to-be-tested terminal in combination with the model information, the environment information and the Lyapunov index; inputting the test data into the to-be-tested terminal, and outputting operation data; analyzing the operation data by using a dynamic threshold value based on the potential energy field, and outputting abnormal data; fusing and classifying the abnormal data based on a pre-training model and the coupling weighting distance, and determining a fault type; and performing early warning according to the fault type. According to the analysis method based on the Lyapunov index and the dynamic threshold value, the test can comprehensively cover various functions and limit states of the equipment, so that potential anomalies and faults can be effectively found.

The invention discloses a water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory, which belongs to the technical field of water conservancy equipment pressure pulsation fault diagnosis, and comprises the following steps: setting a pressure pulsation monitoring point, and collecting a pressure signal; decomposing the pressure signal at each monitoring point by adopting variational mode decomposition (VMD); performing spectrum analysis on each modal function of the pressure signal decomposed by the VMD, and extracting spectrum features; frequency components and pressure amplitude are extracted, and internal flow field state information is obtained; in combination with a chaos theory, obtaining different modal attractors and the maximum value lyapunov exponent lymax; and introducing the modal attractors obtained in the VMD and chaos theory analysis and the lymax into a weighted decision model, and obtaining pressure fluctuation information at different monitoring points in combination with influence weights. According to the water pump pressure pulsation diagnosis method based on the variational mode decomposition and chaos theory, the pressure pulsation occurring in the operation of the water pump is monitored, and damage possibly caused by abnormal pressure pulsation is prevented.

The invention relates to the technical field of flow field measurement, in particular to an ocean flow field measurement method and device, and provides the following scheme: the method comprises the following steps: collecting ocean environment parameters through a vertical sensor, determining an overlook angle of an inclination sensor based on an optimization algorithm, and generating a virtual Doppler measurement array through micro-amplitude scanning; inputting to a chaotic oscillator network to identify a Lyapunov exponent maximum direction, and determining a horizontal included angle of a second inclination sensor; furthermore, a frequency shift signal is collected, and the flow velocity and the flow direction are calculated by combining adjusting parameters, so that high-precision and high-adaptability flow field measurement is realized; the method has a dynamic response capability, and is suitable for direction structure identification and real-time flow direction calculation under complex sea conditions.

The application discloses a method for realizing a conservative charge-controlled memcapacitive hyperchaotic circuit with multiple coexisting attractors. First, a six-dimensional conservative memcapacitive circuit model is constructed by a charge-controlled memcapacitive device, and the memcapacitive characteristics are verified. Then, the balance point and dissipation are used to verify the conservativeness. By changing the system parameters and initial conditions, a larger Lyapunov exponent can be obtained, and it is found that the internal parameter e of the circuit can not only cause the circuit to exhibit complex transient chaotic phenomena, but also has large-scale range chaotic characteristics. The attractor basin, coexisting attractors and complexity reflect the extreme multistability and complex nonlinear dynamic behavior of the circuit. In addition, the initial offset boosting behavior is also found. The application combines the FPGA technology to design a digital conservative charge-controlled memcapacitive hyperchaotic circuit model, and the hardware simulation result is consistent with the numerical calculation result, thereby providing a new idea for the digital realization of a nonlinear storage system.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The invention discloses a harmonic interaction characteristic analysis method and device based on chaotic characteristics, equipment, a storage medium and a program product. The method comprises the following steps: obtaining harmonic impedance corresponding to a plurality of harmonic sources respectively; determining series impedance and parallel admittance of the power transmission line between the first harmonic source and the second harmonic source based on a pre-constructed equivalent circuit model of the power transmission line between the multiple harmonic sources, and determining equivalent connection impedance between the first harmonic source and the second harmonic source based on the series impedance and the parallel admittance; inputting the first harmonic impedance corresponding to the first harmonic source, the second harmonic impedance corresponding to the second harmonic source, the equivalent connection impedance and the Lyapunov exponent into a preset chaotic interaction factor calculation model to obtain a chaotic interaction factor between the first harmonic source and the second harmonic source; and determining an interaction characteristic analysis result between the first harmonic source and the second harmonic source based on the chaos interaction factor. Therefore, the stability of the multi-harmonic source system is improved.

The invention belongs to the field of power system monitoring and fault diagnosis, and particularly relates to a cable branch box health early warning method based on fusion of insulation resistance and leakage current. The method comprises the following steps: acquiring insulation resistance and leakage current signals which are synchronously aligned by using a sensing monitoring unit, and extracting deterministic characteristics through nonlinear preprocessing; performing phase-space reconstruction on the leakage current signal, and calculating the maximum Lyapunov exponent, correlation dimension and other dynamic characteristic indexes so as to quantify the chaotic evolution degree responded by the insulating medium; and constructing an evaluation model based on fusion of insulation resistance and dynamic indexes, calculating a comprehensive health index, comparing the comprehensive health index with a threshold value, and determining an early warning level. According to the method, the qualitative change omen at the initial stage of insulation degradation can be sensitively captured by fusing the macroscopic electrical parameters and the microdynamics evolution characteristics, the advancement and the anti-interference capability of early warning are improved, reliable support is provided for refined operation and maintenance of the cable branch box, and the power supply reliability of a power system is guaranteed.