2980results about "Spectral/fourier analysis" patented technology

The invention relates to the technical field of transformer harmonic monitoring, in particular to a harmonic detection and early warning system for an intelligent transformer, which obtains voltage and current signals of the transformer in real time, performs frequency domain decomposition by using Fourier transform, extracts multi-dimensional harmonic characteristics such as amplitude, phase, frequency and the like, and performs harmonic detection and early warning on the intelligent transformer. And inputting into a deep learning model to train and generate a dynamic harmonic characteristic model. On the basis, a harmonic wave anomaly classification module analyzes harmonic wave signals in real time, carries out type judgment and position association on abnormal harmonic waves, and generates a report containing anomaly types and positions. Through a dynamic early warning module, a power grid load coefficient is calculated based on power grid load state parameters collected in real time, and a harmonic detection threshold is dynamically adjusted in combination with an initially set amplitude and frequency threshold. The application of the dynamic threshold enhances the adaptability of the system to the load state change, and significantly improves the sensitivity and accuracy of harmonic detection and early warning.

A power distribution network cable fault point location method and system, a computer device and a medium, belonging to the technical field of power distribution networks. The location method comprises: S101, when a cable has a fault, a transient signal of a wide-area frequency band is generated, a fault traveling wave containing high-frequency components; a traveling wave line mode component having a relatively small chromatic dispersion is used to perform measurement; decoupling an extracted fault traveling wave signal; S102: the traveling wave voltage and current will have a sharp change when the traveling wave generated by the fault point reaches a measurement end, and the traveling wave head can appear as a high-frequency sudden change in a time-frequency graph, the sudden change point being the position of the wave head; S103: performing EEMD decomposition on the fault traveling wave line mode component, and extracting a first IMF component for Hilbert transform to obtain a time-frequency graph thereof, a sampling moment corresponding to the position of a first frequency sudden change point on the time-frequency graph being the arrival time of the fault traveling wave head; and using a double-end fault location algorithm to calculate a distance with respect to the fault point. The result of the location method has relatively high precision, and, compared with the actual fault position, has a relative error not more than 4%.

The invention relates to the technical field of power system fault monitoring, and discloses a 10kV voltage transformer ground fault real-time monitoring method and system, and the system comprises a ground fault data collection module, a waveform distortion analysis module, a transient signal detection module and the like. Three-phase current, zero-sequence current and voltage waveform data are collected, a current harmonic component proportion, a zero-sequence current high-frequency transient energy value and the like are analyzed, a fault type is determined by combining a phase difference, and a fault position is positioned according to topological parameters and line impedance. And the temperature of the grounding down lead can be monitored, and the fault probability is evaluated and the threshold value is dynamically adjusted by machine learning. According to the invention, real-time accurate monitoring can be realized, fault types and positions can be accurately diagnosed, potential hidden dangers can be comprehensively monitored, the intelligence of a monitoring system is improved, the operation and maintenance cost and risks are effectively reduced, and safe and stable operation of the 10kV power distribution network is ensured.

The invention discloses an intelligent cable detection system and method, and particularly relates to the technical field of cable detection. High-precision harmonic analyzers are installed at the input end and the output end of a cable to capture low-order harmonic nonlinear change data in real time, a distributed optical fiber temperature sensor is used for collecting temperature rise rate data of a hot spot area, the data are preprocessed and subjected to feature extraction, harmonic distortion rate fluctuation features and temperature rise rate deviation features are extracted, and the harmonic distortion rate fluctuation features and the temperature rise rate deviation features are extracted. A data prediction model is constructed, and the accuracy of the on-line monitoring system on the potential risk of the cable is accurately evaluated; according to the method, the cable is divided into different grades according to accuracy values, targeted treatment measures are taken respectively, and especially for incomplete accuracy evaluation, accurate identification and real-time optimization monitoring of cable hidden faults are realized by dynamically predicting the accuracy deviation degree and adjusting the monitoring frequency and the detection range. And the accuracy of early warning of cable faults and the reliability of an online monitoring system are obviously improved.

The present invention relates to the field of fault diagnosis technology, specifically to a fault diagnosis method and system for a photovoltaic inverter, comprising the following steps: based on an operating status data set, analyzing a variety of inverter operating data, identifying the volatility of a variety of operating data, extracting statistical features and frequency domain features of the data, adjusting the detection threshold of abnormal data, and generating abnormal fluctuation detection parameters. In the present invention, the abnormal detection threshold is dynamically adjusted by analyzing the operating data, a more sensitive response is made to the data, the accuracy and response speed of the fault warning are improved, the time window segmentation and signal feature extraction are used to timely discover abnormal fluctuations, detect equipment damage or system failures, and the accuracy of fault type identification is improved by combining the comparison and analysis of data features. By analyzing the component types associated with various fault types, the efficiency and accuracy of locating faulty components are improved, and the reliability and economic benefits of the photovoltaic inverter are enhanced.

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention relates to the technical field of fault positioning, and discloses a power distribution network grounding fault positioning method and system. The method comprises the following steps: acquiring a fault initial transient section signal, an arcing continuous section signal and an arc quenching recovery section signal of the power distribution network, and executing spectral analysis to obtain a fault feature matrix; performing zero-sequence transient frequency spectrum reconstruction on the fault feature matrix to obtain reconstructed frequency spectrum features and time-varying frequency spectrum features; calculating a transient characteristic index and a fault type based on the reconstructed spectrum characteristic and the time-varying spectrum characteristic; performing fault positioning calculation on the power distribution network according to the transient characteristic index and the fault type to obtain an initial fault point position; and performing transient fingerprint matching and probability density accumulation compensation on the initial fault point position to obtain a target fault point position. According to the method, high-precision characterization of the rapid change signal in the short time window is realized, interference of the compensation state on fault positioning is eliminated, and the grounding fault positioning accuracy of the power distribution network is improved.

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention discloses a silver paste coating conductive uniformity test system and a test method, particularly relates to the technical field of silver paste coating conductive test, and is used for solving the problem that the silver paste coating conductive uniformity test is inaccurate. Through a high-precision pressure sensor and a camera device, contact state data of a probe are monitored in real time at each measuring point, a contact state matrix is generated to support accurate correction of contact resistance, after collected data are subjected to standardization processing, a support vector regression model is used for capturing a nonlinear relation, and the accuracy of the contact resistance is improved. The method comprises the following steps: optimizing a compensation model through cross validation and parameter tuning, setting an optimal pressure range to stabilize a contact condition and smoothly process data, correcting time deviation by adopting a multi-channel data acquisition and distributed sampling synchronization algorithm, correcting an error in combination with a compensation coefficient, and finally calculating a conductivity uniformity index through a resistance data matrix. Heat map classification evaluation is generated, a low-degree uniform region is repaired, and the consistency and reliability of a conductive uniformity test are remarkably improved.

According to the harmonic amplification detection method and system of the electric power system, the frequency response of the impedance of the electric power system containing inter-harmonic frequency components is analyzed by obtaining the topological structure and the load distribution condition of the electric power system, and the impedance frequency characteristic scanning is performed on the electric power system at different frequencies by adopting a harmonic injection method; obtaining an impedance frequency response curve; according to the frequency interval of harmonic amplification, the hazard coefficient of the harmonic is evaluated through the voltage distortion rate and the current distortion rate caused by the harmonic, and the type of harmonic source equipment causing harmonic amplification is determined according to the hazard coefficient. According to the scheme, the harmonic source equipment can be detected, the power system can be conveniently maintained in time, and the stability of the power system can be improved.

A system-side harmonic impedance estimation method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an h-th harmonic parameter equivalent circuit simplified on the basis of power grid characteristics, h being a positive integer greater than or equal to 2 (S1); on the basis of the h-th harmonic parameter equivalent circuit, establishing a system-side harmonic impedance estimation mathematical model (S2); acquiring amplitude and phase data of a harmonic voltage and a harmonic current at a PCC (S3); dividing the amplitude data of the harmonic voltage at the PCC into several stable intervals (S4); on the basis of the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals, establishing a variable coefficient regression model between a harmonic voltage phasor and a harmonic current phasor (S5); selecting an optimal window width of a kernel function of the variable coefficient regression model (S6); and calculating a system harmonic impedance on the basis of the variable coefficient regression model and the optimal window width (S7).

The invention discloses a power transmission channel risk early warning evaluation method and system, and relates to the technical field of power transmission channel abnormal condition early warning, and the method comprises the steps: analyzing the current waveform distortion degree caused by a nonlinear load in a power transmission line through monitoring the voltage harmonic distortion rate, and evaluating the influence of harmonic waves on the insulation aging and heating loss of the line and equipment; the real-time gradient of the temperature of the wire is analyzed, abnormal heating conditions of different sections of the wire are judged, and the abnormal rising degree of the temperature of the wire and potential thermal damage accumulation caused by overload and poor contact factors are evaluated; by monitoring and analyzing multi-dimensional indexes such as the voltage harmonic distortion rate, the lead temperature real-time gradient, the sag dynamic deviation rate and the corona discharge intensity, the defect of a traditional monitoring means in the aspect of abnormal heating risk assessment is overcome.

The invention relates to the technical field of line monitoring, in particular to an electrical engineering high-voltage line fault monitoring system. The system comprises a data acquisition module used for acquiring current signals of monitoring nodes on a mining high-voltage line at all moments; the anomaly analysis module is used for constructing a harmonic anomaly characteristic value of each current signal according to current signal change and data mutation characteristics in an adjacent time period of each current signal; obtaining an abnormal score correction value of each current signal based on the probability density of each current signal and the harmonic abnormal characteristic value; the power transmission control module is used for acquiring all abnormal current signals based on the abnormal score correction value by using an abnormal detection algorithm; and obtaining a judgment result of an abnormal fault based on all the abnormal current signals. Therefore, the mining high-voltage line is monitored, the accuracy of monitoring the abnormal fault of the power supply of the mining high-voltage line is improved, and the stability of the power supply of the mining high-voltage line is effectively ensured.

The invention provides a system design method and system supporting automatic testing of multiple radio frequency parameters, and relates to the technical field of testing, and the method comprises the steps: receiving and analyzing a test task, generating a test configuration file according to the information of radio frequency equipment to be tested and multiple radio frequency parameter test items, and using a self-adaptive optimization algorithm to optimize the test configuration file. Dynamically adjusting the test configuration file based on historical test data to generate an optimized test configuration file; controlling a radio frequency signal generator to generate a test signal, inputting the test signal into the radio frequency equipment to be tested, starting a real-time monitoring module, performing multi-dimensional analysis on a digital signal processed by a radio frequency signal analyzer, performing anomaly detection on an analysis result by using a deep learning algorithm, identifying and marking an abnormal data point; and generating a test report according to the analysis result and the anomaly detection result.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention provides an arc fault detection method and device based on lightweight, and a storage medium, and the method comprises the steps: obtaining an arc current signal of a power distribution network load in a preset time period, determining a signal-to-noise ratio parameter, determining a dynamic window length according to the signal-to-noise ratio parameter, carrying out the Hilbert transformation of the arc current signal according to the dynamic window length, and obtaining an arc fault detection result. The method comprises the steps of obtaining m amplitude envelope data, extracting features from the m amplitude envelope data to obtain p time domain statistical features, calculating arc current signals by adopting a preset Fourier transform method to obtain arc current frequency spectrum data, processing the arc current frequency spectrum data to obtain q frequency domain features, and fusing the p time domain statistical features and the q frequency domain features based on a preset feature fusion algorithm to obtain a target arc current feature, and inputting the target arc current feature into a preset lightweight arc fault detection model to obtain an arc fault detection result. Therefore, the accuracy of arc fault detection is improved.

The invention discloses a direct-current arc fault diagnosis method and device with adaptive capacity. The method comprises the following steps: 1) collecting a current alternating-current component when a current direct-current system normally works; 2) extracting time-frequency domain characteristics of a current AC component, wherein the time-frequency domain characteristics comprise a current peak-to-peak value, a standard deviation and an amplitude reconstruction harmonic power sum; 3) performing multi-time-frequency-domain feature fusion based on mahalanobis distance to obtain arc detection features; 4) calculating an arc fault judgment threshold value based on a box plot method; 5) collecting a current AC component in real time to calculate a Mahalanobis distance, comparing the Mahalanobis distance with an arc fault determination threshold, and recording an arc event; the method has the advantages that the system adaptability is high, the detection rate is high, the false alarm rate is low, and the method can be widely applied to high-voltage and low-voltage direct-current power distribution systems such as airplanes, vehicles and ships.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The invention provides a simultaneous multiband superheterodyne receiver based on Rydberg atoms, and relates to the technical field of microwave electric field measurement, probe light provided by a probe light laser and coupling light provided by a coupling light laser oppositely enter an alkali metal atom gas chamber; the microwave signal sources of the multiple wave bands generate two paths of signals, one path of microwave signal is emitted to the space through the local oscillator microwave electric field emission horns of the wave bands, and the microwave electric field receiving horns of the wave bands receive corresponding echo signals. After frequency mixing of each wave band, the microwave electric field emission horn irradiates the alkali metal atom gas chamber at the same time, microwave fields of multiple wave bands are sequentially coupled with multiple pairs of continuous adjacent Rydberg states, and superheterodyne receiving is carried out on microwaves of multiple wave bands at the same time; and superheterodyne and high-sensitivity detection of microwave signals of multiple wave bands can be realized.

The invention provides a frequency converter dynamic fault diagnosis method and system based on a multi-brand load test platform. According to the method, multiple real-time operation parameter sets are acquired, the amplitude attenuation threshold and the frequency fluctuation interval of a voltage waveform fragment are dynamically adjusted according to a load mode configuration file, the dynamic voltage reference parameter corresponding to the current load mode is generated, and then the actual waveform and an expected current phase curve are compared frame by frame; the method comprises the following steps: firstly, calculating to obtain a phase offset, then constructing a multi-dimensional operation characteristic spectrum, then carrying out time sequence cross validation on a sudden change time node of a power factor jump trajectory and an abnormal waveform position in a harmonic distortion incidence matrix, outputting an abnormal mode code, and finally, matching a load adaptation grade constraint condition to generate a diagnosis report; according to the technical scheme provided by the invention, the fault diagnosis precision and the cross-brand system adaptation capability in a complex load scene are remarkably improved, and the problem of misjudgment of a single brand threshold rule in a multi-device scene is effectively solved.

The invention discloses an electromagnetic compatibility testing method and an electromagnetic compatibility testing system for an industrial control power supply, which are used for evaluating conducted interference, radiated interference and immunity performance. The system comprises a load module, a signal connection module, an electromagnetic interference measurement module, an immunity test module, a data processing module and a hardware acceleration module. The load module simulates the actual working state of the industrial control power supply through resistive, inductive, capacitive and dynamic loads; the signal connection module adopts high-frequency shielding and grounding optimization design, so that external interference is reduced; the electromagnetic interference measurement module is combined with the frequency domain analysis and time domain response module to capture interference signal characteristics and power supply dynamic output; the data processing module generates an electromagnetic compatibility report through a comprehensive evaluation algorithm; and the hardware acceleration module realizes real-time processing of high-frequency sampling data by utilizing the FPGA. The method is suitable for the electromagnetic compatibility test of the industrial control power supply in the fields of industrial automation, communication equipment and power electronics, and has wide engineering application value.

The invention relates to the technical field of adaptive control, in particular to an electrical automation adjustment method and system, and the method comprises the following steps: obtaining a three-phase current signal, collecting single-cycle high-frequency sampling, extracting an instantaneous extreme point, recording a time coordinate and an amplitude value, calculating a time interval and a change rate, screening the distribution of the extreme point, and obtaining a harmonic distortion characteristic parameter. According to the method, on the basis of high-frequency sampling, instantaneous extreme point extraction, time interval and change rate calculation, harmonic distortion feature description enhancement, extreme value growth rate and phase drift rate are utilized, time periods with prominent fluctuation amplitude are screened, harmonic change trend prediction precision is improved, and the harmonic change trend prediction precision is improved through zero-sequence component and phase offset calculation in combination with periodic change features. The harmonic source is accurately separated, the harmonic cause identification capability is improved, the compensation current is calculated based on the harmonic source, the injection opportunity is optimized, the compensation parameter is adjusted before the harmonic peak value, and the compensation response speed and the matching precision are improved.

The invention discloses a metering equipment interference signal suppression method and system in an electromagnetic compatibility test, and the method comprises the steps: obtaining an input signal of metering equipment in the electromagnetic compatibility test, performing windowing processing on the input signal by adopting a self-adaptive fractional order window function, and then obtaining a frequency spectrum component of the input signal through self-adaptive fractional order Fourier transform; calculating the amplitude of the frequency spectrum component, comparing the amplitude with a power threshold value, and screening out the frequency spectrum component of the interference signal; establishing a spectrum model of the interference signal based on the interference signal spectrum component; and generating a frequency domain compensation signal considering interference signal phase correction, converting the frequency domain compensation signal into a time domain compensation signal, and superposing the time domain compensation signal and the input signal to obtain a signal after interference suppression. According to the method, the frequency spectrum characteristics of the signals in different frequency bands can be ensured to be clear, the interference signal components can be accurately identified, the energy distribution of the interference signals can be effectively simulated, and the high-efficiency interference suppression capability can still be kept in a variable signal environment.

The invention provides a harmonic detection method and system for a vehicle network electrical system, and relates to the technical field of harmonic detection, and the harmonic detection method for the vehicle network electrical system comprises the steps: obtaining a current signal of a vehicle network bus, and carrying out the band limit processing of the current signal; constructing a variable window width S transformation kernel function with self-adaptive frequency; based on bispectrum analysis of a third-order cumulant, harmonic phase coupling characteristics are extracted, and an enhanced spectrum is generated through principal component projection; generating a dynamic detection threshold by using a non-harmonic interval background noise model; and carrying out harmonic parameter fitting by adopting an exponential decay model with frequency domain priori constraint. According to the method, through a cascade architecture of adaptive time-frequency analysis, high-order statistical feature extraction, dynamic threshold detection and prior constraint fitting, full-process closed-loop optimization from signal preprocessing to parameter optimization is realized, and the method is particularly suitable for accurate detection of heavy-frequency, nonlinear and time-varying harmonics of a vehicle network system; and a diagnosis basis with higher reliability can be provided for electric energy quality treatment.

The invention relates to the technical field of radio monitoring, in particular to a spectrum analysis device and method for a radio monitoring system and a storage medium, and the method comprises the steps: obtaining a corresponding environment noise level in a monitoring process, adjusting a sensitivity threshold value corresponding to signal detection based on the environment noise level, receiving a corresponding to-be-analyzed radio frequency signal, and preprocessing the received to-be-analyzed radio frequency signal based on an adaptive filter; calling a pre-trained spectrum analysis model, inputting the preprocessed to-be-analyzed radio frequency signal into the spectrum analysis model, and obtaining a corresponding spectrum classification result; and calling frequency information and time sequence data corresponding to a radio frequency signal to be analyzed, obtaining corresponding spectrum analysis data based on the spectrum classification result, the frequency information and the time sequence data, and displaying key parameters in the spectrum analysis data through a graphical interface. According to the invention, the effectiveness of signal detection and identification can be improved.

According to the new energy grid-connected electric energy metering method and device, broadband harmonic characteristic information and inter-harmonic characteristic information are respectively obtained according to preprocessed power grid voltage and power grid current; after the broadband harmonic characteristic information and the inter-harmonic characteristic information are input into the influence analysis topology network, a first error compensation parameter is calculated by using an electric energy metering error output by the influence analysis topology network; based on a non-sinusoidal signal simulation model, combining the preprocessed power grid voltage and power grid current with the step wave signal to obtain a non-sinusoidal wave signal; performing error compensation calculation on the non-sine wave signal to obtain a second error compensation parameter; obtaining time-varying error data of the electric energy meter by using historical operation data of the electric energy meter; a target error compensation parameter is calculated based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter, the electric energy meter performs electric energy metering based on the target error compensation parameter, an electric energy metering result is obtained, and the accuracy of new energy grid-connected electric energy metering is improved.

A broadband measurement method and apparatus for a power system, and a computer device, which relate to the technical field of broadband measurement. The method comprises: acquiring a target electrical signal; on the basis of a local mean decomposition algorithm, decomposing in a time domain the target electrical signal into a plurality of signal components having a single frequency; determining the instantaneous frequency of each signal component; on the basis of the instantaneous frequencies, determining a corresponding time-frequency transformation algorithm and a corresponding time-frequency transformation parameter; and on the basis of the corresponding time-frequency transformation parameter, using the corresponding time-frequency transformation algorithm to perform time-frequency transformation on each signal component, so as to obtain a broadband spectrum that comprises frequency-domain information corresponding to each signal component. The method is applicable to spectrum analysis performed on a voltage / current signal that includes a plurality of signal components, the calculation is quickly and precisely performed, the broadband measurement requirements of a new power system can be met, and a strong support is provided for constructing a wide-frequency-oscillation wide-area monitoring system for the power system.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention relates to the technical field of ground fault detection, in particular to a centralized substation terminal with a data real-time acquisition and analysis function. Performing difference on the current effective value sequence to obtain a target moment; obtaining a local number according to the historically connected differential value of the target moment; acquiring a local current segment and a corresponding spectrogram according to the target moment and the local number; obtaining a harmonic ratio coefficient according to the number of peak points in the spectrograms of the target moment and the historical adjacent target moments and the amplitudes of the peak points; obtaining a target coefficient according to a difference value between the target moment and a historical adjacent target moment and a harmonic ratio coefficient, and carrying out clustering; and obtaining a detection reference value according to the discrete characteristics of the target coefficient in the cluster. The abnormal characteristic value is obtained according to the target coefficient and the detection reference value at the current moment; and high-resistance grounding fault monitoring is carried out according to the abnormal characteristic value, so that the fault detection accuracy is improved.