Operation optimization method and system based on joint analysis of power data

Abstract

The present invention provides an operation optimization method and system based on joint analysis of power data. In this method, synchronous sampling data and grid topology parameters of a wide-area power system are obtained; a joint analysis engine is used to perform equipment status assessment, broadband resonance tracing, and harmonic responsibility quantification on the synchronous sampling data and grid topology parameters to generate a grid control strategy; and based on the grid control strategy, the operation of power equipment in the wide-area power system is optimized. In this method, by jointly analyzing equipment status assessment, broadband resonance tracing, and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, which can improve the depth and accuracy of grid analysis, and can meet the needs of high-frequency harmonic analysis through broadband resonance tracing. Intelligent analysis can be achieved for different application scenarios, thereby improving waveform analysis capabilities and frequency response range, and improving analysis results.

Description

Technical Field

[0001] The present invention relates to the field of smart grid monitoring technology, and in particular to an operation optimization method and system based on joint analysis of power data. Background Art

[0002] Wide-area power waveform synchronization monitoring is a power grid monitoring method based on time synchronization technology. By acquiring and analyzing waveforms of electrical quantities such as voltage and current in the power grid with high precision and high sampling rate, it enables real-time perception of the power system's operating status. Power waveforms can be widely used in power quality monitoring, equipment status assessment, resonance tracing, and fault location, helping to promptly identify potential risks and improve the safe and stable operation of the power grid.

[0003] However, the capabilities of current edge nodes are limited, with only basic spectrum analysis capabilities like FFT (Fast Fourier Transform), making it difficult to deeply characterize the complex electromagnetic behavior of the power grid. Furthermore, existing systems generally lack support for ultra-harmonic monitoring in the 2 kHz to 150 kHz frequency band, failing to meet the high-frequency harmonic analysis requirements of new power electronics. This limits application scenarios and results in incomplete and inaccurate analysis. Therefore, there is an urgent need to improve the waveform analysis capabilities and frequency response range of edge nodes to adapt to the trend toward intelligent and refined control of modern power grids. Summary of the Invention

[0004] In order to overcome the shortcomings of the above-mentioned traditional technologies in that the waveform analysis capability and frequency response range of edge nodes are not comprehensive and accurate, the present invention provides an operation optimization method based on joint analysis of power data, comprising:

[0005] Acquire synchronous sampling data and grid topology parameters of wide-area power systems;

[0006] Using a joint analysis engine, the synchronous sampling data and the grid topology parameters are used to perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification to generate a grid control strategy;

[0007] Based on the power grid control strategy, operation of power equipment in the wide-area power system is optimized.

[0008] Optionally, the synchronously sampled data includes a voltage waveform, a current waveform, and an instantaneous power waveform; and using a joint analysis engine to perform device status assessment, broadband resonance tracing, and harmonic responsibility quantification on the synchronously sampled data and the grid topology parameters to generate a grid control strategy includes:

[0009] Performing time-frequency analysis and feature extraction on the voltage waveform and the current waveform to obtain current and voltage features;

[0010] Performing time-frequency analysis and feature extraction on the instantaneous power waveform to obtain instantaneous power features;

[0011] A joint analysis engine is used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data, the current and voltage characteristics, the instantaneous power characteristics, and the grid topology parameters to generate a grid control strategy.

[0012] Optionally, the use of a joint analysis engine to perform device status assessment, broadband resonance tracing, and harmonic responsibility quantification on the synchronously sampled data and the grid topology parameters to generate a control strategy includes:

[0013] Using a joint analysis engine, performing a high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain a time-frequency feature analysis result, and performing a power equipment status assessment on the time-frequency feature analysis result to obtain an equipment status assessment result;

[0014] Performing interharmonic power tracing on the synchronous sampling data to obtain a feature identification result, and performing broadband resonance tracing on the power grid topology parameters and the feature identification result to obtain a broadband resonance tracing result;

[0015] Performing a distorted power-impedance joint calculation on the synchronous sampling data to obtain a joint calculation result; performing a multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation result to obtain a responsibility quantification result;

[0016] A control strategy is generated based on the integration of the equipment status assessment result, the broadband resonance source tracing result and the responsibility quantification result.

[0017] Optionally, performing high-frequency perturbation time-frequency analysis on the synchronously sampled data to obtain a time-frequency feature analysis result includes:

[0018] Performing short-time Fourier transform processing on the synchronous sampling data to obtain signal spectrum changes of the synchronous sampling data;

[0019] Performing multi-resolution analysis on the synchronous sampling data using wavelet transform to obtain local changes of non-stationary signals in the synchronous sampling data;

[0020] Performing nonlinear and non-stationary signal analysis on the synchronous sampling data using Hilbert-Huang transform to obtain a signal decomposition result;

[0021] The signal spectrum change, the local change of the non-stationary signal and the signal decomposition result are fused to obtain a time-frequency feature analysis result.

[0022] Optionally, performing power equipment status assessment on the time-frequency feature analysis result to obtain an equipment status assessment result includes:

[0023] Extracting characteristic indicators of equipment health status from the time-frequency characteristic analysis results;

[0024] The characteristic indicators are input into a state prediction model to output an equipment state assessment result; the state prediction model is constructed using a machine learning algorithm.

[0025] Optionally, performing interharmonic power tracking on the synchronous sampling data to obtain a feature identification result includes:

[0026] Performing frequency domain decomposition on the synchronous sampling data to obtain interharmonic voltage and current components of the synchronous sampling data;

[0027] Calculating active power, reactive power and apparent power for the interharmonic components to obtain a power distribution spectrum;

[0028] Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain a feature identification result;

[0029] The feature identification results include the main interharmonic frequencies and amplitudes thereof, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations.

[0030] Optionally, performing broadband resonance tracing on the grid topology parameters and the characteristic identification results to obtain a broadband resonance tracing result includes:

[0031] Establish impedance models at different frequencies based on grid topology parameters;

[0032] Performing resonance coupling analysis on the feature identification results and the impedance models at different frequencies to obtain a coupling relationship between interharmonic energy and impedance;

[0033] Utilizing the coupling relationship between the interharmonic energy and impedance, combined with the synchronous sampling data and grid topology parameters, the propagation path of the interharmonic power is analyzed to obtain a broadband resonance tracing result;

[0034] Optionally, performing a distorted power-impedance joint calculation on the synchronously sampled data to obtain a joint calculation result includes:

[0035] Performing frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each subharmonic / interharmonic; calculating the distortion power based on the amplitude and phase of each subharmonic / interharmonic;

[0036] Based on the synchronous sampling data, a frequency domain impedance identification method is used to identify dynamic impedance characteristics;

[0037] Dynamically matching the distorted power and the dynamic impedance characteristic to obtain an interaction relationship between the distorted power and the dynamic impedance characteristic;

[0038] Based on the interaction relationship between the distortion power and the dynamic impedance characteristic, a joint calculation result is determined.

[0039] Optionally, performing multi-agent harmonic responsibility quantification analysis on the grid topology parameters and the joint calculation results to obtain responsibility quantification results includes:

[0040] Locating the node area generating the distorted power in the joint calculation result to obtain a plurality of candidate harmonic sources;

[0041] Using grid topology parameters and impedance models, simulating the impact of each candidate harmonic source;

[0042] Using a responsibility allocation algorithm, the responsibility is allocated to each candidate harmonic source to obtain a responsibility weight;

[0043] Based on the influence degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain a responsibility quantification result.

[0044] Optionally, the optimizing the operation of power equipment in the wide-area power system based on the power grid control strategy includes:

[0045] Based on the grid control strategy, output operation optimization instructions and interference source responsibility reports for power equipment in the wide-area power system;

[0046] Based on the operation optimization instruction and the interference source responsibility report, the power system is optimized for grid energy efficiency and equipment operation.

[0047] Optionally, the synchronous sampling data is integrated with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of the energy flow that reflects the operating status of the system.

[0048] On the other hand, an embodiment of the present invention further provides an operation optimization system based on joint analysis of power data, comprising a master layer node, an edge layer node, and power equipment that are communicatively connected to each other;

[0049] The master layer node is used to obtain and send to the edge layer node the synchronous sampling data and grid topology parameters of the wide area power system;

[0050] The edge layer node is configured to utilize a joint analysis engine to perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronously sampled data and the power grid topology parameters, thereby generating a power grid control strategy; based on the power grid control strategy, output operation optimization instructions and interference source responsibility reports for power equipment in the wide-area power system to the analysis system of the master layer node;

[0051] The master station layer node is used to optimize the power grid energy efficiency and equipment operation based on the operation optimization instructions and interference source responsibility report using the analysis system.

[0052] Optionally, the synchronous sampling data includes a voltage waveform, a current waveform, and an instantaneous power waveform; the edge layer node is specifically configured to:

[0053] Performing time-frequency analysis and feature extraction on the voltage waveform and the current waveform to obtain current and voltage features;

[0054] Performing time-frequency analysis and feature extraction on the instantaneous power waveform to obtain instantaneous power features;

[0055] A joint analysis engine is used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data, the current and voltage characteristics, the instantaneous power characteristics, and the grid topology parameters to generate a grid control strategy.

[0056] Optionally, the edge layer node is specifically configured to:

[0057] Using a joint analysis engine, performing a high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain a time-frequency feature analysis result, and performing a power equipment status assessment on the time-frequency feature analysis result to obtain an equipment status assessment result;

[0058] Performing interharmonic power tracing on the synchronous sampling data to obtain a feature identification result, and performing broadband resonance tracing on the power grid topology parameters and the feature identification result to obtain a broadband resonance tracing result;

[0059] Performing a distorted power-impedance joint calculation on the synchronous sampling data to obtain a joint calculation result; performing a multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation result to obtain a responsibility quantification result;

[0060] A power grid control strategy is generated based on the integration of the equipment status assessment result, the broadband resonance source tracing result and the responsibility quantification result.

[0061] Optionally, the edge layer node is specifically configured to:

[0062] Performing short-time Fourier transform processing on the synchronous sampling data to obtain signal spectrum changes of the synchronous sampling data;

[0063] Performing multi-resolution analysis on the synchronous sampling data using wavelet transform to obtain local changes of non-stationary signals in the synchronous sampling data;

[0064] Performing nonlinear and non-stationary signal analysis on the synchronous sampling data using Hilbert-Huang transform to obtain a signal decomposition result;

[0065] The signal spectrum change, the local change of the non-stationary signal and the signal decomposition result are fused to obtain a time-frequency feature analysis result.

[0066] Optionally, the edge layer node is specifically configured to:

[0067] Extracting characteristic indicators of equipment health status from the time-frequency characteristic analysis results;

[0068] The characteristic indicators are input into a state prediction model to output an equipment state assessment result; the state prediction model is constructed using a machine learning algorithm.

[0069] Optionally, the edge layer node is specifically configured to:

[0070] Performing frequency domain decomposition on the synchronous sampling data to obtain interharmonic voltage and current components of the synchronous sampling data;

[0071] Calculating active power, reactive power and apparent power for the interharmonic components to obtain a power distribution spectrum;

[0072] Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain a feature identification result;

[0073] The feature identification results include the main interharmonic frequencies and amplitudes thereof, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations.

[0074] Optionally, the edge layer node is specifically configured to:

[0075] Establish impedance models at different frequencies based on grid topology parameters;

[0076] Performing resonance coupling analysis on the feature identification results and the impedance models at different frequencies to obtain a coupling relationship between interharmonic energy and impedance;

[0077] By utilizing the coupling relationship between the interharmonic energy and impedance, combined with the synchronous sampling data and the grid topology parameters, the propagation path of the interharmonic power is analyzed to obtain a broadband resonance tracing result.

[0078] Optionally, the edge layer node is specifically configured to:

[0079] Performing frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each subharmonic / interharmonic; calculating the distortion power based on the amplitude and phase of each subharmonic / interharmonic;

[0080] Based on the synchronous sampling data, a frequency domain impedance identification method is used to identify dynamic impedance characteristics;

[0081] Dynamically matching the distorted power and the dynamic impedance characteristic to obtain an interaction relationship between the distorted power and the dynamic impedance characteristic;

[0082] Based on the interaction relationship between the distortion power and the dynamic impedance characteristic, a joint calculation result is determined.

[0083] Optionally, the edge layer node is specifically configured to:

[0084] Locating the node area generating the distorted power in the joint calculation result to obtain a plurality of candidate harmonic sources;

[0085] Using grid topology parameters and impedance models, simulating the impact of each candidate harmonic source;

[0086] Using a responsibility allocation algorithm, the responsibility is allocated to each candidate harmonic source to obtain a responsibility weight;

[0087] Based on the influence degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain a responsibility quantification result.

[0088] Optionally, the synchronous sampling data is integrated with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of the energy flow that reflects the operating status of the system.

[0089] On the other hand, an embodiment of the present invention further provides an operation optimization system based on joint analysis of power data, comprising:

[0090] Sampling module, used to obtain synchronous sampling data and grid topology parameters of the wide-area power system;

[0091] A joint analysis module, configured to utilize a joint analysis engine to perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data and the grid topology parameters, and generate a grid control strategy;

[0092] The optimization module is used to optimize the operation of power equipment in the wide-area power system based on the power grid control strategy.

[0093] Optionally, the synchronous sampling data includes a voltage waveform, a current waveform, and an instantaneous power waveform; the joint analysis module is specifically used to:

[0094] Performing time-frequency analysis and feature extraction on the voltage and current waveforms in the synchronous sampling data to obtain current and voltage features;

[0095] Performing time-frequency analysis and feature extraction on the instantaneous power waveform in the synchronous sampling data to obtain instantaneous power features;

[0096] A joint analysis engine is used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data, the current and voltage characteristics, the instantaneous power characteristics, and the grid topology parameters to generate a grid control strategy.

[0097] Optional conjoint analysis modules include:

[0098] an equipment status evaluation unit, configured to perform a high-frequency disturbance time-frequency analysis on the synchronous sampling data using a joint analysis engine to obtain a time-frequency feature analysis result, and perform a power equipment status evaluation on the time-frequency feature analysis result to obtain an equipment status evaluation result;

[0099] A broadband resonance tracing unit is configured to perform interharmonic power tracing on the synchronous sampling data to obtain a feature identification result, and perform broadband resonance tracing on the power grid topology parameters and the feature identification result to obtain a broadband resonance tracing result;

[0100] a harmonic responsibility quantification unit, configured to perform a distorted power-impedance joint calculation on the synchronous sampling data to obtain a joint calculation result; and perform a multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation result to obtain a responsibility quantification result;

[0101] A result integration unit is used to integrate the equipment status evaluation result, the broadband resonance source tracing result and the responsibility quantification result to generate a control strategy.

[0102] Optional equipment status evaluation unit, specifically used for:

[0103] Performing short-time Fourier transform processing on the synchronous sampling data to obtain signal spectrum changes of the synchronous sampling data;

[0104] Performing multi-resolution analysis on the synchronous sampling data using wavelet transform to obtain local changes of non-stationary signals in the synchronous sampling data;

[0105] Performing nonlinear and non-stationary signal analysis on the synchronous sampling data using Hilbert-Huang transform to obtain a signal decomposition result;

[0106] The signal spectrum change, the local change of the non-stationary signal and the signal decomposition result are fused to obtain a time-frequency feature analysis result.

[0107] Optional equipment status evaluation unit, specifically used for:

[0108] Extracting characteristic indicators of equipment health status from the time-frequency characteristic analysis results;

[0109] The characteristic indicators are input into a state prediction model to output an equipment state assessment result; the state prediction model is constructed using a machine learning algorithm.

[0110] Optional, broadband resonant tracing unit, specifically used for:

[0111] Performing frequency domain decomposition on the synchronous sampling data to obtain interharmonic voltage and current components of the synchronous sampling data;

[0112] Calculating active power, reactive power and apparent power for the interharmonic components to obtain a power distribution spectrum;

[0113] Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain a feature identification result;

[0114] The feature identification results include the main interharmonic frequencies and amplitudes thereof, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations.

[0115] Optional, broadband resonant tracing unit, specifically used for:

[0116] Establish impedance models at different frequencies based on grid topology parameters;

[0117] Performing resonance coupling analysis on the feature identification results and the impedance models at different frequencies to obtain a coupling relationship between interharmonic energy and impedance;

[0118] Utilizing the coupling relationship between the interharmonic energy and impedance, combined with the synchronous sampling data and grid topology parameters, the propagation path of the interharmonic power is analyzed to obtain a broadband resonance tracing result;

[0119] Optional harmonic responsibility quantification unit, specifically used for:

[0120] Performing frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each subharmonic / interharmonic; calculating the distortion power based on the amplitude and phase of each subharmonic / interharmonic;

[0121] Based on the synchronous sampling data, a frequency domain impedance identification method is used to identify dynamic impedance characteristics;

[0122] Dynamically matching the distorted power and the dynamic impedance characteristic to obtain an interaction relationship between the distorted power and the dynamic impedance characteristic;

[0123] Based on the interaction relationship between the distortion power and the dynamic impedance characteristic, a joint calculation result is determined.

[0124] Optional harmonic responsibility quantification unit, specifically used for:

[0125] Locating the node area generating the distorted power in the joint calculation result to obtain a plurality of candidate harmonic sources;

[0126] Using grid topology parameters and impedance models, simulating the impact of each candidate harmonic source;

[0127] Using a responsibility allocation algorithm, the responsibility is allocated to each candidate harmonic source to obtain a responsibility weight;

[0128] Based on the influence degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain a responsibility quantification result.

[0129] Optionally, an optimization module is specifically used to output operation optimization instructions and interference source responsibility reports for power equipment in a wide-area power system based on the power grid control strategy; and optimize the power grid energy efficiency and equipment operation of the power system based on the operation optimization instructions and the interference source responsibility report.

[0130] Optionally, the synchronous sampling data is integrated with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of the energy flow that reflects the operating status of the system.

[0131] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0132] The memory is used to store one or more programs;

[0133] When the one or more programs are executed by the at least one processor, the above-mentioned operation optimization method based on joint analysis of power data is implemented.

[0134] On the other hand, the present invention also provides a readable storage medium having an execution program stored thereon, which, when executed, implements the above-mentioned operation optimization method based on joint analysis of power data.

[0135] Compared with the prior art, the present invention has the following beneficial effects:

[0136] The present invention provides an operation optimization method and system based on joint analysis of power data. In this method, synchronous sampling data and grid topology parameters of a wide-area power system are obtained; a joint analysis engine is used to perform equipment status assessment, broadband resonance tracing, and harmonic responsibility quantification on the synchronous sampling data and grid topology parameters to generate a grid control strategy; and based on the grid control strategy, the operation of power equipment in the wide-area power system is optimized. In this method, by jointly analyzing equipment status assessment, broadband resonance tracing, and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, which can improve the depth and accuracy of grid analysis, and can meet the needs of high-frequency harmonic analysis through broadband resonance tracing. Intelligent analysis can be achieved for different application scenarios, thereby improving waveform analysis capabilities and frequency response range, and improving analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] Figure 1 Schematic diagram of the flow of the operation optimization method based on power data joint analysis of the present invention;

[0138] Figure 2 Schematic diagram of the distortion power-impedance joint analysis process of the present invention;

[0139] Figure 3 This is a flow chart of the wide-area power waveform joint analysis method of the present invention;

[0140] Figure 4 This is a schematic diagram of the structure of the operation optimization system based on power data joint analysis of the present invention;

[0141] Figure 5 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0142] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0143] Example 1:

[0144] The present invention provides an operation optimization method based on joint analysis of power data, the flow chart of which is as follows: Figure 1 As shown, including:

[0145] Step 101: Acquire synchronous sampling data and grid topology parameters of a wide-area power system;

[0146] Step 102: Using a joint analysis engine, perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronously sampled data and grid topology parameters to generate a grid control strategy.

[0147] Step 103: Based on the power grid control strategy, optimize the operation of power equipment in the wide-area power system.

[0148] In the embodiment of the present invention, by jointly analyzing the equipment status assessment, broadband resonance tracing and harmonic responsibility quantification of the wide-area power system, the traditional static spectrum analysis is upgraded to dynamic behavior perception, which can improve the depth and accuracy of power grid analysis. In addition, the broadband resonance tracing can meet the needs of high-frequency harmonic analysis, and can realize intelligent analysis for different application scenarios, thereby improving the waveform analysis capability and frequency response range, and improving the analysis effect.

[0149] In one implementation, the wide-area power waveform joint analysis method provided by an embodiment of the present invention can be applied to any device in a wide-area power system. In another implementation, the wide-area power waveform joint analysis method provided by an embodiment of the present invention can be applied to edge-layer nodes in a wide-area power system. The system architecture of this implementation will be described in subsequent embodiments and will not be further described here.

[0150] In step 101 above, the synchronized sampling data of the wide-area power system can be high-precision time-synchronized waveforms from measurement equipment, such as, but not limited to, PMUs (phasor measurement units). High-precision time-synchronized waveforms are synchronously acquired under a precise time reference to ensure that data from different channels remain consistent in time, which is very important for accurately capturing transient phenomena in the power system. In embodiments of the present invention, synchronized sampling data may include not only voltage and current waveforms but also power waveforms (particularly instantaneous power waveforms). Voltage and current waveforms are basic elements of traditional power quality monitoring, but embodiments of the present invention also include power waveforms (such as instantaneous power waveforms). Power waveforms reflect the dynamic process of energy flow in the system and can better reflect the dynamic behavior of the system than simple voltage and current waveforms. In some implementations, synchronized sampling data can be collected by terminal layer nodes and sent to edge layer nodes.

[0151] In step 101 above, the grid topology parameters of the wide-area power system describe information such as the grid structure, line connections, and device relationships. For example, these parameters include line length, transformer ratio, load distribution, filter configuration, and capacitor / reactor location. These parameters determine the harmonic propagation paths and attenuation characteristics in the grid. These parameters form the basis for constructing a system impedance model and evaluating the electrical connections between nodes.

[0152] In the above step 102, the analysis objects include voltage waveforms, current waveforms, and power waveforms. That is to say, in the embodiment of the present invention, not only the voltage waveform and the current waveform are subjected to time-frequency analysis, but also the instantaneous power waveform is creatively subjected to time-frequency analysis and feature extraction. This can more comprehensively reflect the dynamic behavior of the system, such as load changes, abnormal energy flow, harmonic interaction, etc. Instantaneous power fluctuations can reflect abnormal equipment operation status, and energy oscillations caused by resonance will appear as specific frequency components on the power waveform. The difference in power characteristics of different users can be used for harmonic responsibility division. Therefore, the introduction of instantaneous power waveform analysis can more accurately identify nonlinear, dynamic, and coupled behaviors in the system, improve the performance of the joint analysis engine, and make it more accurate in state assessment, tracing, and responsibility division methods, thereby enhancing the intelligent perception and control capabilities of the entire system.

[0153] For example, in step 102 above, time-frequency analysis and feature extraction can be performed on the voltage and current waveforms to obtain current and voltage characteristics; time-frequency analysis and feature extraction can be performed on the instantaneous power waveform to obtain instantaneous power characteristics; and a joint analysis engine can be used to perform device status assessment, broadband resonance tracing, and harmonic responsibility quantification on the synchronously sampled data, current and voltage characteristics, instantaneous power characteristics, and grid topology parameters to generate a grid control strategy. In this example, when performing time-frequency analysis on the voltage and current waveforms, spectrum analysis such as the FFT algorithm can be used to determine whether there are problems such as harmonics and distortion.

[0154] In this example, time-frequency analysis of the instantaneous power waveform can be performed to observe the frequency components present at different time points. For example, wavelet transforms or short-time Fourier transforms (STFTs) can be used to perform time-frequency analysis and obtain instantaneous power analysis results. If high-frequency oscillations are detected at a specific moment based on the instantaneous power analysis results, this indicates possible system resonance or device anomalies. When extracting features from the instantaneous power waveform, feature extraction algorithms or models can be used to extract key features from the instantaneous power analysis results. Key features include, but are not limited to, energy intensity at specific frequencies, the periodicity or aperiodicity of power fluctuations, and the timing of waveform abrupt changes. These key features can assist in determining device aging or failure (i.e., assessing device status), determining the location and cause of resonance (i.e., tracing the source of broadband resonance), and determining which loads or users are introducing more harmonics (i.e., quantifying harmonic contribution).

[0155] Optionally, the synchronous sampling data may also include phase information to improve the accuracy of equipment status assessment, broadband resonance tracing, and harmonic responsibility quantification.

[0156] The joint analysis of power waveforms includes a power equipment status assessment process based on high-frequency disturbance time-frequency feature analysis, a broadband resonance source tracing and active suppression process based on interharmonic power feature identification, and a multi-agent harmonic responsibility quantification analysis process based on the combination of distortion power and broadband impedance. In one implementation, in the above step 102, a joint analysis engine is used to perform high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain time-frequency feature analysis results, and the power equipment status is assessed on the time-frequency feature analysis results to obtain equipment status assessment results; interharmonic power is tracked on the synchronous sampling data to obtain feature identification results; broadband resonance source tracing is performed on the power grid topology parameters and feature identification results to obtain broadband resonance source tracing results; distortion power-impedance joint calculation is performed on the synchronous sampling data to obtain a joint calculation result; multi-agent harmonic responsibility quantification analysis is performed on the power grid topology parameters and the joint calculation result to obtain responsibility quantification results; and a power grid control strategy is generated based on the integration of the equipment status assessment results, broadband resonance source tracing results, and responsibility quantification results.

[0157] In this implementation, the time-frequency characteristic analysis results obtained during power equipment status assessment based on high-frequency disturbance time-frequency characteristic analysis can reflect the high-frequency components in the power waveform and their temporal variation characteristics. High-frequency disturbance time-frequency analysis algorithms include but are not limited to short-time Fourier transform (STFT), wavelet transform (WT), and Hilbert-Huang transform (HHT). High-frequency disturbance time-frequency analysis algorithms can provide the frequency composition of power signals at different times and reveal various disturbance characteristics present in power signals, such as harmonics and transient pulses.

[0158] When performing the high-frequency perturbation time-frequency analysis on the synchronously sampled data and obtaining the time-frequency feature analysis results, the synchronously sampled data can be subjected to a short-time Fourier transform (SFT) to obtain the signal spectrum changes of the synchronously sampled data; multi-resolution analysis can be performed on the synchronously sampled data using a wavelet transform to obtain the local changes of the non-stationary signal in the synchronously sampled data; nonlinear non-stationary signal analysis can be performed on the synchronously sampled data using the Hilbert-Huang transform to obtain the signal decomposition results; and the signal spectrum changes, local changes of the non-stationary signal, and the signal decomposition results can be integrated to obtain the time-frequency feature analysis results. The short-time Fourier transform (SFT) can observe the temporal changes of the signal spectrum by performing a sliding window Fourier transform on the power signal. The wavelet transform has multi-resolution analysis capabilities and is suitable for detecting local changes in non-stationary signals. The Hilbert-Huang transform (HFT) is suitable for analyzing nonlinear non-stationary signals, decomposing the signal into multiple intrinsic mode functions (IMFs) for more detailed analysis. By using these transform algorithms to perform high-frequency perturbation time-frequency analysis on the synchronously sampled data, rich information about the high-frequency perturbation can be extracted from the synchronously sampled data, forming the time-frequency feature analysis results.

[0159] When evaluating the power equipment status using the time-frequency feature analysis results, characteristic indicators of the equipment's health status can be extracted from the time-frequency feature analysis results. These indicators are then input into a state prediction model, which outputs the equipment status evaluation results. The state prediction model is constructed using a machine learning algorithm. Characteristic indicators of equipment health status may include, but are not limited to, energy distribution in specific frequency bands and the frequency of occurrence of certain patterns. Equipment status evaluation results may include, but are not limited to, different levels of classification, such as normal operation, mild anomalies, and severe faults. This approach enables online monitoring and evaluation of power equipment status, timely identification of potential problems, and improved safe and stable operation of the power grid.

[0160] In this implementation, during the broadband resonance tracing and active suppression process based on interharmonic power feature identification, synchronous sampling data is used to capture the interharmonic components in the power system, track their power distribution and dynamic changes, and extract characteristic information; combined with the grid structure parameters, the impedance characteristics of the system in a broadband range are analyzed; then the occurrence mechanism of broadband resonance is identified and traced, based on which the cause of the resonance and governance suggestions can be output to provide support for the stable operation of the power system. This process embodies the closed-loop operation concept of perception-analysis-decision-making in modern smart grids, which is conducive to improving the power quality and operation safety of the grid.

[0161] Interharmonic power tracking involves extracting interharmonic components from synchronously sampled data and calculating their power distribution. For example, when performing interharmonic power tracking on synchronously sampled data to obtain feature identification results, an interharmonic power flow tracking algorithm can be used to perform interharmonic power tracking on the synchronously sampled data to obtain feature identification results. More specifically, the synchronously sampled data can be decomposed in the frequency domain to obtain the interharmonic voltage and current components of the synchronously sampled data. Active power, reactive power, and apparent power are calculated for the interharmonic components to obtain a power distribution spectrum. Based on the amplitude and direction in the power distribution spectrum, the location and intensity changes of the disturbance source are analyzed to obtain feature identification results. Feature identification results include the main interharmonic frequencies and their amplitudes, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations. When performing frequency domain decomposition on the synchronously sampled data, FFT, wavelet transform, PRONY algorithm, or modern high-resolution spectrum estimation methods (such as ESPRIT) can be used to identify interharmonic components in the signal.

[0162] Broadband resonance refers to the resonance phenomenon that occurs within a wide frequency range. It is usually caused by the complex interaction between multiple harmonic sources and system impedance. It is different from simple series or parallel resonance at a single frequency. It has the characteristics of a wide frequency range, many influencing factors, and is difficult to predict. The broadband resonance tracing is to use the grid topology parameters and the previously obtained feature identification results to analyze and locate the root cause of the broadband resonance. For example, when the broadband resonance tracing results are obtained by performing broadband resonance tracing on the grid topology parameters and feature identification results, an impedance model at different frequencies can be established based on the grid topology parameters; the feature identification results are subjected to resonant coupling analysis with the impedance models at different frequencies to obtain the coupling relationship between interharmonic energy and impedance; the coupling relationship between interharmonic energy and impedance is used, combined with synchronous sampling data and grid topology parameters, to analyze the propagation path of interharmonic power and give broadband resonance tracing results.

[0163] The impedance model can be a curve showing the change of system impedance with frequency, which can identify potential resonant frequency points in the system. The coupling relationship can describe the frequency bands in which the interharmonic energy has a strong coupling relationship with the system impedance, thereby determining whether resonance has been triggered. The propagation path of the interharmonic power can include the location of the disturbance source and the area most severely affected by it. The results of broadband resonance tracing include but are not limited to: the main frequency range causing resonance, the responsible equipment or load (such as a certain frequency converter, arc furnace, new energy inverter, etc.), the weak links in the system structure (such as improper configuration of a specific filter, poor cable length matching, etc.), and possible suggestions for improvement measures (such as adjusting the equipment operation mode, adding damping devices, optimizing filter design, etc.).

[0164] The impedance model constructed above can be combined with the distortion power distribution to perform impedance-power joint analysis, thereby accurately identifying the device status, resonant path and its influence range, and enhancing the interpretability and reliability of the diagnostic results. In one implementation, when the distortion power-impedance joint calculation is performed on the synchronous sampling data and the joint calculation result is obtained, the synchronous sampling data can be decomposed in the frequency domain to extract the amplitude and phase of each harmonic / interharmonic; based on the amplitude and phase of each harmonic / interharmonic, the distortion power is calculated; based on the synchronous sampling data, the dynamic impedance characteristics are identified using the frequency domain impedance identification method; the distortion power and dynamic impedance characteristics are dynamically matched to obtain the interaction relationship between the distortion power and the dynamic impedance characteristics; and the joint calculation result is determined based on the interaction relationship between the distortion power and the dynamic impedance characteristics.

[0165] Distortion power refers to the power components generated by non-sinusoidal voltages and currents, primarily from harmonics and interharmonics. This includes, but is not limited to, total harmonic distortion (THD), harmonic active power, harmonic reactive power, and cross-term power. Total harmonic distortion measures the degree to which a voltage or current waveform deviates from an ideal sine wave. Harmonic active power and reactive power represent the contributions of each harmonic to work and energy storage, respectively. Cross-term power represents the non-traditional power components generated by the interaction between voltage and current at different frequencies. In this implementation, frequency domain decomposition using techniques such as FFT and wavelet transforms can be performed to extract the amplitude and phase information of each harmonic / interharmonic, thereby calculating the corresponding distortion power. The system's (local or global) dynamic impedance characteristics influence the harmonic propagation path and amplification effect. In this implementation, frequency domain impedance identification methods can be used to identify the system's dynamic impedance characteristics at specific frequencies, leveraging the relationship between voltage and current changes before and after a disturbance event in synchronously sampled data. These methods can include frequency domain response methods and least squares methods. In this implementation, the joint calculation result may include a data set of distortion power, dynamic impedance characteristics, and the interaction relationship between the two.

[0166] In modern power systems, especially complex systems containing multiple types of distributed power sources, high-power electronic loads, and new energy access units, harmonic pollution is often not caused by a single source, but the result of the joint action of multiple subjects. Therefore, it is necessary to divide the responsibilities of each possible harmonic source. In one implementation method, when the multi-subject harmonic responsibility quantification analysis of the grid topology parameters and the joint calculation results is performed to obtain the responsibility quantification results, a multi-subject game model can be used to perform a multi-subject harmonic responsibility quantification analysis on the grid topology parameters and the joint calculation results to obtain the responsibility quantification results. More specifically, the node area that generates distorted power can be located in the joint calculation results to obtain multiple candidate harmonic sources; the influence of each candidate harmonic source can be simulated using the grid topology parameters and the impedance model; the responsibility allocation algorithm is used to allocate responsibilities to each candidate harmonic source to obtain a responsibility weight; based on the influence of each candidate harmonic source and the responsibility weight, a weighted sum is performed to obtain the responsibility quantification result. In this implementation method, the responsibility allocation algorithm can include an injection method, a proportional method, or a sensitivity analysis method. When using the injection method, it can be assumed that each candidate harmonic source independently injects harmonics of equal intensity. The voltage distortion caused by each candidate harmonic source at the target node can be compared, and then responsibility weights can be assigned. When using the proportional method, the sensitivity coefficient of each candidate harmonic source to the voltage distortion at the target node can be calculated as the responsibility weight. When using the sensitivity analysis method, the sensitivity coefficient of each candidate harmonic source to the voltage distortion at the target node can be calculated as the responsibility weight. The responsibility quantification result is a quantitative description of the responsibility of each potential candidate harmonic source in causing harmonic pollution in the system. For example, it can be expressed as the percentage of the total voltage distortion rate caused by each candidate harmonic source at a specific node or the entire system, or the ranking of each candidate harmonic source's primary contribution to specific subharmonics or broadband harmonics, or a spatial distribution map of harmonic pollution responsibility, or a classification of the responsible parties corresponding to each candidate harmonic source (such as industrial users, new energy stations, rail transit, etc.). This implementation method demonstrates the refined management capabilities of modern power systems for power quality issues and is particularly applicable to the responsibility definition and coordinated control requirements in the context of multi-source coordinated operation in new power systems.

[0167] For example, in Figure 2 In determining responsibility quantification, the first step is to measure harmonics at the point of common coupling (PCC) and construct an equivalent impedance network model. An optimization algorithm is then used to determine the distorted power distribution and impedance parameters at each node, determining the optimal configuration. Finally, the responsibility coefficient (or responsibility weight) for each harmonic source is calculated to clarify interference responsibility. This process combines harmonic measurement, network modeling, and optimization calculations to achieve precise responsibility quantification and formulate governance strategies.

[0168] When generating a grid control strategy based on the integration of the above-mentioned equipment status assessment results, broadband resonance tracing results, and responsibility quantification results, a distributed collaborative control mechanism can be used to dynamically allocate control tasks among multiple power devices based on the equipment status assessment results, broadband resonance tracing results, and responsibility quantification results. Predictive algorithms can also be used to provide early warnings of possible future resonance or equipment failures, allowing control strategies to be adjusted in advance and closed-loop feedback control methods to continuously optimize grid operation. By integrating the three core results of equipment status assessment, broadband resonance tracing, and responsibility quantification analysis, the system can achieve refined and intelligent control of complex power grids, improving the security, stability, and economic efficiency of the grid.

[0169] In one implementation, in step 103, based on the grid control strategy, operational optimization instructions and interference source responsibility reports for power equipment in the wide-area power system can be output. Based on the operational optimization instructions and interference source responsibility reports, the power system is optimized for grid energy efficiency and equipment operation. Based on the grid control strategy, the operational optimization instructions for power equipment outputted can include active suppression instructions and / or energy efficiency optimization suggestions, and the interference source responsibility reports outputted can include responsibility allocation reports. Active suppression instructions aim to dynamically suppress broadband resonance, harmonic disturbances, voltage fluctuations, and other issues in the power grid through real-time intervention. Examples of active suppression instructions include: controlling SVG (Static Var Generator) / SVC (Static Var Compensator) devices to rapidly inject reactive power to offset harmonic currents; switching filter bank states to block resonant paths at specific frequencies; adjusting the output waveform of new energy inverters to reduce total harmonic distortion; enabling energy storage systems to absorb transient energy and mitigate voltage swells / sags; and isolating faulty equipment or abnormal branches to prevent the spread of disturbances. The purpose of energy efficiency optimization recommendations is to optimize energy utilization efficiency, reduce losses, and extend equipment life while ensuring stable grid operation. Energy efficiency optimization recommendations include, for example: recommendations to adjust transformer tap positions to improve voltage stability; recommendations to shut down low-load lines to reduce no-load losses; recommendations to overhaul or rotate equipment during low-load periods; proposals for optimal configuration of reactive power compensation devices; and recommendations for optimizing the layout of distributed power access points to improve local power consumption capacity. Responsibility allocation reports are used to identify the sources of power quality problems in the grid, assign responsibilities, and conduct quantitative analysis to provide a basis for subsequent supervision and governance. Responsibility allocation reports may include the interference source number / name, region, interference type, main frequency range, responsibility level, time distribution characteristics, exceedances, and recommended measures.

[0170] In one implementation, synchronously sampled data is fused with energy flow and information flow. The information flow is the multi-dimensional time-frequency domain feature quantity of the energy flow that reflects the system's operating status. In some implementations, dual-flow coupling of "energy and information" can be achieved through multi-dimensional waveform feature extraction and information fusion technology. Multi-dimensional waveform feature extraction can capture waveform details and establish a mapping relationship with the system's operating status. After coupling the energy flow and information flow, analysis can reflect the overall system performance.

[0171] A specific implementation example Figure 3 As shown, synchronous sampling data and grid topology parameters are obtained. A joint analysis engine is used to perform time-frequency analysis of high-frequency disturbances and equipment status assessment based on the synchronous sampling data to obtain equipment status assessment results. Interharmonic power is tracked based on the synchronous sampling data to obtain feature identification results. Based on the feature identification results and the grid topology data, broadband resonance tracing is performed to obtain broadband resonance tracing results. Distorted power and impedance are jointly calculated on the synchronous sampling data to obtain joint calculation results. Multi-agent harmonic responsibility quantification analysis is performed on the grid topology parameters and the joint calculation results to obtain responsibility quantification results. The equipment status assessment results, broadband resonance tracing results, and responsibility quantification results are integrated to generate a grid control strategy. Based on the grid control strategy, active suppression instructions, energy efficiency optimization suggestions, and responsibility division reports are generated. This is suitable for broadband power quality management scenarios in power grids with a high proportion of renewable energy.

[0172] Example 2:

[0173] Based on the same inventive concept, the present invention also provides an operation optimization system based on joint analysis of power data, comprising:

[0174] Master layer nodes, edge layer nodes and power equipment that communicate with each other;

[0175] The master layer node is used to obtain and send synchronized sampling data and grid topology parameters of the wide area power system to the edge layer nodes;

[0176] Edge-layer nodes utilize a joint analysis engine to evaluate equipment status, trace broadband resonance sources, and quantify harmonic responsibility based on synchronously sampled data and grid topology parameters, generating grid control strategies. Based on these strategies, they output operational optimization instructions and interference source responsibility reports for power equipment in the wide-area power system to the analysis system of the master-layer nodes.

[0177] The master station layer node is used to optimize the grid energy efficiency and equipment operation based on the operation optimization instructions and interference source responsibility reports using the analysis system.

[0178] Optionally, the synchronously sampled data includes a voltage waveform, a current waveform, and an instantaneous power waveform. The edge layer node is specifically used to:

[0179] Perform time-frequency analysis and feature extraction on the voltage and current waveforms to obtain current and voltage features;

[0180] Perform time-frequency analysis and feature extraction on the instantaneous power waveform to obtain the instantaneous power feature;

[0181] Utilizing a joint analysis engine, synchronously sampled data, current and voltage characteristics, instantaneous power characteristics, and grid topology parameters are used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification to generate grid control strategies.

[0182] Optional edge layer nodes, specifically used for:

[0183] Using the joint analysis engine, high-frequency disturbance time-frequency analysis is performed on the synchronous sampling data to obtain time-frequency feature analysis results. The power equipment status assessment is performed on the time-frequency feature analysis results to obtain the equipment status assessment results.

[0184] Perform interharmonic power tracking on synchronous sampling data to obtain feature identification results, and perform broadband resonance tracing on power grid topology parameters and feature identification results to obtain broadband resonance tracing results;

[0185] Perform distorted power-impedance joint calculation on synchronous sampling data to obtain joint calculation results; perform multi-agent harmonic responsibility quantification analysis on power grid topology parameters and joint calculation results to obtain responsibility quantification results;

[0186] The power grid control strategy is generated based on the integration of equipment status assessment results, broadband resonance tracing results and responsibility quantification results.

[0187] Optional edge layer nodes, specifically used for:

[0188] Performing short-time Fourier transform processing on the synchronous sampling data to obtain the signal spectrum change of the synchronous sampling data;

[0189] The synchronous sampling data is subjected to multi-resolution analysis using wavelet transform to obtain the local variation of the non-stationary signal in the synchronous sampling data.

[0190] The synchronous sampling data is subjected to nonlinear non-stationary signal analysis using Hilbert-Huang transform to obtain the signal decomposition result;

[0191] The signal spectrum changes, local changes of non-stationary signals and signal decomposition results are integrated to obtain the time-frequency feature analysis results.

[0192] Optional edge layer nodes, specifically used for:

[0193] Extract characteristic indicators of equipment health status from the time-frequency feature analysis results;

[0194] The characteristic indicators are input into the state prediction model, and the equipment state assessment results are output; the state prediction model is constructed using a machine learning algorithm.

[0195] Optional edge layer nodes, specifically used for:

[0196] Perform frequency domain decomposition on the synchronous sampling data to obtain the interharmonic voltage and current components of the synchronous sampling data;

[0197] Calculate the active power, reactive power and apparent power of the interharmonic components to obtain the power distribution spectrum;

[0198] Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain the feature identification results;

[0199] The feature identification results include the main interharmonic frequencies and their amplitudes, power flow, energy distribution, and the occurrence patterns of interharmonic power component combinations.

[0200] Optional edge layer nodes, specifically used for:

[0201] Establish impedance models at different frequencies based on grid topology parameters;

[0202] The characteristic identification results are analyzed with the impedance model at different frequencies to perform resonance coupling analysis and obtain the coupling relationship between interharmonic energy and impedance.

[0203] By utilizing the coupling relationship between interharmonic energy and impedance, combined with synchronous sampling data and grid topology parameters, the propagation path of interharmonic power is analyzed and the broadband resonance tracing result is obtained.

[0204] Optional edge layer nodes, specifically used for:

[0205] Perform frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each harmonic / interharmonic; calculate the distortion power based on the amplitude and phase of each harmonic / interharmonic;

[0206] Based on synchronous sampling data, the dynamic impedance characteristics are identified using the frequency domain impedance identification method;

[0207] Dynamically match the distortion power and dynamic impedance characteristics to obtain the interaction relationship between the distortion power and dynamic impedance characteristics;

[0208] Based on the interaction between the distortion power and the dynamic impedance characteristics, the joint calculation results are determined.

[0209] Optional edge layer nodes, specifically used for:

[0210] Locate the node area that generates distorted power in the joint calculation results and obtain multiple candidate harmonic sources;

[0211] Using grid topology parameters and impedance models, simulate the impact of each candidate harmonic source;

[0212] Using the responsibility allocation algorithm, the responsibility of each candidate harmonic source is allocated and the responsibility weight is obtained;

[0213] Based on the impact degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain the responsibility quantification result.

[0214] Optionally, the synchronous sampling data is fused with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of the energy flow that reflects the operating status of the system.

[0215] Example 3:

[0216] Based on the same inventive concept, the present invention also provides an operation optimization system based on joint analysis of power data, such as Figure 4 As shown, including:

[0217] Sampling module, used to obtain synchronous sampling data and grid topology parameters of the wide-area power system;

[0218] The joint analysis module is used to use the joint analysis engine to evaluate equipment status, trace the source of broadband resonance, and quantify harmonic responsibility based on synchronous sampling data and grid topology parameters, and generate grid control strategies;

[0219] The optimization module is used to optimize the operation of power equipment in the wide-area power system based on the power grid control strategy.

[0220] Optionally, the synchronously sampled data includes voltage waveform, current waveform and instantaneous power waveform, and the joint analysis module is specifically used to:

[0221] Perform time-frequency analysis and feature extraction on the voltage and current waveforms in the synchronous sampling data to obtain current and voltage features;

[0222] Perform time-frequency analysis and feature extraction on the instantaneous power waveform in the synchronous sampling data to obtain the instantaneous power feature;

[0223] Utilizing a joint analysis engine, synchronously sampled data, current and voltage characteristics, instantaneous power characteristics, and grid topology parameters are used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification to generate grid control strategies.

[0224] Optional conjoint analysis modules include:

[0225] The equipment status evaluation unit is used to use the joint analysis engine to perform high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain time-frequency feature analysis results, and to perform power equipment status evaluation on the time-frequency feature analysis results to obtain equipment status evaluation results;

[0226] The broadband resonance tracing unit is used to track the interharmonic power of the synchronous sampling data to obtain the feature identification results, and to perform broadband resonance tracing on the grid topology parameters and feature identification results to obtain the broadband resonance tracing results;

[0227] The harmonic responsibility quantification unit is used to perform distorted power-impedance joint calculation on synchronous sampling data to obtain joint calculation results; perform multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation results to obtain responsibility quantification results;

[0228] The result integration unit is used to integrate the equipment status assessment results, broadband resonance tracing results and responsibility quantification results to generate a control strategy.

[0229] Optional equipment status evaluation unit, specifically used for:

[0230] Performing short-time Fourier transform processing on the synchronous sampling data to obtain the signal spectrum change of the synchronous sampling data;

[0231] The synchronous sampling data is subjected to multi-resolution analysis using wavelet transform to obtain the local variation of the non-stationary signal in the synchronous sampling data.

[0232] The synchronous sampling data is subjected to nonlinear non-stationary signal analysis using Hilbert-Huang transform to obtain the signal decomposition result;

[0233] The signal spectrum changes, local changes of non-stationary signals and signal decomposition results are integrated to obtain the time-frequency feature analysis results.

[0234] Optional equipment status evaluation unit, specifically used for:

[0235] Extract characteristic indicators of equipment health status from the time-frequency feature analysis results;

[0236] The characteristic indicators are input into the state prediction model, and the equipment state assessment results are output; the state prediction model is constructed using a machine learning algorithm.

[0237] Optional, broadband resonant tracing unit, specifically used for:

[0238] Perform frequency domain decomposition on the synchronous sampling data to obtain the interharmonic voltage and current components of the synchronous sampling data;

[0239] Calculate the active power, reactive power and apparent power of the interharmonic components to obtain the power distribution spectrum;

[0240] Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain the feature identification results;

[0241] The feature identification results include the main interharmonic frequencies and their amplitudes, power flow, energy distribution, and the occurrence patterns of interharmonic power component combinations.

[0242] Optional, broadband resonant tracing unit, specifically used for:

[0243] Establish impedance models at different frequencies based on grid topology parameters;

[0244] The characteristic identification results are analyzed with the impedance model at different frequencies to perform resonance coupling analysis and obtain the coupling relationship between interharmonic energy and impedance.

[0245] By utilizing the coupling relationship between interharmonic energy and impedance, combined with synchronous sampling data and grid topology parameters, the propagation path of interharmonic power is analyzed and broadband resonance tracing results are obtained.

[0246] Optional harmonic responsibility quantification unit, specifically used for:

[0247] Perform frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each harmonic / interharmonic; calculate the distortion power based on the amplitude and phase of each harmonic / interharmonic;

[0248] Based on synchronous sampling data, the dynamic impedance characteristics are identified using the frequency domain impedance identification method;

[0249] Dynamically match the distortion power and dynamic impedance characteristics to obtain the interaction relationship between the distortion power and dynamic impedance characteristics;

[0250] Based on the interaction between the distortion power and the dynamic impedance characteristics, the joint calculation results are determined.

[0251] Optional harmonic responsibility quantification unit, specifically used for:

[0252] Locate the node area that generates distorted power in the joint calculation results and obtain multiple candidate harmonic sources;

[0253] Using grid topology parameters and impedance models, simulate the impact of each candidate harmonic source;

[0254] Using the responsibility allocation algorithm, the responsibility of each candidate harmonic source is allocated and the responsibility weight is obtained;

[0255] Based on the impact degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain the responsibility quantification result.

[0256] Optional, optimization module, specifically used to output operation optimization instructions and interference source responsibility reports for power equipment in the wide-area power system based on the power grid control strategy; based on the operation optimization instructions and interference source responsibility reports, optimize the power grid energy efficiency and equipment operation of the power system.

[0257] Optionally, the synchronous sampling data is fused with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of the energy flow that reflects the operating status of the system.

[0258] Example 4:

[0259] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0260] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of an operation optimization method based on joint analysis of power data in the above embodiment.

[0261] Example 5:

[0262] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). This electronic device-readable storage medium is a memory device within the electronic device, used to store programs and data. It is understood that the storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more executable programs (including program code). It should be noted that the storage medium herein may be high-speed RAM memory or non-volatile memory, such as at least one disk storage device. The processor loading and executing one or more instructions stored in the storage medium can implement the steps of an operation optimization method based on joint analysis of power data in the above-described embodiment.

[0263] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0264] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0265] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0266] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0267] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. An operation optimization method based on joint analysis of power data, characterized in that: include: Acquire synchronous sampling data and grid topology parameters of a wide-area power system; the synchronous sampling data includes voltage waveform, current waveform, and instantaneous power waveform; Using a joint analysis engine, the synchronous sampling data and the grid topology parameters are used to perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification to generate a grid control strategy; Based on the grid control strategy, optimizing the operation of power equipment in the wide-area power system; The use of a joint analysis engine to perform device status assessment, broadband resonance tracing and harmonic responsibility quantification on the synchronously sampled data and the grid topology parameters to generate a grid control strategy includes: Using a joint analysis engine, performing a high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain a time-frequency feature analysis result, and performing a power equipment status assessment on the time-frequency feature analysis result to obtain an equipment status assessment result; Performing interharmonic power tracing on the synchronous sampling data to obtain a feature identification result, and performing broadband resonance tracing on the power grid topology parameters and the feature identification result to obtain a broadband resonance tracing result; Performing a distorted power-impedance joint calculation on the synchronous sampling data to obtain a joint calculation result; performing a multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation result to obtain a responsibility quantification result; A power grid control strategy is generated based on the integration of the equipment status assessment result, the broadband resonance source tracing result and the responsibility quantification result.

2. The method according to claim 1, wherein The use of a joint analysis engine to perform device status assessment, broadband resonance tracing and harmonic responsibility quantification on the synchronously sampled data and the grid topology parameters to generate a grid control strategy includes: Performing time-frequency analysis and feature extraction on the voltage waveform and the current waveform to obtain current and voltage features; Performing time-frequency analysis and feature extraction on the instantaneous power waveform to obtain instantaneous power features; A joint analysis engine is used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data, the current and voltage characteristics, the instantaneous power characteristics, and the grid topology parameters to generate a grid control strategy.

3. The method according to claim 1, wherein The performing high-frequency perturbation time-frequency analysis on the synchronous sampling data to obtain a time-frequency feature analysis result includes: Performing short-time Fourier transform processing on the synchronous sampling data to obtain signal spectrum changes of the synchronous sampling data; Performing multi-resolution analysis on the synchronous sampling data using wavelet transform to obtain local changes of non-stationary signals in the synchronous sampling data; Performing nonlinear and non-stationary signal analysis on the synchronous sampling data using Hilbert-Huang transform to obtain a signal decomposition result; The signal spectrum change, the local change of the non-stationary signal and the signal decomposition result are fused to obtain a time-frequency feature analysis result.

4. The method according to claim 1, wherein The performing of power equipment status evaluation on the time-frequency feature analysis result to obtain the equipment status evaluation result includes: Extracting characteristic indicators of equipment health status from the time-frequency characteristic analysis results; The characteristic indicators are input into a state prediction model to output an equipment state assessment result; the state prediction model is constructed using a machine learning algorithm.

5. The method according to claim 1, wherein The performing interharmonic power tracking on the synchronous sampling data to obtain a feature identification result includes: Performing frequency domain decomposition on the synchronous sampling data to obtain interharmonic voltage and current components of the synchronous sampling data; Calculate the active power, reactive power and apparent power of the interharmonic components to obtain the power distribution spectrum; Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain a feature identification result; The feature identification results include the main interharmonic frequencies and amplitudes thereof, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations.

6. The method according to claim 5, wherein The performing broadband resonance tracing on the grid topology parameters and the characteristic identification results to obtain broadband resonance tracing results includes: Establish impedance models at different frequencies based on grid topology parameters; Performing resonance coupling analysis on the feature identification results and the impedance models at different frequencies to obtain a coupling relationship between interharmonic energy and impedance; By utilizing the coupling relationship between the interharmonic energy and impedance, combined with the synchronous sampling data and the grid topology parameters, the propagation path of the interharmonic power is analyzed to obtain a broadband resonance tracing result.

7. The method according to claim 1, wherein The performing the distorted power-impedance joint calculation on the synchronous sampling data to obtain the joint calculation result includes: Performing frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each subharmonic / interharmonic; calculating the distortion power based on the amplitude and phase of each subharmonic / interharmonic; Based on the synchronous sampling data, a frequency domain impedance identification method is used to identify dynamic impedance characteristics; Dynamically matching the distorted power and the dynamic impedance characteristic to obtain an interaction relationship between the distorted power and the dynamic impedance characteristic; Based on the interaction relationship between the distortion power and the dynamic impedance characteristic, a joint calculation result is determined.

8. The method according to claim 7, wherein The multi-agent harmonic responsibility quantification analysis is performed on the grid topology parameters and the joint calculation results to obtain the responsibility quantification results, including: Locating the node area generating the distorted power in the joint calculation result to obtain a plurality of candidate harmonic sources; Using grid topology parameters and impedance models, simulating the impact of each candidate harmonic source; Using a responsibility allocation algorithm, the responsibility is allocated to each candidate harmonic source to obtain a responsibility weight; Based on the influence degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain a responsibility quantification result.

9. The method according to claim 1, wherein The operation optimization of power equipment in the wide-area power system based on the power grid control strategy includes: Based on the grid control strategy, output operation optimization instructions and interference source responsibility reports for power equipment in the wide-area power system; Based on the operation optimization instruction and the interference source responsibility report, the power system is optimized for grid energy efficiency and equipment operation.

10. The method according to claim 1, wherein The synchronous sampling data is integrated with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of energy flow that reflects the operating status of the system.

11. An operation optimization system based on joint analysis of power data, characterized in that: It includes master layer nodes, edge layer nodes and power equipment that communicate with each other; The master layer node is used to obtain and send synchronous sampling data and grid topology parameters of the wide area power system to the edge layer node; the synchronous sampling data includes voltage waveform, current waveform and instantaneous power waveform; The edge layer node is configured to use a joint analysis engine to perform device status assessment, broadband resonance tracing and harmonic responsibility quantification on the synchronous sampling data and the grid topology parameters, and generate a grid control strategy; Based on the power grid control strategy, output operation optimization instructions and interference source responsibility reports of power equipment in the wide area power system to the analysis system of the master station layer node; The master station layer node is used to optimize the power grid energy efficiency and equipment operation using the analysis system based on the operation optimization instruction and the interference source responsibility report; The edge layer node is specifically used to: Using a joint analysis engine, performing a high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain a time-frequency feature analysis result, and performing a power equipment status assessment on the time-frequency feature analysis result to obtain an equipment status assessment result; Performing interharmonic power tracing on the synchronous sampling data to obtain a feature identification result, and performing broadband resonance tracing on the power grid topology parameters and the feature identification result to obtain a broadband resonance tracing result; Performing a distorted power-impedance joint calculation on the synchronously sampled data to obtain a joint calculation result; Performing a multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation results to obtain a responsibility quantification result; A power grid control strategy is generated based on the integration of the equipment status assessment result, the broadband resonance source tracing result and the responsibility quantification result.

12. The system according to claim 11, wherein The edge layer node is specifically used to: Performing time-frequency analysis and feature extraction on the voltage waveform and the current waveform to obtain current and voltage features; Performing time-frequency analysis and feature extraction on the instantaneous power waveform to obtain instantaneous power features; A joint analysis engine is used to perform equipment status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data, the current and voltage characteristics, the instantaneous power characteristics, and the grid topology parameters to generate a grid control strategy.

13. The system according to claim 11, wherein: The edge layer node is specifically used to: Performing short-time Fourier transform processing on the synchronous sampling data to obtain signal spectrum changes of the synchronous sampling data; Performing multi-resolution analysis on the synchronous sampling data using wavelet transform to obtain local changes of non-stationary signals in the synchronous sampling data; Performing nonlinear and non-stationary signal analysis on the synchronous sampling data using Hilbert-Huang transform to obtain a signal decomposition result; The signal spectrum change, the local change of the non-stationary signal and the signal decomposition result are fused to obtain a time-frequency feature analysis result.

14. The system according to claim 11, wherein: The edge layer node is specifically used to: Extracting characteristic indicators of equipment health status from the time-frequency characteristic analysis results; The characteristic indicators are input into a state prediction model to output an equipment state assessment result; the state prediction model is constructed using a machine learning algorithm.

15. The system according to claim 11, wherein The edge layer node is specifically used to: Performing frequency domain decomposition on the synchronous sampling data to obtain interharmonic voltage and current components of the synchronous sampling data; Calculate the active power, reactive power and apparent power of the interharmonic components to obtain the power distribution spectrum; Based on the amplitude and direction in the power distribution spectrum, the position and intensity changes of the disturbance source are analyzed to obtain a feature identification result; The feature identification results include the main interharmonic frequencies and amplitudes thereof, power flow directions, energy distribution, and the occurrence patterns of interharmonic power component combinations.

16. The system according to claim 15, wherein: The edge layer node is specifically used to: Establish impedance models at different frequencies based on grid topology parameters; Performing resonance coupling analysis on the feature identification results and the impedance models at different frequencies to obtain a coupling relationship between interharmonic energy and impedance; By utilizing the coupling relationship between the interharmonic energy and impedance, combined with the synchronous sampling data and the grid topology parameters, the propagation path of the interharmonic power is analyzed to obtain a broadband resonance tracing result.

17. The system according to claim 11, wherein The edge layer node is specifically used to: Performing frequency domain decomposition on the synchronous sampling data to extract the amplitude and phase of each subharmonic / interharmonic; calculating the distortion power based on the amplitude and phase of each subharmonic / interharmonic; Based on the synchronous sampling data, a frequency domain impedance identification method is used to identify dynamic impedance characteristics; Dynamically matching the distorted power and the dynamic impedance characteristic to obtain an interaction relationship between the distorted power and the dynamic impedance characteristic; Based on the interaction relationship between the distortion power and the dynamic impedance characteristic, a joint calculation result is determined.

18. The system according to claim 17, wherein: The edge layer node is specifically used to: Locating the node area generating the distorted power in the joint calculation result to obtain a plurality of candidate harmonic sources; Using grid topology parameters and impedance models, simulating the impact of each candidate harmonic source; Using a responsibility allocation algorithm, the responsibility is allocated to each candidate harmonic source to obtain a responsibility weight; Based on the influence degree and responsibility weight of each candidate harmonic source, a weighted sum is performed to obtain a responsibility quantification result.

19. The system of claim 11, wherein: The synchronous sampling data is integrated with energy flow and information flow, and the information flow is a multi-dimensional time-frequency domain feature of energy flow that reflects the operating status of the system.

20. An operation optimization system based on joint analysis of power data, characterized in that include: Sampling module, used to obtain synchronous sampling data and grid topology parameters of the wide-area power system; The synchronous sampling data includes a voltage waveform, a current waveform and an instantaneous power waveform; A joint analysis module, configured to utilize a joint analysis engine to perform device status assessment, broadband resonance source tracing, and harmonic responsibility quantification on the synchronous sampling data and the grid topology parameters, and generate a grid control strategy; an optimization module, configured to optimize the operation of power equipment in the wide-area power system based on the power grid control strategy; The joint analysis module includes: The equipment status evaluation unit is used to use the joint analysis engine to perform high-frequency disturbance time-frequency analysis on the synchronous sampling data to obtain time-frequency feature analysis results, and to perform power equipment status evaluation on the time-frequency feature analysis results to obtain equipment status evaluation results; The broadband resonance tracing unit is used to track the interharmonic power of the synchronous sampling data to obtain the feature identification results, and to perform broadband resonance tracing on the grid topology parameters and feature identification results to obtain the broadband resonance tracing results; The harmonic responsibility quantification unit is used to perform distorted power-impedance joint calculation on synchronous sampling data to obtain joint calculation results; perform multi-agent harmonic responsibility quantification analysis on the power grid topology parameters and the joint calculation results to obtain responsibility quantification results; The result integration unit is used to integrate the equipment status assessment results, broadband resonance tracing results and responsibility quantification results to generate a control strategy.

21. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the operation optimization method based on joint analysis of power data as described in any one of claims 1 to 10 is implemented.

22. A readable storage medium, characterized in that An execution program is stored thereon, and when the execution program is executed, the operation optimization method based on joint analysis of power data as described in any one of claims 1 to 10 is implemented.

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