A rapid energy control device integrating measurement, identification and control for network-forming new energy power generation

Abstract

The present application discloses a measurement, identification, and control integrated fast energy control device for network-forming new energy power generation, which relates to the technical field of power system stability control. It includes a data acquisition unit, a disturbance analysis unit, and a control execution unit. The data acquisition unit acquires first operating parameters and extracts first characteristic parameters. The disturbance analysis unit analyzes the characteristic parameters to obtain disturbance information and source locations, and constructs a disturbance characteristic library. The control execution unit calculates compensation parameters, generates control strategies, forms a hierarchical coordinated control structure, and issues adjustment instructions. The present application constructs a complete closed loop from measurement to control, so as to be able to implement fast and accurate energy control for various disturbances, effectively improving the stability and anti-disturbance ability of the network-forming new energy power generation system.

Description

Technical Field

[0001] The present application relates to the technical field of power system stability control, and particularly to a measurement, identification, and control integrated fast energy control device for network-forming new energy power generation. Background Art

[0002] At present, network-forming new energy power generation systems have become an important part of the power system. However, due to the randomness, volatility, and intermittency of new energy, network-forming new energy power generation systems face various disturbance problems such as voltage fluctuations, sharp increases in power change rates, and grid frequency deviations, seriously affecting the stable operation of the system and power supply quality. Moreover, these disturbances often occur intertwined, and it is difficult to accurately identify and locate the disturbance sources, posing a huge challenge to the stable control of the system.

[0003] In the prior art, the control of network-forming new energy power generation systems mostly adopts a scheme in which measurement, identification, and control are separated from each other, suffering from problems such as information transmission lag and slow response speed, and it is difficult to achieve fast response and accurate control of disturbances. At the same time, most of the existing control devices are designed for a single type of disturbance, lacking the comprehensive analysis and coordinated control capabilities for multi-dimensional characteristics, and unable to achieve the integrated integration of measurement data, disturbance identification, and energy control. Therefore, there is an urgent need to develop a measurement, identification, and control integrated fast energy control device for network-forming new energy power generation, which can achieve fast response throughout the process from disturbance feature extraction, disturbance source location to hierarchical coordinated control, and improve the stability and anti-disturbance ability of network-forming new energy power generation systems. Summary of the Invention

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a measurement, identification, and control integrated fast energy control device for network-forming new energy power generation, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a measurement, identification, and control integrated fast energy control device for grid-forming new energy power generation, including: a data acquisition unit, configured to collect first operating parameters of a grid-forming new energy power generation system, and extract first characteristic parameters according to the first operating parameters; the first characteristic parameters include voltage fluctuation characteristics, power change characteristics, and frequency deviation characteristics; the first operating parameters include voltage fluctuation data, power change rate, and grid frequency deviation; a disturbance analysis unit, configured to analyze the first characteristic parameters to obtain disturbance information, perform timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and construct a disturbance characteristic pattern library based on the disturbance information and the disturbance source location information; a control execution unit, configured to calculate compensation parameters according to the disturbance characteristic pattern library, generate a control strategy based on the disturbance source location information, form a hierarchical coordinated control structure through the control strategy, and generate and send adjustment instructions for the first operating parameters to each node of the grid-forming new energy power generation system.

[0008] Preferably, extracting the first characteristic parameters according to the first operating parameters includes extracting voltage fluctuation characteristics of different frequency bands of the voltage fluctuation data; obtaining the power change characteristics by calculating the power change trend of the power change rate; extracting the frequency deviation characteristics of the grid frequency deviation; and correlating the voltage fluctuation characteristics, the power change characteristics, and the frequency deviation characteristics according to a time tag to form a multi-dimensional feature vector.

[0009] Preferably, analyzing the first characteristic parameters to obtain disturbance information includes extracting waveform amplitude, waveform frequency, and phase angle information from the voltage fluctuation characteristics; constructing a voltage disturbance template library; calculating similarity values between the waveform amplitude, the waveform frequency, and the phase angle information and each template in the voltage disturbance template library; and determining a type identifier and a characteristic parameter set of a periodic disturbance according to the similarity values, where the periodic disturbance is included in the disturbance information.

[0010] Preferably, analyzing the first characteristic parameters to obtain disturbance information further includes separating the power change characteristics into a deterministic component and a random component; calculating a statistical characteristic set of the random component; comparing the statistical characteristic set with marked samples in a historical disturbance database to obtain a type identifier of a random disturbance; and determining that the random disturbance is one or more of photovoltaic output fluctuation, wind power fluctuation, or load random change according to the type identifier of the random disturbance, where the random disturbance is included in the disturbance information.

[0011] Preferably, the step of performing timestamp correlation analysis based on the frequency deviation characteristics to locate the disturbance source includes: performing timestamp correlation analysis on the disturbance information to obtain the disturbance source location information, including collecting the frequency deviation characteristics and corresponding timestamp information of multiple measurement points; selecting a reference measurement point, and calculating the time propagation delay of the frequency deviation characteristics of each other measurement point and the reference measurement point; establishing an electrical network model, and mapping the time propagation delay into an electrical distance value; using the electrical distance value to calculate the position coordinates and influence radius of the disturbance source in the electrical network model.

[0012] Preferably, calculating compensation parameters according to the disturbance feature pattern library and generating a control strategy based on the disturbance source location information includes: determining whether the disturbance information is a periodic disturbance or a random disturbance; if it is a periodic disturbance, calculating voltage compensation parameters and reactive power regulation parameters; if it is a random disturbance, calculating power smoothing parameters and active power regulation parameters; identifying the distribution of adjustable devices within the range determined by the disturbance source location information; constructing a control optimization equation, and solving to obtain a control strategy parameter set; the control strategy parameter set includes a list of node identifiers participating in control, adjustment parameters of each node, and execution timings.

[0013] Preferably, the hierarchical coordinated control structure includes: a system layer control module, responsible for generating global control instructions and distributing control strategies to the regional layer control modules; a regional layer control module, responsible for coordinating the operation sequences of multiple node layer control modules within the region; a node layer control module, responsible for executing control actions of specific devices; when the disturbance influence range is limited within a single node, the node layer control module performs the adjustment; when the disturbance influence range spans multiple nodes but is limited within a single region, the regional layer control module coordinates the execution; when the disturbance influence range spans multiple regions, the system layer control module commands the regulation; the influence range is jointly determined by the disturbance source location information and the electrical distance.

[0014] In a second aspect, the present application also provides a measurement, identification, and control integrated fast energy control method for grid-forming new energy power generation, including: collecting first operation parameters of a grid-forming new energy power generation system, and extracting first characteristic parameters according to the first operation parameters; analyzing the first characteristic parameters to obtain disturbance information, performing timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and constructing a disturbance feature pattern library based on the disturbance information and the disturbance source location information; calculating compensation parameters according to the disturbance feature pattern library and generating a control strategy based on the disturbance source location information, forming a hierarchical coordinated control structure through the control strategy, and generating and sending adjustment instructions for the first operation parameters to each node of the grid-forming new energy power generation system.

[0015] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: collecting first operation parameters of a network-forming new energy power generation system, and extracting first characteristic parameters according to the first operation parameters; analyzing the first characteristic parameters to obtain disturbance information, and performing timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source location information; calculating compensation parameters according to the disturbance characteristic pattern library, and generating a control strategy based on the disturbance source location information, forming a hierarchical coordinated control structure through the control strategy, generating and sending adjustment instructions for the first operation parameters to each node of the network-forming new energy power generation system.

[0016] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: collecting first operation parameters of a network-forming new energy power generation system, and extracting first characteristic parameters according to the first operation parameters; analyzing the first characteristic parameters to obtain disturbance information, and performing timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source location information; calculating compensation parameters according to the disturbance characteristic pattern library, and generating a control strategy based on the disturbance source location information, forming a hierarchical coordinated control structure through the control strategy, generating and sending adjustment instructions for the first operation parameters to each node of the network-forming new energy power generation system.

[0017] Implementing the present application has the following beneficial effects: The present application provides a measurement, identification, and control integrated fast energy control device for network-forming new energy power generation. This device integrates system operation parameter measurement, disturbance characteristic identification, and energy collaborative control, and realizes the closed-loop processing of disturbance response. Through the precise measurement of voltage fluctuation data, power change rate, and grid frequency deviation by the data acquisition unit, the rapid identification and positioning of disturbance information by the disturbance analysis unit, and the hierarchical coordinated control of the control execution unit, a complete closed-loop from measurement to control is constructed, enabling the system to implement fast and precise energy control for various disturbances, effectively improving the stability and anti-disturbance ability of the network-forming new energy power generation system, and providing reliable technical support for large-scale new energy grid connection. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the application environment of a measurement, identification, and control integrated fast energy control device used for grid-forming new energy power generation related to the present application;

[0020] Figure 2 It is a schematic diagram of the overall structure of a measurement, identification, and control integrated fast energy control device used for grid-forming new energy power generation related to the present application;

[0021] Figure 3 It is a general flowchart of a measurement, identification, and control integrated fast energy control method used for grid-forming new energy power generation related to the present application;

[0022] Figure 4 It is a computer equipment diagram of a measurement, identification, and control integrated fast energy control method used for grid-forming new energy power generation related to the present application. Detailed implementation manners

[0023] In order to make the objectives, technical solutions, and advantages of the present application more clear, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0024] Grid-forming new energy power generation systems are increasingly widely used in modern power systems. For example, it can be applied to various energy systems such as wind farms, photovoltaic power stations, distributed energy networks, and microgrids. When there are disturbances such as voltage fluctuations, power mutations, or frequency offsets in the power grid, the system needs to quickly identify the disturbance type, accurately locate the disturbance source, and implement effective energy control measures to maintain system stability.

[0025] In related technologies, the measurement, identification, and control functions of new energy power generation systems are usually realized by independent systems respectively, and data needs to be transmitted and processed among the systems. There are not only problems of information transmission delay, but also it is difficult to achieve a fast response to complex disturbances. Especially in the scenario where multiple disturbances occur superimposed, how to achieve accurate identification, location, and coordinated control of disturbances is a relatively difficult technical problem.

[0026] In response to the problems of response lag and poor control effect caused by the separation of measurement, identification and control functions in related technologies, the present application proposes an integrated measurement, identification and control rapid energy control device, which obtains the first operating parameters of a grid-based new energy power generation system through a data acquisition unit, and extracts characteristic parameters through multi-time scale analysis; the disturbance analysis unit quickly identifies different types of disturbances and accurately locates the source of disturbances based on these characteristic parameters, and constructs a disturbance characteristic pattern library; the control execution unit calculates compensation parameters according to the identification results, generates a control strategy, and issues targeted adjustment instructions to each node of the system through a hierarchical coordinated control structure, thereby achieving rapid suppression of disturbances and effective improvement of system stability.

[0027] Figure 1 A schematic diagram of the application environment of the integrated measurement, identification and control rapid energy control device in an embodiment of the present application is shown. Figure 1 In the figure, the grid-type new energy power generation system uses a dotted frame to represent the overall boundary, in which a central control system 100 is provided, and a measurement, identification and control integrated fast energy control device 101 is integrated inside the central control system 100. The system includes wind power generation nodes 102 and photovoltaic power generation nodes 103 distributed in different locations, which are used to realize the power generation function of renewable energy; the energy storage device 104 is located in the middle of the system, which is used to balance power fluctuations and adjust energy supply and demand; the first measurement device 105 and the second measurement device 106 are respectively deployed on the left and right sides of the system, which are used to collect first operating parameters such as voltage fluctuation data, power change rate and grid frequency deviation in real time. The grid access point 107 is located at the bottom of the system and is the connection interface between the grid-type new energy power generation system and the external power grid. The power line 108 represented by the solid line constitutes an energy transmission network, which transmits the electric energy generated by the wind power generation node 102 and the photovoltaic power generation node 103 to the energy storage device 104 through the trunk line for adjustment, and finally merges into the external power grid through the power grid access point 107; at the same time, the first measuring device 105 and the second measuring device 106 are connected to other parts of the system through the power line 108 to monitor the power quality. The communication line 109 represented by the dotted line forms an information transmission network. The integrated measurement, identification and control fast energy control device 101 in the central control system 100 maintains data interaction with each node through the communication line 109, obtains system operating parameters in real time, identifies and analyzes disturbance characteristics, and issues control instructions to each node, realizing the integrated closed-loop management of measurement, identification and control of the entire grid-type new energy power generation system, thereby effectively suppressing various disturbances in the system and improving the stability and reliability of the system.

[0028] Among them, the central control system 100 can be implemented as a dedicated industrial control computer, programmable logic controller (PLC), distributed control system (DCS), fieldbus control system (FCS), embedded control system, or cloud computing-based virtual control center. The integrated measurement, identification, and control fast energy control device 101 can be implemented in hardware form, such as application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), digital signal processor (DSP), etc.; it can also be implemented in the form of software combined with general hardware, such as a measurement and control software system running on an industrial computer; it can also be implemented in the form of collaborative software and hardware design, with some functions realized through hardware acceleration and some functions realized through software algorithms. The wind power generation node 102 can include, but is not limited to, horizontal-axis wind turbines, vertical-axis wind turbines, offshore wind turbine generators, decentralized small wind power generation equipment, or wind farm clusters. The photovoltaic power generation node 103 can include, but is not limited to, crystalline silicon photovoltaic modules, thin-film photovoltaic modules, concentrating photovoltaic (CPV) systems, building-integrated photovoltaic (BIPV) systems, photovoltaic power stations, or distributed photovoltaic systems. The energy storage device 104 can adopt electrochemical energy storage methods, such as lithium-ion batteries, sodium-sulfur batteries, lead-acid batteries, flow batteries, etc.; it can also adopt physical energy storage methods, such as pumped-storage energy storage, compressed air energy storage, flywheel energy storage, etc.; it can also adopt electromagnetic energy storage methods, such as supercapacitors, superconducting magnetic energy storage, etc.; it can also adopt a hybrid system of multiple energy storage technologies. The first measurement device 105 / second measurement device 106 can be implemented as an intelligent electric meter, power quality analyzer, phasor measurement unit (PMU), relay protection device, fault indicator, wireless sensor network node, or power parameter monitoring terminal. The grid access point 107 can be implemented in the form of substations, distribution transformers, grid-connected inverters, power electronic transformers (PET), or intelligent distribution terminals with different voltage levels. The power line 108 can be an overhead line, underground cable, high-voltage direct current transmission line (HVDC), flexible alternating current transmission system (FACTS), or intelligent distribution network. The communication line 109 can adopt wired communication methods, such as optical fiber communication, power line carrier communication (PLC), Ethernet, fieldbus, etc.; it can also adopt wireless communication methods, such as 5G network, LTE-M, narrowband Internet of Things (NB-IoT), LoRa, ZigBee, wireless local area network, etc.; it can also adopt a hybrid network of multiple communication methods to improve the reliability and redundancy of the system.

[0029] In an exemplary embodiment, as Figure 2 shown, an integrated measurement, identification, and control fast energy control device for grid-forming new energy power generation is provided. Taking the application environment in Figure 1 as an example for illustration, the device includes a data acquisition unit, a disturbance analysis unit, and a control execution unit.

[0030] Specifically, as a key component of the integrated measurement, identification, and control fast energy control device, the data acquisition unit is responsible for collecting the first operating parameters of the grid-forming new energy power generation system and extracting the first characteristic parameters based on the first operating parameters. The first characteristic parameters include voltage fluctuation characteristics, power change characteristics, and frequency deviation characteristics; the first operating parameters include voltage fluctuation data, power change rate, and grid frequency deviation. In this application, the data acquisition unit can be a physical entity or a functional module in the central control system 100, and its core function is to comprehensively perceive the operating state of the grid-forming new energy power generation system.

[0031] In an embodiment of this application, the data acquisition unit is deployed at key nodes of the grid-forming new energy power generation system, such as Figure 1 the first measurement device 105 and the second measurement device 106 shown in the figure, for collecting the first operating parameters. The first measurement device 105 can be deployed at the electrical connection point of the wind power generation node 102, and the second measurement device 106 can be deployed at the AC busbar of the photovoltaic power generation node 103. By configuring multiple measurement points, the first operating parameters of each node can be comprehensively collected.

[0032] Among the first operating parameters collected by the data acquisition unit, the voltage fluctuation data includes phase voltage, line voltage, and voltage distortion rate of each node in the system; the power change rate includes active power change rate, reactive power change rate, and apparent power change rate, etc.; the grid frequency deviation includes the difference between the system frequency and the rated frequency and the frequency change rate, etc.

[0033] In an alternative embodiment, the data acquisition unit adopts synchronous sampling technology to ensure that the data at each measurement point has a unified time reference. Specifically, the data acquisition unit can achieve time synchronization of each measurement point through methods such as GPS clock, Network Time Protocol (NTP), or Precision Time Protocol (PTP), so that the collected data has accurate timestamp information, laying a foundation for subsequent time-correlation analysis.

[0034] In another alternative embodiment, the data acquisition unit adopts an adaptive sampling strategy and dynamically adjusts the sampling frequency according to the state of the grid-forming new energy power generation system. When the grid-forming new energy power generation system is in a stable operating state, a lower sampling frequency is adopted to reduce the data transmission and processing burden; when it is detected that the system parameters change rapidly or abnormal fluctuations occur, the sampling frequency is automatically increased to capture transient characteristics and ensure that important disturbance events are not missed.

[0035] Extracting the first characteristic parameters based on the first operating parameters is another key function of the data acquisition unit. The extraction process includes four main steps: extracting voltage fluctuation characteristics, extracting power change characteristics, extracting frequency deviation characteristics, and forming a multi-dimensional feature vector.

[0036] In terms of extracting voltage fluctuation characteristics, the data acquisition unit analyzes the voltage fluctuation data in different frequency bands. In an alternative embodiment, the fast Fourier transform (FFT) method is used to perform frequency-domain decomposition on the voltage fluctuation data, and the harmonic components and amplitude characteristics in different frequency bands are extracted. Specifically, the voltage fluctuation data is decomposed into the fundamental wave and each harmonic, and the amplitude, phase, and harmonic distortion rate of each harmonic are calculated to form a frequency-domain feature vector.

[0037] In another alternative embodiment, the wavelet transform method is used to perform time-frequency analysis on the voltage fluctuation data. Compared with the FFT method, the wavelet transform has the ability of multi-resolution analysis and can simultaneously obtain the local characteristics of the signal in the time domain and the frequency domain. Specifically, by selecting appropriate wavelet basis functions and decomposition scales, the voltage fluctuation data is wavelet decomposed, and the energy distribution characteristics and singularity point characteristics in different frequency bands are extracted to form a time-frequency feature vector.

[0038] In yet another alternative embodiment, the Stockwell transform (abbreviated as S transform) method is used to analyze the voltage fluctuation data. The S transform combines the advantages of the short-time Fourier transform and the wavelet transform, has frequency-dependent resolution and the characteristic of maintaining absolute phase information, and is particularly suitable for analyzing non-stationary signals. Through the S transform, the time-frequency distribution characteristics of the voltage fluctuation data can be extracted, and the disturbance types such as voltage transients, voltage flickers, and voltage sags can be accurately identified.

[0039] The extracted voltage fluctuation characteristics include but are not limited to waveform characteristic parameters and spectrum distribution parameters. The waveform characteristic parameters include peak ratio, crest factor, asymmetry, etc.; the spectrum distribution parameters include harmonic content, frequency band energy distribution, main frequency component, etc. These characteristic parameters together constitute the voltage fluctuation characteristic set for subsequent disturbance analysis.

[0040] In terms of extracting power change characteristics, the data acquisition unit obtains the power change characteristics by calculating the power change trend of the power change rate. In an alternative embodiment, the sliding window method is used to calculate the power change trend. By setting time windows with different widths, the power change rates in the short term, medium term, and long term are calculated respectively to form multi-scale power change characteristics. The short-term window (such as in seconds) captures instantaneous power fluctuations, the medium-term window (such as in minutes) reflects the power adjustment process, and the long-term window (such as in hours) reflects the power balance trend.

[0041] In another alternative embodiment, the recursive least squares (RLS) algorithm is adopted as a classical method for adaptive filtering, which features a fast convergence rate and good tracking performance and is used to calculate the power change trend. During specific implementation, the following steps can be taken: initialize the weight vector and covariance matrix, calculate the prediction error, update the covariance matrix and weight vector, and use the updated weight vector to predict the power change trend. For example, in the scenario of wind farm output fluctuation, by continuously learning the characteristics of wind power fluctuation, accurate prediction of the short-term power change trend can be achieved, providing a basis for disturbance identification.

[0042] In terms of extracting frequency deviation features, the data acquisition unit analyzes and processes the grid frequency deviation. In an alternative embodiment, the zero-crossing detection method is adopted to extract frequency deviation features. This method calculates the frequency by detecting the time interval of the zero-crossing point of the voltage or current waveform, and has the advantages of simple implementation and small computational load. By statistically analyzing the zero-crossing time series, characteristic parameters such as the frequency change rate and frequency offset can be extracted.

[0043] In another alternative embodiment, the phase-locked loop (PLL) technology is adopted to extract frequency deviation features. The PLL technology realizes the locking of the phase of the input signal through closed-loop control and can accurately estimate the grid frequency and its changes. Compared with the zero-crossing detection method, the PLL technology has higher accuracy and anti-interference ability and is especially suitable for use in the case of large waveform distortion and noise interference.

[0044] The extracted frequency deviation features include but are not limited to the frequency change rate and frequency offset. The frequency change rate reflects the system inertia and active power regulation ability; the frequency offset reflects the active power balance state of the system. These characteristic parameters together constitute the frequency deviation feature set for subsequent disturbance analysis.

[0045] After the extraction of voltage fluctuation features, power change features, and frequency deviation features is completed, the data acquisition unit correlates these features according to the time tags to form a multi-dimensional feature vector. By integrating different types of characteristic parameters, a comprehensive capture of the system state is realized, providing richer information for subsequent disturbance analysis.

[0046] In an alternative embodiment, the data acquisition unit adopts the time synchronization method for feature correlation. Specifically, according to the timestamp information of each characteristic parameter, the voltage fluctuation features, power change features, and frequency deviation features within the same time window are combined into a multi-dimensional feature vector. The width of the time window can be adjusted according to actual application requirements to balance the timeliness of feature correlation and the computational burden.

[0047] In another alternative embodiment, when associating the voltage fluctuation feature, the power change feature, and the frequency deviation feature according to the time tag, the data acquisition unit adopts a multi-representation learning method for feature enhancement to improve the expression ability of the multi-dimensional feature vector. The specific implementation steps include: initially associating the three features to form an initial feature matrix; designing a deep neural network structure including a feature encoding layer, a hidden layer, and a feature decoding layer; using an encoder-decoder structure to perform non-linear transformation on the features to extract high-order feature relationships; retaining the original feature information through residual connections to prevent information loss; and assigning dynamic weights to different features through an attention mechanism to enhance the influence of key features.

[0048] Taking the disturbance identification of a photovoltaic power station as an example, when the power fluctuates due to a rapid change in light intensity, the voltage and frequency parameters will also change accordingly. Through the above multi-representation learning method, the temporal correlation characteristics between voltage dips, rapid power changes, and frequency drops can be automatically learned, forming a feature representation of light disturbances and improving the recognition accuracy of such disturbances.

[0049] The construction of the multi-dimensional feature vector makes full use of the correlation between voltage, power, and frequency parameters, realizing information fusion and complementarity between different physical quantities. This fusion method not only improves the expression ability of the features but also enhances the recognition and classification ability of complex disturbances, which is an important embodiment of the integrated design concept of measurement, identification, and control.

[0050] In a comprehensive embodiment of this application, the working process of the data acquisition unit is as follows: First, the data acquisition unit collects the voltage fluctuation data, power change rate, and grid frequency deviation of each node of the grid-forming new energy power generation system in real time; then, the data acquisition unit preprocesses the original data, including filtering, denoising, and outlier processing, to improve the data quality; next, the processed data is respectively subjected to feature extraction to obtain the voltage fluctuation feature, the power change feature, and the frequency deviation feature; finally, the data acquisition unit associates these features according to the time tag to form a multi-dimensional feature vector reflecting the overall state of the system, providing data support for subsequent disturbance analysis and control execution.

[0051] Through the above technical means, the data acquisition unit of this application can quickly and accurately collect the operation parameters of the grid-forming new energy power generation system and extract effective feature parameters, providing a data basis for the integrated fast energy control of measurement, identification, and control. In particular, through multi-time scale analysis and feature fusion technologies, a comprehensive perception and accurate capture of the system state are realized, creating favorable conditions for subsequent disturbance identification and control decision-making.

[0052] Furthermore, the perturbation analysis unit is responsible for analyzing the first characteristic parameter to obtain perturbation information, performing timestamp correlation analysis on the perturbation information to obtain the perturbation source location information, and constructing a perturbation characteristic pattern library based on the perturbation information and the perturbation source location information. The perturbation analysis unit realizes the accurate identification of the perturbation type and the accurate positioning of the perturbation source, providing a solid foundation for the formulation of subsequent control strategies.

[0053] In an embodiment of the present application, the perturbation analysis unit identifies various perturbations in the network-forming new energy power generation system through in-depth analysis of the first characteristic parameter. The analysis process first processes the voltage fluctuation characteristics to identify periodic perturbations; then analyzes the power change characteristics to identify random perturbations; finally, determines the perturbation source location through timestamp correlation analysis and constructs a perturbation characteristic pattern library.

[0054] In terms of analyzing the voltage fluctuation characteristics, the perturbation analysis unit first extracts waveform amplitude, waveform frequency, and phase angle information from the voltage fluctuation characteristics. Among them, the waveform amplitude reflects the intensity of the voltage perturbation, the waveform frequency reflects the periodic characteristics of the perturbation, and the phase angle information reveals the propagation characteristics of the perturbation. These three types of information together constitute a comprehensive description of the voltage perturbation.

[0055] In order to effectively identify various voltage perturbations, the perturbation analysis unit constructs a voltage perturbation template library, which includes harmonic interference templates, oscillatory fluctuation templates, and voltage flicker templates. This template library is constructed using conventional perturbation pattern recognition methods. By collecting and analyzing typical perturbation samples, standard characteristic parameters are extracted to form reference templates for various perturbations. In the network-forming new energy power generation system, the harmonic interference template is mainly used to identify 2-13th harmonics caused by power electronic devices such as inverters and converters; the oscillatory fluctuation template is used to identify 0.1-2Hz low-frequency oscillations caused by the grid connection of new energy sources such as wind power and photovoltaic power, and 2-15Hz local oscillations caused by equipment control loops; the voltage flicker template is used to identify periodic voltage fluctuations caused by operations such as the start and stop of high-power fans and the switching of photovoltaic arrays.

[0056] The perturbation analysis unit calculates the similarity values between the extracted waveform amplitude, waveform frequency, and phase angle information and each template in the voltage perturbation template library. The calculation of the similarity value uses the normalized Euclidean distance method, that is:

[0057] Similarity value = 1 - ||Feature vector - Template vector||2 / ||Template vector||2;

[0058] Among them, the feature vector is composed of waveform amplitude, waveform frequency, and phase angle, and the template vector is the standard eigenvalue of the corresponding perturbation type. The closer the similarity value is to 1, the higher the matching degree. This calculation method can uniformly process features of different dimensions and improve the accuracy of identification.

[0059] Based on the calculated similarity value, the perturbation analysis unit determines the type identifier and the set of characteristic parameters of the periodic perturbation. For example, when detecting the voltage fluctuation at the grid connection point of a wind farm, and through calculating the similarity value, it is found that the matching degree with the low-frequency oscillation template reaches 0.92, which is much higher than that of other templates, then it can be determined that this perturbation is a typical low-frequency oscillation problem of wind turbines. At the same time, the perturbation analysis unit extracts the characteristic parameters such as the frequency (e.g., 0.7 Hz), amplitude (e.g., 3% of the rated voltage), and duration of this oscillation, forming a complete set of characteristic parameters.

[0060] In terms of analyzing the power change characteristics, the perturbation analysis unit separates the power change characteristics into a deterministic component and a random component. In implementation, the empirical mode decomposition (EMD) method can be used for separation. The EMD method can adaptively decompose a signal into a finite number of intrinsic mode functions (IMFs) and a residue term. Among them, the high-frequency IMF components usually correspond to the random component, and the low-frequency IMF components and the residue term correspond to the deterministic component. This separation method is particularly suitable for processing non-linear and non-stationary power change signals, and can effectively capture the random fluctuation characteristics in the new energy power generation system.

[0061] For the separated random component, the perturbation analysis unit calculates its statistical feature set, including mean, variance, skewness, and kurtosis. Taking the power fluctuation of a typical photovoltaic power station as an example, its random component usually has the characteristics of a mean close to zero, a large variance, a slightly negative skewness, and a kurtosis greater than 3, reflecting the fast power fluctuation characteristics caused by light changes. While the random component of wind power fluctuation usually shows the characteristics of a mean close to zero, a moderate variance, a skewness close to zero, and a kurtosis less than 3, reflecting the random characteristics of wind speed changes. These statistical features constitute an important basis for distinguishing different sources of random perturbations.

[0062] The perturbation analysis unit compares the calculated statistical feature set with the labeled samples in the historical perturbation database to obtain the type identifier of the random perturbation. The historical perturbation database is a knowledge base accumulated during the long-term operation of the system, containing the characteristic patterns and labels of different types of random perturbations. This database adopts a hierarchical storage structure, is indexed according to the perturbation type, intensity, and occurrence location, and supports regular updates and self-learning optimization. The comparison process uses the k-nearest neighbor algorithm, calculates the Euclidean distance between the current feature set and the historical samples, selects the k samples with the smallest distance for voting, and determines the most likely perturbation type.

[0063] According to the identified type of random disturbance obtained, the disturbance analysis unit determines that the random disturbance is one or more of photovoltaic output fluctuations, wind power fluctuations, or load random variations. In practical applications, different types of random disturbances may coexist and superimpose on each other. For example, when wind power and photovoltaic are connected simultaneously, a composite disturbance may occur where the wind power decreases and superimposes with the photovoltaic output fluctuations. Through in-depth analysis of statistical characteristics and precise matching of historical samples, the disturbance analysis unit can identify the components and the primary-secondary relationships of such composite disturbances, laying a foundation for subsequent precise control.

[0064] In determining the location of the disturbance source, the disturbance analysis unit first collects the frequency deviation characteristics and corresponding timestamp information of multiple measurement points. In a grid-forming new energy power generation system, frequency is a key indicator reflecting the active power balance state, and its changes will propagate in the system at the speed of electromagnetic waves. By deploying high-precision synchronous measurement devices at key nodes in the system (such as wind farm collection points, photovoltaic power plant connection points, load centers, etc.), the millisecond-level frequency changes and their precise timestamps can be obtained, providing accurate data support for disturbance source location.

[0065] The disturbance analysis unit selects one of the measurement points as the reference measurement point and calculates the time propagation delay between the frequency deviation characteristics of other measurement points and the reference measurement point. The time propagation delay reflects the propagation time difference of the disturbance from the source to each measurement point and is a key parameter for locating the disturbance source. During the calculation process, the cross-correlation analysis method is used to identify the starting point of the frequency fluctuation, accurately calculate the time difference of the same disturbance event observed at different measurement points, and form a time propagation delay matrix.

[0066] Based on the system topology structure and electrical parameters, the disturbance analysis unit establishes an electrical network model and maps the measured time propagation delay into an electrical distance value. The electrical network model is a mathematical abstraction of the actual power system, containing information such as the node impedance matrix, branch impedance parameters, and node connection relationships. During the modeling process, factors such as line parameters, transformer parameters, and generator set parameters are considered to form a comprehensive model that accurately reflects the electrical characteristics of the system. The mapping process uses the following formula:

[0067] Electrical distance value = α × Time propagation delay × V 传播 ;

[0068] Where, α is the correction coefficient, V 传播 is the propagation speed of the disturbance in the system, usually taking the value of the propagation speed of electromagnetic waves in the power system. This mapping relationship converts the time-domain information into spatial position information.

[0069] Using the calculated electrical distance value, the disturbance analysis unit calculates the position coordinates and influence radius of the disturbance source in the electrical network model by using the triangulation method. The triangulation method is based on the electrical distance values of at least three measurement points and determines the position coordinates of the disturbance source by solving a system of nonlinear equations. In specific implementation, the least squares method is used to process redundant observation data to improve the positioning accuracy. For example, when the power of a photovoltaic power station in a certain area of the system drops suddenly due to cloud cover, the specific area where the disturbance occurs can be located by analyzing the time difference of the frequency drop signals observed at each measurement point, and the accuracy can reach the kilometer level, greatly reducing the target range of the control action. The influence radius is determined by analyzing the attenuation characteristics of the frequency fluctuation, which reflects the influence degree of the disturbance on different parts of the system and provides a basis for determining the subsequent control participation range.

[0070] In terms of constructing the disturbance feature pattern library, the disturbance analysis unit comprehensively uses the identified disturbance information and the located disturbance source position information to establish a comprehensive database containing disturbance types, characteristic parameters, and position information. The disturbance feature pattern library adopts a structured storage method and includes the following main fields: disturbance ID, disturbance type (periodic / random), specific disturbance classification (such as harmonic interference, low-frequency oscillation, photovoltaic output fluctuation, etc.), characteristic parameter set (frequency, amplitude, duration, etc.), occurrence location (coordinates, area), influence range, severity, occurrence time, associated events, etc. This structured storage facilitates rapid retrieval and pattern matching and supports subsequent disturbance identification and control decision-making.

[0071] The disturbance feature pattern library not only stores the currently identified disturbance situation but also has the ability of self-learning and adaptation. By analyzing the consistency between the disturbance identification result and the actual system response, the system can continuously optimize the identification algorithm and template parameters to improve the identification accuracy. At the same time, the pattern library also supports seasonal pattern analysis and trend prediction, and can predict the possible disturbance types in a specific period according to historical data, so as to achieve preventive control. This design of the pattern library with learning ability enables the system to continuously adapt to the changing operating environment and disturbance characteristics and continuously improve the effectiveness of energy control.

[0072] Exemplarily, in a network-forming new energy power generation system integrated with wind power, photovoltaics, and conventional power sources, when there is a fluctuation in photovoltaic power output due to cloud changes, the disturbance analysis unit can accurately identify from the power change characteristics that this is a typical random fluctuation in photovoltaic power output, and precisely locate the specific photovoltaic array area where the disturbance occurs through the frequency propagation characteristics. The system will store the characteristic information of this disturbance (such as the fluctuation amplitude is 30% of the rated power, the duration is 2 minutes, and the frequency impact is 0.05 Hz) and the location information (such as the No. 2 photovoltaic power station in the southeast region of the system) into the disturbance characteristic pattern library. When a similar disturbance occurs again under similar meteorological conditions in the future, the system can quickly identify and predict the development trend of the disturbance based on historical experience, so as to call the surrounding energy storage resources or adjustable loads in advance and effectively suppress the system fluctuation.

[0073] Through the above technical means, the disturbance analysis unit of this application realizes the accurate identification and positioning of various disturbances in the network-forming new energy power generation system, laying a solid foundation for the integrated measurement, identification, and control fast energy control. Compared with the traditional separate measurement and identification system, the disturbance analysis unit of this application shortens the disturbance identification time from the second level to the millisecond level, and improves the positioning accuracy from the regional level to the node level, greatly enhancing the response speed and accuracy of the system to complex disturbances. Especially through the organic combination of voltage disturbance template matching, power fluctuation characteristic analysis, and disturbance source location technology based on frequency propagation, a complete set of disturbance analysis methods is formed, which can effectively cope with complex disturbance scenarios in the new energy power generation system and significantly improve the stability and reliability of the system.

[0074] The control execution unit, as the terminal execution link of the integrated measurement, identification, and control fast energy control device, is responsible for calculating the compensation parameters according to the disturbance characteristic pattern library, generating a control strategy based on the disturbance source location information, forming a hierarchical coordinated control structure through the control strategy, and generating and sending adjustment instructions for the first operating parameter to each node of the network-forming new energy power generation system.

[0075] In the embodiment of this application, the control execution unit first judges the disturbance type according to the information in the disturbance characteristic pattern library, then calculates the corresponding compensation parameters for different types of disturbances, generates a control strategy in combination with the disturbance source location information, and finally executes the control action through the hierarchical coordinated control structure.

[0076] The control execution unit determines whether the disturbance information is periodic disturbance or random disturbance by querying the disturbance type identifier in the disturbance feature pattern library. For periodic disturbances, the control execution unit calculates the voltage compensation parameter and the reactive power regulation parameter. The specific implementation method is to obtain the frequency, amplitude and phase information of the disturbance from the disturbance feature pattern library, and then combine the disturbance source location information to calculate the influence degree of the disturbance at each node by using the electrical distance attenuation model. The electrical distance attenuation model refers to the characteristic that the influence of the disturbance weakens as the electrical distance increases when the disturbance propagates along the power system, and it is usually described by an exponential decay function. Taking low-frequency oscillation disturbance as an example, the control execution unit calculates the damping control parameter through the following steps: First, determine the optimal damping coefficient based on the oscillation frequency. Usually, when the oscillation frequency is in the range of 0.2 - 0.7 Hz, the damping coefficient takes a value between 3 and 5. Second, calculate the participation coefficient according to the electrical distance from each node to the disturbance source. The participation coefficient is inversely proportional to the electrical distance. Third, allocate the control quantity in combination with the adjustable capacity of each node to make the total control effect optimal.

[0077] For random disturbances, the control execution unit calculates the power smoothing parameter and the active power regulation parameter. The implementation process is as follows: First, establish a power fluctuation prediction model based on the statistical characteristics (mean, variance, spectrum distribution, etc.) of the disturbance. This model uses the time series analysis method to predict the future power change based on historical power data. Second, construct an electrical topology influence matrix based on the disturbance source location information. This matrix describes the electrical connection relationship and the mutual influence degree between each node of the system. Third, optimize the scheduling control resources by solving the Lagrangian equation. This equation comprehensively considers the control effect and the control cost to obtain the optimal control strategy. For example, for the fluctuation of photovoltaic power output, the control execution unit first calculates the power smoothing parameter: Analyze the main frequency period of the photovoltaic power output fluctuation as 10 seconds, then set the smoothing time constant to 15 - 20 seconds; when the fluctuation amplitude is 20% of the rated power and the system tolerance is strong, set the smoothing capacity to 1.2 times the fluctuation amplitude, that is, 24% of the rated power. Then determine the active power regulation parameters of the energy storage system, including the maximum regulation power of 0.24 times the rated capacity, the regulation rate of 5% of the rated capacity per second, and the regulation direction opposite to the fluctuation. Finally, allocate the regulation tasks according to the response characteristics and positions of each energy storage device. For example, the supercapacitor near the disturbance source is responsible for 80% of the fast response regulation tasks, and the remote battery energy storage system is responsible for 20% of the long-term regulation tasks.

[0078] After determining the compensation parameters, the control execution unit identifies the distribution of adjustable devices within the control range based on the disturbance source location information. The specific implementation method is to first establish an electrical distance coordinate system with the disturbance source location as the center, and then determine the influence range threshold according to the disturbance type and influence degree. ; Finally, select all adjustable devices with an electrical distance less than as candidate control resources. For example, for low-frequency oscillation disturbances, Generally take 50% of the maximum electrical distance of the system ; for harmonic disturbances, take 30%; for random power fluctuations, take 40%. This method of selecting equipment based on electrical distance avoids the blindness of selecting equipment by physical distance in the traditional method and realizes the precise mobilization of control resources.

[0079] The control execution unit constructs a control optimization equation according to the disturbance type, compensation parameters, and the distribution of adjustable devices, and solves to obtain a set of control strategy parameters. The mathematical form of the control optimization equation is:

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] Among them, is the objective function, representing the total control cost, and the optimization goal is to minimize ; is the voltage adjustment amount of the th node, in per-unit value; is the active power adjustment amount of the th node, in MW; is the reactive power adjustment amount of the th node, in MVar; , , are the weight coefficients, corresponding to the weights of voltage, active power, and reactive power adjustments respectively, dimensionless; , are the current active power and reactive power of node , in MW and MVar respectively; , are the lower and upper limits of the active power regulation of node , in MW; , are the lower and upper limits of the reactive power regulation of node , in MVar; is the maximum allowable voltage adjustment amount of node , in per-unit value; is the maximum voltage deviation allowed by the system, in per-unit value; is the total number of nodes participating in the control, dimensionless.

[0086] The specific implementation form of the control strategy parameter set is as follows: The list of node identifiers participating in the control is stored in the order of priority, and the priority is positively correlated with the control effect and response speed of the nodes; The adjustment parameters of each node include three parts: the adjustment target value, the adjustment rate, and the adjustment time limit; The execution timing adopts the form of time tags, which specifies the time points when each node starts to execute the adjustment. Usually, the nodes with high priority execute first, and the nodes with low priority execute later, forming a ladder response mode. This detailed design of the control strategy parameter set ensures the accurate execution of control instructions and the efficient utilization of resources.

[0087] To execute the control strategy efficiently, the control execution unit forms a hierarchical coordinated control structure, including a system layer control module, a regional layer control module, and a node layer control module. The implementation of the hierarchical control structure adopts a master-slave architecture. The system layer control module serves as the main controller, responsible for global decision-making; The regional layer control module serves as the intermediate coordinator, responsible for the allocation of control resources within the region; The node layer control module serves as the execution terminal, directly interacting with physical devices.

[0088] The calculation method of the disturbance influence range is as follows: First, obtain the position coordinates of the disturbance source from the disturbance characteristic pattern library and the initial intensity of the disturbance , where is set according to the disturbance type. For periodic disturbances:

[0089] ;

[0090] where is the disturbance amplitude, is the reference amplitude;

[0091] For random disturbances:

[0092] ;

[0093] where is the disturbance standard deviation, is the reference standard deviation.

[0094] Then calculate the influence degree of the disturbance on each node :

[0095] ;

[0096] where is the attenuation coefficient. For low-frequency oscillation disturbances , for harmonic disturbances , for random perturbations ; is the electrical distance from the node to the perturbation source.

[0097] Finally, determine the influence range , take the minimum radius that includes all of the nodes, where is the influence threshold, usually taken as 0.05. When it is necessary to determine whether the perturbation influence crosses regions, compare with the electrical distance to the regional boundary . If , it is considered that the perturbation influence crosses regions.

[0098] When the perturbation influence range is limited within a single node, the node - layer control module performs the regulation. The execution mode of the node - layer control is: directly send the control parameters to the local controller, and realize the transfer of control instructions at the device layer through the field bus or industrial Ethernet. The response time is usually in the millisecond level. This direct control method avoids the delay of multi - level transmission and is suitable for handling local small - range perturbations.

[0099] When the perturbation influence range spans multiple nodes but is limited within a single region, the regional - layer control module coordinates the execution. The execution mode of the regional - layer control is: first, decompose the control task into multiple subtasks and assign them to relevant nodes; then establish a coordination mechanism between nodes to ensure the consistency of control actions; finally, supervise the execution process and handle possible conflicts. This coordinated control method balances the consistency of centralized control and the flexibility of distributed control and is suitable for handling medium - range perturbations.

[0100] When the perturbation influence range spans multiple regions, the system - layer control module commands and regulates. The execution mode of the system - layer control is: first, formulate a global control strategy, clarify the control objectives and limiting conditions of each region; then assign the control tasks to relevant regional control modules; finally, establish a coordination mechanism between regions to ensure the coordinated control of the whole system. This hierarchical control method is suitable for handling large - range system - level perturbations and can achieve global optimal control.

[0101] The design concept of measurement, identification, and control integration is fully reflected in the control execution unit. Compared with traditional separate systems, this application realizes the direct utilization of disturbance characteristic parameters and disturbance source location information, and reduces the time for generating control strategies from seconds to milliseconds. The specific implementation method is to achieve fast data transfer through a shared memory mechanism, avoiding the delays caused by data format conversion and network transmission in traditional methods. At the same time, the control execution unit adopts a real-time optimization algorithm to dynamically adjust the control strategy according to the latest disturbance information, realizing the closed-loop optimization of the control process. This seamlessly integrated measurement, identification, and control integration design significantly improves the system response speed and control accuracy, providing a strong guarantee for the stable operation of the network-forming new energy power generation system.

[0102] Through the above technical means, the control execution unit of this application realizes fast and precise control of the disturbances in the network-forming new energy power generation system. In particular, by closely integrating the disturbance characteristics and location information to form targeted control strategies, and adopting a dynamic hierarchical control structure based on the disturbance influence range, the accuracy and efficiency of control are improved, solving the control problem of complex disturbances in the new energy power generation system, and providing a strong guarantee for the stable operation of the system.

[0103] Based on the same inventive concept, the embodiment of this application also provides a measurement, identification, and control integration fast energy control method for network-forming new energy power generation. The implementation solutions provided by this method to solve problems are similar to the implementation solutions recorded in the above device. Therefore, the specific limitations in one or more embodiments of the measurement, identification, and control integration fast energy control method for network-forming new energy power generation provided below can refer to the limitations on the measurement, identification, and control integration fast energy control device for network-forming new energy power generation in the above text, and will not be repeated here.

[0104] In an exemplary embodiment, as Figure 3 shown, a measurement, identification, and control integration fast energy control method for network-forming new energy power generation is provided, including:

[0105] Collect the first operating parameters of the network-forming new energy power generation system, and extract the first characteristic parameters according to the first operating parameters;

[0106] Analyze the first characteristic parameters to obtain disturbance information, and perform timestamp correlation analysis on the disturbance information to obtain the disturbance source location information. Based on the disturbance information and the disturbance source location information, construct a disturbance characteristic pattern library;

[0107] Calculate the compensation parameters according to the disturbance characteristic pattern library, and generate a control strategy based on the disturbance source location information. Through the control strategy, form a hierarchical coordinated control structure, and generate and send adjustment instructions for the first operating parameters to each node of the network-forming new energy power generation system.

[0108] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structural diagram may be as shown in Figure 4 . The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner. The wireless manner can be implemented through Wi-Fi, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a fast energy control method for measurement, discrimination, and control integration used in networked new energy power generation. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0109] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0110] In an embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0111] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0112] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0114] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0115] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope recorded in this application.

[0116] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A rapid energy control device for measurement, identification and control integration used in network-forming new energy power generation, characterized in that Comprising: A data acquisition unit, configured to acquire first operating parameters of a network-forming new energy power generation system, and extract first characteristic parameters according to the first operating parameters; The first characteristic parameters include voltage fluctuation characteristics, power change characteristics, and frequency deviation characteristics; the first operating parameters include voltage fluctuation data, power change rate, and grid frequency deviation; A disturbance analysis unit, configured to analyze the first characteristic parameters to obtain disturbance information, perform timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and construct a disturbance characteristic pattern library based on the disturbance information and the disturbance source location information; A control execution unit, configured to calculate compensation parameters according to the disturbance characteristic pattern library, generate a control strategy based on the disturbance source location information, form a hierarchical coordinated control structure through the control strategy, generate and issue adjustment instructions for the first operating parameters to each node of the network-forming new energy power generation system; Analyzing the first characteristic parameters to obtain disturbance information includes extracting waveform amplitude, waveform frequency, and phase angle information from the voltage fluctuation characteristics; constructing a voltage disturbance template library; calculating similarity values between the waveform amplitude, the waveform frequency, and the phase angle information and each template in the voltage disturbance template library; determining a type identifier and a set of characteristic parameters of periodic disturbances according to the similarity values, where the periodic disturbances are included in the disturbance information; further including separating the power change characteristics into deterministic components and random components; calculating a statistical feature set of the random components; comparing the statistical feature set with marked samples in a historical disturbance database to obtain a type identifier of random disturbances; determining that the random disturbances are one or more of photovoltaic output fluctuations, wind power fluctuations, or load random changes according to the type identifier of the random disturbances, where the random disturbances are included in the disturbance information; Performing timestamp correlation analysis on the disturbance information to obtain disturbance source location information includes acquiring frequency deviation characteristics and corresponding timestamp information of multiple measurement points; selecting a reference measurement point, and calculating the time propagation delay of the frequency deviation characteristics of other each measurement point and the reference measurement point; establishing an electrical network model, and mapping the time propagation delay into an electrical distance value; calculating the position coordinates and influence radius of the disturbance source in the electrical network model by using the electrical distance value.

2. The integrated measurement, identification and control fast energy control device for grid-forming new energy power generation according to claim 1, characterized in that: Extracting first characteristic parameters according to the first operating parameters includes extracting voltage fluctuation characteristics of different frequency bands of the voltage fluctuation data; obtaining the power change characteristics by calculating the power change trend of the power change rate; extracting the frequency deviation characteristics of the grid frequency deviation; correlating the voltage fluctuation characteristics, the power change characteristics, and the frequency deviation characteristics according to time tags to form a multi-dimensional feature vector.

3. The integrated measurement, identification and control fast energy control device for grid-forming new energy power generation according to claim 2, characterized in that: Calculate compensation parameters based on the disturbance feature pattern library, and generate a control strategy based on the disturbance source location information, including: determining whether the disturbance information is periodic disturbance or random disturbance; if it is periodic disturbance, calculate voltage compensation parameters and reactive power regulation parameters; if it is random disturbance, calculate power smoothing parameters and active power regulation parameters; identify the distribution of controllable devices within the range determined by the disturbance source location information; construct a control optimization equation, and solve to obtain a set of control strategy parameters; the set of control strategy parameters includes a list of node identifiers participating in control, adjustment parameters for each node, and execution timings.

4. The integrated measurement, identification and control fast energy control device for grid-forming new energy power generation according to claim 3, characterized in that: The hierarchical coordinated control structure includes: A system layer control module, responsible for generating global control instructions and distributing control strategies to the regional layer control modules; A regional layer control module, responsible for coordinating the operation sequences of multiple node layer control modules within the region; A node layer control module, responsible for executing control actions of specific devices; When the disturbance influence range is limited within a single node, the adjustment is executed by the node layer control module; when the disturbance influence range spans multiple nodes but is limited within a single region, it is coordinated and executed by the regional layer control module; when the disturbance influence range spans multiple regions, it is commanded and regulated by the system layer control module; The influence range is jointly determined by the disturbance source location information and the electrical distance.

5. A measurement, identification, and control integrated fast energy control method for grid-forming new energy power generation, implemented using the measurement, identification, and control integrated fast energy control device for grid-forming new energy power generation as described in any one of claims 1 to 4, characterized in that: Collect the first operating parameters of the grid-forming new energy power generation system, and extract first characteristic parameters according to the first operating parameters; Analyze the first characteristic parameters to obtain disturbance information, perform timestamp correlation analysis on the disturbance information to obtain disturbance source location information, and construct a disturbance feature pattern library based on the disturbance information and the disturbance source location information; Calculate compensation parameters based on the disturbance feature pattern library, generate a control strategy based on the disturbance source location information, form a hierarchical coordinated control structure through the control strategy, and generate and send adjustment instructions for the first operating parameters to each node of the grid-forming new energy power generation system.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the measurement, identification, and control integrated fast energy control method for grid-forming new energy power generation as described in claim 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the measurement, identification, and control integrated fast energy control method for grid-forming new energy power generation as described in claim 5.

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