Measurement, identification and control integrated rapid energy control device used for network construction type new energy power generation

Abstract

The invention discloses a measurement, identification and control integrated rapid energy control device used for network construction type new energy power generation, and relates to the technical field of power system stability control, the measurement, identification and control integrated rapid energy control device comprises a data acquisition unit, a disturbance analysis unit and a control execution unit, the data acquisition unit acquires a first operation parameter and extracts a first characteristic parameter; the disturbance analysis unit analyzes the characteristic parameters to obtain disturbance information and a source position, and constructs a disturbance characteristic library; and the control execution unit calculates compensation parameters, generates a control strategy, forms a hierarchical coordination control structure and issues an adjustment instruction. According to the invention, a complete closed loop from measurement to control is constructed, so that rapid and accurate energy control can be carried out on various disturbances, and the stability and anti-disturbance capability of the network construction type new energy power generation system are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system stability control, and in particular to an integrated rapid energy control device for measurement, identification and control used in grid-connected renewable energy power generation. Background Art

[0002] At present, the grid-type renewable energy power generation system has become an important part of the power system. However, due to the randomness, volatility and intermittency of renewable energy, the grid-type renewable energy power generation system faces a variety of disturbances such as voltage fluctuations, sharp increase in power change rate and grid frequency deviation, which seriously affect the stable operation and power supply quality of the system. Moreover, these disturbances often occur in an intertwined manner, and the source of disturbance is difficult to accurately identify and locate, which brings great challenges to the stable control of the system.

[0003] In the prior art, the control of grid-type new energy power generation systems mostly adopts a solution that separates measurement, identification and control, which has problems such as information transmission lag and slow response speed, making it difficult to achieve rapid response and precise control of disturbances. At the same time, most existing control devices are designed for a single disturbance type, lacking the ability to comprehensively analyze and coordinate multi-dimensional features, and unable to achieve the integrated integration of measurement data, disturbance identification and energy control. Therefore, it is urgent to develop a fast energy control device that integrates measurement, identification and control for grid-type new energy power generation, which can achieve rapid response of the entire process from disturbance feature extraction, disturbance source location to hierarchical coordinated control, and improve the stability and anti-disturbance capability of the grid-type new energy power generation system. Summary of the invention

[0004] In view of the above-mentioned problems, this application is proposed.

[0005] Therefore, the present application provides an integrated rapid energy control device for measurement, identification and control used in grid-based new energy power generation, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, this application provides the following technical solutions: In the first aspect, the present application provides an integrated measurement, identification and control rapid energy control device for grid-type renewable energy power generation, including: a data acquisition unit, used to acquire a first operating parameter of a grid-type renewable energy power generation system, and extract a first characteristic parameter based on the first operating parameter; the first characteristic parameter includes a voltage fluctuation characteristic, a power change characteristic and a frequency deviation characteristic; the first operating parameter includes voltage fluctuation data, a power change rate and a grid frequency deviation; a disturbance analysis unit, used to analyze the first characteristic parameter to obtain disturbance information, and 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, used to calculate compensation parameters based on the disturbance characteristic pattern library, and generate a control strategy based on the disturbance source location information, and form a hierarchical coordinated control structure through the control strategy, and generate and issue adjustment instructions for the first operating parameters to each node of the grid-type renewable energy power generation system.

[0007] Preferably, the first characteristic parameter is extracted according to the first operating parameter, including extracting the 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 associating the voltage fluctuation characteristics, the power change characteristics and the frequency deviation characteristics according to time tags to form a multi-dimensional feature vector.

[0008] Preferably, the first characteristic parameter is analyzed to obtain disturbance information, including extracting waveform amplitude, waveform frequency and phase angle information from the voltage fluctuation characteristics; constructing a voltage disturbance template library; calculating a similarity value 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 identification and a characteristic parameter set of a periodic disturbance based on the similarity value, the periodic disturbance being included in the disturbance information.

[0009] Preferably, analyzing the first characteristic parameter to obtain disturbance information also includes 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 labeled samples in a historical disturbance database to obtain a type identification of the random disturbance; determining, based on the type identification of the random disturbance, that the random disturbance is one or more of photovoltaic output fluctuations, wind power fluctuations, or random load changes, and the random disturbance is included in the disturbance information.

[0010] 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 of multiple measurement points and the corresponding timestamp information; selecting a reference measurement point, calculating the time propagation delay between the frequency deviation characteristics of other measurement points and the reference measurement point; establishing an electrical network model, mapping the time propagation delay to an electrical distance value; and using the electrical distance value to calculate the position coordinates and influence radius of the disturbance source in the electrical network model.

[0011] Preferably, compensation parameters are calculated according to the disturbance characteristic pattern library, and a control strategy is generated based on the disturbance source position information, including: judging 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 controllable devices within the range determined by the disturbance source position information; constructing a control optimization equation and solving it to obtain a control strategy parameter set; the control strategy parameter set includes a list of node identifiers participating in the control, adjustment parameters of each node and an execution sequence.

[0012] Preferably, the hierarchical coordinated control structure includes: a system layer control module, which is responsible for generating global control instructions and distributing control strategies to the regional layer control unit; a regional layer control module, which is responsible for coordinating the operation sequence of multiple node layer control modules in the region; a node layer control module, which is responsible for executing control actions of specific equipment; when the disturbance impact range is limited to a single node, the node layer control module performs adjustment; when the disturbance impact range spans multiple nodes but is limited to a single region, the regional layer control module coordinates execution; when the disturbance impact range spans multiple regions, the system layer control module directs and regulates; the impact range is determined by the disturbance source location information and the electrical distance.

[0013] In the second aspect, the present application also provides an integrated measurement, identification and control rapid energy control method for grid-type renewable energy power generation, including: collecting a first operating parameter of the grid-type renewable energy power generation system, and extracting a first characteristic parameter based on the first operating parameter; analyzing the first characteristic parameter to obtain disturbance information, and performing timestamp correlation analysis on the disturbance information to obtain disturbance source position information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source position information; calculating compensation parameters based on the disturbance characteristic pattern library, and generating a control strategy based on the disturbance source position information, forming a hierarchical coordinated control structure through the control strategy, and generating and issuing adjustment instructions for the first operating parameters to each node of the grid-type renewable energy power generation system.

[0014] In the third aspect, the present application also provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: collecting a first operating parameter of a grid-type new energy power generation system, and extracting a first characteristic parameter based on the first operating parameter; analyzing the first characteristic parameter to obtain disturbance information, and performing timestamp correlation analysis on the disturbance information to obtain disturbance source position information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source position information; calculating compensation parameters based on the disturbance characteristic pattern library, and generating a control strategy based on the disturbance source position information, forming a hierarchical coordinated control structure through the control strategy, and generating and issuing adjustment instructions for the first operating parameter to each node of the grid-type new energy power generation system.

[0015] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when executed by a processor: collecting a first operating parameter of a grid-type new energy power generation system, and extracting a first characteristic parameter based on the first operating parameter; analyzing the first characteristic parameter to obtain disturbance information, and performing a 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 based on 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, and generating and issuing adjustment instructions for the first operating parameter to each node of the grid-type new energy power generation system.

[0016] The implementation of this application has the following beneficial effects: This application provides an integrated rapid energy control device for measurement, identification and control used in grid-type renewable energy power generation. The device integrates system operation parameter measurement, disturbance feature identification and energy coordinated control, and realizes 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 rapid and accurate energy control of various disturbances, effectively improving the stability and anti-disturbance capability of the grid-type renewable energy power generation system, and providing reliable technical support for large-scale renewable energy grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a schematic diagram of the application environment of a fast energy control device with integrated measurement, identification and control for grid-building new energy power generation involved in the present application; Figure 2 This is a schematic diagram of the overall structure of a fast energy control device with integrated measurement, identification and control for grid-building new energy power generation involved in the present application; Figure 3 This is an overall flow chart of a rapid energy control method for integrated measurement, identification and control used in grid-building new energy power generation involved in this application; Figure 4 This is a computer equipment diagram of a rapid energy control method involving integrated measurement, identification and control for grid-based renewable energy power generation. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying 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.

[0020] Grid-connected renewable energy power generation systems are increasingly being used in modern power systems. For example, they can be applied to various energy systems such as wind farms, photovoltaic power stations, distributed energy networks, and microgrids. When the grid experiences disturbances such as voltage fluctuations, power mutations, or frequency shifts, the system needs to quickly identify the type of disturbance, accurately locate the source of the disturbance, and implement effective energy control measures to maintain system stability.

[0021] In related technologies, the measurement, identification and control functions of new energy power generation systems are usually implemented by independent systems, and data needs to be transmitted and processed between systems. Not only does this cause information transmission delays, but it is also difficult to achieve rapid response to complex disturbances. Especially in scenarios where multiple disturbances overlap, how to accurately identify, locate and coordinately control disturbances is a relatively difficult technical problem.

[0022] 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.

[0023] 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.

[0024] Among them, the central control system 100 can be implemented as a dedicated industrial control computer, a programmable logic controller (PLC), a distributed control system (DCS), a fieldbus control system (FCS), an embedded control system or a virtual control center based on cloud computing. The integrated rapid energy control device 101 can be implemented in the form of hardware, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), etc.; it can also be implemented in the form of a combination of software and general hardware, such as a measurement and control software system running on an industrial-grade computer; it can also be implemented in the form of software and hardware collaborative design, with some functions implemented by hardware acceleration and some functions implemented by software algorithms. The wind power generation node 102 may include but is not limited to horizontal axis wind turbines, vertical axis wind turbines, offshore wind turbines, distributed small wind power generation equipment or wind farm clusters. The photovoltaic power generation node 103 may include but is not limited to crystalline silicon photovoltaic modules, thin-film photovoltaic modules, concentrated 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, fluid batteries, etc.; it can also adopt physical energy storage methods, such as pumped 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 measuring device 105 / the second measuring device 106 can be implemented as a smart meter, a power quality analyzer, a phasor measurement unit (PMU), a relay protection device, a fault indicator, a wireless sensor network node or a power parameter monitoring terminal. The grid access point 107 can be implemented in the form of substations of different voltage levels, distribution transformers, grid-connected inverters, power electronic transformers (PET) or smart distribution terminals. The power line 108 can be an overhead line, an underground cable, a high-voltage direct current transmission line (HVDC), a flexible alternating current transmission system (FACTS) or a smart distribution network. The communication line 109 can adopt wired communication methods, such as optical fiber communication, power line carrier communication (PLC), Ethernet, field bus, 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.

[0025] In an exemplary embodiment, Figure 2 As shown, a fast energy control device integrating measurement, identification and control for grid-building new energy power generation is provided. Figure 1 Taking the application environment in as an example, the device includes a data acquisition unit, a disturbance analysis unit and a control execution unit.

[0026] Specifically, as a key component of the integrated measurement, identification and control rapid energy control device, the data acquisition unit is responsible for collecting the first operating parameters of the grid-type 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. Its core function is to achieve comprehensive perception of the operating status of the grid-type new energy power generation system.

[0027] In one embodiment of the present application, the data acquisition unit is deployed at a key node of a grid-type new energy power generation system, such as Figure 1 The first measuring device 105 and the second measuring device 106 shown are used to collect the first operating parameters. The first measuring device 105 can be deployed at the electrical connection point of the wind power generation node 102, and the second measuring device 106 can be deployed at the AC busbar of the photovoltaic power generation node 103. By configuring multiple measuring points, the first operating parameters of each node can be fully collected.

[0028] Among the first operating parameters collected by the data acquisition unit, the voltage fluctuation data include the phase voltage, line voltage and voltage distortion rate of each node in the system; the power change rate includes the active power change rate, reactive power change rate and apparent power change rate; the grid frequency deviation includes the difference between the system frequency and the rated frequency and the frequency change rate.

[0029] In an optional embodiment, the data acquisition unit uses synchronous sampling technology to ensure that the data of each measurement point has a unified time reference. Specifically, the data acquisition unit can achieve time synchronization of each measurement point through 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.

[0030] In another optional embodiment, the data acquisition unit adopts an adaptive sampling strategy to dynamically adjust the sampling frequency according to the state of the grid-type new energy power generation system. When the grid-type new energy power generation system is in a stable operating state, a lower sampling frequency is used to reduce the data transmission and processing burden; when a rapid change or abnormal fluctuation of system parameters is detected, the sampling frequency is automatically increased to capture transient characteristics to ensure that important disturbance events are not missed.

[0031] Extracting the first characteristic parameter according to the first operating parameter 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 characteristic vector.

[0032] In terms of extracting voltage fluctuation characteristics, the data acquisition unit analyzes different frequency bands of voltage fluctuation data. In an optional embodiment, the fast Fourier transform (FFT) method is used to perform frequency domain decomposition on the voltage fluctuation data to extract the harmonic components and amplitude characteristics of different frequency bands. 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.

[0033] In another optional embodiment, a wavelet transform method is used to perform time-frequency analysis on the voltage fluctuation data. Compared with the FFT method, the wavelet transform has multi-resolution analysis capabilities 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 subjected to wavelet decomposition, and the energy distribution characteristics and singular point characteristics of different frequency bands are extracted to form a time-frequency feature vector.

[0034] In another optional embodiment, the Stockwell transform (S transform for short) method is used to analyze the voltage fluctuation data. The S transform combines the advantages of short-time Fourier transform and wavelet transform, has frequency-related resolution and the characteristics 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 disturbance types such as voltage transients, voltage flickers, and voltage sags can be accurately identified.

[0035] The extracted voltage fluctuation characteristics include but are not limited to waveform characteristic parameters and spectrum distribution parameters. Waveform characteristic parameters include peak ratio, crest factor, asymmetry, etc.; spectrum distribution parameters include harmonic content, frequency band energy distribution, main frequency component, etc. These characteristic parameters together constitute the voltage fluctuation feature set for subsequent disturbance analysis.

[0036] 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 optional embodiment, the sliding window method is used to calculate the power change trend. By setting time windows of different widths, the short-term, medium-term and long-term power change rates are calculated respectively to form multi-scale power change characteristics. The short-term window (such as seconds) captures instantaneous power fluctuations, the medium-term window (such as minutes) reflects the power adjustment process, and the long-term window (such as hours) reflects the power balance trend.

[0037] In another optional embodiment, a recursive least squares (RLS) algorithm is used as a classic method of adaptive filtering, which has the characteristics of fast convergence speed and good tracking performance, and is used to calculate the power change trend. In specific implementation, the following steps can be followed: initializing the weight vector and covariance matrix, calculating the prediction error, updating the covariance matrix and weight vector, and using the updated weight vector to predict the power change trend. For example, in the scenario of wind farm output fluctuation, by continuously learning the wind power fluctuation characteristics, an accurate prediction of the short-term power change trend can be achieved, providing a basis for disturbance identification.

[0038] In terms of extracting frequency deviation characteristics, the data acquisition unit analyzes and processes the power grid frequency deviation. In an optional embodiment, a zero crossing detection method is used to extract frequency deviation characteristics. 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 amount of calculation. By statistically analyzing the zero crossing time series, characteristic parameters such as frequency change rate and frequency offset can be extracted.

[0039] In another optional embodiment, a phase-locked loop (PLL) technology is used to extract the frequency deviation feature. PLL technology locks 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, PLL technology has higher accuracy and anti-interference ability, and is particularly suitable for use in situations where waveform distortion and noise interference are large.

[0040] 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 capability; the frequency offset reflects the system active power balance state. These characteristic parameters together constitute the frequency deviation feature set for subsequent disturbance analysis.

[0041] After extracting the voltage fluctuation characteristics, power change characteristics and frequency deviation characteristics, the data acquisition unit associates these characteristics according to the time tag to form a multi-dimensional feature vector. By integrating different types of feature parameters, the system status can be fully captured, providing richer information for subsequent disturbance analysis.

[0042] In an optional embodiment, the data acquisition unit uses a time synchronization method to perform feature association. Specifically, based on the timestamp information of each feature parameter, the voltage fluctuation feature, power change feature, and frequency deviation feature within the same time window are combined into a multidimensional feature vector. The width of the time window can be adjusted according to actual application requirements to balance the timeliness and computational burden of feature association.

[0043] In another optional embodiment, when the voltage fluctuation feature, power change feature and frequency deviation feature are associated according to the time label, the data acquisition unit uses a multiple representation learning method to enhance the feature and improve the expression ability of the multi-dimensional feature vector. The specific implementation steps include: preliminary association of 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 nonlinear transformation on the features and extract high-order feature relationships; retaining the original feature information through residual connections to prevent information loss; and giving dynamic weights to different features through an attention mechanism to enhance the impact of key features.

[0044] Taking the disturbance identification of photovoltaic power plants as an example, when the light intensity changes rapidly and causes power fluctuations, the voltage and frequency parameters will also change accordingly. Through the above-mentioned multiple representation learning method, the timing correlation characteristics between voltage sag, rapid power change and frequency drop can be automatically learned to form a characteristic representation of light disturbances, thereby improving the recognition accuracy of such disturbances.

[0045] The construction of multi-dimensional feature vectors makes full use of the correlation between voltage, power and frequency parameters, and realizes the information fusion and complementarity between different physical quantities. This fusion method not only improves the expression ability of 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.

[0046] In a comprehensive embodiment of the present 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-connected new energy power generation system in real time; then, the data acquisition unit pre-processes the original data, including filtering, denoising and outlier processing, to improve the data quality; then, the processed data is subjected to feature extraction to obtain voltage fluctuation characteristics, power change characteristics and frequency deviation characteristics; finally, the data acquisition unit associates these characteristics according to time tags to form a multi-dimensional feature vector reflecting the overall state of the system, providing data support for subsequent disturbance analysis and control execution.

[0047] Through the above technical means, the data acquisition unit of this application can quickly and accurately collect the operating parameters of the grid-type new energy power generation system and extract effective characteristic parameters, providing a data basis for integrated rapid energy control of measurement, identification and control. In particular, through multi-time scale analysis and feature fusion technology, comprehensive perception and accurate capture of the system state are achieved, creating favorable conditions for subsequent disturbance identification and control decisions.

[0048] Furthermore, the disturbance analysis unit is responsible for analyzing the first characteristic parameter to obtain disturbance information, and performing timestamp correlation analysis on the disturbance information to obtain the disturbance source location information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source location information. The disturbance analysis unit realizes the accurate identification of the disturbance type and the accurate location of the disturbance source, providing a solid foundation for the formulation of subsequent control strategies.

[0049] In one embodiment of the present application, the disturbance analysis unit identifies various disturbances in the grid-type new energy power generation system by deeply analyzing the first characteristic parameter. The analysis process first processes the voltage fluctuation characteristics to identify periodic disturbances; then analyzes the power change characteristics to identify random disturbances; finally, the disturbance source location is determined through timestamp correlation analysis, and a disturbance characteristic pattern library is constructed.

[0050] In terms of analyzing voltage fluctuation characteristics, the disturbance 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 disturbance, the waveform frequency reflects the periodic characteristics of the disturbance, and the phase angle information reveals the propagation characteristics of the disturbance. These three types of information together constitute a comprehensive description of the voltage disturbance.

[0051] In order to effectively identify various types of voltage disturbances, the disturbance analysis unit constructs a voltage disturbance template library, including harmonic interference templates, oscillation fluctuation templates and voltage flicker templates. The template library is constructed using conventional disturbance pattern recognition methods. By collecting and analyzing typical disturbance samples, standard feature parameters are extracted to form reference templates for various disturbances. In the grid-type new energy power generation system, the harmonic interference template is mainly used to identify 2-13 harmonics caused by power electronic equipment such as inverters and converters; the oscillation fluctuation template is used to identify 0.1-2Hz low-frequency oscillations caused by wind power, photovoltaics and other new energy grid-connected 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 starting and stopping high-power wind turbines and switching on and off photovoltaic arrays.

[0052] The disturbance analysis unit calculates the similarity value between the extracted waveform amplitude, waveform frequency and phase angle information and each template in the voltage disturbance template library. The similarity value is calculated using the normalized Euclidean distance method, that is: Similarity value = 1-||feature vector-template vector||2 / ||template vector||2; The feature vector is composed of waveform amplitude, waveform frequency and phase angle, and the template vector is the standard feature value of the corresponding disturbance type. The closer the similarity value is to 1, the higher the matching degree is. This calculation method can uniformly process features of different dimensions and improve the accuracy of recognition.

[0053] According to the calculated similarity value, the disturbance analysis unit determines the type identification and characteristic parameter set of the periodic disturbance. For example, when the voltage fluctuation at the wind farm grid connection point is detected, by calculating the similarity value, it is found that its matching degree with the low-frequency oscillation template reaches 0.92, which is much higher than the matching degree of other templates. It can be determined that the disturbance is a typical low-frequency oscillation problem of the wind turbine. At the same time, the disturbance analysis unit extracts the characteristic parameters of the oscillation frequency (such as 0.7Hz), amplitude (such as 3% of the rated voltage) and duration to form a complete characteristic parameter set.

[0054] In terms of analyzing the power variation characteristics, the disturbance analysis unit separates the power variation characteristics into deterministic components and random components. In implementation, the empirical mode decomposition (EMD) method can be used for separation. The EMD method can adaptively decompose the signal into a finite number of intrinsic mode functions (IMFs) and a residual term, where the high-frequency IMF component usually corresponds to the random component, and the low-frequency IMF component and the residual term correspond to the deterministic component. This separation method is particularly suitable for processing nonlinear and non-stationary power variation signals, and can effectively capture the random fluctuation characteristics in the new energy power generation system.

[0055] For the separated random components, the disturbance 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 components usually have the characteristics of a mean close to zero, a large variance, a slightly negative skewness, and a kurtosis greater than 3, reflecting the rapid power fluctuation characteristics caused by changes in light. The random components of wind power fluctuations are usually characterized by 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 characteristics constitute an important basis for distinguishing different sources of random disturbances.

[0056] The disturbance analysis unit compares the calculated statistical feature set with the labeled samples in the historical disturbance database to obtain the type identification of the random disturbance. The historical disturbance database is a knowledge base accumulated during the long-term operation of the system, which contains the characteristic patterns and labels of different types of random disturbances. The database adopts a hierarchical storage structure, indexed by disturbance type, intensity and location, and supports regular updates and self-learning optimization. The comparison process uses the k-nearest neighbor algorithm to calculate 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 disturbance type.

[0057] According to the random disturbance type identification obtained, the disturbance analysis unit determines that the random disturbance is one or more of photovoltaic output fluctuations, wind power fluctuations, or random load changes. In practical applications, different types of random disturbances may exist at the same time and overlap with each other. For example, when wind power and photovoltaics are connected at the same time, a composite disturbance may occur in which wind power drops and photovoltaic output fluctuations overlap. The disturbance analysis unit can identify the components and primary and secondary relationships of this composite disturbance through in-depth analysis of statistical features and precise matching of historical samples, laying the foundation for subsequent precise control.

[0058] In terms of 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 the grid-type 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 of the system (such as wind farm collection points, photovoltaic power station grid connection points, load centers, etc.), millisecond-level frequency changes and their precise timestamps can be obtained, providing accurate data support for the location of disturbance sources.

[0059] The disturbance analysis unit selects one of the measurement points as the reference measurement point and calculates the frequency deviation characteristics of the other measurement points and the time propagation delay of the reference measurement point. The time propagation delay reflects the difference in propagation time from the source of the disturbance to each measurement point and is a key parameter for locating the source of the disturbance. 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 between different measurement points observing the same disturbance event, and form a time propagation delay matrix.

[0060] Based on the system topology and electrical parameters, the disturbance analysis unit establishes an electrical network model and maps the measured time propagation delay to an electrical distance value. The electrical network model is a mathematical abstraction of the actual power system, which contains information such as the node impedance matrix, branch impedance parameters, and node connection relationships. In 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: Electrical distance value = α ×Time propagation delay× V 传播 ; in, α is the correction factor, V 传播 The propagation speed of the disturbance in the system is usually taken as the propagation speed of electromagnetic waves in the power system. This mapping relationship converts time domain information into spatial position information.

[0061] Using the calculated electrical distance value, the disturbance analysis unit uses the triangulation method to calculate the position coordinates and influence radius of the disturbance source in the electrical network model. 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 set of nonlinear equations. In specific implementation, the least squares method is used to process redundant observation data to improve positioning accuracy. For example, when a photovoltaic power station in a certain area of ​​the system causes a sudden drop in power due to cloud cover, the specific area where the disturbance occurs can be located by analyzing the time difference of the frequency drop signal observed at each measurement point, with an accuracy of kilometers, greatly reducing the target range of the control action. The influence radius is determined by analyzing the attenuation characteristics of the frequency fluctuations, reflecting the degree of influence of the disturbance on different parts of the system, and providing a basis for determining the scope of subsequent control participation.

[0062] In terms of building a disturbance characteristic pattern library, the disturbance analysis unit comprehensively utilizes the identified disturbance information and the located disturbance source location information to establish a comprehensive database containing disturbance types, characteristic parameters and location information. The disturbance characteristic pattern library adopts a structured storage method and contains 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), impact range, severity, occurrence time, related events, etc. This structured storage facilitates rapid retrieval and pattern matching, and supports subsequent disturbance identification and control decisions.

[0063] The disturbance feature pattern library not only stores the currently identified disturbances, but also has self-learning and adaptive capabilities. By analyzing the consistency between the disturbance identification results and the actual system response, the system can continuously optimize the identification algorithm and template parameters to improve the accuracy of identification. At the same time, the pattern library also supports seasonal pattern analysis and trend prediction, and can predict the type of disturbance that may occur in a specific period of time based on historical data, thereby achieving preventive control. This pattern library design with learning capabilities enables the system to continuously adapt to the changing operating environment and disturbance characteristics, and continuously improve the effectiveness of energy control.

[0064] For example, in a grid-type new energy power generation system that integrates wind power, photovoltaic power and conventional power sources, when there is a fluctuation in photovoltaic output due to changes in cloud cover, the disturbance analysis unit can accurately identify from the power change characteristics that this is a typical random fluctuation in photovoltaic output, and accurately 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 a fluctuation amplitude of 30% of the rated power, a duration of 2 minutes, and a frequency impact of 0.05Hz) and location information (such as the No. 2 photovoltaic power station in the southeast area of ​​the system) in the disturbance characteristic pattern library. When similar disturbances occur 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, thereby calling surrounding energy storage resources or adjustable loads in advance to effectively suppress system fluctuations.

[0065] Through the above technical means, the disturbance analysis unit of the present application realizes the accurate identification and precise positioning of various disturbances in the grid-type new energy power generation system, laying a solid foundation for the integrated rapid energy control of measurement, identification and control. Compared with the traditional separate measurement and identification system, the disturbance analysis unit of the present application shortens the disturbance identification time from seconds to milliseconds, and improves the positioning accuracy from the regional level to the node level, greatly improving the system's response speed and accuracy to complex disturbances. In particular, through the organic combination of voltage disturbance template matching, power fluctuation characteristic analysis and frequency propagation-based disturbance source positioning technology, a complete set of disturbance analysis methods has been formed, which can effectively cope with complex disturbance scenarios in new energy power generation systems and significantly improve the stability and reliability of the system.

[0066] As the terminal execution link of the integrated measurement, identification and control rapid energy control device, the control execution unit is responsible for calculating the compensation parameters according to the disturbance characteristic pattern library, and generating the control strategy based on the disturbance source position information. Through the control strategy, a hierarchical coordinated control structure is formed, and adjustment instructions for the first operating parameters are generated and issued to each node of the grid-based new energy power generation system.

[0067] In an embodiment of the present application, the control execution unit first determines the disturbance type based on 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 position information, and finally executes the control action through a hierarchical coordinated control structure.

[0068] The control execution unit determines whether the disturbance information is a periodic disturbance or a random disturbance by querying the disturbance type identifier in the disturbance characteristic pattern library. For periodic disturbances, the control execution unit calculates the voltage compensation parameters and reactive power regulation parameters. The specific implementation method is to obtain the frequency, amplitude and phase information of the disturbance from the disturbance characteristic pattern library, and then combine the disturbance source location information to calculate the influence of the disturbance on each node using the electrical distance attenuation model. The electrical distance attenuation model refers to the characteristic that when the disturbance propagates along the power system, its influence decreases as the electrical distance increases, and is usually described by an exponential attenuation function. Taking low-frequency oscillation disturbance as an example, the control execution unit calculates the damping control parameters through the following steps: First, the optimal damping coefficient is determined based on the oscillation frequency. Usually, when the oscillation frequency is in the range of 0.2-0.7Hz, the damping coefficient is between 3-5; second, the participation coefficient is calculated based on the electrical distance from each node to the disturbance source, and the participation coefficient is inversely proportional to the electrical distance; third, the control quantity is allocated in combination with the node adjustable capacity to optimize the overall control effect.

[0069] For random disturbances, the control execution unit calculates power smoothing parameters and active power regulation parameters. The implementation process is as follows: First, a power fluctuation prediction model is established based on the statistical characteristics of the disturbance (mean, variance, spectrum distribution, etc.). The model uses a time series analysis method to predict future power changes based on historical power data; second, an electrical topology influence matrix is ​​constructed based on the location information of the disturbance source. The matrix describes the electrical connection relationship and mutual influence between the nodes of the system; third, the scheduling control resources are optimized by solving the Lagrange equation. The equation comprehensively considers the control effect and control cost to obtain the optimal control strategy. For example, in response to photovoltaic output fluctuations, the control execution unit first calculates the power smoothing parameters: the main frequency period of photovoltaic output fluctuations is analyzed as 10 seconds, and the smoothing time constant is set to 15-20 seconds; when the fluctuation amplitude is 20% of the rated power and the system has a strong tolerance, the smoothing capacity is set to 1.2 times the fluctuation amplitude, that is, 24% of the rated power; then the active power regulation parameters of the energy storage system are determined, 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 is opposite to the fluctuation; finally, the regulation tasks are allocated according to the response characteristics and location of each energy storage device. For example, the supercapacitor close to 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.

[0070] After determining the compensation parameters, the control execution unit identifies the distribution of controllable 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; then determine the impact range threshold according to the disturbance type and impact degree. ; Finally, select the electrical distance less than All controllable devices are considered as candidate control resources. For example, for low-frequency oscillation disturbances, Usually take the maximum electrical distance of the system 50%; for harmonic disturbance, Pick 30%; for random power fluctuations, Pick This device selection method based on electrical distance avoids the blindness of selecting devices based on physical distance or administrative divisions in traditional methods, and realizes the precise mobilization of control resources.

[0071] The control execution unit constructs the control optimization equation according to the disturbance type, compensation parameters and distribution of adjustable devices, and solves it to obtain the control strategy parameter set. The mathematical form of the control optimization equation is: ; ; ; ; ; in, is the objective function, which represents the total control cost. The optimization goal is to make Minimum; For the The voltage adjustment of each node, in per unit value; For the Active power adjustment of each node, in MW; For the The reactive power adjustment of each node, in MVar; , , are weight coefficients, corresponding to the weights of voltage, active power and reactive power adjustment, dimensionless; , For Node The current active power and reactive power, in MW and MVar respectively; , For Node The lower and upper limits of active power regulation, in MW; , For Node The lower and upper limits of reactive power regulation are expressed in MVar; For Node The maximum voltage adjustment allowed, in per unit value; The maximum voltage deviation allowed by the system, in per unit value; is the total number of nodes involved in control, dimensionless.

[0072] The specific implementation form of the control strategy parameter set is as follows: the node identification list participating in the control is stored in a priority-ordered manner, and the priority is positively correlated with the control effect and response speed of the node; the adjustment parameters of each node include the adjustment target value, adjustment rate, and adjustment time limit; the execution sequence adopts the form of a time tag, which specifies the time point when each node starts to execute the adjustment. Usually, high-priority nodes are executed first, and low-priority nodes are executed later, forming a tiered response mode. This detailed design of the control strategy parameter set ensures the accurate execution of control instructions and the efficient use of resources.

[0073] In order to efficiently execute the control strategy, the control execution unit forms a hierarchical coordination control structure, including system layer control module, regional layer control module and node layer control module. The hierarchical control structure adopts a master-slave architecture. The system layer control module is the main controller responsible for global decision-making; the regional layer control module is the intermediate coordinator responsible for the allocation of control resources in the region; the node layer control module is the execution terminal and directly interacts with the physical device.

[0074] The calculation method of the disturbance influence range is as follows: first obtain the disturbance source position coordinates from the disturbance characteristic pattern library and the initial intensity of the disturbance ,in According to the disturbance type setting, for periodic disturbances: ; in, is the disturbance amplitude, is the reference amplitude; For random perturbations: ; in, is the disturbance standard deviation, is the reference standard deviation.

[0075] Then calculate the disturbance at each node The degree of influence : ; in, is the attenuation coefficient, for low-frequency oscillation disturbances , for harmonic disturbances , for random disturbances ; For Node The electrical distance to the source of the disturbance.

[0076] Finally determine the scope of impact , Take all The minimum radius that all nodes are included in, where is the impact threshold, usually 0.05. When it is necessary to determine whether the disturbance impact crosses regions, Electrical distance to area boundary For comparison, if The disturbance is considered to affect cross-regions.

[0077] When the disturbance is limited to a single node, the node-level control module performs the adjustment. The node-level control execution mode is: directly send the control parameters to the local controller, and realize the control command transmission of the device layer through the fieldbus or industrial Ethernet. The response time is usually in milliseconds. This direct control method avoids the delay of multi-level transmission and is suitable for handling local small-scale disturbances.

[0078] When the disturbance affects multiple nodes but is limited to a single area, the regional layer control module coordinates the execution. The regional layer control execution method is: first, the control task is decomposed into multiple subtasks and assigned to relevant nodes; then a coordination mechanism is established between nodes to ensure the consistency of control actions; finally, the execution process is supervised to deal with 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 disturbances.

[0079] When the disturbance affects multiple areas, the system-level control module will be in charge of the control. The execution method of system-level control is as follows: first, formulate a global control strategy to clarify the control objectives and constraints of each area; then assign the control tasks to the relevant regional control modules; finally, establish a coordination mechanism between regions to ensure the coordinated control of the entire system. This hierarchical control method is suitable for handling large-scale system-level disturbances and can achieve global optimal control.

[0080] The design concept of integrated measurement, identification and control is fully reflected in the control execution unit. Compared with the traditional separate system, the present application realizes the direct use of disturbance characteristic parameters and disturbance source location information, and the time for control strategy generation is shortened from seconds to milliseconds. The specific implementation method is to realize rapid data transmission through a shared memory mechanism, avoiding the delay of 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, thereby realizing closed-loop optimization of the control process. This seamlessly integrated measurement, identification and control design significantly improves the system response speed and control accuracy, and provides a strong guarantee for the stable operation of the grid-based new energy power generation system.

[0081] Through the above technical means, the control execution unit of this application realizes the rapid and accurate control of the disturbance of the grid-type new energy power generation system. In particular, by closely integrating the disturbance characteristics with the position information, forming a targeted control strategy, and adopting a dynamic hierarchical control structure based on the disturbance influence range, the accuracy and efficiency of the control are improved, the control problem of complex disturbances in the new energy power generation system is solved, and a strong guarantee is provided for the stable operation of the system.

[0082] Based on the same inventive concept, the embodiment of the present application also provides a fast energy control method for integrated measurement, identification and control used in grid-type renewable energy power generation. The implementation scheme for solving the problem provided by the method is similar to the implementation scheme recorded in the above-mentioned device, so the specific limitations in one or more embodiments of the fast energy control method for integrated measurement, identification and control used in grid-type renewable energy power generation provided below can be referred to the limitations of the fast energy control device for integrated measurement, identification and control used in grid-type renewable energy power generation above, and will not be repeated here.

[0083] In an exemplary embodiment, Figure 3 As shown, a rapid energy control method for integrated measurement, identification and control for grid-building new energy power generation is provided, comprising: Collecting a first operating parameter of the grid-building new energy power generation system, and extracting a first characteristic parameter according to the first operating parameter; Analyze the first characteristic parameter 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; Compensation parameters are calculated based on the disturbance characteristic pattern library, and a control strategy is generated based on the disturbance source position information. A hierarchical coordinated control structure is formed through the control strategy, and an adjustment instruction for the first operating parameter is generated and sent to each node of the grid-type new energy power generation system.

[0084] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. 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 to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through Wi-Fi, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a fast energy control method for integrated measurement, detection and control for network-based new energy power generation is realized. 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, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0085] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure 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 certain components, or have a different arrangement of components.

[0086] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0087] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0088] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0089] 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 used 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 must comply with relevant regulations.

[0090] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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), magnetic 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0091] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this application.

[0092] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation, characterized in that: include: A data acquisition unit, used for acquiring a first operating parameter of the grid-building new energy power generation system, and extracting a first characteristic parameter according to the first operating parameter; 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 parameter to obtain disturbance information, perform timestamp correlation analysis on the disturbance information to obtain disturbance source position information, and construct a disturbance characteristic pattern library based on the disturbance information and the disturbance source position information; A control execution unit is used to calculate compensation parameters according to the disturbance characteristic pattern library, and generate a control strategy based on the disturbance source position 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 grid-based new energy power generation system.

2. The fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 1, characterized in that: Extracting the first characteristic parameter according to the first operating parameter includes extracting the 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 power grid frequency deviation; associating 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. A fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 2, characterized in that: Analyzing the first characteristic parameter 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 a similarity value 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 identification and a characteristic parameter set of a periodic disturbance according to the similarity value, wherein the periodic disturbance is included in the disturbance information.

4. The fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 3, characterized in that: Analyzing the first characteristic parameter to obtain disturbance information also includes separating the power change characteristics into deterministic components and random components; calculating a statistical feature set of the random component; comparing the statistical feature set with labeled samples in a historical disturbance database to obtain a type identification of the random disturbance; determining, based on the type identification of the random disturbance, that the random disturbance is one or more of photovoltaic output fluctuations, wind power fluctuations, or random load changes, and the random disturbance is included in the disturbance information.

5. The fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 4, characterized in that: Performing timestamp correlation analysis on the disturbance information to obtain the location information of the disturbance source, including collecting frequency deviation characteristics of multiple measurement points and corresponding timestamp information; selecting a reference measurement point, calculating the frequency deviation characteristics of other measurement points and the time propagation delay of the reference measurement point; establishing an electrical network model, mapping the time propagation delay to an electrical distance value; and using the electrical distance value to calculate the location coordinates and influence radius of the disturbance source in the electrical network model.

6. A fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 5, characterized in that: Compensation parameters are calculated according to the disturbance characteristic pattern library, and a control strategy is generated based on the disturbance source position information, including: judging 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 adjustment parameters; if it is a random disturbance, calculating power smoothing parameters and active power adjustment parameters; identifying the distribution of controllable devices within the range determined by the disturbance source position information; constructing a control optimization equation and solving it to obtain a control strategy parameter set; the control strategy parameter set includes a list of node identifiers participating in the control, adjustment parameters of each node and an execution sequence.

7. A fast energy control device for integrated measurement, identification and control used in grid-building new energy power generation as claimed in claim 6, characterized in that: The hierarchical coordination control structure includes: The system-level control module is responsible for generating global control instructions and distributing control strategies to the regional-level control units; The regional layer control module is responsible for coordinating the operation sequence of multiple node layer control modules in the region; The node layer control module is responsible for executing the control actions of specific devices; When the disturbance impact range is limited to a single node, the node layer control module performs the regulation; when the disturbance impact range spans multiple nodes but is limited to a single area, the area layer control module coordinates the execution; when the disturbance impact range spans multiple areas, the system layer control module directs and regulates; The influence range is determined by the disturbance source location information and the electrical distance.

8. A fast energy control method for integrated measurement, identification and control for grid-building type renewable energy power generation, using the fast energy control device for integrated measurement, identification and control for grid-building type renewable energy power generation as claimed in any one of claims 1 to 7, characterized in that: Collecting a first operating parameter of the grid-building new energy power generation system, and extracting a first characteristic parameter according to the first operating parameter; Analyzing the first characteristic parameter to obtain disturbance information, performing timestamp correlation analysis on the disturbance information to obtain disturbance source position information, and constructing a disturbance characteristic pattern library based on the disturbance information and the disturbance source position information; Compensation parameters are calculated according to the disturbance characteristic pattern library, and a control strategy is generated based on the disturbance source position information. A hierarchical coordinated control structure is formed through the control strategy to generate and send adjustment instructions for the first operating parameters to each node of the grid-based new energy power generation system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the fast energy control method for integrated measurement, identification and control used in grid-based new energy power generation described in claim 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the fast energy control method of integrated measurement, identification and control for grid-based new energy power generation described in claim 8 are implemented.

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