Power distribution network intelligent fusion terminal test system

Abstract

The invention relates to the technical field of intelligent fusion terminals, and particularly discloses a test system for an intelligent fusion terminal of a power distribution network, which is used for solving the problems of single data source, simple rule judgment, insufficient utilization of wind power intermittent characteristics and multi-source information and lagging regulation and control capability in the transient process of rapid change of wind power output in the prior art. The wind power output parameters and the wind power fluctuation parameters output by the wind turbine generator in the monitoring time period are acquired in real time, the wind speed data flow and the wind direction data flow in the wind power output parameters are acquired, and the wind power interference factor calculation model is constructed based on the wind speed data flow and the wind direction data flow to calculate the wind power interference factor. By extracting the wind power output parameters and the wind power fluctuation parameters, constructing the hierarchical model, generating the wind power output simulation power value and judging whether the wind power fluctuation is normal or not, the adaptability and reliability of the power distribution network when the wind power output changes rapidly are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fusion terminals, and more specifically, to a distribution network intelligent fusion terminal testing system. Background Art

[0002] With the continuous expansion of the scale and complexity of the distribution network, there are a large number of wind farms connected to the distribution network in coastal areas. When the wind power increases at night, the wind power output increases suddenly. The uncertainty of load demand and the intermittent output of renewable energy have a serious impact on the voltage stability of the distribution network. The uncertainty of wind power will also cause the wind power output power to fluctuate in a short period of time, making the voltage level unstable. If there is no flexible and adjustable voltage control strategy, it may cause frequent voltage limit violations and control command conflicts. Traditional detection methods for wind power are mostly when the wind power increases at night, causing the output to rise or fall suddenly in a short period of time. Data records show that the power curve is not stable and peaks and troughs appear frequently. If the data analysis strategy is not optimized for intermittent characteristics, normal power fluctuations may be misjudged as equipment failures or data anomalies. The judgment method is too single. Once faced with the rapid fluctuations and intermittent characteristics of wind power output, misjudgment and malfunction are prone to occur.

[0003] With the deepening application of information technology, big data analysis, and edge computing technology in distribution networks, existing technologies use massive distribution network operation data to perform refined perception and optimization control of its operation status, such as Figure 1 The schematic diagram of the cloud-edge collaborative architecture of the existing distribution network is shown in the figure. The cloud-edge collaborative architecture of the existing distribution network includes a four-layer solution of "cloud-pipe-edge-end", in which the cloud platform layer is the distribution cloud master station, the pipeline layer is the network management layer; the edge computing is the intelligent fusion terminal, a distributed intelligent agent with data storage, computing and analysis capabilities, which can provide autonomous decision-making and services on-site or nearby; the end device is the physical device at the end of the distribution network. The intelligent fusion terminal is located at the edge of the distribution network, close to the wind farm access point and sensitive load area. In the face of voltage fluctuations caused by sudden increases or decreases in wind power output, the terminal can directly access the on-site measurement device to obtain node electrical quantity and wind power data in real time. However, in the transient process of rapid changes in wind power output, the mode of centralized analysis and then issuing instructions only through the cloud platform has communication delays and decision lags, and there are problems such as a single data source and simple rule judgment, which does not fully utilize the intermittent characteristics of wind power and multi-source information, and the regulation capability lags.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distribution network intelligent fusion terminal testing system, which is used to solve the problems of communication delay and decision lag in the existing mode of rapid change of wind power output in the transient process, which is only carried out through centralized analysis on the cloud platform and then issuing instructions, and there is a single data source and simple rule judgment, and the intermittent characteristics of wind power and multi-source information are not fully utilized, and the regulation capability is lagging, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A distribution network intelligent fusion terminal test system includes a multi-source data fusion module, a wind power interference analysis module and a wind power fluctuation simulation module. The multi-source data fusion module is used to obtain in real time the wind power output parameters and wind power fluctuation parameters output by a wind turbine during a monitoring period; the wind power output parameters include wind speed data stream, wind direction data stream, output power data stream and rated power; the wind power fluctuation parameters include wind speed fluctuation amplitude and wind speed fluctuation frequency; the wind power interference analysis module is used to obtain the wind speed data stream and wind direction data stream in the wind power output parameters, and construct a wind power interference factor calculation model based on the wind speed data stream and the wind direction data stream to calculate the wind power interference factor; the wind power fluctuation simulation module is used to extract the wind power output parameters and calculate the wind power interference factor. The wind power fluctuation parameters are combined with the basic output, periodic fluctuation and intermittent fault characteristics to build a hierarchical model, generate a wind power output simulation power value, and judge whether the wind power fluctuation is normal; the wind power fluctuation simulation module includes a basic wind power output analysis unit; the basic wind power output analysis unit is used to obtain the wind speed data stream, output power data stream and rated power in the wind power output parameters during the monitoring period, and obtain the rated wind speed of the wind turbine to reach the rated power, extract the minimum wind speed when the wind turbine starts to generate electricity and the maximum wind speed when the wind turbine stops running based on the wind speed data stream, and build a basic wind power output model based on the rated wind speed, minimum wind speed and maximum wind speed to calculate the basic output power. The formula of the basic wind power output model is:

[0008]

[0009] Where: P base (v t ) is the wind speed v t The corresponding basic output power, P rated is the rated power, v t is the wind speed corresponding to time t, v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v rated It is the rated wind speed at which the fan unit reaches rated power.

[0010] As a further solution of the present invention, the wind power interference analysis module includes a first wind power interference analysis unit; the first wind power interference analysis unit is used to construct a first wind power interference factor calculation model according to the wind speed data stream to obtain a first wind power interference factor, extract the wind speed data stream within the monitoring period, and arrange it in descending order to obtain a first sequence (v cut_in ,v2,…,v j ,…v cut_out ), where v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v2 is the second wind speed in numerical value, and v j is the jth wind speed in numerical order, and the formula for the first wind power interference factor calculation model is:

[0011]

[0012] Where: μ1 is the first wind power interference factor, m is the number of values ​​in the first sequence, v j is the jth wind speed in numerical order, v cut_out is the maximum wind speed when the fan unit stops running, v cut_in It is the minimum wind speed when the wind turbine starts to generate electricity.

[0013] As a further solution of the present invention, the wind power interference analysis module also includes a second wind power interference analysis unit; the second wind power interference analysis unit is used to construct a second wind power interference factor calculation model according to the wind direction data stream to obtain a second wind power interference factor, extract the wind direction data stream within the monitoring period, and arrange them in descending order to obtain a second sequence (θ cut_in ,θ2,…,θ h ,…θ cut_out ), where θ cut_in is the minimum wind direction data when the wind turbine starts generating electricity, θ cut_out is the maximum wind direction data when the fan unit stops running, θ2 is the second wind direction data in numerical value ranking, θ h is the wind direction data ranked by the hth value, and the formula for the second wind power interference factor calculation model is:

[0014]

[0015] Where: μ2 is the second wind power interference factor, H is the number of values ​​in the second sequence, θ h is the wind direction data with the hth numerical value, θ cut_out is the maximum wind direction data when the fan unit stops running, θ cut_in It is the minimum wind direction data when the wind turbine starts to generate electricity.

[0016] As a further solution of the present invention, the wind power fluctuation simulation module also includes a wind power periodic fluctuation analysis unit, an intermittent fault simulation unit, a fluctuation power synthesis unit and a fluctuation abnormality judgment unit; the wind power periodic fluctuation analysis unit is used to obtain the wind speed fluctuation amplitude and wind speed fluctuation frequency in the wind power fluctuation parameters, and construct a periodic fluctuation model to calculate the wind power periodic fluctuation power; the intermittent fault simulation unit is used to obtain the basic output power, and construct an intermittent fault power analysis model according to the basic output power to obtain the intermittent fault power. The formula of the intermittent fault power analysis model is:

[0017] P outage (t) = P base (v t )·H(tt outage );

[0018] Where: P outage (t) is the intermittent fault power at time t, P base (v t ) is the wind speed v t The corresponding basic output power, t outage is the time when the fault occurs, H(tt outage ) is the fault indication function.

[0019] As a further solution of the present invention, the fluctuating power synthesis unit is used to obtain the first wind power interference factor, the second wind power interference factor, the basic output power, the wind power periodic fluctuation power and the intermittent fault power, and superimpose them to obtain the final wind power output simulation power value:

[0020] P wind (t) = |1-μ1-μ2|×(P outage (t)+P base (v t )+P cycle (t));

[0021] Where: P wind (t) is the final wind power output simulation power value, μ1 is the first wind power interference factor, μ2 is the second wind power interference factor, P outage (t) is the intermittent fault power at time t, P base (v t ) is the wind speed v t The corresponding basic output power, P cycle (t) is the periodic fluctuation power of wind power at time t.

[0022] As a further scheme of the present invention, the fluctuation abnormality judgment unit is used to extract the first wind power interference factor and the second wind power interference factor, and evaluate whether the wind power output simulation power value is normal based on the first wind power interference factor and the second wind power interference factor. If the sum of the first wind power interference factor and the second wind power interference factor is greater than or equal to 1, the wind power output simulation power value is abnormal, and at this time there is an abnormality in the wind power fluctuation; if the sum of the first wind power interference factor and the second wind power interference factor is less than 1, the wind power output simulation power value is normal, and at this time there is no abnormality in the wind power fluctuation.

[0023] The technical effects and advantages of the intelligent fusion terminal test system for distribution network of the present invention are as follows: the present invention obtains the wind power output parameters and wind force fluctuation parameters output by wind turbines in real time during the monitoring period, and comprehensively reflects the wind power output characteristics by integrating multi-source data such as wind speed, wind direction, and output power, thereby avoiding the one-sidedness of information caused by a single data source and improving the accuracy of decision-making; the wind speed data stream and wind direction data stream in the wind power output parameters are obtained, and a wind power interference factor calculation model is constructed based on the wind speed data stream and the wind direction data stream to calculate the wind power interference factor; a hierarchical model is constructed by extracting the wind power output parameters and the wind force fluctuation parameters, and a wind power output model is generated. The simulated power value is used to build a hierarchical model using the basic output, periodic fluctuations and intermittent fault characteristics of wind power output, and the intermittent characteristics of wind power output are deeply explored to provide a basis for the control strategy; through real-time data collection and analysis, abnormal wind power fluctuations are discovered in time, communication delays and decision lags are reduced, and the response speed and reliability of the system are improved; based on multi-source information and intermittent characteristics of wind power, more refined control strategies are formulated to enhance the control capabilities of the system and ensure the stable operation of the distribution network; through comprehensive analysis of multi-source data, the simplicity of rule judgment is reduced, the risk of misjudgment and malfunction caused by rapid changes in wind power output is reduced, and the safety of the system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the cloud-edge collaborative architecture of the existing distribution network provided by the present invention;

[0025] Figure 2 A schematic diagram of the structure of a distribution network intelligent fusion terminal test system provided by the present invention;

[0026] Figure 3 A schematic diagram of a flow chart of the wind power fluctuation simulation module provided by the present invention for judging whether the wind power fluctuation is normal. DETAILED DESCRIPTION

[0027] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Figure 2 The present invention provides a schematic diagram of the structure of a distribution network intelligent fusion terminal test system. Figure 2 As shown, a distribution network intelligent fusion terminal test system includes a multi-source data fusion module, a wind power interference analysis module and a wind power fluctuation simulation module, the multi-source data fusion module is connected to the wind power interference analysis module and the wind power fluctuation simulation module respectively, and the wind power interference analysis module is connected to the wind power fluctuation simulation module;

[0029] The multi-source data fusion module is used to obtain the wind power output parameters and wind power fluctuation parameters of the wind turbines in real time during the monitoring period; the wind power output parameters include wind speed data stream, wind direction data stream, output power data stream and rated power; the wind power fluctuation parameters include wind speed fluctuation amplitude and wind speed fluctuation frequency;

[0030] Among them, the wind speed data stream, wind speed fluctuation amplitude and wind speed fluctuation frequency are obtained through real-time monitoring of the wind speed sensor, and the wind direction data stream is obtained through real-time monitoring of the wind direction sensor; the output power data stream is obtained through real-time monitoring of the wind turbine internal monitoring system (SCADA);

[0031] The wind power interference analysis module is used to obtain the wind speed data stream and wind direction data stream in the wind power output parameters, and build a wind power interference factor calculation model based on the wind speed data stream and wind direction data stream to calculate the wind power interference factor;

[0032] The wind power fluctuation simulation module is used to extract wind power output parameters and wind power fluctuation parameters, combine basic output, periodic fluctuations and intermittent fault characteristics, build a hierarchical model, generate wind power output simulation power values, and determine whether the wind power fluctuation is normal.

[0033] Real-time acquisition of wind speed, wind direction, output power and rated power wind power output parameters, while capturing wind speed fluctuation amplitude and frequency wind power fluctuation parameters, solves the problem of single data source in existing technologies, provides complete, real-time, multi-dimensional data support, and improves the accuracy of subsequent analysis; constructs a wind power interference factor calculation model, quantitatively analyzes the impact of wind speed and wind direction on wind power output, outputs interference factors, and is used to evaluate the root causes of wind power fluctuations, implement root cause analysis of wind power fluctuations, and help identify interference sources of power fluctuations; combines basic output, periodic fluctuations, and intermittent fault characteristics to build a hierarchical model, generate wind power output simulation power values, simulate normal fluctuations and abnormal fluctuations (such as intermittent faults) in wind farm operation, reproduce real wind power output scenarios, and provide wind farms with Grid-connected testing and control optimization provide real power fluctuation scenarios. The automated diagnosis function reduces manual intervention, improves system operation efficiency, provides abnormal wind power fluctuation warnings, provides a time window for rapid intervention and control, reduces the risk of misjudgment and missed judgment, and improves the reliability of protection strategies. Through multi-module data sharing and interaction, the dynamic impact of wind farm fluctuations on the voltage, frequency and power flow distribution of the distribution network is analyzed, the coordination between wind farms and distribution networks is improved, and the impact of wind power fluctuations on grid operation is reduced. Based on the results of interference analysis and fluctuation simulation, the system can adjust protection thresholds, control strategies and operating modes to adapt to a variety of wind power fluctuation scenarios, make up for the problem of lagging control capabilities in existing technologies, achieve real-time response to wind power fluctuations, and reduce the negative impact on grid stability.

[0034] Specifically, the wind power interference analysis module includes a first wind power interference analysis unit and a second wind power interference analysis unit;

[0035] The first wind power interference analysis unit is used to construct a first wind power interference factor calculation model according to the wind speed data stream to obtain a first wind power interference factor, extract the wind speed data stream within the monitoring period, and arrange them in descending order to obtain a first sequence (v cut_in ,v2,…,v j ,…v cut_out ), where v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v2 is the second wind speed in numerical value, and v j is the jth wind speed in numerical order, and the formula for the first wind power interference factor calculation model is:

[0036]

[0037] Where: μ1 is the first wind power interference factor, m is the number of values ​​in the first sequence, v j is the jth wind speed in numerical order, v cut_outis the maximum wind speed when the fan unit stops running, v cut_in The minimum wind speed when the wind turbine starts to generate electricity;

[0038] The second wind power interference analysis unit is used to construct a second wind power interference factor calculation model according to the wind direction data stream to obtain a second wind power interference factor, extract the wind direction data stream within the monitoring period, and arrange them in descending order to obtain a second sequence (θ cut_in ,θ2,…,θ h ,…θ cut_out ), where θ cut_in is the minimum wind direction data when the wind turbine starts generating electricity, θ cut_out is the maximum wind direction data when the fan unit stops running, θ2 is the second wind direction data in numerical value ranking, θ h is the wind direction data ranked by the hth value, and the formula for the second wind power interference factor calculation model is:

[0039]

[0040] Where: μ2 is the second wind power interference factor, H is the number of values ​​in the second sequence, θ h is the wind direction data with the hth numerical value, θ cut_out is the maximum wind direction data when the fan unit stops running, θ cut_in It is the minimum wind direction data when the wind turbine starts to generate electricity.

[0041] The first wind power interference analysis unit and the second wind power interference analysis unit conduct quantitative analysis on the interference factors of wind speed and wind direction, which provides important support for the identification of the causes of wind power fluctuations, regulation optimization and prediction; the first wind power interference analysis unit and the second wind power interference analysis unit respectively construct interference factor calculation models through wind speed data streams and wind direction data streams to quantify the interference effects of wind speed and wind direction on wind power output; by sorting wind speed data and calculating interference factors, the distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are reflected, and the potential impact of wind speed changes on wind power output is quantitatively described; by the size of the interference factor, it is quickly determined whether the wind speed fluctuates significantly within a certain monitoring period, thereby identifying the time period or area with high interference; by sorting wind direction data and calculating interference factors, the wind speed ... sorting wind direction data and calculating interference factors, it is quickly determined whether the wind speed fluctuates significantly within a certain monitoring period, thereby identifying the time period or area with high interference; by sorting wind direction data and calculating interference factors, the wind speed distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are reflected, and the wind speed distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are reflected, and the wind speed distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are reflected, and the wind speed distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are analyzed, and the wind speed distribution characteristics of wind speed in different intervals (from minimum wind speed to maximum wind speed) are analyzed The wind direction can be quantified within a specified interval (from the minimum wind direction to the maximum wind direction) to directly reflect the impact of wind direction changes on the power generation efficiency of wind turbines. The numerical value of the second wind power interference factor can help identify time periods with large wind direction deviations, thereby adjusting the wind turbine's windward angle to maximize power generation efficiency. Combining the wind speed interference factor with the second wind power interference factor for analysis can identify the combined impact of wind speed and wind direction fluctuations on wind power output, provide multi-dimensional interference input for the wind power fluctuation simulation module, and improve the accuracy and authenticity of power output simulation. Through real-time calculation of wind speed and the second wind power interference factor, early warning can be given when wind power fluctuations are severe, providing a decision-making basis for the regulation of the distribution network. Combined with the analysis results of the interference factor, the charging and discharging strategy of the energy storage system can be adjusted to smooth the impact of wind power fluctuations on the distribution network.

[0042] Specifically, the wind power fluctuation simulation module includes a basic wind power output analysis unit, a wind power cycle fluctuation analysis unit, an intermittent fault simulation unit, a fluctuation power synthesis unit and a fluctuation abnormality judgment unit; the basic wind power output analysis unit is connected to the wind power cycle fluctuation analysis unit, the wind power cycle fluctuation analysis unit is connected to the intermittent fault simulation unit, the intermittent fault simulation unit is connected to the fluctuation power synthesis unit, and the fluctuation power synthesis unit is connected to the fluctuation abnormality judgment unit.

[0043] The basic wind power output analysis unit is used to obtain the wind speed data stream, output power data stream and rated power in the wind power output parameters during the monitoring period, and obtain the rated wind speed of the wind turbine to reach the rated power. Based on the wind speed data stream, the minimum wind speed when the wind turbine starts to generate electricity and the maximum wind speed when the wind turbine stops running are extracted. According to the rated wind speed, minimum wind speed and maximum wind speed, a basic wind power output model is constructed to calculate the basic output power. The formula of the basic wind power output model is:

[0044]

[0045] Where: P base (v t) is the wind speed v t The corresponding basic output power, P rated is the rated power, v t is the wind speed corresponding to time t, v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v rated The rated wind speed at which the wind turbine reaches rated power;

[0046] The wind power fluctuation simulation module accurately simulates the power fluctuation characteristics of wind farms under various working conditions through the collaboration of various units, providing support for distribution network intelligent terminal testing, wind power dispatch optimization and grid stability analysis; extracts wind power output parameters (such as wind speed, output power and rated power), and builds a basic wind power output model based on the wind speed range to simulate the basic power output of wind farms under different wind speed conditions. It can adapt to different wind speed conditions (low wind speed, medium wind speed and high wind speed), from zero power output to rated power.

[0047] The wind power periodic fluctuation analysis unit is used to obtain the wind speed fluctuation amplitude and wind speed fluctuation frequency in the wind power fluctuation parameters, and to construct a periodic fluctuation model to calculate the wind power periodic fluctuation power. The formula of the periodic fluctuation model is:

[0048] P cycle (t) = A cycle ·sin(2πf cycle t+φ);

[0049] Where: P cycle (t) is the periodic fluctuation power of wind power at time t, A cycle is the periodic wind speed fluctuation amplitude, f cycle is the wind speed fluctuation frequency, t is the time t, where, φ is the initial phase of the wave, P cycle (t0) is the periodic fluctuation power of wind power at the initial time t0, which is calculated by fitting based on historical data;

[0050] The intermittent fault simulation unit is used to obtain the basic output power. An intermittent fault power analysis model is constructed according to the basic output power to obtain the intermittent fault power. The formula of the intermittent fault power analysis model is:

[0051] P outage (t) = P base (v t )·H(tt outage );

[0052] Where: P outage (t) is the intermittent fault power at time t, P base (v t) is the wind speed v t The corresponding basic output power, t outage is the time when the fault occurs, H(tt outage ) is a fault indication function, which is used to control the intermittent fault to take effect at the time of fault occurrence, before which the output power is in a normal state;

[0053] By modeling the characteristics of periodic fluctuations and intermittent faults, it helps to simulate the complex power fluctuation characteristics in the actual wind farm operating environment, and provide support for wind power regulation, fault diagnosis and grid coordination; obtain the amplitude and frequency of wind speed fluctuations, and build a periodic fluctuation model, which can truly reproduce the periodic changes in wind farm power and reflect the operating characteristics of the wind farm under stable wind speed fluctuation conditions; improve the simulation accuracy of wind power periodic fluctuations by fitting historical data and dynamically adjusting model parameters; use fault indication functions to simulate sudden faults, dynamically adjust wind power output, build an intermittent fault power analysis model, and calculate the power output changes at the time of the fault; simulate intermittent faults in wind farms The instantaneous impact of abnormalities (such as wind turbine shutdown and grid abnormality) on power output can be used to provide real data for grid control and protection testing. The impact of faults on grid power balance is calculated based on the change in output power to support the evaluation of fault tolerance. The charging and discharging strategies of energy storage equipment can be adjusted according to intermittent fault periods and power changes to smooth the impact on the grid. The periodic fluctuation model and the intermittent fault model are combined to cover multi-dimensional scenarios of normal fluctuations and abnormal fluctuations, and the characteristics of periodic fluctuations and intermittent faults are comprehensively considered to provide data support for optimizing control strategies, thereby improving the accuracy and practicality of wind power fluctuation simulation and providing important support for wind farm operation management and grid control.

[0054] The fluctuating power synthesis unit is used to obtain the first wind power interference factor, the second wind power interference factor, the basic output power, the wind power periodic fluctuation power and the intermittent fault power, and superimpose them to obtain the final wind power output simulation power value:

[0055] P wind (t) = |1-μ1-μ2|×(P outage (t)+P base (v t )+P ctcle (t));

[0056] Where: P wind (t) is the final wind power output simulation power value, μ1 is the first wind power interference factor, μ2 is the second wind power interference factor, P outage (t) is the intermittent fault power at time t, P base (v t ) is the wind speed v t The corresponding basic output power, P cycle(t) is the periodic fluctuation power of wind power at time t;

[0057] The abnormal fluctuation judgment unit is used to extract the first wind power interference factor and the second wind power interference factor, and evaluate whether the wind power output simulation power value is normal based on the first wind power interference factor and the second wind power interference factor. If the sum of the first wind power interference factor and the second wind power interference factor is greater than or equal to 1, the wind power output simulation power value is abnormal, and the wind power fluctuation is abnormal at this time; if the sum of the first wind power interference factor and the second wind power interference factor is less than 1, the wind power output simulation power value is normal, and the wind power fluctuation is not abnormal at this time.

[0058] Through multi-dimensional power superposition and abnormality identification, the wind power output characteristics are fully simulated and monitored to provide support for wind farm operation optimization and distribution network coordination; by superimposing basic output power, periodic fluctuation power, intermittent fault power, and combining the first wind power interference factor and the second wind power interference factor, a comprehensive wind power output simulation power value is generated; based on the first wind power interference factor and the second wind power interference factor, it is judged in real time whether the wind power fluctuation is normal to ensure the rapid identification of abnormal fluctuations. Through the analysis results of the interference factors, the main sources of abnormal fluctuations (wind speed interference or wind direction interference) can be quickly identified. Through real-time abnormality identification, abnormal fluctuations can be avoided from causing serious impacts on the operation of wind turbines or distribution networks; provide real and dynamic wind power data for the protection strategy test of the intelligent fusion terminal, and optimize the protection logic through the abnormal identification results. Based on the dynamic adjustment of the interference factors, the protection strategy is optimized to improve the robustness of the intelligent terminal in complex fluctuation scenarios.

[0059] The embodiment of the present invention obtains the wind power output parameters and wind force fluctuation parameters output by the wind turbine group in real time during the monitoring period, and comprehensively reflects the wind power output characteristics by integrating multi-source data such as wind speed, wind direction, and output power, thereby avoiding the one-sidedness of information caused by a single data source and improving the accuracy of decision-making; obtains the wind speed data stream and wind direction data stream in the wind power output parameters, and constructs a wind power interference factor calculation model based on the wind speed data stream and the wind direction data stream to calculate the wind power interference factor; extracts the wind power output parameters and wind force fluctuation parameters, constructs a hierarchical model, generates a wind power output simulation power value, and uses the wind power output Based on the basic output, periodic fluctuations and intermittent fault characteristics, a hierarchical model is constructed to deeply explore the intermittent characteristics of wind power output and provide a basis for the control strategy; through real-time data collection and analysis, abnormal wind power fluctuations are discovered in time, communication delays and decision lags are reduced, and the response speed and reliability of the system are improved; based on multi-source information and intermittent characteristics of wind power, more refined control strategies are formulated to enhance the control capabilities of the system and ensure the stable operation of the distribution network; through comprehensive analysis of multi-source data, the simplicity of rule judgment is reduced, the risk of misjudgment and malfunction caused by rapid changes in wind power output is reduced, and the safety of the system is improved.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0061] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distribution network intelligent fusion terminal test system, comprising a multi-source data fusion module, a wind power interference analysis module and a wind power fluctuation simulation module, characterized in that: The multi-source data fusion module is used to obtain the wind power output parameters and wind power fluctuation parameters of the wind turbines in real time during the monitoring period; the wind power output parameters include wind speed data stream, wind direction data stream, output power data stream and rated power; Wind power fluctuation parameters include wind speed fluctuation amplitude and wind speed fluctuation frequency; the wind power interference analysis module is used to obtain the wind speed data stream and wind direction data stream in the wind power output parameters, and build a wind power interference factor calculation model based on the wind speed data stream and wind direction data stream to calculate the wind power interference factor; The wind power fluctuation simulation module is used to extract wind power output parameters and wind power fluctuation parameters, combine basic output, periodic fluctuation and intermittent fault characteristics, build a hierarchical model, generate wind power output simulation power value, and judge whether the wind power fluctuation is normal; The wind power fluctuation simulation module includes a basic wind power output analysis unit; the basic wind power output analysis unit is used to obtain the wind speed data stream, output power data stream and rated power in the wind power output parameters during the monitoring period, and obtain the rated wind speed of the wind turbine to reach the rated power. Based on the wind speed data stream, the minimum wind speed when the wind turbine starts to generate electricity and the maximum wind speed when the wind turbine stops running are extracted. According to the rated wind speed, minimum wind speed and maximum wind speed, a basic wind power output model is constructed to calculate the basic output power. The formula of the basic wind power output model is: Where: P base (v t ) is the wind speed v t The corresponding basic output power, P rated is the rated power, v t is the wind speed corresponding to time t, v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v rated It is the rated wind speed at which the fan unit reaches rated power.

2. The distribution network intelligent fusion terminal test system according to claim 1 is characterized in that: The wind power interference analysis module includes a first wind power interference analysis unit; the first wind power interference analysis unit is used to construct a first wind power interference factor calculation model according to the wind speed data stream to obtain the first wind power interference factor, extract the wind speed data stream within the monitoring period, and arrange it in descending order to obtain a first sequence (v cut_in , v2,…,v j ,…v cut_out ), where v cut_in is the minimum wind speed when the wind turbine starts generating electricity, v cut_out is the maximum wind speed when the fan unit stops running, v2 is the second wind speed in numerical value, and v j is the jth wind speed in numerical order, and the formula for the first wind power interference factor calculation model is: Where: μ1 is the first wind power interference factor, m is the number of values ​​in the first sequence, v j is the jth wind speed in numerical order, v cut_out is the maximum wind speed when the fan unit stops running, v cut_in It is the minimum wind speed when the wind turbine starts to generate electricity.

3. The distribution network intelligent fusion terminal test system according to claim 2 is characterized in that: The wind power interference analysis module also includes a second wind power interference analysis unit; the second wind power interference analysis unit is used to construct a second wind power interference factor calculation model according to the wind direction data stream to obtain a second wind power interference factor, extract the wind direction data stream within the monitoring period, and arrange them in descending order to obtain a second sequence (θ cut_in ,θ2,…,θ h ,…θ cut_out ), where θ cut_in is the minimum wind direction data when the wind turbine starts generating electricity, θ cut_out is the maximum wind direction data when the fan unit stops running, θ2 is the second wind direction data in numerical value ranking, θ h is the wind direction data ranked by the hth value, and the formula for the second wind power interference factor calculation model is: Where: μ2 is the second wind power interference factor, H is the number of values ​​in the second sequence, θ h is the wind direction data with the hth numerical value, θ cut_out is the maximum wind direction data when the fan unit stops running, θ cut_in It is the minimum wind direction data when the wind turbine starts to generate electricity.

4. The distribution network intelligent fusion terminal test system according to claim 1, characterized in that: The wind power fluctuation simulation module also includes a wind power periodic fluctuation analysis unit, an intermittent fault simulation unit, a fluctuation power synthesis unit and a fluctuation abnormality judgment unit; the wind power periodic fluctuation analysis unit is used to obtain the wind speed fluctuation amplitude and wind speed fluctuation frequency in the wind power fluctuation parameters, and to construct a periodic fluctuation model to calculate the wind power periodic fluctuation power; the intermittent fault simulation unit is used to obtain the basic output power, and to construct an intermittent fault power analysis model based on the basic output power to obtain the intermittent fault power. The formula of the intermittent fault power analysis model is: P outage (t)=P base (v t )·H(t-t outage ); Where: P outage (t) is the intermittent fault power at time t, P base (v t ) is the wind speed v t The corresponding basic output power, t outage is the time when the fault occurs, H(tt outage ) is the fault indication function.

5. The distribution network intelligent fusion terminal test system according to claim 4 is characterized in that: The fluctuating power synthesis unit is used to obtain the first wind power interference factor, the second wind power interference factor, the basic output power, the wind power periodic fluctuation power and the intermittent fault power, and superimpose them to obtain the final wind power output simulation power value: P wind (t)=|1-μ1-μ2|×(P outage (t)+P base (v t )+P cycle (t)); Where: P wind (t) is the final wind power output simulation power value, μ1 is the first wind power interference factor, μ2 is the second wind power interference factor, P outage (t) is the intermittent fault power at time t, P base (v t ) is the wind speed v t The corresponding basic output power, P cycle (t) is the periodic fluctuation power of wind power at time t.

6. The distribution network intelligent fusion terminal test system according to claim 4, characterized in that: The fluctuation abnormality discrimination unit is used to extract the first wind power interference factor and the second wind power interference factor, and evaluate whether the wind power output simulation power value is normal based on the first wind power interference factor and the second wind power interference factor. If the sum of the first wind power interference factor and the second wind power interference factor is greater than or equal to 1, the wind power output simulation power value is abnormal, and at this time, the wind power fluctuation is abnormal; If the sum of the first wind power interference factor and the second wind power interference factor is less than 1, the wind power output simulation power value is normal, and the wind power fluctuation is not abnormal.

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