Multi-dimensional instability dominant factor detection method and system based on cyclic DFT (Discrete Fourier Transform) method

Abstract

The invention discloses a multi-dimensional instability dominant factor detection method and system based on a cyclic DFT method. The method comprises the steps of obtaining a detection data set; determining a static voltage stability margin, a static limit current and a static limit power angle of the power distribution network; based on a single-machine control mode, a power distribution network voltage control state parameter is used as an optimization variable, and a static limit current and a static limit power angle are used as control variables; sequentially determining a coupling model of a multi-dimensional instability dominant factor of the power distribution network, a distribution function of the influence of the instability factor on the stability of the power distribution network, a self-adaptive optimization model of partition scheduling of the power distribution network and a historical data distribution function of the voltage of the power distribution network, and correcting broadband measurement data in a detection data set; obtaining steady-state characteristic quantities in different broadband oscillation modes; when the variable quantity of the steady-state characteristic quantity in any broadband oscillation mode is larger than the static voltage stability margin of the power distribution network, it is judged that the broadband oscillation mode is unstable, the steady-state characteristic quantity in the broadband oscillation mode serves as a detection result, and decision support is provided for power grid dispatching.

Description

Technical Field

[0001] The present invention belongs to the field of power system control, and in particular, relates to a multi-dimensional instability dominant factor detection method and system based on a cyclic DFT method that is suitable for a high-proportion renewable energy power system. Background Art

[0002] In recent years, the application of renewable energy and power electronic equipment in power grids has made great progress. The power system is developing towards "high proportion of renewable energy" and "high proportion of power electronic equipment", which has become an important technical feature of the new power system. Since the stability analysis of the traditional power system is derived from the characteristics of the synchronous machine, it is difficult to adapt to the rapid development of "double high", and the "double high" system often has broadband oscillations, which leads to reduced system stability and decreased voltage quality at weak nodes. To solve the above situation, a grid-type new energy + energy storage construction scheme is proposed to improve the strength and stability of the power grid, which can change the virtual inertia provision mode from passive control to autonomous support. To realize this scheme, multi-dimensional instability dominant factor analysis technology and broadband oscillation mode real-time extraction technology are proposed. From the operating principle, the former can comprehensively consider a variety of main factors that can cause the instability of the power system to dispatch electric energy to achieve rapid balance of electric energy to improve system stability, and the latter can accurately analyze the modal frequency and realize broadband monitoring function.

[0003] In the prior art, the resolution of traditional frequency analysis is closely related to the selected length of the data window. The longer the selected length of the data window, the higher the resolution. However, a data window that is too long requires a large amount of data calculation, which is not suitable for implementation on the device. At the same time, the analysis of frequency and amplitude has a large lag, which is not suitable for real-time online dynamic calculation. The power system is a complex whole. The dominant factors of instability are power angle and voltage. It is generally difficult for us to judge the order of occurrence of voltage stability and power angle stability. It may be that only one instability state occurs, or it may be that voltage stability and power angle stability appear in an unstable state at the same time. For the discrimination of power angle instability, the research on power system is relatively mature now, and many discrimination methods have been proposed; however, there are few studies on the discrimination of voltage instability at this stage, especially the comprehensive discrimination of voltage instability and power angle instability. There are also many deviations in the results of studying voltage stability and power angle stability individually. There is little research on the discrimination of the two at home and abroad. There is no standard that can comprehensively analyze and discriminate voltage stability and power angle stability, and there is no unified statement on the relationship between the mutual influence of voltage stability and power angle stability. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a multi-dimensional instability dominant factor detection method and system based on the cyclic DFT method. By analyzing the weak nodes, the impact of renewable resources on the static voltage stability of the distribution network, and the characteristics of renewable resources, a scheduling set for active distribution network voltage control is constructed. Voltage stability is achieved through multi-party decision-making, so that electric energy can be balanced faster, and the stability and strength of the power grid are improved. In addition, the system adopts a cyclic discrete Fourier transform (DFT) correction algorithm based on a sliding data window, which can effectively avoid the influence of frequency leakage under the premise of accurately analyzing the modal frequency, and avoid the lag problem caused by a large amount of data calculation. The perception function of the power grid is improved, the risk of oscillation is prevented and accurate early warning is given, and decision support is provided for power grid dispatching.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a multi-dimensional instability dominant factor detection method based on a cyclic DFT method, comprising:

[0007] A sliding data window is used to obtain broadband measurement data, and a cyclic discrete Fourier transform method is used to determine the broadband oscillation modal phasor of the broadband measurement data in the sliding data window at the current moment; based on the broadband oscillation modal phasor obtained in the sliding data window at the current moment, the broadband oscillation modal phasor in the sliding data window at the next moment is predicted; if broadband oscillation is determined to occur according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set;

[0008] According to the calculation results of the power flow of the distribution network system, the static voltage stability indexes of the weak nodes and the new energy grid-connected points in the distribution network are determined, and the minimum value of each static voltage stability index is used as the static voltage stability margin of the distribution network; the curves of the active power and reactive current components output by the AC side of the grid-connected converter and the converter power angle are drawn respectively; the extreme value points on the curves of the active power and reactive current components are extracted as the static limiting current, and the extreme value points on the curves of the active power and converter power angle are extracted as the static limiting power angle;

[0009] Based on the single-machine control mode, the voltage control state parameters of the distribution network are used as optimization variables, and the static limit current and static limit power angle are used as control variables to establish a coupling model of the dominant factors of multi-dimensional instability of the distribution network; based on the coupling model of the dominant factors of multi-dimensional instability of the distribution network, the distribution function of the influence of the instability factors on the stability of the distribution network is obtained; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, an adaptive optimization model for the zoning dispatch of the distribution network is established; based on the adaptive optimization model for the zoning dispatch of the distribution network, the historical data distribution function of the distribution network voltage is obtained by using the closed-loop control feature set of the output current of the grid-connected converter in each dispatching zone and the voltage control state parameters of the distribution network in each dispatching zone;

[0010] The broadband measurement data in the detection data set are corrected based on the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching zone, and the steady-state characteristic quantities of the distribution network under different broadband oscillation modes are obtained; when the change of the steady-state characteristic quantities under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability due to the broadband oscillation mode, and the steady-state characteristic quantities under the broadband oscillation mode are used as the detection results of the dominant factors of multi-dimensional instability.

[0011] Preferably, the model of the cyclic discrete Fourier transform method satisfies the following relationship:

[0012]

[0013] In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

[0014] Preferably, if it is determined that broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set;

[0015] If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

[0016] Preferably, the new energy grid connection point includes a PQ type node and a PV type node; wherein the active power and reactive power of the PQ type node are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of the PV type node are both constant values;

[0017] According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship:

[0018]

[0019] Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ij are the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

[0020] Preferably, the minimum value of the static voltage stability index of each node is used as the static voltage stability margin of the distribution network, satisfying the following relationship:

[0021]

[0022] In the formula, It is the static voltage stability margin of the distribution network.

[0023] Preferably, respectively drawing the curves of active power and reactive current component outputted by the AC side of the grid-connected converter and the curve of the converter power angle includes:

[0024] Based on the fundamental frequency equivalent circuit of the AC side of the grid-connected converter in the dq axis rotating coordinates, the active power output of the AC side of the grid-connected converter satisfies the following relationship:

[0025] P out =1.5(V od I Q +V oq I P )

[0026] Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq are the d-axis component and q-axis component of the grid-connected point voltage, I Q is the reactive current component, I P is the active current component;

[0027] When the output power of the grid-connected converter is maximum, the active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship:

[0028]

[0029] Where V g is the grid voltage amplitude, x g is the equivalent reactance between the grid connection point and the grid, δ is the converter power angle;

[0030] Based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively. out -I Q , the curve P of active power and converter power angle out -δ.

[0031] Preferably, the coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship:

[0032]

[0033] Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

[0034] Preferably, the distribution function of the impact of the instability factor on the stability of the distribution network satisfies the following relationship:

[0035] k l =M+(b i +s(e))

[0036] In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

[0037] Preferably, the adaptive optimization model for distribution network partition scheduling satisfies the following relationship:

[0038]

[0039] Where r(w) is the adaptive optimization function of partition scheduling, and Φ is the closed-loop control feature set of the output current of the grid-connected converter.

[0040] Preferably, the historical data distribution function of the distribution network voltage satisfies the following relationship:

[0041]

[0042] Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ wis the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

[0043] Preferably, the steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship:

[0044]

[0045] Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

[0046] Preferably, the steady-state characteristic quantities of the distribution network under different broadband oscillation modes include but are not limited to the voltage amplitude and phase under each broadband oscillation mode obtained by broadband measurement, the closed-loop control characteristics of the output current of the grid-connected converter, and the historical data of the distribution network voltage.

[0047] The present invention also proposes a multi-dimensional instability dominant factor detection system based on a cyclic DFT method, comprising:

[0048] A data processing module is used to obtain broadband measurement data by using a sliding data window, and to determine the broadband oscillation modal phasor of the broadband measurement data in the sliding data window at the current moment by using a cyclic discrete Fourier transform method; based on the broadband oscillation modal phasor obtained in the sliding data window at the current moment, predict the broadband oscillation modal phasor in the sliding data window at the next moment; if broadband oscillation is determined to occur according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, then record the broadband measurement data in the sliding data window at the current moment as a detection data set;

[0049] The index establishment module is used to determine the static voltage stability index of the weak nodes and the new energy grid connection points in the distribution network according to the power flow calculation results of the distribution network system, and take the minimum value of each static voltage stability index as the static voltage stability margin of the distribution network; respectively draw the curve of the active power and reactive current component output on the AC side of the grid-connected converter and the curve of the converter power angle; extract the extreme value point on the curve of the active power and reactive current component as the static limiting current, and extract the extreme value point on the curve of the active power and converter power angle as the static limiting power angle;

[0050] The instability dominant factor detection module is used to establish a coupling model of the multi-dimensional instability dominant factors of the distribution network based on the single-machine control mode, with the distribution network voltage control state parameters as the optimization variables, and the static limit current and the static limit power angle as the control variables; based on the coupling model of the multi-dimensional instability dominant factors of the distribution network, the distribution function of the influence of the instability factors on the stability of the distribution network is obtained; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, an adaptive optimization model for the distribution network partition scheduling is established; based on the adaptive optimization model for the distribution network partition scheduling, the grid-connected converters in each scheduling partition are used to select the optimal control parameters of the distribution network. The closed-loop control feature set of the output current and the distribution network voltage control state parameters of each dispatching zone are used to obtain the historical data distribution function of the distribution network voltage; the broadband measurement data in the detection data set are corrected by the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching zone to obtain the steady-state characteristic quantities of the distribution network under different broadband oscillation modes; when the change of the steady-state characteristic quantity under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability in the broadband oscillation mode, and the steady-state characteristic quantity under the broadband oscillation mode is used as the detection result of the multi-dimensional instability dominant factor.

[0051] The model of the cyclic discrete Fourier transform method satisfies the following relationship:

[0052]

[0053] In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

[0054] If it is determined that broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set;

[0055] If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

[0056] New energy grid-connected points include PQ type nodes and PV type nodes; among them, the active power and reactive power of PQ type nodes are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of PV type nodes are both constant values;

[0057] According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship:

[0058]

[0059] Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ij are the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

[0060] The minimum value of the static voltage stability index of each node is taken as the static voltage stability margin of the distribution network, satisfying the following relationship:

[0061]

[0062] In the formula, It is the static voltage stability margin of the distribution network.

[0063] Draw the curves of active power and reactive current components output by the AC side of the grid-connected converter and the converter power angle, including:

[0064] Based on the fundamental frequency equivalent circuit of the AC side of the grid-connected converter in the dq axis rotating coordinates, the active power output of the AC side of the grid-connected converter satisfies the following relationship:

[0065] P out =1.5(V od I Q +V oq I P )

[0066] Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq are the d-axis component and q-axis component of the grid-connected point voltage, I Q is the reactive current component, I P is the active current component;

[0067] When the output power of the grid-connected converter is maximum, the active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship:

[0068]

[0069] Where V g is the grid voltage amplitude, x g is the equivalent reactance between the grid connection point and the grid, δ is the converter power angle;

[0070] Based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively. out -I Q , the curve P of active power and converter power angle out -δ.

[0071] The coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship:

[0072]

[0073] Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

[0074] The distribution function of the impact of instability factors on the stability of the distribution network satisfies the following relationship:

[0075] k l =M+(b i +s(e))

[0076] In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

[0077] The adaptive optimization model for distribution network zoning dispatch satisfies the following relationship:

[0078]

[0079] Where r(w) is the adaptive optimization function of partition scheduling, and Φ is the closed-loop control feature set of the output current of the grid-connected converter.

[0080] The historical data distribution function of the distribution network voltage satisfies the following relationship:

[0081]

[0082] Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ w is the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

[0083] The steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship:

[0084]

[0085] Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

[0086] The steady-state characteristic quantities of the distribution network under different broadband oscillation modes include but are not limited to the voltage amplitude and phase under each broadband oscillation mode obtained by broadband measurement, the closed-loop control characteristics of the grid-connected converter output current, and the historical data of the distribution network voltage.

[0087] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0088] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0089] The beneficial effect of the present invention is that, compared with the prior art, at least the method proposed by the present invention is adapted to the multi-dimensional support decision-making system of the high-proportion renewable energy power system, and the scheduling set of active distribution network voltage control is constructed by analyzing the weak nodes, the impact of renewable resources on the static voltage stability of the distribution network, and the characteristics of renewable resources. Voltage stability is achieved through multi-party decision-making, so that electric energy can be balanced faster, and the stability and strength of the power grid are improved. In addition, the system adopts a cyclic DFT correction algorithm based on a sliding data window, which can effectively avoid the influence of frequency leakage under the premise of accurately analyzing the modal frequency, and avoids the lag problem caused by large amounts of data calculation. The grid perception function is improved, oscillation risks are prevented and accurate warnings are given, providing decision support for grid dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 The present invention proposes a flow chart of a method for analyzing the multi-dimensional instability dominant factors of a high-proportion renewable energy power system. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0092] The present invention proposes a multi-dimensional instability dominant factor detection method based on the cyclic DFT method, which can be adapted to the multi-dimensional instability dominant factor support decision-making of a high proportion of renewable energy power system, such as Figure 1 As shown, including:

[0093] Step 1, using a sliding data window to obtain broadband measurement data; using a cyclic discrete Fourier transform method to determine the broadband oscillation modal phasor of the broadband measurement data in the sliding data window; based on the broadband oscillation modal phasor obtained in the sliding data window at the current moment, predict the broadband oscillation modal phasor in the sliding data window at the next moment; if broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, then record the broadband measurement data in the sliding data window at the current moment as a detection data set.

[0094] The full name of broadband measurement is broadband multi-frequency signal measurement, which means to achieve unified measurement of signals in a wide frequency domain or wide bandwidth. At present, broadband measurement has been preliminarily defined in relevant standards: based on high-speed sampling of no less than 12.8kHz, unified measurement of fundamental, harmonic and interharmonic signals in the range of 0 to 2500Hz in the power grid is achieved.

[0095] In a non-limiting preferred embodiment, a cyclic DFT method based on a sliding data window is used to perform broadband monitoring on the system to obtain broadband measurement data. The traditional extraction method processes a large amount of data, is not suitable for implementation on the device, and has a large lag in the analysis of frequency and amplitude, and cannot meet the requirements of real-time online dynamic operations. The cyclic DFT method based on the sliding data window can analyze very accurate modal frequencies while retaining the transient change trend information of the amplitude. The cyclic DFT method is used to calculate the modal frequency and modal phasor (including amplitude and phase) for fixed-interval sampling data, which can effectively avoid the influence of frequency leakage.

[0096] In the embodiment, the broadband measurement data is stored in an array, a sliding data window is used to slide on the array, and the broadband measurement data in the sliding data window is obtained during the sliding of the window. The broadband measurement data is transformed from the time domain to the frequency domain using a cyclic DFT method to obtain a broadband oscillation mode; wherein the model of the cyclic DFT method satisfies the following relationship:

[0097]

[0098] In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

[0099] Based on the model of the cyclic DFT method, each point in x(n) is multiplied by cos(2π·i·n / N) and sin(2π·i·n / N) when m=i to obtain the real and imaginary parts of x(i), where i is the sampling point number. Thus, the broadband oscillation modal phasor (including amplitude and phase) is obtained.

[0100] If it is determined that broadband oscillation occurs according to the broadband oscillation modal phasor prediction value in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as the detection data set.

[0101] In the present invention, the amplitude and phase of the broadband oscillation modal phasor are predicted by the broadband measurement data collected by the sliding data window. If the predicted value meets the broadband oscillation condition of the system, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set. When the broadband oscillation is predicted to occur at the next moment, the previous broadband measurement data needs to be detected to find the dominant factor of multi-dimensional instability, so as to give an early warning of system instability.

[0102] If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

[0103] In the embodiment, a waveform diagram of the equivalent impedance of the grid-type inverter changing with the current is output through a large number of simulations and experiments. After a large number of simulations and experiments, it is found that when the "double-high" system has a broadband oscillation, the equivalent impedance of the inverter is a negative resistance capacitance. As the current increases, the equivalent impedance phase remains unchanged but the amplitude increases sharply. At this time, the power system exhibits a subsynchronous oscillation. Under the broadband oscillation characteristics of the "double-high" system, the traditional extraction method processes a large amount of data, is not suitable for implementation on the device, and has a large lag in the analysis of frequency and amplitude, and is not suitable for real-time online dynamic operations. The cyclic DFT correction algorithm based on the sliding data window can analyze very accurate modal frequencies without losing the transient change trend information of the amplitude. The DFT correction algorithm is used for the fixed-interval sampling data to calculate the modal frequency and modal phasor (i.e., amplitude and phase), which can effectively avoid the influence of frequency leakage. And through the cyclic DFT correction algorithm of the sliding data window, it is judged whether the system is in a broadband oscillation state, and if it is in this state, the multi-dimensional support decision system is started.

[0104] Through the cyclic DFT correction algorithm of the sliding data window, the accuracy of modal frequency sampling can be guaranteed while reducing the amount of data processing. It can solve the problem of large lag in frequency and amplitude analysis of traditional modal sampling and is more suitable for device implementation and real-time online dynamic calculation.

[0105] Step 2: According to the power flow calculation results of the distribution network system, the static voltage stability indicators of the weak nodes and the new energy grid connection points in the distribution network are determined, and the minimum value of each static voltage stability indicator is used as the static voltage stability margin of the distribution network.

[0106] When the node voltage exceeds the limit, voltage collapse often occurs at the weakest node, causing the power system to collapse completely. Therefore, more attention should be paid to weak nodes during power dispatch. Methods for determining weak nodes include:

[0107] The continuous power flow method is used to generate the PV curve of the node under the power grid operation scenario, and the inflection point on the PV curve is taken as the weak node; the voltage gradually decreases with the increase of active power, and the slope of the decrease is small before the voltage inflection point arrives. When the voltage crosses the inflection point, the corresponding curvature becomes larger. The nodes that are easier to reach the inflection point are weaker. The weak nodes are extracted based on this characteristic; the voltage amplitude and active power of the weak node constitute the characteristic data of the weak node, which satisfies the following relationship:

[0108] Z k =(U k ,P k )

[0109] In the formula, Z k is the characteristic data of the kth weak node, U k is the voltage amplitude of the kth weak node, P k is the active power of the kth weak node.

[0110] In general, there are only PQ nodes and balancing nodes in the power grid. When renewable energy is connected to the grid, the number and type of grid nodes will change. Since the grid connection of renewable energy will not affect the frequency regulation of the system, it can be approximately considered that the output active power is a constant value. As for the operation mode of reactive power and voltage, it needs to be analyzed according to the actual situation. New energy grid connection points include PQ type nodes and PV type nodes; among them, the active power and reactive power of PQ type nodes are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of PV type nodes are constant values.

[0111] According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship:

[0112]

[0113] Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ijare the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

[0114] The minimum value of the static voltage stability index of each node is taken as the static voltage stability margin of the distribution network, satisfying the following relationship:

[0115]

[0116] In the formula, It is the static voltage stability margin of the distribution network.

[0117] Static voltage stability index L of node j j The smaller the distance from 0, the worse the static voltage stability of node j is, and the smaller the static voltage stability margin of the distribution network is, the more unstable the distribution network voltage is.

[0118] The most developed renewable energy sources at present include solar energy, wind energy, geothermal energy, biomass energy, and ocean energy. These renewable resources are volatile and intermittent. At the same time, the distribution of renewable energy in different regions is also different. In order to incorporate the characteristics of renewable energy into the consideration of power distribution, the uncertainty of power generation should be predicted under different time scales and multi-scenario operation requirements. Among them, the time scale is divided into medium- and long-term predictions and short-term and ultra-short-term predictions. According to the method, it includes the backup setting method, stochastic optimization method, chance constrained programming method and robust optimization method. The new energy power generation power uncertainty prediction unit is used to cluster historical data to obtain multi-type clusters to describe the uncertainty of power.

[0119] In order to deal with the power uncertainty brought by renewable energy generation, the present invention adopts a method of discretization and clustering based on historical data to generate multi-type clusters to describe the power uncertainty. This method is essentially a stochastic optimization method for multiple discrete scenarios. The stochastic optimization method generates and extracts random scenarios from the probability distribution according to the uncertain variables (actual and predicted power errors of renewable energy), and then discretizes the continuous probability distribution into the form of multiple scenario sets.

[0120] In the embodiment, a random optimization method is used to obtain active power and reactive power, and to calculate apparent power; the optimized apparent power, active power and reactive power are used to calculate the static voltage stability index of the new energy grid connection point, thereby eliminating the influence of the uncertainty of new energy power generation on the calculation result of the static voltage stability index.

[0121] Step 3, respectively draw the curves of active power and reactive current components outputted on the AC side of the grid-connected converter and the curve of the converter power angle; extract the extreme points on the curves of active power and reactive current components as the static limiting current, and extract the extreme points on the curves of active power and converter power angle as the static limiting power angle.

[0122] One end of the grid-connected converter is connected to the grid, and the other end is connected to new energy or energy storage equipment. The distribution network is regarded as a single-machine infinite system, assuming that the equivalent impedance between the grid connection point and the infinite grid is R g 、x g , the phase of the grid connection point is 0°.

[0123] The grid voltage vector V o ∠0° is decomposed into dq axis. When the q-axis component of the grid-connected point voltage is zero, the output power of the grid-connected converter is maximum. At this time, the d-axis is oriented to the grid-connected point voltage vector V o ∠0°.

[0124] Based on the fundamental frequency equivalent circuit of the AC side of the renewable resource grid-connected converter in the dq axis rotating coordinates, the renewable resource grid-connected converter is equivalent to a single-machine infinite system, and the voltage amplitude at the grid connection point satisfies the following relationship:

[0125]

[0126] Where U o is the voltage amplitude at the grid connection point, V g is the grid voltage amplitude, I Q is the reactive current component, I P is the active current component.

[0127] From the base frequency equivalent circuit, it can be seen that the active power output on the AC side of the renewable resource grid-connected converter satisfies the following relationship:

[0128] P out =1.5(V od I Q +V oq I P )

[0129] Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq They are the d-axis component and q-axis component of the grid-connected point voltage respectively.

[0130] When the PLL dynamics are neglected, the d-axis component is oriented directly on the grid-connected voltage vector V o ∠0°, at this time V oq is zero, and the active power output on the AC side of the grid-connected converter satisfies the following relationship:

[0131]

[0132] Reactive current component I Q The following relationship is satisfied between the converter power angle δ:

[0133] I Q x g =V g sinδ

[0134] The active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship:

[0135]

[0136] In the above formula, the active power P output by the AC side of the grid-connected converter is out is the reactive current component I Q and the function of the converter power angle δ, based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively out -I Q , the curve P of active power and converter power angle out -δ; Extract P out -I Q The extreme point on the curve is taken as the static limiting current, and P is extracted out The extreme point on the -δ curve is taken as the static limit power angle.

[0137] In the embodiment, the static voltage stability margin of the distribution network is first determined, and then the static limiting current and the static limiting power angle are used as steady-state characteristic quantities of the distribution network.

[0138] Step 4, based on the single-machine control mode, with the distribution network voltage control state parameters as optimization variables, and the static limit current and static limit power angle as control variables, establish a coupling model of the dominant factors of multi-dimensional instability of the distribution network; based on the coupling model of the dominant factors of multi-dimensional instability of the distribution network, obtain the distribution function of the influence of the instability factors on the stability of the distribution network; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, establish an adaptive optimization model for the distribution network zoning scheduling; based on the adaptive optimization model for the distribution network zoning scheduling, with the closed-loop control feature set of the output current of the grid-connected converter in each scheduling zone and the distribution network voltage control state parameters of each scheduling zone, obtain the spatial distribution function of the historical data of the distribution network voltage.

[0139] Specifically, the coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship:

[0140]

[0141] Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

[0142] In the embodiment, the static limiting current and the static limiting power angle are used as the control independent variables, and a single-machine control method of a grid-connected converter is adopted to identify the dominant factors of multi-dimensional instability in the distribution network, thereby comprehensively considering the coupling effect of multiple factors such as the dominant factors of multi-dimensional instability and the distribution of power load morphological parameters and the static voltage stability margin.

[0143] When the system is connected to a weak AC system, the impact of weak nodes on system stability is analyzed by the dataset, the uncertainty prediction of renewable energy, and the influence of each factor causing system instability in the set of factors affecting system stability disturbance analysis of renewable resources. Therefore, based on the coupling model of the dominant factors of multi-dimensional instability of the distribution network, the distribution function of the influence of instability factors on the stability of the distribution network is obtained, which satisfies the following relationship:

[0144] k l =M+(b i +s(e))

[0145] In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

[0146] Based on the generalized characteristic analysis method defined by static characteristics, the closed-loop control characteristics of the output current of the grid-connected converter are obtained according to the distribution network voltage control state parameters. According to the distribution of power load morphological parameters, the distribution network voltage is partitioned, and an adaptive optimization model for distribution network partition scheduling is established, which satisfies the following relationship:

[0147]

[0148] Where r(w) is the adaptive optimization function of partition scheduling, Φ is the closed-loop control feature set of the output current of the grid-connected converter;

[0149] In the embodiment, the solution of the adaptive optimization model for distribution network partition scheduling is the scheduling instructions of each scheduling partition.

[0150] When each dispatching partition responds to the multi-dimensional support decision-making instructions of the distribution network, each partition dispatching model has an optimal solution. Therefore, the historical data distribution function of the voltage of the entire distribution network satisfies the following relationship:

[0151]

[0152] Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ w is the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

[0153] Step 5, using the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching partition, correct the broadband measurement data in the detection data set to obtain the steady-state characteristic quantity of the distribution network under different broadband oscillation modes; when the change of the steady-state characteristic quantity under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability due to the broadband oscillation mode, and the steady-state characteristic quantity under the broadband oscillation mode is used as the detection result of the multi-dimensional instability dominant factor.

[0154] The steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship:

[0155]

[0156] Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

[0157] In an embodiment, steady-state characteristic quantities of the distribution network under different wide-band oscillation modes include, but are not limited to, voltage amplitude and phase under each wide-band oscillation mode obtained by wide-band measurement, closed-loop control characteristics of the output current of the grid-connected converter, and historical data of the distribution network voltage; when the change in the characteristic quantity under any wide-band oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network is unstable due to the wide-band oscillation mode, and the characteristic quantity under the wide-band oscillation mode is used as the detection result of the multi-dimensional instability dominant factor.

[0158] The method proposed in the present invention can adapt to a high proportion of new energy power systems, can balance electric energy more accurately and quickly, and the multi-dimensional support control decision-making technology takes more comprehensive considerations when scheduling electric energy, so that the voltage of each node can be more stable, effectively improving the stability of the system.

[0159] The present invention also proposes a multi-dimensional instability dominant factor detection system based on a cyclic DFT method, comprising:

[0160] A data processing module is used to obtain broadband measurement data by using a sliding data window, and to determine the broadband oscillation modal phasor of the broadband measurement data in the sliding data window at the current moment by using a cyclic discrete Fourier transform method; based on the broadband oscillation modal phasor obtained in the sliding data window at the current moment, predict the broadband oscillation modal phasor in the sliding data window at the next moment; if broadband oscillation is determined to occur according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, then record the broadband measurement data in the sliding data window at the current moment as a detection data set;

[0161] The index establishment module is used to determine the static voltage stability index of the weak nodes and the new energy grid connection points in the distribution network according to the power flow calculation results of the distribution network system, and take the minimum value of each static voltage stability index as the static voltage stability margin of the distribution network; respectively draw the curve of the active power and reactive current component output on the AC side of the grid-connected converter and the curve of the converter power angle; extract the extreme value point on the curve of the active power and reactive current component as the static limiting current, and extract the extreme value point on the curve of the active power and converter power angle as the static limiting power angle;

[0162] The instability dominant factor detection module is used to establish a coupling model of the multi-dimensional instability dominant factors of the distribution network based on the single-machine control mode, with the distribution network voltage control state parameters as the optimization variables, and the static limit current and the static limit power angle as the control variables; based on the coupling model of the multi-dimensional instability dominant factors of the distribution network, the distribution function of the influence of the instability factors on the stability of the distribution network is obtained; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, an adaptive optimization model for the distribution network partition scheduling is established; based on the adaptive optimization model for the distribution network partition scheduling, the grid-connected converters in each scheduling partition are used to select the optimal control parameters of the distribution network. The closed-loop control feature set of the output current and the distribution network voltage control state parameters of each dispatching zone are used to obtain the historical data distribution function of the distribution network voltage; the broadband measurement data in the detection data set are corrected by the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching zone to obtain the steady-state characteristic quantities of the distribution network under different broadband oscillation modes; when the change of the steady-state characteristic quantity under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability in the broadband oscillation mode, and the steady-state characteristic quantity under the broadband oscillation mode is used as the detection result of the multi-dimensional instability dominant factor.

[0163] The model of the cyclic discrete Fourier transform method satisfies the following relationship:

[0164]

[0165] In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

[0166] If it is determined that broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set;

[0167] If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

[0168] New energy grid-connected points include PQ type nodes and PV type nodes; among them, the active power and reactive power of PQ type nodes are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of PV type nodes are both constant values;

[0169] According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship:

[0170]

[0171] Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ij are the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

[0172] The minimum value of the static voltage stability index of each node is taken as the static voltage stability margin of the distribution network, satisfying the following relationship:

[0173]

[0174] In the formula, It is the static voltage stability margin of the distribution network.

[0175] Draw the curves of active power and reactive current components output by the AC side of the grid-connected converter and the converter power angle, including:

[0176] Based on the fundamental frequency equivalent circuit of the AC side of the grid-connected converter in the dq axis rotating coordinates, the active power output of the AC side of the grid-connected converter satisfies the following relationship:

[0177] P out =1.5(V od I Q +V oq I P )

[0178] Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq are the d-axis component and q-axis component of the grid-connected point voltage, I Q is the reactive current component, I P is the active current component;

[0179] When the output power of the grid-connected converter is maximum, the active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship:

[0180]

[0181] Where V g is the grid voltage amplitude, x g is the equivalent reactance between the grid connection point and the grid, δ is the converter power angle;

[0182] Based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively. out -I Q , the curve P of active power and converter power angle out -δ.

[0183] The coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship:

[0184]

[0185] Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

[0186] The distribution function of the impact of instability factors on the stability of the distribution network satisfies the following relationship:

[0187] k l =M+(bi +s(e))

[0188] In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

[0189] The adaptive optimization model for distribution network zoning dispatch satisfies the following relationship:

[0190]

[0191] Where r(w) is the adaptive optimization function of partition scheduling, and Φ is the closed-loop control feature set of the output current of the grid-connected converter.

[0192] The historical data distribution function of the distribution network voltage satisfies the following relationship:

[0193]

[0194] Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ w is the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

[0195] The steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship:

[0196]

[0197] Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

[0198] The steady-state characteristic quantities of the distribution network under different broadband oscillation modes include but are not limited to the voltage amplitude and phase under each broadband oscillation mode obtained by broadband measurement, the closed-loop control characteristics of the grid-connected converter output current, and the historical data of the distribution network voltage.

[0199] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0200] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0201] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0202] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method, characterized in that: include: A sliding data window is used to acquire broadband measurement data, and a cyclic discrete Fourier transform method is used to determine the broadband oscillation modal phasor of the broadband measurement data within the sliding data window at the current moment; Based on the obtained broadband oscillation modal phasor in the sliding data window at the current moment, predict the broadband oscillation modal phasor in the sliding data window at the next moment; If it is determined that broadband oscillation occurs according to the broadband oscillation modal phasor prediction value in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set; According to the power flow calculation results of the distribution network system, the static voltage stability indexes of the weak nodes and new energy grid connection points in the distribution network are determined, and the minimum value of each static voltage stability index is used as the static voltage stability margin of the distribution network; Draw the curves of active power and reactive current component outputted by the AC side of the grid-connected converter and the curve of converter power angle respectively; extract the extreme value points on the curves of active power and reactive current component as the static limiting current, and extract the extreme value points on the curves of active power and converter power angle as the static limiting power angle; Based on the single-machine control mode, the voltage control state parameters of the distribution network are used as optimization variables, and the static limit current and static limit power angle are used as control variables to establish a coupling model of the dominant factors of multi-dimensional instability of the distribution network; based on the coupling model of the dominant factors of multi-dimensional instability of the distribution network, the distribution function of the influence of the instability factors on the stability of the distribution network is obtained; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, an adaptive optimization model for the zoning dispatch of the distribution network is established; based on the adaptive optimization model for the zoning dispatch of the distribution network, the historical data distribution function of the distribution network voltage is obtained by using the closed-loop control feature set of the output current of the grid-connected converter in each dispatching zone and the voltage control state parameters of the distribution network in each dispatching zone; The broadband measurement data in the detection data set are corrected based on the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching zone, and the steady-state characteristic quantities of the distribution network under different broadband oscillation modes are obtained; when the change of the steady-state characteristic quantities under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability due to the broadband oscillation mode, and the steady-state characteristic quantities under the broadband oscillation mode are used as the detection results of the dominant factors of multi-dimensional instability.

2. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 1 is characterized in that: The model of the cyclic discrete Fourier transform method satisfies the following relationship: In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

3. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 2 is characterized in that: If it is determined that broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set; If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

4. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 1 is characterized in that: New energy grid-connected points include PQ type nodes and PV type nodes; among them, the active power and reactive power of PQ type nodes are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of PV type nodes are both constant values; According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship: Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ij are the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

5. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 4 is characterized in that: The minimum value of the static voltage stability index of each node is taken as the static voltage stability margin of the distribution network, satisfying the following relationship: In the formula, It is the static voltage stability margin of the distribution network.

6. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 1 is characterized in that: Draw the curves of active power and reactive current components output by the AC side of the grid-connected converter and the converter power angle, including: Based on the fundamental frequency equivalent circuit of the AC side of the grid-connected converter in the dq axis rotating coordinates, the active power output of the AC side of the grid-connected converter satisfies the following relationship: P out =1.5(V od I Q +V oq I P ) Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq are the d-axis component and q-axis component of the grid-connected point voltage, I Q is the reactive current component, I P is the active current component; When the output power of the grid-connected converter is maximum, the active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship: Where V g is the grid voltage amplitude, x g is the equivalent reactance between the grid connection point and the grid, δ is the converter power angle; Based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively. out -I Q , the curve P of active power and converter power angle out -δ.

7. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 1 is characterized in that: The coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship: Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

8. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 7 is characterized in that: The distribution function of the impact of instability factors on the stability of the distribution network satisfies the following relationship: k l =M+(b i +s(e)) In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

9. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 8, characterized in that: The adaptive optimization model for distribution network zoning dispatch satisfies the following relationship: Where r(w) is the adaptive optimization function of partition scheduling, and Φ is the closed-loop control feature set of the output current of the grid-connected converter.

10. The method for detecting the dominant factors of multi-dimensional instability based on the cyclic DFT method according to claim 9, characterized in that: The historical data distribution function of the distribution network voltage satisfies the following relationship: Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ w is the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

11. The method for detecting the dominant factors of multidimensional instability based on the cyclic DFT method according to claim 10, characterized in that: The steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship: Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

12. The method for detecting the dominant factors of multi-dimensional instability based on the cyclic DFT method according to claim 11, characterized in that: The steady-state characteristic quantities of the distribution network under different broadband oscillation modes include but are not limited to the voltage amplitude and phase under each broadband oscillation mode obtained by broadband measurement, the closed-loop control characteristics of the grid-connected converter output current, and the historical data of the distribution network voltage.

13. A multi-dimensional instability dominant factor detection system based on a cyclic DFT method, used to implement the method described in any one of claims 1 to 12, characterized in that: include: A data processing module is used to obtain broadband measurement data by using a sliding data window, and to determine the broadband oscillation modal phasor of the broadband measurement data in the sliding data window at the current moment by using a cyclic discrete Fourier transform method; based on the broadband oscillation modal phasor in the sliding data window at the current moment, predict the broadband oscillation modal phasor in the sliding data window at the next moment; If it is determined that broadband oscillation occurs according to the broadband oscillation modal phasor prediction value in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set; The index establishment module is used to determine the static voltage stability index of weak nodes and new energy grid connection points in the distribution network according to the power flow calculation results of the distribution network system, and take the minimum value of each static voltage stability index as the static voltage stability margin of the distribution network; Draw the curves of active power and reactive current component outputted by the AC side of the grid-connected converter and the curve of converter power angle respectively; extract the extreme value points on the curves of active power and reactive current component as the static limiting current, and extract the extreme value points on the curves of active power and converter power angle as the static limiting power angle; The instability dominant factor detection module is used to establish a coupling model of the multi-dimensional instability dominant factors of the distribution network based on the single-machine control mode, with the distribution network voltage control state parameters as the optimization variables, and the static limit current and the static limit power angle as the control variables; based on the coupling model of the multi-dimensional instability dominant factors of the distribution network, the distribution function of the influence of the instability factors on the stability of the distribution network is obtained; using the distribution function of the influence of the instability factors on the stability of the distribution network and the closed-loop control feature set of the output current of the grid-connected converter, an adaptive optimization model for the distribution network partition scheduling is established; based on the adaptive optimization model for the distribution network partition scheduling, the grid-connected converters in each scheduling partition are used to select the optimal control parameters of the distribution network. The closed-loop control feature set of the output current and the distribution network voltage control state parameters of each dispatching zone are used to obtain the historical data distribution function of the distribution network voltage; the broadband measurement data in the detection data set are corrected by the historical data distribution function of the distribution network voltage and the distribution network voltage control state parameters of each dispatching zone to obtain the steady-state characteristic quantities of the distribution network under different broadband oscillation modes; when the change of the steady-state characteristic quantity under any broadband oscillation mode is greater than the static voltage stability margin of the distribution network, it is determined that the distribution network has instability in the broadband oscillation mode, and the steady-state characteristic quantity under the broadband oscillation mode is used as the detection result of the multi-dimensional instability dominant factor.

14. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 13 is characterized in that: The model of the cyclic discrete Fourier transform method satisfies the following relationship: In the formula, x(m) is the mth frequency domain signal, x(n) is the nth time domain signal, and N is the number of discrete time domain signals. Therefore, the input of the discrete Fourier transform is N discrete time domain signals, and the output is N discrete frequency domain point signals.

15. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 14 is characterized in that: If it is determined that broadband oscillation occurs according to the predicted value of the broadband oscillation modal phasor in the sliding data window at the next moment, the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set, and the sliding data window is continued to be moved to collect sample data, and the broadband measurement data in the sliding data window at the current moment is recorded as a detection data set; If it is determined that no broadband oscillation will occur according to the predicted value of the broadband oscillation mode phasor in the sliding data window at the next moment, the sliding data window is continued to be moved to collect sample data.

16. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 13, characterized in that: New energy grid-connected points include PQ type nodes and PV type nodes; among them, the active power and reactive power of PQ type nodes are opposite to the active power and reactive power of the load, and the active power and voltage amplitude of PV type nodes are both constant values; According to the calculation results of the power flow of the distribution network system, the static voltage stability index of weak nodes, PQ type nodes and PV type nodes is obtained by the following relationship: Where, L j is the static voltage stability index of node j, node j includes weak nodes, PQ type nodes and PV type nodes, Z ij , R ij and X ij are the impedance, resistance and reactance of the line between node i and node j respectively, satisfying S j , P j and Q j are the apparent power, active power and reactive power of node j respectively, satisfying U i is the voltage amplitude at node i.

17. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 16 is characterized in that: The minimum value of the static voltage stability index of each node is taken as the static voltage stability margin of the distribution network, satisfying the following relationship: In the formula, It is the static voltage stability margin of the distribution network.

18. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 13, characterized in that: Draw the curves of active power and reactive current components output by the AC side of the grid-connected converter and the converter power angle, including: Based on the fundamental frequency equivalent circuit of the AC side of the grid-connected converter in the dq axis rotating coordinates, the active power output of the AC side of the grid-connected converter satisfies the following relationship: P out =1.5(V od I Q +V oq I P ) Where P out is the active power output of the AC side of the grid-connected converter, V od 、V oq are the d-axis component and q-axis component of the grid-connected point voltage, I Q is the reactive current component, I P is the active current component; When the output power of the grid-connected converter is maximum, the active power output on the AC side of the grid-connected converter and the converter power angle δ satisfy the following relationship: Where V g is the grid voltage amplitude, x g is the equivalent reactance between the grid connection point and the grid, δ is the converter power angle; Based on the above formula, the curves P of the active power and reactive current components output by the AC side of the grid-connected converter are drawn respectively. out -I Q , the curve P of active power and converter power angle out -δ.

19. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 13, characterized in that: The coupling model of the dominant factors of multi-dimensional instability of the distribution network satisfies the following relationship: Where M is the coupling function of the dominant factors of multi-dimensional instability of the distribution network; b i is the static limiting current on the i-th transmission line; n is the number of transmission lines; s(e) is the static limiting power angle, and C is the voltage control state parameter of the distribution network.

20. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 19, characterized in that: The distribution function of the impact of instability factors on the stability of the distribution network satisfies the following relationship: k l =M+(b i +s(e)) In the formula, k l is the distribution function of the impact of instability factors on the stability of the distribution network.

21. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 20, characterized in that: The adaptive optimization model for distribution network zoning dispatch satisfies the following relationship: Where r(w) is the adaptive optimization function of partition scheduling, and Φ is the closed-loop control feature set of the output current of the grid-connected converter.

22. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 21, characterized in that: The historical data distribution function of the distribution network voltage satisfies the following relationship: Where F is the historical data distribution function of the distribution network voltage, is the optimal solution of the partition scheduling model, corresponding to the scheduling instruction of the scheduling partition w, Φ w is the closed-loop control feature set of the grid-connected converter output current in the dispatching partition w, is the distribution network voltage control state parameter of dispatch partition w, and W is the total number of dispatch partitions.

23. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 22, characterized in that: The steady-state characteristic quantities of the distribution network under different broadband oscillation modes satisfy the following relationship: Where m is the broadband measurement data in the detection data set, and t is the steady-state characteristic quantity of the distribution network under different broadband oscillation modes.

24. The multi-dimensional instability dominant factor detection system based on the cyclic DFT method according to claim 23, characterized in that: The steady-state characteristic quantities of the distribution network under different broadband oscillation modes include but are not limited to the voltage amplitude and phase under each broadband oscillation mode obtained by broadband measurement, the closed-loop control characteristics of the grid-connected converter output current, and the historical data of the distribution network voltage.

25. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

26. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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