Emergency control coordination method for power angle stability of power system and related equipment

Abstract

The invention relates to the technical field of electric power system control, and discloses an emergency control coordination method for power angle stability of an electric power system and related equipment, and by obtaining time domain simulation data of a system test fault and constructing a power angle stability margin, early warning can be performed before the power angle of the system is unstable. The early warning mechanism is helpful for taking measures in advance and preventing the system from entering an unstable state. And when the power angle of the system is indeed unstable, the effects of different control measures can be quickly predicted through the constructed power angle stable sample set and the trained convolutional neural network model, so that quick response is realized, and measures are taken. By constructing the emergency control optimization model considering the control sensitivity, the effects and the sensitivity of various control measures can be comprehensively considered, so that the optimal emergency control strategy is determined, and the control on the system stability is effectively improved according to the optimal emergency control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and specifically to an emergency control coordination method for power angle stability of a power system and related equipment. Background Technique

[0002] Since the 21st century, the demand for electric energy has gradually increased, and the requirement of green development has promoted the energy transformation of the new power system, enabling large-scale integration of renewable new energy and power electronic devices into the grid. At the same time, to achieve efficient, low-loss, and long-distance power transmission, the scale of AC-DC interconnection has also gradually expanded. However, along with the sustainable development transformation of the power system, the new power system exhibits higher dimensions, more complex dynamic characteristics, and strong nonlinear characteristics, posing a severe challenge to the safe and stable operation of the power grid.

[0003] In actual operation, large-scale renewable energy generation will change the grid structure and power flow distribution, thus affecting the overall transient power angle stability of the system. When a large disturbance or serious fault may lead to system instability, emergency control measures need to be initiated to maintain the safe and stable operation of the system and continuous power supply. The commonly used transient stability emergency control measures at present include generator tripping, load shedding, DC transmission emergency power modulation, local islanding, etc. Among them, generator tripping is the simplest, most mature, and most widely used control measure. In addition, considering economic and reliability issues, only implementing generator tripping control can no longer fully meet the stability requirements of complex power systems, and the coordinated cooperation with other control resources needs to be considered. Load shedding is a control measure with significant effects and wide application for solving transient power angle instability problems, and can assist generator tripping measures in coordinated emergency control. Therefore, it is urgent to evaluate the enhancement effects of various controllable resources on the power angle stability margin under different operating modes in combination with the actual grid operating characteristics, so as to provide a basis for formulating the optimal coordinated control strategy.

[0004] At present, when implementing the measures of generator tripping and load shedding, the pre - planned control mode of offline predetermination - online matching is usually followed. Based on the decision table generated offline or online, the emergency control generator tripping strategy that matches the operating conditions of the system is selected. However, the accuracy and speed of simulation calculations are limited, and the selection of generator tripping strategies through the strategy table cannot fully adapt to the flexible and changeable operating scenarios of modern power systems. For multiple faults, cascading faults, or other situations where the operating state of the power system changes continuously and rapidly, there is a risk of strategy mismatch. The development of the Wide Area Measurement System (WAMS) and artificial intelligence technology provides strong support for realizing the control mode of "real - time decision - making and real - time matching". The method based on neural networks gets rid of the limitations of traditional physical models, can comprehensively consider multi - dimensional information, autonomously learn the highly non - linear relationship between the operating mode and the system stable state, and adaptively provide control measures according to the operating conditions of the system. However, the basic deep - learning network is difficult to fully utilize the complex dynamic characteristics of the power system, and the prediction accuracy and generalization ability of the model need to be further improved. Summary of the Invention

[0005] In order to overcome the defects existing in the above - mentioned prior art, the purpose of the present invention is to provide an emergency control coordination method for power system power angle stability and related equipment, so as to solve the technical problem of how to improve the system stability control through the power angle stability of the power system in the prior art.

[0006] The present invention is realized through the following technical solutions:

[0007] In the first aspect, the present invention provides an emergency control coordination method for power system power angle stability, including:

[0008] Obtain the time - domain simulation data of the system test fault, judge the stability of the system power angle through the power angle stability margin based on the time - domain simulation data. When the system power angle becomes unstable, construct a power angle stable sample set based on the time - domain simulation data, and construct a pre - planned control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stable sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model;

[0009] Obtain the key electrical quantity time - series information when the system fails, and judge the stability of the system power angle through the power angle stability margin based on the key electrical quantity time - series information. When the system power angle becomes unstable and the pre - planned control fails, predict the sensitivity of the corresponding control point through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0010] Preferably, in obtaining the time-domain simulation data of the system test faults, the time-domain simulation data includes the operation mode data of the system, typical fault data, and alternative control measure data; among them, the key electrical parameter time-series information is determined according to the operation mode data and the typical fault data, and the power angle stability margin is constructed through the key electrical parameter time-series information.

[0011] Further, in the step of constructing the power angle stability sample set based on the time-domain simulation data, the power angle stability sample set is constructed by using the key electrical parameter time-series information before and after the fault occurrence of the system and the control variables of the corresponding alternative control measure data as the sample input features, and the power angle stability margin output by the power angle stability margin as the output label.

[0012] Further, in the step of judging the stability of the system power angle by constructing the power angle stability margin through the time-domain simulation data, the fault clearing time is set by the dichotomy method based on the key electrical parameter time-series information to determine the critical clearing time under different faults in the time-domain simulation, and the transient stability margin index is determined according to the critical clearing time under different faults. The expression of the transient stability margin index is as follows:

[0013] η = T cmax -T c

[0014] Wherein, T cmax represents the critical clearing time of the power system, and T c represents the fault clearing time;

[0015] Wherein, when the transient stability margin index is greater than zero, the system power angle is stable, otherwise the system power angle is unstable.

[0016] Preferably, in the step of constructing a pre-planned control mismatch scenario according to the system power angle instability situation, the construction process of the pre-planned control mismatch scenario is as follows:

[0017] Based on the transient stability mechanism, analyze and obtain the influence on the transient stability of the system;

[0018] According to the influence, obtain the key factors affecting the system stability as the mismatch inducement;

[0019] According to the mismatch inducement, apply the corresponding pre-planned control measures according to the actual strategy table to construct a pre-planned control mismatch scenario.

[0020] Preferably, in the step of predicting the sensitivity of the corresponding control point through the control measure sensitivity prediction model based on the mismatch scenario, the specific process is as follows:

[0021] Input the obtained key electrical quantity time-series information into the control measure sensitivity prediction model;

[0022] Establish the correlation between control measures and the change in transient stability margin based on the chronological information of the key electrical quantities, where the expression is as follows:

[0023]

[0024] In the formula: η g represents the transient stability margin of the system after taking emergency control measures, and η0 represents the transient stability margin of the system without taking emergency control measures; N represents the number of control points; Δu i represents the control adjustment amount of the i-th control point; represents the partial derivative of η with respect to Δu i ;

[0025] Determine the sensitivity of the control point according to the correlation, and the expression is as follows:

[0026]

[0027] In the formula, Δu k represents the control adjustment amount of the k-th control point, T cmax,k represents the critical clearing time for removing the generator / load at point k, T cmax,0 represents the initial critical clearing time, Δη k represents the change in the power angle stability margin of the system after applying the control measure Δu k at point k to the instability scenario.

[0028] Preferably, in the step of constructing an emergency control optimization model based on sensitivity and determining the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, the emergency control optimization model takes the minimum of the generator tripping amount and the total load shedding amount as the optimization objective, and takes static security constraints and transient stability constraints as the constraint conditions, where the objective function expression is as follows:

[0029]

[0030] where, Δu G,i and Δu L,j are respectively the generator tripping amount at the i-th node and the load shedding amount at the j-th node; c G,i represents the tripping cost of the i-th generator node, c L,j is the load shedding cost of the j-th load node, representing the unit control cost of each control measure amount;

[0031] The static security constraints include state variable constraints and control variable constraints;

[0032] The expression of the state variable constraints is as follows:

[0033] s.t. Pimin ≤P G,i ≤P imax

[0034] Q imin ≤Q G,i ≤Q imax

[0035] V jmin ≤V L,j ≤V jmax

[0036] Among them, P G,i is the active power of the generator at node i, and P imax , P imin are the upper and lower limits of the active power of the generator at node i respectively; Q G,i is the reactive power of the generator at node i, and Q imax , Q imin are the upper and lower limits of the reactive power of the generator at node i respectively; V L,j is the bus voltage at node j, and V jmax , V jmin are the upper and lower limits of the bus voltage at node j respectively; The expressions of the control variable constraints are as follows:

[0037] 0 ≤ Δu G,i ≤ Δu Gmax

[0038] 0 ≤ Δu L,j ≤ Δu Lmax

[0039] Among them, Δu G,i and Δu L,j are the generator tripping amount at the i-th node and the load shedding amount at the j-th node respectively; Δu Gmax is the maximum value of the generator tripping amount at node i; Δu Lmax is the maximum value of the load shedding amount at node j;

[0040] The expressions of the transient stability constraints are as follows:

[0041] η0 + S G,i Δu G,i + S L,j Δu L,j ≥ η stable

[0042] Among them, η0 represents the initial transient stability margin of the system, η stable represents the transient stability threshold, and S G,i , S L,j are the sensitivities of the generator tripping and load shedding control measures respectively.

[0043] Second aspect, the present invention further provides an emergency control coordination system for power system power angle stability, including:

[0044] A simulation processing and prediction model construction module, configured to obtain time-domain simulation data of system test faults, construct a power angle stability margin based on the time-domain simulation data to judge the stability of the system power angle. When the system power angle becomes unstable, construct a power angle stability sample set based on the time-domain simulation data, and construct a pre-plan control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stability sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model;

[0045] A control optimization and stability coordination processing module, configured to obtain the key electrical quantity time series information of the system failure, and judge the stability of the system power angle based on the key electrical quantity time series information and the power angle stability margin. When the system power angle becomes unstable and the pre-plan control fails, predict the sensitivity of the corresponding control point through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0046] Third aspect, the present invention further provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the emergency control coordination method for power system power angle stability as described above are implemented.

[0047] Fourth aspect, the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the emergency control coordination method for power system power angle stability as described above are implemented.

[0048] Compared with the prior art, the present invention has the following beneficial technical effects:

[0049] The present invention provides an emergency control coordination method for the power angle stability of a power system. By obtaining the time-domain simulation data of system test faults and constructing a power angle stability margin, the present invention can give an early warning before the power angle of the system becomes unstable. This early warning mechanism helps to take measures in advance to prevent the system from entering an unstable state. When the power angle of the system does become unstable, it can quickly predict the effects of different control measures through the constructed power angle stability sample set and the trained convolutional neural network model, so as to quickly respond and take measures. By constructing an emergency control optimization model considering control sensitivity, the effects and sensitivities of various control measures can be comprehensively considered, so as to determine the optimal emergency control strategy, and the control of system stability is effectively improved according to the optimal emergency control strategy.

[0050] Furthermore, based on the power transmission system with large-scale new energy access, the present invention analyzes the influencing mechanism of system power angle stability, excavates the key factors affecting system power angle stability and extracts the inducement of emergency control mismatch; considering typical operation modes and fault types, combining traditional pre-planned control measures and mismatch inducements, it simulates the mismatch mode of traditional power angle stability pre-planned control, providing a test scenario for formulating non-pre-planned control strategies. At the same time, the present invention considers the control sensitivities of generator tripping and load shedding, and proposes a method for constructing and solving an emergency control optimization model for power angle stability, realizing the coordinated cooperation between controllable resources. The proposed method can ensure the safe and stable operation of the system under the mismatch scenario of pre-planned control when the operation mode changes, effectively complementing the three lines of defense of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the emergency control coordination method for the power angle stability of the power system in the embodiment of the present invention;

[0052] Figure 2 It is an emergency control flow chart for power angle stability in the embodiment of the present invention;

[0053] Figure 3 It is a schematic structural diagram of the simulation system in the embodiment of the present invention;

[0054] Figure 4 It is the mismatch simulation result of the pre-planned control measure when the wind power replacement ratio of the whole system changes in the embodiment of the present invention;

[0055] Figure 5 It is the mismatch simulation result of the pre-planned control measure when the wind power recovery rate changes in the embodiment of the present invention;

[0056] Figure 6 It is the system power angle response curve after the coordinated control strategy acts in Scenario 2 when the wind power replacement ratio of the whole system changes in the embodiment of the present invention;

[0057] Figure 7It is the system power angle response curve after the coordinated control strategy in Scenario 2 when the wind power recovery rate changes in the embodiment of the present invention;

[0058] Figure 8 It is a schematic diagram of the emergency control coordination system for the power angle stability of the power system in the embodiment of the present invention;

[0059] In the figure: 1. Simulation processing and prediction model construction module; 2. Control optimization and stability coordination processing module. Specific implementation manners

[0060] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] The present invention will be further described in detail below in conjunction with the accompanying drawings:

[0062] The purpose of the present invention is to provide an emergency control coordination method and related equipment for the power angle stability of the power system to solve the technical problem of how to improve the system stability control through the power angle stability of the power system in the prior art.

[0063] See Figure 1 , the present invention provides an emergency control coordination method for the power angle stability of the power system, including:

[0064] Step 1, obtain the time-domain simulation data of the system test fault, judge the stability of the system power angle through the power angle stability margin constructed by the time-domain simulation data. When the system power angle is unstable, construct a power angle stable sample set based on the time-domain simulation data, and construct a pre-plan control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stable sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model;

[0065] Specifically, in obtaining the time-domain simulation data of the system test fault, the time-domain simulation data includes the operation mode data, typical fault data and alternative control measure data of the system; among them, the key electrical parameter time series information is determined according to the operation mode data and the typical fault data, and the power angle stability margin is constructed through the key electrical parameter time series information.

[0066] Among them, in the step of constructing the power angle stability sample set based on the time-domain simulation data, the power angle stability sample set is constructed by using the time-series information of the key electrical parameters of the system before and after the fault and the control variables of the corresponding alternative control measure data as the sample input features, and the power angle stability margin output by the power angle stability margin as the output label.

[0067] Among them, in the step of judging the stability of the system power angle by constructing the power angle stability margin through the time-domain simulation data, the bisection method is used to set the fault clearing time based on the time-series information of the key electrical parameters to determine the critical clearing time under different faults in the time-domain simulation, and the transient stability margin index is determined according to the critical clearing time under different faults. The expression of the transient stability margin index is as follows:

[0068] η=T cmax -T c (1)

[0069] Among them, T cmax represents the critical clearing time of the power system, and T c is the fault clearing time;

[0070] Among them, when the transient stability margin index is greater than zero, the system power angle is stable; otherwise, the system power angle is unstable.

[0071] In the present invention, during the transient stability margin evaluation process, first, the initial operating conditions are set, and the off-line system is simulated. For different operating modes and fault settings, the original electrical characteristics such as the generator power angle, bus voltage, and electromagnetic power at the fault clearing moment are collected, and the stability margin of the system under different operating conditions is calculated.

[0072] Specifically, in the step of constructing the pre-scheme control mismatch scenario according to the system power angle instability situation, the construction process of the pre-scheme control mismatch scenario is as follows:

[0073] Based on the transient stability mechanism, the influence on the system transient stability is analyzed;

[0074] According to the influence, the key factors affecting the system stability are obtained as the mismatch inducement;

[0075] According to the mismatch inducement, the corresponding pre-scheme control measures are applied according to the actual strategy table to construct the pre-scheme control mismatch scenario.

[0076] Taking the direct-drive permanent magnet synchronous wind turbine unit connected to the power grid as an example, the transient stability mechanism of the new energy access system is analyzed, and the two-machine system is transformed into a single-machine infinite system.

[0077] Assume that the access system is an infinite system. After the wind power is connected, the active power outputs of the direct-drive wind turbine W and the synchronous generator G become

[0078] Pw = k wr P G (2)

[0079] P Gw = (1 - k wr )P G (3)

[0080] Wherein, P G is the active power output of the synchronous generator before the wind power is connected to the system and before the fault occurs, P w is the active power output of the direct-drive wind turbine, P Gw is the active power output of the direct-drive wind turbine before the fault occurs after the wind power is connected to the system, and k wr is defined as the wind power replacement ratio of the whole system, that is, the sum of the ratios of the active power output of the direct-drive wind turbine to the original active power output of the synchronous machine.

[0081] Adopt the classical second-order model for analysis, and describe the transient characteristics of the synchronous machine with the following second-order rotor motion equation:

[0082]

[0083] Wherein, P e is the electromagnetic power of the synchronous generator, P m is the mechanical power of the synchronous generator, ω0 is the angular frequency at steady state, M is the inertia of the synchronous generator, and δ is the power angle of the synchronous generator.

[0084] After the wind power is connected, the rotor motion equation of the synchronous generator becomes

[0085]

[0086] Wherein, δ w 、P mw 、P ew 、M w are respectively the power angle, mechanical power, electromagnetic power, and inertia of the synchronous generator after the wind turbine is connected. Define the power angle change amount Δδ = δ w - δ, indicating the change value of the power angle of the synchronous machine before and after the wind power is connected. Taking the difference between the rotor motion equations of the synchronous machine in the two states before and after the wind power is connected, the differential equation of Δδ can be obtained as:

[0087]

[0088] The power angle change amount Δδ can reflect the impact of the wind power connected to the system on the transient stability of the synchronous machine. Δδ > 0 indicates that the power angle difference between the synchronous machines becomes larger after the wind turbine is connected, and the transient stability of the system decreases. On the contrary, the transient stability of the system increases.

[0089] Analyze from the perspective of transient energy. Assume that the fault critical clearing time before the wind power is connected to the system is t cr. During the period from the occurrence of the fault to the clearance of the fault, for a single-machine infinite-bus system, the accelerating power during the fault is P acc = |P m - P e | = P m - P e . After the fault is cleared, the decelerating power is P dea = |P m - P e | = P e - P m . If P acc / M increases and P dea / M decreases after the wind power is connected, then, when the fault duration is t cr , the accelerating area of the synchronous machine is larger than the decelerating area, which indicates that the critical clearing time decreases and the transient stability level of the system decreases after the wind power is connected; conversely, if P acc / M decreases and P dea / M increases after the wind power is connected, then, when the fault duration is t cr , the accelerating area of the synchronous machine is smaller than the decelerating area, indicating that the critical clearing time increases and the transient stability level of the system improves after the wind power is connected.

[0090] Thus, the key to transient stability analysis is to derive the relationship between the electromagnetic power P e of the synchronous machine and δ. For this purpose, the following simplification is adopted:

[0091] 1) Considering the most severe three-phase short-circuit fault, the electromagnetic power of the synchronous machine is approximately 0 during the fault.

[0092] 2) Regarding the direct-drive wind turbine as a power injection model during the fault period and for a period of time after the fault is cleared;

[0093] Assuming that the steady-state active power output of the direct-drive wind turbine before the fault is P w , the power value during the fault is P0, and the wind turbine resumes its active power output at a preset rate k after the fault is cleared. The specific expressions of the active power output of the wind turbine in each stage are as follows:

[0094]

[0095] where: t f represents the fault occurrence time; t c represents the fault clearing time; P0 represents the power value of the wind turbine during the fault, and the parameter k is used to represent the recovery rate of the active power output of the wind turbine.

[0096] 3) Adopting the DC power flow method;

[0097] Before the fault occurs, since only the trend of system power flow is concerned, the DC power flow method can be used to replace the conventional power flow calculation method. During the fault, it is considered that the electromagnetic power of the synchronous machine is 0. After the fault is cleared, the direct-drive wind turbine is regarded as a constant power source. Assuming that there is no voltage stability problem in the system and only the angle stability problem exists, the load can be regarded as a constant power. The DC power flow calculation can correctly estimate the change trend of the electromagnetic power of the synchronous machine within a certain range, and thus can be used to judge the change trend of the deceleration power of the synchronous generator set. The simple proof is as follows.

[0098] Before connecting the wind turbine, under the classical second-order model, the expression form of the electromagnetic power of the synchronous machine is

[0099]

[0100] where E is the internal electromotive force of the synchronous machine, U is the terminal voltage of the generator, θ is the power angle of the generator with the phase angle of the generator's own terminal voltage as the reference, and X′ d represents the transient reactance of the generator.

[0101] After connecting the wind turbine, the above formula becomes

[0102]

[0103] where E w , U w , θ w , X′ dw and are the internal electromotive force of the synchronous machine, the terminal voltage of the node, the power angle of the generator with the phase angle of the generator's own terminal voltage as the reference, the transient reactance of the generator, and the synchronous inertia respectively after the wind turbine is connected. When adopting the classical second-order model of the synchronous generator, the internal electromotive force of the synchronous generator remains unchanged after the fault occurs; at the same time, on the premise that the system voltage recovers quickly, there is U w ≈U≈1; and after the wind power is connected, M w X' dw =MX' d .

[0104] Therefore, from Equation (9) - Equation (8), we can get:

[0105]

[0106] When -π < θ w +θ < π, the sign of Δ(P w / M) can be judged by the sign of θ e -θ, that is, the change trend of P e / M. Therefore, using the DC power flow method for approximate calculation can, to a certain extent, guide the judgment of the change in transient stability before and after the wind turbine is connected.

[0107] In summary, based on the equal - area criterion, the access of new - energy units affects the system power - angle stability by influencing the steady - state power angle, accelerating area, and decelerating area.

[0108] Before the fault occurs, the direct - drive wind turbine is regarded as a constant - power source, and the steady - state power - angle change Δδ0 is estimated using the DC power flow.

[0109] Before the wind power is connected to the system, there is

[0110] δ0 = P G x (11)

[0111] In the formula, δ0 is the steady - state power angle, and x represents the equivalent impedance from the internal - potential node of the synchronous generator to the infinite bus.

[0112] After the wind power is connected to the system,

[0113] δ 0w = P Gw x=(1 - k wr )P G x (12)

[0114] Δδ0 = δ 0w -δ0=-k wr P G x<0 (13)

[0115] In the formula, δ 0w is the steady - state power angle after the wind turbine is connected. From Δδ0 < 0, it can be seen that the connection of the direct - drive wind turbine reduces the steady - state power angle of the synchronous machine.

[0116] When a three - phase short - circuit fault occurs in the system, during the fault, the synchronous - machine P e ≈0. Before the direct - drive wind turbine is connected to the system, the accelerating power during the fault satisfies

[0117] P acc =|P m -P e |≈P m = P G (14)

[0118] In the formula, P acc is the accelerating power of the single - machine infinite - bus system during the fault. After the direct - drive wind turbine is connected to the system, the accelerating power becomes

[0119] P accw =|P mw -P ew |≈P mw =(1 - k wr )P G (15)

[0120] In the formula, P accwThe accelerating power of the single-machine infinite system during the fault of the wind turbine connected to the system. According to Equations (6), (14), and (15), during the fault, Δδ satisfies Equation (16), indicating that when the synchronous machine directly reduces its output after the wind power is connected to the system, the accelerating area of the synchronous machine during the fault will be reduced.

[0121]

[0122] Before the wind power is connected, the approximate expression of the electromagnetic power of the synchronous machine within a short time after the fault is cleared can be obtained from Equation (12):

[0123]

[0124] Therefore, the decelerating power before the wind power is connected is

[0125]

[0126] In the formula, P dea is the decelerating power of the single-machine infinite system after the fault is cleared. After the wind power is connected, it can be seen from Equation (13) that the electromagnetic power of the synchronous machine satisfies within a short time after the fault is cleared

[0127]

[0128] Thus, the decelerating power after the wind power is connected is

[0129]

[0130] After the fault is cleared, Δδ satisfies

[0131]

[0132] In the formula, P deaw is the decelerating power of the single-machine infinite system after the wind turbine is connected to the system and the fault is cleared. It can be seen from this that the decelerating area of the synchronous machine increases after the fault is cleared.

[0133] To sum up, after the direct-drive wind turbine is connected to the system, the synchronous machine directly reduces its output, the steady-state power angle decreases, the accelerating area decreases, and the decelerating area increases, which can improve the transient stability level. Further, according to Equations (13), (16), and (21), it can be known that the wind power replacement ratio k wr and the wind power recovery rate k are both key factors affecting the power angle stability of the system. The larger the wind power replacement ratio k wr of the system, the larger the reduction in the steady-state power angle, the reduction in the accelerating area, and the increase in the decelerating area, and the better the impact on the transient stability of the system.

[0134] When the pre-arranged control can make the system return to stability under the operating scenario with a large k wr , in the case of k wrThere is a risk of pre - planned control mismatch in smaller operating scenarios; similarly, the smaller the wind power recovery rate \(k\), the larger the increase in the deceleration area, which is more conducive to system transient stability. Therefore, when pre - planned control can restore system stability in an operating scenario with a smaller \(k\), there is a risk of pre - planned control mismatch in an operating scenario with a larger \(k\). Based on the above incentives for stable control mismatch, a typical pre - planned control mismatch scenario for angle stability can be constructed, that is, when online implementing pre - planned control measures based on a strategy table, the system cannot be stabilized.

[0135] Step 2: Obtain the time - series information of key electrical quantities when the system fails, and judge the stability of the system angle based on the time - series information of key electrical quantities and the angle stability margin. When the system angle becomes unstable and the pre - planned control fails, predict the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0136] Specifically, in the step of predicting the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, the specific process is as follows:

[0137] Input the obtained time - series information of key electrical quantities into the control measure sensitivity prediction model;

[0138] Establish the correlation between control measures and the change in transient stability margin based on the time - series information of key electrical quantities, and the expression is as follows:

[0139]

[0140] In the formula: \(\eta\) g represents the system transient stability margin after taking emergency control measures, \(\eta_0\) represents the system transient stability margin without taking emergency control measures; \(N\) is the number of control points; \(\Delta u\) i represents the control adjustment amount of the \(i\) - th control point; represents the partial derivative of \(\eta\) with respect to \(\Delta u\) i ;

[0141] Determine the sensitivity of the control points according to the correlation, and the expression is as follows:

[0142]

[0143] In the formula, \(\Delta u\) k represents the control adjustment amount of the \(k\) - th control point, \(T\) cmax,k is the critical clearing time for removing the generator / load at point \(k\), \(T\) cmax,0is the initial critical clearing time, Δη k represents the change in the power angle stability margin of the system after applying the k-point control measure Δu to the instability scenario k

[0144] Specifically, in the step of constructing an emergency control optimization model based on sensitivity and determining the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, the emergency control optimization model takes the minimum of the total generator tripping amount and the total load shedding amount as the optimization objective, and takes static security constraints and transient stability constraints as the constraint conditions. The expression of the objective function is as follows:

[0145]

[0146] where, Δu G,i and Δu L,j are the generator tripping amount of the i-th node and the load shedding amount of the j-th node respectively; c G,i is the tripping cost of the i-th generator node, and c L,j is the load shedding cost of the j-th load node, representing the unit control cost of each control measure quantity.

[0147] At the same time, in the actual power grid, all variables in the system are within reasonable constraint ranges. The constraint conditions of emergency control are divided into two categories. One is static security constraints, which means that the system should be within the acceptable range of the stable operating point of the system when it reaches a new equilibrium point after emergency control; the other is transient stability constraints, which usually include power angle stability, voltage stability, frequency stability, etc. In this invention, transient power angle stability is mainly studied. A reasonable generator tripping and load shedding strategy should keep the unstable system in transient stability, and the stability margin is greater than a certain threshold. Since the influence of control input on system state and stability index has non-linear characteristics, the sensitivity method is used to establish a linear relationship between transient stability margin and emergency control variables.

[0148] The static security constraints include state variable constraints and control variable constraints;

[0149] The expression of the state variable constraints is as follows:

[0150] s.t. P imin ≤P G,i ≤P imax (25)

[0151] Q imin ≤Q G,i ≤Q imax (26)

[0152] V jmin ≤V L,j ≤V jmax (27) ​

[0153] Among them, P G,i is the active power of the generator at node i, P imax , P imin are respectively the upper and lower limits of the active power of the generator at node i; Q G,i is the reactive power of the generator at node i, Q imax , Q imin are respectively the upper and lower limits of the reactive power of the generator at node i; V L,j is the bus voltage at node j, V jmax , V jmin are respectively the upper and lower limits of the bus voltage at node j;

[0154] The expressions for the constraints of the control variables are as follows:

[0155] 0 ≤ Δu G,i ≤ Δu Gmax (28)

[0156] 0 ≤ Δu L,j ≤ Δu Lmax (29)

[0157] Among them, Δu G,i and Δu L,j are respectively the generator tripping amount at the i-th node and the load shedding amount at the j-th node; Δu Gmax is the maximum value of the generator tripping amount at node i; Δu Lmax is the maximum value of the load shedding amount at node j;

[0158] The expressions for the transient stability constraints are as follows:

[0159] η0 + S G,i Δu G,i + S L,j Δu L,j ≥ η stable (30)

[0160] Among them, η0 represents the initial transient stability margin of the system, η stable represents the transient stability threshold, S G,i , S L,j are respectively the sensitivities of the generator tripping and load shedding control measures.

[0161] Based on the power transmission system with large-scale new energy access, the present invention analyzes the influencing mechanism of system power angle stability, excavates the key factors affecting system power angle stability, and extracts the inducement of emergency control mismatch. Considering typical operation modes and fault types, combined with traditional pre-planned control measures and mismatch inducements, the mismatch mode of traditional power angle stability pre-planned control is simulated to provide a test scenario for formulating non-pre-planned control strategies. At the same time, considering the control sensitivity of generator tripping and load shedding, a method for constructing and solving an optimization model for emergency control of power angle stability is proposed, realizing the coordinated cooperation among controllable resources. The proposed method can ensure the safe and stable operation of the system under the mismatch scenario of pre-planned control when the operation mode changes, effectively complementing the three lines of defense of the power system.

[0162] The specific flowchart of the present invention is as Figure 2 shown. In the offline generation stage, time-domain simulation data is obtained, the system is judged for stability based on the power angle stability margin, and corresponding combined alternative emergency control measures are applied. The key electrical quantities and control variables of the system before and after the fault occurrence are used as sample input features, and the power angle stability margin is used as the output label to construct a power angle stability sample set. The convolutional neural network model is trained to determine the model structure and parameters, and the model performance is evaluated using the test set, and finally a sensitivity prediction model is generated.

[0163] In the online determination and application stage, when a fault occurs, the time series values of state variables are collected in real time by means of an online measurement system, and the existing stability margin evaluation index is used to determine whether the system is stable. If a power angle instability phenomenon occurs, pre-planned control measures are executed. If the pre-planned control is mismatched, for the mismatch scenario, based on the proposed sensitivity prediction model, the improvement amount of the power angle stability margin under different alternative control measures is predicted, and the corresponding control sensitivity is calculated. Subsequently, an emergency control optimization model considering control sensitivity is established, and the optimization problem including the objective function (24) and system constraints (25)-(30) is solved by CPLEX to determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures. Finally, the transient stability of the system after applying the control is judged. If the system units still cannot recover synchronous stability, the data-driven emergency control will be executed again until stability is restored.

[0164] Embodiment 1

[0165] According to Figure 3As shown in the figure, this embodiment provides an emergency control coordination method for the power angle stability of a power system. A mismatch scenario is constructed for the simplified simulation system of the power grid in northern Shaanxi to verify the mismatch mechanism. The test system is constructed based on the 800 kV grid structure, with a base capacity of 100 MW. The system contains two ±800 kV DC lines, and the rated power of the DC system is 4000 MW. For the possible mismatch causes of power angle stability obtained from the analysis, by setting the operating conditions corresponding to the mismatch causes in PSASP, a traditional pre-planned control mismatch scenario for power angle stability is generated, and the mismatch mechanism is verified at the same time.

[0166] 1) Change in the wind power replacement ratio of the whole system

[0167] For the sending-end system with wind power access, the lower the wind power replacement ratio of the whole system, the more unstable the system. Therefore, the following possible pre-planned control measure mismatch scenarios are set:

[0168] Scenario 1: The thermal power output accounts for 90% (wind turbine replacement ratio 10%)

[0169] Scenario 2: Change the number of thermal power units, adjust the wind power output, and the thermal power output accounts for 95% (wind turbine replacement ratio 5%)

[0170] Fault setting: A three-phase short-circuit fault of N-1 occurs between Shuofang and Yuheng at 1 s, and the fault is cleared after the line is disconnected 0.1 s later. The system still experiences power angle instability.

[0171] The pre-planned control measure is: 0.2 s after the fault occurs, cut off the generators at the sending-end Shaanxi**, Shanqing**, and Shandian** bus nodes of the system, with a cut-off capacity of 262 MW.

[0172] The simulation results are as Figure 4 shown. Taking the above pre-planned control measures in Scenario 1 can achieve power angle stability, while taking the above pre-planned control measures in Scenario 2 results in power angle instability, that is, the phenomenon of pre-planned control measure mismatch occurs. This phenomenon is consistent with the conclusion obtained in the previous section. Without considering voltage and frequency instability, the higher the wind power replacement ratio, the more stable the power angle of the system.

[0173] 2) Change in the wind power recovery rate

[0174] Since the smaller the active power recovery rate of the wind turbine, the more stable the system, the following possible pre-planned control measure mismatch scenarios are set:

[0175] Scenario 1: The thermal power output accounts for 20%, the new energy output accounts for 80%, and the wind turbine recovery rate is 20% of the rated power / s

[0176] Scenario 2: The thermal power output accounts for 20%, the new energy output accounts for 80%, and the wind turbine recovery rate is 100% of the rated power / s

[0177] Fault setting: A N-1 three-phase short-circuit fault occurs at Shuofang - Yuheng at 1 s, and the fault is cleared after 0.1 s of line disconnection. The system still experiences power angle instability.

[0178] The pre - planned control measures applied are as follows: At 0.2 s after the fault occurs, generators at the bus nodes of Shanshen**, Shanhao**, Shanqing**G1, Shaanxi**G1, Shanguo**G1, and Shandian**G1 are tripped, and the tripping capacity is 785 MW.

[0179] The simulation results are as Figure 5 shown. Taking the above - mentioned pre - planned control measures in Scenario 1 can achieve power angle stability, while taking the above - mentioned pre - planned control measures in Scenario 2 results in power angle instability, that is, the phenomenon of pre - planned control measure mismatch occurs. This phenomenon is consistent with the conclusion obtained in the previous section. Without considering voltage and frequency instability, the smaller the wind power recovery rate, the more stable the system power angle.

[0180] To ensure the safe and stable operation of the system in the scenario where pre - planned control fails, a non - pre - planned coordinated emergency control strategy is proposed. First, a sensitivity prediction model is established based on this system, comprehensively considering different operation modes and fault types. A data set is established using the simulation software PSASP. Combining the actual situation of the northern Shaanxi power grid, operation modes with new energy penetration rates of 50%, 60%, 70%, and 80% are set in this simulation system. At the same time, the system load level is set from 90% to 110% and increased in steps of 10%. Under the corresponding operation modes, the actual power grid fault conditions are simulated, and different fault types are set, including 8 typical fault scenarios such as three - phase ground short - circuit, DC blocking, and complex AC - DC faults. Among them, the AC fault location is set at 10% - 90% of the AC line, and 9 fault points are increased in steps of 10%. The fault duration is 0.1 s - 0.25 s and increased in steps of 0.03 s. The total simulation time is 10 s, and a total of 4320 basic scenarios are generated through simulation. According to the proposed stability margin evaluation index, the system stability is judged. Under the basic scenarios, 3846 stable samples and 474 unstable samples are obtained by calculating the stability margin through time - domain simulation. 16 groups of bus nodes in the system are selected to provide potential generator tripping and load shedding control capacities, and their corresponding control quantities are shown in Table 1 (benchmark capacity 100 MW). For the unstable situation of the system, different combined generator tripping control schemes are formed by tripping the control quantity of a single node or the control quantities of two nodes respectively; for the stable situation of the system, the control measure quantity is set to 0. According to the above - mentioned combination of operation conditions and control measures, the corresponding power angle stability margin indexes before and after the system emergency control measures are calculated, and the operation data under each operation scenario are collected as input features, with the system power angle stability margin as the output label. All samples are divided into a training set and a test set in a ratio of 4:1. A convolutional neural network model is trained based on the generated training set samples, and the prediction effect of the model is evaluated according to the test set.

[0181] Table 1 Each bus node and its controllable capacity

[0182]

[0183]

[0184] The prediction accuracy of the trained convolutional neural network model is 98.76%, and the regression prediction effect is good, which can accurately evaluate the transient stability margin of the system. When applied online, for a certain non-specific scenario of the similar power angle instability mode, the above model can be used to predict the improvement effect of each control measure on the system stability margin after instability, mine the control sensitive points, and provide a basis for formulating emergency control strategies.

[0185] Taking the mismatch scenario constructed by changing the wind power replacement ratio of the whole system as an example, when the fan replacement ratio is 5%, the pre - planned control fails. Under this operation mode, the trained convolutional neural network model is used to predict the transient stability margin of the system, and the sensitivities of each control measure are calculated. The results are shown in Table 2 and Table 3.

[0186] Table 2 Prediction sensitivity of generator tripping control points

[0187]

[0188] Table 3 Prediction sensitivity of load shedding control points

[0189]

[0190]

[0191] From the obtained sensitivities of control measures and related constraint conditions, the optimization problem can be solved to obtain the optimal coordinated emergency control strategy. Set the transient stability threshold Mstable = 0.3, the frequency fluctuation range is less than 0.2Hz, the unit control cost c G,i of generator tripping and the unit control cost c L,j of load shedding are both set to 1.0, and the ranges of generator tripping ratio and load shedding ratio are set from 0 to 1. Use the Cplex solver to solve the optimization problems from Equation (24) to Equation (30), and the optimal coordinated emergency control strategy is shown in Table 4.

[0192] Table 4 Optimal coordinated emergency control strategy

[0193]

[0194] After the fault is cleared in 0.1 seconds, the above optimal emergency control strategy is implemented. Figure 6 The power angle response curve of the system is shown. The results show that after implementing the stability control measures, the units operate synchronously and the system resumes stability.

[0195] Similarly, in the scenario of structural mismatch with the wind power recovery rate changing, after adopting this non - pre - planned control strategy, the power angle response curve of the system is as shown in Figure 7 shown. The results show that this control strategy can also make the system return to stability, verifying the effectiveness of the optimal emergency control strategy.

[0196] Embodiment 2

[0197] According to Figure 8 shown, this embodiment provides an emergency control coordination system for the power angle stability of a power system, including:

[0198] A simulation processing and prediction model construction module, which is used to obtain the time - domain simulation data of system test faults, construct a power angle stability margin through the time - domain simulation data to judge the stability of the system power angle. When the system power angle becomes unstable, construct a power angle stability sample set based on the time - domain simulation data, and construct a pre - planned control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stability sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model;

[0199] A control optimization and stability coordination processing module, which is used to obtain the time - series information of key electrical quantities when the system fails, and judge the stability of the system power angle based on the power angle stability margin through the time - series information of key electrical quantities. When the system power angle is unstable and the pre - planned control fails, predict the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0200] Embodiment 3

[0201] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as an emergency control coordination program for the power angle stability of a power system.

[0202] When the processor executes the computer program, it realizes the steps of the above - mentioned emergency control coordination method for the power angle stability of a power system, such as:

[0203] Obtain the time - domain simulation data of system test faults, construct a power angle stability margin through the time - domain simulation data to judge the stability of the system power angle. When the system power angle is unstable, construct a power angle stability sample set based on the time - domain simulation data, and construct a pre - planned control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stability sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model;

[0204] Obtain the time - series information of key electrical quantities when the system fails, and judge the stability of the system power angle based on the power - angle stability margin through the time - series information of key electrical quantities. When the system power angle becomes unstable and the pre - planned control fails, predict the sensitivity of the corresponding control points through the control - measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator - tripping and load - shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0205] Alternatively, when the processor executes the computer program, it realizes the functions of each module in the above - mentioned system. For example:

[0206] The simulation processing and prediction model construction module 1 is used to obtain the time - domain simulation data of the system test fault, construct the power - angle stability margin through the time - domain simulation data to judge the stability of the system power angle. When the system power angle is unstable, construct a power - angle stability sample set based on the time - domain simulation data, and construct a pre - planned control mismatch scenario according to the system power - angle instability situation; train a convolutional neural network model based on the power - angle stability sample set and determine the model structure and parameters to obtain the control - measure sensitivity prediction model;

[0207] The control optimization and stability coordination processing module 2 is used to obtain the time - series information of key electrical quantities when the system fails, and judge the stability of the system power angle based on the power - angle stability margin through the time - series information of key electrical quantities. When the system power angle is unstable and the pre - planned control fails, predict the sensitivity of the corresponding control points through the control - measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator - tripping and load - shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

[0208] For example, the computer program can be divided into the simulation processing and prediction model construction module 1 and the control optimization and stability coordination processing module 2;

[0209] The specific functions of each module are as follows:

[0210] The simulation processing and prediction model construction module 1 is used to obtain the time - domain simulation data of the system test fault, construct the power - angle stability margin through the time - domain simulation data to judge the stability of the system power angle. When the system power angle is unstable, construct a power - angle stability sample set based on the time - domain simulation data, and construct a pre - planned control mismatch scenario according to the system power - angle instability situation; train a convolutional neural network model based on the power - angle stability sample set and determine the model structure and parameters to obtain the control - measure sensitivity prediction model;

[0211] The control optimization and stability coordination processing module 2 is used to obtain the timing information of key electrical quantities when the system fails, and judge the stability of the system power angle based on the power angle stability margin through the timing information of key electrical quantities. When the system power angle becomes unstable and the pre - planned control fails, the sensitivity of the corresponding control point is predicted based on the mismatch scenario through the control measure sensitivity prediction model, an emergency control optimization model is constructed based on the sensitivity, and the optimal emergency control strategy for coordinating generator tripping and load shedding control measures is determined through the emergency control optimization model. Emergency control coordination is carried out according to the optimal emergency control strategy until the system units return to stability.

[0212] The mobile terminal can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The mobile terminal may include, but is not limited to, a processor and a memory.

[0213] The processor may be a central processing unit (CPU), or may also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the mobile terminal, connecting various parts of the entire mobile terminal through various interfaces and lines.

[0214] The memory can be used to store the computer program and / or module. The processor realizes various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0215] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high - speed random - access memory, and may also include non - volatile memory, such as a hard disk, a memory, a plug - in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid - state storage devices.

[0216] Example 4

[0217] The present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps of the emergency control coordination method for power angle stability of a power system.

[0218] If the modules / units integrated in the mobile terminal are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0219] Based on such an understanding, to implement all or part of the processes in the above methods, the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above aggregation reinforcement learning resource scheduling method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate forms, etc.

[0220] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0221] It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An emergency control coordination method for power angle stability of a power system, characterized in that, Including: Obtain the time-domain simulation data of system test faults, construct the power angle stability margin based on the time-domain simulation data to judge the stability of the system power angle. When the system power angle becomes unstable, construct a power angle stability sample set based on the time-domain simulation data, and construct a pre-planned control mismatch scenario according to the system power angle instability situation; Train a convolutional neural network model based on the power angle stability sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model; Obtain the time-series information of key electrical quantities when the system fails, and judge the stability of the system power angle based on the power angle stability margin through the time-series information of key electrical quantities. When the system power angle becomes unstable and the pre-planned control fails, predict the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine the optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

2. The emergency control coordination method for power angle stability of a power system according to claim 1, characterized in that, In the step of obtaining the time-domain simulation data of system test faults, the time-domain simulation data includes the operation mode data of the system, typical fault data, and alternative control measure data; Among them, determine the time-series information of key electrical parameters according to the operation mode data and typical fault data, and construct the power angle stability margin through the time-series information of key electrical parameters.

3. An emergency control coordination method for power angle stability of a power system according to claim 2, characterized in that In the step of constructing the power angle stability sample set based on the time-domain simulation data, the power angle stability sample set is constructed by using the time-series information of key electrical parameters of the system before and after the fault and the control variables of the corresponding alternative control measure data as sample input features, and the power angle stability margin output by the power angle stability margin as the output label.

4. An emergency control coordination method for power angle stability of a power system according to claim 2, characterized in that, In the step of judging the stability of the system power angle by constructing the power angle stability margin through the time-domain simulation data, use the dichotomy method to set the fault clearing time based on the time-series information of key electrical parameters to determine the critical clearing time under different faults in the time-domain simulation, and determine the transient stability margin index according to the critical clearing time under different faults. The expression of the transient stability margin index is as follows: η = T cmax -T c Among them, T cmax represents the critical clearing time of the power system, and T c represents the fault clearing time; Among them, when the transient stability margin index is greater than zero, the system power angle is stable; otherwise, the system power angle is unstable.

5. An emergency control coordination method for power angle stability of a power system according to claim 1, characterized in that, In the step of constructing a pre-planned control mismatch scenario according to the system power angle instability situation, the construction process of the pre-planned control mismatch scenario is as follows: Based on the transient stability mechanism, analyze the influence on the transient stability of the system; Obtain the key factors affecting the system stability according to the influence as the mismatch inducement; Apply the corresponding pre-planned control measures according to the mismatch inducement based on the actual strategy table to construct a pre-planned control mismatch scenario.

6. The emergency control coordination method for power angle stability of a power system according to claim 1, characterized in that, In the step of predicting the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, the specific process is as follows: Input the obtained time-series information of key electrical quantities into the control measure sensitivity prediction model; Establish the correlation relationship between the control measure and the change amount of the transient stability margin according to the time-series information of key electrical quantities, and the expression is as follows: Where: η g represents the transient stability margin of the system after taking emergency control measures, and η0 represents the transient stability margin of the system without taking emergency control measures; N represents the number of control points; Δu i represents the control adjustment amount of the i-th control point; represents the partial derivative of η with respect to Δu i ; Determine the sensitivity of the control point according to the correlation relationship, and the expression is as follows: where, Δu k represents the control adjustment amount of the k-th control point, T cmax,k represents the critical clearing time for removing the generator / load at point k, T cmax,0 represents the initial critical clearing time, Δη k represents the change in the power angle stability margin of the system after applying the control measure Δu at point k k to the instability scenario.

7. An emergency control coordination method for power angle stability of a power system according to claim 1, characterized in that In the step of constructing an emergency control optimization model based on sensitivity and determining an optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, the emergency control optimization model takes the minimum of the generator tripping amount and the total load shedding amount as the optimization objective, and takes static security constraints and transient stability constraints as constraint conditions, where the expression of the objective function is as follows: where, Δu G,i and Δu L,j are the generator tripping amount of the i-th node and the load shedding amount of the j-th node respectively; c G,i represents the tripping cost of the i-th generator node, and c L,j represents the load shedding cost of the j-th load node, indicating the unit control cost of each control measure quantity; The static security constraints include state variable constraints and control variable constraints; The expression of the state variable constraints is as follows: s.t.P imin ≤P G,i ≤P imax Q imin ≤Q G,i ≤Q imax V jmin ≤V L,j ≤V jmax Among them, P G,i is the active power of the generator at node i, P imax , P imin are the upper and lower limits of the active power of the generator at node i respectively; Q G,i is the reactive power of the generator at node i, Q imax , Q imin are the upper and lower limits of the reactive power of the generator at node i respectively; V L,j is the bus voltage at node j, V jmax , V jmin are the upper and lower limits of the bus voltage at node j respectively; The expressions of the control variable constraints are as follows: 0 ≤ Δu G , i ≤ Δu G max 0 ≤ Δu L,j ≤ Δu Lmax where, Δu G,i and Δu L,j are the generator tripping amount of the i-th node and the load shedding amount of the j-th node respectively; Δu Gmax is the maximum value of the generator tripping amount of node i; Δu Lmax is the maximum value of the load shedding amount of node j; The expression of the transient stability constraints is as follows: η0 + S G,i Δu G,i + S L,j Δu L,j ≥ η stable Among them, η0 represents the initial transient stability margin of the system, and η stable represents the transient stability threshold, and S G,i , S L,j are the sensitivities of the generator tripping and load shedding control measures respectively.

8. An emergency control coordination system for power angle stability of a power system, characterized in that, Including: A simulation processing and prediction model construction module, configured to obtain time-domain simulation data of system test faults, construct a stability judgment of the system power angle through the power angle stability margin based on the time-domain simulation data. When the system power angle becomes unstable, construct a power angle stability sample set based on the time-domain simulation data, and construct a pre-plan control mismatch scenario according to the system power angle instability situation; train a convolutional neural network model based on the power angle stability sample set and determine the model structure and parameters to obtain a control measure sensitivity prediction model; A control optimization and stability coordination processing module, configured to obtain the key electrical quantity time series information of the system when a fault occurs, and construct a stability judgment of the system power angle through the power angle stability margin based on the key electrical quantity time series information. When the system power angle becomes unstable and the pre-plan control fails, predict the sensitivity of the corresponding control points through the control measure sensitivity prediction model based on the mismatch scenario, construct an emergency control optimization model based on the sensitivity, determine an optimal emergency control strategy for coordinating generator tripping and load shedding control measures through the emergency control optimization model, and perform emergency control coordination according to the optimal emergency control strategy until the system units return to stability.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the emergency control coordination method for power system power angle stability according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the emergency control coordination method for power system power angle stability according to any one of claims 1-7.

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