A method and device for generating fault data of a hydroelectric power station excitation system

Abstract

The application discloses a kind of hydroelectric station excitation system fault data generation method, device, belongs to hydroelectric station excitation system fault technical field.The method of the present application includes the following steps: building hydroelectric station hydroelectric generator set single machine and grid model;The hydroelectric station hydroelectric generator set single machine and grid model contains excitation system model, speed regulator model, generator model, water turbine model and grid model;Through in different input state working condition, using improved large field search algorithm in each iteration process to search adjustable excitation parameter in excitation system model is simulated by hydroelectric station hydroelectric generator set single machine and grid model after, to obtain fault data.The present application first builds hydroelectric station hydroelectric generator set single machine and grid model, then based on this model carries out variable parameter and variable working condition and variable excitation parameter experiment can find out fault data.

Description

TECHNICAL FIELD

[0001] The application relates to a method and device for generating fault data of an excitation system of a hydropower station, and belongs to the technical field of excitation system faults of hydropower stations. BACKGROUND

[0002] Improper parameter setting of an excitation system of a hydropower station easily leads to abnormal output of a regulator. In actual production and operation of the hydropower station, if the abnormal regulation of the excitation system is not found in time, voltage imbalance and other problems may be caused, and then the output power of the generator is unstable, thereby affecting the operation stability of the unit or the power grid, and even causing equipment damage in serious cases. For example, the operation of the hydropower station unit once caused frequency oscillation of the power grid, and the reason was that forced oscillation in the excitation system caused the forced oscillation. The forced oscillation is mainly caused by the output of the PSS (Power System Stabilizer). According to an analysis report of a certain research unit, due to the superposition of the low-field output PSS output, the amplitude-frequency and phase-frequency characteristics of the AVR (Automatic Voltage Regulator) link are changed, and therefore the PSS parameters for compensating the phase may not be completely suitable. The greater the low-field output is, the greater the influence of the PSS output may be. The above problem is only a typical representative of improper parameter setting, and cannot fully reflect the regulation problems caused by improper parameter setting. Therefore, how to avoid improper parameter setting of the excitation and ensure the safety of the power system and related electrical equipment has become a technical problem to be solved.

[0003] The acquisition and application of the fault data generated by the actual operation of the excitation system of the current hydropower station mainly depend on the integrity, timeliness, confidentiality and storage of the data recording, for example, a method can directly extract data in the power station monitoring device, since the excitation system of the hydropower station mostly does not have an independent fault recording device, the operation personnel usually call data from the monitoring system, and the monitoring data generally do not contain intermediate scalar data of excitation, such as the adjustment output of AVR, the output of PSS and other key data, which leads to the lack of key characteristics in fault analysis and diagnosis, and the quantitative analysis is more difficult, at the same time, due to the privacy of these operation data, usually involving sensitive information, the acquisition and sharing are limited by strict privacy and security regulations, therefore, it is particularly difficult to collect different fault type data of the excitation system of the hydropower station under different operating conditions. Another method is field test method, which finds out the optimal adjustment parameter by simulating the influence of different adjustment parameters on voltage output under different working conditions, but in the actual simulation process, due to the limitation of power generation conditions and safety factors, the tested parameters are limited, and the operation state of the whole parameter domain of the excitation system cannot be reflected, and it is also not easy to find out the parameter set that can induce adjustment failure. Therefore, based on the above problems, a detailed model of the excitation system of the hydropower station is established by using professional simulation modeling technology, and the fault data is generated by simulating different fault scenes, which has become the mainstream technical means. In recent years, some researchers at home and abroad have proposed some methods for generating fault data of the excitation system, the existing research mainly focuses on the faults caused by the damage of the excitation system itself, but the method still has certain limitations, and the adjustment failure caused by improper parameter configuration is not considered, therefore, it is urgent to invent a data generation method for abnormal output of the regulator caused by improper parameter configuration of the excitation system of the hydropower station to make up for the deficiency of the existing excitation fault data. SUMMARY

[0004] The application provides a hydropower station excitation system fault data generation method and device, which first builds a single unit of a hydropower station hydro-generator set into a power grid model, then carries out variable parameter and variable working condition (variable frequency, variable reactive power) and variable excitation parameter experiment based on the model to find out fault data.

[0005] The technical scheme of the application is as follows:

[0006] According to the first aspect of the application, a hydropower station excitation system fault data generation method is provided, which comprises the following steps: building a single unit of a hydropower station hydro-generator set into a power grid model; the single unit of the hydropower station hydro-generator set into the power grid model comprises an excitation system model, a speed regulator model, a generator model, a water turbine model and a power grid model; through the improved large domain search algorithm, the adjustable excitation parameters in the excitation system model are searched in each iteration process under different input state working conditions, and then the single unit of the hydropower station hydro-generator set into the power grid model is simulated to obtain fault data.

[0007] Further, the improved large domain search algorithm is used to search the adjustable excitation parameters in the excitation system model in each iteration process, specifically:

[0008] S1: Select the adjustable excitation parameters in the excitation system model, determine the excitation system model parameter set X i =[x i 1,x i 2 ,x i 3 ,…x j i m ]Specific parameter composition; Wherein x i j i represents the jth adjustable excitation parameter under the input state δ i ;

[0009] S2: Construct the discretization encoding method of the excitation system model parameter set X i =[x i 1 ,x i 2 ,x i 3 ,…x j i m ]; m i ;

[0010] S3: Set the first input state δ1, that is, determine (W G 1 ,V G 1 ), and determine other input states, a total of z input states are set; Wherein (W G 1 ,V G 1 ) represents the given frequency per unit value W G and the given voltage per unit value V G of the generator terminal under the first input state;

[0011] S4: Construct the scaling generation method of X 1 , determine the number of sets composed of X 1 u, and construct the G 1 (old) set, that is u>2, u1,u2∈u, represents the uth adjustable excitation parameter set;

[0012] S5: Perform parameter search on G 1 (old);

[0013] S6: Construct the evaluation function in the search process to simulate the search result Y 1The evaluation value exceeding a preset threshold value is determined as fault data;

[0014] S7: After repeating steps S5-S6, G 1 is selected 1 is the adjustable excitation parameter set corresponding to the fault data in the first input state.

[0015] S8: Change the input state, change δ1 to δ2 in S3, continue to execute S3-S7 to select G 2 , repeat z times until G 1 (total)={G 2 (total)={G Z (total)={G Output result; is represented as the output result generated by each adjustable excitation parameter set in G i in the input state δ i .

[0016] Further, the discrete coding method of the adjustable parameters of the excitation model is as follows: for each parameter in X i , randomly extract it from a selectable set containing D elements, and the value of any parameter x j i can be represented as x j i ∈W j , W j ={w j(1) i ,w j(2) i ,w j(3) i …w j(d) i …w j(D) i}; wherein W j is the selectable value set of the adjustable excitation parameter x j i , w j(d) i is the dth value result of the jth parameter in the ith input state.

[0017] Further, the calculation formula of the dth value w j(d) i of the jth parameter in the ith input state is:

[0018]

[0019] Wherein, min(x) j i ), max(x j i ) represent x respectively j i The minimum and maximum values ​​of .

[0020] Furthermore, the input state δ i The setting range is as follows: V G i ∈[0.8,1.2];W G i ∈[1-0.004,1+0.004]; where, V G i W G i This represents the per-unit value of the generator terminal voltage and the per-unit value of the hydro-generator set under the i-th input state.

[0021] Furthermore, regarding G i (old) Perform parameter search, as follows:

[0022] The specific operation strategy of the first local search strategy is as follows: under the i-th input state, from G... i The set of excitation parameters randomly selected from (old) is the l-th group. Any j-th parameter x in j i In {w j(1) i ,w j(2) i ,w j(3) i …w j(d) i …w j(D) i The new set of excitation parameters (group l) is generated by exchanging it with other values ​​within the set range.

[0023] The specific operation strategy of the second local search strategy is as follows: under the i-th input state, from G... i Any g-th parameter x from the l1-th set of excitation parameters randomly selected in (old) g i The current value and any p-th parameter x of the randomly selected l2-th group of excitation parameters. p i The current value is mapped in its respective set, and g ≠ p.

[0024] Furthermore, the specific process of repeatedly executing steps S5-S6 is as follows:

[0025] S51, set in G i (old) represents the maximum number of iterations for the search, with a total of z possible states; the initial state i = 1, and the number of iterations gen = 1.

[0026] S52. Execute the first local search strategy and evaluate the set of excitation parameters participating in the first local search strategy using the evaluation function. If either of the two conditions in the first evaluation rule is met, then... Store G i In the (new) set, G at the same time i Removed from the (old) set If neither condition is met, execute S53;

[0027] S53. Execute the second local search strategy and evaluate the two sets of excitation parameters participating in the second local search strategy using the evaluation function. If either of the two conditions in the second or third evaluation rule is met, then the set of excitation parameters that meet the conditions is stored in G. i In the (new) set, G at the same time i Removed from the (old) set or If either of the two conditions in the second evaluation rule is satisfied, and either of the two conditions in the third evaluation rule is satisfied, then both sets of excitation parameters that satisfy the conditions are stored in G. i In the (new) set, G at the same time i Removed from the (old) set and Otherwise, execute S54;

[0028] S54. Repeat S52-S53 until the termination condition of the current state is met, then proceed to the next state, until all z states have been executed and the process ends.

[0029] Furthermore, the first evaluation rule is specifically as follows:

[0030]

[0031] in, This is the new set of excitation parameters (V) generated based on the first local search strategy; G i The per-unit voltage value is given to the generator terminal under the i-th input state; The adjustable excitation parameter combination under the i-th input state is as follows The fault data evaluation function during configuration; data_fault is the generation condition of the excitation fault data: if Any one of the two conditions is met, then put Into the G i (new) set, while G i (old) set is removed

[0032] Further, the second evaluation rule is specifically:

[0033]

[0034] Wherein, The new 11th set of excitation parameters generated according to the second local search strategy; V G i The given voltage per unit at the generator end under the i-th input state; The adjustable excitation parameter combination under the i-th input state is The fault data evaluation function during configuration; data_fault is the generation condition of the excitation fault data: if Any one of the two conditions is met, then put Into the G i (new) set, while G i (old) set is removed

[0035] Further, the third evaluation rule is specifically:

[0036]

[0037] Wherein, The new 12th set of excitation parameters generated according to the second local search strategy; V G i The given voltage per unit at the generator end under the i-th input state; The adjustable excitation parameter combination under the i-th input state is The fault data evaluation function during configuration; data_fault is the generation condition of the excitation fault data: if Any one of the two conditions is met, then put Into the G i (new) set, while G i (old) set is removed

[0038] According to a second aspect of the present application, a hydroelectric station excitation system fault data generation device is provided, comprising: a model construction module for building a hydroelectric station hydroelectric generator set single machine and grid model; the hydroelectric station hydroelectric generator set single machine and grid model comprises an excitation system model, a speed regulator model, a generator model, a water turbine model and a grid model; an obtaining module for obtaining fault data by simulating the hydroelectric station hydroelectric generator set single machine and grid model after searching the adjustable excitation parameters in the excitation system model in each iteration process by using the improved large domain search algorithm under different input state conditions.

[0039] The present application has the following advantages:

[0040] Firstly, the present application solves the problem of fault identification caused by excitation system multi-parameter adjustment and improves the efficiency of excitation system fault data generation. Specifically, due to the complexity of hydroelectric generator excitation system parameters and the large number of parameters, traditional manual methods or exhaustive methods cannot cope with the complex fault waveform identification caused by parameter changes; the present application can efficiently cope with changes in more than ten excitation parameters through the design of ILDSA algorithm, and quickly locate fault data through the domain search strategy, providing a powerful tool for system fault analysis and optimization. In addition, by designing the improved domain search algorithm (ILDSA), the fault data caused by improper configuration of the hydroelectric generator excitation system parameters can be automatically and efficiently identified and searched; compared with the traditional enumeration method or exhaustive algorithm, the ILDSA algorithm can quickly and accurately find fault waveforms, greatly improving the efficiency and accuracy of the data generation process and avoiding the tedious work of manual checking.

[0041] Secondly, the present application builds a hydroelectric station hydroelectric generator set single machine and grid model comprising an excitation system, a speed regulator, a water turbine, a generator and a grid, which covers multiple complex system components; based on this model, various operating conditions and parameter variation experiments are carried out, so that the fault data of the excitation system can be comprehensively and efficiently identified, and the accuracy and applicability of fault diagnosis are improved.

[0042] Thirdly, the present application provides a reasonable reference for the parameter configuration of the hydroelectric station excitation system through the generation and analysis of fault data; by reasonably configuring the excitation system parameters, the faults caused by improper parameters can be effectively avoided, the stability and reliability of the hydroelectric generator set are improved, and the overall operation efficiency and safety of the hydroelectric station are improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a hydroelectric station hydroelectric generator set single machine and grid operation model;

[0044] Figure 2 is a fault waveform curve. DETAILED DESCRIPTION

[0045] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work under the premise that the embodiments in the present application fall within the scope of protection of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in any manner without conflict.

[0046] Since the number of excitation parameters is as many as more than ten, the method of searching by using the exhaustion method and manually checking cannot be completed, and thus the present application provides a hydropower station excitation system fault data generation method and device, so that the algorithm can efficiently search for the fault data of the hydropower station excitation system, and provide a reasonable reference basis for excitation parameter configuration.

[0047] As shown in Figures 1-2 According to a first aspect of the embodiments of the present application, a hydropower station excitation system fault data generation method is provided, which comprises: building a hydropower station hydro-generator unit single-machine integrated power grid model as shown in Figure 1 The hydropower station hydro-generator unit single-machine integrated power grid model comprises an excitation system model, a governor model, a generator model, a hydraulic turbine model and a power grid model; by searching for adjustable excitation parameters in the excitation system model in each iteration process by using an improved large domain search algorithm ILDSA under different input state conditions, simulation is performed on the hydropower station hydro-generator unit single-machine integrated power grid model to obtain fault data.

[0048] The governor model is as follows:

[0049] C1(s)=D1(s)*Y1(s);

[0050]

[0051] W ERR (s)=W G (s)-W1(s);

[0052] Y g (s)=W ERR (s)*c1(s);

[0053] In the formula, C1(s) is a transfer function of the governor model, D1(s) is a frequency dead zone single-machine integrated power grid model of the speed regulating system, Y1(s) is an actuator model of the single-machine integrated speed regulating system; T y is a servomotor non-moving time constant, and s is a Laplace factor; K P is a controller PID proportional link gain, KI Kp is the controller PID proportional gain D Kd is the controller PID derivative gain; b p Kt is the controller integral gain; T 1v Td is the derivative time constant; e fH Wu is the upper bound of the frequency range of the isolated grid; e fL Wl is the lower bound of the frequency dead zone of the isolated grid; W ERR (s) is the frequency error, W G (s) is the given frequency of the hydro-generator unit, W1(s) is the terminal frequency of the generator, Y g (s) is the governor output.

[0054] The hydro-turbine model is as follows:

[0055]

[0056] P m (s) = P1(s) * Y g (s) ;

[0057] In the formula, P1(s) is the transfer function of the hydro-turbine model, T w is the water flow inertia time constant, a and b are the hydro-turbine model coefficients, P m (s) is the mechanical power of the hydro-turbine.

[0058] The excitation system model is as follows:

[0059] [V ERR (s) + P2(s) ] * E(s) = E f (s) ;

[0060] V G (s) - V t (s) = V ERR (s) ;

[0061]

[0062] In the formula, V ERR (s) is the difference between the given voltage and the terminal voltage of the generator, P2(s) is the transfer function of the PSS, E(s) is the overall transfer function of the excitation system, E f (s) is the excitation voltage input to the generator by the excitation system, V G (s) is the given voltage at the terminal of the generator, V t (s) is the transfer function of the terminal voltage output by the generator, V ERR (s) is the transfer function of another input of the excitation system other than the PSS; E AVR (s), E OL (s), E LL(s), E1(s) are transfer functions of AVR part, over excitation limiter, under excitation limiter part and output after low value gate respectively, V L1 is output of low value gate, V H is output voltage of under excitation limiter, V AVR is output voltage of AVR part, V L2 is output voltage of over excitation limiter; K, K A , K F , K H1 , K L , K S , K r , K p are gains of transfer function; K V is model selection factor, taking value of 0 or 1; T1, T2...T 25 , T H1 , T H2 , T L1 , T L2 are time constants of transfer function; G U (s), G L (s), G B (s) are transfer functions of different branches of PSS; ω n is system frequency. In above parameters, decision parameters are: T H1 , T H2 , T L1 , T L2 , K, K H1 , K L , K V , etc. Optimal algorithm can be determined by optimization of decision parameters.

[0063] The generator model is as follows:

[0064]

[0065]

[0066] In the formula, G(s) is transfer function of generator model, P e (s) is transfer function of generator output active power, Q e (s) is transfer function of generator output reactive power, E f (s) and P m (s) are excitation voltage input to generator and mechanical power of water turbine from excitation system respectively; M is moment of inertia, ζ is damping coefficient, R s is stator resistance, X s is synchronous reactance, L is stator inductance, φ is phase difference of terminal voltage and stator current; j represents imaginary number; V t(s) is the transfer function of the generator output terminal voltage, I a (s) is the transfer function of the generator stator current; δ(s) is the Laplace transform of the generator rotor phase angle δ(t); W1(s) is the Laplace transform of the generator rotor angular velocity ω1(t), i.e. the generator terminal frequency.

[0067] The power grid model is as follows:

[0068]

[0069] In the formula, H(s) is the transfer function of the power grid model, W1(s) is the generator terminal frequency, P m (s) is the mechanical power of the hydraulic turbine, M is the moment of inertia, X s is the synchronous reactance, and ζ is the damping coefficient.

[0070] According to the above model transfer function, by changing the generator terminal given voltage unit value V G and the given frequency unit value W G of the hydro-generator unit, different input states δ i are formed. G i ,V G i ), i ∈ {1, 2, 3…z}, wherein δ i represents the i-th input state, (W G i ,V G i ) represents the generator terminal given voltage unit value V G and the given frequency unit value W G of the hydro-generator unit in the i-th input state (here expressed as W G , V G ). W G , V G are actually constants, which are different from W G (s), V G expressed in the above theoretical model. Z represents that z states are generated in the fault generation experiment, and the input state set can be represented as δ = {δ1, δ2, δ3…δ z} = {(W G 1 ,V G 1 ), (W G 2 ,V G 2 ), … (W G z ,V G z )}.

[0071] Set certain input state δ i , in the state of δ i by changing the excitation model parameter set X i =[x1 i ,x2 i ,x3 i …x j i …X m i ], j∈{1,2,3…m}, through the water turbine generator set single machine and grid model simulation after the power plant thus produce different output results Y i =[y1 i ,y2 i ,y3 i …y q i …y k i ], q∈{1,2,3…k}(such as Figure 2 According to the excitation model parameter set corresponding output results, the characteristics of the output results include active power, reactive power, generator terminal frequency, generator output terminal voltage); wherein X i represents the adjustable excitation parameter set under the input state δ i , x j i represents the jth adjustable excitation parameter under the input state δ i , Y i represents the ith output result generated by x i under the input state δ i , y q i represents the qth output characteristics of Y i under the input state δ i .

[0072] The main operation steps of the improved large domain search algorithm ILDSA for the excitation model adjustable excitation parameter X i =[x1 i ,x2 i ,x3 i …x j i …x m i ] include:

[0073] S1. Select the excitation model adjustable excitation parameter, determine the excitation model parameter set X i =[x1 i ,x2 i ,x3 i …xj i …x m i ]Specific parameter composition; wherein, x j i represents the jth adjustable excitation parameter under the input state δ i ;

[0074] S2. Construct the discrete coding method of the excitation model parameter set X i =[x1 i ,x2 i ,x3 i …x j i …x m i ;

[0075] S3. Set the first input state δ1, that is, determine (W G 1 ,V G 1 ), and determine other input states, a total of z input states are set; wherein, (W G 1 ,V G 1 ) represents the given generator terminal voltage reference value V G and the given hydro-generator set frequency reference value W G under the first input state;

[0076] S4. Construct the scaling generation method of X 1 , determine the number of sets u composed of X 1 , and construct the G 1 (old) set, that is, u>2, u1, u2∈u, represents the uth set of adjustable excitation parameters;

[0077] S5. Perform parameter search on G 1 (old);

[0078] S6. Construct the evaluation function CFEI in the search process to evaluate the simulation results Y 1 of each search, and determine that the evaluation value exceeds the preset threshold value as the fault data;

[0079] S7. After repeating steps S5-S6, select the parameter set corresponding to the fault data in the G 1 (old) set to construct the set G 1 (new), that is, the parameter set corresponding to the fault data under the first input state;

[0080] S8. Change the input state, change δ1 in S3 to δ2, continue to execute S3-S7 to select G 2 (new), repeat z times, until the set G(total) = {G 1 (new), G 2 (new)…G Z (new)} is formed, then the algorithm stops, and the corresponding output results of each element of G(total) are recorded. The output results are represented as the output results generated by each set of adjustable excitation parameters in G i (new) under the input state δ i (new).

[0081] By way of example, the adjustable excitation parameters X i of the excitation model can be: i x2 i x3 i …x j i …x m i

[0082] Table 1 Set of adjustable excitation parameters

[0083] Parameter number Parameter name Symbol Value range x1 i ]] Excitation AVR part time constant [CAT 19 ]]> [0.01,100] x2 i ]] Excitation AVR part time constant [CAT 20 ]]> [0.01,100] x3 i ]] Excitation AVR part time constant [CAT 21 ]]> [0.01,100] x4 i ]] Excitation AVR part time constant [CAT 22 ]]> [0.01,100] x5 i ]] Excitation AVR part gain K (0,100] x6 i ]]> Excitation AVR part model selection factor K V ]]> 0 or 1 x7 i ]] Excitation low excitation limit part time constant [CAT H1 ]]> [0.01,100] x8 i ]] Excitation low excitation limit part time constant [CAT H2 ]]> [0.01,100] x9 i ]] Excitation low excitation limit part gain K H1 ]]> [-50, 50] but not 0 x 10 i ]] Excitation over excitation limit part time constant [CAT L1 ]]> [0.01,100] x 11 i ]]> Excitation over excitation limit part time constant [CAT L2 ]]> [0.01,100] x 12 i ]]> Excitation over excitation limit part gain K L ]]> (0,50] x 13 i ]]> PSS part gain K S ]]> (0,100] x 14 i ]]> PSS part gain K r ]]> (0,100] x 15 i ]]> PSS part gain K p ]]> (0,100]

[0084] Further, the discretization encoding method of the adjustable parameters of the excitation model is specifically:

[0085] The selection rule for each parameter in X i is that each is randomly selected from a selectable set containing D elements, for example, the value of any parameter x j i can be represented as x j i ∈ W j , W j = {w j(1) i ,w j(2) i ,w j(3) i …w j(d) i …w j(D) i}, where W j is the selectable value set of the adjustable excitation parameter x j i , and w j(d) i is the dth value result of the jth parameter of the ith input state. ​

[0086] The dth value calculation formula of the jth parameter of the ith input state in the above formula is:

[0087]

[0088] Wherein, min(x j i ), max(x j i ) respectively represent the minimum value, maximum value of x j i , see the value range of Table 1, if min(x j i ) cannot take the value of 0, then take the minimum two digits close to 0.01. For example, the excitation adjustable parameter corresponds to x5 i in Table 1, if the current is the first input state, then min(x5 1 ) = 0.01, max(x5 1 ) = 100, D = 10, d = 5, then w 5(5) 1 = 0.01 + 5 * [(100-0.01) / 10] = 49.96.

[0089] Further, the setting range of the input state δ i is as follows: V G i ∈[0.8,1.2]; W G i ∈[1-0.004,1+0.004].

[0090] Further, the scale generation method is specifically:

[0091] To construct the uth set of adjustable excitation parameters It is explained as follows: Any jth parameter x j i In the optional {w j(1) i ,w j(2) i ,w j(3) i …w j(d) i …w j(D) i} collection is randomly generated.

[0092] Further, parameter search is carried out on G i (old), specifically as follows:

[0093] The first local search strategy is defined as swap_local, and the specific operation strategy is: under the ith input state, any jth parameter x i in the randomly selected il set of excitation parameters in G (old) is exchanged with other values in the set range {w j i , w j(1) i , w j(2) i , w j(3) i …w j(d) i …w j(D) i} to obtain a new parameter x j i (new) = w j(3) i , and r = rand(1, D), rand(1, D) represents a random integer in [1, D], then the new parameter x j i (new) = w j(r) i , r ≠ 3;

[0094] The second local search strategy is defined as map_global, and the specific operation strategy is: under the ith input state, any jth parameter x i in the randomly selected il set of excitation parameters in G j (old) is exchanged with other values in the set range {w i , w p i , w j i , w j(1) i …w j(2) i …w j(3) i} to obtain a new parameter x j(k1) i (new) = w j(D) i , and r = rand(1, D), rand(1, D) represents a random integer in [1, D], then the new parameter x J(k1) i (new) = w p i , r ≠ 3; p(1) i , w p(2) i , w p(3)i …w p(k2) i …w p(D) i} is w p(k2) i , exchange k1, k2, then x j i is w j(k2) i , x p i is w p(k1) i , form a new parameter x j i (new), xp i (new) to construct the excitation parameter set.

[0095] Further, the evaluation function CFEI evaluation method is as follows:

[0096]

[0097] N = (t end i -t start i ) / f sample i

[0098] Where, F CFEI (X i ) is the fault data evaluation function when the adjustable excitation parameter combination is X i configured under the i-th input state, V t i (t) is the machine terminal voltage output per unit value at time t under the i-th input state, t start i is the disturbance start time under the i-th input state, t end i is the disturbance end time under the i-th input state, f sample i is the simulation sampling frequency under the i-th input state, V G i is the generator terminal given voltage per unit value under the i-th input state.

[0099] Further, the search strategy of ILDSA and the CFEI evaluation rules are as follows:

[0100] S51, set the total number of iterations searched in G i (old) as gen, and the total number of states is z; the initial state i = 1, and the initial iteration number gen = 1;

[0101] S52, execute the first local search strategy, using F CFEI evaluate the excitation parameter set participating in the first local search strategy

[0102] wherein, is the new l-th excitation parameter set generated according to the first local search strategy; data_fault is the generation condition of the excitation fault data; if any one of the two conditions is satisfied, then is stored in G i (new) set, while i (old) set is removed if neither of the two conditions is satisfied, execute S53;

[0103] S53, execute the second local search strategy, using F CFEI evaluate the two excitation parameter sets participating in the second local search strategy

[0104]

[0105] if or any one of the conditions is satisfied, then the excitation parameter set satisfying the condition is stored in G i (new) set, while i (old) set is removed or if and any one of the conditions is satisfied, then the two excitation parameter sets satisfying the condition are both stored in G i (new) set, while i (old) set is removed and otherwise, execute S54;

[0106] S54, at the end of each iteration, the iteration number is incremented by 1, and S52-S53 are repeatedly executed until the termination condition of the current state is reached, and then the next state is entered, until all z states are executed and the end; the termination condition of the current state is: the maximum iteration number is reached; or all data in G i (old) is removed (i.e. any of the above termination conditions is satisfied, and the search of the next state is directly entered).

[0107] To verify the effectiveness of the ILDSA proposed in the present application and the accuracy of the model, the following specific test parameters are set: the algorithm is programmed in Matlab 2022a, and the hardware environment is tested on a PC with an Intel core i7 2.80 GHz and a memory of 16 G. The simulation parameters are set as shown in the following table: the adjustable parameter table of the excitation system is set as shown in Table 1, and the inherent parameters or identified parameters of the speed regulator and the water turbine are set as shown in Table 2 y T w = 1.6 s, a = 1, b = 0.5, K P = 4, K I = 3, K D = 1, b P = 4%, e fl = e fH = 0.05 Hz. The inherent parameters of the excitation system are set as shown in Table 2.

[0108] Table 2 Set of fixed parameters of excitation

[0109] [CD AT T1] 36 [T2] 0.00746 [CD AT T3] 0.2587 [CD AT T4] 3.448 [ T5 ] 21.517 [T6] 23.548 ​ 54.3264 [T8] 13.48 [T9] 6 [CAT 10 ]]> 60 [CAT 11 ]]> 76 [CAT 12 ]]> 17 [CAT 13 ]]> 0.046 [CAT 14 ]]> 0.476 [CAT 15 ]]> 1.43 [CAT 16 ]]> 0.00036 [CAT 17 ]]> 0.0306 [CAT 18 ]]> 0.65 [CAT 23 ]]> 0.004 [CAT 24 ]]> 0.04 [CAT 25 ]]> 0.02 K A ]]> 7 K F ]]> 1

[0110] The inherent parameters of the generator and its grid system are set as M = 12969.11 kg·m 2 , ζ = 0 pu, R s = 2.8544 × 10 -3 pu, X s = 1.305 pu, L = 0 pu. The ILDSA algorithm is set as: gen = 100, u = 100, z = 1, D = 5, δ1 = (1.004, 1.1).

[0111] Supplementary partial field failure waveform curve and parameter list 3:

[0112] Table 3 Adjustable parameter values during excitation failure

[0113] Parameter number Parameter name Symbol Value x1 1 ]] Excitation AVR part time constant [CAT 19 ]]> 100 x2 1 ]] Excitation AVR part time constant [CAT 20 ]]> 100 x3 1 ]]> Excitation AVR part time constant [CAT 21 ]]> 10 x4 1 ]] Excitation AVR part time constant [CAT 22 ]]> 1 x5 1 ]]> Excitation AVR part gain K 1 x6 1 ]]> Excitation AVR part model selection factor K V ]]> 1 x7 1 ]]> Excitation low excitation limit part time constant [CAT H1 ]]> 1 x8 1 ]]> Excitation low excitation limit part time constant [CAT H2 ]]> 10 x9 1 ]] Excitation low excitation limit part gain K H1 ]]> -10 x 10 1 ]]> Excitation over excitation limit part time constant [CAT L1 ]]> 9 x 11 1 ]]> Excitation over excitation limit part time constant [CAT L2 ]]> 6 x 12 1 ]]> Excitation over excitation limit part gain K L ]]> 1.5 x 13 1 ]]> PSS part gain K S ]]> 1 x 14 1 ]]> PSS part gain K r ]]> 1 x 15 1 ]]> PSS part gain K p ]]> 80

[0114] According to the technical solution, the water-turbine-generator-unit overall mathematical model is constructed, and the water-turbine-generator-unit overall mathematical model comprises a speed regulator model (including a mechanical model and an electrical model), an excitation system model (including a low excitation limit model, a PSS model, and an over-excitation limit model), a water-turbine model, a generator model, and a power grid model. By inputting working conditions to different models, searching for excitation parameters in each iteration process by using the ILDSA, and simulating, possible faults of the excitation system are found out, that is, the excitation system parameters possibly causing abnormal adjustment are searched for continuously, including related parameters (see Table 1 for a set of excitation adjustable parameters) such as an excitation AVR part time constant, an excitation AVR part gain, excitation low excitation and over-excitation limit, etc. After changing the values of the parameters in the adjustable range and simulating, the result simulation is judged by using a designed fault comprehensive evaluation index (CFEI) in the process of searching for unreasonable excitation parameters in each iteration of the ILDSA, and the fault data are screened and marked, and finally the parameter configuration data with fault marks are generated.

[0115] According to a second aspect of the embodiments of the present application, a hydropower station excitation system fault data generation device is provided, comprising: a model construction module, configured to build a single water-turbine-generator-unit of a hydropower station into a power grid model; the single water-turbine-generator-unit of the hydropower station into the power grid model comprises an excitation system model, a speed regulator model, a generator model, a water-turbine model, and a power grid model; an obtaining module, configured to search for adjustable excitation parameters in the excitation system model in each iteration process by using an improved large domain search algorithm under different input state working conditions, and then simulate by using the single water-turbine-generator-unit of the hydropower station into the power grid model to obtain fault data. The parts not described in detail for each module in the above can be referred to the related description of the embodiments.

[0116] The specific embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the present application.

Claims

1. A method for generating excitation system fault data for a hydroelectric power plant, characterized by, The method comprises the following steps: The water turbine generator unit single machine is connected into the power grid model; the water turbine generator unit single machine connected into the power grid model comprises an excitation system model, a speed regulator model, a generator model, a water turbine model and a power grid model; The improved large domain search algorithm is used to search the adjustable excitation parameters in the excitation system model in each iteration process under different input state conditions, and then the water turbine generator unit single machine connected into the power grid model is simulated to obtain fault data; The improved large domain search algorithm is used to search the adjustable excitation parameters in the excitation system model in each iteration process, and the specific process is as follows: S1: Select adjustable excitation parameters in the excitation system model to determine the parameter set of the excitation system model. Specific parameter composition; among which, Indicated as in input state The next One adjustable excitation parameter; S2: constructing a parameter set of the excitation system model discretization encoding method; S3: set the first input state , determine , and determine other input states, a total of input states are set; wherein, is expressed as the given frequency unit value of the hydroelectric generating set in the first input state and the given voltage unit value at the generator end ; S4: constructing a large-scale generation method of determining the number of sets of constitutions, i.e. , each element in the set is not equal, , represents the first set of adjustable excitation parameter sets; S5: to conducting a parameter search; S6: Constructing an evaluation function in the search process to evaluate the simulation results of each search Evaluation is performed, and if the evaluation value exceeds a preset threshold, the data is determined as fault data. S7: after repeating steps S5-S6, selecting The adjustable excitation parameter set corresponding to the fault data in the set, constituting a set That is, the adjustable excitation parameter set corresponding to the fault data in the first input state S8: change the input state to that in S3 , and continue to execute S3-S7 , select , and repeat execution times until a set is formed , then the algorithm stops, and the output results corresponding to each element are recorded ; The output results are represented as the output results generated by each group of adjustable excitation parameter sets in under the input state . ​ 2. The method of claim 1, wherein, The discretization encoding method for the adjustable parameters of the excitation system model is as follows: For Each parameter in the array is derived from a string containing Randomly selected from an optional set of elements, any parameter The values ​​of can all be expressed as ;in, Adjustable excitation parameters The set of optional numerical values, For the first The first input state The parameter of the first The result of the selected value.

3. The method of claim 2, wherein, The first parameter of the first input state is calculated by the following formula: The first​ ； wherein , respectively denote the minimum value, the maximum value of .

4. The method of claim 1, wherein, The input state The setting range is as follows: ; ]; wherein, represents the generator terminal given voltage unit value and the hydroelectric generating set given frequency unit value under the i-th input state.

5. The method of claim 1, wherein, To A parameter search was performed as follows: The specific operation strategy of the first local search strategy is: any one parameter in the first set of excitation parameters randomly selected from the set of excitation parameters under the first input state ​​​​​​​​ The specific operation strategy of the second local search strategy is as follows: in the first input state, any first parameter in the first set of excitation parameters randomly selected from the second set of excitation parameters is mapped with the current value of any second parameter in the second set of excitation parameters randomly selected from the first set of excitation parameters. ≠ .​​​​​​​ 6. The method of claim 1, wherein, The specific process of repeatedly executing steps S5-S6 is as follows: S51, set in the maximum number of iterations of the search, the number of states in total is z; the initial state , the number of iterations ; S52. Execute the first local search strategy and evaluate the set of excitation parameters participating in the first local search strategy using the evaluation function. If either of the two conditions in the first evaluation rule is met, then... deposit In the set, at the same time Remove from set If neither of the two conditions is met, execute S53. S53, performing a second local search strategy, evaluating two sets of excitation parameter sets participating in the second local search strategy by using the evaluation function : if any one of the two conditions in the second evaluation rule or the third evaluation rule is satisfied, storing the set of excitation parameters satisfying the condition in the set, while eliminating the set of excitation parameters satisfying the condition in the set; : if any one of the two conditions in the second evaluation rule is satisfied and any one of the two conditions in the third evaluation rule is satisfied, storing both sets of excitation parameters satisfying the conditions in the set, while eliminating both sets of excitation parameters satisfying the conditions in the set; : otherwise, performing S54; S54, repeatedly execute S52-S53 until the termination condition of the current state is reached, and then enter the next state, until all z states are executed and end; wherein, a new first set of excitation parameters generated according to a first local search strategy; a new second set of excitation parameters generated according to a second local search strategy; a new first set of excitation parameters generated according to a first local search strategy; a new second set of excitation parameters generated according to a second local search strategy; a new first set of excitation parameters generated according to a first local search strategy; a new second set of excitation parameters generated according to a second local search strategy.

7. The method of claim 6, wherein, The first evaluation rule is as follows: ； wherein, is the generator terminal given voltage reference value in the first input state; is the generator terminal given voltage reference value in the second input state; is the generator terminal given voltage reference value in the third input state; is the generator terminal given voltage reference value in the fourth input state; is the fault data evaluation function when the adjustable excitation parameter combination is configured in the fourth input state; is the generation condition of the excitation fault data: if any one of the two conditions is met, it is stored in the set, and the set is removed from the set. ​​​​​ 8. The method of claim 6, wherein, The second evaluation rule is as follows: ； in, For the first The per-unit value of the generator terminal given voltage under various input conditions; For the first The adjustable excitation parameter combination under the input state is as follows Fault data evaluation function during configuration; Conditions for generating excitation fault data: If If either of the two conditions is met, then... deposit In the set, at the same time Remove from set .

9. The method of claim 6, wherein, The third evaluation rule is as follows: ； in, For the first The per-unit value of the generator terminal given voltage under various input conditions; For the first The adjustable excitation parameter combination under the input state is as follows Fault data evaluation function during configuration; Conditions for generating excitation fault data: If If either of the two conditions is met, then... deposit In the set, at the same time Remove from set .

10. An apparatus for performing the method of generating fault data for a hydroelectric plant excitation system as recited in claim 1, characterized by: It comprises: The model construction module is used to build a water turbine generator unit single machine connected into the power grid model; The water turbine generator unit single machine connected into the power grid model comprises an excitation system model, a speed regulator model, a generator model, a water turbine model and a power grid model; The obtaining module is used to search the adjustable excitation parameters in the excitation system model in each iteration process under different input state conditions by using the improved large domain search algorithm, and then simulate the water turbine generator unit single machine connected into the power grid model to obtain fault data.

Images