Parameter optimization method for low voltage tripping device to reduce load loss during voltage sag

Abstract

A method for optimizing the parameters of a low-voltage tripping device to reduce load losses under voltage sags is provided. Based on the operating characteristics of the low-voltage tripping device under different parameter configurations, a load loss quantitative evaluation system is established. According to the load loss quantitative evaluation system, a low-voltage tripping device parameter optimization problem is established with the goal of minimizing economic losses, and the optimization problem is solved to obtain a parameter optimization configuration scheme for the low-voltage tripping device. The load loss quantitative evaluation system established by the present invention has low error and high precision. The load loss quantitative evaluation system established by this method can calculate the load loss amount under different low-voltage tripping device parameter configurations, provide support for verifying improvement measures for low-voltage tripping device parameters, and can effectively reduce load losses under voltage sags.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag. Background Art

[0002] Low-voltage trip devices, as crucial accessories in circuit breakers, provide low-voltage protection and are widely used in modern power systems. A voltage sag in the main grid can trigger a large number of low-voltage trip devices, seriously threatening the safe and stable operation of the power grid and even causing widespread power outages and significant economic losses. Load loss incidents caused by low-voltage trip device activation are a common occurrence across the country, attracting widespread attention. Therefore, research on methods to mitigate load loss during voltage sags is of great significance.

[0003] Establishing an accurate load loss quantification evaluation system is fundamental to researching methods to reduce load loss during voltage sags. Voltage sag sensitivity tests were conducted to develop a voltage tolerance curve for low-voltage trip devices, revealing their operating characteristics under voltage sags. Based on this, users were categorized by their reported capacity, and a load loss model was developed for each user category based on the voltage tolerance curve of the low-voltage trip device. However, this modeling approach did not consider the parameter configuration of the low-voltage trip device, resulting in significant errors.

[0004] In research on reducing load losses during voltage sags, delay elements should be added to low-voltage tripping devices for equipment less affected by voltage sags. The delay setting should take into account both the grid disconnection time and the equipment's ability to withstand voltage sags. However, existing solutions lack detailed settings for voltage and delay settings for different types of users. Summary of the Invention

[0005] The object of the present invention is to provide a method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag, so as to reduce load loss during voltage sag.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag comprises the following steps:

[0008] S1, based on the operating characteristics of the low-voltage trip device under different parameter configurations, establish a load loss quantitative evaluation system;

[0009] S2, based on the load loss quantitative evaluation system, establishes a low-voltage tripping device parameter optimization problem with the goal of minimizing economic losses, solves the optimization problem, and obtains a parameter optimization configuration scheme for the low-voltage tripping device.

[0010] A further improvement of the present invention is that, in step S1, the load loss quantitative evaluation system is as follows:

[0011]

[0012] Among them, C j is the load tripping ratio of the jth type of user, where U is the voltage at the low voltage tripping device end, U setj is the voltage setting value of the low-voltage tripping device, t is the duration of the voltage sag, T1 and T2 are the boundary values of the voltage sag duration in the fuzzy area, the non-action area, and the action area, respectively. j is the slope of the linear function, b j is the intercept of the linear function on the y-axis.

[0013] A further improvement of the present invention is that the load tripping ratio of the j-th type of user is obtained by solving the voltage tolerance curve of the low-voltage tripping device of each type of user.

[0014] A further improvement of the present invention is that the specific process of step S2 is: classifying users according to the settings of the voltage and delay setting value of the low-voltage trip device, and defining this classification as the first-level classification of users; then classifying users again according to their sensitivity to voltage sag, and defining this classification as the second-level classification of users; establishing an objective function with the goal of minimizing economic losses based on the load loss quantitative evaluation system, the economic losses of each type of user caused by low-voltage operation and power outages, and solving the value of the proportion of users of the u-th second-level classification in the j-th first-level classification of substation i, to obtain the low-voltage trip device parameter configuration of each type of user.

[0015] A further improvement of the present invention is that the objective function for minimizing economic losses is:

[0016]

[0017] where α u , β u ,λ u P are the economic loss coefficients of users in the uth category due to power outage caused by the action of the low-voltage trip device, low-voltage operation caused by the failure of the low-voltage trip device to operate, and low-voltage operation caused by the failure to install the low-voltage trip device; i is the load of substation i; C j is the load tripping ratio of the jth type of user; k iju is the proportion of users of the uth category in the jth category of the first category of substation i; U fi and t fi are the voltage sag amplitude and duration of substation i under fault f, respectively; M and F are the substation set and fault set, respectively; N and R are the set of primary user and secondary user types, respectively;

[0018] The voltage sag amplitude and duration constraints are:

[0019]

[0020] The user ratio constraint is:

[0021]

[0022] in, is the initial proportion of users of the uth second-level category in the jth first-level category of substation i.

[0023] A further improvement of the present invention is that the parameter optimization configuration model of the low-voltage trip device is composed of an objective function with the goal of minimizing economic losses, voltage sag amplitude and duration constraints, and user ratio constraints. The parameter optimization configuration model of the low-voltage trip device is converted into a random chance constrained programming model. The objective function of the random chance constrained programming model is:

[0024]

[0025] Constraints:

[0026]

[0027] Among them, δ is the confidence level given in advance by the decision maker, is the pessimistic value of the objective function before conversion, and R is the set of secondary user types.

[0028] A further improvement of the present invention is that a firefly algorithm is used to solve a random chance constrained programming model to obtain a parameter optimization configuration scheme for the low-voltage tripping device.

[0029] A further improvement of the present invention is that the firefly algorithm is used to solve the random chance constrained programming model to obtain the parameter optimization configuration scheme of the low voltage trip device, which includes the following steps:

[0030] Step 1: Set the number of firefly population individuals V, the initial maximum attraction β0, the light absorption coefficient γ, the step size factor α0, the maximum number of iterations B, and the number of iterations k = 0;

[0031] Step 2: Randomly initialize the position of each firefly i=1,2…,V and calculate the objective function value of each firefly As the maximum fluorescence brightness

[0032] Step 3: Calculate the relative brightness of each firefly in the group and attractiveness where r ijis the distance between fireflies i and j; the direction of firefly movement is determined by relative brightness, and each firefly moves towards the individual with the highest relative brightness to it;

[0033] Step 4: Update the formula according to the position Update the spatial position of each firefly Where rand is a uniformly distributed random real number greater than or equal to 0 and less than 1;

[0034] Step 5: According to the updated position of each firefly Recalculate the brightness of each firefly That is, the objective function value And according to calculate

[0035] Step 6: When the maximum number of iterations B is reached, let Otherwise, the number of iterations k increases by 1, and the process returns to the third step to conduct the next search.

[0036] Step 7: Output the global extreme point x p and the optimal individual value f(x p ).

[0037] Compared with existing technologies, the present invention has the following advantages: It establishes a load loss quantification evaluation system based on the voltage sag operating characteristics of the low-voltage trip device. As a result, the established load loss quantification evaluation system has low error and high accuracy. The load loss quantification evaluation system established by this method can calculate the load loss under different low-voltage trip device parameter configurations, providing support for verifying improvement measures for low-voltage trip device parameters.

[0038] Furthermore, the present invention establishes a low-voltage tripping device parameter optimization configuration model with the goal of minimizing economic losses. The setting scheme of the low-voltage tripping device voltage and delay setting value obtained by solving the low-voltage tripping device parameter optimization configuration model can effectively reduce the load loss under voltage sag. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the comprehensive voltage tolerance curve of the low voltage trip device.

[0040] Figure 2 The comprehensive voltage tolerance curves of the low-voltage trip device for each user type are shown in Figure 1. (a) is the comprehensive voltage tolerance curve of the low-voltage trip device with the parameter configuration of {0.4, 0s}, (b) is the comprehensive voltage tolerance curve of the low-voltage trip device with the parameter configuration of {0.5, 0s}, and (c) is the comprehensive voltage tolerance curve of the low-voltage trip device with the parameter configuration of {0.6, 0s}.

[0041] Figure 3 Flowchart of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] See also Figure 3 The present invention provides a method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag, the method comprising the following two steps:

[0044] S1. Based on the operating characteristics of the low-voltage trip device under different parameter configurations, a load loss quantitative evaluation system is established to reduce the error of the load loss quantitative evaluation system;

[0045] S2, established a low-voltage tripping device parameter optimization problem with the goal of minimizing economic losses, and proposed a parameter optimization configuration scheme for the low-voltage tripping device based on the firefly algorithm to solve the optimization problem, thereby reducing load losses under voltage sag.

[0046] The specific process of the present invention is as follows:

[0047] First, users are classified according to the voltage and delay setting value of the low-voltage tripping device, and the load tripping ratio C of each type of user is used to determine the load tripping ratio. j Calculate the load loss P of the substation sag .Right now

[0048] P sag =∑PC j k j (1)

[0049] Where, P is the load of the substation, C j is the load tripping ratio of the jth type of user, k j is the ratio of the j-th user load to the total load P of the site.

[0050] From the above formula, we can see that the key to constructing a quantitative evaluation system for load loss is to solve the load tripping ratio C of each type of user. j .

[0051] Secondly, according to the voltage tolerance curve of the low voltage trip device of each type of user (such as Figure 1 ) Solve for the load tripping ratio C j The voltage tolerance curve of the low voltage trip device is as follows: Figure 1As shown. Figure 1 It can be seen that the load tripping ratio C of the jth type of user is j is a piecewise function. When the voltage sag amplitude and duration at the low voltage tripping device end are Figure 1 In the inaction area, C j =0; if in Figure 1 In the action area, C j =0; if in Figure 1 In the fuzzy area, the low-voltage tripping device partially operates, so it is also necessary to determine the load tripping ratio C of the j-th type of user in the fuzzy area. j .

[0052] Considering that the lower the voltage sag amplitude is, the more the low-voltage tripping device will operate, in order to simplify the model and analysis, a simple linear curve is used to describe the relationship between the load tripping ratio and the voltage sag amplitude in the fuzzy area, that is, C j =a j U+b j where a j is the slope of the linear function, b j The intercept of the linear function on the y-axis. The load tripping ratio function C of the j-th type of user j for:

[0053]

[0054] Among them, U is the voltage at the low voltage tripping device end, U setj is the voltage setting value of the low-voltage tripping device, t is the duration of the voltage sag, T1 and T2 are the boundary values of the voltage sag duration between the fuzzy area, the non-action area and the action area respectively.

[0055] The voltage action setting value of the low-voltage tripping device for the jth category user is set to U setj , that is, when the voltage is greater than U setj When , the low voltage tripping device will not act, so we can know that the starting point of the linear function in the fuzzy region (U setj ,0) should be (U setj , 0), substitute this point into C j =a j U+b j In the middle, we can get b j =-a j U setj Therefore, the only unknown parameter to be determined is a j .

[0056] Finally, the load tripping ratio C of the j-th type of user is solved by the least squares method. j =a j Ua j U setj The unknown parameter a inj .

[0057] Assume that users are divided into n categories, and the load proportion of the j-th category user in substation i is k ij , under fault f, the voltage sag amplitude of substation i is U fi The duration of the voltage sag is t fi . Due to the voltage sag amplitude U fi It is the substation voltage rather than the user end voltage. The voltage sag amplitude U fi Pre-multiplication voltage proportional coefficient k u To replace the user's overall voltage sag amplitude. u U fi and voltage sag duration t fi Substituting into the above formula (2), we can get the load tripping ratio C of the j-th type of user in substation i: ij .Right now

[0058]

[0059] Then the total load tripping ratio C of substation i is ti for:

[0060] C ti =k i1 C i1 +k i2 C i2 +……+k in C in (4)

[0061] Among them, k i1 is the load proportion of the first category users in substation i, C i1 is the load tripping ratio of the first category of users in substation i, k i2 is the load proportion of the second type of users in substation i, C i2 is the load tripping ratio of the second category users in substation i, k in is the load proportion of the nth type of user in substation i, C in is the load tripping ratio of the nth type of user in substation i.

[0062] Assume that the number of substations affected by the fault is m, and the actual load tripping ratio of substation i is C tri , then the square sum of the load tripping ratio deviation S is:

[0063]

[0064] In order to minimize the deviation, the above formula (5) is applied to a j Take the derivative and set it to 0, that is

[0065]

[0066] Among them, when C ij =0 or C ij =1, A ij = 0. When C ij =a j k u U fi -a j U setj When A ij =k ij (k u U fi -U setj ). Therefore, in order to find all unknown parameters a j The selected fault f should make the load tripping ratio function C of at least two substations ij All functions are in the fuzzy region of n types of users and the number of substations affected by the fault is m≥2.

[0067] Solve the above equation (6) and get a j and b j The value of load loss is used to complete the construction of the load loss quantitative evaluation system.

[0068] Based on the load loss quantitative evaluation system, an optimization problem is established and solved to optimize the parameters of the low-voltage tripping device to reduce load loss during voltage sag. The specific steps are as follows:

[0069] First, users are classified according to the voltage and delay setting values of the low-voltage trip device. This classification is defined as the first-level user classification. Based on this classification, users are further classified according to their sensitivity to voltage sags. This classification is defined as the second-level user classification. Based on the economic losses caused by low voltage operation and power outages to each type of user, an objective function with the goal of minimizing economic losses is established:

[0070]

[0071] Among them, α u , β u ,λ u P are the economic loss coefficients of users in the uth category due to power outage caused by the action of the low-voltage trip device, low-voltage operation caused by the failure of the low-voltage trip device to operate, and low-voltage operation caused by the failure to install the low-voltage trip device; i is the load of substation i; C j is the load tripping ratio function of the jth type of first-level classification user; k iju is the proportion of users of the uth category in the jth category of the first category of substation i; U fi and t fiare the voltage sag amplitude and duration of substation i under fault f, respectively; M and F are the substation set and fault set, respectively; N and R are the sets of primary and secondary user types, respectively.

[0072] From the objective function, we can know that the proportion k of users in the u-th category of the j-th category of the first-level classification of substation i is calculated. iju The value of will get the low voltage trip device parameter configuration for each type of user.

[0073] The voltage sag amplitude and duration constraints are:

[0074]

[0075] The user ratio constraint is:

[0076]

[0077] in, is the initial proportion of users of the uth second-level category in the jth first-level category of substation i.

[0078] The comprehensive objective function and constraint conditions form the low-voltage trip device parameter optimization configuration model.

[0079] Secondly, the fault f in the model occurs randomly, C j and α u , β u ,λ u are related to the voltage and duration of the substation under fault f, so C j and α u , β u ,λ u is a random variable, and the optimization problem is a random optimization problem.

[0080] In order to solve the optimization problem, the model is converted into a random chance constrained programming model, and the objective function after conversion is:

[0081]

[0082] The new constraints are:

[0083]

[0084] Among them, δ is the confidence level given in advance by the decision maker, is the pessimistic value of the objective function before transformation.

[0085] Therefore, the converted low-voltage trip device parameter optimization configuration model is:

[0086] Objective function:

[0087]

[0088] Constraints:

[0089]

[0090] Finally, in order to solve the low-voltage trip device parameter optimization configuration model, the present invention uses the firefly algorithm to solve it, and the steps are as follows:

[0091] Step 1: Initialize the basic parameters of the algorithm. Set the number of firefly population V, the initial maximum attraction β0, the light absorption coefficient γ, the step size α0, the maximum number of iterations B, and the number of iterations k = 0.

[0092] Step 2: Randomly initialize the position of each firefly (i=1,2…,V) and calculate the objective function value of each firefly As the maximum fluorescence brightness

[0093] Step 3: Calculate the relative brightness of each firefly in the group and attractiveness where r ij is the distance between fireflies i and j; the direction of movement of the fireflies is determined by the relative brightness, and each firefly moves towards the individual with the largest relative brightness to it.

[0094] Step 4: Update the formula according to the position Update the spatial position of each firefly Where rand is a uniformly distributed random real number greater than or equal to 0 and less than 1.

[0095] Step 5: According to the updated position of each firefly Recalculate the brightness of each firefly That is, the objective function value And according to calculate

[0096] Step 6: When the maximum number of iterations B is reached, let And go to the next step; otherwise, the number of iterations k increases by 1, and returns to the third step to perform the next search.

[0097] Step 7: Output the global extreme point x p and the optimal individual value f(x p ).

[0098] The embodiment of the present invention is further described below by using an example. The following is only an example of the embodiment of the present invention, and the embodiment of the present invention is not limited thereto.

[0099] Taking an accident in a certain area where 977.4MW of load was lost due to large-scale operation of low-voltage tripping devices as an example, the accuracy of the load loss quantitative evaluation system and the rationality of the low-voltage tripping device parameter optimization configuration model were verified.

[0100] The accident caused a total of six 330kV substations to lose load. The substations are denoted as A, B, C, D, E, and F. The load P before the substation failure and the load loss caused by the accident are summarized in Table 1 below.

[0101] Table 1 Substation load loss

[0102]

[0103] According to actual research, the majority of low-voltage trip devices in this region are from Hangzhou Shen Electric, Zhejiang Alstom, Jiangsu Kaifan, and Zhejiang Chint. The delay interval for each low-voltage trip device is 1 second, and the delay time setting is mostly within 3 seconds. The voltage setting values are mostly set at 0.4, 0.5, and 0.6 pu. Therefore, users can be divided into 13 categories based on their low-voltage trip device parameter configuration (including those without a low-voltage trip device installed). Each category of users uses a low-voltage trip device from Hangzhou Shen Electric, Zhejiang Alstom, Jiangsu Kaifan, and Zhejiang Chint. Taking substation A as an example, the user classification and proportion are summarized in Table 2 below (parameter configuration is expressed as {voltage setting value, delay time}).

[0104] Table 2 Classification and load ratio of users in substation A

[0105]

[0106] First, draw the voltage tolerance curves of user types {0.4, 0s}, {0.5, 0s} and {0.6, 0s} as shown in the figure. Figure 2 As shown in (a), (b) and (c).

[0107] Secondly, a load loss quantitative evaluation system was constructed based on the plotted voltage withstand curves. Since the voltage withstand curve with added delay is shifted rightward along the time axis by a corresponding amount of time, the load tripping proportional function with added delay can be calculated by determining the load tripping proportional function with no delay.

[0108] By calculating the actual loss ratio of substation load C tr , user proportion k, voltage sag duration t = 68ms, voltage sag amplitude U of substations A, B, C, D, E, and F T =[0.51, 0.538, 0.484, 0.481, 0.534, 0.497], voltage proportional coefficient k u=0.5 and the initial points (0.4,0), (0.5,0), (0.6,0) of the user types {0.4,0s}, {0.5,0s}, {0.6,0s} are substituted into the formula of the load loss tripping proportional function. Using the least squares method, the load tripping proportional function for the user types {0.4,0s}, {0.5,0s}, {0.6,0s} can be obtained as follows:

[0109]

[0110]

[0111]

[0112] Taking {0.4, 1s} as an example, the load tripping proportional function of this type is to shift the load tripping proportional function of {0.4, 0s} to the right by 1s. Therefore, the load tripping proportional function of the user type {0.4, 1s} is:

[0113]

[0114] By analogy, other load tripping proportional functions with increased delay can be obtained, completing the establishment of a quantitative evaluation system for load loss.

[0115] Finally, the voltage sag amplitude U of substations A, B, C, D, E, and F in the accident is calculated. T , voltage proportional coefficient k u , voltage sag duration t, load P and user proportion k are substituted into the established load loss quantitative evaluation system to calculate the load loss of this accident. The accuracy of the load loss evaluation system is verified by comparing the actual load loss of each substation. The results are shown in Table 3 below.

[0116] Table 3 Comparison between calculated and actual values of substation load loss

[0117]

[0118]

[0119] The results show that the constructed load loss quantitative evaluation system has high accuracy.

[0120] During the process of optimizing the parameters of the low-voltage trip device, we first divided users into three categories based on their sensitivity to voltage sags: users with precision instruments (such as hospitals and high-tech enterprises), and industrial users without precision instruments (such as residents and hotels). Through field research, we determined the proportion of substation load contributed by each user. Based on the economic losses incurred by each user category during power outages and low-voltage operation, we developed economic loss coefficients α, β, and λ for each user category.

[0121] Secondly, a parameter optimization model for low-voltage tripping devices in Shaanxi Province was constructed, with a confidence level δ set to 0.9. Various faults were simulated using PSASP software, and the voltage at each substation during the fault period was recorded, assuming that each fault had the same probability of occurrence. The model was solved using a MATLAB program with the firefly population size V = 600, maximum attraction β0 = 0.2, light absorption coefficient γ = 1, step size α0 = 0.25, and maximum number of iterations B = 5000. The data was then substituted into the load loss quantification evaluation system to calculate the load loss at the optimized substation. The results are shown in the table below.

[0122] Table 4 Load loss of substation after optimization

[0123]

[0124] The optimized scheme loses a total of 221.36MW of load, which is a total reduction of 756.04MW of load loss compared to the 977.4MW loss before optimization. The results show that the low-voltage trip device parameter optimization configuration scheme has a good effect.

Claims

1. A method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag, characterized in that: The following steps are involved: S1, based on the operating characteristics of the low-voltage trip device under different parameter configurations, establish a load loss quantitative evaluation system; S2, based on the load loss quantitative evaluation system, establishes a low-voltage trip device parameter optimization problem with the goal of minimizing economic losses, solves the optimization problem, and obtains a parameter optimization configuration scheme for the low-voltage trip device; The specific process of step S2 is as follows: users are classified according to the settings of the voltage and delay setting value of the low-voltage trip device, and this classification is defined as the first-level classification of users. Then, users are further classified according to their sensitivity to voltage sag, and this classification is defined as the second-level classification of users. Based on the load loss quantitative evaluation system and the economic losses of each type of user due to low voltage operation and power outages, an objective function with the goal of minimizing economic losses is established, and the proportion of users in the second-level classification of category u in the first-level classification of category j of substation i is solved to obtain the parameter configuration of the low-voltage trip device for each type of user. The objective function with the goal of minimizing economic losses is: where α u , β u ,λ u P are the economic loss coefficients of users in the uth category due to power outage caused by the action of the low-voltage trip device, low-voltage operation caused by the failure of the low-voltage trip device to operate, and low-voltage operation caused by the failure to install the low-voltage trip device; i is the load of substation i; C j is the load tripping ratio of the jth type of user; k iju is the proportion of users of the uth category in the jth category of the first category of substation i; U fi and t fi are the voltage sag amplitude and duration of substation i under fault f, respectively; M and F are the substation set and fault set, respectively; N and R are the set of primary user and secondary user types, respectively; The voltage sag amplitude and duration constraints are: The user ratio constraint is: in, is the initial proportion of users of the uth second-level category in the jth first-level category of substation i.

2. The method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag according to claim 1, characterized in that: In step S1, the load loss quantitative evaluation system is as follows: Among them, C j is the load tripping ratio of the jth type of user, where U is the voltage at the low voltage tripping device end, U setj is the voltage setting value of the low-voltage tripping device, t is the duration of the voltage sag, T1 and T2 are the boundary values of the voltage sag duration in the fuzzy area, the non-action area, and the action area, respectively. j is the slope of the linear function, b j is the intercept of the linear function on the y-axis.

3. The method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag according to claim 2, characterized in that: The load tripping ratio of the jth type of user is obtained based on the voltage tolerance curve of the low-voltage tripping device of each type of user.

4. The method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag according to claim 1, characterized in that: The objective function with the goal of minimizing economic losses, the voltage sag amplitude and duration constraints, and the user ratio constraints constitute the low-voltage trip device parameter optimization configuration model. The low-voltage trip device parameter optimization configuration model is converted into a random chance constrained programming model. The objective function of the random chance constrained programming model is: Constraints: Among them, δ is the confidence level given in advance by the decision maker, is the pessimistic value of the objective function before conversion, and R is the set of secondary user types.

5. The method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag according to claim 4, characterized in that: The firefly algorithm is used to solve the random chance constrained programming model and the parameter optimization configuration scheme of the low voltage trip device is obtained.

6. The method for optimizing parameters of a low-voltage tripping device for reducing load loss during voltage sag according to claim 5, characterized in that: The firefly algorithm is used to solve the random chance constrained programming model to obtain the parameter optimization configuration scheme of the low-voltage trip device, which includes the following steps: Step 1: Set the number of firefly population individuals V, the initial maximum attraction β0, the light absorption coefficient γ, the step size factor α0, the maximum number of iterations B, and the number of iterations k = 0; Step 2: Randomly initialize the position of each firefly i=1,2…,V and calculate the objective function value of each firefly As the maximum fluorescence brightness Step 3: Calculate the relative brightness of each firefly in the group and attractiveness where r ij is the distance between fireflies i and j; the direction of firefly movement is determined by relative brightness, and each firefly moves towards the individual with the highest relative brightness to it; Step 4: Update the formula according to the position Update the spatial position of each firefly Where rand is a uniformly distributed random real number greater than or equal to 0 and less than 1; Step 5: According to the updated position of each firefly Recalculate the brightness of each firefly That is, the objective function value And according to calculate Step 6: When the maximum number of iterations B is reached, let And go to the next step; otherwise, the number of iterations k increases by 1, and returns to the third step to perform the next search; Step 7: Output the global extreme point x p and the optimal individual value f(x p ).

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