Fault current limiter multi-objective optimization configuration method and system based on improved GSA algorithm

Abstract

The invention discloses a fault current limiter multi-objective optimization configuration method and system based on an improved GSA algorithm, and the method comprises the steps: determining the range and the number upper limit of branches where fault current limiters are installed according to a short-circuit current out-of-limit node and a short-circuit current control level of a current power grid; calculating short-circuit current considering nonlinearity of the fault current limiter, and establishing a current limiting effect evaluation index based on the short-circuit current; establishing a fault current limiter multi-target system and constraint conditions which comprehensively consider the economy and the current limiting effect; and adopting an improved GSA algorithm, adaptively adjusting a weight coefficient and a learning factor, and obtaining an optimal configuration scheme of the fault current limiter. According to the technical scheme, the problems that nonlinearity of the current limiter is not considered in a traditional optimal configuration model, the layout range of the current limiter is wide, the solving search amount is large, and a solving algorithm is prone to falling into local optimum are solved, and the aim of achieving the maximum current limiting effect with the minimum configuration capacity can be achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power, and particularly relates to a multi-objective optimal configuration method and system for a fault current limiter based on an improved GSA algorithm. Background Technique

[0002] With the continuous advancement of the new power system and the steady growth of power loads, the power grid structure has become more complex, the connections are closer, and the load aggregation is more obvious, resulting in a significant increase in the short-circuit current level of the power grid, which affects the safe and stable operation of the power system. Traditional methods for limiting short-circuit current mainly include hierarchical and zonal control and busbar splitting operation, etc., which can achieve a good current-limiting effect. However, the above methods involve disadvantages such as basic power grid construction and incomplete networks, and are no longer applicable under the background of the new power system. Therefore, at present, system operators have begun to widely use fault current limiters to limit short-circuit current. The impedance of this device is 0 under normal power grid operation conditions, and it will not increase network losses. During a fault, it can quickly put the impedance into operation to limit the short-circuit current magnitude, and the investment in the fault current limiter is relatively small and the construction period is also short. It is the best choice with both economy and effectiveness under the background of the current new power system.

[0003] However, in a complex network structure, there are often multiple nodes where the short-circuit current exceeds the limit. How to reasonably and effectively arrange the fault current limiter is a major difficulty in current research. In current research, optimization models are mostly established with objectives such as investment economy, network loss, and current-limiting effect, and intelligent solution algorithms such as the PSO algorithm and genetic algorithm are used to solve and obtain the optimal configuration scheme of the final fault current limiter. However, there are still the following problems: ① In the optimization process, multiple short-circuit current calculations are involved. The nonlinearity of the current limiter is not considered in the current calculations, resulting in inaccurate current-limiting evaluation results and thus inaccurate optimization models; ② There are many short-circuit over-standard points in the power grid, the layout range is wide, and the solution search volume is large; ③ Traditional intelligent algorithms are prone to falling into local optima, and the obtained solutions have room for further improvement.

[0004] In response to the above problems, the applicant has conducted in-depth research, and this case has arisen. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-objective optimal configuration method and system for a fault current limiter based on an improved GSA algorithm, which overcomes the problems of the traditional optimal configuration model not considering the nonlinearity of the current limiter, the wide layout range of the current limiter and the large solution search volume, and the solution algorithm being prone to falling into local optima, and can achieve the goal of achieving the maximum current-limiting effect with the minimum configuration capacity.

[0006] To achieve the above object, the solution of the present invention is:

[0007] A multi-objective optimal configuration method for a fault current limiter based on an improved GSA algorithm includes the following steps:

[0008] Step 1: Determine the branch range and upper limit of the number of fault current limiters to be installed according to the current grid short-circuit current over-limit nodes and short-circuit current control level;

[0009] Step 2: Calculate the short-circuit current considering the non-linearity of the fault current limiter, and establish an evaluation index for the current limiting effect based on this;

[0010] Step 3: Establish a multi-objective system and constraint conditions for the fault current limiter that comprehensively considers economy and current limiting effect;

[0011] Step 4: Adopt an improved GSA algorithm to adaptively adjust the weight coefficient and learning factor to obtain the optimal configuration scheme of the fault current limiter.

[0012] In the above Step 1, the specific content of determining the branch range for installing the fault current limiter is as follows.

[0013] Step 11: Calculate the short-circuit current before the access of the fault current limiter and determine the over-limit nodes;

[0014] Step 12: Select the top n branches with the highest sensitivity as the installation node range of the fault current limiter according to the sensitivity of the short-circuit current of the over-limit node to the branch impedance.

[0015] In the above Step 12, the sensitivity of the short-circuit current to the branch impedance is characterized by the sensitivity of the self-impedance of the short-circuit current over-limit node to the branch impedance. The sensitivity η j of the self-impedance of the over-limit node i to the impedance of the branch j is calculated by the following formula:

[0016]

[0017] where I is the set of short-circuit current over-limit nodes; X ii is the self-impedance of the over-limit node i; X j is the impedance of the branch j.

[0018] The specific content of the above Step 2 is as follows.

[0019] Step 21: Write out the nodal admittance matrix after the access of the fault current limiter satisfying where the impedance X j of the branch with the fault current limiter is a function of the branch voltage U j ;

[0020] Step 22: Solve it by an iterative method. Let k be the number of iterations. According to the voltage U k-1 at the (k - 1)th time, back substitution is used to obtain the voltage U k at the kth time, and the branch impedance at the kth time is recalculated

[0021] Step 23: Update the branch currents according to the new impedance, and sum them to obtain the node short-circuit current. When the difference between the two adjacent short-circuit currents calculated is less than the given standard, end the iteration to obtain the final short-circuit current considering the nonlinearity of the fault current limiter.

[0022] The specific content of the above step 3 is as follows:

[0023] Economy includes minimizing the fixed cost of the fault current limiter, and the expression is:

[0024] minγ FCL N FCL

[0025] In the formula, γ FCL is the installation cost per unit capacity of the fault current limiter; N FCL is the installed capacity of the fault current limiter;

[0026] The current limiting effect target is:

[0027]

[0028] In the formula, is the node short-circuit current before current limiting; I′ j is the short-circuit current after the access of the fault current limiter;

[0029] The above-mentioned constraint conditions include power balance constraint, line power constraint and node voltage constraint.

[0030] In the above step 4, the improved GSA algorithm includes an adaptively adjusted weight coefficient and an adaptively updated learning factor;

[0031]

[0032] Among them, f is the fitness of the current solution; ω min and ω max are the weight limits;

[0033] c 1 = c 2 = c max -(c max -c min )*t / T max

[0034] Among them, c max and c min are the learning factor limits respectively; T max is the maximum number of iterations; t is the current iteration number.

[0035] A multi-objective optimal configuration system for a fault current limiter based on an improved GSA algorithm includes

[0036] A fault current limiter installation parameter determination module, configured to determine the branch range and the upper limit of the number of branches where the fault current limiter is installed according to the current grid short-circuit current over-limit node and the short-circuit current control level;

[0037] A current-limiting effect evaluation index construction module, configured to establish a current-limiting effect evaluation index based on the short-circuit current considering the nonlinearity of the fault current limiter;

[0038] A multi-objective system and constraint condition construction module, configured to comprehensively consider economy and current-limiting effect, and establish a multi-objective system and constraint conditions for the fault current limiter; and,

[0039] A fault current limiter configuration module, configured to adopt an improved GSA algorithm to adaptively adjust the weight coefficient and the learning factor to obtain the optimal configuration scheme of the fault current limiter.

[0040] A computer device, including 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, the steps of the multi-objective optimization configuration method of the fault current limiter based on the improved GSA algorithm as described above are implemented.

[0041] A computer-readable storage medium, the computer-readable storage medium stores a computer program; characterized in that: when the computer program is executed by a processor, the steps of the multi-objective optimization configuration method of the fault current limiter based on the improved GSA algorithm as described above are implemented.

[0042] After adopting the above solution, the present invention first determines the branch range and the upper limit of the number of branches where the fault current limiter is installed according to the current grid short-circuit current over-limit node and the short-circuit current control level; proposes a fast calculation method for the short-circuit current considering the nonlinearity of the fault current limiter, and based on this, establishes a current-limiting effect evaluation index; establishes a multi-objective system and constraint conditions for the fault current limiter that comprehensively consider economy and current-limiting effect; proposes an improved GSA algorithm, adaptively adjusts the weight and the learning factor, enhances the global search ability of the algorithm, and obtains the optimal configuration scheme of the fault current limiter. This method overcomes the problems that the traditional optimization configuration model does not consider the nonlinearity of the current limiter, the layout range of the current limiter is wide and the solution search amount is large, and the solution algorithm is easy to fall into local optimum, etc., and can achieve the goal of realizing the maximum current-limiting effect with the minimum configuration capacity. Description of the Drawings

[0043] Figure 1 is a flowchart of the present invention. Detailed Embodiment

[0044] Hereinafter, in conjunction with the drawings, the technical solutions and beneficial effects of the present invention will be described in detail.

[0045] As Figure 1As shown in the figure, the present invention provides a multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm, including the following steps:

[0046] Step 1: Determine the branch range and the upper limit of the number of installations of the current limiter according to the current grid short-circuit current over-limit nodes and the short-circuit current control level; among them, determining the branch range for installing the fault current limiter specifically includes:

[0047] Step 11: Calculate the short-circuit current before the access of the fault current limiter and determine the over-limit nodes; here, the short-circuit current can be calculated according to the conventional short-circuit current calculation method, which will not be elaborated here;

[0048] Step 12: Calculate the sensitivity of the short-circuit current of the over-limit nodes to the branch impedance, and select the first n branches as the installation node range of the fault current limiter. Preferably, the sensitivity between the short-circuit current and the branch impedance cannot be directly calculated, and the short-circuit current is closely related to the self-impedance of the node. Therefore, in the embodiment of the present invention, the sensitivity of the self-impedance of the short-circuit current over-limit node to the branch impedance is used, and the calculation formula is as follows:

[0049]

[0050] In the formula, I is the set of short-circuit current over-limit nodes; X ii is the self-impedance of the over-limit node; X j is the impedance of branch j, j = 1, 2, 3,..., representing the jth branch within the power grid range. The larger the value of formula (1), the more obvious the effect of installing the fault current limiter on branch j; arrange η 1 , η 2 , η 3 ,... in order, and select the first n branches corresponding to the first n data from large to small as the installation node range of the fault current limiter. In this embodiment, 20% of the total number of power grid branches at the current voltage level can be selected as the installation node range of the fault current limiter.

[0051] Among them, the upper limit of the number of installations of the fault current limiter can be determined according to the cost and planning;

[0052] Step 2: Propose a fast calculation method for short-circuit current considering the nonlinearity of the fault current limiter, and establish an evaluation index for the current limiting effect based on this;

[0053] The conventional short-circuit current calculation assumes that the branch impedance is a constant value, but the fault current limiter shows a nonlinear characteristic. Therefore, the short-circuit current calculation method considering the nonlinear characteristic is updated to:

[0054] Step 21: Write the node admittance matrix after the access of the fault current limiter Satisfy Among them, the branch impedance X j of the branch with the fault current limiter is the branch voltage Uj function of (voltage difference between both ends of the branch)

[0055] Step 22, due to the non-linear characteristics, an iterative method is adopted for solving. Let k be the number of iterations. According to the voltage U at the (k - 1)th time k-1 , the voltage U at the kth time is obtained by forward-backward substitution k , and the branch impedance at the kth time is recalculated

[0056] Step 23, update the branch currents according to the new impedance, sum them to obtain the node short-circuit current. When the difference between the two adjacent short-circuit currents calculated is less than the given standard, the iteration ends, and the final short-circuit current considering the non-linearity of the fault current limiter is obtained

[0057] Step 3, establish a multi-objective system and constraint conditions of the fault current limiter considering economy and current-limiting effect comprehensively

[0058] Step 31, the established multi-objectives include two aspects: economy and current-limiting effect. Among them, the economy includes the fixed cost of the fault current limiter and the loss of increased line loss after the fault current limiter is connected. Since the increase in line loss is very small under normal operation mode and can be ignored, the economy is mainly expressed as the minimum fixed cost of the fault current limiter:

[0059] minγ FCL N FCL (2)

[0060] In the formula, γ FCL is the installation cost per unit capacity of the fault current limiter; N FCL is the installation capacity of the fault current limiter

[0061] The current-limiting effect is characterized by the degree of decrease in the short-circuit current of the nodes where the short-circuit current exceeds the standard after the fault current limiter is connected. Among them, the current-limiting effect target is:

[0062]

[0063] In the formula is the node short-circuit current before current limiting; I′ j is the short-circuit current after the fault current limiter is connected, and is calculated by the method in Step 2

[0064] Step 32, the constraint conditions of the multi-objective optimization model of the fault current limiter mainly include power balance constraint, line power constraint, node voltage constraint, etc

[0065] Step 4, propose an improved GSA algorithm, adaptively adjust the weight and learning factor, enhance the global search ability of the algorithm, and combine the constraint conditions to solve the multi-objective system of the fault current limiter to obtain the optimal configuration scheme of the fault current limiter

[0066] The gravitational search algorithm (GSA) assumes that the law of universal gravitation applies among the particles in the search space. The optimal space is the equilibrium point where each particle reaches a stable operation under the action of universal gravitation. However, this algorithm has certain drawbacks. In the optimization process, it only depends on the positions of the current particles, lacks overall information, and is prone to falling into local optima. To address this problem, the present invention improves the weight and learning factor links in the GSA algorithm. Among them, the weight coefficient is changed from a fixed coefficient to an adaptive adjustment to improve its global optimization ability:

[0067]

[0068] In the formula, f is the fitness of the current solution, f avg and f min represent the average value and the minimum value of f; ω min and ω max are the weight limits.

[0069] Update of the learning factor. The learning factor consists of two parts: c1 representing self-optimization and c2 representing global optimization. If the learning factor remains unchanged during the iteration process, the algorithm is prone to falling into local optima. Therefore, it is necessary to adaptively update the learning factor. The formula is as follows:

[0070] c 1 = c 2 = c max -(c max - c min ) * t / T max (5)

[0071] In the formula, c max and c min are the learning factor limits respectively; T max is the maximum number of iterations; t is the current iteration number.

[0072] An embodiment of the present invention also provides a multi-objective optimal configuration system for a fault current limiter based on the improved GSA algorithm, including

[0073] A fault current limiter installation parameter determination module, configured to determine the branch range and the upper limit of the number of fault current limiters to be installed according to the current grid short-circuit current over-limit nodes and the short-circuit current control level;

[0074] A current limiting effect evaluation index construction module, configured to establish a current limiting effect evaluation index based on the short-circuit current considering the nonlinearity of the fault current limiter;

[0075] A multi-objective system and constraint condition construction module, configured to comprehensively consider economy and current limiting effect, and establish a multi-objective system and constraint conditions for the fault current limiter; and

[0076] The fault current limiter configuration module is configured to adaptively adjust the weight coefficient and the learning factor by using an improved GSA algorithm to obtain the optimal configuration scheme of the fault current limiter.

[0077] An embodiment of the present invention further provides another computer device, including a processor and a memory configured to store a computer program that can run on the processor; wherein, when the processor is configured to run the computer program, it executes the method steps in the foregoing embodiment.

[0078] In practical applications, the foregoing processor includes a Field-Programmable Gate Array (FPGA), and the processor may be a Central Processing Unit (CPU) or a Digital Signal Processor (DSP). It can be understood that for different devices, the electronic devices for implementing the functions of the foregoing processor may also be others, and the embodiments of the present invention do not make specific limitations.

[0079] The foregoing memory may be a volatile memory, such as a Random-Access Memory (RAM); or a non-volatile memory, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD), or a Solid-State Drive (SSD); or a combination of the foregoing types of memories, and provides instructions and data to the processor.

[0080] In an exemplary embodiment, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program.

[0081] Optionally, the computer-readable storage medium can be applied to any one of the methods in the embodiments of the present invention, and the computer program enables the computer to execute the corresponding processes implemented by the processor in each of the methods in the embodiments of the present invention. For the sake of brevity, it will not be elaborated herein.

[0082] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the various components shown or discussed can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0083] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0084] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0085] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 or steps for implementing the functions specified in one block or a plurality of blocks.

[0087] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm, characterized in that it includes the following steps: Step 1, according to the current grid short-circuit current over-limit nodes and the short-circuit current control level, determine the branch range and the upper limit of the number of branches where the fault current limiter is installed; Step 2, calculate the short-circuit current considering the nonlinearity of the fault current limiter, and based on this, establish an evaluation index for the current limiting effect; Step 3, establish a multi-objective system and constraint conditions for the fault current limiter considering economy and current limiting effect; Step 4, adopt an improved GSA algorithm to adaptively adjust the weight coefficient and the learning factor to obtain the optimal configuration scheme of the fault current limiter.

2. The multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm according to claim 1, characterized in that: In the said Step 1, the specific content of determining the branch range where the fault current limiter is installed is, Step 11, calculate the short-circuit current before the fault current limiter is connected, and determine the over-limit nodes; Step 12, according to the sensitivity of the short-circuit current of the said over-limit nodes to the branch impedance, select the top n branches with the highest sensitivity as the installation node range of the fault current limiter.

3. The multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm according to claim 2, characterized in that: In the said step 12, the sensitivity of the short-circuit current to the branch impedance is characterized by the sensitivity of the self-impedance of the short-circuit current over-limit node to the branch impedance. The sensitivity η j of the self-impedance of the over-limit node i to the impedance of the branch j is calculated by the following formula: where I is the set of nodes with short-circuit current exceeding the limit; X ii is the self-impedance of the over-limit node i; X j is the impedance of branch j.

4. The multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm according to claim 1, characterized in that: The specific content of the said Step 2 is, Step 21, write out the nodal admittance matrix after the fault current limiter is connected Satisfy Among them, the branch impedance X of the fault current limiter j is the function of the branch voltage U j ; Step 22, solve it in an iterative manner. Let k be the number of iterations, and based on the voltage U at the (k - 1)th time k-1 , perform forward-backward substitution to obtain the voltage U at the kth time k , and recalculate the branch impedance at the kth time Step 23, update the branch current according to the new impedance, sum to obtain the node short-circuit current. When the difference between the two adjacent short-circuit currents calculated is less than the given standard, end the iteration to obtain the final short-circuit current considering the nonlinearity of the fault current limiter.

5. The multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm according to claim 1, characterized in that: The specific content of the said Step 3 is, The economy includes minimizing the fixed cost of the fault current limiter, and the expression is: minγ FCL N FCL where γ FCL is the installation cost per unit capacity of the fault current limiter; N FCL is the installed capacity of the fault current limiter; The current limiting effect target is: In the formula, is the short-circuit current of the node before current limiting; I′ j is the short-circuit current after the fault current limiter is connected; The said constraint conditions include power balance constraint, line power constraint and node voltage constraint.

6. The multi-objective optimization configuration method for a fault current limiter based on an improved GSA algorithm according to claim 1, characterized in that: In the said Step 4, the improved GSA algorithm includes an adaptively adjusted weight coefficient and an adaptively updated learning factor; where f is the fitness of the current solution; ω min and ω max are weight limits; c 1 = c 2 = c max -(c max - c min ) * t / T max Among them, c max and c min are respectively the learning factor limits; T max is the maximum number of iterations; t is the current number of iterations.

7. A multi-objective optimization configuration system for a fault current limiter based on an improved GSA algorithm, characterized in that: It includes, A fault current limiter installation parameter determination module configured to determine the branch range and the upper limit of the number of branches where the fault current limiter is installed according to the current grid short-circuit current over-limit nodes and the short-circuit current control level; A current limiting effect evaluation index construction module configured to establish an evaluation index for the current limiting effect based on the short-circuit current considering the nonlinearity of the fault current limiter; A multi-objective system and constraint condition construction module configured to establish a multi-objective system and constraint conditions for the fault current limiter considering economy and current limiting effect; and, A fault current limiter configuration module configured to adopt an improved GSA algorithm to adaptively adjust the weight coefficient and the learning factor to obtain the optimal configuration scheme of the fault current limiter.

8. A computer device, 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, the steps of the multi-objective optimal configuration method of the fault current limiter based on the improved GSA algorithm according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program; Characterized in that: When the computer program is executed by a processor, the steps of the multi-objective optimal configuration method of the fault current limiter based on the improved GSA algorithm according to any one of claims 1 to 6 are implemented.

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