Fault position identification method and system based on new energy power system

Abstract

The invention discloses a fault position identification method and system based on a new energy power system, and the method comprises the steps: collecting fault data in a new energy power system line, extracting fault currents at two sides of the line according to a fault scene, and carrying out the EMD decomposition of the fault currents, thereby extracting the fault features of a system side IMFsi and a station side IMFwi; selecting IMF components with fault feature representativeness to carry out Kendall correlation coefficient calculation; a weighting coefficient mi corresponding to the Taui is constructed, a protection relevancy value rho is constructed in combination with the correlation coefficient Taui, optimization solution is conducted on the weighting coefficient mi through a raccoon longnose optimization algorithm COA, and therefore the optimized weighting coefficient mi is determined; and determining a protection constant value sigma according to the protection relevancy value rho, and further judging whether the fault is an internal fault or an external fault by adopting the protection constant value rho. According to the method, the criterion construction is completed by utilizing different fault characteristics of two sides of the system after decomposition when the system fails and performing similarity calculation, and the criterion accuracy is improved by combining an optimization algorithm, so that the protection principle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system safety assessment, and in particular to a fault location identification method and system based on a new energy power system. Background Art

[0002] The new energy power system has the characteristics of clean and low carbon, safe and controllable, flexible and efficient. Building a new power system with new energy as the main body is an important countermeasure to actively respond to the strategic goals of carbon peak and carbon neutrality. Compared with traditional synchronous power sources, new energy is connected to the grid through a large number of power electronic devices. Affected by its own control strategy, the fault characteristics after the fault are not obvious, resulting in a decrease in the action performance of traditional protection methods, which may face the risk of refusal to operate and false operation.

[0003] In the construction of new protection principles for new power systems, some scholars have used the generalized S-transform amplitude matrix to extract the transient energy characteristics of current and voltage, and the phase angle matrix to extract polarity characteristics. However, the accuracy and precision of feature extraction are affected by factors such as signal quality and system parameters, making it uncertain, such as [Wei Guo, Xu Wenwen, Wang Xuguang, et al. Active distribution network fault location method based on generalized S transform [J]. Electrical Measurement and Instrumentation, 2021].

[0004] There are also studies that use the LMD energy relative entropy of the two sections of transient zero-sequence current in the fault section to select the line, and use the relative entropy of each component to determine the fault section, but the calculation is relatively complex and the workload is huge, such as [Xu Yanchun, Zhao Caicai, Sun Sihan, et al. Active distribution network fault location method based on improved LMD and energy relative entropy [J]. China Electric Power, 2021].

[0005] The invention patent with publication number CN118645971A discloses a differential protection method and system for renewable energy access. The differential protection method is used to ensure that the differential protection can work accurately when renewable energy is connected, thereby improving the accuracy of differential protection in identifying system faults. The differential protection method can only identify faults, but cannot determine the location of faults inside or outside the zone. Although this protection method is simple, its accuracy is not high, and it is no longer applicable to renewable energy power systems. Summary of the invention

[0006] Purpose of the invention: In order to solve the problems arising from the above-mentioned prior art, the present invention provides a fault location identification method based on a new energy power system, which solves the problem that when a fault occurs in a new power system, the fault characteristics are not obvious, making it difficult to distinguish the fault location. The present invention also provides a fault location identification system based on a new energy power system.

[0007] Technical solution: According to a first aspect of the present invention, a method for identifying a fault location based on a new energy power system is provided, the method comprising:

[0008] Collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line.

[0009] Select the IMF components that are representative of the fault characteristics to calculate the Kendall correlation coefficient, that is, for the selected IMFs i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section;

[0010] Construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ;

[0011] The protection setting σ is determined according to the protection correlation value ρ, and then the protection setting σ is used to judge whether the fault is inside or outside the zone.

[0012] Furthermore, the method also includes:

[0013] The weighting coefficient m is calculated using the coati optimization algorithm COA. i Perform optimization to determine the optimized weighting coefficient m i ;

[0014] Furthermore, the method also includes:

[0015] Establishing a sample library for the new energy power system under different fault scenarios and when faults occur at different locations;

[0016] Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first fixed value σ 1 , calculate |ρ| for the fault samples in the area, and |ρ| is less than the second fixed value σ 2 Therefore, the protection setting

[0017] Further, including:

[0018] The fault currents on both sides of the line are extracted according to the fault scenario, and the EMD is decomposed to extract the IMFs on the system side. i and station-side IMFw i The fault characteristics include:

[0019] The characteristic components are extracted by EMD, and the fault section is determined by comparing different fault characteristics. Therefore, the local characteristic IMF component r of the fault signal is decomposed using formula (1): k (t) Used for fault diagnosis.

[0020]

[0021] Where: n is the number of IMF components that meet the EMD constraint conditions; res is the residual component after decomposition;

[0022] The use of EMD requires the following two conditions to be met:

[0023] (1) The difference between the number of extreme points and the number of zero-crossing points in all IMF signals can only be 0 or 1;

[0024] (2) For any point in the signal, the mean of its upper and lower envelopes is 0;

[0025] After EMD decomposition, the IMF components and residuals of the original signal are obtained. Representative IMF components need to be selected for feature analysis. The information used for fault feature analysis is mainly the IMF components obtained by decomposition.

[0026] The IMF components on both sides of the new energy power system line are recorded as system-side IMFs i and station-side IMFw i , where i=1 to 7, when a fault occurs in the new energy power system, the fault IMF component has the following fault characteristics:

[0027] Feature 1: IMFs 1 、IMF 1 The basic information of the fault is retained. Although the characteristics are not clear, there are still differences in amplitude and phase, which have the characteristics of preliminary judgment;

[0028] Feature 2: IMFs 2 、IMFs 3 、IMF 2 、IMF 3 The amplitude characteristics of the fault information are retained. When the fault occurs outside the area, the information on both sides is basically the same, but when the fault occurs inside the area, there is a difference between the two.

[0029] Feature 3: IMFs 4-7 、IMF 4-7The polarity characteristics of fault information are highlighted. When there is an out-of-area fault, the information change trends on both sides are opposite, while when there is an in-area fault, the information change trends on both sides are the same. Therefore, the corresponding IMFs are selected according to the above characteristics. i 、IMF i Just perform basic fault analysis.

[0030] Further, including:

[0031] The selection of IMF components representative of fault characteristics to calculate the Kendall correlation coefficient specifically includes:

[0032] The Kendall correlation coefficient is defined as follows:

[0033]

[0034] Where: N is the number of data in the data window, C i The calculated two-sided IMFs i 、IMF i The number of consistent pairs, D i The calculated two-sided IMFs i 、IMF i Number of divergent pairs;

[0035] For selected IMFs i 、IMF i Component sequence, select a cycle data window after the fault, and calculate the correlation coefficient Tau of the IMF component i ;

[0036] Therefore, when a fault occurs inside or outside the line, Tau 1-2 It only has clear differences in different time periods and is not stable; Tau 3 Only the initial period is unclear; Tau 4-7 The difference is relatively stable.

[0037] Further, including:

[0038] The weighting coefficient m is constructed i , and combined with the correlation coefficient Tau i Construct the protection correlation value ρ, which is specifically expressed as:

[0039]

[0040] Wherein, n is the number of correlation coefficients involved in the calculation, preferably, n=5 here.

[0041] Further, including:

[0042] The weighting coefficient m is calculated by using the coati optimization algorithm COA. iPerform optimization to determine the optimized weighting coefficient m i , including:

[0043] The best individual position in the current population Assuming that is the location of the prey, half of the predators in the design population will chase the prey, while the other half will wait for the arrival of the prey. Therefore, formula (4) is used to simulate the location of the predators in the population. When the prey lands, it will be at a random location in the search space. Based on this location, the waiting predators will move in the search space and use formulas (5) and (6) to update their positions.

[0044] When the new position of each hunter improves the value of the objective function, it will be updated to the new position, otherwise it will remain at the original position;

[0045] M i As the optimization algorithm objective function To solve the problem, faults are set at different locations from several kilometers outside the reverse area to several kilometers outside the forward area of ​​the line, and the correlation coefficient Tau of each IMF component on both sides of the line is calculated. i A sample library is constructed and used as the input of the optimization algorithm. The optimization goal is that the similarity value ρ is less than the set threshold for S / 2 consecutive points after the fault, where S is the number of sampling points in one cycle.

[0046] The optimization process is:

[0047]

[0048] Where: M is the population size, t is the current number of iterations, lb j The lower bound of the j-th dimension variable in time-varying time, ub j represents the upper bound of the j-th dimension variable, fitness(·) is used to calculate the fitness, r represents a random number between [0,1], I represents a random integer from the integer set {1,2}, represents the calculated new position, Indicates the j-th dimension variable value of the i-th individual in the current iteration.

[0049] Further, including:

[0050] The protection constant σ is determined according to the protection correlation value ρ. Considering the positive and negative nature of the correlation coefficient, the absolute value of the similarity value ρ is used to construct the protection criterion:

[0051]

[0052] Among them, σ is the protection constant.

[0053] On the other hand, the present invention also provides a fault location determination system based on a new energy power system, the system comprising:

[0054] The data decomposition module is used to collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line.

[0055] The fault section determination module is used to select IMF components that are representative of fault characteristics to calculate the Kendall correlation coefficient. i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section;

[0056] Coefficient optimization module, used to construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ, and use the coati optimization algorithm COA to adjust the weight coefficient m i Perform optimization to determine the optimized weighting coefficient m i ;

[0057] The fault location judgment module is used to determine the protection setting value σ according to the protection correlation value ρ, and then use the protection setting value σ to judge whether it is an internal or external fault.

[0058] Furthermore, the system also includes:

[0059] A protection setting value calculation module, used for establishing a sample library for the new energy power system when faults occur at different locations under different fault scenarios;

[0060] Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first fixed value σ 1 , calculate |ρ| for the fault samples in the area, and |ρ| is less than the second fixed value σ 2 Therefore, the protection setting

[0061] Finally, the present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed, the method for identifying the fault location based on a new energy power system described in the above claims is executed.

[0062] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0063] The present invention proposes a new principle of power system protection based on multimodal information analysis at both ends of the line and Kendall correlation. When a fault occurs in the new power system, it can accurately identify faults inside and outside the area, so that the protection can act correctly and quickly. First, the EMD method is selected to extract fault characteristics, which is easy to implement, and the full-phase fault current signal is selected, which will not be affected by transient and steady-state quantities. Then, the Kendall similarity is used to solve the problem of difficulty in determining the fault location caused by the unstable signal generated by the system after the new energy is connected. Finally, after the preliminary construction of the protection criterion, it is further proposed to use the COA optimization algorithm to optimize the weight coefficient in the criterion, so that the protection accuracy of the protection setting is greatly improved.

[0064] Therefore, the present invention completes the criterion construction by utilizing the different fault characteristics after decomposition on both sides of the system when the system fails and performing similarity calculation, and improves the accuracy of the criterion by combining the optimization algorithm, thereby realizing the protection principle. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic diagram of a new energy grid-connected fault location according to an embodiment of the present invention;

[0066] Figure 2 is a schematic diagram of the EMD decomposition seventh-order IMF function according to an embodiment of the present invention;

[0067] Figure 3 This is a fault data diagram at 15 km outside the system side area according to an embodiment of the present invention; wherein,

[0068] (a) Original current on both sides of the line for a fault 15 km outside the system side area;

[0069] (b) A fault 15 km outside the system side area, IMF function components after EMD decomposition of fault current on both sides;

[0070] (c) Kendal similarity of IMF function components on both sides of the fault 15 km outside the system side area;

[0071] (d) Calculated ρ value for a fault 15 km outside the system side area;

[0072] Figure 4 This is a fault data diagram of a location 15 km outside the system side area and with a ground resistance of 10Ω according to an embodiment of the present invention; wherein,

[0073] (a) Original current on both sides of the fault line 15 km outside the system side area with a ground impedance of 10Ω;

[0074] (b) IMF function components after EMD decomposition of fault current on both sides of the fault with ground impedance of 10Ω and 15km outside the system side area;

[0075] (c) Kendal similarity of IMF function components on both sides of the fault at 15 km outside the system side area and with a ground impedance of 10Ω;

[0076] (d) ρ value calculated for a fault 15 km outside the system side area with a ground impedance of 10Ω;

[0077] Figure 5 is a fault current data diagram at 50 km in the area according to an embodiment of the present invention; wherein,

[0078] (a) 50 km fault in the area, original current on both sides of the line;

[0079] (b) 50km fault in the area, IMF function components after EMD decomposition of fault current on both sides;

[0080] (c) Kendal similarity of IMF function components on both sides of a 50 km fault in the area;

[0081] (d) Calculated ρ value for a 50 km fault in the area;

[0082] Figure 6 This is the fault data diagram when the area is 50km and the ground resistance is 10Ω according to the embodiment of the present invention; wherein,

[0083] (a) 50km fault in the area, ground resistance 10Ω, original current on both sides of the line;

[0084] (b) 50km fault in the area, grounding resistance 10Ω, IMF function components after EMD decomposition of fault current on both sides;

[0085] (c) Kendal similarity of the IMF function components on both sides of the fault with a distance of 50 km and a ground resistance of 10 Ω in the area;

[0086] (d) Calculated ρ value for a 50km fault in the area and a ground resistance of 10Ω;

[0087] Figure 7 This is the fault data diagram at 15 km outside the station side area according to the embodiment of the present invention; wherein,

[0088] (a) The original current on both sides of the line at a fault 15 km outside the station side area;

[0089] (b) Fault 15 km outside the station side, IMF function components after EMD decomposition of fault current on both sides;

[0090] (c) Kendal similarity of the IMF function components on both sides of the fault 15 km outside the station side area;

[0091] (d) Calculated ρ value for a fault 15 km outside the station side area;

[0092] Figure 8 This is the fault data diagram of the station side area 15km outside and the ground resistance 10Ω described in the embodiment of the present invention; wherein,

[0093] (a) Original current on both sides of the fault line with a ground impedance of 10Ω and a distance of 15 km from the station side;

[0094] (b) IMF function components after EMD decomposition of fault current on both sides of the fault with ground impedance of 10Ω and 15 km outside the station side;

[0095] (c) Kendall similarity of IMF function components on both sides of the fault with ground impedance of 10Ω and 15 km outside the station side;

[0096] (d) ρ value calculated for a fault 15 km outside the station side area with a ground impedance of 10Ω;

[0097] Fig. 9 is a flow chart of a novel power system protection method according to an embodiment of the present invention;

[0098] Fig.10 It is a schematic diagram of the terminal hardware structure of each embodiment of the fault location identification method based on the new energy power system described in the embodiment of the present invention. DETAILED DESCRIPTION

[0099] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0100] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0101] Example 1

[0102] In order to solve the problem that the unstable signal generated by the system after the new energy is connected in the current new power system, which makes it difficult to determine the fault location, the embodiment of the present invention proposes a new power system protection principle based on multi-modal information analysis at both ends of the line and Kendall correlation, which specifically includes the following steps:

[0103] S10 collects fault data in the lines of the new energy power system, extracts the fault currents on both sides of the line according to the fault scenario, and performs EMD decomposition on them to extract the IMFs on the system side. i and station-side IMFw i Fault characteristics; the new energy power system includes a station side and a system side. The station side includes multiple photovoltaic power generation groups under grid-type control, and the system side is the power grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by the converter and transmitted to the power grid side using the transmission line.

[0104] This embodiment does not limit the structure corresponding to the specific power system, and there may be one or more specific fault points.

[0105] The fault data in this embodiment may be a fault on the station side or a fault on the system side, and the power system fault conditions generally include out-of-area faults and in-area faults. Out-of-area faults refer to faults that occur outside the feeder segment set by the system protection device. In-area faults refer to faults that occur inside the feeder segment set by the system protection device. Therefore, faults in the new energy power system can be divided into in-area faults on the station side, out-of-area faults on the station side, in-area faults on the system side, and out-of-area faults on the system side.

[0106] However, when a fault occurs in the power system at first, the fault signal is an unstable time-varying distribution signal, and the fault feature is not obvious, so it is difficult to extract the feature quantity for fault judgment by common methods. Based on this, this embodiment provides a detailed description of fault location judgment.

[0107] Optionally, in this embodiment, since the fault signal exists in various forms, the original fault signal X(t) on both sides of the line is first decomposed by empirical mode decomposition. The biggest advantage of EMD decomposition is its strong adaptability, and it can decompose the original signal by frequency step by step according to the scale characteristics of the signal itself, making the original signal characteristics clearer while removing noise. No matter what shape the signal is, its low-frequency and high-frequency parts will always have differences, so using it for decomposition is an effective method.

[0108] Generally, characteristic components can be extracted through EMD, and the fault section can be determined by comparing different fault characteristics. Therefore, the local characteristic IMF component r of the fault signal is decomposed using formula (1): k (t) Used for fault diagnosis.

[0109]

[0110] Where: n is the number of IMF components that meet the EMD constraints; res is the residual component after decomposition.

[0111] The use of EMD requires the following two conditions to be met:

[0112] (1) The difference between the number of extreme points and the number of zero-crossing points in all IMF signals can only be 0 or 1;

[0113] (2) For any point in the signal, the mean of its upper and lower envelopes is 0.

[0114] After EMD decomposition, the IMF components and residuals of the original signal are obtained. Representative IMF components need to be selected for feature analysis. The information used for fault feature analysis is mainly for the decomposed IMF components. Suppose the IMF components on both sides of the line are the system side IMFs i and station-side IMFw i , where i=1-7.

[0115] In this embodiment, the number of components is obtained based on the EMD decomposition itself, which can obtain up to 7 IMFs and their corresponding residuals, while the system-side IMFs i and station-side IMFw i The components on both sides are obtained according to the fault signals on the corresponding sides, and this embodiment does not limit the components on both sides, because one of the system side or the station side may not have a fault, so there is no fault signal on the corresponding side. In this embodiment, both sides have faults, so there are components IMFs on the corresponding side. i and IMF i .

[0116] In one case of this embodiment, when faults occur on both the system side and the station side, it is found that the faulty IMF component has the following fault characteristics:

[0117] Feature 1: IMFs 1 、IMF 1 The basic information of the fault is retained. Although the characteristics are not clear, there are still differences in amplitude and phase, which have the characteristics of preliminary judgment;

[0118] Feature 2: IMFs 2 、IMFs 3 、IMF 2 、IMF 3 The amplitude characteristics of the fault information are retained. When the fault occurs outside the area, the information on both sides is basically the same, but when the fault occurs inside the area, there is a difference between the two.

[0119] Feature 3: IMFs 4-7 、IMF 4-7 The polarity characteristics of fault information are highlighted. When there is an out-of-zone fault, the information change trends on both sides are opposite, while when there is an in-zone fault, the information change trends on both sides are the same.

[0120] Therefore, in this embodiment, the corresponding IMFs are selected according to specific needs or characteristics. i 、IMF i Perform basic fault analysis. If you need to analyze the polarity characteristics of fault information, select the corresponding IMFs 4-7 and IMF 4-7 , analyze the amplitude characteristics and select the corresponding IMFs 2 、IMFs 3 、IMF 2 、IMF 3 , this application does not limit the specific selection.

[0121] S20 selects IMF components that are representative of fault characteristics to calculate the Kendall correlation coefficient, that is, for the selected IMFs i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , thereby determining the fault section.

[0122] In this embodiment, when a fault occurs inside or outside the line area, the difference in amplitude or polarity of the IMF component characteristics on both sides of the line is an ordered series with unknown distribution, and it is difficult to construct a protection criterion using a threshold comparison form. Therefore, the correlation coefficient of the representative IMF components is calculated.

[0123] This embodiment does not limit the specific correlation calculation method, such as the following correlation methods are all available: Pearson correlation coefficient, which is used to measure the linear relationship between two variables and is applicable to continuous, linear, and normally distributed data; Spearman rank correlation coefficient, which is used for non-parametric data and is applicable to continuous, ordered, or data that does not conform to the normal distribution; Kendall rank correlation coefficient, which is applicable to ordered classification or rank data, and calculates correlation through sequential relationships.

[0124] The best way is that in this embodiment, the Kendall rank correlation coefficient is used to calculate the correlation. Since the Kendall correlation is essentially to check how many pairs of the same order there are in the sequence, and then to judge the correlation of the IMF component data with component sequences more accurately, this embodiment can introduce the Kendall correlation coefficient Tau i To reflect the differences in current characteristics of faults in different areas, the correlation of the signal sequences decomposed on both sides is calculated.

[0125] For example, the principle of Kendall correlation coefficient is as follows: Assume two random variables X and Y, and the number of elements contained in the two variables is N, the i-th (1≤i≤N) combination is expressed as (X i ,Y i ). If X i >X j , Yi >Y j If X appears at the same time, the i-th combination and the j-th combination are considered to be consistent. i >X j , Y i <Y j This means that the two combinations are inconsistent, that is, there is a disagreement.

[0126] Therefore, when a fault occurs in the system, the difference in amplitude or polarity of the IMF component characteristics after empirical mode decomposition of the fault current on both sides of the line is an ordered series with unknown distribution, making it difficult to construct a protection criterion using a threshold comparison form. In this embodiment, the introduction of the Kendall correlation coefficient can better reflect the correlation of the fault characteristics of each IMF component. i 、IMF i Component sequence, select a data window for a period of time after the fault. In this embodiment, it is preferred to select a cycle data window to calculate the correlation coefficient Tau of the IMF component i .

[0127] For example, in this embodiment, when a fault occurs inside or outside the line, the correlation coefficient Tau i There are different characteristics:

[0128] Tau 1-2 It only has clear differences in different time periods and is not stable;

[0129] Tau 3 Only the initial period is unclear;

[0130] Tau 4-7 The difference is relatively stable.

[0131] Therefore, in this embodiment, considering Tau 4-7 The characteristics are consistent. Therefore, in order to simplify the calculation, only Tau is used in the subsequent criterion construction. 4 , Tau 5 .

[0132] The Kendall correlation coefficient is calculated by the following formula, and the Kendall correlation coefficient is defined as follows:

[0133]

[0134] Where: N is the number of data in the data window, C i The calculated two-sided IMFs i 、IMF i The number of consistent pairs, D i The calculated two-sided IMFs i 、IMF i Number of divergent pairs.

[0135] In this embodiment, the IMFs on both sides i 、IMF i The specific manifestation of the agreement is: if the IMFs i >IMFs j 、IMF i >IMF j If they appear at the same time, the i-th combination and the j-th combination are considered to be consistent, where the i-th combination is (IMFs i ,IMF i ), the jth combination is (IMFs j ,IMF j ), where i and j are not greater than N. Otherwise, if IMFs i >IMFs j 、IMF i >IMF j If they do not appear at the same time, it means that the two combinations are divergent.

[0136] Therefore, in this embodiment, for example, the IMFs on both sides obtained by decomposing in step 1 can be i 、IMF i The component sequences are respectively substituted into formula (2) to calculate the corresponding Tau i value, and then according to the obtained Tau i The value constructs the protection correlation value ρ.

[0137] S30 builds corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection correlation value ρ.

[0138] In this embodiment, Tau can also be directly used i The fault interval is judged, that is, if it is less than the correlation coefficient Tau, it is an internal fault, and if it is greater than the correlation coefficient Tau, it is an external fault. However, the accuracy of this method is relatively weak.

[0139] Therefore, this embodiment finds that the accuracy of judging the fault interval by only using the Tau on both sides of the fault section line is insufficient, and misjudgment may occur, resulting in reduced protection performance. i It is difficult to accurately identify faults, especially in high resistance faults and weak system faults. 4-5 Also loses high sensitivity.

[0140] Therefore, in order to improve the accuracy of fault interval judgment, this embodiment combines Tau 1-5The differences in amplitude and polarity are compared, and the principle of multi-information fusion is applied to construct a comprehensive similarity value using weighted Where: n is the number of correlation coefficients involved in the calculation, which is 5 in this application; m i Corresponding to Tau i The weighting coefficient of .

[0141] It should be noted that the selection of the above parameters does not limit the specific value range, that is, the number of correlation coefficients involved in the calculation can be selected according to different situations. For example, if the calculation is not simplified, 6 or 7 can be selected. Or if stability is not considered, 4 can be selected.

[0142] Among them, m i For Tau i The corresponding weighting coefficient, in this embodiment, is not specifically limited. First, it can be set based on experience. It is set as a fixed value and directly participates in the calculation of the formula.

[0143] Therefore, considering the positive and negative nature of the correlation coefficient, the absolute value of the similarity value ρ is used to construct the protection criterion:

[0144]

[0145] Among them, σ is the protection constant.

[0146] S40 determines the protection setting value σ according to the protection correlation value ρ, and then uses the protection setting value σ to judge whether it is an internal or external fault.

[0147] Example 2

[0148] On the basis of step S30 in embodiment 1, this embodiment optimizes and solves the weighting coefficient, that is, an optimization algorithm is used to optimize the initial weighting coefficient, and finally an optimized weighting coefficient is obtained.

[0149] Optimally, the weighting coefficient m is calculated using the coati optimization algorithm COA i Perform optimization to determine the optimized weighting coefficient m i .

[0150] In this embodiment, in order to make the similarity value ρ have clear and stable differences when there are faults inside and outside the line area, the long-nosed coati optimization algorithm is introduced to m i Search for optimal value.

[0151] The coati optimization algorithm simulates the behavior of the coati in North America when they cooperate with predators and when they escape from their predators. It has the advantages of no need to set control parameters, high efficiency, and strong balance ability. It has stronger performance than other optimization algorithms. The algorithm is mainly aimed at its exploration process, iContinuously searching for the best result makes the criterion ρ more accurate.

[0152] For example, the optimization process is: the best individual position in the current population Assuming that is the position of the prey, in the algorithm, half of the predators in the population will chase the prey, while the other half will wait for the arrival of the prey. Therefore, the position of the predators in the population is simulated using equation (3); when the prey lands, it will be at a random position in the search space, and based on this position, the waiting predators will move in the search space, using equations (4) and (5) to update the position. When the new position of each predator improves the value of the objective function, the new position is acceptable, otherwise, it will remain at the original position.

[0153] In this embodiment, combined with the above embodiment, in order to achieve the optimization process, first, m i As the optimization algorithm objective function To solve the problem, different types of faults are set at 30 km outside the reverse area and 30 km outside the forward area of ​​the line, and the correlation coefficient Tau of each IMF component on both sides of the line is calculated. i A sample library is constructed and used as the input of the optimization algorithm. The optimization goal is that the similarity value ρ is less than the threshold for S / 2 consecutive points after the fault, where S is the number of sampling points in one cycle. The optimization is iterated until the optimization goal is reached and the optimal solution of the weighted coefficient is obtained.

[0154] It should be noted that this embodiment does not limit the specific distance outside the reverse zone and the forward zone of the specific line. This is set according to specific needs, and the number of sampling points and the threshold are also selected and set according to specific requirements.

[0155] The specific optimization process is:

[0156]

[0157] Where: M is the population size, t is the current number of iterations, lb j The lower bound of the j-th dimension variable in time-varying time, ub j represents the upper bound of the j-th dimension variable, and fitness(·) is used to calculate the fitness. r represents a random number between [0,1], and I represents a random integer from the integer set {1,2}. represents the calculated new position, Represents the j-th dimension variable value of the i-th individual in the current iteration.

[0158] In this embodiment, after the optimal solution is obtained, the protection setting value σ is determined according to the protection correlation value ρ, and then the protection setting value σ is used to judge whether the fault is inside or outside the zone.

[0159] That is, in this embodiment, considering the positive and negative nature of the correlation coefficient, the absolute value of the similarity value ρ is used to construct the protection criterion:

[0160]

[0161] Among them, σ is the protection constant.

[0162] In terms of the calculation of the protection setting value, this embodiment does not make specific restrictions. Preferably, more accurate data is obtained by the following method, namely: a sample library as mentioned in step 3 is established for different types of faults in different fault scenarios of the system, and the protection setting value is calculated based on the current information on both sides of the system circuit collected in real time. Based on the out-of-zone fault samples, |ρ| is calculated separately and is greater than σ 1 , the fault samples in the area are calculated separately |ρ| and are all less than σ 2 , the protection setting value is set to

[0163] In this embodiment, σ 1 and σ 2 The value of is set according to the data in the sample library. For example, the ρ calculated based on the out-of-zone fault samples in the sample library includes multiple ρ 1 ,ρ 2 ,...ρ m , m is the total number of samples outside the area in the sample library, and σ 1 The selection principle is: make |ρ 1 |,|ρ 2 |,...|ρ m The minimum value of |ρ i | min Also greater than σ 1 , that is, 1 <|ρ i | min Similarly, the ρ calculated based on the fault samples in the sample library includes multiple ρ 1 ,ρ 2 ,...ρ p , and σ 2 The selection principle is: make |ρ 1 |,|ρ 2 |,...|ρ p The minimum value of |ρ j | max Also less than σ 2 , that is, 2 >|ρ j | max , and the specific value can be set according to needs.

[0164] Therefore, it can be seen from the above-described embodiments 1 and 2 that the overall protection judgment process is as follows: Fig. 9 As shown:

[0165] Step 1: Collect different fault data of the system and use the simulation model to expand the fault scenario; decompose the fault current on both sides of the line after the fault based on EMD and extract the fault characteristics;

[0166] Step 2: Select the IMF components that are representative of the fault characteristics to calculate the Kendall correlation coefficient;

[0167] Step 3: Determine the weighting coefficient m i , construct the protection relevance value ρ, and use the COA optimization algorithm to adjust the weight coefficient m i Perform optimization solution;

[0168] Step 4: Calculate the protection setting σ. For the real-time sampled fault information, the protection judgment is implemented based on |ρ|≤σ and |ρ|>σ.

[0169] In order to verify the effectiveness of this method, the following example is given: When a new energy system fails, the following Figure 1 The simulation model shown is used to simulate fault conditions with different locations, types and ground impedances.

[0170] When a single-phase grounding fault occurs in a certain area of ​​the system, the fault current signals on both sides are decomposed by EMD to obtain the IMF function components as follows: Figure 2 As shown, the two sides of the high-order function waveform are basically consistent, and it is impossible to extract relevant effective information to judge the fault section. Therefore, this application selects the first five-order IMF function components as the basis for fault feature analysis.

[0171] Power system faults include out-of-area faults and in-area faults. Out-of-area faults refer to faults that occur outside the feeder segment set by the system protection device. In-area faults refer to faults that occur inside the feeder segment set by the system protection device.

[0172] Scenario 1: Set the new energy system to have an out-of-area fault, as shown in the F4 and F5 positions, and a single-phase grounding fault occurs at different distances from the busbars on both sides. The fault occurs at 2s. Extract the full-phase current on the station side and the system side after the fault occurs. Figure 3 (a) and Figure 4 In (a), perform EMD decomposition, and compare the IMF functions on both sides. Figure 3 (b) Figure 4 As shown in (b) of Figure 1, it can be seen that after the fault outside the zone occurs, as is consistent with the above analysis, there is a large difference in the waveform of the IFM function on both sides in the first five orders, and it can be clearly observed that the waveform is different from that when the fault occurs inside the zone. Figure 5(b) has obvious distinction and can be used as a basis for judgment and analysis.

[0173] Scenario 2: Set the new energy system to fail in the area, as shown in the F1, F2, and F3 positions, and a single-phase grounding fault occurs at different distances from the busbars on both sides. The fault occurs at 2s. Extract the full-phase current on the station side and the system side after the fault occurs. The original current signals at F2 and 50km away from both sides are as follows: Figure 5 As shown in (a), the EMD decomposition is performed to obtain the IMF function on both sides Figure 5 (b) in contrast Figure 2 As shown, it can be clearly observed that after the fault occurs, the high-order IMF functions have a high similarity, making it difficult to compare the fault characteristics, while the IFM functions have large differences in the first five orders, which can be used as the basis for constructing the criterion.

[0174] Scenario 3: Set the new energy system to have an internal and external fault with grounding resistance. The fault locations are located at different distances inside and outside the zone, and a single-phase grounding fault occurs. The fault occurs at 2s. Extract the full-phase current on the station side and the system side after the fault occurs. Figure 6 (a) Figure 7 (a) and Figure 8 In (a), EMD decomposition is performed, and the schematic diagram of the comparison of the IMF function components on both sides is shown as follows Figure 6 (b) Figure 7 (b) and Figure 8 As shown in (b) of the figure, it can be seen that after a system fault occurs, a larger waveform feature can be present after adding the grounding resistance. This shows that in this case, the IMF function component can also be used as the basis for constructing the criterion.

[0175] The above three scenarios cover various areas and fault conditions that may exist when new energy units are connected to the grid. Therefore, this patent calculates the similarity between the two sides of the IMF function component to form a protection criterion. The Kendall similarity Tau calculated for different fault sections, different fault distances and different grounding resistances is shown in Tables 1, 2 and 3.

[0176] At the same time, according to the different grounding impedances and the types of blocked faults, the COA optimization algorithm is used to determine the values ​​of each m for criterion construction.

[0177] Table 1 Kendall similarity values ​​calculated at different fault distances within the area

[0178]

[0179]

[0180] Table 2 Kendall similarity values ​​calculated at different fault distances outside the zone

[0181]

[0182] Table 3 Kendall similarity values ​​calculated after adding 10 ohm grounding impedance at different fault distances inside and outside the zone

[0183]

[0184] like Figure 3 (c)~ Figure 8 The Kendall similarity calculation curve in the corresponding window shown in (c) is basically consistent with the data in Table 1, Table 2, and Table 3, and has strong discrimination. The feasibility of using the Kendall coefficient to judge the fault section is further verified. The model parameters and operation results mentioned in this patent are brought into the COA algorithm for optimization results, and the m value is optimized and solved, which can be optimized to meet the value of m in various fault conditions. i =-2,-1.2,-0.8,-1.5,-1.6. The optimization results are only calculated under various parameter conditions of this patent.

[0185] This can improve the criterion ρ:

[0186]

[0187] Among them, n=5.

[0188]

[0189] Among them: Through the calculation of a large number of data samples, the data results in the above table are brought into the criterion ρ, and it can be calculated that σ=2.6 under various parameter conditions of this application, as shown in Figure 4 and Figure 3 (d)~ Figure 8 As shown in (d) in .

[0190] Substituting Tau1~Tau5 in the above table into the criterion in turn, the calculation results can be obtained as shown in Table 4 and Figure 3 (d)~ Figure 8 As shown in (d) in Figure 2, when σ is set to 2.6, the absolute value of ρ for faults within the zone is less than 2.6, while for faults outside the zone, the absolute value of ρ is greater than 2.6. Therefore, this criterion can accurately judge faults within and outside the zone and ensure accurate protection action.

[0191] Table 4 Calculated ρ values ​​under different fault conditions

[0192]

[0193]

[0194] Example 3

[0195] On the other hand, the present invention also provides a fault location identification system based on a new energy power system, the system comprising:

[0196] The data decomposition module is used to collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line.

[0197] The fault section determination module is used to select IMF components that are representative of fault characteristics to calculate the Kendall correlation coefficient. i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section;

[0198] Coefficient optimization module, used to construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ, and use the coati optimization algorithm COA to adjust the weight coefficient m i Perform optimization to determine the optimized weighting coefficient m i ;

[0199] The fault location judgment module is used to determine the protection setting value σ according to the protection correlation value ρ, and then use the protection setting value σ to judge whether it is an internal or external fault.

[0200] A protection setting value calculation module, used for establishing a sample library for the new energy power system when faults occur at different locations under different fault scenarios;

[0201] Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first fixed value σ 1 , calculate |ρ| for the fault samples in the area, and |ρ| is less than the second fixed value σ 2 Therefore, the protection setting

[0202] Other technical features of the fault location determination system based on the renewable energy power system described in the present invention are similar to the corresponding fault location determination method based on the renewable energy power system, and will not be repeated here.

[0203] Example 4

[0204] Reference Fig.10 , Fig.10 The figure is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0205] like Fig.10 As shown, the terminal may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a network interface 1003, and a memory 1004. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The network interface 1003 may optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1004 may be a high-speed RAM memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1004 may optionally be a storage device independent of the aforementioned processor 1001.

[0206] Those skilled in the art will understand that Fig.10 The terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0207] like Fig.10 As shown, the memory 1004 as a computer storage medium may include an operating system, a data storage module, a network communication module, and a fault location identification program based on a new energy power system.

[0208] exist Fig.10 In the terminal shown, the network interface 1003 is mainly used to connect to the background server and perform data communication with the background server; the processor 1001 can call the fault location identification program based on the new energy power system stored in the memory 1004, and perform the following operations:

[0209] Collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line.

[0210] Select the IMF components that are representative of the fault characteristics to calculate the Kendall correlation coefficient, that is, for the selected IMFs i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section;

[0211] Construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ;

[0212] The protection setting σ is determined according to the protection correlation value ρ, and then the protection setting σ is used to judge whether the fault is inside or outside the zone.

[0213] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0214] The weighting coefficient m is calculated using the coati optimization algorithm COA. i Perform optimization to determine the optimized weighting coefficient m i .

[0215] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0216] Establishing a sample library for the new energy power system under different fault scenarios and when faults occur at different locations;

[0217] Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first fixed value σ 1 , calculate |ρ| for the fault samples in the area, and |ρ| is less than the second fixed value σ 2 Therefore, the protection setting

[0218] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0219] The fault currents on both sides of the line are extracted according to the fault scenario, and the EMD is decomposed to extract the IMFs on the system side. i and station-side IMFw i The fault characteristics include:

[0220] The characteristic components are extracted by EMD, and the fault section is determined by comparing different fault characteristics. Therefore, the local characteristic IMF component r of the fault signal is decomposed using formula (1): k (t) Used for fault diagnosis.

[0221]

[0222] Where: n is the number of IMF components that meet the EMD constraint conditions; res is the residual component after decomposition;

[0223] The use of EMD requires the following two conditions to be met:

[0224] (1) The difference between the number of extreme points and the number of zero-crossing points in all IMF signals can only be 0 or 1;

[0225] (2) For any point in the signal, the mean of its upper and lower envelopes is 0;

[0226] After EMD decomposition, the IMF components and residuals of the original signal are obtained. Representative IMF components need to be selected for feature analysis. The information used for fault feature analysis is mainly the IMF components obtained by decomposition.

[0227] The IMF components on both sides of the new energy power system line are recorded as system-side IMFs i and station-side IMFw i , where i=1 to 7, when a fault occurs in the new energy power system, the fault IMF component has the following fault characteristics:

[0228] Feature 1: IMFs 1 、IMF 1 The basic information of the fault is retained. Although the characteristics are not clear, there are still differences in amplitude and phase, which have the characteristics of preliminary judgment;

[0229] Feature 2: IMFs 2 、IMFs 3 、IMF 2 、IMF 3 The amplitude characteristics of the fault information are retained. When the fault occurs outside the area, the information on both sides is basically the same, but when the fault occurs inside the area, there is a difference between the two.

[0230] Feature 3: IMFs 4-7 、IMF 4-7 The polarity characteristics of fault information are highlighted. When there is an out-of-area fault, the information change trends on both sides are opposite, while when there is an in-area fault, the information change trends on both sides are the same. Therefore, the corresponding IMFs are selected according to the above characteristics. i 、IMF i Just perform basic fault analysis.

[0231] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0232] The selection of IMF components representative of fault characteristics to calculate the Kendall correlation coefficient specifically includes:

[0233] The Kendall correlation coefficient is defined as follows:

[0234]

[0235] Where: N is the number of data in the data window, C i The calculated two-sided IMFs i 、IMF i The number of consistent pairs, D i The calculated two-sided IMFs i 、IMF i Number of divergent pairs;

[0236] For selected IMFs i 、IMF i Component sequence, select a cycle data window after the fault, and calculate the correlation coefficient Tau of the IMF component i ;

[0237] Therefore, when a fault occurs inside or outside the line, Tau 1-2 It only has clear differences in different time periods and is not stable; Tau 3 Only the initial period is unclear; Tau 4-7 The difference is relatively stable.

[0238] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0239] The weighting coefficient m is constructed i , and combined with the correlation coefficient Tau i Construct the protection correlation value ρ, which is specifically expressed as:

[0240]

[0241] Wherein, n is the number of correlation coefficients involved in the calculation, and here n=5.

[0242] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0243] The weighting coefficient m is calculated by using the coati optimization algorithm COA.i Perform optimization to determine the optimized weighting coefficient m i , including:

[0244] The best individual position in the current population Assuming that is the location of the prey, half of the predators in the design population will chase the prey, while the other half will wait for the arrival of the prey. Therefore, formula (3) is used to simulate the location of the predators in the population. When the prey lands, it will be at a random location in the search space. Based on this location, the waiting predators will move in the search space and use formulas (4) and (5) to update their positions.

[0245] When the new position of each hunter improves the value of the objective function, it is updated to the new position, otherwise it will remain at the original position;

[0246] M i As the optimization algorithm objective function To solve the problem, faults are set at different locations from several kilometers outside the reverse area to several kilometers outside the forward area of ​​the line, and the correlation coefficient Tau of each IMF component on both sides of the line is calculated. i A sample library is constructed and used as the input of the optimization algorithm. The optimization goal is that the similarity value ρ is less than the set threshold for S / 2 consecutive points after the fault, where S is the number of sampling points in one cycle.

[0247] The optimization process is:

[0248]

[0249] Where: M is the population size, t is the current number of iterations, lb j The lower bound of the j-th dimension variable in time-varying time, ub j represents the upper bound of the j-th dimension variable, fitness(·) is used to calculate the fitness, r represents a random number between [0,1], I represents a random integer from the integer set {1,2}, represents the calculated new position, Indicates the j-th dimension variable value of the i-th individual in the current iteration.

[0250] Further, the processor 1001 may call a fault location identification program based on a new energy power system stored in the memory 1004, and further perform the following operations:

[0251] The protection constant σ is determined according to the protection correlation value ρ. Considering the positive and negative nature of the correlation coefficient, the absolute value of the similarity value ρ is used to construct the protection criterion:

[0252]

[0253] Among them, σ is the protection constant.

[0254] In addition, it can be understood by a person skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the control terminal to implement the process steps of the embodiment of the above method.

[0255] Therefore, the present invention also provides a computer-readable storage medium, which stores a fault location identification program based on a new energy power system. When the fault location identification program based on the new energy power system is executed by a processor, the various steps of the fault location identification method based on the new energy power system described in the above embodiment are implemented.

[0256] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0257] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0258] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0259] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0260] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0261] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0262] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0263] 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 equivalents, the present invention is also intended to include these modifications and variations.

[0264] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A fault location identification method based on a new energy power system, characterized in that: The method includes: Collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line. Select the IMF components that are representative of the fault characteristics to calculate the Kendall correlation coefficient, that is, for the selected IMFs i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section; Construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ, determine the protection setting value σ according to the protection relevance value ρ, and then use the protection setting value v to judge whether it is an internal or external fault.

2. The fault location identification method based on the new energy power system according to claim 1, characterized in that: The method further includes: The weighting coefficient m is calculated using the coati optimization algorithm COA. i Perform optimization to determine the optimized weighting coefficient m i .

3. The fault location identification method based on the new energy power system according to claim 1 or 2, characterized in that: The method further includes: Establishing a sample library for the new energy power system under different fault scenarios and when faults occur at different locations; Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first constant σ1, and |ρ| is calculated for the in-zone fault samples, and |ρ| is less than the second constant σ2. Therefore, the protection constant 4. The fault location identification method based on the new energy power system according to claim 2, characterized in that: The fault currents on both sides of the line are extracted according to the fault scenario, and the EMD is decomposed to extract the IMFs on the system side. i and station-side IMFw i The fault characteristics include: The characteristic components are extracted by EMD, and the fault section is determined by comparing different fault characteristics. Therefore, the local characteristic IMF component r of the fault signal is decomposed using formula (1): k (t) For fault diagnosis; Where: n is the number of IMF components that meet the EMD constraint conditions; res is the residual component after decomposition; The use of EMD requires the following two conditions to be met: (1) The difference between the number of extreme points and the number of zero-crossing points in all IMF signals can only be 0 or 1; (2) For any point in the signal, the mean of its upper and lower envelopes is 0; After EMD decomposition, the IMF components and residuals of the original signal are obtained. Representative IMF components need to be selected for feature analysis. The information used for fault feature analysis is mainly the IMF components obtained by decomposition. The IMF components on both sides of the new energy power system line are recorded as system-side IMFs i and station-side IMFw i , where i=1 to 7, when a fault occurs in the new energy power system, the fault IMF component has the following fault characteristics: Feature 1: IMFs1 and IMFw1 retain the basic information of the fault. Although the features are not clear, they still have the differences in amplitude and phase, which are the characteristics of preliminary judgment; Feature 2: IMFs2, IMFs3, IMFw2, and IMFw3 retain the amplitude characteristics of the fault information. When the fault occurs outside the area, the information on both sides is basically the same, but when the fault occurs inside the area, there is a difference between the two. Feature 3: IMFs 4-7 、IMF 4-7 The polarity characteristics of fault information are highlighted. When there is an out-of-area fault, the information change trends on both sides are opposite, while when there is an in-area fault, the information change trends on both sides are the same. Therefore, the corresponding IMFs are selected according to the above characteristics. i 、IMF i Just perform basic fault analysis.

5. The fault location identification method based on the new energy power system according to claim 4, characterized in that: The selection of IMF components representative of fault characteristics to calculate the Kendall correlation coefficient specifically includes: The Kendall correlation coefficient is defined as follows: Where: N is the number of data in the data window, C i The calculated two-sided IMFs i 、IMF i The number of consistent pairs, D i The calculated two-sided IMFs i 、IMF i Number of divergent pairs; For selected IMFs i 、IMF i Component sequence, select the wave data window for a period of time after the fault, and calculate the correlation coefficient Tau of the IMF component i ; Therefore, when a fault occurs inside or outside the line, Tau 1-2 There are only clear differences in different periods and no stability; Tau3 is only unclear in the initial period; Tau 4-7 The difference is relatively stable.

6. The fault location identification method based on the new energy power system according to claim 5, characterized in that: The weighting coefficient m is constructed i , and combined with the correlation coefficient Tau i Construct the protection correlation value ρ, which is specifically expressed as: Among them, n is the number of correlation coefficients involved in the calculation.

7. The fault location identification method based on the new energy power system according to claim 6, characterized in that: The weighting coefficient m is calculated by using the coati optimization algorithm COA. i Perform optimization to determine the optimized weighting coefficient m i , specifically including: The best individual position in the current population Assuming that is the location of the prey, half of the predators in the design population will chase the prey, while the other half will wait for the arrival of the prey. Therefore, formula (4) is used to simulate the location of the predators in the population. When the prey lands, it will be at a random location in the search space. Based on this location, the waiting predators will move in the search space and use formulas (5) and (6) to update their positions. When the new position of each hunter improves the value of the objective function, it is updated to the new position, otherwise it will remain at the original position; M i As the optimization algorithm objective function To solve the problem, faults are set at different locations from several kilometers outside the reverse area to several kilometers outside the forward area of ​​the line, and the correlation coefficient Tau of each IMF component on both sides of the line is calculated. i A sample library is constructed and used as the input of the optimization algorithm. The optimization goal is that the similarity value ρ is less than the set threshold for S / 2 consecutive points after the fault, where S is the number of sampling points of a time wave. The optimization process is: Where: M is the population size, t is the current number of iterations, lb j The lower bound of the j-th dimension variable in time-varying time, ub j represents the upper bound of the j-th dimension variable, fitness(·) is used to calculate the fitness, r represents a random number between [0,1], I represents a random integer from the integer set {1,2}, represents the calculated new position, Indicates the j-th dimension variable value of the i-th individual in the current iteration.

8. The fault location identification method based on the new energy power system according to claim 1 or 2, characterized in that: The protection constant σ is determined according to the protection correlation value ρ. Considering the positive and negative nature of the correlation coefficient, the absolute value of the similarity value ρ is used to construct the protection criterion: Among them, σ is the protection constant.

9. A fault location determination system based on a new energy power system, characterized in that: The system includes: The data decomposition module is used to collect fault data in the new energy power system line, extract the fault current on both sides of the line according to the fault scenario, and perform EMD decomposition on it to extract the IMFs on the system side. i and station-side IMFw i The new energy power system includes a station side and a system side. The station side includes a plurality of photovoltaic power generation groups under grid-type control. The system side is the grid side. The electric energy generated by the photovoltaic power generation group is converted into alternating current by a converter and transmitted to the grid side by an external transmission line. The fault section determination module is used to select IMF components that are representative of fault characteristics to calculate the Kendall correlation coefficient. i 、IMF i Component sequence calculates the correlation coefficient Tau of the IMF component i , so as to determine the fault section; Coefficient optimization module, used to construct the corresponding Tau i The weighting coefficient m i , and combined with the correlation coefficient Tau i Construct the protection relevance value ρ; The fault location judgment module is used to determine the protection setting value σ according to the protection correlation value ρ, and then use the protection setting value σ to judge whether it is an internal or external fault.

10. The fault location determination system based on the new energy power system according to claim 9, characterized in that: The system also includes: A protection setting value calculation module, used for establishing a sample library for the new energy power system when faults occur at different locations under different fault scenarios; Traverse the sample library, where the corresponding |ρ| is calculated for the out-of-zone fault samples, and |ρ| is greater than the first constant ρ1, and |ρ| is calculated for the in-zone fault samples, and |ρ| is less than the second constant σ2. Therefore, the protection constant 11. The fault location identification method based on the new energy power system according to claim 9 or 10, characterized in that: The method further includes: The weighting coefficient m is calculated using the coati optimization algorithm COA. i Perform optimization to determine the optimized weighting coefficient m i .

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