Distributed photovoltaic low-voltage transformer area vulnerability assessment method and system, terminal and medium

Abstract

A distributed photovoltaic-containing low-voltage transformer area vulnerability evaluation method, system, terminal and medium are characterized in that the method comprises the following steps: constructing a low-voltage transformer area state vulnerability evaluation index system, collecting and utilizing power distribution network operation parameters, and calculating vulnerability evaluation indexes by the low-voltage transformer area state vulnerability evaluation index system; constructing the vulnerability evaluation indexes into a decision matrix, and calculating adjustment coefficients for different indexes in the decision matrix so as to obtain correlation coefficients between the indexes and the information amount of each index; calculating the objective correction weight of each index by using the information amount and the correction variance of each index, and collecting the subjective weight of each index to obtain the comprehensive weight of each index; and determining the optimal solution and the worst solution of each index in the decision matrix by adopting a TOPSIS model, and evaluating the vulnerability degree of the low-voltage transformer area through the comprehensive weight and the distance between the current index and the optimal solution as well as the worst solution.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and more specifically, to a vulnerability assessment method, system, terminal, and medium for low-voltage power distribution areas with distributed photovoltaics. Background Art

[0002] With the transformation of the energy structure, the access scale of distributed photovoltaics in 0.4 kV low-voltage power distribution areas is increasing day by day, making the power flow distribution in low-voltage power distribution areas become complex. The intermittency and randomness of distributed photovoltaic output will affect the voltage index of the power distribution area nodes, causing voltage rise, resulting in problems such as transformer overload, heavy overload of equipment lines, and three-phase imbalance, which have a greater impact on the vulnerability of the distribution network. And the vulnerability of the distribution network is an important evaluation index for the power grid to maintain stability and normal power supply capacity when suffering from disturbances or faults. The higher the vulnerability of the distribution network, the greater the impact of the power grid fault.

[0003] A reasonable evaluation index system is the basis for the vulnerability assessment of the distribution network and can reflect the true safety situation of the distribution network. However, the previous distribution network evaluation index systems mostly used reliability indexes, which reflected the power supply capabilities such as the power outage frequency, power outage duration, and lack of power supply in the distribution network for a period of time, and mostly focused on the vulnerability assessment research of the distribution network above 10 kV. In addition, the previous distribution network evaluation index systems did not consider the impact of the randomness and volatility of distributed photovoltaic output on the vulnerability of low-voltage power distribution areas, and had certain limitations.

[0004] In view of the above problems, there is an urgent need for a vulnerability assessment method, system, terminal, and medium for low-voltage power distribution areas with distributed photovoltaics. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the present invention provides a low-voltage vulnerability assessment index system established based on the random volatility of distributed photovoltaic power and the characteristics of low-voltage power distribution areas, and comprehensively evaluates the vulnerability of the power distribution area.

[0006] The present invention adopts the following technical solutions.

[0007] In the first aspect of the present invention, it relates to a vulnerability assessment method for low-voltage power distribution areas with distributed photovoltaic power generation. The method includes the following steps: constructing an evaluation index system for the vulnerability of the low-voltage power distribution area state, collecting the operating parameters of the distribution network and using the evaluation index system for the vulnerability of the low-voltage power distribution area state to calculate the vulnerability assessment index; constructing the vulnerability assessment index into a decision matrix, calculating the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index; calculating the objective correction weight of each index by using the information amount of each index and the corrected variance, and obtaining the comprehensive weight of each index by integrating the subjective weights of each index; using the TOPSIS model to determine the optimal solution and the worst solution of each index in the decision matrix, and evaluating the vulnerability degree of the low-voltage power distribution area through the comprehensive weight, the distance between the current index and the optimal solution and the worst solution.

[0008] Preferably, constructing an evaluation index system for the vulnerability of the low-voltage power distribution area state, collecting the operating parameters of the distribution network and using the evaluation index system for the vulnerability of the low-voltage power distribution area state to calculate the vulnerability assessment index, including: the vulnerability assessment index includes the transformer voltage over-limit index, the equipment line overload index, the three-phase unbalance degree index, the power balance index and the island operation index.

[0009] Preferably, constructing the vulnerability assessment index into a decision matrix, calculating the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index, including: using the element value a mn in the decision matrix, calculating the adjustment coefficient of each column index in the decision matrix as:

[0010]

[0011] In the formula, the calculation method of f(a mn ) is as follows:

[0012]

[0013] k is an arbitrary constant, which is 0.1 or 0.2 in one embodiment.

[0014] Preferably, constructing the vulnerability assessment index into a decision matrix, calculating the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index, including: using the m-th and n-th column index vectors A m , A n of the decision matrix A, and the adjustment coefficients B vm , B vn corresponding to the two column index vectors, calculating the correlation coefficient between each index; traversing the correlation coefficient between the current index vector and other index vectors, and integrating the adjustment coefficient of the current index vector to calculate the information amount of the current index vector.

[0015] Preferably, the objective corrected weight of each index is calculated using the information amount and corrected variance of each index, and the subjective weights of all indexes are combined to obtain the comprehensive weight of each index, including: the objective corrected weight of each index is:

[0016]

[0017] In the formula, C n is the information amount of the current index vector A n V mn is the variance of the nth vulnerability index of the evaluation object m after K t times of sub-sampling; aiming at the minimum variance, iterative improvement is implemented on the objective corrected weight of each index.

[0018] Preferably, the objective corrected weight of each index is calculated using the information amount and corrected variance of each index, and the subjective weights of all indexes are combined to obtain the comprehensive weight of each index, including:

[0019] The subjective weight ω n ″ of each index is determined by the expert scoring method;

[0020] The comprehensive weight of each index is:

[0021]

[0022] Preferably, the TOPSIS model is used to determine the optimal solution and the worst solution of each index in the decision matrix, and the vulnerability degree of the low-voltage power distribution area is evaluated through the comprehensive weight, the distance between the current index and the optimal solution and the worst solution, including: selecting the optimal solution n and the worst solution in each index vector a After adding an error factor to each index vector, the weighted corrected cosine similarity distance between each evaluation object and the optimal solution and the worst solution is determined; the distances from the evaluation object to the optimal solution and the worst solution of each index are calculated, so as to calculate the closeness degree between the evaluation object and the optimal solution.

[0023] In a second aspect of the present invention, there is provided a vulnerability assessment system for a low-voltage power distribution area with distributed photovoltaic power generation. The system implements a vulnerability assessment method for a low-voltage power distribution area with distributed photovoltaic power generation in the first aspect of the present invention. The system includes a collection module, a calculation module, a weight module, and an evaluation module. The collection module is configured to construct an evaluation index system for the vulnerability of the low-voltage power distribution area state, collect the operation parameters of the distribution network and use them to calculate the vulnerability assessment index by using the evaluation index system for the vulnerability of the low-voltage power distribution area state. The calculation module is configured to construct the vulnerability assessment index into a decision matrix, calculate the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index. The weight module is configured to calculate the objective corrected weight of each index by using the information amount of each index and the corrected variance, and obtain the comprehensive weight of each index by integrating the subjective weights of each index. The evaluation module is configured to determine the optimal solution and the worst solution of each index in the decision matrix by using the TOPSIS model, and evaluate the vulnerability degree of the low-voltage power distribution area through the comprehensive weight, the distance between the current index and the optimal solution and the worst solution.

[0024] In a third aspect of the present invention, there is provided a terminal including a processor and a storage medium. The storage medium is used to store instructions. The processor is configured to operate according to the instructions to execute the steps of the method described in the first aspect of the present invention.

[0025] In a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps of the method described in the first aspect of the present invention are implemented.

[0026] The beneficial effects of the present invention are as follows. Compared with the prior art, a vulnerability assessment index system for a low-voltage power distribution area is established in the present invention in view of the random volatility of distributed photovoltaic power and the characteristics of the low-voltage power distribution area, and the vulnerability of the power distribution area is comprehensively evaluated, which can solve the problem that the vulnerability of the low-voltage power distribution area cannot be accurately evaluated.

[0027] The beneficial effects of the present invention also include:

[0028] 1. The present invention takes into account the influence brought by the access of distributed photovoltaic power to the power distribution area, adds an index related to the volatility of photovoltaic power output, and constructs an evaluation index system for the vulnerability of the low-voltage power distribution area state.

[0029] 2. The present invention uses a combined weighting method based on improved CRITIC to determine the index weights of the power distribution area, takes into account the volatility of each index data itself while taking into account the correlation between each index, improves the CRITIC method by using the adjustment parameter method, and corrects the obtained objective weights by using the variance of the multiple sampling results of the vulnerability index.

[0030] 3. The present invention establishes a vulnerability assessment model for low-voltage power distribution areas based on an improved TOPSIS algorithm. By adding an error factor to correct the cosine similarity distance and performing weighted processing, the gap between the closeness degrees can be amplified, enabling the cosine similarity to better replace the Euclidean distance, making the calculation results more accurate, improving the objectivity of the assessment model, and thus obtaining more accurate and reliable vulnerability assessment results, which can more truly reflect the operating conditions of the low-voltage power distribution areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic flow chart of a method for assessing the vulnerability of a low-voltage power distribution area with distributed photovoltaic power generation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To make the objectives, technical solutions and advantages of the present invention clearer and more accurate, the technical solutions of the present invention will be described in detail below through multiple specific embodiments. The embodiments adopted by the present invention are only used to explain the present invention and do not limit the content of the present invention.

[0033] Figure 1 It is a schematic flow chart of a method for assessing the vulnerability of a low-voltage power distribution area with distributed photovoltaic power generation according to the present invention. As Figure 1 shown, in the first aspect of the present invention, it relates to a method for assessing the vulnerability of a low-voltage power distribution area with distributed photovoltaic power generation.

[0034] Step 1: Construct an evaluation index system for the state vulnerability of the low-voltage power distribution area, collect the operating parameters of the distribution network and use the evaluation index system for the state vulnerability of the low-voltage power distribution area to calculate the vulnerability assessment index.

[0035] Input the collected operating parameters of the distribution network into the constructed evaluation index system for the state vulnerability of the low-voltage power distribution area, and output the vulnerability assessment index. The evaluation index system for the state vulnerability of the low-voltage power distribution area includes the transformer voltage over-limit index, the equipment line overload index, the three-phase unbalance degree index, the power balance index and the island operation index.

[0036] The access of distributed photovoltaic power generation to the power distribution area will cause the transformer voltage to rise and increase the risk of over-limit. The photovoltaic output volatility is an index to measure the fluctuation degree of photovoltaic output within a certain period of time. If the volatility is too high, it will cause the transformer voltage to exceed the limit. The photovoltaic output volatility is used to measure the probability of transformer voltage over-limit and is used to calculate the value of the transformer voltage over-limit index. The larger the value, the higher the vulnerability of the transformer.

[0037] The transformer voltage over-limit index is:

[0038]

[0039] In the formula, λ is the photovoltaic output volatility, and there is

[0040] Pi is the measured value of the photovoltaic output at the \(i\)-th moment, is the average value of the photovoltaic output, that is, N i is the number of data samples,

[0041] V i is the real-time voltage value of the transformer substation at the \(i\)-th moment, V i are the upper and lower limits of the real-time voltage value \(V\) of the transformer substation at the \(i\)-th moment, respectively. i of the upper and lower limits.

[0042] Under the photovoltaic access scenario, the frequency of voltage over-limit increases, resulting in heavy overload of equipment lines. The heavier the overload index value \(R\) of the equipment lines, L the greater the vulnerability of the operation state of the equipment lines.

[0043] The overload index of the equipment lines is:

[0044]

[0045] In the formula, \(\rho\) is the probability of heavy overload of the equipment lines,

[0046] \(l\) is the line number, \(N\) l is the total number of equipment lines,

[0047] P l is the active power of line \(l\), is the maximum allowable active power on line \(l\).

[0048] In one embodiment, the Monte Carlo algorithm is used to calculate the probability of heavy overload of the equipment lines. By a large number of random samplings to simulate the actual operation scenario, the frequency of overload events is statistically counted, and then the probability is estimated.

[0049] The calculation process of the Monte Carlo method is:

[0050] (1) Input the distribution network data. Set the overload condition. When , it is determined as overload.

[0051] In the formula, \(S\) l (\(t\)) and \(S\) Nl are the load and rated capacity of the \(l\)-th line at time \(t\), respectively, and \(C\) eff is the threshold of line heavy overload.

[0052] (2) Conduct \(n\) samplings. According to the probability distribution of line heavy overload, randomly extract \(n\) samples to obtain a large number of random number samples that conform to this distribution.

[0053] (3) Check whether each of the obtained samples meets the overload condition, and record the number of overloads \(N\) overload .

[0054] (4) Calculate the overload probability. The estimated value of the overload probability is:

[0055] Taking the three-phase unbalance degree as the basis for measuring the vulnerability of the low-voltage substation area, the unbalance of the three-phase load will lead to a reduction in the power supply efficiency of the line and the substation transformer. The larger its value, the greater the vulnerability of the substation area.

[0056] The three-phase current unbalance degree ε at time t I,t is expressed as:

[0057]

[0058] In the formula, I A,t , I B,t and I C,t are the secondary-side currents of phases A, B, and C respectively, ε I is the CT transformation ratio,

[0059] The three-phase voltage unbalance degree ε at time t I,t is expressed as:

[0060]

[0061] In the formula, V A,t , V B,t , V C,t are the secondary-side voltages of phases A, B, and C respectively, ε U is the PT transformation ratio.

[0062] The output of photovoltaic power has characteristics such as randomness, volatility, and intermittency. Therefore, the access of distributed photovoltaic power introduces a high degree of uncertainty to the power balance of the substation transformer. The power balance index is used to measure the vulnerability of the low-voltage substation area. The larger the power balance index, the greater the vulnerability.

[0063] The power balance index is:

[0064]

[0065] In the formula,

[0066] P load , Q load are the active power and reactive power required by the load respectively,

[0067] P pv is the output power of the distributed photovoltaic power,

[0068] Q f is the power factor of the load, and there is Q L and Q C are the reactive powers of the inductor and capacitor of the load on the inverter side of the substation area respectively, and P is the active power of the substation area,

[0069] L and C are the inductance and capacitance of the load respectively.

[0070] Taking the actual frequency of the substation area as a measure of the islanding operation index, when islanding occurs, the actual frequency of the system will change due to different load sizes and natures. By judging whether the frequency exceeds the limit, it can be detected whether there is an islanding operation situation. The larger the value of the frequency, the greater the risk of islanding operation and the greater the vulnerability of the substation area.

[0071] The islanding operation index is:

[0072]

[0073] R is the load resistance on the inverter side of the substation area.

[0074] Step 2: Construct the vulnerability assessment index into a decision matrix, calculate the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information volume of each index.

[0075] In one embodiment of the present invention, the evaluation object can be one of multiple substation areas. In another embodiment, it is the vulnerability assessment index collected and calculated at different times in a substation area, that is, the state of the substation area at different times.

[0076] Suppose there are M evaluation objects, and each substation area participates in N evaluation indexes. Construct the decision matrix A as:

[0077]

[0078] In the formula, the element a mn in the matrix is the nth index value of the mth evaluation object, and m and n are any integers from 1 to M and 1 to N respectively.

[0079] Since the samples are not standardized, the measurement unit and the average unit are different, and there will be errors when using the standard deviation to compare their variation degrees. Therefore, the present invention is improved, and the adjustment coefficient B vn is:

[0080]

[0081] In the formula, the calculation method of f(a mn ) is as follows:

[0082]

[0083] k is an arbitrary constant, which is 0.1 or 0.2 in one embodiment.

[0084] max m∈[1,2,…,M] (|a mn |) is the maximum value of the absolute values of the elements in the nth column of the decision matrix.

[0085] Step 3: Calculate the objective correction weights of each indicator using the information amount and corrected variance of each indicator, and obtain the comprehensive weights of each indicator by integrating the subjective weights of each indicator.

[0086] Construct the vulnerability assessment indicators into a decision matrix, calculate the adjustment coefficients for each different indicator in the decision matrix, so as to obtain the correlation coefficients between the indicators and the information amounts of each indicator, including: using the m-th and n-th column indicator vectors A m 、A n of the decision matrix A, and the adjustment coefficients B vm 、B vn corresponding to the two column indicator vectors, calculate the correlation coefficients between each indicator; traverse the correlation coefficients between the current indicator vector and other indicator vectors, and integrate the adjustment coefficients of the current indicator vector to calculate the information amount of the current indicator vector.

[0087] The correlation coefficient between each indicator is:

[0088]

[0089] In the formula, A m 、A n are the m-th and n-th columns of the decision matrix A respectively. B vm 、B vn are the adjustment coefficients of the m-th and n-th columns of the decision matrix A respectively.

[0090] Among them, the information amount C n contained in the n-th indicator is:

[0091]

[0092] The information amount C n is the product of the index variability and conflict, and is used to measure the weight ratio of this indicator in the entire comprehensive evaluation system.

[0093] The present invention corrects the obtained objective weights using the variance of the multiple sampling results of the vulnerability indicators. The variance represents the degree of deviation of a random variable from its mean value, and the larger its value, the greater the impact of the process of distributed photovoltaic random power output on the vulnerability indicators of the evaluation object. That is, with the goal of minimizing the variance, iterative improvement is implemented on the objective correction weights of each indicator.

[0094] After K t times of sampling, the variance V mn of the n-th vulnerability index of the evaluation object m can be expressed as:

[0095]

[0096] In the formula, is a random variable that contains K t times of sub-sampled evaluation object m's nth vulnerability index, and E(·) is to calculate the expectation.

[0097] In summary, the objective correction weight of each index is obtained as follows:

[0098]

[0099] Secondly, the present invention also supports determining the subjective weight based on the expert scoring method. Select multiple representative and authoritative experts to score the vulnerability assessment indicators, summarize and analyze the scoring results of the experts, calculate the average score of each indicator, feedback the initially summarized scoring results to each expert, and invite them to revise their scores according to the feedback results. According to the multi-round feedback and revised expert scoring results, use the mathematical analysis method to calculate the subjective weight ω n ″.

[0100] Based on the above two weight calculation methods, the present invention also supports determining the comprehensive weight based on the minimum information entropy increase. The comprehensive weight is calculated by the method of minimizing the sum of the information entropy increases of the comprehensive weight, subjective weight, and objective weight as follows:

[0101]

[0102] Use the above comprehensive weight to weight the original decision matrix A to obtain the input matrix. Input the input matrix into the TOPSIS model to evaluate the vulnerability of the substation area.

[0103] Step 4, use the TOPSIS model to determine the optimal solution and the worst solution of each indicator in the decision matrix, and evaluate the vulnerability of the low-voltage substation area through the comprehensive weight, the distance between the current indicator and the optimal solution and the worst solution.

[0104] The TOPSIS model ranks the evaluation objects as a whole according to the relative closeness of the evaluation object to the ideal solution, and then determines the relative advantages and disadvantages. First, establish the optimal vector and the worst vector matrix through samples. The closer each object is to the optimal vector and the farther it is from the worst vector, the higher the evaluation level. However, the traditional Euclidean distance calculation ignores the correlation between indicators, resulting in a deviation in the calculation result. Therefore, the improved TOPSIS algorithm is adopted using the modified cosine similarity distance.

[0105] The improved TOPSIS model first determines the optimal solution n in each indicator A and the worst solution as follows:

[0106]

[0107] On this basis, the weighted modified cosine similarity distance between each evaluation object and the optimal solution and the worst solution is determined. f is the number of the optimal solution or the worst solution.

[0108] The cosine similarity method mainly distinguishes differences in terms of direction. The results calculated using cosine similarity are extremely small, resulting in a relatively close distance between each evaluation object and the optimal solution and the worst solution. The obtained relative closeness differences are small, making it impossible to accurately measure the pros and cons of each evaluation object and causing deviation in the evaluation results. Therefore, an error factor is added to magnify the gap between the closeness degrees, enabling cosine similarity to better replace the Euclidean distance. Thus, the error factor σ is:

[0109]

[0110] The error factor σ describes the average value of the positive and negative ideal solutions of all indicators.

[0111] The weighted cosine similarity distance is:

[0112]

[0113] In the formula, X + / - is a vector composed of or under the 1st to Nth indicators.

[0114] Generally, the average value of the positive and negative ideal solutions is used as the starting point of the vector in the attribute space to calculate the cosine similarity between each alternative solution and the positive and negative ideal solutions, in order to magnify the gap between the closeness degrees. However, it cannot meet the situation where objects with high similarity need to be significantly distinguished. The present invention uses the average value of the positive and negative ideal solutions of all indicators as the error factor and uses the logarithmic function to correct the distances from each evaluation object to the optimal solution and the worst solution.

[0115] The distance from each evaluation object to the optimal solution is:

[0116]

[0117] The distance from each evaluation object to the worst solution is:

[0118]

[0119] Thus, the closeness degree between the evaluation object m and the optimal solution is obtained as:

[0120]

[0121] D m is the comprehensive evaluation value of the current evaluation object. The closer D m is to 0, the higher the vulnerability of the evaluation object m.

[0122] In the second aspect of the present invention, it relates to a vulnerability assessment system for low-voltage power distribution areas with distributed photovoltaic power generation. The system is implemented by using the vulnerability assessment method for low-voltage power distribution areas with distributed photovoltaic power generation described in the first aspect of the present invention. The system includes a collection module, a calculation module, a weight module, and an evaluation module. The collection module is used to construct an evaluation index system for the state vulnerability of the low-voltage power distribution area, collect the operating parameters of the distribution network and use them to calculate the vulnerability assessment indexes by using the evaluation index system for the state vulnerability of the low-voltage power distribution area. The calculation module is used to construct the vulnerability assessment indexes into a decision matrix, calculate the adjustment coefficients for each different index in the decision matrix, so as to obtain the correlation coefficients between the indexes and the information content of each index. The weight module is used to calculate the objective corrected weights of each index by using the information content of each index and the corrected variance, and obtain the comprehensive weights of each index by integrating the subjective weights of each index. The evaluation module is used to adopt the TOPSIS model to determine the optimal solution and the worst solution of each index in the decision matrix, and evaluate the vulnerability degree of the low-voltage power distribution area through the comprehensive weights, the distance between the current index and the optimal solution and the worst solution.

[0123] In the third aspect of the present invention, it relates to a terminal, including a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to execute the steps of the method described in the first aspect of the present invention.

[0124] In the fourth aspect of the present invention, it relates to a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method described in the first aspect of the present invention.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that there are still contents in the technical solutions of the present invention that can be modified or equivalently replaced for the specific implementation manners of the present invention. Any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A vulnerability assessment method for low-voltage power distribution areas with distributed photovoltaic power, characterized in that, The method includes the following steps: Construct an evaluation index system for the vulnerability of the low-voltage substation area state, collect the operation parameters of the distribution network and use the evaluation index system for the vulnerability of the low-voltage substation area state to calculate the vulnerability evaluation index; Construct the vulnerability evaluation index into a decision matrix, calculate the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index; Calculate the objective correction weight of each index by using the information amount of each index and the corrected variance, and obtain the comprehensive weight of each index by integrating the subjective weights of each index; Adopt the TOPSIS model to determine the optimal solution and the worst solution of each index in the decision matrix, and evaluate the vulnerability degree of the low-voltage substation area through the comprehensive weight, the distance between the current index and the optimal solution and the worst solution.

2. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 1, characterized in that: The construction of the evaluation index system for the vulnerability of the low-voltage substation area state, collecting the operation parameters of the distribution network and using the evaluation index system for the vulnerability of the low-voltage substation area state to calculate the vulnerability evaluation index includes: The vulnerability evaluation index includes the substation transformer voltage over-limit index, the equipment line overload index, the three-phase unbalance degree index, the power balance index and the island operation index.

3. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 2, characterized in that: The construction of the vulnerability evaluation index into a decision matrix, calculating the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index includes: Using the element values a in the decision matrix mn , the adjustment coefficients for each column index in the decision matrix are calculated as follows: where f(a mn ) is calculated as follows: k is an arbitrary constant, which is 0.1 or 0.2 in one embodiment.

4. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 3, characterized in that: The construction of the vulnerability evaluation index into a decision matrix, calculating the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information amount of each index includes: Using the m-th and n-th column index vectors A m and A n , and the adjustment coefficients B vm and B vn corresponding to the two column index vectors, calculate the correlation coefficients between the various indicators; Traverse the correlation coefficient between the current index vector and other index vectors, and integrate the adjustment coefficients of the current index vector to calculate the information amount of the current index vector.

5. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 4, characterized in that: The calculation of the objective correction weight of each index by using the information amount of each index and the corrected variance, and the obtaining of the comprehensive weight of each index by integrating the subjective weights of each index includes: The objective correction weight of each index is: where C n is the information content of the current index vector A n , V mn is the variance of the nth vulnerability index of the evaluation object m after K t times of subsampling; Taking the minimum variance as the goal, iteratively improve the objective correction weight of each index.

6. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 5, characterized in that: The calculation of the objective correction weight of each index by using the information amount of each index and the corrected variance, and the obtaining of the comprehensive weight of each index by integrating the subjective weights of each index includes: The subjective weight ω of each index n ″Determined by the expert scoring method; The comprehensive weight of each index is:

7. A method for evaluating the vulnerability of a low-voltage substation area with distributed photovoltaics according to claim 6, characterized in that: The optimal and worst solutions of each index in the decision matrix are determined by using the TOPSIS model, and the vulnerability degree of the low-voltage power distribution area is evaluated by the comprehensive weight, the distance between the current index and the optimal and worst solutions, including: Select each index vector A n and the optimal solution and the worst solution After adding an error factor to each index vector, the weighted modified cosine similarity distance between each evaluation object and the optimal and worst solutions is determined; Calculate the distances from the evaluation object to the optimal and worst solutions of each index, so as to calculate the closeness degree between the evaluation object and the optimal solution.

8. A vulnerability assessment system for a low-voltage power distribution area with distributed photovoltaic; characterized in that: The system is implemented by using the vulnerability assessment method for a low-voltage power distribution area with distributed photovoltaic according to any one of claims 1-7; The system includes a collection module, a calculation module, a weight module and an evaluation module; The collection module is used to construct an evaluation index system for the vulnerability of the low-voltage power distribution area state, collect the operation parameters of the distribution network and use them to calculate the vulnerability assessment indexes by using the evaluation index system for the low-voltage power distribution area state; The calculation module is used to construct the vulnerability assessment indexes into a decision matrix, calculate the adjustment coefficient for each different index in the decision matrix, so as to obtain the correlation coefficient between the indexes and the information content of each index; The weight module is used to calculate the objective modified weight of each index by using the information content of each index and the modified variance, and obtain the comprehensive weight of each index by aggregating the subjective weights of each index; The evaluation module is used to determine the optimal and worst solutions of each index in the decision matrix by using the TOPSIS model, and evaluate the vulnerability degree of the low-voltage power distribution area by the comprehensive weight, the distance between the current index and the optimal and worst solutions.

9. A terminal, including a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the steps of the method according to any one of claims 1-7 are implemented.

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