Power distribution network security domain processing method, system and platform based on evaluation system

Abstract

The invention discloses a power distribution network security domain processing method, system and platform based on an evaluation system, and the method comprises the steps: generating and obtaining first data corresponding to a power distribution network through the method, and constructing an index system corresponding to the power distribution network according to the first data; constructing a power distribution network evaluation system according to the index system in combination with a combined weighting method combining an entropy weight method and an analytic hierarchy process, and generating an evaluation score corresponding to the power distribution network; wherein the evaluation score is power distribution network safety score data; and based on the evaluation score, generating a safety boundary corresponding to the power distribution network, and processing and generating a safety domain corresponding to the power distribution network according to the safety boundary, and a system and a platform corresponding to the method can enable calculation and generation of the safety domain of the power distribution network to be more accurate. According to the scheme, the technical problems of weak safety performance, complex safety domain calculation and poor reliability of a traditional power distribution network can be solved, and the safety level of the power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid processing, and particularly relates to a distribution network security domain processing method, system and platform based on an evaluation system. Background Art

[0002] At present, there are significant differences between the dynamic characteristics of new energy devices and those of synchronous motors. Due to the randomness and volatility of their output, the uncertainty faced by the power system increases, and the structure becomes more complex, having a significant impact on the stability of the distribution network, which is highly likely to cause safety and stability problems.

[0003] The concept of "security domain" was initially proposed by Hnyilicza E in 1975 to discuss the stability issues of power systems at a computer application conference in the power industry. A relatively complete theoretical framework has been formed in terms of the theory, modeling, and mechanism characteristics of the transmission system. Compared with traditional methods, the "region" method of the transmission security region can provide the specific position of the system operating point in the security region, enabling the control end to obtain the overall security measures of the system and significantly reducing the workload of security assessment. Online security assessment and operation based on the "region" concept have been widely applied in actual transmission networks.

[0004] Considering the significant differences in the security concepts between the transmission system and the distribution system, as well as the particularity of the new energy high-penetration distribution network, traditional analysis methods need further development and improvement. The calculation of the distribution network security domain needs to be considered more comprehensively to ensure the stable operation of the distribution network.

[0005] Therefore, aiming at the technical problems and defects of weak security performance, incomplete evaluation system, complex calculation of security domain, and poor reliability of the above distribution network, it is urgent to design and develop a distribution network security domain processing method, system and platform based on an evaluation system. Summary of the Invention

[0006] To overcome the deficiencies and difficulties of the above-mentioned prior art, the purpose of the present invention is to provide a distribution network security domain processing method, system and platform based on an evaluation system, which comprehensively evaluates the security of the distribution network by using the analytic hierarchy process, making the calculation and generation of the distribution network security domain more accurate.

[0007] The first object of the present invention is to provide a distribution network security domain processing method based on an evaluation system; the second object of the present invention is to provide a distribution network security domain processing system based on an evaluation system; the third object of the present invention is to provide a distribution network security domain processing platform based on an evaluation system.

[0008] The first object of the present invention is achieved as follows: The method includes the following steps:

[0009] Generate and obtain first data corresponding to the distribution network, and construct an index system corresponding to the distribution network according to the first data; wherein, the first data is basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation area parameter data;

[0010] According to the index system, and in combination with a combined weighting method combining the entropy weight method and the analytic hierarchy process, construct a distribution network evaluation system, and at the same time generate an evaluation score corresponding to the distribution network; wherein, the evaluation score is distribution network safety score data;

[0011] Based on the evaluation score, generate a safety boundary corresponding to the distribution network, and process according to the safety boundary to generate a safety domain corresponding to the distribution network.

[0012] Further, the generating and obtaining first data corresponding to the distribution network, and constructing an index system corresponding to the distribution network according to the first data, further includes:

[0013] Based on the first data, calculate and generate corresponding first-level index data and second-level index data respectively; wherein, the first-level index data includes safety index data, economic index data, and flexibility index data; the second-level index data includes voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, new energy power generation proportion RPG, line maximum load rate MLF, net load volatility FRL.

[0014] Further, the generating and obtaining first data corresponding to the distribution network, and constructing an index system corresponding to the distribution network according to the first data, further includes:

[0015] Calculate and generate first-level index data and second-level index data, wherein the calculation formula is as follows:

[0016] The safety index includes:

[0017] Voltage deviation index VEI:

[0018]

[0019] In the formula: U i,t represents the actual node voltage value of node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network;

[0020] Voltage qualification rate VQR:

[0021]

[0022] Where: N v,t represents the number of nodes with qualified distribution network voltage at time t;

[0023] Node reverse load θ:

[0024]

[0025] Where: N2 represents the total number of distributed photovoltaics connected to the j-th node, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of the j-th node, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics;

[0026] The economic indicators include:

[0027] Reactive power compensation qualification rate RPQ:

[0028]

[0029] Where: N Q,i represents the number of nodes with reactive power configuration meeting the requirements in the distribution network at time t;

[0030] Average network loss rate ALR:

[0031]

[0032] Where, P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of distribution network branches;

[0033] New energy power generation ratio RPG:

[0034]

[0035] Where, P pv,t represents the total output of distributed photovoltaics in the distribution network at time t, P load,t represents the total load demand of the distribution network at time t;

[0036] The flexibility indicators include:

[0037] Line maximum load rate MLF:

[0038]

[0039] Where, p l,t represents the active power flowing through branch l at time t; represents the maximum allowable active power that branch l can flow through;

[0040] Net load volatility FRL:

[0041]

[0042] Wherein, P t and P t-1 respectively represent the net load values of the distribution network at time t and t - 1.

[0043] Furthermore, the method of constructing a distribution network evaluation system in real time according to the index system and combining the combined weighting method of entropy weight method and analytic hierarchy process, and generating an evaluation score corresponding to the distribution network further includes:

[0044] Index positive transformation:

[0045] Among the secondary index data, the voltage deviation index VEI is a reverse index, the voltage qualification rate VQR is a positive index, the node reverse load θ is a reverse index, the reactive power compensation qualification rate RPQ is a positive index, the average line loss rate ALR is a reverse index, the proportion of new energy power generation RPG is a positive index, the maximum line load rate MLF is a reverse index, and the net load volatility FRL is a reverse index. The voltage deviation index VEI, the node reverse load θ, the average line loss rate ALR, the maximum line load rate MLF, and the net load volatility FRL are transformed into positive indexes, and the transformation formulas are as follows:

[0046] Suppose there are m objects to be evaluated and n evaluation indexes, and a data matrix X can be formed,

[0047] X = (x ij ) m×n (9)

[0048] Suppose the matrix of the elements in the data matrix after the index positive transformation is X', and the elements in it are x′ ij , then the index positive transformation formula:

[0049] Positive indexes are as follows:

[0050] x′ ij = x ij (10)

[0051] Reverse indexes are as follows:

[0052] x′ ij = max(x ij ) - x ij (11)

[0053] Wherein, x′ ij is the data after positive transformation; max(x ij ) is the maximum value in the data matrix X; Index standardization processing: The formula is as follows:

[0054]

[0055] Wherein, rij is the matrix data after standardization;

[0056] Combined weighting method:

[0057] Weight assignment based on entropy weight method:

[0058] The basic steps of assigning weights to n indicators divided into m health levels by the entropy weight method are as follows:

[0059] According to the index evaluation level division standard, calculate the probability that the index data falls within each evaluation level interval, and construct the probability distribution matrix P:

[0060]

[0061] In the formula, p ij is the probability that the j-th indicator falls within the i-th evaluation level interval;

[0062] The degree of dispersion of the indicator data implies the amount of information contained in the indicator, which is represented by information entropy; calculate the information entropy E based on the probability distribution of the indicator data j :

[0063]

[0064] The information entropy reflects the degree of dispersion of the indicator, reflects the amount of information contained in the indicator, and reflects the importance of the indicator; calculate the entropy weight method weight H of the indicator based on the information entropy j :

[0065]

[0066] Weight assignment based on analytic hierarchy process: Based on the nine-level scale method, use subjective judgment to measure the relative importance of any two indicators to form a judgment matrix M; find the maximum eigenvalue λ of the judgment matrix M max and the eigenvector ξ corresponding to the maximum eigenvalue, normalize the eigenvector ξ to solve, and the analytic hierarchy process weight W of the lower-level indicator relative to the upper level can be obtained j ;

[0067] When constructing the judgment matrix, there may be some errors in the subjective judgment of the importance between indicators, and the judgment matrix M needs to be checked for consistency; the calculation formula of the consistency ratio CR of the judgment matrix M is as follows:

[0068]

[0069] In the formula, CI is the consistency index, RI is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix M, and n is the order of the judgment matrix M;

[0070] Combined weighting method based on entropy weight method and analytic hierarchy process:

[0071] The combined method weight S of the j-th index j is:

[0072]

[0073] In the formula, n is the number of indexes, H j is the entropy weight method weight of the j-th index, W j is the analytic hierarchy process weight of the j-th index;

[0074] Evaluation score health level division:

[0075] Based on the combined weighting method to calculate the weights, the sum of the product of the grade division threshold of the secondary index evaluation score and the secondary index weight is the grade division threshold of the primary index evaluation score, and the sum of the product of the grade division threshold of the primary index evaluation score and the primary index weight is the grade division threshold of the evaluation score; the grade division standard is calculated, and the grades are excellent, good, and poor;

[0076] Distribution network grade evaluation:

[0077] Take the multi-dimensional data of the distribution network of the scene to be evaluated, calculate the secondary index value and perform standardized processing to obtain the secondary index evaluation score; the sum of the product of the secondary index evaluation score and the corresponding index weight is the evaluation score of the primary index; the sum of the product of the primary index evaluation score and the corresponding index weight is the evaluation score of the secondary index, and the grade is evaluated according to the score.

[0078] Furthermore, based on the evaluation score, generating a safety boundary corresponding to the distribution network, and processing according to the safety boundary to generate a safety domain corresponding to the distribution network, further includes:

[0079] Determine the maximum value P vmax and the minimum value P vmin of the active power output of distributed photovoltaic, divide the distributed photovoltaic output interval [P vmin , P vmax , first, calculate the active power outputs of two distributed photovoltaic devices connected to the distribution network as P v1min , P v2min , perform power flow calculation, judge the grade of the distribution network state, which is excellent, good or poor, and record the working point coordinates (P v1min , P v2min ) composed of P v1min , P v2min ); take the calculation step size as h, and calculate the active power outputs of two distributed photovoltaic devices connected to the distribution network as P v1 = P v1min + h, P v2 = Pv2min +h to perform power flow calculation, determine the level of the distribution network status, which can be excellent, good, or poor, and record the operating point coordinates (P v1 , P v2 ) composed of; accumulate the step size to P v1 , P v2 ); when the step size reaches P v1max , P v2max , end the power flow calculation and record the operating point coordinates (P v1max , P v2max ); according to the criteria for dividing levels, classify the operating point coordinates. If the status corresponding to the operating point coordinates is excellent, the coordinate point is marked green; if the status corresponding to the operating point coordinates is good, the coordinate point is marked blue; if the status corresponding to the operating point coordinates is poor, the coordinate point is marked red; perform fitting of the upper and lower boundaries based on the recorded operating points of different levels to obtain the safety domain of the distribution network containing the evaluation system.

[0080] The second object of the present invention is achieved as follows: The system is used to implement the method for processing the safety domain of the distribution network based on the evaluation system; the system includes:

[0081] The first data generation unit is used to generate and obtain the first data corresponding to the distribution network, and construct an index system corresponding to the distribution network according to the first data; wherein, the first data is the basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation parameter data;

[0082] The second data generation unit is used to construct an evaluation system for the distribution network according to the index system and in combination with the combined weighting method of entropy weight method and analytic hierarchy process, and generate an evaluation score corresponding to the distribution network at the same time; wherein, the evaluation score is the safety score data of the distribution network;

[0083] The third data generation unit is used to generate a safety boundary corresponding to the distribution network based on the evaluation score, and process the safety boundary to generate a safety domain corresponding to the distribution network.

[0084] Further, the first data generation unit further includes:

[0085] The first generation module is used to calculate and generate corresponding first-level index data and second-level index data respectively based on the first data; wherein, the first-level index data includes safety index data, economic index data, and flexibility index data; the second-level index data includes voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, new energy power generation ratio RPG, line maximum load rate MLF, and net load volatility FRL.

[0086] Furthermore, the first data generation unit further includes:

[0087] A first calculation module for calculating and generating primary index data and secondary index data, where the calculation formulas are as follows:

[0088] The security indicators include:

[0089] Voltage deviation index VEI:

[0090]

[0091] In the formula: U i,t represents the actual node voltage value of node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network;

[0092] Voltage qualification rate VQR:

[0093]

[0094] In the formula: N v,t represents the number of nodes with qualified distribution network voltage at time t;

[0095] Node reverse load θ:

[0096]

[0097] In the formula: N2 represents the total number of distributed photovoltaics connected to node j, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of node j, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics;

[0098] The economic indicators include:

[0099] Reactive power compensation qualification rate RPQ:

[0100]

[0101] In the formula: N Q,i represents the number of nodes in the distribution network where the reactive power configuration meets the requirements at time t;

[0102] Average network loss rate ALR:

[0103]

[0104] In the formula, P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of branches in the distribution network;

[0105] Proportion of new energy power generation RPG:

[0106]

[0107] In the formula, P pv,t represents the total distributed photovoltaic output of the distribution network at time t, and P load,t represents the total load demand of the distribution network at time t;

[0108] The flexibility index includes:

[0109] The maximum line load rate MLF:

[0110]

[0111] In the formula, p l,t represents the active power flowing through branch l at time t; represents the maximum allowable active power that can flow through branch l;

[0112] The net load volatility FRL:

[0113]

[0114] In the formula, P t and P t-1 respectively represent the net load values of the distribution network at time t and time t - 1.

[0115] The third object of the present invention is achieved as follows: It includes a processor, a memory, and a control program for the distribution network security domain processing platform based on an evaluation system; wherein, in the processor, the control program for the distribution network security domain processing platform based on the evaluation system is executed, the control program for the distribution network security domain processing platform based on the evaluation system is stored in the memory, and the control program for the distribution network security domain processing platform based on the evaluation system implements the method for the distribution network security domain processing based on the evaluation system.

[0116] The present invention generates and obtains first data corresponding to the distribution network through a method, and constructs an index system corresponding to the distribution network according to the first data; wherein, the first data is the basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation area parameter data; according to the index system, and in combination with a combined weighting method combining the entropy weight method and the analytic hierarchy process, a distribution network evaluation system is constructed, and at the same time, an evaluation score corresponding to the distribution network is generated; wherein, the evaluation score is the distribution network security score data; based on the evaluation score, a security boundary corresponding to the distribution network is generated, and a security domain corresponding to the distribution network is generated according to the security boundary processing, as well as a system and a platform corresponding to the method, which can make the calculation and generation of the distribution network security domain more accurate.

[0117] That is to say, the technical problems of weak safety performance, complex safety domain calculation and poor reliability of the traditional distribution network can be solved by the solution of the present invention, and the safety level of the distribution network is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0119] Figure 1 Schematic flow chart of a method for processing a safety domain of a distribution network based on an evaluation system according to the present invention;

[0120] Figure 2 Schematic flow chart of a specific embodiment of a method for processing a safety domain of a distribution network based on an evaluation system according to the present invention;

[0121] Figure 3 Schematic architecture diagram of a system for processing a safety domain of a distribution network based on an evaluation system according to the present invention;

[0122] Figure 4 Schematic architecture diagram of a platform for processing a safety domain of a distribution network based on an evaluation system according to the present invention;

[0123] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0124] For better understanding of the purpose, technical solutions and advantages of the present invention, the following further describes the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0125] The present invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0126] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0127] In addition, if there are descriptions such as "first" and "second" in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. Secondly, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0128] Preferably, a method for processing a distribution network security domain based on an evaluation system according to the present invention is applied to one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0129] The terminal may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal can interact with the customer through means such as a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0130] The present invention aims to implement a method, a system, and a platform for processing a distribution network security domain based on an evaluation system.

[0131] As Figure 1 shown, it is a flowchart of the method for processing a distribution network security domain based on an evaluation system provided by an embodiment of the present invention.

[0132] In this embodiment, the method for processing a distribution network security domain based on an evaluation system can be applied to a terminal with a display function or a fixed terminal. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0133] The method for processing a distribution network security domain based on an evaluation system can also be applied to a hardware environment composed of a terminal and a server connected to the terminal through a network. The network includes but is not limited to: a wide area network, a metropolitan area network, or a local area network. The method for processing a distribution network security domain based on an evaluation system in an embodiment of the present invention can be executed by the server, can also be executed by the terminal, or can be jointly executed by the server and the terminal.

[0134] For example, for a distribution network security domain processing terminal that needs to perform processing based on an evaluation system, the distribution network security domain processing function provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. Again, the method provided by the present invention can also run on devices such as servers in the form of a Software Development Kit (SDK), providing an interface for the distribution network security domain processing function in the form of the SDK. The terminal or other devices can implement the distribution network security domain processing function through the provided interface. The present invention will be further described below with reference to the accompanying drawings.

[0135] As Figure 1 - Figure 2 shown, the present invention provides a method for processing a distribution network security domain based on an evaluation system. The method includes the following steps:

[0136] S1. Generate and obtain first data corresponding to the distribution network, and construct an index system corresponding to the distribution network according to the first data; wherein, the first data is basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation area parameter data;

[0137] S2. According to the index system, and in combination with a combined weighting method that combines the entropy weight method and the analytic hierarchy process, construct a distribution network evaluation system, and at the same time generate an evaluation score corresponding to the distribution network; wherein, the evaluation score is distribution network security score data;

[0138] S3. Based on the evaluation score, generate a safety boundary corresponding to the distribution network, and process the safety boundary to generate a safety domain corresponding to the distribution network.

[0139] The step of generating and obtaining first data corresponding to the distribution network and constructing an index system corresponding to the distribution network according to the first data further includes:

[0140] S11. Based on the first data, calculate and generate corresponding first-level index data and second-level index data respectively; wherein, the first-level index data includes safety index data, economic index data, and flexibility index data; the second-level index data includes voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, new energy power generation ratio RPG, line maximum load rate MLF, and net load volatility FRL.

[0141] The step of generating and obtaining first data corresponding to the distribution network and constructing an index system corresponding to the distribution network according to the first data further includes:

[0142] S12. Calculate and generate the first-level index data and the second-level index data. The calculation formulas are as follows:

[0143] The security indicators include:

[0144] Voltage deviation index VEI:

[0145]

[0146] In the formula: U i,t represents the actual node voltage value of node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network;

[0147] Voltage qualification rate VQR:

[0148]

[0149] In the formula: N v,t represents the number of nodes with qualified distribution network voltage at time t;

[0150] Node reverse load θ:

[0151]

[0152] In the formula: N2 represents the total number of distributed photovoltaics connected to node j, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of node j, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics;

[0153] The economic indicators include: Reactive power compensation qualification rate RPQ:

[0154]

[0155] In the formula: N Q,i represents the number of nodes in the distribution network where the reactive power configuration meets the requirements at time t;

[0156] Average network loss rate ALR:

[0157]

[0158] In the formula, P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of distribution network branches;

[0159] Proportion of new energy power generation RPG:

[0160]

[0161] In the formula, Ppv,t Denote the total distributed PV output of the distribution network at time t as P load,t Denote the total load demand of the distribution network at time t;

[0162] The flexibility indicators include:

[0163] Maximum line load factor MLF:

[0164]

[0165] In the formula, p l,t Denote the active power flowing through branch l at time t; p l max Denote the maximum allowable active power that can flow through branch l;

[0166] Net load volatility FRL:

[0167]

[0168] In the formula, P t and P t-1 Denote the net load values of the distribution network at time t and t-1 respectively.

[0169] According to the above index system, combined with the combined weighting method of entropy weight method and analytic hierarchy process, a distribution network evaluation system is constructed in real time, and at the same time, an evaluation score corresponding to the distribution network is generated, which also includes:

[0170] S21. Index positive normalization:

[0171] The secondary index data voltage deviation index VEI is a negative index, the voltage qualification rate VQR is a positive index, the node reverse load θ is a negative index, the reactive power compensation qualification rate RPQ is a positive index, the average line loss rate ALR is a negative index, the proportion of new energy power generation RPG is a positive index, the maximum line load rate MLF is a negative index, and the net load volatility FRL is a negative index. Convert the voltage deviation index VEI, node reverse load θ, average line loss rate ALR, maximum line load rate MLF, and net load volatility FRL into positive indexes. The conversion formulas are as follows:

[0172] Suppose there are m objects to be evaluated and n evaluation indexes, and a data matrix X can be formed,

[0173] X = (x ij ) m×n (9)

[0174] Suppose the matrix after the elements in the data matrix are processed by index positive normalization is X', and the elements in it are x′ ij , then the index positive normalization formula:

[0175] Positive index:

[0176] x′ ij = x ij (10)

[0177] Inverse index:

[0178] x′ ij = max(x ij ) - x ij (11)

[0179] In the formula, x′ ij is the data after positive transformation; max(x ij ) is the maximum value in the data matrix X;

[0180] S22. Index standardization processing: The formula is as follows:

[0181]

[0182] In the formula, r ij is the standardized matrix data;

[0183] S23. Combined weighting method:

[0184] Weight assignment based on entropy weight method:

[0185] The basic steps for entropy weight method weighting of n indicators divided into m health levels are as follows:

[0186] According to the index evaluation level division standard, calculate the probability that the index data falls within each evaluation level interval, and construct the probability distribution matrix P:

[0187]

[0188] In the formula, p ij is the probability that the jth indicator falls within the ith evaluation level interval;

[0189] The degree of dispersion of the indicator data implies the amount of information contained in the indicator, which is represented by information entropy; calculate the information entropy E j based on the probability distribution of the indicator data:

[0190]

[0191] Information entropy reflects the degree of dispersion of the indicator, reflects the amount of information contained in the indicator, and reflects the importance of the indicator; calculate the entropy weight method weight H j of the indicator based on information entropy:

[0192]

[0193] Weight assignment based on analytic hierarchy process:

[0194] Based on the nine-level scale method, the relative importance of any two indicators is measured using subjective judgment to form a judgment matrix M; the maximum eigenvalue λ of the judgment matrix M is obtained. max The eigenvector ξ corresponding to the maximum eigenvalue is obtained, and the eigenvector ξ is normalized to solve, and the analytic hierarchy process weight W of the lower-level indicator relative to the upper level can be obtained. j ;

[0195] When constructing the judgment matrix, there may be some errors in the subjective judgment of the importance between indicators, and the judgment matrix M needs to be checked for consistency; the calculation formula for the consistency ratio CR of the judgment matrix M is as follows:

[0196]

[0197] In the formula, CI is the consistency index, and RI is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix M, and n is the order of the judgment matrix M;

[0198] Combined weighting method based on entropy weight method and analytic hierarchy process:

[0199] The combined method weight S of the jth indicator j is:

[0200]

[0201] In the formula, n is the number of indicators, H j is the entropy weight method weight of the jth indicator, and W j is the analytic hierarchy process weight of the jth indicator;

[0202] S24. Evaluation score health level division: Based on the combined weighting method to calculate the weights, the sum of the product of the grade division threshold of the secondary indicator evaluation score and the secondary indicator weight is the grade division threshold of the primary indicator evaluation score, and the sum of the product of the grade division threshold of the primary indicator evaluation score and the primary indicator weight is the grade division threshold of the evaluation score; the grade division standard is calculated, and the grade division is excellent, good, and poor;

[0203] S25. Distribution network level evaluation: Take the multi-dimensional data of the distribution network of the scene to be evaluated, calculate the secondary indicator values and standardize them to obtain the secondary indicator evaluation scores; the sum of the product of the secondary indicator evaluation scores and the corresponding indicator weights is the evaluation score of the primary indicator; the sum of the product of the primary indicator evaluation scores and the corresponding indicator weights is the evaluation score of the secondary indicator, and the grade is evaluated according to the score.

[0204] Based on the evaluation score, a safety boundary corresponding to the distribution network is generated, and a safety domain corresponding to the distribution network is generated according to the safety boundary, and further includes:

[0205] S31. Determine the maximum value \(P\) of the active power output of the distributed photovoltaic system vmax and the minimum value \(P\) vmin , and divide the output range of the distributed photovoltaic system into \([P\) vmin , \(P\) vmax . First, measure the active power outputs of two distributed photovoltaic devices connected to the distribution network as \(P\) v1min and \(P\) v2min . Conduct a power flow calculation to determine the level of the distribution network status, which can be excellent, good, or poor. Record the working point coordinates \((P\) v1min , \(P\) v2min ) formed by \(P\) v1min and \(P\) v2min ; Take the calculation step size as \(h\), and measure the active power outputs of two distributed photovoltaic devices connected to the distribution network as \(P\) v1 = \(P\) v1min + \(h\) and \(P\) v2 = \(P\) v2min + \(h\). Conduct a power flow calculation to determine the level of the distribution network status, which can be excellent, good, or poor. Record the working point coordinates \((P\) v1 , \(P\) v2 ) formed by \(P\) v1 and \(P\) v2 ; When the step size is accumulated to \(P\) v1max and \(P\) v2max , end the power flow calculation and record the working point coordinates \((P\) v1max , \(P\) v2max ); According to the classification criteria of the levels, classify the working point coordinates. If the status corresponding to the working point coordinates is excellent, the coordinate point is marked as green; if the status corresponding to the working point coordinates is good, the coordinate point is marked as blue; if the status corresponding to the working point coordinates is poor, the coordinate point is marked as red; Fit the upper and lower boundaries based on the recorded working points of different levels to obtain the safety region of the distribution network containing the evaluation system.

[0206] Specifically, in the embodiment of the present invention, a method for processing the safety region of a distribution network based on an evaluation system is provided, and the specific method steps involved are as follows:

[0207] S01: Obtain the network topology information, branch node parameters, and distribution transformer area parameters of the distribution network, and construct indicators based on the basic information of the distribution network to form a distribution network indicator system;

[0208] S02: Use a combined weighting method that combines the entropy weight method and the analytic hierarchy process to construct a distribution network evaluation system and generate an evaluation score corresponding to the distribution network;

[0209] S03: Based on the evaluation score, generate a safety boundary corresponding to the distribution network, and process the safety boundary to generate a safety region corresponding to the distribution network.

[0210] That is, the first step: construct an index system; the second step is to construct an evaluation system according to the combined weighting method; the third step is to calculate the safety domain.

[0211] Specifically, in step S01, the grid topology information and branch node parameters of the distribution network are collected and obtained, including the number of network nodes, the number of branches, the line impedance value, etc. of the distribution network; and based on the first data, the corresponding primary index data and secondary index data are respectively calculated and generated; among them, the primary index data includes safety index data, economic index data, and flexibility index data; the secondary index data includes voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, new energy power generation ratio RPG, line maximum load rate MLF, and net load volatility FRL.

[0212] Generating and obtaining the first data corresponding to the distribution network, and constructing an index system corresponding to the distribution network according to the first data further includes:

[0213] Calculating and generating primary index data and secondary index data, where the calculation formulas are as follows:

[0214] The safety index includes: voltage deviation index VEI:

[0215]

[0216] In the formula: U i,t represents the actual node voltage value of node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network;

[0217] Voltage qualification rate VQR:

[0218]

[0219] In the formula: N v,t represents the number of nodes with qualified distribution network voltage at time t;

[0220] Node reverse load θ:

[0221]

[0222] In the formula: N2 represents the total number of distributed photovoltaics connected to the j-th node, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of the j-th node, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics;

[0223] The economic index includes:

[0224] Reactive power compensation qualification rate RPQ:

[0225]

[0226] Where: N Q,i represents the number of nodes in the distribution network where the reactive power configuration meets the requirements at time t;

[0227] Average network loss rate ALR:

[0228]

[0229] Where, P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of branches in the distribution network;

[0230] Proportion of new energy power generation RPG:

[0231]

[0232] Where, P pv,t represents the total output of distributed photovoltaics in the distribution network at time t, and P load,t represents the total load demand of the distribution network at time t;

[0233] The flexibility index includes:

[0234] Maximum line load rate MLF:

[0235]

[0236] Where, p l,t represents the active power flowing through branch l at time t; p l max represents the maximum allowable active power that branch l can flow through;

[0237] Net load volatility FRL:

[0238]

[0239] Where, P t and P t-1 respectively represent the net load values of the distribution network at time t and time t - 1.

[0240] In step S02, the method of constructing a distribution network evaluation system in real time according to the index system and combining the combined weighting method of entropy weight method and analytic hierarchy process, and generating an evaluation score corresponding to the distribution network, further includes:

[0241] Index positive normalization:

[0242] The secondary index data voltage deviation index VEI is a negative index, the voltage qualification rate VQR is a positive index, the node reverse load θ is a negative index, the reactive power compensation qualification rate RPQ is a positive index, the average network loss rate ALR is a negative index, the new energy power generation ratio RPG is a positive index, the line maximum load rate MLF is a negative index, and the net load volatility FRL is a negative index. Convert the voltage deviation index VEI, the node reverse load θ, the average network loss rate ALR, the line maximum load rate MLF, and the net load volatility FRL into positive indexes. The conversion formula is as follows:

[0243] Suppose there are m objects to be evaluated and n evaluation indicators, and a data matrix X can be formed.

[0244] X = (x ij ) m×n (9)

[0245] Let the matrix after the elements in the data matrix are normalized be X', and the elements in it are x′ ij . Then the index normalization formula:

[0246] Positive index:

[0247] x′ ij = x ij (10)

[0248] Negative index:

[0249] x′ ij = max(x ij ) - x ij (11)

[0250] In the formula, x′ ij is the data after normalization; max(x ij ) is the maximum value in the data matrix X;

[0251] Index standardization processing: The formula is as follows:

[0252]

[0253] In the formula, r ij is the matrix data after standardization;

[0254] Combined weighting method:

[0255] Weight assignment based on entropy weight method:

[0256] The basic steps of entropy weight method for weighting n indicators divided into m health levels are as follows:

[0257] According to the index evaluation level division standard, calculate the probability that the index data falls in each evaluation level interval, and construct a probability distribution matrix P:

[0258]

[0259] where p ij is the probability that the j-th index falls within the i-th evaluation grade interval;

[0260] The degree of dispersion of the index data implies the amount of information contained in the index, which is represented by information entropy; calculate the information entropy E based on the probability distribution of the index data j :

[0261]

[0262] Information entropy reflects the degree of dispersion of the index, reflects the amount of information contained in the index, and reflects the importance of the index; calculate the entropy weight method weight H of the index based on information entropy j :

[0263]

[0264] Weight assignment based on the analytic hierarchy process:

[0265] Based on the nine-level scale method, subjectively measure the relative importance of any two indicators to form a judgment matrix M; find the maximum eigenvalue λ of the judgment matrix M max and the eigenvector ξ corresponding to the maximum eigenvalue, normalize and solve the eigenvector ξ, and the analytic hierarchy process weight W of the lower-level index relative to the upper level can be obtained j ;

[0266] When constructing the judgment matrix, there may be some errors in the subjective judgment of the importance between indicators, and the judgment matrix M needs to be checked for consistency; the calculation formula for the consistency ratio CR of the judgment matrix M is as follows:

[0267]

[0268]

[0269] where CI is the consistency index, RI is the average random consistency index; λ max is the maximum eigenvalue of the judgment matrix M, and n is the order of the judgment matrix M;

[0270] Combined weighting method based on the entropy weight method and the analytic hierarchy process:

[0271] The combined method weight S of the j-th index j is:

[0272]

[0273] where n is the number of indicators, Hj is the weight of the entropy weight method for the j-th index, W j The weight of the analytic hierarchy process for the j-th index;

[0274] Evaluation score health level division:

[0275] Based on the combined weighting method to calculate the weights, the sum of the product of the grading threshold of the secondary index evaluation score and the secondary index weight is the grading threshold of the primary index evaluation score, and the sum of the product of the grading threshold of the primary index evaluation score and the primary index weight is the grading threshold of the evaluation score; calculate the grading standard, and the grading is excellent, good, and poor;

[0276] Distribution network level evaluation:

[0277] Take the multi-dimensional data of the distribution network of the scene to be evaluated, calculate the secondary index value and perform standardized processing to obtain the secondary index evaluation score; the sum of the product of the secondary index evaluation score and the corresponding index weight is the evaluation score of the primary index; the sum of the product of the primary index evaluation score and the corresponding index weight is the evaluation score of the secondary index, and determine the level according to the score evaluation.

[0278] In step S03, based on the evaluation score, generating a safety boundary corresponding to the distribution network, and generating a safety domain corresponding to the distribution network according to the safety boundary, further includes:

[0279] Determine the maximum value P of the active power output of distributed photovoltaic vmax and the minimum value P vmin , divide the distributed photovoltaic output interval [P vmin , P vmax . First, calculate the active power outputs of two distributed photovoltaic devices connected to the distribution network as P v1min , P v2min , perform power flow calculation, judge the level of the distribution network state, which is excellent, good or poor, and record the working point coordinates (P v1min , P v2min ) composed of P v1min , P v2min ; take the calculation step size as h, calculate the active power outputs of two distributed photovoltaic devices connected to the distribution network as P v1 = P v1min + h, P v2 = P v2min + h, perform power flow calculation, judge the level of the distribution network state, which is excellent, good or poor, and record the working point coordinates (P v1 , P v2 ) composed of P v1 , P v2 ; accumulate the step size to P v1max , P v2maxWhen the power flow calculation ends, record the coordinates of the operating point (P v1max , P v2max ); according to the classification criteria, classify the coordinates of the operating point. If the status corresponding to the coordinates of the operating point is excellent, the coordinate point is marked green; if the status corresponding to the coordinates of the operating point is good, the coordinate point is marked blue; if the status corresponding to the coordinates of the operating point is poor, the coordinate point is marked red; fit the upper and lower boundaries according to the recorded operating points of different levels to obtain the distribution network security region containing the evaluation system.

[0280] To achieve the above object, the present invention also provides a distribution network security region processing system based on an evaluation system, as Figure 3 shown. The system is used to implement the distribution network security region processing method based on the evaluation system; specifically, the system includes:

[0281] The first data generation unit is used to generate and obtain the first data corresponding to the distribution network, and construct an index system corresponding to the distribution network according to the first data; wherein, the first data is the basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation parameter data.

[0282] The second data generation unit is used to construct a distribution network evaluation system according to the index system and in combination with a combined weighting method combining the entropy weight method and the analytic hierarchy process, and simultaneously generate an evaluation score corresponding to the distribution network; wherein, the evaluation score is the distribution network security score data.

[0283] The third data generation unit is used to generate a safety boundary corresponding to the distribution network based on the evaluation score, and process the safety boundary to generate a safety region corresponding to the distribution network.

[0284] The first data generation unit further includes:

[0285] The first generation module is used to calculate and generate corresponding first-level index data and second-level index data based on the first data; wherein, the first-level index data includes safety index data, economic index data, and flexibility index data; the second-level index data includes voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, new energy power generation ratio RPG, line maximum load rate MLF, and net load volatility FRL.

[0286] The first data generation unit further includes:

[0287] The first calculation module is used to calculate and generate first-level index data and second-level index data, and the calculation formulas are as follows:

[0288] The safety index includes:

[0289] Voltage deviation index VEI:

[0290]

[0291] Where: U i,t represents the actual value of the node voltage at node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network;

[0292] Voltage qualification rate VQR:

[0293]

[0294] Where: N v,t represents the number of nodes with qualified distribution network voltage at time t;

[0295] Node reverse load θ:

[0296]

[0297] Where: N2 represents the total number of distributed photovoltaics connected to node j, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of node j, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics;

[0298] Economic indicators include:

[0299] Reactive power compensation qualification rate RPQ:

[0300]

[0301] Where: N Q,i represents the number of nodes in the distribution network where the reactive power configuration meets the requirements at time t;

[0302] Average network loss rate ALR:

[0303]

[0304] Where, P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of branches in the distribution network;

[0305] Proportion of new energy power generation RPG:

[0306]

[0307] Where, P pv,t represents the total output of distributed photovoltaics in the distribution network at time t, P load,t represents the total load demand of the distribution network at time t;

[0308] The flexibility indicators include:

[0309] The maximum line load factor MLF:

[0310]

[0311] Where p l,t represents the active power flowing through branch l at time t; p l max represents the maximum allowable active power that can flow through branch l;

[0312] The net load volatility FRL:

[0313]

[0314] Where P t and P t-1 respectively represent the net load values of the distribution network at time t and time t-1.

[0315] In the embodiment of the system solution of the present invention, the method steps involved in the processing of the distribution network security domain based on the evaluation system have been elaborated above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be elaborated here.

[0316] To achieve the above object, the present invention also provides a distribution network security domain processing platform based on an evaluation system, as Figure 4 shown, including a processor, a memory, and a control program for the distribution network security domain processing platform based on the evaluation system; wherein, the processor executes the control program for the distribution network security domain processing platform based on the evaluation system, the control program for the distribution network security domain processing platform based on the evaluation system is stored in the memory, and the control program for the distribution network security domain processing platform based on the evaluation system implements the method steps of the distribution network security domain processing based on the evaluation system. For example:

[0317] S1. Generate and obtain the first data corresponding to the distribution network, and construct an index system corresponding to the distribution network according to the first data; wherein, the first data is the basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation parameter data;

[0318] S2. According to the index system, and in combination with the combined weighting method combining the entropy weight method and the analytic hierarchy process, construct a distribution network evaluation system, and at the same time generate an evaluation score corresponding to the distribution network; wherein, the evaluation score is the distribution network security score data;

[0319] S3. Generate a safety boundary corresponding to the distribution network based on the evaluation score, and process to generate a safety domain corresponding to the distribution network according to the safety boundary.

[0320] The specific details of the steps have been elaborated above and will not be repeated here.

[0321] In the embodiment of the present invention, the distribution network safety domain processing platform based on the evaluation system is built with a processor, which can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions, including the combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors and various control chips, etc. The processor uses various interfaces and lines to connect to each component, runs or executes the programs or units stored in the memory, and calls the data stored in the memory to execute various functions of the distribution network safety domain processing based on the evaluation system and process data.

[0322] The memory is used to store program codes and various data, is installed in the distribution network safety domain processing platform based on the evaluation system, and realizes the high-speed and automatic access of programs or data during operation.

[0323] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store data.

[0324] The present invention generates and obtains first data corresponding to a distribution network through a method, and constructs an index system corresponding to the distribution network according to the first data; wherein, the first data is basic information data of the distribution network, including grid topology information data, branch node parameter data, and distribution substation area parameter data; according to the index system, and in combination with a combined weighting method combining the entropy weight method and the analytic hierarchy process, a distribution network evaluation system is constructed, and at the same time an evaluation score corresponding to the distribution network is generated; wherein, the evaluation score is safety score data of the distribution network; based on the evaluation score, a safety boundary corresponding to the distribution network is generated, and a safety domain corresponding to the distribution network is generated according to the safety boundary, and a system and a platform corresponding to the method can make the calculation and generation of the safety domain of the distribution network more accurate.

[0325] That is to say, through the solution of the present invention, the technical problems of weak safety performance, complex safety domain calculation, and poor reliability of the traditional distribution network can be solved, and the safety level of the distribution network is improved.

[0326] In addition, according to the nature and characteristics of the safety domain of the distribution network, the present invention solution proposes an index system for the distribution network, and then uses a combined weighting method combining the entropy weight method and the analytic hierarchy process to obtain different evaluation scores of the distribution network, so as to calculate the safety domain of the distribution network, which is more scientifically based than calculating the safety domain of the distribution network relying only on a single index. The safety index system proposed by the present invention solution considers three aspects of factors: safety, economy, and flexibility, and comprehensively evaluates the safety characteristics of the distribution network, providing a theoretical and data basis for the calculation of the safety domain of the distribution network.

[0327] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent of the present invention shall be subject to the appended claims.

Claims

1. A distribution network security domain processing method based on an evaluation system, characterized in that: The method comprises the steps of: Generate and obtain first data corresponding to the distribution network, and construct an indicator system corresponding to the distribution network according to the first data; wherein the first data is basic information data of the distribution network, including grid topology information data, branch node parameter data and distribution station area parameter data; According to the index system, combined with the combined weighting method of the entropy weight method and the hierarchical analysis method, a distribution network evaluation system is constructed, and an evaluation score corresponding to the distribution network is generated; wherein the evaluation score is the distribution network security score data; Based on the evaluation score, a safety boundary corresponding to the power distribution network is generated, and a safety domain corresponding to the power distribution network is generated according to the safety boundary processing.

2. A distribution network security domain processing method based on an evaluation system according to claim 1, characterized in that: The generating and acquiring first data corresponding to the distribution network, and constructing an indicator system corresponding to the distribution network according to the first data, further includes: Based on the first data, the corresponding first-level indicator data and second-level indicator data are calculated and generated respectively; wherein, the first-level indicator data include safety indicator data, economic indicator data, and flexibility indicator data; the second-level indicator data include voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, proportion of new energy power generation RPG, maximum line load rate MLF, and net load fluctuation rate FRL.

3. A distribution network security domain processing method based on an evaluation system according to claim 1 or 2, characterized in that: The generating and acquiring first data corresponding to the distribution network, and constructing an indicator system corresponding to the distribution network according to the first data, further includes: Calculate and generate the primary indicator data and secondary indicator data, where the calculation formula is as follows: Safety indicators include voltage excursion index VEI: Where: U i,t represents the actual value of the node voltage at node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network; Voltage qualification rate VQR: Where: N v,t Indicates the number of nodes with qualified voltage in the distribution network at time t; Node reverse load θ: Where: N2 represents the total number of distributed photovoltaics connected to the jth node, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of the j-th node, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics; Economic indicators include: Reactive power compensation qualification rate RPQ: Where: N Q,i It represents the number of nodes in the distribution network whose reactive power configuration meets the requirements at time t; Average network loss rate ALR: Where P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of distribution network branches; Renewable energy generation ratio RPG: Where P pv,t represents the total output of distributed photovoltaic power generation in the distribution network at time t, P load,t represents the total load demand of the distribution network at time t; Flexibility indicators include: Line maximum load factor MLF: In the formula, p l,t represents the active power flowing through branch l at time t; Indicates the maximum active power allowed to flow through branch l; Net load fluctuation rate FRL: Where P t and P t-1 They represent the net load values ​​of the distribution network at time t and time t-1 respectively.

4. A distribution network security domain processing method based on an evaluation system according to claim 1, characterized in that: The method of constructing a distribution network evaluation system in real time based on the indicator system and combining the combined weighting method of the entropy weight method and the hierarchical analysis method, and generating an evaluation score corresponding to the distribution network, also includes: Positive index conversion: The secondary index data voltage offset index VEI is a reverse index, voltage qualification rate VQR is a positive index, node reverse load θ is a reverse index, reactive power compensation qualification rate RPQ is a positive index, average network loss rate ALR is a reverse index, new energy power generation ratio RPG is a positive index, line maximum load rate MLF is a reverse index, net load fluctuation rate FRL is a reverse index. The voltage offset index VEI, node reverse load θ, average network loss rate ALR, line maximum load rate MLF, net load fluctuation rate FRL are converted into positive indicators. The conversion formula is as follows: Suppose there are m objects to be evaluated and n evaluation indicators, which can form a data matrix X. X=(x ij ) m×n (9) Suppose the matrix after the index positive processing of the elements in the data matrix is ​​X', and the element is x' ij , then the indicator positive formula: Positive indicators: x′ ij =x ij (10) Contrarian Indicators: x′ ij =max(x ij )-x ij (11) In the formula, x′ ij is the data after forward processing; max(xij) is the maximum value in the data matrix X; indicator standardization processing: the formula is as follows: In the formula, r ij is the standardized matrix data; Combined weighting method, weight assignment based on entropy weight method: The basic steps of entropy weighting for n indicators divided into m health levels are as follows: According to the index evaluation grade classification standard, the probability of the index data falling in each evaluation level interval is calculated, and the probability distribution matrix P is constructed: In the formula, p ij is the probability that the jth indicator falls within the i-th evaluation level interval; The discrete degree of the indicator data implies the amount of information contained in the indicator, which is expressed by information entropy. The information entropy E is calculated based on the probability distribution of the indicator data. j : Information entropy reflects the degree of dispersion of the indicator, reflects the amount of information contained in the indicator, and reflects the importance of the indicator; the entropy weight method weight H of the indicator calculated based on information entropy j : Weight assignment based on hierarchical analysis method: Based on the nine-level scaling method, subjective judgment is used to measure the relative importance of any two indicators to form a judgment matrix M; the maximum eigenvalue λ of the judgment matrix M is calculated. max The eigenvector ξ corresponding to the maximum eigenvalue is normalized and solved to obtain the AHP weight W of the lower-level index relative to the previous level. j ; When constructing the judgment matrix, there may be some errors in the subjective judgment of the importance of each indicator, so it is necessary to perform consistency check on the judgment matrix M. The calculation formula for the consistency ratio CR of the judgment matrix M is as follows: In the formula, CI is the consistency index, RI is the average random consistency index; max is the maximum eigenvalue of the judgment matrix M, n is the order of the judgment matrix M; Combined weighting method based on entropy weight method and hierarchical analysis method: The combined weight S of the jth index j for: In the formula, n is the number of indicators, H j is the entropy weight of the jth indicator, W j The AHP weight of the jth indicator; Assessment score health level classification: The weights are calculated based on the combined weighting method. The sum of the product of the grade division threshold of the secondary indicator evaluation score and the secondary indicator weight is the grade division threshold of the primary indicator evaluation score, and the sum of the product of the grade division threshold of the primary indicator evaluation score and the primary indicator weight is the grade division threshold of the evaluation score. The grade division standard is calculated and the grades are divided into excellent, good and poor. Distribution network level assessment: Take the multi-dimensional data of the distribution network of the scenario to be evaluated, calculate the secondary indicator value and standardize it to get the secondary indicator evaluation score; the sum of the product of the secondary indicator evaluation score and the corresponding indicator weight is the evaluation score of the primary indicator; the sum of the product of the primary indicator evaluation score and the corresponding indicator weight is the evaluation score of the secondary indicator, and the level of the evaluation is determined according to the score.

5. A distribution network security domain processing method based on an evaluation system according to claim 1, characterized in that: The method of generating a safety boundary corresponding to the distribution network based on the evaluation score, and generating a safety domain corresponding to the distribution network according to the safety boundary processing, further includes: Determine the maximum value P of distributed photovoltaic active output vmax and the minimum value P vmin , divide the distributed photovoltaic output range [P vmin , P vmax ], firstly, the active output of the two distributed photovoltaic devices connected to the distribution network is calculated as P v1min , P v2min , perform power flow calculations, determine the level of the distribution network status, whether it is excellent, good or poor, and record the P v1min , P v2min The coordinates of the working point (P v1min , P v2min ); Take the calculation step length as h, and calculate the active output of the two distributed photovoltaic devices connected to the distribution network as P v1 =P v1min +h、P v2 =P v2min +h, perform power flow calculation, determine the level of the distribution network status, whether it is excellent, good or poor, and record it by P v1 , P v2 The coordinates of the working point (P v1 , P v2 ); Accumulate step length to P v1max , P v2max When the power flow calculation is finished, the coordinates of the working point (P v1max , P v2max ); According to the classification standard, the coordinates of the working points are classified. If the state corresponding to the working point coordinates is excellent, the coordinate point is marked in green; if the state corresponding to the working point coordinates is good, the coordinate point is marked in blue; if the state corresponding to the working point coordinates is poor, the coordinate point is marked in red; according to the recorded working points of different levels, the upper and lower boundaries are fitted to obtain the distribution network safety domain containing the evaluation system.

6. A distribution network security domain processing system based on an evaluation system, characterized in that: The system is used to implement the distribution network security domain processing method based on the evaluation system as described in any one of claims 1 to 5; the system includes: A first data generating unit is used to generate and obtain first data corresponding to the distribution network, and to construct an indicator system corresponding to the distribution network according to the first data; wherein the first data is basic information data of the distribution network, including grid topology information data, branch node parameter data and distribution station area parameter data; A second data generating unit is used to construct a distribution network evaluation system based on the indicator system and in combination with a combined weighting method that combines an entropy weight method with a hierarchical analysis method, and to generate an evaluation score corresponding to the distribution network; wherein the evaluation score is distribution network safety score data; The third data generating unit is used to generate a safety boundary corresponding to the distribution network based on the evaluation score, and generate a safety domain corresponding to the distribution network according to the safety boundary processing.

7. A distribution network security domain processing system based on an evaluation system according to claim 6, characterized in that: The first data generating unit further includes: The first generating module is used to calculate and generate corresponding primary indicator data and secondary indicator data based on the first data; wherein the primary indicator data include safety indicator data, economic indicator data, and flexibility indicator data; the secondary indicator data include voltage deviation index VEI, voltage qualification rate VQR, node reverse load θ, reactive power compensation qualification rate RPQ, average network loss rate ALR, proportion of new energy power generation RPG, line maximum load rate MLF, and net load fluctuation rate FRL.

8. A distribution network security domain processing system based on an evaluation system according to claim 6 or 7, characterized in that: The first data generating unit further includes: The first calculation module is used to calculate and generate the primary index data and the secondary index data, wherein the calculation formula is as follows: Safety indicators include: Voltage Excursion Index VEI: Where: U i,t represents the actual value of the node voltage at node i at time t; U e,i represents the rated voltage value of node i; N represents the total number of nodes in the distribution network; Voltage qualification rate VQR: Where: N v,t Indicates the number of nodes with qualified voltage in the distribution network at time t; Node reverse load θ: Where: N2 represents the total number of distributed photovoltaics connected to the jth node, p pv,i,t represents the output of the i-th distributed photovoltaic at time t; N3 represents the total load of the j-th node, p load,i,t represents the i-th load demand at time t; N1 is the total number of nodes connected to distributed photovoltaics; Economic indicators include: Reactive power compensation qualification rate RPQ: Where: N Q,i It represents the number of nodes in the distribution network whose reactive power configuration meets the requirements at time t; Average network loss rate ALR: Where P loss,t represents the total network loss of the distribution network at time t, and L represents the total number of distribution network branches; Renewable energy generation ratio RPG: Where P pv,t represents the total output of distributed photovoltaic power generation in the distribution network at time t, P load,t represents the total load demand of the distribution network at time t; Flexibility indicators include: Line maximum load factor MLF: In the formula, p l,t represents the active power flowing through branch l at time t; Indicates the maximum active power allowed to flow through branch l; Net load fluctuation rate FRL: Where P t and P t-1 They represent the net load values ​​of the distribution network at time t and time t-1 respectively.

9. A distribution network security domain processing platform based on an evaluation system, characterized in that: It includes a processor, a memory and a distribution network security domain processing platform control program based on an evaluation system; wherein the distribution network security domain processing platform control program based on an evaluation system is executed on the processor, the distribution network security domain processing platform control program based on an evaluation system is stored in the memory, and the distribution network security domain processing platform control program based on an evaluation system implements the distribution network security domain processing method based on an evaluation system as described in any one of claims 1 to 5.

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