A method for identifying weak links in power systems based on power flow distribution entropy

Abstract

The present invention discloses a method for identifying weak links in a power system based on current distribution entropy. Using the PSD-Edit simulation software, a simulation analysis of power system data in a certain region is performed to collect information about all substations and the operating current data on their outgoing lines. In the simulation software, the output of the new energy plant connected to a certain substation is proportionally increased to simulate the current impact on the node. Alternatively, one of the lines #imgabs0# is disconnected to simulate a line breakage fault. The entropy values and weights of the nodes and lines are calculated to obtain their weak indicators. Finally, the weak indicators of the nodes and lines are arranged in ascending order to obtain a table of their weak indicators. The present invention introduces for the first time a current channel variable C=1 / ln#imgabs1#, where #imgabs2# is the number of current channels connected to a component (substation or line). Combined with the objective entropy weight method, this method reduces the error in the application of the traditional current entropy method in identifying weak links in the power system, provides a reasonable basis for power grid monitoring, and has industrial application value.

Description

Technical Field

[0001] The present invention belongs to the field of weak link identification of power systems, and in particular relates to a weak link identification method based on power flow distribution entropy and objective entropy weight method. Background Art

[0002] As global fossil energy consumption continues to grow, climate change caused by carbon emissions is becoming increasingly pronounced, and achieving a low-carbon energy transition has become a global consensus. The large-scale deployment of renewable energy power plants is a key factor in this energy transition. As my country's energy transition continues to advance, wind and solar power plants are being connected to the power system. Compared to the traditional power system, the most significant characteristic of the new power system is that its power structure will shift from a power generation structure dominated by controllable and continuous coal-fired power generation to a power generation structure dominated by renewable energy power generation with highly uncertain and less controllable output. As one of the world's largest and most complex man-made systems, the power system is inherently vulnerable to various emergencies. With the large-scale deployment of renewable energy plants such as wind farms and photovoltaic power stations, the uncertainty and complexity of the power system will increase dramatically, posing unprecedented challenges to its stability.

[0003] "Entropy" is a natural law that can express the degree of chaos and disorder in a system. Entropy theory is widely used in various scientific fields such as system science and informatics, with fruitful results. Information entropy is a probabilistic description used to judge the certainty of the state of the system: the more stable the system, the smaller its entropy value and the closer it is to 0; the more chaotic the system, the larger its entropy value. For a generalized complex system, entropy can be used as a measure of the chaos and disorder of the distribution state. The essence of the operation of the power system is energy balance, and its energy balance can be described based on the entropy change of the power flow through the component. The formula for defining the power flow entropy of the component is: Where: η i is the power flow impact rate of component i; N is the total number of system components.

[0004] The power flow entropy of the power system can describe the distribution law of the power flow in a certain state. The more uniform the distribution, the more stable the system. In an ideal situation, the system power flow is evenly distributed on the line, the power flow impact is evenly distributed, and the power flow impact rate η of each line is i =1 / N, at this time, the system's power flow entropy reaches its maximum value lnN, and the system is in the most stable state.

[0005] Traditional applications of entropy methods in power systems fail to consider, or only consider, the impact of the severity of component flow conditions under a single operating mode on vulnerability indicators. Furthermore, entropy is defined as summing the entropy values of all states to derive a system's disorder index. In power systems, "state" refers to the number of flow channels connected to a component. When applying this entropy definition, a component with a large number of outgoing lines may have an excessively high entropy value, potentially interfering with the formation of its vulnerability indicator. Summary of the Invention

[0006] To avoid excessive entropy values due to a large number of outgoing lines in a component, which would interfere with the formation of its weak index, the present invention provides a method for identifying weak links in a power system based on power flow distribution entropy. The method includes a node power flow distribution entropy method and a line power flow distribution entropy method. The specific steps are as follows:

[0007] Node power flow distribution entropy method:

[0008] Step 1: Use PSD-Edit simulation software to simulate and analyze the power system data of a certain region, collecting information about all substations and the operating conditions and flow data on their outgoing lines;

[0009] Step 2: In the simulation software, proportionally increase the output of the renewable energy plant connected to a certain substation to simulate the impact of the power flow on the node, i.e., the substation;

[0010] Step 3: Calculate the node entropy and its weight according to the method proposed in the present invention to obtain the node weakness index;

[0011] Step 4: All the substations counted are operated according to step 3, and the substations are arranged in descending order according to their respective weakness indicators to obtain the final node weakness indicator table.

[0012] Optionally, the node power flow distribution entropy model can be expressed as the following formula:

[0013]

[0014] Among them, H n (a) is the node weakness index of node a based on the power flow distribution entropy. a is the power flow channel variable of node a, C a =1 / lnN, where N is the number of power flow channels connected to node a. a is the power flow weight of node a. is the flow impact rate of branch k connected to node a, which is the proportion of flow impact distributed on the branch.

[0015] Optionally, in step 2, the output of the renewable energy plants connected to a particular substation is proportionally increased to simulate the impact of the current on the node, and the output of all renewable energy plants connected to that substation is proportionally increased. If substation A has only one outgoing line connected to substation B, the renewable energy installed capacity around substation A is counted as that of substation B to facilitate the subsequent calculation of the node's current weight.

[0016] Optionally, the specific formula for the node weakness index in step 3 is as follows:

[0017]

[0018] in, is the flow increment of branch k after node a is impacted by the flow, is the power flow of branch k; is the current of branch k after node a is impacted by the current.

[0019] From this, the total power flow impact on node a can be obtained as follows:

[0020]

[0021] Where N is the number of power flow channels connected to node a.

[0022] Then the power flow impact rate of node a on branch k is:

[0023]

[0024] The percentage of the installed capacity of renewable energy around the available node to the total installed capacity of renewable energy in the system is used as the weight of the node; the weakness index of node a based on the power flow distribution entropy is:

[0025]

[0026] Compared to the definition of information entropy, entropy is used to describe the degree of energy distribution balance in power systems. The more concentrated the energy distribution, the smaller the entropy value, and the weaker the component (i.e., substation). Therefore, the ranking of weakness indicators is from low to high.

[0027] Line power flow distribution entropy method:

[0028] Step 1: Use PSD-Edit simulation software to simulate and analyze the power system data of a certain region and collect the operating flow data information on all lines of a certain voltage level;

[0029] Step 2: Disconnect one of the lines in the simulation software a , simulate a line disconnection fault; Step 3: according to the method proposed by the present invention, calculate the line entropy value and its weight to obtain the line weakness index;

[0030] Step 4: Follow the steps in step 3 for all the statistical lines, and arrange the lines in descending order according to their respective weakness indicators to obtain the final line weakness indicator table.

[0031] Optionally, in step 1, the line operating condition flow information selects a per-unit value to reduce the impact between lines of different voltage levels.

[0032] Optionally, the specific formula for the line weakness index in step 3 is as follows:

[0033] Assume that a branch line in the system a Disconnect, its adjacent branch l b The trend is becomes (branch l a The adjacent branches of the transformers at both ends are divided by l a All outgoing lines except b The flow increment on is:

[0034]

[0035] At this time, branch l a For branch l b The power flow transfer ratio is:

[0036]

[0037] Where M is the branch l a Number of connected tidal channels.

[0038] Obtaining the route l based on the objective entropy weight method a The power flow weight Ω a ; Line l a Weakness index based on power flow distribution entropy:

[0039]

[0040] Among them G a =1 / lnM, For branch l a For branch l j The current transfer ratio.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] The method for identifying weak links in a power system based on power flow entropy of the present invention, on the one hand, introduces the power flow channel variable C a =1 / lnN and G a=1 / lnM, where N is the number of flow channels connected to the node (substation), and M is the number of flow channels connected to the line. This reduces the error caused by excessive entropy due to too many lines around the component. On the other hand, considering that a single data cannot accurately describe the weight of the flow that the component is subjected to during the actual operation of the power system, an objective entropy weight method is adopted. Combined with multiple sets of typical power grid operation mode data, a weight is assigned to each line as comprehensively as possible in an objective manner, avoiding interference from human factors and greatly reducing the impact of unequal line flow conditions on weak link identification. This further improves the accuracy of the identification method, provides a reasonable basis for power grid monitoring, and has industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the method for identifying weak links in a power system according to the present invention.

[0044] Figure 2 This is a flow chart of the power system weak node identification based on power flow entropy according to the present invention.

[0045] Figure 3 This is a flow chart of the power system weak line identification based on power flow entropy according to the present invention.

[0046] Figure 4 This is the geographical wiring diagram of Datong power grid in summer 2022.

[0047] Figure 5 These are the 220kV system node weakness indicators in Datong area measured using the traditional entropy method and the entropy method described in the present invention.

[0048] Figure 6 The line weakness index of the 220kV system in Datong area is measured using the traditional entropy method and the entropy method described in the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to specific embodiments.

[0050] This invention addresses the potential for significant power flow impacts on certain nodes caused by the uncertainty of renewable energy generation, as well as the potential for overloads or re-loads on adjacent lines after an M-1 fault on a line. By combining node weighting and line entropy weighting, this method proposes node and line weakness indicators based on power flow distribution entropy. This method effectively reduces the errors in traditional entropy methods used to identify weak links in power systems. The following is a detailed description of the implementation plan, using simulation data from the 220kV power grid in Datong as an example.

[0051] Reference Figure 2 As shown, Figure 2 The method for identifying weak nodes in a power system based on power flow entropy comprises the following steps:

[0052] Step 1: Using the PSD-Edit simulation software, taking Datong's 220 kV power system in the summer of 2022 as an example, with wind power at 10% output and photovoltaic power at 50% output, collect information on all 220 kV substations and their outgoing line condition flow data.

[0053] Step 2: Increase the output of all renewable energy sources around a substation by 20%, i.e., wind power by 30% and photovoltaic power by 70%, to simulate the situation where the substation is impacted by the renewable energy flow. Statistical data on the outgoing lines of the substation (node) at this time is collected.

[0054] Step 3: Using the concept of power flow distribution entropy, calculate the power flow impact rate of the node to all its power flow channels:

[0055]

[0056]

[0057] in, is the flow increment of branch k after node a is impacted by the flow, is the power flow of branch k; is the current of branch k after node a is impacted by the current, and N is the number of current channels connected to the component (a certain substation).

[0058] The percentage of the installed capacity of renewable energy around the node to the total installed capacity of renewable energy in the system is used as the weight of the node ω a ;

[0059] The power flow impact rate of node a on branch k is:

[0060] The final node weakness index of the substation is obtained:

[0061]

[0062] Among them, the flow channel variable C a =1 / lnN, N is the number of tidal channels connected to the component, and the summation sign belongs to the numerator.

[0063] Step 4: Traverse all statistical substations in steps 2 and 3 to obtain the ranking table of weak indexes of Datong 220kV system nodes, refer to Figure 5 As shown, Figure 5 The traditional entropy method is used, that is, the tidal channel variable C is not introduced. a =1 / lnN and node weight ω a A method for identifying weak indicators, and the top three substation information and their indicators among the indicators obtained by the method of the present invention.

[0064] Reference Figure 3 As shown, Figure 3 The method for identifying weak lines in a power system based on power flow entropy includes the following steps:

[0065] Step 1: Using the PSD-Edit simulation software, take Datong's 220kV power system in the summer of 2022 as an example, with wind power output at 10% and photovoltaic power output at 50%, and collect the power flow data of all 220kV lines.

[0066] Step 2: Disconnect a line in the simulation software to simulate the fault condition of system line M-1, and collect the power flow data on the adjacent lines of the line at this time;

[0067] Step 3: Using the concept of power flow distribution entropy, calculate the power flow transfer ratio of the disconnected line to one of its adjacent lines:

[0068]

[0069] in, For line l a Before disconnection, its adjacent branch l b The working condition trend on For line l a Disconnect the rear branch b The current on line l a The number of connected branches is also called line l a The number of adjacent tidal channels; a Calculate adjacent lines one by one to get line l a Power flow transfer ratio of all adjacent lines.

[0070] The calculation process of line power flow weight based on the objective entropy weight method is as follows:

[0071] 1) Select the same grid structure of a certain area power grid, and calculate the data under T different operating modes. Calculate the power flow of each line under the system working condition and record it in the form of a matrix: A = (a ij ) T*M

[0072] 2) Normalize the tidal flow data:

[0073]

[0074] Among them, max i＝1,2,...,T (a ij ) and min i＝1,2,...,T (a ij ) are the maximum and minimum values of the power flows of all lines respectively;

[0075] 3) Divide the statistical attribute j, that is, the power flow data on different lines in each operation mode, into statistical intervals. Divide the attribute j into T intervals. Each interval can be expressed as:

[0076] 4) Count the normalized values a′ of all the flow data ij Distributed in a certain range Number σ=0,1,...,T-1;

[0077] 5) Calculate each a′ in the statistical attribute j ij The probability distribution of :

[0078]

[0079] 6) Calculate the entropy value of attribute j:

[0080]

[0081] 7) H j Normalization processing:

[0082]

[0083] 8) Repeat the above steps to obtain the normalized entropy value of all attributes j, and use the following formula to obtain the weight of each attribute j:

[0084]

[0085] The final weak index of the line is obtained:

[0086]

[0087] Among them, the flow channel variable G a =1 / lnM, M is the circuit l a Number of adjacent tidal channels, Ω a For line l a The tidal current weight, and the summation sign belongs to the numerator.

[0088] Step 4: Traverse all statistical lines in steps 2 and 3 to obtain the ranking table of weak indicators of Datong 220kV system lines, refer to Figure 6 As shown, Figure 6 The traditional entropy method is used, that is, the tidal channel variable G is not introduced. a =1 / lnM and line weight Ω a A method for identifying weak indicators, and the top five line information and their indicators among the indicators obtained by the method of the present invention.

[0089] The present invention takes into account the uncertainty of renewable energy power generation in the new power system. Based on the concept of power flow entropy, the present invention proposes a weak link identification technology based on node power flow distribution entropy and line power flow distribution entropy for the power flow impact on the nodes where the new energy plants are concentrated. The weight analysis method is combined to assign reasonable weights to the nodes and lines, and a weak link identification index in the new power system is proposed to identify the weak links in the system accordingly.

[0090] The present invention addresses the following problems in the application of traditional entropy method in power system: the influence of the weight of component working condition flow on weak index under one operation mode is not considered or is only considered; and when defining entropy, the entropy value of a component may be too large due to the large number of outgoing lines, which interferes with the formation of its weak index. The objective entropy weight method is adopted to combine multiple sets of power grid operation data to assign weights to component flow in an objective way, avoiding the interference of human factors; and the flow channel variables C=1 / lnN and G are introduced. a =1 / lnM, where N is the number of power flow channels connected to the node (substation), and M is the line l a The number of adjacent power flow channels is increased, thereby reducing the error caused by excessive entropy due to too many lines around the component.

[0091] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Any equivalent structural or functional transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied to other related technical fields, are also included in the scope of protection of the present invention.

Claims

1. A method for identifying weak links in a power system based on power flow distribution entropy, comprising a node power flow distribution entropy method and a line power flow distribution entropy method, and comprising the following specific steps: Step 1: Use PSD-Edit simulation software to simulate and analyze the power system data of a certain region, and collect all substation information and power flow data on the line; Step 2: In the simulation software, proportionally increase the output of a new energy plant connected to a substation or disconnect a line to simulate the power flow impact on the node or line M-1 failure; Step 3: Use the node power flow distribution entropy model to calculate the power flow distribution entropy and its weight of all nodes to obtain the node weakness index; use the line power flow distribution entropy model to calculate the power flow distribution entropy and its weight of all lines to obtain the line weakness index; The node power flow distribution entropy model adopts the following formula: in, H n (a) is the node weakness index of node a based on the power flow distribution entropy; C a is the power flow channel variable of node a; C a =1 / lnN, N is the number of power flow channels connected to node a; ω a is the power flow weight of node a, is the flow impact rate of branch k connected to node a, which is the proportion of flow impact distributed on the branch; Step 4: All the statistical substations and lines are operated according to step 3, and the weakness indicators of the nodes are arranged in order from low to high, and finally a node weakness indicator table is obtained.

2. The method for identifying weak links in a power system based on power flow distribution entropy according to claim 1, wherein the specific formula of the node weakness index in step 3 is as follows: in, is the flow increment of branch k after node a is impacted by the flow, is the power flow of branch k; is the current of branch k after node a is impacted by the current; From this, the total power flow impact on node a can be obtained as follows: Where N is the number of power flow channels connected to node a; Then the power flow impact rate of node a on branch k is: The percentage of the installed capacity of renewable energy around the node to the total installed capacity of renewable energy in the system is used as the power flow weight of the node.

3. The method for identifying weak links in a power system based on power flow distribution entropy according to claim 2, wherein: In step 2, the output of the renewable energy plant connected to a certain substation is increased proportionally to simulate the impact of the current on the node, and the output of all renewable energy plants connected to the substation is increased proportionally; if substation a has only one outgoing line connected to substation b, the renewable energy installed capacity around substation a is counted as that of substation b, so as to facilitate the subsequent calculation of the node's current weight.

4. A method for identifying weak links in a power system based on power flow distribution entropy according to any one of claims 1 to 3, characterized in that: The weak link identification method also includes a line power flow distribution entropy method, and the specific steps are as follows: Step 1: Use PSD-Edit simulation software to simulate and analyze the power system data of a certain region and collect the operating flow data information on all lines of a certain voltage level; Step 2: Disconnect one of the lines in the simulation software a , simulate line disconnection fault; Step 3: Calculate the line entropy value and its weight according to the line power distribution entropy model to obtain the line weakness index; the line power distribution entropy model adopts the following formula Among them G a =1 / lnM, M is the branch l a The number of connected tidal channels, For branch l a For branch l j Power flow transfer ratio, Ω a For line l a The tidal force weight of H l (a) is line l a Weak indicators; Step 4: Follow the steps in step 3 for all the statistical lines, and arrange the lines in descending order according to their respective weakness indicators to obtain the final line weakness indicator table.

5. The method for identifying weak links in a power system based on power flow distribution entropy according to claim 4, characterized in that: In step 1 of the line power flow distribution entropy method, the per-unit value of the line operating condition power flow information is selected to reduce the impact between lines of different voltage levels.

6. The method for identifying weak links in a power system based on power flow distribution entropy according to claim 4 or 5, characterized in that: Step 3 of the line power distribution entropy method uses the concept of power distribution entropy to calculate the power transfer ratio of the disconnected line to one of its adjacent lines: in, For line l a Before disconnection, the adjacent branch l b The working condition trend on For line l a Disconnect the rear branch b The current on line M is a The number of connected branches is also called line l a Number of adjacent flow channels; for line l a The adjacent lines are calculated one by one to obtain the power flow transfer ratio of all adjacent lines of the line.

7. The method for identifying weak links in a power system based on power flow distribution entropy according to claim 4 or 5, characterized in that: Step 3 of the line flow distribution entropy method uses the objective entropy weight method to calculate the line flow weight. The specific formula is as follows: 1) Select the same grid structure of a certain area power grid and the data under T different operating modes. Calculate the flow of each line under the system working condition and record it in matrix form: A = (a ij ) T*M 2) Normalize the tidal flow data: Among them, max i＝1,2,...,T (a ij ) and min i＝1,2,...,T (a ij ) are the maximum and minimum values of the power flows of all lines respectively; 3) Divide the statistical attribute j, that is, the power flow data on different lines in each operation mode, into statistical intervals. Divide the attribute j into T intervals. Each interval can be expressed as: 4) Count the normalized values a′ of all the flow data ij Distributed in a certain range Number σ=0,1,...,T-1; 5) Calculate each a′ in the statistical attribute j ij The probability distribution of : 6) Calculate the entropy value of attribute j: 7) H j Normalization processing: 8) Repeat the above steps to obtain the normalized entropy values of all attributes j, and use the following formula to obtain the power flow weight of each attribute j: The final weak index of the line is obtained: Among them, the flow channel variable G a =1 / lnM, M is the circuit l a Number of adjacent tidal channels, Ω a For line l a The trend weight.

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