Power system analysis device, power system analysis method, and program

Abstract

To provide a power system analysis device capable of automatically performing classification of characteristics of an unstable power system state and extraction of a system cross section having a high risk of system stability.SOLUTION: A power system analysis device according to an embodiment includes: a system analysis model construction unit that constructs a system analysis model for analyzing a power system for a plurality of cross sections based on scenario data indicating time-series data regarding analysis conditions of the power system; a power flow calculation unit that performs power flow calculation using the system analysis model for the plurality of cross sections; a stability calculation unit that calculates stability of power transmission in the power system based on a result of the power flow calculation; a data analysis unit configured to group vector data indicating a power flow calculation result of each cross section based on the stability calculation result or a constraint condition generated from the stability calculation result and the power flow calculation result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a power system analysis device, a power system analysis method, and a program. [Background technology]

[0002] The master plan of the Organization for Cross-regional Coordination of Transmission Operators (OCCTO) envisages multiple scenarios based on factors such as the amount of renewable energy introduced and its weighting, and proposes measures to strengthen facilities as needed from a cost-effective perspective. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-94441 Summary of the Invention [Problem to be solved by the invention]

[0004] Previously, power system operators extracted high-risk time sections using rules or manually based on system operation rules and changes in renewable energy demand, but the future introduction of large amounts of renewable energy and fluctuations in renewable energy output could lead to enormous changes in system conditions. In this case, the number of patterns of time sections with high stability risk will also become enormous, posing a challenge in that it will take a great deal of time and effort to consider measures that take future high-risk sections into account.

[0005] The problem to be solved by the present invention is to provide a power system analysis device, a power system analysis method, and a program that are capable of automatically classifying the characteristics of unstable power system states and extracting system cross sections that pose a high risk to system stability. [Means for solving the problem]

[0006] An electric power system analysis device according to one embodiment includes a system analysis model construction unit that constructs a system analysis model for analyzing an electric power system for multiple cross sections based on scenario data indicating time-series data related to analysis conditions of the electric power system; a power flow calculation unit that performs power flow calculations using the system analysis model for the multiple cross sections; a stability calculation unit that calculates the stability of power transmission in the electric power system based on the power flow calculation results; and a data analysis unit that groups vector data indicating the power flow calculation results for each cross section based on the stability calculation results or constraint conditions generated from the stability calculation results and the power flow calculation results. [Effects of the Invention]

[0007] According to this embodiment, it is possible to automatically classify the characteristics of unstable power system states and extract system sections with high system stability risks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a schematic configuration of a power system analysis device according to a first embodiment. [Figure 2] 1 is a diagram of the target equipment of the system analysis base model data. [Figure 3] FIG. 10 is a diagram showing an example of node data in a certain future cross section. [Figure 4] FIG. 10 is a diagram showing an example of branch data at a certain future cross section. [Figure 5] FIG. 10 is a diagram showing an example of trans data at a certain future cross section. [Figure 6] FIG. 10 is a diagram showing an example of generator data in a certain future cross section. [Figure 7] 3 is a flowchart showing the procedure of the power system analysis operation performed by the power system analysis device according to the first embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a power flow calculation result of a node. [Figure 9] FIG. 10 is a diagram illustrating an example of a power flow calculation result of a branch. [Figure 10]FIG. 10 is a diagram illustrating an example of a power flow calculation result of a generator. [Figure 11] FIG. 10 is a diagram illustrating an example of a calculation result of stability. [Figure 12] 10 is a flowchart illustrating an example of a procedure for labeling processing. [Figure 13] FIG. 10 is a diagram illustrating an example of clustering of power flow calculation results. [Figure 14] FIG. 1 is a diagram illustrating an example of instability in a power system. [Figure 15] FIG. 10 is a diagram showing an example of the results of label processing of each future cross section for each accident case that may occur in the power system. [Figure 16] FIG. 10 is a diagram showing an example of a result of grouping by accident case. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. The present invention is not limited to the following embodiments.

[0010] (First embodiment) Fig. 1 is a block diagram showing a schematic configuration of a power system analysis device according to a first embodiment. The power system analysis device 1 shown in Fig. 1 includes a data acquisition unit 10, a calculation unit 20, a storage unit 30, and a data output unit 40. The power system analysis device 1 groups the stability of power supply in a future time period (future time) in the power system based on set constraint conditions. Each unit will be described below.

[0011] The data acquisition unit 10 acquires scenario data D11, system analysis base model data D12, etc. For example, the data acquisition unit 10 acquires these data from external organizations or external systems via a network. Each piece of data will now be described.

[0012] The scenario data D11 indicates time-series data related to the analysis conditions of the power system. The scenario data D11 indicates, for example, the total installed capacity of renewable energy power generation facilities, the demand scale of the power supply area of ​​the power system, the output and operating capacity of synchronous generators installed in the power system, etc., in time series. The renewable energy power generation facilities include, for example, solar power generation facilities and wind power generation facilities.

[0013] The system analysis base model data D12 is data relating to target facilities for system analysis, such as nodes, branches, transformers, generators, and loads of a power system. The system analysis base model data D12 will now be further described with reference to the drawings.

[0014] 2 is a diagram of the target facility of the system analysis base model data D12. In this diagram, nodes 101, transformers 102, branches 103, loads 104, and generators 105 are illustrated in the same manner as in the actual topology of the power system.

[0015] The node 101 corresponds to a substation or a switchyard. At least one of a transformer 102, a load 104, and a generator 105 is connected to the node 101. The transformer 102 is disposed between two nodes 101. The transformer 102 transforms a voltage input from one node 101 to a predetermined voltage and outputs it to the other node 101. The branch 103 is a transmission line connecting the two nodes 101. The load 104 is a consumer that consumes power, or a renewable energy power generation device that supplies power to the power grid. The generator 105 is a synchronous generator that operates in synchronization with other generators 105.

[0016] In this embodiment, the data acquiring unit 10 acquires the system analysis base model data D12 of the future cross section for each of the node 101, the transformer 102, the branch 103, and the generator 105. That is, the data acquiring unit 10 acquires the system analysis base model data D12 along a future time series.

[0017] Fig. 3 is a diagram showing an example of node data in a certain future cross section. The node data D12a shown in Fig. 3 shows the output values ​​of active power and reactive power for the generator 105 and the load 104 connected to each node 101 identified by the node name. The node data D12a also shows the designated voltage value and the reactive power value adjusted by the phase modifying equipment for each node. In the node data D12a shown in Fig. 3, the output value of active power for the load 104 connected to node c is a negative value. This indicates that the load 104 is a renewable energy power generation device.

[0018] Fig. 4 is a diagram showing an example of branch data for a certain future cross section. In the branch data D12b shown in Fig. 4, the connection source (From) and connection destination (To) nodes, resistance, inductive reactance X, and capacitive reactance Yc are shown in association with each branch 103 identified by its branch name.

[0019] Fig. 5 is a diagram showing an example of transformer data for a certain future cross section. In the transformer data D12c shown in Fig. 5, the source and destination nodes, the inductive reactance X, and the tap ratio are shown in association with each transformer 102 identified by its transformer name.

[0020] Fig. 6 is a diagram showing an example of generator data for a certain future cross section. The generator data D12d shown in Fig. 6 shows the connected node, operating capacity, rated output, and inertia in association with each generator 105 identified by its generator name. Although not shown in Fig. 6, other generator data includes the generator reactance, armature resistance, excitation control system model (AVR, PSS), governor model, etc.

[0021] When the data acquisition unit 10 acquires the above-described scenario data D11 and system analysis base model data D12, it outputs the acquired data to the calculation unit 20. Now, returning to FIG. 1, the configuration of the calculation unit 20 will be described.

[0022] 1, the calculation unit 20 includes a system analysis model construction unit 21, a power flow calculation unit 22, a stability calculation unit 23, and a data analysis unit 24. The calculation unit 20 is configured by, for example, a computer that executes a predetermined program.

[0023] The system analysis model construction unit 21 constructs a system analysis model of multiple cross sections using the scenario data D11 and the system analysis base model data D12 acquired by the data acquisition unit 10. The power flow calculation unit 22 calculates the power flow of the power system based on the system analysis model of multiple cross sections constructed by the system analysis model construction unit 21. The stability calculation unit 23 calculates the stability of the multiple cross sections based on the system analysis model of multiple cross sections constructed by the system analysis model construction unit 21 and the power flow calculation results by the power flow calculation unit 22. The data analysis unit 24 sets constraint conditions for grouping based on the stability calculation results calculated by the stability calculation unit 23, and groups vector data indicating the power flow calculation results based on the set constraint conditions.

[0024] The storage unit 30 is configured with a storage medium such as a semiconductor memory or a hard disk. The storage unit 30 is connected to the calculation unit 20. The storage unit 30 controls the writing and reading of data indicating various calculation results by the calculation unit 20. Furthermore, if the calculation unit 20 is configured with a computer, the storage unit 30 also stores a program for causing the computer to execute the program.

[0025] The data output unit 40 is configured by, for example, a liquid crystal display, a plasma display, etc. The data output unit 40 displays various data including the calculation results of the calculation unit 20. The displayed data includes, for example, the analysis results of the data analysis unit 24.

[0026] The operation of analyzing a power system by the above-described power system analysis device 1 will be described below.

[0027] FIG. 7 is a flowchart showing the procedure of the power system analysis operation by the power system analysis device 1 according to the first embodiment.

[0028] 7, first, the data acquisition unit 10 receives the scenario data D11 and the system analysis base model data D12 (step S1). The acquired data is transmitted to the calculation unit 20.

[0029] Next, the system analysis model construction unit 21 constructs a system analysis model for multiple cross sections (step S2). In step S2, the system analysis model construction unit 21 rewrites the values ​​of the system analysis base model shown in Figures 3 to 6 based on the contents of the scenario data D11.

[0030] For example, the output value of the active power of the generator indicated in the node data D12a is rewritten to the value of the time-series data of the output of each power source indicated in the scenario data D11. Also, the output value of the active power of the load indicated in the node data D12a is rewritten to the value of the time-series data of the power demand by location indicated in the scenario data D11. However, since the load connected to node c is a renewable energy power generation device, the output value of the active power is rewritten to the value of the time-series data of the renewable energy output indicated in the scenario data D11. The specified voltage value of the node data D12a and the reactive power value of the phase modifying equipment are automatically adjusted according to the voltage distribution indicated in the scenario data D11.

[0031] Next, power flow calculation unit 22 calculates the power flow of the power system using the system analysis model constructed by system analysis model construction unit 21 (step S3). Step S3 will be described below.

[0032] In power system power flow analysis, the power flow calculation unit 22 calculates the power flow at each node and branch by inputting the system analysis model constructed by the system analysis model construction unit 21 into a power flow calculation model. The power flow calculation model is installed in software for performing system congestion assessment or asset management, for example. In this embodiment, an existing power flow calculation model may be used.

[0033] FIG. 8 is a diagram showing an example of a power flow calculation result for a node. In FIG. 8, the voltage and phase of each node in each future cross section are shown in association with the active power and reactive power of the generator, the active power and reactive power of the load, and the reactive power of the phase modifying equipment. The values ​​of the active power, reactive power, and reactive power of the phase modifying equipment are values ​​shown in node data D12a, which is one of the system analysis models. In other words, the power flow calculation result for a node indicates the voltage and phase of the node derived by a mathematical model that uses the values ​​shown in node data D12a for the generator, load, and phase modifying equipment as parameters (the above example shows a PQ-specified node, but for a PV-specified node for which a voltage is specified, the phase and reactive power are shown).

[0034] FIG. 9 shows an example of a branch power flow calculation result. FIG. 9 shows the active and reactive power flows, active and reactive power losses, of each branch for each future cross section. The active and reactive power losses are calculated based on the values ​​of resistance, inductive reactance, and capacitive capacitance shown in branch data D12b. In other words, the branch power flow calculation result indicates the active and reactive power flows, active and reactive power losses of the branch derived by the mathematical model. The branch power flow calculation result may also include the phase difference angle, which is the phase difference between the substations connected to both ends of each branch.

[0035] Fig. 10 is a diagram showing an example of the power flow calculation results of the generator 105. In Fig. 10, the operating capacity of each generator 105 in each future cross section is shown for each connected node. The calculation results shown in Figs. 8 to 10 are stored in the storage unit 30.

[0036] Next, the stability calculation unit 23 calculates the stability of power transmission in the power system based on the calculation result of the power flow calculation unit 22 (step S4). Step S4 will now be described with reference to FIG.

[0037] FIG. 11 is a diagram showing an example of the stability calculation result. In the example shown in FIG. 11, the stability calculation unit 23 calculates the internal phase difference angle δ as one of the indices of the synchronous stability of the four generators 105. The internal phase difference angle δ indicates the transient stability of the generators in the event of a fault. Note that the stability indices are not limited to synchronous stability. The stability indices may include, for example, at least one of synchronous stability, voltage stability, frequency stability, and steady-state stability.

[0038] Next, data analysis unit 24 performs data analysis of the calculation results of power flow calculation unit 22 and the calculation results of stability calculation unit 23 (step S5). In step S5, data analysis unit 24 first performs labeling processing on the calculation results of stability calculation unit 23. Here, the labeling processing will be described with reference to Fig. 12.

[0039] Fig. 12 is a flowchart showing an example of the procedure for labeling processing. In this flowchart, the data analysis unit 24 first determines whether or not an out-of-step generator exists based on the calculation results of the stability at each cross section (step S51). In step S51, as shown in Fig. 11, if there is a generator whose internal phase difference angle δ exceeds a preset threshold value δth (for example, 180 degrees), the data analysis unit 24 determines that there is an out-of-step generator. Conversely, if there is no generator whose internal phase difference angle δ exceeds the threshold value δth, the data analysis unit 24 determines that there is no out-of-step generator.

[0040] If there is no out-of-step generator, the data analysis unit 24 determines whether the amplitude ratio α is greater than 0.1 in the waveform (see FIG. 11) showing the change over time in the internal phase difference angle of each generator (step S52). If the amplitude ratio α is 0.1 or less, the data analysis unit 24 sets a stability label 0, which indicates that the synchronization of the generators is stable (step S53). On the other hand, if the amplitude ratio α is greater than 0.1, the data analysis unit 24 sets an oscillation continuation label 1, which indicates that the oscillation of the internal phase difference angle is continuing (step S54).

[0041] If there is a step-out generator, the data analysis unit 24 determines whether the deceleration energy is equal to or less than the acceleration energy in the four synchronous generators (step S55). If the deceleration energy is greater than the acceleration energy, the data analysis unit 24 sets an N-wave step-out label 2 indicating that the oscillation of the internal phase difference angle is the second wave or later (step S56). If the deceleration energy is equal to or less than the acceleration energy, the data analysis unit 24 sets a 1-wave step-out label 3 indicating that the oscillation of the internal phase difference angle is the first wave (step S57).

[0042] Once the above-described labeling process is completed, the data analysis unit 24 uses the set labels to set constraint conditions for grouping the calculation results of the power flow calculation unit 22. For example, the data analysis unit 24 sets a first constraint condition that groups the stable label 0 separately from the N-wave step-out label 2 and the 1-wave step-out label 3. Alternatively, the data analysis unit 24 sets a second constraint condition that groups the stable label 0 separately from the vibration continuation label 1, the N-wave step-out label 2, and the 1-wave step-out label 3.

[0043] When the first constraint condition is set, the vibration continuation label 1 may belong to either the stable group to which the stable label 0 belongs, or the out-of-step group to which the N-wave out-of-step label 2 and the 1-wave out-of-step label 3 belong. The data analysis unit 24 determines the group to which the vibration continuation label 1 belongs based on the similarity of the power flow calculation results.

[0044] Furthermore, when the second constraint condition is set, the data analysis unit 24 may forcibly assign the vibration continuation label 1 to the out-of-step group. In this case, among the cross sections of the vibration continuation label 1, the cross sections close to the stable label are also absorbed into the out-of-step group.

[0045] The constraints are not limited to the first and second constraints described above. For example, if there are multiple out-of-step generators in a certain future cross section, the data analysis unit 24 may extract any number of out-of-step generators in order of the earliest out-of-step time, and set a constraint that separates the extracted group of out-of-step generators from other out-of-step generators. For example, as shown in Figure 11, the top two out-of-step generators in terms of out-of-step time are extracted.

[0046] Furthermore, when there are multiple out-of-step generators in a certain future cross section, the data analysis unit 24 may group them according to the out-of-step locus. Here, the out-of-step locus is the location where the internal phase difference angle (the phase angle difference between both ends of the transmission line) twists (exceeds 180 degrees, for example) earliest. For example, in Figure 11, branch c is set as an out-of-step locus group, separate from the other groups.

[0047] After the constraint setting process is complete, the data analyzer 24 generates a single vector data set that combines the multidimensional quantities indicated in the power flow calculation results for the nodes and branches for each future cross-section. The data analyzer 24 then clusters the vector data based on the constraints. The quantities combined as vector data include, for example, the node voltage, phase, active power, reactive power, and reactive power input from the phase modifying equipment. The quantities also include the branch's active power flow, reactive power flow, active power loss, reactive power loss, phase difference angle, and generator operating capacity.

[0048] Fig. 13 is a diagram showing an example of clustering results of power flow calculation. Fig. 13 shows an example of the clustering results of two-dimensional vector data that combines the renewable energy outputs of all nodes in the future time horizon and the active power flows of all branches in the future time horizon. The renewable energy outputs of the nodes are calculated as the active power outputs of the loads in the power flow calculation results of the nodes shown in Fig. 8.

[0049] 13, the power flow calculation results are clustered into three clusters: a first cluster C1, a second cluster C2, and a third cluster C3. The first cluster C1 is further clustered into three clusters.

[0050] Fig. 14 is a diagram showing an example of instability in a power system. In Fig. 14, the horizontal axis represents the number of data items belonging to each cluster shown in Fig. 13, and the vertical axis represents instability, which is the reciprocal of stability.

[0051] As shown in Figures 13 and 14, in this embodiment, the synchronization states of the generators are clearly separated into a stable group (second cluster C2 and third cluster C3) and an unstable group (first cluster C1). The unstable group is further subdivided according to the characteristics of the power system, and the characteristics of the subdivided system state are extracted. For example, the unstable group is clustered according to the causes of instability, such as a decrease in inertia / synchronizing force, a heavy power flow on a specific transmission line, and a voltage drop at a specific substation.

[0052] In the above-described constrained clustering, labels indicate the boundaries of the groups. When the unstable group is further subdivided based on the results of the power flow calculation, the boundaries of the subdivision are not known, which poses a clustering problem. In this embodiment, the data analysis unit 24 performs clustering using a method called COP k-means, based on constraints generated from the power flow calculation results and the training information, i.e., the stability calculation results.

[0053] However, the clustering method used by the data analysis unit 24 is not limited to the above-mentioned constrained clustering. Another clustering method is a supervised clustering method in which, without generating constraint conditions, stability calculation results or indices obtained from the stability calculation results are input together with power flow calculation results, and clustering is performed.

[0054] Supervised clustering methods include fully supervised clustering, semi-supervised clustering, and transductive clustering. In fully supervised clustering, supervised information exists in multiple object sets (power flow calculation results for each cross section). In semi-supervised clustering, supervised information exists in a single object set. In transductive clustering, no new objects are classified.

[0055] Here, a method of clustering for each accident case that may occur in a power system will be described with reference to FIGS.

[0056] Fig. 15 shows an example of the results of label processing for each future cross section for each accident case that may occur in a power system. In Fig. 15, the label value for each cross section is shown for each accident case. In each accident case, a step-out of a synchronous generator occurs in a future cross section where the label value is 5 or less.

[0057] Figure 16 shows an example of the results of grouping by accident case. In Figure 16, the first clusters C11, C21, and C31 are clusters of an unstable group that includes a generator that has lost synchronization. The second clusters C12, C22, and C32 and the third clusters C13, C23, and C33 are clusters of a stable group that does not include a generator that has lost synchronization.

[0058] In the example shown in Figure 16, different high-risk cross sections are extracted for each accident case. When the user selects at least one arbitrary accident case (such as an accident case that the user particularly wants to keep an eye on or an accident case that requires management) from among multiple accident cases as the first accident case, the data analysis unit 24 clusters each selected accident case with constraints and selects a cross section from each unstable group that satisfies predetermined conditions, such as the least stable cross section or the cross section closest to the center of gravity. The selected cross section is displayed as the first risk cross section D1, as shown in Figure 16, to be distinguished from the other cross sections.

[0059] Furthermore, if there is a first risk cross section D1 common to the selected accident cases, the data analysis unit 24 identifies the first risk cross section D1 as a second risk cross section D2. The second risk cross section D2 is displayed as being distinguishable from the first risk cross section D1. Note that the second risk cross section D2 does not have to be common to all accident cases and extracted as the first risk cross section D1. The second risk cross section D2 may be, for example, a cross section extracted as the first risk cross section D1 in a second accident case, which is an accident case further specified by the user from the first accident case. Furthermore, the second risk cross section D2 may be a cross section extracted as the first risk cross section D1 in a specific number or more of accident cases.

[0060] When the data analysis by the data analysis unit 24 is completed, the data output unit 40 finally displays the data analysis results (step S6). In step S6, for example, the data output unit 40 displays clustering images such as those shown in Fig. 13 and Fig. 16, or an image showing the stability and instability of clusters such as that shown in Fig. 14.

[0061] According to the present embodiment described above, the calculation unit 20 can automatically classify the characteristics of unstable power system states and extract system sections with high system stability risks.

[0062] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel system described in this specification can be embodied in various other forms. Furthermore, various omissions, substitutions, and modifications can be made to the forms of the system described in this specification without departing from the spirit of the invention. The appended claims and their equivalents are intended to cover such forms and modifications that fall within the scope and spirit of the invention. [Explanation of symbols]

[0063] 1: Power system analysis device 21: System analysis model construction department 22: Tidal flow calculation section 23:Stability calculation part 24: Data analysis department

Claims

1. a system analysis model constructing unit that constructs system analysis models for analyzing the power system for a plurality of cross sections based on scenario data that indicates time-series data related to analysis conditions of the power system; a power flow calculation unit that performs power flow calculations using the system analysis model for the plurality of cross sections; a stability calculation unit that calculates stability of power transmission in the power system based on the power flow calculation result; a data analysis unit that groups vector data indicating the power flow calculation results for each cross section based on the stability calculation results or constraint conditions generated from the stability calculation results and the power flow calculation results.

2. 2. The power system analysis device according to claim 1, wherein the vector data includes at least one of a phase, an active power, a reactive power, and a reactive power input from a phase modifying device of a node of the power system, an active power flow, a reactive power flow, an active power loss, a reactive power loss, and a phase difference angle of a branch of the power system, and an operating capacity of a generator installed in the power system.

3. The power system analyzer according to claim 1 , wherein the stability calculation results include at least one of synchronous stability, voltage stability, frequency stability, and steady-state stability.

4. 2. The power system analysis device according to claim 1, wherein the data analysis unit sets a stable label and an unstable label for each cross section based on the evaluation result of the stability of each cross section, and groups the vector data under a constraint that a cross section with an unstable label and a cross section with a stable label are not in the same group.

5. 2. The power system analysis device according to claim 1, wherein the stability calculation results are obtained by labeling each cross section as stable or unstable based on the stability evaluation results for each cross section, setting one or more thresholds for one or more indicators indicating the stability, setting stability index exceedance labels that indicate whether each stability indicator for each cross section is exceeded, and grouping the vector data under constraints regarding the similarity of the stable labels / unstable labels and the stability index exceedance labels for each cross section.

6. the stability calculation result is a result indicating the synchronization stability, 4. The power system analysis device according to claim 3, wherein the data analysis unit sets a label for each cross section according to whether or not a synchronous loss of a synchronous generator occurs based on an evaluation result of the synchronous stability of each cross section, and groups the vector data under a constraint that a cross section with a synchronous loss and a cross section without a synchronous loss are not grouped together.

7. 6. The power system analysis device according to claim 5, wherein the data analysis unit extracts groups of out-of-step generators in any order, starting from the group containing cross sections with out-of-step, in descending order of the time when they lose synchronism, and sets constraints that separate cross sections with different groups of out-of-step generators.

8. The power system analysis device according to claim 5 , wherein the data analysis unit further sets a constraint for separating cross sections with different out-of-step loci from each other for a group including a cross section with out-of-step locus.

9. The power system analyzer according to claim 1 , wherein the data analysis unit performs grouping by supervised clustering or constrained clustering.

10. 5. The power system analysis device according to claim 4, wherein the data analysis unit sets a stable label and an unstable label for each cross section for a plurality of accident cases specified by a user based on the evaluation results of the stability of each cross section, groups the vector data under a constraint that cross sections labeled with an unstable label and cross sections labeled with a stable label are not in the same group, extracts first risk cross sections selected based on a stability index from each group containing cross sections labeled with an unstable label, and extracts cross sections that have become the first risk cross sections in a predetermined number or more of accident cases from the plurality of accident cases as second risk cross sections.

11. constructing a system analysis model for analyzing a plurality of cross sections of the power system based on scenario data indicating time-series data related to analysis conditions of the power system; A power flow calculation is performed using the system analysis model for the multiple cross sections, calculating stability of power transmission in the power system based on the power flow calculation result; grouping vector data indicating the power flow calculation results for each cross section based on the stability calculation results or constraint conditions generated from the stability calculation results and the power flow calculation results; Power system analysis method.

12. A process of constructing a system analysis model for analyzing a plurality of cross sections of the power system based on scenario data indicating time-series data related to analysis conditions of the power system; A process of performing a power flow calculation using the system analysis model for the plurality of cross sections; calculating stability of power transmission in the power system based on the power flow calculation result; a process of grouping vector data indicating the power flow calculation results for each cross section based on the stability calculation results or constraint conditions generated from the stability calculation results and the power flow calculation results; A program that causes a computer to execute the following.

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