A method for locating subsynchronous oscillation sources in power systems based on measurement data

Abstract

The present invention discloses a method for locating a subsynchronous oscillation source of an electric power system based on measurement data, and belongs to the technical field of electric power systems. The method for locating a subsynchronous oscillation source of an electric power system based on measurement data includes: obtaining measurement data of each unit during the occurrence of subsynchronous oscillation, constructing a subsynchronous oscillation signal sequence; normalizing the subsynchronous oscillation signal sequence and performing bandpass filtering; converting the oscillation data into an ordinal pattern, calculating the dynamic symbolized transfer entropy, and determining the causal relationship of subsynchronous oscillation propagation; constructing an oscillation propagation causal network, determining the location of the oscillation source or the oscillation area where it is located; and calculating a weighted causal influence index when there are multiple oscillation sources. The present invention, based on the consideration of causal relationships, can accurately realize the positioning of subsynchronous oscillation sources in multiple scenarios such as single oscillation sources and multiple oscillation sources by relying on measurement data, thereby solving the problem of poor accuracy of subsynchronous oscillation source positioning in the case of dynamic changes in the power grid by existing positioning methods.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for locating a subsynchronous oscillation source of a power system based on measurement data. Background Art

[0002] my country is vigorously developing renewable energy power generation, and the grid-connected capacity of renewable energy sources such as wind power and photovoltaics continues to increase. With the development of high-proportion renewable energy grid integration, particularly wind power, and high-voltage direct current (HVDC) transmission technology, power electronic equipment is being widely integrated into the system's source, grid, and load components to ensure a secure and reliable power supply. This new power system is trending towards a high proportion of power electronic equipment and renewable energy. This can easily lead to the presence of subsynchronous and supersynchronous harmonic components in power signals, resulting in the increasingly prominent problem of subsynchronous oscillations in the power system, which seriously threatens the safe and stable operation of the power system. Therefore, timely and accurate identification of subsynchronous oscillation sources in the system plays a vital role in reducing wind and solar power curtailment caused by oscillation events.

[0003] AC / DC hybrid power grids with a high proportion of renewable energy have complex topologies and diverse operating states. Traditional preemptive approaches based on physical models exhibit high-dimensional, nonlinear, and time-varying characteristics, with large variations in dynamic timescales. This makes it difficult to accurately describe the system's dynamics and presents the curse of dimensionality. Furthermore, oscillation source location methods based on physical mechanism analysis construct location criteria by studying the physical mechanisms of oscillation generation. This often requires specific physical assumptions and simplified conditions, which can lead to inaccurate location in actual systems. Therefore, there is an urgent need for subsynchronous oscillation source location methods that are independent of system models and can generalize across multiple scenarios.

[0004] Prior art CN119029931A discloses a subsynchronous oscillation positioning method and platform for a wind power grid-connected system based on instantaneous power. When a subsynchronous oscillation disturbance occurs, the instantaneous voltage and instantaneous current at the wind power grid connection point are detected; the instantaneous power at the grid connection point is obtained based on the instantaneous voltage and instantaneous current; the subsynchronous period is obtained based on the instantaneous power; the DC component and the secondary AC component after the disturbance are obtained based on the subsynchronous period and instantaneous power; the subsynchronous power is obtained based on the subsynchronous period, the DC component after the disturbance, and the secondary AC component after the disturbance; and whether the wind turbine is a subsynchronous oscillation source is determined based on the subsynchronous power. This synchronous oscillation positioning method has precise positioning, simple calculation, short time, low workload, and strong applicability. However, the above-mentioned prior art has poor versatility and poor positioning capabilities for multiple vibration sources. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for locating a subsynchronous oscillation source in a power system based on measurement data, so as to solve the problem that the existing positioning methods have poor accuracy in locating the subsynchronous oscillation source under dynamic changes in the power grid.

[0006] To achieve the above object, the present invention provides a method for locating a subsynchronous oscillation source in a power system based on measurement data, comprising the following steps:

[0007] S1. Obtaining measurement data of electrical quantities of each unit during subsynchronous oscillation of the power system and constructing a subsynchronous oscillation signal sequence;

[0008] S2. normalize the subsynchronous oscillation signal sequence, perform bandpass filtering on the normalized oscillation data, and retain the oscillation component of the subsynchronous frequency for subsequent calculations;

[0009] S3, converting the processed oscillation data into an ordinal pattern, calculating the dynamic symbolic transfer entropy, and determining the causal relationship of subsynchronous oscillation propagation;

[0010] S4. Using nodes as vertices and oscillation causal coefficients as edge weights, construct an oscillation propagation causal network, calculate out-degree strength, in-degree strength, and node oscillation propagation strength, and determine the location of the oscillation source or the oscillation region where it is located;

[0011] S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution of each source, and output the oscillation source position and contribution results.

[0012] Preferably, in said S1, the measurement data acquired includes voltage, current and active power.

[0013] Preferably, in S2, the subsynchronous oscillation signal sequence normalization method is a zero-mean normalization method, which is given by the following formula:

[0014] ;

[0015] in, is the oscillation data value after normalization, is the original value of this group of oscillation data, is the mean of the oscillating data, is the standard deviation, is the number of oscillation data, For the The original value of the oscillation data.

[0016] Preferably, in S3, the method for converting the processed oscillation data into an ordinal pattern is:

[0017] S31, extracting a length of A subsequence of , with adjacent elements spaced by d , the elements in the data window of this subsequence are represented as:

[0018] ;

[0019] in, Indicates the first elements;

[0020] S32. Sort the elements in the data window and obtain the sorted index to reflect the position of the original element. The sorted index is expressed by the following formula:

[0021] ;

[0022] in, Indicates the first The index of the position of the element after sorting;

[0023] S33. Convert the sorted index of each position into a Base number, converts the sort index into a unique integer encoded hash value , hash value for:

[0024] ;

[0025] The ordinal pattern of the oscillation signal generated by the symbolic method of permutation entropy is of length An array of , where each element in the array corresponds to a unique integer code in a data window.

[0026] Preferably, in S3, the dynamic symbolic transfer entropy calculation method is:

[0027] The symbolically generated oscillation signal sequence is divided into several overlapping windows by means of a sliding window, and the window size is , the sliding step length is s ,dynamic transfer entropy is calculated independently in each window to generate a causal strength sequence;

[0028] The calculation result of the transfer entropy between two sets of oscillation sequences is obtained by the following formula:

[0029] ;

[0030] in, is the joint probability density function, For the known and hour The conditional probability of Known hour The marginal probability of Indicates the oscillation signal sequence corresponding to the measurement data of a certain electrical quantity, Indicates the oscillation signal sequence corresponding to another set of measurement data of the electrical quantity, Indicates time delay;

[0031] Conditional probability and marginal probability , obtained by the following formula:

[0032] ;

[0033] ;

[0034] in, Indicates the number of occurrences of joint events, which refer to the state transition of the target sequence , conditional status , joint state ;

[0035] The dynamic symbolic transfer entropy is calculated by counting the occurrences of joint events in each window and is expressed as:

[0036] .

[0037] Preferably, in S4, the method for constructing the oscillation propagation causal network is:

[0038] S41. Calculate the difference in transfer entropy between two groups of variables, i.e., the net transfer entropy, as the oscillation causal coefficient. This coefficient is non-negative. The direction is that the variable with larger transfer entropy points to the variable with smaller transfer entropy, which represents the causal strength and direction of oscillation propagation. When When , the calculation formula of the oscillation causal coefficient is expressed as:

[0039] ;

[0040] S42. Take each unit node as the vertex, the oscillation causal coefficient value as the edge weight, and set a threshold. When the oscillation causal coefficient is less than the threshold, there is no obvious causal relationship between the two variables. The edge between the two variables is removed, and the edges greater than the threshold are retained to construct an oscillation propagation causal network.

[0041] Preferably, in S4, the method for calculating the node out-degree strength, in-degree strength and node oscillation propagation strength is:

[0042] Outdegree intensity , penetration strength and node oscillation propagation intensity The calculation formula is:

[0043] ;

[0044] ;

[0045] ;

[0046] in, For nodes To Node The oscillatory causal coefficient of For nodes To Node Oscillation causal coefficient; node oscillation propagation intensity OPI When it is greater than 0, it is determined to be an oscillation source or the location is in the oscillation area.

[0047] Preferably, in S5, the weighted causal influence index in the multi-oscillation source scenario is calculated by the following formula:

[0048] ;

[0049] ;

[0050] in, WCII is the weighted causal influence index, Node The out-degree strength, 、 is the weighting coefficient; Dynamic influence integral, reflecting the node The continuous influence during the oscillation process, For nodes To Node Dynamic symbolic transfer entropy.

[0051] The advantages and positive effects of the method for locating subsynchronous oscillation sources in power systems based on measurement data described in the present invention are: on the basis of considering causal relationships, the present invention gets rid of dependence on system models, has high physical interpretability, and can accurately locate subsynchronous oscillation sources in multiple scenarios such as single oscillation sources and multiple oscillation sources by relying solely on measurement data, thereby improving the accuracy of subsynchronous oscillation source positioning.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of an embodiment of the present invention;

[0054] Figure 2 This is a test scenario for an embodiment of the present invention;

[0055] Figure 3 is a positioning map applied to a test scenario in an embodiment of the present invention;

[0056] Figure 4 This is a graph of indicator results when an embodiment of the present invention is applied to a test scenario. DETAILED DESCRIPTION

[0057] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is usually placed when in use. These are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention. In the description of the present invention, it should also be noted that, unless otherwise expressly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] In this application, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0060] like Figure 1 A method for locating a subsynchronous oscillation source in a power system based on measurement data includes the following steps:

[0061] S1. Obtain measurement data of electrical quantities of each unit during subsynchronous oscillation of the power system and construct a subsynchronous oscillation signal sequence.

[0062] The acquired measurement data include voltage, current and active power.

[0063] S2. Normalize the subsynchronous oscillation signal sequence, and perform bandpass filtering on the normalized oscillation data, retaining the oscillation component of the subsynchronous frequency for subsequent calculations.

[0064] The normalization method of the subsynchronous oscillation signal sequence is the zero-mean normalization method, which is given by the following formula:

[0065] ;

[0066] in, is the oscillation data value after normalization, is the original value of this group of oscillation data, is the mean of the oscillating data, is the standard deviation, is the number of oscillation data, For the The original value of the oscillation data.

[0067] S3. Convert the processed oscillation data into an ordinal pattern, calculate the dynamic symbolic transfer entropy, and determine the causal relationship of subsynchronous oscillation propagation.

[0068] The method to convert the processed oscillation data into an ordinal pattern is:

[0069] S31, extracting a length of A subsequence of , with adjacent elements spaced by d , the elements in the data window of this subsequence are represented as:

[0070] ;

[0071] in, Indicates the first elements.

[0072] S32. Sort the elements in the data window and obtain the sorted index to reflect the position of the original element. The sorted index is expressed by the following formula:

[0073] ;

[0074] in, Indicates the first The index of the element after sorting.

[0075] S33. Convert the sorted index of each position into a Base number, converts the sort index into a unique integer encoded hash value The function of the hash algorithm is to map different inputs (here, different sort index combinations) to different output integers, and this mapping is unique, making it easy to quickly identify and distinguish different subsequence features. for:

[0076] ;

[0077] The signal subsequence is processed by combining the symbolic method of permutation entropy, and the generated oscillation signal ordinal pattern is of length Each element in the array corresponds to a unique integer code within a data window. Each hash value represents a characteristic of the corresponding window (i.e., subsequence). This unique integer code can be used to identify the window in subsequent analysis (such as determining whether it is an oscillation source). Representing complex signal subsequence characteristics with simple integer codes facilitates data processing and analysis.

[0078] The calculation method of dynamic symbolic transfer entropy is:

[0079] The symbolically generated oscillation signal sequence is divided into several overlapping windows by means of a sliding window, and the window size is , the sliding step length is s ,The dynamic transfer entropy is calculated independently in each window to generate a causal strength sequence.

[0080] Transfer entropy is a metric used to measure the direction and strength of information transfer between two sequences. In the context of subsynchronous oscillation analysis in power systems, calculating dynamic transfer entropy can analyze the causal relationships and information transfer between oscillation signals within a window, thereby generating a causal strength sequence that reflects the changes in the strength of the causal relationships between oscillation signals within different windows.

[0081] The calculation result of the transfer entropy between two sets of oscillation sequences is obtained by the following formula:

[0082] ;

[0083] in, is the joint probability density function, For the known and hour The conditional probability of Known hour The marginal probability of Indicates the oscillation signal sequence corresponding to the measurement data of a certain electrical quantity, Indicates the oscillation signal sequence corresponding to another set of measurement data of the electrical quantity, Indicates time delay;

[0084] Conditional probability and marginal probability , obtained by the following formula:

[0085] ;

[0086] ;

[0087] in, Indicates the number of occurrences of joint events, which refer to the state transition of the target sequence , conditional status , joint state .

[0088] The dynamic symbolic transfer entropy is calculated by counting the occurrences of joint events in each window and is expressed as:

[0089] .

[0090] S4. Using nodes as vertices and oscillation causal coefficients as edge weights, construct an oscillation propagation causal network, and calculate the out-degree strength, in-degree strength, and node oscillation propagation strength to determine the location of the oscillation source or the oscillation area.

[0091] The method for constructing an oscillation propagation causal network is:

[0092] S41. Calculate the difference in transfer entropy between the two groups of variables, i.e., the net transfer entropy, as the oscillation causal coefficient. This coefficient is non-negative and points from the variable with greater transfer entropy to the variable with smaller transfer entropy, characterizing the causal strength and direction of oscillation propagation. When , the calculation formula of the oscillation causal coefficient is expressed as:

[0093] .

[0094] S42. Take each unit node as the vertex, the oscillation causal coefficient value as the edge weight, and set the threshold to 0.1. When the oscillation causal coefficient is less than the threshold, the transfer entropy between the two variables is similar in size, and it is considered that there is no obvious causal relationship between the two variables. The edge between the two variables is removed, and the edges greater than the threshold are retained to construct the oscillation propagation causal network.

[0095] The method for calculating node out-degree strength, in-degree strength and node oscillation propagation strength is:

[0096] Outdegree intensity , penetration strength and node oscillation propagation intensity The calculation formula is:

[0097] ;

[0098] ;

[0099] ;

[0100] in, For nodes To Node The oscillatory causal coefficient of For nodes To Node Oscillation causal coefficient; node oscillation propagation intensity OPIWhen the value is greater than 0, it indicates that the node has a significant impact on other nodes but is less affected by other nodes. This indicates that the node is the source of oscillation events in other nodes and is therefore determined to be the oscillation source or located in the oscillation area. OPI The larger the value is, the greater the impact on the electrical measurement data of other nodes is, and it is the dominant oscillation source.

[0101] S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution of each source, and output the oscillation source position and contribution results.

[0102] The weighted causal influence index in the multi-vibration source scenario is calculated by the following formula:

[0103] ;

[0104] ;

[0105] in, WCII is the weighted causal influence index, Node The out-degree strength quantifies the total net output information of the node to the entire network. 、 is the weighting coefficient, which is set to 0.5 to comprehensively consider the causal information during the oscillation propagation process. Dynamic influence integral, reflecting the node The continuous influence during the oscillation process, For nodes To Node The weighted causal influence index more accurately displays the importance of each unit and reflects the influence of each oscillation source in the propagation of subsynchronous oscillations.

[0106] In order to verify the effectiveness of the subsynchronous oscillation source positioning method of the present invention, the method of the present invention is applied to the improved two-area system for testing. Figure 2 As shown in the figure, DFIG1, a 5MW DFIG-1 wind farm, is connected to bus 6. The wind farm already includes series compensation and short-distance transmission lines. A single wind turbine is used to represent the entire wind farm. 30 wind turbines are connected, the wind speed is set to 8 m / s, and the line series compensation degree is 32.7%. DFIG2, a 30-wind turbine farm, is added to bus 5. The wind speed is 11 m / s, the line series compensation degree is set to 18%, and all series compensation capacitors are put into operation at 11 s. The system experiences subsynchronous oscillation caused by the dynamic interaction between the DFIG-1 wind turbines and the series compensation lines.

[0107] Collect the active power of each unit as the oscillation signal and calculate the oscillation causal coefficient between each unit pair OCC and node oscillation propagation intensity OPIThe results are shown in Table 1. Gen1 is generator set 1, Gen2 is generator set 2, Gen3 is generator set 3, Gen4 is generator set 4, Wind1 is double-fed wind turbine set 1, and Wind2 is double-fed wind turbine set 2. The causal network of system oscillation propagation is constructed as follows: Figure 3 , OCC Causal relationships with values ​​below the threshold are connected by dashed lines, indicating no clear causal transmission relationship. With the exception of two wind farms, the out-degrees of the remaining turbine nodes are very low, while the out-degree strengths of the two wind farms are very high. The node causal strengths are all greater than 0, and the mutual oscillation causal coefficient is very small, indicating that there is no clear causal relationship between the two wind farms. Therefore, both wind farms can be considered the dominant oscillation sources of this sub-synchronous oscillation event. This is consistent with the assumptions, verifying the effectiveness of the proposed method for localization in a multi-oscillation source scenario.

[0108] Table 1 Oscillation causal coefficients between units and node oscillation propagation intensity

[0109] ;

[0110] In order to further quantify the impact of each unit in the propagation of SSO events, the normalized index of the multi-oscillation source contribution of each unit is calculated. WCII , the contribution ranking results are as follows Figure 4 Normalization of two wind farm oscillation sources WCII These indicators are significantly higher than those of other units by over 30%, indicating that these two oscillation sources contribute significantly more to the oscillation propagation than other units, playing a key role in the oscillation propagation, with Wind Farm 1 having a greater impact. The proposed indicators can intuitively characterize the contribution of each unit to the system oscillation propagation, providing valuable insights for identifying the dominant oscillation source and initiating subsequent emergency oscillation control measures.

[0111] Therefore, the method for locating subsynchronous oscillation sources in power systems based on measurement data described in the present invention, while taking into account causal relationships, gets rid of dependence on system models and relies on measurement data to accurately locate subsynchronous oscillation sources in multiple scenarios such as single oscillation sources and multiple oscillation sources, thereby solving the problem of poor accuracy of subsynchronous oscillation source positioning in existing positioning methods under dynamic changes in the power grid.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for locating subsynchronous oscillation sources in a power system based on measurement data, characterized in that: The following steps are involved: S1. Obtaining measurement data of electrical quantities of each unit during subsynchronous oscillation of the power system and constructing a subsynchronous oscillation signal sequence; S2. normalize the subsynchronous oscillation signal sequence, perform bandpass filtering on the normalized oscillation data, and retain the oscillation component of the subsynchronous frequency for subsequent calculations; S3, converting the processed oscillation data into an ordinal pattern, calculating the dynamic symbolic transfer entropy, and determining the causal relationship of subsynchronous oscillation propagation; S4. Using nodes as vertices and oscillation causal coefficients as edge weights, construct an oscillation propagation causal network, calculate out-degree strength, in-degree strength, and node oscillation propagation strength, and determine the location of the oscillation source or the oscillation region where it is located; S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution of each source, and output the oscillation source location and contribution results; In S3, the method for converting the processed oscillation data into an ordinal pattern is: S31, extracting a length of A subsequence of , with adjacent elements spaced by , the elements in the data window of this subsequence are represented as: ； in, Indicates the first elements; S32. Sort the elements in the data window and obtain the sorted index to reflect the position of the original element. The sorted index is expressed by the following formula: ； in, Indicates the first The index of the position of the element after sorting; S33. Convert the sorted index of each position into a Base number, converts the sort index into a unique integer encoded hash value , hash value for: ； The ordinal pattern of the oscillation signal generated by the symbolic method of permutation entropy is of length An array of , each element in the array corresponds to a unique integer code in a data window; In S5, the weighted causal influence index in the multi-vibration source scenario is calculated by the following formula: ； ； in, is the weighted causal influence index, Node The out-degree strength, 、 is the weighting coefficient; Dynamic influence integral, reflecting the node The continuous influence during the oscillation process, For nodes To Node Dynamic symbolic transfer entropy.

2. The method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 1, characterized in that: In S1, the measurement data obtained include voltage, current and active power.

3. The method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 2, characterized in that: In S2, the subsynchronous oscillation signal sequence normalization method is a zero-mean normalization method, which is given by the following formula: ； in, is the oscillation data value after normalization, is the original value of this group of oscillation data, is the mean of the oscillating data, is the standard deviation, is the number of oscillation data, For the The original value of the oscillation data.

4. The method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 3, characterized in that: In S3, the dynamic symbolic transfer entropy calculation method is: The symbolically generated oscillation signal sequence is divided into several overlapping windows by means of a sliding window, and the window size is , the sliding step length is ,dynamic transfer entropy is calculated independently in each window to generate a causal strength sequence; The calculation result of the transfer entropy between two sets of oscillation sequences is obtained by the following formula: ； in, is the joint probability density function, For the known and hour The conditional probability of Known hour The marginal probability of Indicates the oscillation signal sequence corresponding to the electrical quantity measurement data, Represents The oscillation signal sequence corresponding to another set of measurement data of the same electrical quantity, Indicates time delay; Conditional probability and marginal probability , obtained by the following formula: ； ； in, Indicates the number of occurrences of joint events, which refer to the state transition of the target sequence , conditional status , joint state ; The dynamic symbolic transfer entropy is calculated by counting the occurrences of joint events in each window and is expressed as: 。 5. The method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 4, characterized in that: In S4, the method for constructing the oscillation propagation causal network is: S41. Calculate the difference in transfer entropy between two groups of variables, i.e., the net transfer entropy, as the oscillation causal coefficient. This coefficient is non-negative and points from the variable with larger transfer entropy to the variable with smaller transfer entropy, characterizing the causal strength and direction of oscillation propagation. When , the calculation formula of the oscillation causal coefficient is expressed as: ； S42. Take each unit node as the vertex, the oscillation causal coefficient value as the edge weight, and set a threshold. When the oscillation causal coefficient is less than the threshold, there is no obvious causal relationship between the two variables. The edge between the two variables is removed, and the edges greater than the threshold are retained to construct an oscillation propagation causal network.

6. The method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 5, characterized in that: In S4, the method for calculating the node out-degree strength, in-degree strength and node oscillation propagation strength is: Outdegree intensity , penetration strength and node oscillation propagation intensity The calculation formula is: ； ； ； in, For nodes To Node The oscillatory causal coefficient of For nodes To Node Oscillation causal coefficient; node oscillation propagation intensity OPI When it is greater than 0, it is determined to be an oscillation source or the location is in the oscillation area.

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