Measurement data-based power system subsynchronous oscillation source positioning method

Abstract

The invention discloses a power system subsynchronous oscillation source positioning method based on measurement data, and belongs to the technical field of power systems. The power system subsynchronous oscillation source positioning method based on the measurement data comprises the following steps: obtaining the measurement data of each unit in a subsynchronous oscillation process, and constructing a subsynchronous oscillation signal sequence; normalizing the subsynchronous oscillation signal sequence and carrying out band-pass filtering; converting oscillation data into an ordinal number mode, calculating a dynamic symbolization transfer entropy, and determining a subsynchronous oscillation propagation causal relationship; constructing an oscillation propagation causal network, and determining an oscillation source or an oscillation region position; and when a multi-oscillation-source condition exists, calculating a weighted causal influence index. On the basis of considering the causal relationship, the method can accurately achieve the positioning of the subsynchronous oscillation source in multiple scenes such as a single oscillation source and multiple oscillation sources through the measurement data, and solves the problem that the positioning accuracy of the subsynchronous oscillation source is poor under the dynamic change condition of a power grid through an existing positioning method.

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 in a power system based on measurement data. Background Art

[0002] China vigorously develops new energy power generation, and the grid-connected capacity of new energy sources such as wind power and photovoltaic power continues to increase. With the development of high-proportion new energy grid connection represented by wind energy and high-voltage direct current transmission technology, in order to ensure the safe and reliable supply of electric power, power electronic devices are widely connected to various parts of the system source, grid, and load. The new power system shows a development trend of high-proportion power electronic devices and high-proportion renewable new energy, which easily leads to the existence of sub / supersynchronous harmonic components in electric power signals, thus causing the increasingly prominent problem of subsynchronous oscillation in the power system, seriously threatening the safe and stable operation of the power system. Therefore, accurately locating the subsynchronous oscillation source in the system in a timely manner plays an important role in reducing the curtailment of wind and light caused by oscillation events.

[0003] The AC / DC hybrid power grid containing a high proportion of renewable energy has a complex topology and diverse operating states. The traditional physical model-based pre-plan method shows high-dimensional, non-linear, and time-varying characteristics, with a large dynamic time scale difference, making it difficult to accurately describe the dynamic laws of the system and suffering from the curse of dimensionality problem. At the same time, the oscillation source location method based on physical mechanism analysis constructs location criteria by studying the physical mechanism of oscillation occurrence, usually requiring the introduction of specific physical assumptions and simplification conditions, which may lead to inaccurate location in the actual system. Therefore, there is an urgent need for a subsynchronous oscillation source location method that can get rid of the dependence on the system model and achieve multi-scenario generalization.

[0004] The prior art CN119029931A discloses a method and platform for locating subsynchronous oscillation in 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 according to the instantaneous voltage and instantaneous current; the subsynchronous period is obtained according to the instantaneous power; the disturbed DC component and the disturbed second-order AC component are obtained according to the subsynchronous period and the instantaneous power; the subsynchronous power is obtained according to the subsynchronous period, the disturbed DC component, and the disturbed second-order AC component; and it is determined whether the wind turbine is a subsynchronous oscillation source according to the subsynchronous power, which has the advantages of accurate location, simple and fast calculation, small workload, and strong applicability for synchronous oscillation location. However, the above prior art has poor generality and poor ability to locate multiple vibration sources. Summary of the Invention

[0005] The object 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 of poor accuracy in locating the subsynchronous oscillation source by the existing location methods under the condition of 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: S1. Obtain the measurement data of the electrical quantities of each unit during the subsynchronous oscillation process in the power system, and construct a subsynchronous oscillation signal sequence; S2. Perform normalization processing on the subsynchronous oscillation signal sequence, and perform band-pass filtering on the normalized oscillation data, and retain the oscillation components of the subsynchronous frequency for subsequent calculations; 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; S4. Construct an oscillation propagation causal network with nodes as vertices and oscillation causal coefficients as edge weights, 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 region; S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution degree of each source, and output the oscillation source location and contribution degree results.

[0007] Preferably, in S1, the obtained measurement data includes voltage, current, and active power.

[0008] Preferably, in S2, the normalization method for the subsynchronous oscillation signal sequence is the zero-mean normalization method, which is given by the following formula: ; where, is the value of the oscillation data after normalization, is the original value of this group of oscillation data, is the mean value of the oscillation data, is the standard deviation, is the number of oscillation data, is the original value of the

[0009] Preferably, in S3, the method for converting the processed oscillation data into an ordinal pattern is as follows: S31. Extract a subsequence with a length of from the subsynchronous oscillation signal sequence, and the adjacent elements are spaced by d , and the elements in the data window of this subsequence are expressed as: ; where, represents the th element in the original subsynchronous oscillation signal sequence; S32. Sort the elements in the data window, and obtain the sorted index to reflect the position of the original elements. The sorted index is expressed by the following formula: ; Among them, represents the position index of the -th element in the data window after sorting; S33. Convert the sorting index of each position into a -ary number, and convert the sorting index into a unique integer-encoded hash value , and the hash value is: ; The ordinal pattern of the oscillation signal generated by combining the symbolic method of permutation entropy is an array of length , and each element in the array corresponds to a unique integer encoding in the data window.

[0010] Preferably, in the above S3, the calculation method of dynamic symbolic transfer entropy is: Divide the oscillation signal sequence generated by symbolization into several overlapping windows by means of a sliding window, the window size is , and the sliding step is s . Independently calculate the dynamic transfer entropy within each window to generate a causal intensity sequence; The calculation result of the transfer entropy between two groups of oscillation sequences is obtained by the following formula: ; Among them, is the joint probability density function, is the conditional probability of and when , is the marginal probability of when , represents the oscillation signal sequence corresponding to the measured data of a certain electrical quantity, represents the oscillation signal sequence corresponding to another group of measured data of this electrical quantity, represents the time delay; The conditional probability and the marginal probability are obtained by the following formula: ; ; Among them, represents the number of occurrences of the joint event, and the joint event refers to the state transition of the target sequence , the conditional state , and the joint state ; The dynamic symbolic transfer entropy is calculated by statistically counting the number of occurrences of joint events within each window and is expressed as: 。

[0011] Preferably, in the step S4, the method for constructing the oscillation propagation causal network is as follows: S41. Calculate the difference in transfer entropy between two sets of variables, i.e., the net transfer entropy, as the oscillation causal coefficient. This coefficient is non - negative. The direction is from the variable with a larger transfer entropy to the variable with a smaller transfer entropy, which represents the causal strength and direction of oscillation propagation. When holds, the calculation formula for the oscillation causal coefficient is expressed as: ; S42. Take each unit node as a vertex, and the value of the oscillation causal coefficient as the weight of the edge. Set a threshold. When the oscillation causal coefficient is less than the threshold, there is no obvious causal relationship between the two variables, and the edge between the two variables is removed. Retain the edges with values greater than the threshold to construct the oscillation propagation causal network.

[0012] Preferably, in the step S4, the methods for calculating the out - degree strength, in - degree strength, and node oscillation propagation strength of a node are as follows: Out - degree strength 、In - degree strength and node oscillation propagation strength The calculation formulas are: ; ; ; Among them, is the oscillation causal coefficient from node to node , is the oscillation causal coefficient from node to node ; When the node oscillation propagation strength OPI is greater than 0, it is determined as an oscillation source or the position is in the oscillation region.

[0013] Preferably, in the step S5, the weighted causal influence index in the multi - oscillation - source scenario is calculated by the following formula: ; ; Among them, WCII is the weighted causal influence index, i.e., the out - degree strength of node , 、 are the weighted coefficients; is the dynamic influence integral, which reflects the continuous influence of node during the oscillation process, is the node To the node Dynamic symbolic transfer entropy

[0014] The advantages and positive effects of the method for locating subsynchronous oscillation sources in a power system based on measurement data according to the present invention are as follows: On the basis of considering causal relationships, the present invention gets rid of the dependence on the system model, has high physical interpretability, and can accurately locate subsynchronous oscillation sources in multiple scenarios such as single oscillation sources and multiple oscillation sources only relying on measurement data, improving the accuracy of subsynchronous oscillation source location.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0016] Figure 1 Is a flowchart of an embodiment of the present invention; Figure 2 Is a test scenario of an embodiment of the present invention; Figure 3 Is a location map of an embodiment of the present invention applied to a test scenario; Figure 4 Is an index result map of an embodiment of the present invention applied to a test scenario. Detailed Embodiments

[0017] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "installed", "connected" 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 directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside 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 situations.

[0018] 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 belonging to the technical field of this application. If there is any inconsistency, it shall be based on the meaning described in this specification or the meaning obtained according to the content recorded in this specification. 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.

[0019] The following will describe in detail the embodiments of the present invention in conjunction with the accompanying drawings.

[0020] As Figure 1 shown. A method for locating the subsynchronous oscillation source of a power system based on measurement data includes the following steps: S1. Obtain the measurement data of the electrical quantities of each unit during the subsynchronous oscillation of the power system, and construct a subsynchronous oscillation signal sequence.

[0021] The obtained measurement data includes voltage, current, and active power.

[0022] S2. Normalize the subsynchronous oscillation signal sequence, and perform band-pass filtering on the normalized oscillation data to retain the oscillation components at the subsynchronous frequency for subsequent calculations.

[0023] The normalization method for the subsynchronous oscillation signal sequence is the zero-mean normalization method, which is given by the following formula: ; where is the normalized oscillation data value, is the original value of this group of oscillation data, is the mean value of the oscillation data, is the standard deviation, is the number of oscillation data, is the th original value of the oscillation data.

[0024] 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.

[0025] The method for converting the processed oscillation data into an ordinal pattern is as follows: S31. Extract a subsequence of length from the subsynchronous oscillation signal sequence, with an adjacent element interval of d . The elements in the data window of this subsequence are represented as: ; where represents the th element in the original subsynchronous oscillation signal sequence.

[0026] S32. Sort the elements in the data window, and obtain the sorted index to reflect the position of the original elements. The sorted index is represented by the following formula: ; where represents the position index of the th element in the data window after sorting.

[0027] S33. Convert the sorting index at each position into a radix number, and convert the sorting index into a unique integer-encoded hash value . The role of the hash algorithm is to map different inputs (here, different combinations of sorting indexes) to different output integers, and this mapping is unique, facilitating the quick identification and differentiation of different subsequence features. The hash value is: ; Process the signal subsequence in combination with the symbolization method of permutation entropy, and the generated ordinal pattern of the oscillation signal is an array with a length of . Each element in the array corresponds to a unique integer encoding in a data window. Each hash value represents a feature of the corresponding window (i.e., subsequence), and this unique integer encoding can be used as an identifier for the window in subsequent analyses (such as analyzing whether it is an oscillation source, etc.), representing the complex signal subsequence features with simple integer encodings for convenient data processing and analysis.

[0028] The calculation method of dynamic symbolized transfer entropy is: Divide the oscillation signal sequence generated by symbolization into several overlapping windows by means of a sliding window. The window size is , and the sliding step is s . Independently calculate the dynamic transfer entropy within each window to generate a causal strength sequence.

[0029] Transfer entropy is an index used to measure the direction and strength of information transfer between two sequences. In the scenario of subsynchronous oscillation analysis in the power system, by calculating the dynamic transfer entropy, the causal relationship and information transfer situation between the oscillation signals within the window can be analyzed, and then a causal strength sequence can be generated, which reflects the strength change of the causal relationship between the oscillation signals in different windows.

[0030] The calculation result of the transfer entropy between two groups of oscillation sequences is obtained by the following formula: ; where is the joint probability density function, is the conditional probability of and when , is the marginal probability of when , represents the oscillation signal sequence corresponding to the measured data of a certain electrical quantity, represents the oscillation signal sequence corresponding to another set of measured data of this electrical quantity, represents the time delay; Conditional probability and marginal probability , are obtained by the following formula: ; ; wherein, represents the occurrence times of the joint event, and the joint event refers to the state transition of the target sequence , conditional state , joint state .

[0031] The dynamic symbolic transfer entropy is calculated by statistically counting the occurrence times of joint events in each window, and is expressed as: .

[0032] S4. Taking the nodes as vertices and the oscillatory causal coefficient as the edge weight, construct an oscillatory propagation causal network, and calculate the out-degree strength, in-degree strength and node oscillatory propagation strength to determine the position of the oscillation source or the oscillation region where it is located.

[0033] The method for constructing the oscillatory propagation causal network is as follows: S41. Calculate the difference in transfer entropy between two sets of variables, i.e., the net transfer entropy, as the oscillatory causal coefficient. This coefficient is non-negative, and the direction is from the variable with a larger transfer entropy to the variable with a smaller transfer entropy, characterizing the causal strength and direction of oscillatory propagation. When , the calculation formula of the oscillatory causal coefficient is expressed as: .

[0034] S42. Taking each unit node as a vertex and the value of the oscillatory causal coefficient as the weight of the edge, set the threshold to 0.1. When the oscillatory causal coefficient is less than the threshold, the transfer entropy between the two variables is similar, and it is considered that there is no obvious causal relationship between the two variables. Remove the edge between the two variables and retain the edges greater than the threshold to construct an oscillatory propagation causal network.

[0035] The methods for calculating the out-degree strength, in-degree strength and node oscillatory propagation strength of the node are as follows: Out-degree strength , in-degree strength and node oscillatory propagation strength The calculation formulas are: ; ; ; wherein, is the oscillatory causal coefficient from node to node , is the node To the node Oscillation causal coefficient; Node oscillation propagation intensity OPI When it is greater than 0, it indicates that this node has a greater impact on other nodes, is less affected by other nodes, is the source of oscillation events for other nodes, and is determined to be an oscillation source or the location is in the oscillation area. OPI The larger it is, the greater its impact on the electrical quantity measurement data of other nodes, and it is the dominant oscillation source.

[0036] S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution degree of each source, and output the oscillation source location and contribution degree results.

[0037] The weighted causal influence index in the scenario of multiple oscillation sources is calculated by the following formula: ; ; Wherein, WCII is the weighted causal influence index, i.e., the out-degree strength of node , which quantifies the total net output information volume of the node to the whole network. , are weighted coefficients, set to 0.5 to comprehensively consider the causal information in the oscillation propagation process. is the dynamic influence integral, reflecting the continuous influence of node during the oscillation process, is the dynamic symbolic transfer entropy from node to node . The weighted causal influence index more accurately shows the importance of each unit and represents the influence of each oscillation source in the process of sub-synchronous oscillation propagation.

[0038] To verify the effectiveness of the sub-synchronous oscillation source location method of the present invention, the method of the present invention is applied to an improved two-area system for testing. As Figure 2 shown, a No. 1 doubly-fed wind farm DFIG1 is incorporated into bus 6, a 5MW doubly-fed wind turbine is used, the wind farm already includes series compensation and short-distance transmission lines, a single wind turbine is used to equivalent the whole wind farm, 30 wind turbine units are connected, the wind speed is set to 8m / s, and the line series compensation degree is 32.7%; a No. 2 doubly-fed wind farm DFIG2 with 30 wind turbines is added at bus 5, the wind speed is 11m / s, and the line series compensation degree is set to 18%. All series compensation capacitors are put into operation at 11s, and sub-synchronous oscillation occurs due to the dynamic interaction between the doubly-fed wind turbines and the series compensation lines in the system.

[0039] Collect the active power of each unit as the oscillation signal, and calculate the oscillation causal coefficient OCC between each pair of units and the node oscillation propagation intensityOPI , as shown in Table 1, Gen1 is the No. 1 generating unit, Gen2 is the No. 2 generating unit, Gen3 is the No. 3 generating unit, Gen4 is the No. 4 generating unit, Wind1 is the No. 1 doubly-fed wind turbine unit, and Wind2 is the No. 2 doubly-fed wind turbine unit. Construct the causal network of system oscillation propagation as Figure 3 , OCC . The causal relationships with values less than the threshold are connected by dashed lines, indicating no obvious causal transmission relationship. Except for the two wind farms, the out-degrees of the remaining unit nodes are very small, while the out-degree intensities of the two wind farms are very large. The node causal intensities are all greater than 0, and the oscillation causal coefficients between them are very small, indicating no obvious causal relationship between the two wind farms. It can be determined that both wind farms are the dominant oscillation sources of this subsynchronous oscillation event. Consistent with the preset, the effectiveness of the proposed method in locating in a multi-oscillation source scenario is verified.

[0040] Table 1 Oscillation causal coefficients and node oscillation propagation intensities between units ;

[0041] To further quantify the influence of each unit in the propagation of SSO events, calculate the normalized index of the contribution of each unit to multi-oscillation sources WCII , and the contribution ranking results are as Figure 4 shown. The normalized WCII indices of the two wind farm oscillation sources are respectively more than 30% higher than those of other units, indicating that the contributions of these two oscillation sources to the oscillation propagation are far greater than those of other units, playing a key role in this oscillation propagation, and the influence caused by the No. 1 wind farm is greater. The proposed index can intuitively characterize the contribution degree of each unit to the system oscillation propagation, which has important reference value for identifying the dominant oscillation source and taking subsequent oscillation emergency control measures.

[0042] Therefore, by using the method for locating subsynchronous oscillation sources in a power system based on measurement data described in the present invention, on the basis of considering causal relationships, getting rid of the dependence on the system model, and relying on measurement data, the location of subsynchronous oscillation sources in multiple scenarios such as single oscillation source and multi-oscillation sources can be accurately realized, and the problem of poor accuracy in locating subsynchronous oscillation sources by existing location methods under the condition of dynamic changes in the power grid is solved.

[0043] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for locating the subsynchronous oscillation source of a power system based on measurement data, characterized in that It includes the following steps: S1. Obtain the measurement data of the electrical quantities of each unit during the subsynchronous oscillation process of the power system, and construct a subsynchronous oscillation signal sequence; S2. Perform normalization processing on the subsynchronous oscillation signal sequence, and perform band-pass filtering on the normalized oscillation data, and retain the oscillation components of the subsynchronous frequency for subsequent calculations; 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; S4. Construct an oscillation propagation causal network with nodes as vertices and oscillation causal coefficients as edge weights, 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; S5. When there are multiple oscillation sources, calculate the weighted causal influence index, evaluate the contribution degree of each source, and output the results of the oscillation source location and contribution degree.

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

3. A 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 normalization method of the subsynchronous oscillation signal sequence is the zero-mean normalization method, which is given by the following formula: ； Among them, is the normalized oscillation data value, is the original value of this set of oscillation data, is the mean value of the oscillation data, is the standard deviation, is the number of oscillation data, is the original value of the nth oscillation data.

4. A 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 method of converting the processed oscillation data into an ordinal pattern is: S31. Extract a subsequence of length from the subsynchronous oscillation signal sequence, with an adjacent element interval of d . The elements in the data window of this subsequence are represented as: ； Among them, represents the th element in the original subsynchronous oscillation signal sequence; S32. Sort the elements in the data window, and obtain the sorted index to reflect the position of the original elements. The sorted index is represented by the following formula: ； Among them, represents the position index of the -th element in the sorted data window; S33. Convert the sorting index at each position into a base number, and convert the sorting index into a unique integer-encoded hash value , and the hash value is: ； The ordinal pattern of the oscillation signal generated by the symbolic method combined with permutation entropy is an array of length , and each element in the array corresponds to a unique integer code in a data window.

5. A method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 4, characterized in that In S3, the calculation method of the dynamic symbolic transfer entropy is: The oscillatory signal sequence generated by symbolization is segmented into a number of overlapping windows by means of a sliding window, the window size being , and the sliding step size being s . The dynamic transfer entropy is calculated independently within each window to generate a causal strength sequence; The calculation result of the transfer entropy between two groups of oscillation sequences is obtained by the following formula: ； Among them, is the joint probability density function, is the conditional probability of and when ; is the marginal probability of when ; represents 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, represents the time delay; Conditional probability and marginal probability are obtained by the following formula: ； ； Among them, represents the occurrence times of the combined event, and the combined event refers to the state transition of the target sequence , conditional state , combined state ; The dynamic symbolic transfer entropy is calculated by counting the occurrence times of joint events in each window, and is expressed as: 。 6. The method for locating a subsynchronous oscillation source of a power system based on measurement data according to claim 5, wherein In S4, the method of constructing the oscillation propagation causal network is: S41. Calculate the difference in transfer entropy between two sets of variables, i.e., the net transfer entropy, as the oscillatory causal coefficient. This coefficient is non - negative, and the direction is from the variable with a larger transfer entropy to the variable with a smaller transfer entropy, characterizing the causal strength and direction of oscillatory propagation; when , the calculation formula for the oscillatory causal coefficient is expressed as: ； S42. Take each unit node as a vertex and the oscillation causal coefficient value as the weight of the edge, set a threshold. When the oscillation causal coefficient is less than the threshold, there is no obvious causal relationship between the two variables, and the edge between the two variables is removed, and the edges greater than the threshold are retained to construct the oscillation propagation causal network.

7. A method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 6, characterized in that In S4, the methods of calculating the out-degree strength, in-degree strength, and node oscillation propagation strength of the node are: Out-degree strength , in-degree strength and node oscillation propagation strength The calculation formulas are as follows: ； ； ； Among them, is the oscillation causality coefficient from node to node ; is the oscillation causality coefficient from node to node ; when the node oscillation propagation intensity OPI is greater than 0, it is determined as an oscillation source or the location is in the oscillation region.

8. A method for locating a subsynchronous oscillation source in a power system based on measurement data according to claim 7, characterized in that In S5, the weighted causal influence index in the multi-oscillation source scenario is calculated by the following formula: ； ； Among them, WCII is the weighted causal influence index, that is, the out-degree strength of node ; , are the weighting coefficients; is the dynamic influence integral, reflecting the continuous influence of node during the oscillation process; is the dynamic symbolic transfer entropy from node to node .

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