Information management device and analysis method for power system based on artificial intelligence

Abstract

The invention discloses an information management device and an analysis method for an electric power system based on artificial intelligence, and belongs to the technical field of information analysis, and the method comprises the steps: removing dispersed parameters in information of a source electric power system, determining a to-be-processed numerical value group in the information of the source electric power system, and carrying out the calculation to obtain a base value and an abnormal minimum threshold value; then, the attached key metric value of each operation parameter is determined through the anomalous value, so that the purposes of centralizing the numerical value group benchmark to be processed, removing disturbance and improving the accuracy of analyzing and cleaning the information of the electric power system are achieved, and the method is suitable for selecting the anomalous values of the operation parameters of all the electric power equipment of the electric power system. The defects that in the prior art, the information of the power system in different positions and different states is tedious, latent and diversified and is disturbed and even confused by complicated factors, and no platform or method for selecting and estimating the information of the power system is suitable for all power equipment of the power system are effectively overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of information analysis, and particularly relates to an information management device and analysis method for a power system based on artificial intelligence. Background Art

[0002] The whole composed of the power generation part of a power plant, power transmission and distribution lines, a substation, and various electrical equipment of users is called a power system.

[0003] In practical applications, the current monitoring device of the power system often uses the prior art solution mentioned in the patent publication number "CN208285075U", where a sensing component is connected to a processing module. The sensing component is used to collect the operating parameters of electrical equipment and transmit them to the processing module. The operating parameters of the electrical equipment are the information of the power system. The processing module is used to compare the transmitted information of the power system with a predefined critical quantity. If the information of the power system is higher than the predefined critical quantity, the processing module determines that the power system is operating abnormally.

[0004] Before comparing the transmitted information of the power system with the predefined critical quantity, the information of the power system often needs to be analyzed and cleaned. The principle of this analysis and cleaning is as follows: Based on the operating parameters of electrical equipment, information anomaly estimation under the combination of anomaly and deviation is used to analyze and clean abnormal values. The information anomalies of the power system in different positions and different states are complex, latent, diversified, and are affected and even confused by complex factors. Therefore, although there are several methods for selecting and estimating information anomalies of the power system, there is no platform and method applicable to all electrical equipment of the power system. Summary of the Invention

[0005] To solve the defects in the prior art, the present invention proposes an information management device and analysis method for a power system based on artificial intelligence, effectively avoiding the defects in the prior art that the information anomalies of the power system in different positions and different states are complex, latent, diversified, and are affected and even confused by complex factors, and there is no platform and method for selecting and estimating information anomalies of the power system applicable to all electrical equipment of the power system.

[0006] The present invention adopts the following technical solutions.

[0007] An information analysis method for a power system based on artificial intelligence, comprising:

[0008] The sensing component collects the operating parameters of the power equipment and transmits them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module analyzes and cleans the information of the power system. According to the information of the power system after analysis and cleaning compared with the predefined critical quantity, if the information of the power system is higher than the predefined critical quantity, the processing module determines that the power system is operating abnormally.

[0009] A method for analyzing and cleaning the information of the power system includes:

[0010] Step1: Obtain the information of the source power system for digitally determining information anomalies; here, the information of the source power system includes various types of operating parameters and the parameter values of each operating parameter.

[0011] Step2: Determine the base value according to the parameter value of each operating parameter.

[0012] Step3: Determine the abnormal minimum threshold according to the base value and the parameter value of each operating parameter.

[0013] Step4: Determine the abnormal quantity value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal minimum threshold.

[0014] Step5: Determine the additional key measurement value of each operating parameter according to the abnormal quantity value of each operating parameter.

[0015] Further, the information of the source power system is the operating parameters of the power equipment transmitted by the sensing component to the processing module.

[0016] Further, the categories of operating parameters include the voltage values, current values, and power values of different power equipment.

[0017] Further, Step5 specifically includes:

[0018] Step5-1: Select one of the operating parameters that meet the predefined state and determine it as the reference key degree.

[0019] Step5-2: Determine the action amount of each operating parameter in each power equipment, and determine the reference value of each action amount according to the reference key degree.

[0020] Step5-3: Determine the instant key degree of each of the other operating parameters except those registered as the reference key degree according to the reference key degree and the reference value.

[0021] Step5-4: Determine the additional key measurement value of each operating parameter according to the reference key degree of the operating parameter that meets the predefined state, the instant key degree of each of the other operating parameters except those registered as the reference key degree, and the abnormal quantity value of each operating parameter.

[0022] Further, the preset state is the operating parameter with the largest parameter value in the cluster with the most parameters after clustering all the operating parameters using the K-means method; the benchmark criticality is one;

[0023] The benchmark criticality is the detection parameter benchmark criticality, and the criticality of the specifically set detection target parameter is one.

[0024] Further, the operation equation for the action amount of each operating parameter is:

[0025]

[0026] Here, D jk is the action amount of the k-th operating parameter of the j-th power device; b jk is the abnormal value of the k-th operating parameter of the j-th power device; n is the total amount of all operating parameters.

[0027] Further, the abnormal value of the k-th operating parameter of the j-th power device is the quotient obtained by dividing the base value by the parameter value of this operating parameter.

[0028] Further, the operation equation for the reference value of each action amount is:

[0029]

[0030] Here, T jk is the reference value of the k-th operating parameter of the j-th power device, zd(D j ) is the action amount of the operating parameter with the lowest action amount value among all operating parameters; zg(D j ) is the action amount of the operating parameter with the highest numerical value among all operating parameters.

[0031] Further, the operation equation for the instant criticality of each operating parameter is:

[0032]

[0033] Here, X jk is the instant criticality of the k-th operating parameter of the j-th power device.

[0034] Further, the operation equation for the attached criticality measurement value of each operating parameter is:

[0035] Xw jk =b jk *X jk ;

[0036] Here, Xw jk is the attached criticality measurement value of the k-th operating parameter of the j-th power device.

[0037] Further, Step 2 specifically includes:

[0038] Step 2-1: Based on the parameter values of each operating parameter, determine the difference obtained by subtracting the parameter value of each operating parameter from the median of the parameter values of each operating parameter; based on the difference obtained by subtracting the parameter value of each operating parameter from the median of the parameter values of each operating parameter, obtain the current operating parameter group after removing the dispersion parameters within the parameter value of each operating parameter;

[0039] Step 2-2: Based on the current operating parameter group, determine the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group; based on the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group, obtain the next operating parameter group after removing the dispersion parameters within the current operating parameter group;

[0040] Step 2-3: Until there are no dispersion parameters in the next operating parameter group, determine the median of this operating parameter group as the base value.

[0041] Further, the operation equation of the abnormal minimum threshold is:

[0042] Db = C + L * E p ;

[0043] Here, Db is the abnormal minimum threshold of the operating parameter; p is the number of cycles; L is the value-taking factor; C is the base value; E p is the difference of the operating parameter group after the p-th cycle.

[0044] The value of L is determined by itself according to the specific state of the information of the source power system, and the value-taking range of L is two to three.

[0045] Further, the method for determining the abnormal value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal minimum threshold specifically includes:

[0046] According to the ratio obtained by dividing the parameter value of each operating parameter by the abnormal minimum threshold, determine whether the ratio is higher than 1;

[0047] When the ratio is not less than 1, set the operating parameter value as the information for analyzing and cleaning the power system;

[0048] When the ratio is less than 1, remove the operating parameter value.

[0049] An information management device for a power system based on artificial intelligence, comprising:

[0050] The sensing component is connected to the processing module. The sensing component is used to collect the operating parameters of the power equipment and transmit them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module is used to analyze and clean the information of the power system. According to the information of the power system after analysis and cleaning compared with the predefined critical quantity, if the information of the power system is higher than the predefined critical quantity, the processing module determines that the power system is operating abnormally;

[0051] The modules running on the processing module include:

[0052] The acquisition module, which is used to acquire the information of the source power system and is used to digitally determine information anomalies; here, the information of the source power system includes various types of operating parameters and the parameter values of each operating parameter;

[0053] The first determination module, which is used to determine the base value according to the parameter values of each operating parameter;

[0054] The second determination module, which is used to determine the lowest anomaly threshold according to the base value and the parameter values of each operating parameter;

[0055] The third determination module, which is used to determine the anomaly value of each operating parameter according to the ratio of the parameter value of each operating parameter to the lowest anomaly threshold;

[0056] The fourth determination module, which is used to determine the additional key metric value of each operating parameter according to the anomaly value of each operating parameter.

[0057] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0058] By removing the scattered parameters in the information of the source power system, determining the numerical group to be processed from the information of the source power system, and calculating the base value and the lowest anomaly threshold; then, by determining the additional key metric value of each operating parameter through the anomaly value, the centralization of the numerical group benchmark to be processed is achieved, the disturbance is removed, and the accuracy of analyzing and cleaning the information of the power system is improved, which is applicable to the selection of abnormal values of the operating parameters of all power equipment in the power system. Brief Description of the Drawings

[0059] Figure 1 is the flowchart of the method for analyzing the information of the power system based on artificial intelligence described in the present invention;

[0060] Figure 2 is the partial structure diagram of the information management device of the power system based on artificial intelligence described in the present invention. Detailed Embodiments

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will, in conjunction with the accompanying drawings in the embodiments of the present invention, clearly and completely describe the technical solutions of the present invention. The embodiments described in this application are only partial embodiments of the present invention, rather than all embodiments. According to the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] As Figure 1 shown, a method for information analysis of a power system based on artificial intelligence according to the present invention includes:

[0063] The sensing component collects the operating parameters of the power equipment and transmits them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module analyzes and cleans the information of the power system. According to the information of the power system after analysis and cleaning compared with the predefined critical amount, if the information of the power system is higher than the predefined critical amount, the processing module determines that the power system is operating abnormally.

[0064] The method for analyzing and cleaning the information of the power system includes:

[0065] Step1: Obtain the information of the source power system for digitally determining information anomalies; here, the information of the source power system includes various types of operating parameters and the parameter values of each operating parameter.

[0066] Step2: Determine the base value according to the parameter value of each operating parameter.

[0067] Step3: Determine the abnormal minimum threshold according to the base value and the parameter value of each operating parameter.

[0068] Step4: Determine the abnormal value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal minimum threshold.

[0069] Step5: Determine the additional key metric value of each operating parameter according to the abnormal value of each operating parameter.

[0070] By removing the scattered parameters of the information of the source power system, determining the value group to be processed within the information of the source power system, and thereby calculating the base value and the abnormal minimum threshold; then determining the additional key metric value of each operating parameter through the abnormal value to achieve the centralization of the value group benchmark to be processed, remove disturbances, and improve the accuracy of analyzing and cleaning the information of the power system, which is applicable to the selection of abnormal values of the operating parameters of all power equipment in the power system.

[0071] In a preferred but non-limiting embodiment of the present invention, the information of the source power system is the operating parameters of the power equipment transmitted by the sensing component to the processing module.

[0072] In a preferred but non-limiting embodiment of the present invention, the categories of operating parameters include the voltage values, current values, and power values of different power devices.

[0073] In a preferred but non-limiting embodiment of the present invention, Step5 specifically includes:

[0074] Step5-1: Select one of the operating parameters that meet the preset state and determine it as the reference criticality;

[0075] Step5-2: Determine the action amount of each operating parameter in each power device, and determine the reference value of each action amount according to the reference criticality;

[0076] Step5-3: According to the reference criticality and the reference value, determine the instantaneous criticality of each of the other operating parameters that are not registered as the reference criticality;

[0077] Step5-4: According to the reference criticality of the operating parameters that meet the preset state, the instantaneous criticality of each of the other operating parameters that are not registered as the reference criticality, and the abnormal value of each operating parameter, determine the additional criticality measurement value of each operating parameter.

[0078] From Step5-1 to Step5-4, it successively completes the determination and exploration of parameter criticality (that is, determining the reference criticality), determining the action amount of each operating parameter of each power device, benchmarking the action amount (that is, performing a normalization operation to determine each reference value according to the reference criticality), parameter additional criticality (that is, instantaneous criticality), and centralization of logarithmic values (that is, determining the additional criticality measurement value), achieving the benchmarking of logarithmic values, improving the accuracy of analyzing and cleaning the information of the power system, and being applicable to the abnormal selection of all power devices.

[0079] In a preferred but non-limiting embodiment of the present invention, the preset state is the operating parameter with the largest parameter value in the cluster with the most parameters after clustering all operating parameters using the K-means method; the reference criticality is one;

[0080] The reference criticality is the exploration parameter reference criticality, and the specifically set exploration target parameter criticality is one.

[0081] In a preferred but non-limiting embodiment of the present invention, the calculation equation for the action amount of each operating parameter is:

[0082]

[0083] Here, D jk is the action amount of the kth operating parameter of the jth power device; b jkis the inverse constant value of the k-th operating parameter of the j-th power device; n is the total number of all operating parameters.

[0084] In a preferred but non-limiting embodiment of the present invention, the inverse constant value of the k-th operating parameter of the j-th power device is the quotient obtained by dividing the base value by the parameter value of this operating parameter.

[0085] In a preferred but non-limiting embodiment of the present invention, the operation equation of the reference value of each action quantity is:

[0086]

[0087] Here, T jk is the reference value of the k-th operating parameter of the j-th power device, and the value range of T jk is from zero to one; zd(D j ) is the action quantity of the operating parameter with the lowest action quantity value within the action quantities of all operating parameters; zg(D j ) is the action quantity of the operating parameter with the highest numerical value among the action quantities of all operating parameters.

[0088] In a preferred but non-limiting embodiment of the present invention, the operation equation of the immediate criticality of each operating parameter is:

[0089]

[0090] Here, X jk is the immediate criticality of the k-th operating parameter of the j-th power device.

[0091] In a preferred but non-limiting embodiment of the present invention, the operation equation of the attached criticality measurement value of each operating parameter is:

[0092] Xw jk = b jk *X jk ;

[0093] Here, Xw jk is the attached criticality measurement value of the k-th operating parameter of the j-th power device.

[0094] In a preferred but non-limiting embodiment of the present invention, Step2 specifically includes:

[0095] Step2-1: According to the parameter value of each operating parameter, determine the difference obtained by subtracting the parameter value of each operating parameter from the median of the parameter value of each operating parameter; based on the difference obtained by subtracting the parameter value of each operating parameter from the median of the parameter value of each operating parameter, obtain the current operating parameter group after removing the dispersion parameters within the parameter value of each operating parameter.

[0096] Step2-2: Determine the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group; based on the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group, obtain the next operating parameter group after removing the scattered parameters within the current operating parameter group;

[0097] Step2-3: Until there are no scattered parameters in the next operating parameter group, determine the median of this operating parameter group as the base value.

[0098] That is, take the value obtained by subtracting 3 times the difference from the median as the lowest threshold of the current operating parameter group, take the value obtained by adding 3 times the difference to the median as the highest threshold of the current operating parameter group, and take the values of each operating parameter whose parameter values are between the lowest threshold and the highest threshold of the current operating parameter group as the next operating parameter group. Use subtracting 3 times the difference or adding 3 times the difference from the median to remove the outliers of all types of operating parameters, and the ratio of the outliers to all operating parameters is from forty-six per thousand to two point six seven percent, which means that removing the outliers basically has little disturbance to the parameters. Therefore, using subtracting 3 times the difference or adding 3 times the difference from the median to calculate the base value is the most accurate.

[0099] In a preferred but non-limiting embodiment of the present invention, the operation equation for the abnormal lowest threshold is:

[0100] Db = C + L * E p ;

[0101] Here, Db is the abnormal lowest threshold of the operating parameter; p is the number of cycles; L is the value-taking factor; C is the base value; E p is the difference of the operating parameter group after the pth cycle.

[0102] The value of L is determined by itself according to the specific state of the information of the source power system, and the value range of L is from two to three.

[0103] Performing cyclic improvement using the base value can reduce the negative effect of outliers and more accurately reflect the dispersion level of the parameters.

[0104] In a preferred but non-limiting embodiment of the present invention, the method for determining the abnormal value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal lowest threshold specifically includes:

[0105] Determine whether the ratio obtained by dividing the parameter value of each operating parameter by the abnormal lowest threshold is higher than 1;

[0106] When the ratio is not less than 1, set the operating parameter value as the information of the analyzed and cleaned power system;

[0107] When the ratio is less than 1, remove the value of this operating parameter.

[0108] Removing the values of abnormal operating parameters strengthens the improvement.

[0109] As Figure 2 shown, an information management device for a power system based on artificial intelligence according to the present invention includes:

[0110] The sensing component is connected to the processing module. The sensing component is used to collect the operating parameters of the power equipment and transmit them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module is used to analyze and clean the information of the power system. According to the information of the power system after analysis and cleaning compared with the predefined critical quantity, if the information of the power system is higher than the predefined critical quantity, the processing module determines that the power system is operating abnormally;

[0111] The modules running on the processing module include:

[0112] An acquisition module, which is used to acquire the information of the source power system and is used to digitally determine information anomalies; here, the information of the source power system includes various types of operating parameters and the parameter values of each operating parameter;

[0113] A first determination module, which is used to determine the base value according to the parameter value of each operating parameter;

[0114] A second determination module, which is used to determine the abnormal minimum threshold according to the base value and the parameter value of each operating parameter;

[0115] A third determination module, which is used to determine the abnormal value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal minimum threshold;

[0116] A fourth determination module, which is used to determine the additional key metric value of each operating parameter according to the abnormal value of each operating parameter.

[0117] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0118] By removing the scattered parameters in the information of the source power system, determining the value group to be processed in the information of the source power system, and calculating the base value and the abnormal minimum threshold therefrom; then determining the additional key metric value of each operating parameter through the abnormal value, to achieve the centralization of the value group benchmark to be processed, remove the disturbance, improve the accuracy of analyzing and cleaning the information of the power system, and is applicable to the selection of abnormal values of the operating parameters of all power equipment in the power system.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An information analysis method for power system based on artificial intelligence, characterized in that: include: The sensor component collects the operating parameters of the power equipment and transmits them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module analyzes and cleans the information of the power system. According to the comparison between the analyzed and cleaned power system information and the pre-defined critical quantity, if the power system information is higher than the pre-defined critical quantity, the processing module determines that the power system is operating abnormally. The method of information analysis and cleaning of the power system includes: Step 1: Obtain information from the source power system to digitally identify abnormal information; Here, the information of the source power system includes multiple categories of operating parameters and parameter values ​​of each operating parameter; Step 2: Determine the base value based on the parameter value of each operating parameter; Step 3: Determine the lowest abnormal threshold based on the base value and the parameter value of each operating parameter; Step 4: Determine the abnormal value of each operating parameter based on the ratio of the parameter value of each operating parameter to the lowest abnormal threshold; Step 5: According to the abnormal value of each operating parameter, determine the key measurement value of each operating parameter.

2. The information analysis method of the power system based on artificial intelligence according to claim 1 is characterized in that: The information of the source power system is the operating parameters of the power equipment transmitted by the sensor component to the processing module; The categories of operating parameters include voltage, current and power values ​​of different electrical equipment.

3. The information analysis method of the power system based on artificial intelligence according to claim 2 is characterized in that: Step 5 specifically includes: Step 5-1: Select one of the operating parameters that meets the pre-set state and identify it as the benchmark criticality; Step 5-2: Identify the action amount of each operating parameter in each power equipment, and determine the reference value of each action amount according to the reference criticality; Step 5-3: Based on the baseline criticality and the baseline value, determine the immediate criticality of each operating parameter other than the registered baseline criticality; Step 5-4: Determine the additional critical metric value of each operating parameter based on the baseline criticality of the operating parameter that meets the pre-set status, the instantaneous criticality of each operating parameter other than the registered baseline criticality, and the abnormal value of each operating parameter.

4. The information analysis method of the power system based on artificial intelligence according to claim 3 is characterized in that: The pre-set state is the operating parameter with the largest parameter value in the cluster with the most parameters after clustering all the operating parameters using the K-means method; the benchmark criticality is one; The base criticality is the base criticality of the search parameter, and the criticality of the parameter specifically set for the search purpose is one.

5. The information analysis method of the power system based on artificial intelligence according to claim 4 is characterized in that: The operational equation for the action of each operating parameter is: Here, D jk is the action quantity of the kth operating parameter of the jth power equipment; b jk is the abnormal value of the kth operating parameter of the jth power equipment; n is the total amount of all operating parameters; The abnormal value of the kth operating parameter of the jth power equipment is the quotient obtained by dividing the base value by the parameter value of the operating parameter.

6. The information analysis method of the power system based on artificial intelligence according to claim 5 is characterized in that: The calculation equation of the reference value of each action is: Here, T jk is the reference value of the kth operating parameter of the jth power equipment, zd(D j ) is the action of the operating parameter with the lowest value among all the action of the operating parameters; zg(D j ) is the action of the operating parameter with the highest value among all the actions of the operating parameters; The operational equation for the immediate criticality of each operating parameter is: Here, X jk It is the immediate criticality of the kth operating parameter of the jth power equipment.

7. The information analysis method of the power system based on artificial intelligence according to claim 6 is characterized in that: The calculation equation for the key metric value attached to each operating parameter is: Xw jk =b jk *X jk ; Here, Xw jk It is the attached key metric value of the kth operating parameter of the jth power equipment.

8. The information analysis method of the power system based on artificial intelligence according to claim 7 is characterized in that: Step 2 specifically includes: Step 2-1: According to the parameter value of each operating parameter, determine the difference obtained by subtracting the parameter value of each operating parameter from the median value of the parameter value of each operating parameter; According to the difference obtained by subtracting the parameter value of each operating parameter from the median value of each operating parameter, the scattered parameters in the parameter value of each operating parameter are removed to obtain the current operating parameter group; Step 2-2: According to the current operating parameter group, determine the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group; according to the difference obtained by subtracting the current operating parameter group from the median of the current operating parameter group, remove the scattered parameters in the current operating parameter group to obtain the next operating parameter group; Step 2-3: Until there are no scattered parameters in the next operating parameter group, the median of the operating parameter group is determined as the base value.

9. The information analysis method of the power system based on artificial intelligence according to claim 8 is characterized in that: The calculation equation of the abnormal minimum threshold is: Db=C+L*E p ; Here, Db is the lowest abnormal threshold of the operating parameter; p is the cycle frequency; L is the value factor; C is the base value; E p is the difference in the operating parameter group after the pth cycle. The value of L is determined based on the specific state of the information of the source power system, and the value range of L is 2 to 3; The method for determining the abnormal value of each operating parameter based on the ratio of the parameter value of each operating parameter to the abnormal minimum threshold value specifically includes: According to the ratio obtained by dividing the parameter value of each operating parameter by the minimum abnormal threshold, determine whether the ratio is higher than 1; When the ratio is not less than 1, the operating parameter value is set to the information of the power system after the analysis and cleaning; When the ratio is lower than 1, the operating parameter value is removed.

10. An information management device for a power system based on artificial intelligence, characterized in that: include: The sensor component is connected to the processing module. The sensor component is used to collect the operating parameters of the power equipment and transmit them to the processing module. The operating parameters of the power equipment are the information of the power system. The processing module is used to analyze and clean the information of the power system. According to the comparison between the analyzed and cleaned power system information and the pre-defined critical quantity, if the power system information is higher than the pre-defined critical quantity, the processing module determines that the power system is operating abnormally. The modules running on the processing module include: An acquisition module is used to acquire information of a source power system for digitally identifying information abnormality; here, the information of the source power system includes multiple categories of operating parameters and parameter values ​​of each operating parameter; A recognition module 1, which is used to recognize a base value according to a parameter value of each operating parameter; Identification module 2, which is used to identify the lowest abnormal threshold value based on the base value and the parameter value of each operating parameter; The identification module 3 is used to identify the abnormal value of each operating parameter according to the ratio of the parameter value of each operating parameter to the abnormal minimum threshold value; Identification module four is used to identify the attached key measurement value of each operating parameter based on the abnormal value of each operating parameter.

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