CN117347791B - Power grid fault online identification system and method based on big data - Google Patents
Power grid fault online identification system and method based on big data Download PDFInfo
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Abstract
The invention discloses an online power grid fault identification system and method based on big data, and belongs to the technical field of power fault identification. The invention solves the problem of poor feasibility of the existing identification method, acquires power data through the power SCADA system, and generates power operation work logs from the power data; the electric power operation work log is led into a power grid fault prediction model for training, so that a power grid fault prediction report is obtained; comparing the real-time power data in the prediction report with the historical normal monitoring data, so as to obtain whether the power grid at the position fails or not; the first-level fault signal, the second-level fault signal and the zero-level fault signal are sequentially divided according to the difference of the comparison values, and red light early warning, yellow light early warning and green light notification are sequentially sent out through the cloud platform; therefore, the function of online identifying faults of the power grid is realized, the efficiency of online identifying the faults of the power grid is ensured, and the feasibility of the existing power fault early warning identification method is improved.
Description
Technical Field
The invention relates to the technical field of power failure recognition, in particular to an on-line power grid failure recognition system and method based on big data.
Background
With the rapid development of internet technology, data has become one of the important resources for the future development of enterprises; the big data technology plays an important role in the power failure recognition and intelligent early warning technology, so that the intelligent power grid management technology also becomes a core link of a power grid operation system and is a powerful support and power for promoting the intelligent development of the power grid; in the intelligent power grid management technology, huge information flow is generated in the system every moment, and a huge amount of data resources can be obtained by performing calculation and analysis on the system and effectively processing various data; and then the large data technology is used for deep mining of the power grid management data information, so that a large amount of effective information in the power grid can be found, hidden fault factors in the power grid can be found in time, and more effective information is provided for normal operation of the power grid.
However, with the rapid development of the power industry, the number and scale of power equipment are continuously enlarged, and the requirements for early warning and identifying power faults are increasingly high; at present, the existing traditional power failure early warning method cannot meet the increasing demand, so that the feasibility of the existing power failure early warning and identifying method is reduced.
Therefore, the existing requirements are not met, and an online power grid fault identification system and method based on big data are provided.
Disclosure of Invention
The invention aims to provide an on-line power grid fault identification system and method based on big data, wherein the electric power SCADA system is used for collecting electric power data, generating an electric power operation work log and guiding the electric power operation work log into a grid fault prediction model for training, so as to obtain a grid fault prediction report; comparing the real-time power data in the prediction report with the historical normal monitoring data, so as to obtain whether the power grid at the position fails or not; the first-level fault signal, the second-level fault signal and the zero-level fault signal are sequentially divided according to the difference of the comparison values, and red light early warning, yellow light early warning and green light notification are sequentially sent out through the cloud platform; therefore, the function of online identifying the faults of the power grid is realized, and the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: an on-line power grid fault identification system based on big data, comprising:
The data acquisition module is used for acquiring power data of the power equipment based on a sensor deployed in the power SCADA system, so that data parameters of the power equipment are obtained, and the acquired power data are sent to the data processing unit for preprocessing;
the data processing unit is used for receiving the power data sent by the data acquisition module, preprocessing and cleaning the power data, obtaining processed power data, and sending the processed power data to the data checking unit;
The model training unit is used for creating a data training model and training the model based on the power data collected in the past in the power SCADA system, so as to obtain an optimized power grid fault prediction model;
the data checking unit is used for receiving the power data sent by the data processing unit, and accessing the power data into the power grid fault prediction model for checking, so that a power grid fault prediction report is obtained, the fault position is positioned by combining the topological structure of the actual power grid, and the prediction report and the fault position are transmitted to the cloud platform;
the cloud platform is used for receiving the prediction report sent by the data inspection unit and sending a fault early warning notification based on the content of the prediction report;
Wherein, the data acquisition module further includes:
The real-time receiving module is used for receiving data information acquired by a sensor deployed in the electric SCADA system in real time;
The time interval acquisition module is used for acquiring the data acquisition time interval of the sensor deployed in the power-based SCADA system;
The time threshold setting module is used for setting a first time threshold and a second time threshold according to the data acquisition time interval, wherein the first time threshold and the second time threshold are obtained through the following formula:
Wherein Ty1 and Ty2 represent a first time threshold and a second time threshold, respectively; ty01 and Ty02 represent an initial first time threshold and a second time threshold, respectively; c01 represents the data volume formed by the data acquired by the sensor in one acquisition period with the sensor acquisition time interval smaller than the initial first time threshold; c02 represents the amount of data formed by the data acquired by the sensor in one acquisition period, wherein the acquisition time interval of the sensor is equal to or greater than the initial first time threshold, but is smaller than the initial second time threshold; c03 represents the data amount formed by the data acquired by the sensor in one acquisition period, wherein the acquisition time interval of the sensor is equal to or greater than the initial second time threshold; cz represents the total data volume based on one acquisition cycle of sensors deployed in the power SCADA system; e represents a constant; n represents the number of sensors; t i denotes a data acquisition time interval corresponding to the ith sensor;
The data set dividing module is used for dividing the data information acquired by the sensor according to the first time threshold and the second time threshold to obtain a first data set, a second data set and a third data set;
The data transmission time interval setting module is used for setting corresponding data transmission time intervals of the first data set, the second data set and the third data set, and transmitting data corresponding to the first data set, the second data set and the third data set to the data processing unit according to the data transmission time intervals;
the first data set is formed by data acquired by a sensor with a sensor acquisition time interval smaller than a first time threshold;
The second data set is formed by data acquired by the sensor, wherein the acquisition time interval of the sensor is equal to or greater than the first time threshold, but is smaller than the second time threshold;
The third data set is formed by data acquired by the sensor, wherein the acquisition time interval of the data acquired by the sensor is equal to or greater than the second time threshold;
The data transmission time interval setting module includes:
The first information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the first data set as first information data;
A first data transmission time interval setting module, configured to set a first data transmission time interval corresponding to the first data set according to the first information data; the first data transmission time interval is obtained through the following formula:
Wherein Tc1 represents a first data transmission time interval corresponding to the first data set; m1 represents the number of sensors corresponding to the first data set; t01 i represents a data acquisition time interval of the ith sensor corresponding to the first data set; ty1 represents a first time threshold; cm01 represents the acquired data amount of one acquisition period corresponding to the first data set; cz represents the total data volume based on one acquisition cycle of sensors deployed in the power SCADA system; tc01 represents an initial first data transmission time interval corresponding to the first data set;
the second information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the second data set to be used as second information data;
A second data transmission time interval setting module, configured to set a second data transmission time interval corresponding to the second data set according to the second information data; the second data transmission time interval is obtained through the following formula:
Wherein Tc2 represents a second data transmission time interval corresponding to the second data set; m2 represents the number of sensors corresponding to the second data set; t02 i represents a data acquisition time interval of the ith sensor corresponding to the second data set; ty1 represents a first time threshold; ty2 represents a second time threshold; cm02 represents the acquired data amount of one acquisition cycle corresponding to the second data set; cz represents the total data volume based on one acquisition cycle of sensors deployed in the power SCADA system; tc02 represents an initial second data transmission time interval corresponding to the second data set;
The third information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the third data set to be used as third information data;
a third data transmission time interval setting module, configured to set a third data transmission time interval corresponding to the third data set according to the third information data; the third data transmission time interval is obtained through the following formula:
wherein Tc3 represents a third data transmission time interval corresponding to a third data set; m3 represents the number of sensors corresponding to the third data set; t03i represents a data acquisition time interval of the ith sensor corresponding to the third data set; ty2 represents a second time threshold; cm03 represents the acquired data amount of one acquisition period corresponding to the third data set; cz represents the total data volume based on one acquisition cycle of sensors deployed in the power SCADA system; tc03 represents an initial third data transmission time interval corresponding to the third data set.
Further, the sensor deployed in the power-based SCADA system in the data acquisition module includes: photoelectric sensors, infrared sensors, speed sensors, acceleration sensors, temperature sensors, humidity sensors, magnetic field sensors, GIS sensors, vibration sensors, ripple sensors, RFID tags, GPS devices, radiation sensors, heat-sensitive sensors, and energy consumption sensors.
Further, the data processing unit includes:
The preprocessing module is used for preprocessing and cleaning the power data and generating corresponding power operation work logs from the processed power data;
The feature extraction module is used for extracting parameter features of the content in the power operation work log so as to obtain a parameter feature set; the content for extracting the parameter features comprises the following steps: the duration of the fault exceeds 180 seconds, the location of the fault has been identified, and the cause of the fault has been determined;
And the data storage module is used for storing the power operation work log and the parameter feature set according to time sequence, thereby being used as a power data text base.
Further, the model training unit includes:
the sample acquisition module is based on the past parameter feature set of the power SCADA system stored in the data processing unit, and is used as a feature sample set;
The model creation module is used for guiding the characteristic sample set into the support vector machine for training and optimizing parameters of the support vector machine, so that a power grid fault prediction model is built.
Further, the data verification unit includes:
The data reading module is used for receiving the power operation work log sent by the data processing unit in real time, and importing the power operation work log into the power grid fault prediction model for training, so that a power grid fault prediction result is obtained;
The fault output module is used for outputting a prediction report of the power grid fault prediction model, analyzing various abnormal states of the power quality and finding out abnormal points;
And the wireless transmission module is used for transmitting the output prediction report to the cloud platform based on the wireless communication technology for monitoring and early warning.
Further, the fault output module analyzes the electric energy abnormal state for the prediction report, specifically:
Comparing the current monitoring data with the historical normal monitoring data to obtain whether the power distribution node has faults or not, thereby realizing the positioning of the faults; and performing fault grading on the power grid through a fault influence factor analysis result to generate corresponding early warning reminding.
Further, the power grid is classified according to the fault influence factor analysis result, and corresponding early warning reminding is generated, specifically:
When the analysis result shows that the power data greatly exceeds the historical normal monitoring data, judging the power data as a fault dangerous signal, generating a first-level fault signal according to the fault dangerous signal, and transmitting the first-level fault signal to a cloud platform to send out a red light fault early warning notice; when the analysis result shows that the electric power data is less than the historical normal monitoring data, judging that the electric power data is a swing fault signal, generating a secondary fault signal according to the swing fault signal, and transmitting the signal to a cloud platform to send out yellow light fault early warning notification; and when the analysis result shows that the power data does not exceed the historical normal monitoring data, judging as a safety and stability signal, generating a zero-level fault signal according to the safety and stability signal, transmitting the zero-level fault signal to a cloud platform to send out a green light notice, and respectively generating corresponding text samples and transmitting the text samples to the cloud platform for output description.
The implementation method of the power grid fault online identification system based on big data comprises the following steps:
S1, collecting power data of power equipment through a sensor deployed in an electric SCADA system, and preprocessing and cleaning the power data to generate an electric operation work log;
S2, extracting a characteristic set of the historical power data, and importing the characteristic set serving as a characteristic sample set into a support vector machine for training, so that a power grid fault prediction model is obtained;
S3, importing the electric power operation work log generated in the S1 into a power grid fault prediction model for training, and outputting a power grid fault prediction report for analysis;
S4, comparing the real-time power data with the historical normal monitoring data to obtain whether the power grid at the position fails or not, and dividing a failure level according to the difference of comparison values;
S5, dividing the power data with larger comparison value difference into first-level fault signals, and sending out red light early warning through the cloud platform; dividing the electric power data with the common comparison value gap into secondary fault signals, and sending out yellow light early warning through a cloud platform; and dividing the power data with the comparison value not generating the gap into zero-order fault signals, and sending a green light notice through the cloud platform, so that the efficiency of online identification of the power grid faults is ensured.
Compared with the prior art, the invention has the beneficial effects that:
The invention collects power data through the power SCADA system and generates power operation work logs from the power data; the electric power operation work log is led into a power grid fault prediction model for training, so that a power grid fault prediction report is obtained; comparing the real-time power data in the prediction report with the historical normal monitoring data, so as to obtain whether the power grid at the position fails or not; the first-level fault signal, the second-level fault signal and the zero-level fault signal are sequentially divided according to the difference of the comparison values, and red light early warning, yellow light early warning and green light notification are sequentially sent out through the cloud platform; therefore, the function of online identifying faults of the power grid is realized, the efficiency of online identifying the faults of the power grid is ensured, and the feasibility of the existing power fault early warning identification method is improved.
Drawings
FIG. 1 is a diagram of an online power grid fault identification system based on big data;
fig. 2 is a flowchart of the online power grid fault identification method based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that the number and the scale of the existing power equipment are continuously enlarged, the requirements for early warning and identifying the power faults are higher and higher; the conventional power failure early warning method cannot meet the increasing demand, so that the technical problem of feasibility of the existing power failure early warning and identifying method is reduced, referring to fig. 1-2, the present embodiment provides the following technical scheme:
An on-line power grid fault identification system based on big data, comprising:
The data acquisition module is used for acquiring power data of the power equipment based on a sensor deployed in the power SCADA system, so that data parameters of the power equipment are obtained, and the acquired power data are sent to the data processing unit for preprocessing; specifically, the electric SCADA system is: a data acquisition and monitoring control system; in the power system, on-site power equipment can be monitored and controlled to realize various functions such as data acquisition, equipment control, measurement, parameter adjustment, various signal alarms and the like; the method comprises the steps that data acquisition is carried out on electric equipment to be monitored through various sensors deployed in an electric SCADA system, so that various data of operation equipment in an electric power grid are obtained; wherein, the sensor deployed in the power SCADA based system in the data acquisition module specifically comprises: photoelectric sensors, infrared sensors, speed sensors, acceleration sensors, temperature sensors, humidity sensors, magnetic field sensors, gas sensor, vibration sensors, ripple sensors, RFID tags, GPS devices, radiation sensors, heat sensitive sensors, and energy consumption sensors.
The data processing unit is used for receiving the power data sent by the data acquisition module, preprocessing and cleaning the power data, obtaining processed power data, and sending the processed power data to the data checking unit; specifically, the electric power operation log is formed by receiving electric power data acquired by the electric power SCADA system, removing and correcting invalid data, performing data cleaning, data formatting and data normalization on the residual data, and storing the residual data in a document.
The model training unit is used for creating a data training model and training the model based on the power data collected in the past in the power SCADA system, so as to obtain an optimized power grid fault prediction model; specifically, a characteristic sample set is formed by screening out high-quality power data collected in the past by the power SCADA system and extracting a characteristic set of the power data; and importing the characteristic sample set into a support vector machine for training, thereby obtaining a power grid fault prediction model, and predicting the fault point of the real-time power data through the power grid fault prediction model.
The data checking unit is used for receiving the power data sent by the data processing unit, and accessing the power data into the power grid fault prediction model for checking, so that a power grid fault prediction report is obtained, the fault position is positioned by combining the topological structure of the actual power grid, and the prediction report and the fault position are transmitted to the cloud platform; specifically, by taking the power grid fault prediction model as a reference, real-time power data acquired by the power SCADA system is generated into a power operation work log, and the power operation work log is led into the power grid fault prediction model for training, so that a power grid fault prediction report is obtained, the position of a power grid fault is further positioned, and early warning notification is made through a cloud platform, so that the feasibility of the existing power fault early warning and identifying method is improved.
The cloud platform is used for receiving the prediction report sent by the data inspection unit and sending a fault early warning notification based on the content of the prediction report; specifically, the cloud platform is: the monitoring platform consists of a display terminal and a warning lamp, and is connected with the data checking unit through a wireless communication technology; the fault positioning result can be displayed through the display terminal, and the early warning result can be subjected to lamplight warning through the warning lamp; wherein, the warning light divide into in proper order: red, yellow and green lights, three colors representing a primary fault, a secondary fault and a zero-order fault in sequence, so as to remind the presence user of knowing the dangerous degree of each fault point.
The working principle of the above matters is as follows: collecting power data through a power SCADA system, and generating power operation work logs from the power data; the electric power operation work log is led into a power grid fault prediction model for training, so that a power grid fault prediction report is obtained; comparing the real-time power data in the prediction report with the historical normal monitoring data, so as to obtain whether the power grid at the position fails or not; the first-level fault signal, the second-level fault signal and the zero-level fault signal are sequentially divided according to the difference of the comparison values, and red light early warning, yellow light early warning and green light notification are sequentially sent out through the cloud platform; therefore, the function of online identifying faults of the power grid is realized, the efficiency of online identifying the faults of the power grid is ensured, and the feasibility of the existing power fault early warning identification method is improved.
Specifically, the data acquisition module further includes:
The real-time receiving module is used for receiving data information acquired by a sensor deployed in the electric SCADA system in real time;
The time interval acquisition module is used for acquiring the data acquisition time interval of the sensor deployed in the power-based SCADA system;
The time threshold setting module is used for setting a first time threshold and a second time threshold according to the data acquisition time interval, wherein the first time threshold and the second time threshold are obtained through the following formula:
Wherein T y1 and T y2 represent a first time threshold and a second time threshold, respectively; t y01 and T y02 represent an initial first time threshold and a second time threshold, respectively; c 01 represents the data volume formed by the data acquired by the sensor in one acquisition period with the sensor acquisition time interval smaller than the initial first time threshold; c 02 represents the data volume formed by the data acquired by the sensor in one acquisition period, wherein the acquisition time interval of the sensor is equal to or greater than the initial first time threshold value, but is smaller than the initial second time threshold value; c 03 represents the data amount formed by the data acquired by the sensor in one acquisition period with the sensor acquisition time interval equal to or greater than the initial second time threshold; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; e represents a constant; n represents the number of sensors; t i represents a data acquisition time interval corresponding to the ith sensor;
The data set dividing module is used for dividing the data information acquired by the sensor according to the first time threshold and the second time threshold to obtain a first data set, a second data set and a third data set;
The data transmission time interval setting module is used for setting corresponding data transmission time intervals of the first data set, the second data set and the third data set, and transmitting data corresponding to the first data set, the second data set and the third data set to the data processing unit according to the data transmission time intervals;
the first data set is formed by data acquired by a sensor with a sensor acquisition time interval smaller than a first time threshold;
The second data set is formed by data acquired by the sensor, wherein the acquisition time interval of the sensor is equal to or greater than the first time threshold, but is smaller than the second time threshold;
The third data set is a data set formed by data acquired by the sensor at a sensor acquisition time interval equal to or greater than a second time threshold.
The technical effects of the technical scheme are as follows: the data instantaneity is improved: the real-time property of the data can be better reflected by receiving the data information acquired by the sensor in real time and setting different time thresholds according to the data acquisition time interval and dividing the data information into different data sets. The data with shorter sensor acquisition time interval is transmitted and processed more quickly, which is helpful for monitoring the state of the power system in real time.
Data transmission optimization: the corresponding data transmission time intervals are set according to different data sets, so that data transmission can be managed more effectively, important data can be transmitted timely, and loads on network and system resources are reduced. This helps to improve the efficiency and reliability of data transmission.
The resource utilization efficiency is improved: by intelligently managing data according to the sensor acquisition time interval, system resources can be better utilized, unnecessary data transmission and processing cost is reduced, and therefore the utilization efficiency of the resources is improved.
The data processing efficiency is improved: the data information is divided into different data sets, so that data processing can be performed more pertinently, unnecessary data processing time and resource waste are reduced, and the data processing efficiency is improved.
In summary, according to the technical scheme, different time thresholds are set according to the acquisition time interval and the data volume of the sensor, so that data transmission and processing are optimized, data instantaneity, transmission efficiency and resource utilization efficiency are improved, and better monitoring and management of data of the power system are facilitated. This is of great importance for the performance and reliability of the electrical SCADA system.
Specifically, the data transmission time interval setting module includes:
The first information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the first data set as first information data;
A first data transmission time interval setting module, configured to set a first data transmission time interval corresponding to the first data set according to the first information data; the first data transmission time interval is obtained through the following formula:
Wherein T c1 represents a first data transmission time interval corresponding to the first data set; m 1 represents the number of sensors corresponding to the first dataset; t 01i denotes a data acquisition time interval of the i-th sensor corresponding to the first data set; t y1 denotes a first time threshold; c m01 represents the acquired data quantity of one acquisition period corresponding to the first data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c01 denotes an initial first data transmission time interval corresponding to the first data set;
the second information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the second data set to be used as second information data;
A second data transmission time interval setting module, configured to set a second data transmission time interval corresponding to the second data set according to the second information data; the second data transmission time interval is obtained through the following formula:
Wherein T c2 represents a second data transmission time interval corresponding to the second data set; m 2 represents the number of sensors corresponding to the second dataset; t 02i denotes a data acquisition time interval of the ith sensor corresponding to the second data set; t y1 denotes a first time threshold; t y2 denotes a second time threshold; c m02 represents the acquired data quantity of one acquisition period corresponding to the second data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c02 denotes an initial second data transmission time interval corresponding to the second data set;
The third information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the third data set to be used as third information data;
a third data transmission time interval setting module, configured to set a third data transmission time interval corresponding to the third data set according to the third information data; the third data transmission time interval is obtained through the following formula:
Wherein T c3 represents a third data transmission time interval corresponding to a third data set; m 3 represents the number of sensors corresponding to the third dataset; t 03i denotes a data acquisition time interval of the ith sensor corresponding to the third data set; t y2 denotes a second time threshold; c m03 represents the acquired data quantity of one acquisition period corresponding to the third data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c03 denotes an initial third data transmission time interval corresponding to the third data set.
The technical effects of the technical scheme are as follows: personalized setting of data transmission time intervals: the data transmission time interval of each data set is dynamically set according to the acquisition time interval and the time threshold of the sensor corresponding to the different data sets (the first data set, the second data set and the third data set). In this way, the data of different data sets can have personalized time arrangement during transmission, and the data acquisition characteristics of different sensors can be better adapted.
Flexibility of sensor data transmission: by intelligently adjusting the data transmission time interval according to the sensor acquisition time interval, the flexibility of data transmission can be improved. The data set with shorter acquisition time interval of the sensor can be transmitted more frequently so as to ensure real-time performance, and the data set with longer acquisition time interval can be moderately reduced in transmission times according to the requirement, so that resource consumption is reduced.
The resource utilization efficiency is improved: by intelligently setting the data transmission time interval, network and system resources can be utilized more effectively, unnecessary data transmission and processing cost is reduced, and the utilization efficiency of the resources is improved.
Stability of data transmission: according to the algorithm of data acquisition time interval and time threshold value setting, data transmission can be performed more stably, excessive data congestion or overlong transmission interval is avoided, and therefore stability of data transmission is improved.
In summary, according to the technical scheme, the sensor data transmission time interval is intelligently set, so that individuation and flexibility of data transmission are realized, the resource utilization efficiency and the stability of data transmission are improved, and the transmission and processing of sensor data in the electric SCADA system are better managed.
The data processing unit includes:
The preprocessing module is used for preprocessing and cleaning the power data and generating corresponding power operation work logs from the processed power data; specifically, parameter data of the electric power grid in the monitoring area is collected through the electric power SCADA system, and at the moment, the electric power data comprises: and after the data is cleaned, formatted and normalized, the residual data is cached into a document, so that a corresponding electric power operation work log is generated.
The feature extraction module is used for extracting parameter features of the content in the power operation work log so as to obtain a parameter feature set; the content for extracting the parameter features comprises the following steps: the duration of the fault exceeds 180 seconds, the location of the fault has been identified, and the cause of the fault has been determined; specifically, the feature extraction module can perform feature extraction on the power data acquired by the power SCADA system, so that a parameter feature set can be obtained; if the content in the currently extracted power operation work log is used as a training power grid fault prediction model, the extracted parameter feature set can be classified as a feature sample set.
The data storage module is used for storing the power operation work log and the parameter feature set according to time sequence, and is used as a power data text base; specifically, by storing the generated power operation logs and the generated parameter feature sets in the past and the existing power operation logs, a power data text base is formed, and the collected power data is backed up to prevent loss; meanwhile, high-quality sample parameters can be provided for the later-stage training power grid fault prediction model, so that the prediction precision of the power grid fault prediction model is improved.
The model training unit includes:
the sample acquisition module is used for acquiring any parameter characteristic set based on the previous parameter characteristic set of the electric power SCADA system stored in the data processing unit through a wireless transmission technology, thereby being used as a characteristic sample set for training a power grid fault prediction model.
The model creation module is used for guiding the characteristic sample set into the support vector machine for training and optimizing parameters of the support vector machine so as to build a power grid fault prediction model; specifically, a feature sample set with a fault result is guided into a support vector machine for training, so that a fault prediction sample is obtained, and a training model for predicting the power grid fault is built.
The data inspection unit includes:
The data reading module is used for receiving the power operation work log sent by the data processing unit in real time, and importing the power operation work log into the power grid fault prediction model for training, so that a power grid fault prediction result is obtained; specifically, the currently generated power operation work log is imported into a power grid fault prediction model for training, so that fault and possible fault results are obtained; and judging a specific position according to the output result and the power grid topology result, and transmitting the final result to the cloud platform for early warning notification.
The fault output module is used for outputting a prediction report of the power grid fault prediction model, analyzing various abnormal states of the power quality and finding out abnormal points; the fault output module analyzes the electric energy abnormal state of the prediction report, and specifically comprises the following steps: comparing the current monitoring data with the historical normal monitoring data to obtain whether the power distribution node has faults or not, thereby realizing the positioning of the faults; performing fault grading on the power grid through a fault influence factor analysis result, and generating corresponding early warning reminding; in one embodiment, such as: the distribution points of the power grid in a certain area are as follows: t1, T2, T3..T9, training the monitoring data of the ten monitoring points through a power grid fault prediction model, obtaining that the difference between the monitoring data of T1, T3 and T5 and the historical normal monitoring data of T1, T3 and T5 is larger, the difference between the monitoring data of T2, T4 and T6 and the historical normal monitoring data of T2, T4 and T6 is smaller, the monitoring data of T7, T8 and T9 and the historical normal monitoring data of T7, T8 and T9 are equal, and further obtaining through prediction report analysis: the positions T1, T3, T5, T2, T4 and T6 are fault points or fault points possibly occur, and T7, T8 and T9 are normal areas; and analyzing the prediction report by the method so as to obtain an abnormal point of the power quality.
The power grid is classified according to fault influence factor analysis results to generate corresponding early warning reminding, and the method specifically comprises the following steps:
When the analysis result shows that the power data greatly exceeds the historical normal monitoring data, judging the power data as a fault dangerous signal, generating a first-level fault signal according to the fault dangerous signal, and transmitting the first-level fault signal to a cloud platform to send out a red light fault early warning notice; when the analysis result shows that the electric power data is less than the historical normal monitoring data, judging that the electric power data is a swing fault signal, generating a secondary fault signal according to the swing fault signal, and transmitting the signal to a cloud platform to send out yellow light fault early warning notification; when the analysis result shows that the power data does not exceed the historical normal monitoring data, judging as a safety and stability signal, generating a zero-level fault signal according to the safety and stability signal, transmitting the zero-level fault signal to a cloud platform to send out a green light notice, and respectively generating corresponding text samples and transmitting the text samples to the cloud platform for output description; the above embodiments are followed, for example: the method comprises the steps of importing monitoring data of T1, T2 and T3..T9 power grid distribution points of a certain area into a power grid fault prediction model for training, and analyzing an output prediction report to obtain: the difference between the monitoring data of T1, T3 and T5 and the historical normal monitoring data of T1, T3 and T5 is larger, the monitoring data indicate that faults occur at the monitoring data, so that a fault danger signal is judged, a primary fault signal is generated according to the fault danger signal, and the primary fault signal is transmitted to a cloud platform to send out a red light fault early warning notice; the difference between the monitoring data of T2, T4 and T6 and the historical normal monitoring data of T2, T4 and T6 is smaller, and the difference indicates that faults are possible to occur at the monitoring data, and the monitoring data are judged to be swing fault signals, so that a secondary fault signal is generated and transmitted to a cloud platform to send out yellow lamp fault early warning notification; the monitoring data of T7, T8 and T9 are equal to the historical normal monitoring data of T7, T8 and T9, the monitoring data indicate that the monitoring data are in a normal running state, the monitoring data are judged to be safe and stable signals, zero-order fault signals are generated according to the safe and stable signals, and the zero-order fault signals are transmitted to a cloud platform to send out green light notification; therefore, the function of online identifying faults of the power grid is realized, the online identifying efficiency of the power grid faults is ensured, and the feasibility of the existing power fault early warning and identifying method is improved.
And the wireless transmission module is used for transmitting the output prediction report to the cloud platform based on the wireless communication technology for monitoring and early warning.
In order to better show the use flow of the power grid fault online identification system based on big data, the embodiment provides an implementation method of the power grid fault online identification system based on big data, and the implementation method comprises the following steps:
S1, collecting power data of power equipment through a sensor deployed in an electric SCADA system, and preprocessing and cleaning the power data to generate an electric operation work log;
S2, extracting a characteristic set of the historical power data, and importing the characteristic set serving as a characteristic sample set into a support vector machine for training, so that a power grid fault prediction model is obtained;
S3, importing the electric power operation work log generated in the S1 into a power grid fault prediction model for training, and outputting a power grid fault prediction report for analysis;
S4, comparing the real-time power data with the historical normal monitoring data to obtain whether the power grid at the position fails or not, and dividing a failure level according to the difference of comparison values;
S5, dividing the power data with larger comparison value difference into first-level fault signals, and sending out red light early warning through the cloud platform; dividing the electric power data with the common comparison value gap into secondary fault signals, and sending out yellow light early warning through a cloud platform; and dividing the power data with the comparison value not generating the gap into zero-order fault signals, and sending a green light notice through the cloud platform, so that the efficiency of online identification of the power grid faults is ensured.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. On-line power grid fault identification system based on big data, which is characterized by comprising:
The data acquisition module is used for acquiring power data of the power equipment based on a sensor deployed in the power SCADA system, so that data parameters of the power equipment are obtained, and the acquired power data are sent to the data processing unit for preprocessing;
the data processing unit is used for receiving the power data sent by the data acquisition module, preprocessing and cleaning the power data, obtaining processed power data, and sending the processed power data to the data checking unit;
The model training unit is used for creating a data training model and training the model based on the power data collected in the past in the power SCADA system, so as to obtain an optimized power grid fault prediction model;
the data checking unit is used for receiving the power data sent by the data processing unit, and accessing the power data into the power grid fault prediction model for checking, so that a power grid fault prediction report is obtained, the fault position is positioned by combining the topological structure of the actual power grid, and the prediction report and the fault position are transmitted to the cloud platform;
the cloud platform is used for receiving the prediction report sent by the data inspection unit and sending a fault early warning notification based on the content of the prediction report;
Wherein, the data acquisition module further includes:
The real-time receiving module is used for receiving data information acquired by a sensor deployed in the electric SCADA system in real time;
The time interval acquisition module is used for acquiring the data acquisition time interval of the sensor deployed in the power-based SCADA system;
The time threshold setting module is used for setting a first time threshold and a second time threshold according to the data acquisition time interval, wherein the first time threshold and the second time threshold are obtained through the following formula:
Wherein T y1 and T y2 represent a first time threshold and a second time threshold, respectively; t y01 and T y02 represent an initial first time threshold and a second time threshold, respectively; c 01 represents the data volume formed by the data acquired by the sensor in one acquisition period with the sensor acquisition time interval smaller than the initial first time threshold; c 02 represents the data volume formed by the data acquired by the sensor in one acquisition period, wherein the acquisition time interval of the sensor is equal to or greater than the initial first time threshold value, but is smaller than the initial second time threshold value; c 03 represents the data amount formed by the data acquired by the sensor in one acquisition period with the sensor acquisition time interval equal to or greater than the initial second time threshold; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; e represents a constant; n represents the number of sensors; t i represents a data acquisition time interval corresponding to the ith sensor;
The data set dividing module is used for dividing the data information acquired by the sensor according to the first time threshold and the second time threshold to obtain a first data set, a second data set and a third data set;
The data transmission time interval setting module is used for setting corresponding data transmission time intervals of the first data set, the second data set and the third data set, and transmitting data corresponding to the first data set, the second data set and the third data set to the data processing unit according to the data transmission time intervals;
the first data set is formed by data acquired by a sensor with a sensor acquisition time interval smaller than a first time threshold;
The second data set is formed by data acquired by the sensor, wherein the acquisition time interval of the sensor is equal to or greater than the first time threshold, but is smaller than the second time threshold;
The third data set is formed by data acquired by the sensor, wherein the acquisition time interval of the data acquired by the sensor is equal to or greater than the second time threshold;
The data transmission time interval setting module includes:
The first information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the first data set as first information data;
A first data transmission time interval setting module, configured to set a first data transmission time interval corresponding to the first data set according to the first information data; the first data transmission time interval is obtained through the following formula:
Wherein T c1 represents a first data transmission time interval corresponding to the first data set; m 1 represents the number of sensors corresponding to the first dataset; t 01i denotes a data acquisition time interval of the i-th sensor corresponding to the first data set; t y1 denotes a first time threshold; c m01 represents the acquired data quantity of one acquisition period corresponding to the first data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c01 denotes an initial first data transmission time interval corresponding to the first data set;
the second information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the second data set to be used as second information data;
A second data transmission time interval setting module, configured to set a second data transmission time interval corresponding to the second data set according to the second information data; the second data transmission time interval is obtained through the following formula:
Wherein T c2 represents a second data transmission time interval corresponding to the second data set; m 2 represents the number of sensors corresponding to the second dataset; t 02i denotes a data acquisition time interval of the ith sensor corresponding to the second data set; t y1 denotes a first time threshold; t y2 denotes a second time threshold; c m02 represents the acquired data quantity of one acquisition period corresponding to the second data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c02 denotes an initial second data transmission time interval corresponding to the second data set;
The third information acquisition module is used for extracting the acquisition time interval of the sensor corresponding to the third data set to be used as third information data;
a third data transmission time interval setting module, configured to set a third data transmission time interval corresponding to the third data set according to the third information data; the third data transmission time interval is obtained through the following formula:
Wherein T c3 represents a third data transmission time interval corresponding to a third data set; m 3 represents the number of sensors corresponding to the third dataset; t 03i denotes a data acquisition time interval of the ith sensor corresponding to the third data set; t y2 denotes a second time threshold; cm03 represents the acquired data amount of one acquisition period corresponding to the third data set; c z represents the total data volume based on one acquisition cycle of deployed sensors in the power SCADA system; t c03 denotes an initial third data transmission time interval corresponding to the third data set.
2. The big data based power grid fault online identification system of claim 1, wherein: the sensor deployed in the power SCADA based system in the data acquisition module comprises: photoelectric sensors, infrared sensors, speed sensors, acceleration sensors, temperature sensors, humidity sensors, magnetic field sensors, GIS sensors, vibration sensors, ripple sensors, RFID tags, GPS devices, radiation sensors, heat-sensitive sensors, and energy consumption sensors.
3. The big data based power grid fault online identification system of claim 1, wherein: the data processing unit includes:
The preprocessing module is used for preprocessing and cleaning the power data and generating corresponding power operation work logs from the processed power data;
The feature extraction module is used for extracting parameter features of the content in the power operation work log so as to obtain a parameter feature set; the content for extracting the parameter features comprises the following steps: the duration of the fault exceeds 180 seconds, the location of the fault has been identified, and the cause of the fault has been determined;
And the data storage module is used for storing the power operation work log and the parameter feature set according to time sequence, thereby being used as a power data text base.
4. The big data based power grid fault online identification system of claim 1, wherein: the model training unit includes:
the sample acquisition module is based on the past parameter feature set of the power SCADA system stored in the data processing unit, and is used as a feature sample set;
The model creation module is used for guiding the characteristic sample set into the support vector machine for training and optimizing parameters of the support vector machine, so that a power grid fault prediction model is built.
5. The big data based power grid fault online identification system of claim 1, wherein: the data verification unit includes:
The data reading module is used for receiving the power operation work log sent by the data processing unit in real time, and importing the power operation work log into the power grid fault prediction model for training, so that a power grid fault prediction result is obtained;
The fault output module is used for outputting a prediction report of the power grid fault prediction model, analyzing various abnormal states of the power quality and finding out abnormal points;
And the wireless transmission module is used for transmitting the output prediction report to the cloud platform based on the wireless communication technology for monitoring and early warning.
6. The big data based power grid fault online identification system of claim 5, wherein: the fault output module analyzes the electric energy abnormal state of the prediction report, and specifically comprises the following steps:
Comparing the current monitoring data with the historical normal monitoring data to obtain whether the power distribution node has faults or not, thereby realizing the positioning of the faults; and performing fault grading on the power grid through a fault influence factor analysis result to generate corresponding early warning reminding.
7. The big data based power grid fault online identification system of claim 6, wherein: the power grid is classified according to fault influence factor analysis results to generate corresponding early warning reminding, and the method specifically comprises the following steps:
When the analysis result shows that the power data greatly exceeds the historical normal monitoring data, judging the power data as a fault dangerous signal, generating a first-level fault signal according to the fault dangerous signal, and transmitting the first-level fault signal to a cloud platform to send out a red light fault early warning notice; when the analysis result shows that the electric power data is less than the historical normal monitoring data, judging that the electric power data is a swing fault signal, generating a secondary fault signal according to the swing fault signal, and transmitting the signal to a cloud platform to send out yellow light fault early warning notification; and when the analysis result shows that the power data does not exceed the historical normal monitoring data, judging as a safety and stability signal, generating a zero-level fault signal according to the safety and stability signal, transmitting the zero-level fault signal to a cloud platform to send out a green light notice, and respectively generating corresponding text samples and transmitting the text samples to the cloud platform for output description.
8. A method for implementing the big data based power grid fault online identification system as claimed in any one of claims 1-7, wherein: the method comprises the following steps:
S1, collecting power data of power equipment through a sensor deployed in an electric SCADA system, and preprocessing and cleaning the power data to generate an electric operation work log;
S2, extracting a characteristic set of the historical power data, and importing the characteristic set serving as a characteristic sample set into a support vector machine for training, so that a power grid fault prediction model is obtained;
S3, importing the electric power operation work log generated in the S1 into a power grid fault prediction model for training, and outputting a power grid fault prediction report for analysis;
S4, comparing the real-time power data with the historical normal monitoring data to obtain whether the power grid at the position fails or not, and dividing a failure level according to the difference of comparison values;
S5, dividing the power data with larger comparison value difference into first-level fault signals, and sending out red light early warning through the cloud platform; dividing the electric power data with the common comparison value gap into secondary fault signals, and sending out yellow light early warning through a cloud platform; and dividing the power data with the comparison value not generating the gap into zero-order fault signals, and sending a green light notice through the cloud platform, so that the efficiency of online identification of the power grid faults is ensured.
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