CN113095708A - Power quality analysis system and method based on big data - Google Patents
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Abstract
The invention relates to a big data-based power quality analysis system and a big data-based power quality analysis method, and aims to solve the problems that a power grid system cannot provide good power quality and the capacity and the installation site of power quality control equipment cannot be accurately quantified due to various factors in the prior art. The power quality analysis system comprises a monitoring data query module, a power quality analysis module, a statistical analysis module and a system configuration module. The electric energy quality analysis method comprises the following steps: 1) monitoring the power quality through terminal monitoring equipment of a monitoring point to generate terminal monitoring data; 2) acquiring terminal monitoring data of a monitoring point in real time and off-line through data access; 3) performing real-time and off-line analysis and calculation tasks based on a big data processing platform Cloudera; 4) classifying and integrating terminal monitoring data, and storing the terminal monitoring data in an Hbase form; 5) and an interface for inquiring and operating the power quality analysis is provided for a power quality analysis user through a file interface, a database and a visual display mode.
Description
Technical Field
The invention relates to a power quality analysis system and method based on big data.
Background
Along with the rapid development of Chinese economy, people's lives are more and more intelligent, the number of used high-power electrical appliances is continuously increased, the requirements on the quality of electric energy are further higher and higher, and the electricity utilization safety is also more and more emphasized by people. In practical application, various factors can cause that a power grid system cannot provide good power quality, such as unbalance of three-phase voltage or current, power grid harmonic waves, voltage flicker, voltage distortion, frequency deviation and the like, and the capacity and the installation place of power quality treatment equipment cannot be accurately quantized, so that the production and life quality of the whole society is influenced, and therefore, the power quality needs to be analyzed so as to monitor and treat the power quality in time.
Disclosure of Invention
The invention aims to solve the problems that a power grid system cannot provide good power quality and the capacity and the installation place of power quality treatment equipment cannot be accurately quantified due to various factors in the prior art, and provides a power quality analysis system and method based on big data.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a power quality analysis system based on big data is characterized in that: the system comprises a monitoring data query module, a power quality analysis module, a statistical analysis module and a system configuration module;
the monitoring data query module comprises a power quality index query unit and a power quality region query unit; the power quality index query unit is used for querying and displaying monitoring data and power quality indexes according to user conditions; the power quality region query unit is used for displaying power quality index data according to the GIS on the user map;
the power quality analysis module comprises a power quality early warning unit, a power quality evaluation unit and a power quality prediction unit; the electric energy quality early warning unit is used for carrying out real-time online monitoring analysis on main indexes of electric energy quality, setting an early warning threshold value and an early warning grade, and carrying out real-time early warning according to set early warning conditions; the electric energy quality evaluation unit is used for analyzing and evaluating the electric energy quality big data, finding out unqualified electric energy quality monitoring data, mining the unqualified electric energy quality monitoring data by using an Apriori association rule and an FP-Tree algorithm, finding out the intrinsic characteristics and rules which are hidden in the data and influence the electric energy quality from the massive electric energy quality big data, and finding out the reasons influencing the electric energy quality so as to evaluate the electric energy quality; the power quality prediction unit is used for predicting the power quality in the region by combining power quality historical data, power quality real-time monitoring data, power distribution network line distribution data and geographic information data and through time and space correlation analysis;
the statistical analysis module is used for counting the monitoring data, analyzing the power quality index, performing multi-dimensional statistical analysis according to monitoring points, monitoring areas, index types and monitoring events, and displaying the statistical analysis result;
the system configuration module comprises a user configuration unit, an equipment configuration unit and a data configuration unit; the user configuration unit is used for configuring user roles and user authority information; the equipment configuration unit is used for carrying out parameter configuration and state configuration on the monitoring equipment; the data configuration unit is used for maintaining parameters of the database and configuring a path for importing and exporting data.
Furthermore, the statistical analysis result of the statistical analysis module is shown in the form of a graph and a table, and the export function of Excel and HTML is provided.
The power quality analysis method based on the big data is characterized by comprising the following steps of:
1) monitoring the power quality through terminal monitoring equipment of a monitoring point to generate terminal monitoring data;
2) acquiring terminal monitoring data of a monitoring point in real time and off-line through data access;
3) performing real-time and off-line analysis and calculation tasks based on a big data processing platform Cloudera;
4) classifying and integrating terminal monitoring data, and storing the terminal monitoring data in an Hbase form;
5) and an interface for inquiring and operating the power quality analysis is provided for a power quality analysis user through a file interface, a database and a visual display mode.
Further, in step 1), the terminal monitoring data includes three-phase voltages ua, ub, uc of the power grid, three-phase currents ial, ibl, icl of the load, three-phase currents ias, ibs, ics of the general power quality control device, three-phase voltage deviation DUabc, three-phase voltage fluctuation DUabc, three-phase voltage unbalance sUabc, three-phase current deviation dibc, three-phase current fluctuation dlabc, harmonic distortion rate thdibc, power factor index DPF, and line loss rate dP.
Further, in the step 3), the real-time and offline analysis and calculation task based on the big data processing platform Cloudera is realized by performing statistical analysis, including statistical algorithm or analysis algorithm, on the terminal monitoring data stored in the big data processing platform Cloudera through a MapReduce parallel calculation technology;
the statistical algorithm comprises statistics of the maximum value, the minimum value, the average value, the 95% probability and the qualification rate of the terminal monitoring data, wherein the statistical realization process of the maximum value, the minimum value and the average value of the terminal monitoring data is as follows:
a) the method comprises the steps that terminal monitoring data filtering of one channel or one monitoring point is completed in a Map stage, and the maximum value, the minimum value and the average value of the terminal monitoring data of the channel or the monitoring point in a time period are obtained;
b) monitoring data (v) for a terminal by Map operation1,v2,...,vk) Converting to generate i as key word and the distortion of voltage and current as key value (i, (v)1i,v2i,...,vki) As input data for the Reduce phase;
c) performing classified statistics on input data according to the keywords i in Reduce stage, and calculating the minimum value minMaximum value maxAnd average value avg；
The implementation process of the analysis algorithm is as follows:
a) averagely dividing the distortion rate into 10 grades according to the result of comparing the maximum distortion rate in the three-phase voltage or current with the national standard value, and analogizing the grade severity degree in turn;
b) filtering the terminal monitoring data in the Map stage, and reading the total distortion rate value corresponding to each channel or monitoring point;
c) and respectively calculating the evaluation grade according to the total distortion value of each channel or monitoring point in the Reduce stage, and inputting the grade of each channel or monitoring point.
Further, in step 3), the big data processing platform Cloudera includes an index and running state analysis module and a transient event analysis module;
the index and running state analysis module runs according to the following steps:
a) the verification system particularly selects 1T amount of test data for the storage and statistical analysis capability of mass data, and performs statistical calculation on terminal monitoring data on a big data processing platform Cloudera;
b) counting the maximum value, the minimum value and the average value of the power quality data of the monitoring points with complete account information every day, and storing the results into an Hbase database and a MySQL database;
c) counting the voltage deviation, the frequency deviation and the overrun time and the counting time of the long-time flicker index of each monitoring point every day;
d) counting the conditions that the total voltage and power quality distortion of each monitoring point per day exceeds the standard, the negative sequence voltage unbalance exceeds the standard, the power quality and voltage content exceeds the standard, the inter-power quality and voltage content exceeds the standard and the power quality and current exceeds the standard;
e) obtaining running state statistics of the terminal monitoring equipment by calculating the online rate, integrity rate and accuracy rate of the terminal monitoring equipment at the monitoring point;
f) after acquiring the information of the monitoring point account and the terminal monitoring data in a period of time, obtaining the online condition, the data index uploading amount and the data correctness condition of the terminal monitoring equipment of the monitoring point, the condition of the operating monitoring point in the account and the data amount to be uploaded by comparison, and obtaining a calculation result by a ratio calculation and a region-by-region and time aggregation calculation method;
the transient event analysis module operates according to the following steps:
a) polymerizing ABC three-phase sag of a single-point monitoring point;
firstly, extracting A, B, C voltage sag time of each phase recorded at the same time from a voltage sag time list of a single monitoring point; then sequencing the residual voltage of A, B, C phase voltage in the sag time; finding the phase, amplitude and corresponding duration of the minimum residual voltage sag time; merging the 3 voltage sag times into 1 voltage sag event;
b) aggregating multiple sag times within 1 minute of a single monitoring point;
counting from 0:00 every day and ending 24:00 the day; automatically finishing sequencing a plurality of sag times of the same monitoring point within 1 day according to the time sequence, finishing time sequencing after aggregation of a plurality of sag times within 1 minute by taking the recorded initial moment of the sag occurrence for the first time as a statistical starting point, then starting aggregation of sag events within 1 minute for the second time, and so on;
c) aggregating the sag amplitude and the duration of the power grid;
after the interception of the one-minute time period is completed, sequencing the residual voltages of the multiple sag times within 1 minute, and finding out the sag time information of the minimum residual voltage, wherein the voltage sag amplitude value after the multiple sag events are aggregated within 1 minute is the minimum residual voltage in the sag events within 1 minute, and the duration time is the duration time of the minimum residual voltage within 1 minute;
d) by selecting provinces, cities, substations and monitoring points, daily data, monthly data and annual data of short-time interruption, voltage sag, frequency, voltage and harmonic waves are inquired, and are classified according to the characteristic assignment and the duration of the short-time interruption, the voltage sag and the voltage sag, and time and frequency are classified and summarized.
Further, in the step 4), the classifying and integrating the terminal monitoring data and storing the terminal monitoring data in the Hbase form specifically includes integrating the non-relational data, the relational data and the real-time push data respectively and storing the non-relational data, the relational data and the real-time push data into the non-relational database, the relational database and the distributed file system respectively.
Further, in the step 5), the visually displaying specifically includes displaying the electric energy quality analysis result on a Web browser by using HTML, CSS, JSP, or Javascript technology, and performing system cross-browser displaying on the electric energy quality analysis result by using a Jquery frame and a standard Javascript code style.
The invention has the beneficial effects that:
according to the electric energy quality analysis system and method based on the big data, the electric energy quality is subjected to statistical analysis based on the big data, the capacity and the installation place of electric energy quality equipment can be accurately quantized, meanwhile, manual intervention is reduced, and the stability and the safety of a power grid are greatly improved.
Drawings
FIG. 1 is a model diagram of a big data based power quality analysis system of the present invention;
FIG. 2 is a flow chart of a big data based power quality analysis method of the present invention;
FIG. 3 is a functional block diagram of a big data based power quality analysis method of the present invention;
fig. 4 is a schematic diagram of a statistical method in the big data-based power quality analysis method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the big data based power quality analysis system provided by the present invention includes a monitoring data query module, a power quality analysis module, a statistical analysis module, and a system configuration module;
the monitoring data query module comprises a power quality index query unit and a power quality region query unit. The power quality index query unit is used for querying and displaying the monitoring data and the power quality index according to user conditions; and the power quality region query unit is used for displaying power quality index data according to the GIS on the user map.
The power quality analysis module comprises a power quality early warning unit, a power quality evaluation unit and a power quality prediction unit. The electric energy quality early warning unit is used for carrying out real-time online monitoring analysis on main indexes of electric energy quality, setting an early warning threshold value and an early warning grade, and carrying out real-time early warning according to set early warning conditions; the electric energy quality evaluation unit is used for analyzing and evaluating the electric energy quality big data, finding out unqualified electric energy quality index monitoring data, mining the unqualified electric energy quality data by using an Apriori association rule and an FP-Tree algorithm, finding out the intrinsic characteristics and rules which are hidden in the data and influence the electric energy quality from the massive electric energy quality big data, and finding out the reasons influencing the electric energy quality, thereby evaluating the electric energy quality; the power quality prediction unit is used for predicting the power quality in the region by combining power quality historical data, power quality real-time monitoring data, power distribution network line distribution data and geographic information data and through time and space correlation analysis.
The statistical analysis module is used for counting the monitoring data, analyzing the power quality index, performing statistical analysis according to monitoring points, monitoring areas, index types and monitoring event multi-dimensionality, displaying the statistical analysis result in the form of a graph and a table, and providing the export function of Excel and HTML.
The system configuration module comprises a user configuration unit, a device configuration unit and a data configuration unit. The user configuration unit is used for configuring user roles and user authority information; the equipment configuration unit is used for carrying out parameter configuration and state configuration on the monitoring equipment; the data configuration unit is used for maintaining parameters of the database and configuring a path for importing and exporting data.
The power quality analysis system is developed to conform to J2EE standard specifications, basic frameworks such as SpringMVC and MyBatis are integrated, a full-function MVC module for constructing a Web application program is provided, programming technologies such as a plane object and the like are used, the coupling degree of each part of power quality analysis service logic is reduced, the repeatability of power quality analysis is improved, a control reversal technology is used, a mode is provided for binding each part with the service of each part, and all related services required by an object are enabled to be available.
The invention provides a power quality analysis method based on big data, which operates in the system and comprises the following steps:
1) monitoring the power quality through terminal monitoring equipment of a monitoring point to generate terminal monitoring data;
2) acquiring terminal monitoring data of a monitoring point in real time and off-line through data access;
3) performing real-time and off-line analysis and calculation tasks based on a big data processing platform Cloudera;
4) classifying and integrating terminal monitoring data, and storing the terminal monitoring data in an Hbase form;
5) and an interface for inquiring and operating the power quality analysis is provided for a power quality analysis user through a file interface, a database and a visual display mode.
Specifically, as shown in fig. 2, the method is implemented by running a data source layer, a data acquisition layer, a calculation and processing layer, a storage layer and an access layer.
And in the data source layer, monitoring the power quality through terminal monitoring equipment of a monitoring point to generate terminal monitoring data serving as a data source. The terminal monitoring data comprise three-phase voltages ua, ub and uc of a power grid, three-phase currents ial, ibl and icl of a load, three-phase currents ias, ibs and ics of a universal power quality control device, three-phase voltage deviation DUabc, three-phase voltage fluctuation dUabc, three-phase voltage unbalance sUabc, three-phase current deviation DIabc, three-phase current fluctuation dIabc, harmonic distortion rate THDIabc, a power factor index DPF and line loss rate dP.
And in the data acquisition layer, acquiring terminal monitoring data of the monitoring points in a real-time data acquisition mode and an off-line data acquisition mode.
And data storage and data statistical analysis are carried out on a computing and processing layer and a storage layer through a big data processing platform Cloudera.
Specifically, real-time and offline calculation tasks are performed based on the big data processing platform Cloudera, that is, statistical analysis is performed on the terminal monitoring data stored in the big data processing platform Cloudera through a MapReduce parallel calculation technology, and commonly used algorithms include a statistical algorithm and an analysis algorithm.
The statistical algorithm includes statistics of the maximum value, the minimum value, the average value, the 95% probability and the qualification rate of the terminal monitoring data, wherein the statistical implementation process of the maximum value, the minimum value and the average value of the terminal monitoring data is shown in fig. 4 and includes:
a) the method comprises the steps that terminal monitoring data filtering of one channel or one monitoring point is completed in a Map stage, and the maximum value, the minimum value and the average value of the terminal monitoring data of the channel or the monitoring point in a time period are obtained;
b) monitoring data (v) for a terminal by Map operation1,v2,...,vk) Converting to generate i as key word and the distortion of voltage and current as key value (i, (v)1i,v2i,...,vki) As input data for the Reduce phase;
c) performing classified statistics on input data according to the keywords i in Reduce stage, and calculating the minimum value minMaximum value maxAnd average value avg。
The implementation process of the analysis algorithm comprises the following steps:
a) averagely dividing the distortion rate into 10 grades according to the result of comparing the maximum distortion rate in the three-phase voltage or current with the national standard value, and analogizing the grade severity degree in turn;
b) filtering the terminal monitoring data in the Map stage, and reading the total distortion rate value corresponding to each channel or monitoring point;
c) and respectively calculating the evaluation grade according to the total distortion value of each channel or monitoring point in the Reduce stage, and inputting the grade of each channel or monitoring point.
The big data processing platform Cloudera comprises an index and running state analysis module and a transient event analysis module. The index and running state analysis module and the transient event analysis module obtain data from big data, then obtain basic data and other auxiliary data from the relational database, perform statistical calculation, transmit the calculation result to the relational database through an interface or directly send the calculation result to a foreground application program, and the foreground program reads the data in the relational database to perform data display.
The index and running state analysis module runs according to the following steps:
a) the verification system particularly selects 1T amount of test data for the storage and statistical analysis capability of mass data, and performs statistical calculation on terminal monitoring data on a big data processing platform Cloudera;
b) counting the maximum value, the minimum value and the average value of the power quality data of the monitoring points with complete account information every day, and storing the results into an Hbase database and a MySQL database;
c) counting the voltage deviation, the frequency deviation and the overrun time and the counting time of the long-time flicker index of each monitoring point every day;
d) counting the conditions that the total voltage and power quality distortion of each monitoring point per day exceeds the standard, the negative sequence voltage unbalance exceeds the standard, the power quality and voltage content exceeds the standard, the inter-power quality and voltage content exceeds the standard and the power quality and current exceeds the standard;
e) obtaining running state statistics of the terminal monitoring equipment by calculating the online rate, integrity rate and accuracy rate of the terminal monitoring equipment at the monitoring point;
f) after acquiring the information of the monitoring point account and the terminal monitoring data in a period of time, the online condition, the data index uploading amount and the data correctness condition of the terminal monitoring equipment of the monitoring point, the condition of the monitoring point operating in the account and the data amount to be uploaded are obtained by comparison, and the calculation result is obtained by a ratio calculation and a region-by-region and time aggregation calculation method.
The transient event comprises three types of voltage sag, voltage rise and short-time interruption, and each type of time has three indexes, namely a transient time starting moment, a transient event duration and a transient event residual voltage, namely a characteristic amplitude. The transient index data of each monitoring point has the occurrence frequency of transient events with different characteristic amplitudes and different durations.
And counting the voltage dips occurring within 1 minute for one time, wherein the residual voltage is the minimum residual voltage of the voltage dips occurring within 1 minute for several times, and the duration is the duration of the voltage dip where the minimum residual voltage is located within 1 minute.
The transient event analysis module operates according to the following steps:
a) polymerizing ABC three-phase sag of a single-point monitoring point;
firstly, extracting A, B, C voltage sag time of each phase recorded at the same time from a voltage sag time list of a single monitoring point; then sequencing the residual voltage of A, B, C phase voltage in the sag time; finding the phase, amplitude and corresponding duration of the minimum residual voltage sag time; merging the 3 voltage sag times into 1 voltage sag event;
b) aggregating multiple sag times within 1 minute of a single monitoring point;
counting from 0:00 every day and ending 24:00 the day; automatically finishing sequencing a plurality of sag times of the same monitoring point within 1 day according to the time sequence, finishing time sequencing after aggregation of a plurality of sag times within 1 minute by taking the recorded initial moment of the sag occurrence for the first time as a statistical starting point, then starting aggregation of sag events within 1 minute for the second time, and so on;
c) aggregating the sag amplitude and the duration of the power grid;
after the interception of the one-minute time period is completed, sequencing the residual voltages of the multiple sag times within 1 minute, and finding out the sag time information of the minimum residual voltage, wherein the voltage sag amplitude value after the multiple sag events are aggregated within 1 minute is the minimum residual voltage in the sag events within 1 minute, and the duration time is the duration time of the minimum residual voltage within 1 minute;
d) by selecting provinces, cities, substations and monitoring points, daily data, monthly data and annual data of short-time interruption, voltage sag, frequency, voltage and harmonic waves are inquired, and are classified according to the characteristic assignment and the duration of the short-time interruption, the voltage sag and the voltage sag, and time and frequency are classified and summarized.
The classification and integration of the terminal monitoring data and the storage in the Hbase form are specifically that non-relational data, relational data and real-time pushed data are respectively integrated and stored in a non-relational database, a relational database and a distributed file system.
The multi-source heterogeneous data come from a plurality of data sources, including different databases, data sets collected by different devices in work and the like. The operating system and the management system of different data sources are different, the storage mode and the logic structure of data are different, and the generation time, the use place and the code protocol of data are also different.
The data association analysis is to find out the association between data which is seemingly irregular, so as to find out the regularity, the development trend and the like among things.
An Apriori algorithm firstly traverses a database to determine a frequent item set, then carries out pruning according to a support degree threshold value, and finally calculates the reliability according to the support degree, thereby determining the association rule.
The support degree is calculated in such a way that if the DUabc is greater than a national standard specified value, k _ DUabc is 1.01 × k _ DUabc; if the DUabc satisfies the national standard specified value and is less than 0.5 times the national standard specified value, k _ DUabc is 0.99 × k _ DUabc; if the two situations are not applicable, the k _ DUabc is kept unchanged for a period of time, and the change situations of other power quality phenomena are observed.
And at an access layer, providing an interface for querying and operating the power quality analysis for a power quality analysis user through a power quality analysis system based on big data. The electric energy quality analysis system shows the man-machine interaction for a user, is positioned on a mobile terminal client, a PC desktop, a large screen and other terminals used by a final user of the electric energy quality analysis system, shows an electric energy quality analysis result on a Web browser by adopting HTML, CSS, JSP or Javascript technology, and carries out system cross-browser display on the electric energy quality analysis result by adopting a Jquery framework and a standard Javascript code style, and mainly realizes the functions of user registration, user permission, data query, electric energy quality analysis, electric energy quality evaluation, electric energy quality prediction, statistical analysis and the like.
The principle of the big data-based power quality analysis method is shown in fig. 3, and a data layer acquires power quality data, distribution network distribution data, GIS data and power user data by collecting terminal monitoring data of monitoring points. The support layer realizes functions of a service bus, uniform security authentication, CMS, resource service, a business process engine, MQ, CA certificate management, data analysis service, data prediction service and the like through a big data processing platform Cloudera. The support layer is a tie connection between the data layer and the application layer. The application layer realizes functions of user registration, user authority, data query, power quality assessment, power quality analysis, power quality prestoring, statistical analysis report forms and the like through the power quality analysis system. The presentation layer displays the electric energy quality analysis result to the user through the webpage, the multimedia, the terminal and the report.
In practical application, a power grid model is obtained through the method, and N samples are extracted from the determined big data samples; counting all fault types in the power grid model and power quality management compensation schemes corresponding to the fault types respectively, and taking the power quality management compensation schemes under the fault types as samples for simulation to obtain transient data corresponding to the power quality compensation schemes under the fault types respectively; and determining the current fault type of the power grid model, and finding out the corresponding fault type and the power quality management compensation scheme thereof from all the counted fault types.
Claims (8)
1. The utility model provides a power quality analysis system based on big data which characterized in that: the system comprises a monitoring data query module, a power quality analysis module, a statistical analysis module and a system configuration module;
the monitoring data query module comprises a power quality index query unit and a power quality region query unit; the power quality index query unit is used for querying and displaying monitoring data and power quality indexes according to user conditions; the power quality region query unit is used for displaying power quality index data according to the GIS on the user map;
the power quality analysis module comprises a power quality early warning unit, a power quality evaluation unit and a power quality prediction unit; the electric energy quality early warning unit is used for carrying out real-time online monitoring analysis on main indexes of electric energy quality, setting an early warning threshold value and an early warning grade, and carrying out real-time early warning according to set early warning conditions; the electric energy quality evaluation unit is used for analyzing and evaluating the electric energy quality big data, finding out unqualified electric energy quality monitoring data, mining the unqualified electric energy quality monitoring data by using an Apriori association rule and an FP-Tree algorithm, finding out the intrinsic characteristics and rules which are hidden in the data and influence the electric energy quality from the massive electric energy quality big data, and finding out the reasons influencing the electric energy quality so as to evaluate the electric energy quality; the power quality prediction unit is used for predicting the power quality in the region by combining power quality historical data, power quality real-time monitoring data, power distribution network line distribution data and geographic information data and through time and space correlation analysis;
the statistical analysis module is used for counting the monitoring data, analyzing the power quality index, performing multi-dimensional statistical analysis according to monitoring points, monitoring areas, index types and monitoring events, and displaying the statistical analysis result;
the system configuration module comprises a user configuration unit, an equipment configuration unit and a data configuration unit; the user configuration unit is used for configuring user roles and user authority information; the equipment configuration unit is used for carrying out parameter configuration and state configuration on the monitoring equipment; the data configuration unit is used for maintaining parameters of the database and configuring a path for importing and exporting data.
2. The big-data based power quality analysis system of claim 1, wherein: the statistical analysis result of the statistical analysis module is shown in the form of a graph and a table, and the export function of Excel and HTML is provided.
3. A big data based power quality analysis method operating in the big data based power quality analysis system of claim 1, comprising the steps of:
1) monitoring the power quality through terminal monitoring equipment of a monitoring point to generate terminal monitoring data;
2) acquiring terminal monitoring data of a monitoring point in real time and off-line through data access;
3) performing real-time and off-line analysis and calculation tasks based on a big data processing platform Cloudera;
4) classifying and integrating terminal monitoring data, and storing the terminal monitoring data in an Hbase form;
5) and an interface for inquiring and operating the power quality analysis is provided for a power quality analysis user through a file interface, a database and a visual display mode.
4. The big-data-based power quality analysis method according to claim 3, wherein: in the step 1), the terminal monitoring data comprise three-phase voltages ua, ub and uc of a power grid, three-phase currents ial, ibl and icl of a load, three-phase currents ias, ibs and ics of a universal power quality control device, three-phase voltage deviation DUabc, three-phase voltage fluctuation DUabc, three-phase voltage unbalance sUabc, three-phase current deviation dibc, three-phase current fluctuation dlabc, harmonic distortion rate thdibc, a power factor index DPF and line loss rate dP.
5. The big-data-based power quality analysis method according to claim 4, wherein: in the step 3), the real-time and offline analysis and calculation task based on the big data processing platform Cloudera is realized by performing statistical analysis on the terminal monitoring data stored in the big data processing platform Cloudera through a MapReduce parallel calculation technology, wherein the statistical analysis comprises a statistical algorithm or an analysis algorithm;
the statistical algorithm comprises statistics of the maximum value, the minimum value, the average value, the 95% probability and the qualification rate of the terminal monitoring data, wherein the statistical realization process of the maximum value, the minimum value and the average value of the terminal monitoring data is as follows:
a) the method comprises the steps that terminal monitoring data filtering of one channel or one monitoring point is completed in a Map stage, and the maximum value, the minimum value and the average value of the terminal monitoring data of the channel or the monitoring point in a time period are obtained;
b) monitoring data (v) for a terminal by Map operation1,v2,...,vk) Converting to generate i as key word and the distortion of voltage and current as key value (i, (v)1i,v2i,...,vki) As input data for the Reduce phase;
c) performing classified statistics on input data according to the keywords i in Reduce stage, and calculating the minimum value minMaximum value maxAnd average value avg；
The implementation process of the analysis algorithm is as follows:
a) averagely dividing the distortion rate into 10 grades according to the result of comparing the maximum distortion rate in the three-phase voltage or current with the national standard value, and analogizing the grade severity degree in turn;
b) filtering the terminal monitoring data in the Map stage, and reading the total distortion rate value corresponding to each channel or monitoring point;
c) and respectively calculating the evaluation grade according to the total distortion value of each channel or monitoring point in the Reduce stage, and inputting the grade of each channel or monitoring point.
6. The big-data-based power quality analysis method according to claim 5, wherein: in step 3), the big data processing platform Cloudera comprises an index and running state analysis module and a transient event analysis module;
the index and running state analysis module runs according to the following steps:
a) the verification system particularly selects 1T amount of test data for the storage and statistical analysis capability of mass data, and performs statistical calculation on terminal monitoring data on a big data processing platform Cloudera;
b) counting the maximum value, the minimum value and the average value of the power quality data of the monitoring points with complete account information every day, and storing the results into an Hbase database and a MySQL database;
c) counting the voltage deviation, the frequency deviation and the overrun time and the counting time of the long-time flicker index of each monitoring point every day;
d) counting the conditions that the total voltage and power quality distortion of each monitoring point per day exceeds the standard, the negative sequence voltage unbalance exceeds the standard, the power quality and voltage content exceeds the standard, the inter-power quality and voltage content exceeds the standard and the power quality and current exceeds the standard;
e) obtaining running state statistics of the terminal monitoring equipment by calculating the online rate, integrity rate and accuracy rate of the terminal monitoring equipment at the monitoring point;
f) after acquiring the information of the monitoring point account and the terminal monitoring data in a period of time, obtaining the online condition, the data index uploading amount and the data correctness condition of the terminal monitoring equipment of the monitoring point, the condition of the operating monitoring point in the account and the data amount to be uploaded by comparison, and obtaining a calculation result by a ratio calculation and a region-by-region and time aggregation calculation method;
the transient event analysis module operates according to the following steps:
a) polymerizing ABC three-phase sag of a single-point monitoring point;
firstly, extracting A, B, C voltage sag time of each phase recorded at the same time from a voltage sag time list of a single monitoring point; then sequencing the residual voltage of A, B, C phase voltage in the sag time; finding the phase, amplitude and corresponding duration of the minimum residual voltage sag time; merging the 3 voltage sag times into 1 voltage sag event;
b) aggregating multiple sag times within 1 minute of a single monitoring point;
counting from 0:00 every day and ending 24:00 the day; automatically finishing sequencing a plurality of sag times of the same monitoring point within 1 day according to the time sequence, finishing time sequencing after aggregation of a plurality of sag times within 1 minute by taking the recorded initial moment of the sag occurrence for the first time as a statistical starting point, then starting aggregation of sag events within 1 minute for the second time, and so on;
c) aggregating the sag amplitude and the duration of the power grid;
after the interception of the one-minute time period is completed, sequencing the residual voltages of the multiple sag times within 1 minute, and finding out the sag time information of the minimum residual voltage, wherein the voltage sag amplitude value after the multiple sag events are aggregated within 1 minute is the minimum residual voltage in the sag events within 1 minute, and the duration time is the duration time of the minimum residual voltage within 1 minute;
d) by selecting provinces, cities, substations and monitoring points, daily data, monthly data and annual data of short-time interruption, voltage sag, frequency, voltage and harmonic waves are inquired, and are classified according to the characteristic assignment and the duration of the short-time interruption, the voltage sag and the voltage sag, and time and frequency are classified and summarized.
7. The big-data-based power quality analysis method according to any one of claims 3 to 6, wherein: in the step 4), the classifying and integrating the terminal monitoring data and storing the terminal monitoring data in the Hbase form specifically includes integrating non-relational data, relational data and real-time pushed data respectively and storing the integrated data in a non-relational database, a relational database and a distributed file system respectively.
8. The big-data-based power quality analysis method according to claim 7, wherein: in the step 5), the visualized display specifically includes displaying the electric energy quality analysis result on a Web browser by adopting HTML, CSS, JSP or Javascript technology, and performing system cross-browser display on the electric energy quality analysis result by adopting Jquery framework and specification Javascript code style.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115018395A (en) * | 2022-08-08 | 2022-09-06 | 南京佑友软件技术有限公司 | Power quality analysis method |
| CN116110577A (en) * | 2022-11-16 | 2023-05-12 | 荣科科技股份有限公司 | Health monitoring analysis method and system based on big data |
| CN116995816A (en) * | 2023-09-25 | 2023-11-03 | 国网山东省电力公司淄博供电公司 | Power supply data processing platform and method based on artificial intelligence |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103872782A (en) * | 2014-03-31 | 2014-06-18 | 国家电网公司 | Electric energy quality data comprehensive service system |
-
2021
- 2021-04-23 CN CN202110442246.4A patent/CN113095708A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103872782A (en) * | 2014-03-31 | 2014-06-18 | 国家电网公司 | Electric energy quality data comprehensive service system |
Non-Patent Citations (4)
| Title |
|---|
| 刁长玉;: "基于在线监测系统的电能质量问题研究", 科学技术创新, no. 07, pages 76 - 77 * |
| 周国亮: "基于大数据的电能质量监测分析系统设计与实现", 智能电网, vol. 2, no. 9, pages 11 - 15 * |
| 娄国娇: "基于大数据的电能质量分析应用研究", 中国优秀硕士学位论文全文数据库（电子期刊）, no. 9, pages 6 - 36 * |
| 郭晓乾: "基于大数据的电能质量监测分析系统设计与实现", 软件导刊, vol. 19, no. 8, pages 182 - 185 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115018395A (en) * | 2022-08-08 | 2022-09-06 | 南京佑友软件技术有限公司 | Power quality analysis method |
| CN116110577A (en) * | 2022-11-16 | 2023-05-12 | 荣科科技股份有限公司 | Health monitoring analysis method and system based on big data |
| CN116110577B (en) * | 2022-11-16 | 2024-04-30 | 荣科科技股份有限公司 | Health monitoring analysis method and system based on big data |
| CN116995816A (en) * | 2023-09-25 | 2023-11-03 | 国网山东省电力公司淄博供电公司 | Power supply data processing platform and method based on artificial intelligence |
| CN116995816B (en) * | 2023-09-25 | 2024-02-23 | 国网山东省电力公司淄博供电公司 | Power supply data processing platform and method based on artificial intelligence |
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