CN118962559A - A method and system for intelligent detection of synchronization accuracy of fault indicators - Google Patents
A method and system for intelligent detection of synchronization accuracy of fault indicators Download PDFInfo
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Abstract
本发明公开了一种故障指示器同步精度智能检测方法及系统，涉及智慧电能管理技术领域，解决了现有技术中，用电系统进行电能消耗时无法对电能消耗进行数据采集监测分析，无法保证数据采集的准确性的技术问题，具体为数据采集监测分析单元对电能消耗进行数据采集监测分析，对实时监测的用电系统进行电能数据采集，根据采集的电能数据构建电能数据集合并对电能数据集合进行数据比对处理，完成后得到建模分析集合；数据建模分析预测单元对建模分析集合进行建模分析预测，并通过建模分析预警生成预测正常信号或者预测故障信号。
The present invention discloses a fault indicator synchronization accuracy intelligent detection method and system, which relate to the field of intelligent electric energy management technology, and solve the technical problem in the prior art that when an electric power system consumes electric energy, it is impossible to collect, monitor and analyze data on electric energy consumption, and the accuracy of data collection cannot be guaranteed. Specifically, a data collection, monitoring and analysis unit collects data on electric energy consumption, collects electric energy data from the electric power system monitored in real time, constructs an electric energy data set based on the collected electric energy data, and performs data comparison processing on the electric energy data set, and obtains a modeling analysis set after completion; a data modeling, analysis and prediction unit performs modeling, analysis and prediction on the modeling and analysis set, and generates a predicted normal signal or a predicted fault signal through a modeling and analysis early warning.
Description
Technical Field
The invention relates to the technical field of intelligent electric energy management, in particular to a fault indicator synchronization precision intelligent detection method and system.
Background
The intelligent electric energy management is a comprehensive management system integrating modern information technology, internet of things technology and big data analysis technology, and aims to realize safe, efficient, environment-friendly and economic operation of an electric power system; the importance of intelligent power management is increasingly highlighted; firstly, it helps to improve the electric energy utilization efficiency, reduces the electric energy waste. The link with excessively high electric energy consumption can be timely found by monitoring the use condition of the electric energy in real time, and corresponding measures are taken for adjustment and improvement.
In the prior art, when the power consumption is carried out by the power system, data acquisition, monitoring and analysis cannot be carried out on the power consumption, the accuracy of data acquisition cannot be guaranteed, meanwhile, modeling analysis and prediction cannot be carried out on a modeling analysis set, so that the prediction efficiency of real-time power parameters is low, and in addition, parameter monitoring and early warning cannot be carried out on the real-time power parameters, so that the power parameter management and control efficiency is reduced.
Aiming at the technical defects, an intelligent detection method and system for the synchronous precision of the fault indicator are provided.
Disclosure of Invention
The invention aims to solve the problems and provide a fault indicator synchronization precision intelligent detection method and system.
The aim of the invention can be achieved by the following technical scheme:
the intelligent detection system for the synchronous precision of the fault indicator is used for monitoring electric energy parameters of an electric power system and comprises a monitoring center cloud platform, wherein the monitoring center cloud platform is in communication connection with a data acquisition, monitoring and analysis unit, a data modeling, analysis and prediction unit, a data real-time parameter early warning unit and an electric power management strategy evaluation unit;
The data acquisition, monitoring and analysis unit is used for carrying out data acquisition, monitoring and analysis on the electric energy consumption, carrying out electric energy data acquisition on the electric energy system monitored in real time through the fault indicator, constructing an electric energy data set according to the acquired electric energy data, carrying out data comparison processing on the electric energy data set, and obtaining a modeling analysis set after completion;
The data modeling analysis prediction unit performs modeling analysis prediction on the modeling analysis set, generates a predicted normal signal or a predicted fault signal through modeling analysis early warning, and when the prediction is normal, the data real-time parameter early warning unit performs real-time parameter early warning on the power consumption system electric energy data, acquires real-time early warning parameters, substitutes a formula to acquire real-time parameter early warning coefficients in the power consumption system operation process, generates a high early warning signal or a low early warning signal according to coefficient threshold comparison, and transmits the high early warning signal or the low early warning signal to the monitoring center cloud platform;
The power management strategy evaluation unit performs power management strategy evaluation on power data of the power utilization system during early warning, acquires power scheduling duration data and power control speed data, and deduces whether the power management strategy evaluation is qualified or not according to data comparison.
As a preferred embodiment of the invention, the data acquisition monitoring analysis unit operates as follows:
collecting electric energy data of the power utilization system monitored in real time; and randomly selecting operation time according to the operation time period of the power utilization system, constructing an electric energy data acquisition time period according to the time sequence by the selected operation time, acquiring electric energy data in the electric energy data acquisition time period, and carrying out aggregation sequencing on each electric energy data according to the same type of data to construct an electric energy data set.
As a preferred embodiment of the invention, floating deviation information and floating duration information are obtained, wherein the floating deviation information and the floating duration information are respectively the non-intersection time length of the deviation value of any two electric energy data values in each electric energy data set and the numerical deviation of the numerical floating span peak value of the electric energy data of the type corresponding to the fault moment, and the numerical floating time period of the electric energy data of the type corresponding to the fault moment and the duration time of the instantaneous floating of the data in the current electric energy data set;
If the floating deviation information exceeds the value deviation threshold value or the floating duration information does not exceed the non-intersection duration threshold value, eliminating the current value in the electric energy data set, marking the processed electric energy data set as a modeling analysis set, and sending the modeling analysis set to a monitoring center cloud platform;
If the floating deviation information does not exceed the numerical deviation threshold value and the floating duration information exceeds the non-intersection duration threshold value, carrying out numerical preservation on the current numerical value in the electric energy data set, marking the reserved electric energy data set as a modeling analysis set, and sending the modeling analysis set to a monitoring center cloud platform.
As a preferred embodiment of the present invention, the modeling analysis prediction process is as follows:
carrying out curve construction on the electric energy data values in the modeling analysis set, substituting and connecting the values of all subsets in the modeling analysis set by taking the time interval duration corresponding to the adjacent electric energy data values as the abscissa adjacent interval duration and taking the electric energy data values as the ordinate so as to construct an electric energy data real-time curve; and meanwhile, the time period constructed by the historical operation time period acquisition time point comprises the power consumption system fault time period, the power consumption system fault occurrence time and the power consumption system fault ending time.
As a preferred embodiment of the invention, the electric energy data values of the historical operation time points in the historical operation time period are synchronously substituted into a coordinate system, an electric energy data historical curve is constructed, the curve angle is marked as a fault risk angle according to the curve angle formed by the corresponding curve peak points of the power utilization system fault time period in the electric energy data historical curve, and meanwhile, the corresponding slope average is marked as a fault risk slope according to the curve slope average of each time point in the corresponding curve between the power utilization system fault occurrence time and the ending time in the electric energy data historical curve.
As a preferred implementation mode of the invention, if the curve angle formed by the corresponding curve points of any time in the constructed curve in the modeling analysis set does not exceed the fault risk angle, or the slope of the corresponding curve at any adjacent time in the constructed curve in the modeling analysis set does not exceed the slope average value of the curve, judging that the data of the current modeling analysis set is abnormal in prediction;
If the curve angle formed by the corresponding curve points at any time in the constructed curves in the modeling analysis set exceeds the fault risk angle and the slope of the corresponding curve at any adjacent time in the constructed curves in the modeling analysis set exceeds the slope average value of the curve, judging that the data prediction of the current modeling analysis set is normal.
As a preferred embodiment of the invention, the real-time early warning parameters comprise the excessive amount of the instantaneous increment span and the increment span mean value of the floating of the data of the same type of electric energy at the adjacent time in real time in the running process of the electric system, the synchronous increment time interval span value of the floating duration of the data of different types of electric energy in the running process of the electric system, and the continuous increment speed of the data of the current floating electric energy type after the data of any type of electric energy in the running process of the electric system are floated.
As a preferred implementation mode of the invention, if the real-time parameter early-warning coefficient exceeds the real-time parameter early-warning coefficient threshold value in the operation process of the power utilization system, judging that the data real-time parameter early-warning is abnormal in the operation process of the power utilization system; if the real-time parameter early warning coefficient does not exceed the real-time parameter early warning coefficient threshold value in the operation process of the power utilization system, judging that the real-time parameter early warning of the data in the operation process of the power utilization system is normal.
As a preferred implementation mode of the invention, the electric energy scheduling duration data and the electric energy control speed data are respectively the overlapping duration of the electric energy supply buffer duration corresponding to the electric energy acquisition alternative strategy during early warning of the electric energy data of the electric energy utilization system and the electric energy utilization system early warning operation duration, and the maximum control speed ratio corresponding to the floating span growth speed of the electric energy data after the execution of the electric energy acquisition alternative strategy during early warning of the electric energy data of the electric energy utilization system is completed.
As a preferred embodiment of the invention, if the power scheduling duration data exceeds the overlapping duration threshold or the power control speed data does not exceed the maximum control speed duty ratio threshold, determining that the power management strategy evaluation of the power utilization system is inefficient; if the electric energy scheduling duration data does not exceed the overlapping duration threshold and the electric energy control speed data exceeds the maximum control speed duty ratio threshold, judging that the electric energy management strategy evaluation of the electric power utilization system is efficient.
As a preferred embodiment of the invention, a fault indicator synchronization precision intelligent detection method comprises the following steps:
Step one: data acquisition and monitoring, data acquisition and monitoring analysis on electric energy consumption, electric energy data acquisition on a power utilization system monitored in real time, electric energy data set construction according to set sequencing, data comparison processing on the electric energy data set, and modeling analysis set obtaining after completion;
Step two: data modeling analysis prediction, modeling analysis prediction is carried out on a modeling analysis set, and a predicted normal signal or a predicted fault signal is generated through modeling analysis early warning;
Step three: real-time parameter early warning of data, real-time parameter early warning of electric energy data of the power utilization system, acquisition of real-time early warning parameters, substitution of the real-time early warning parameters into a formula to acquire real-time parameter early warning coefficients in the operation process of the power utilization system, and comparison of coefficient threshold values to generate high early warning signals or low early warning signals;
Step four: and (3) carrying out electric energy management strategy evaluation when the electric energy data of the power utilization system are early-warned, acquiring electric energy scheduling duration data and electric energy control speed data, and deducing whether the electric energy management strategy evaluation is qualified or not according to data comparison.
Compared with the prior art, the invention has the beneficial effects that:
1. In the invention, the data acquisition, monitoring and analysis are carried out on the electric energy consumption, the real-time electric energy consumption data are acquired and processed, the abnormal quantity of the electric energy consumption data is removed, the instantaneous value during the operation of the power utilization system influences the electric energy consumption detection accuracy, and meanwhile, the deviation of the electric energy management and control caused by the accidental deviation data of the power utilization system is avoided; and carrying out modeling analysis prediction on the modeling analysis set, comparing and analyzing the historical time period data curve with the current data modeling set, deducing whether the current modeling analysis set is abnormal or not, and facilitating the prediction and analysis of the trend of the electric energy data of the current electric energy system, deducing the abnormal probability of the electric energy data of the current electric energy system in time, thereby being beneficial to improving the intelligent control efficiency of the electric energy data, ensuring the operation qualification of the electric energy system and reducing the system operation influence caused by the electric energy data abnormality of the electric energy system.
2. In the invention, real-time parameter early warning is carried out on the electric energy data of the power utilization system, whether the electric energy data in the current power utilization system has abnormal parameters is inferred through real-time parameter analysis, and real-time efficiency monitoring is carried out on the operation of the power utilization system so as to ensure the management and control efficiency of the electric energy data, ensure that fault prediction can be carried out in time in the monitoring process of the electric energy data, and ensure the operation efficiency of the power utilization system; and carrying out electric energy management strategy evaluation when electric energy data of the electric energy utilization system are early-warned, deducing whether the electric energy control efficiency of the current electric energy utilization system meets the actual requirement or not, avoiding that the electric energy utilization system cannot be timely regulated when the electric energy data are abnormal, reducing the operation efficiency of the electric energy utilization system, and ensuring that the electric energy data are abnormal and the integral operation of the electric energy utilization system is not influenced.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a block diagram of a fault indicator synchronization accuracy intelligent detection system according to an embodiment of the present invention;
fig. 2 is a flowchart of a fault indicator synchronization accuracy intelligent detection method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, 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.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
An embodiment 1, please refer to fig. 1, is a fault indicator synchronization accuracy intelligent detection system for monitoring electric energy parameters of an electric power system, which comprises a monitoring center cloud platform, wherein the monitoring center cloud platform is in communication connection with a data acquisition monitoring analysis unit, a data modeling analysis prediction unit, a data real-time parameter early warning unit and an electric power management strategy evaluation unit;
The monitoring center cloud platform generates a data acquisition monitoring analysis signal and sends the data acquisition monitoring analysis signal to the data acquisition monitoring analysis unit, the data acquisition monitoring analysis unit performs data acquisition monitoring analysis on the power consumption through the fault indicator after receiving the data acquisition monitoring analysis signal, performs acquisition processing on real-time power consumption data, eliminates abnormal quantity of the power consumption data, influences the instantaneous value of the power consumption system during operation on the power consumption detection accuracy, and simultaneously avoids deviation of power management and control caused by accidental deviation data of the power consumption system;
Collecting electric energy data of the electric power utilization system monitored in real time, wherein the electric energy data comprise electric energy consumption parameters such as voltage, current, electric quantity and the like existing in the electric power utilization system in real time; randomly selecting operation time according to the operation time period of the power utilization system, constructing an electric energy data acquisition time period according to the time sequence by the selected operation time, acquiring electric energy data in the electric energy data acquisition time period, and carrying out aggregation sequencing on each electric energy data according to the same type of data to construct an electric energy data aggregation;
Acquiring the value deviation of the value of any two electric energy data in each electric energy data set and the value deviation of the value floating span peak value of the electric energy data of the type corresponding to the fault moment, acquiring the non-intersection time length corresponding to the value floating time period of the electric energy data of the type corresponding to the fault moment and the duration time period of the value instantaneous floating in the current electric energy data set, and respectively marking the value deviation of any two electric energy data in each electric energy data set and the value floating span peak value of the electric energy data of the type corresponding to the fault moment, the value floating time period of the electric energy data of the type corresponding to the fault moment and the non-intersection time length corresponding to the duration time period of the value instantaneous floating in the current electric energy data set as floating deviation information and floating duration information, and comparing the value deviation threshold value and the non-intersection time length threshold value with each other:
if the value deviation of the value of any two electric energy data in each electric energy data set and the value deviation of the value floating span peak value of the electric energy data of the type corresponding to the fault moment exceed a value deviation threshold value, or the value floating period of the electric energy data of the type corresponding to the fault moment and the duration period corresponding to the instantaneous floating of the value in the current electric energy data set do not exceed a non-intersection duration threshold value, the current value in the electric energy data set is removed, the processed electric energy data set is marked as a modeling analysis set, and the modeling analysis set is sent to a monitoring center cloud platform;
if the value deviation of the value of any two electric energy data in each electric energy data set and the value deviation of the value floating span peak value of the electric energy data of the type corresponding to the fault moment do not exceed a value deviation threshold value, and the value floating period of the electric energy data of the type corresponding to the fault moment and the duration period of the instantaneous floating of the value in the current electric energy data set exceed a non-intersection duration threshold value, carrying out value reservation on the current value in the electric energy data set, marking the reserved electric energy data set as a modeling analysis set, and sending the modeling analysis set to a monitoring center cloud platform;
Meanwhile, a data modeling analysis prediction signal is generated and sent to a data modeling analysis prediction unit, after the data modeling analysis prediction unit receives the data modeling analysis prediction signal, modeling analysis prediction is carried out on a modeling analysis set, a data curve in a historical period is compared with a current data modeling set to judge whether the current modeling analysis set is abnormal or not, so that the prediction analysis of the trend of the electric energy data of the current electric energy system through the real-time electric energy system is facilitated, the abnormal probability of the electric energy data of the current electric energy system is deduced in time, the intelligent control efficiency of the electric energy data is improved, the operation qualification of the electric energy system is guaranteed, and the system operation influence caused by the electric energy data abnormality of the electric energy system is reduced;
Carrying out curve construction on the electric energy data values in the modeling analysis set, substituting and connecting the values of all subsets in the modeling analysis set by taking the time interval duration corresponding to the adjacent electric energy data values as the abscissa adjacent interval duration and taking the electric energy data values as the ordinate so as to construct an electric energy data real-time curve; meanwhile, the historical operation time period of the power utilization system is collected and historical operation time points are selected, the adjacent interval time length of the collected time points is consistent with the interval time length of the corresponding sequence in the current modeling analysis set, and meanwhile, the time period constructed by the collection time point of the historical operation time period comprises the power utilization system fault time period, the power utilization system fault occurrence time and the power utilization system fault ending time;
synchronously substituting the electric energy data values of the historical operation time points in the historical operation time period into a coordinate system, constructing an electric energy data historical curve, marking the curve angle as a fault risk angle according to the curve angle formed by the corresponding curve peak point of the power utilization system fault time period in the electric energy data historical curve, and marking the corresponding slope average value as a fault risk slope according to the curve slope average value of each time point in the corresponding curve between the power utilization system fault occurrence time and the ending time in the electric energy data historical curve;
If the curve angle formed by the corresponding curve points at any moment in the construction curve in the modeling analysis set does not exceed the fault risk angle, or the slope of the curve corresponding to any adjacent moment in the construction curve in the modeling analysis set does not exceed the curve slope average value, judging that the data of the current modeling analysis set is predicted abnormally, generating a predicted fault signal and sending the predicted fault signal to a monitoring center cloud platform, setting the moment point of the corresponding curve part in the modeling analysis set as a risk moment after the monitoring center cloud platform receives the predicted fault signal, tracing and monitoring the running process at the risk moment, and overhauling the equipment related to the power utilization system at the current risk moment;
If the curve angle formed by the curve points corresponding to any time in the constructed curves in the modeling analysis set exceeds the fault risk angle and the slope of the curve corresponding to any adjacent time in the constructed curves in the modeling analysis set exceeds the slope average value of the curve, judging that the data of the current modeling analysis set are predicted to be normal, generating a predicted normal signal and transmitting the predicted normal signal to a monitoring center cloud platform;
Generating a data real-time parameter early-warning signal and sending the data real-time parameter early-warning signal to a data real-time parameter early-warning unit, carrying out real-time parameter early-warning on electric energy data of the power utilization system after the data real-time parameter early-warning unit receives the data real-time parameter early-warning signal, deducing whether the electric energy data in the current power utilization system has abnormal parameters or not through real-time parameter analysis, and carrying out real-time efficiency monitoring on the operation of the power utilization system to ensure the management and control efficiency of the electric energy data, so that fault prediction can be carried out in time in the monitoring process of the electric energy data, and the operation efficiency of the power utilization system is ensured;
Acquiring the excessive amount of the instantaneous increment span and the increment span mean value of the floating of the real-time adjacent time values of the same type of electric energy data in the operation process of the electric system, simultaneously acquiring the span value of the synchronous increment time interval of the floating duration of the different type of electric energy data in the operation process of the electric system, and respectively marking the excessive amount of the instantaneous increment span and the increment span mean value of the floating of the real-time adjacent time values of the same type of electric energy data in the operation process of the electric system and the synchronous increment time interval span value of the floating duration of the different type of electric energy data in the operation process of the electric system as DCL and KDZ;
The continuous increasing speed of the current floating electric energy type data after the electric energy data of any type float in the operation process of the power utilization system is obtained, and the continuous increasing speed of the current floating electric energy type data after the electric energy data of any type float in the operation process of the power utilization system is marked as ZSD;
Uniformly marking the acquired data as real-time early warning parameters, and substituting the real-time early warning parameters into a formula to acquire real-time parameter early warning coefficients YJ in the operation process of the power utilization system, wherein the formula is as follows: wherein mkj, mkj, mkj are preset proportional coefficients, and beta is taken as an error correction factor, and the value is 0.98;
Comparing the real-time parameter early-warning coefficient YJ with a real-time parameter early-warning coefficient threshold value in the operation process of the power utilization system:
If the real-time parameter early-warning coefficient YJ exceeds the real-time parameter early-warning coefficient threshold value in the operation process of the power utilization system, judging that the data real-time parameter early-warning is abnormal in the operation process of the power utilization system, generating a high early-warning signal and sending the high early-warning signal to a monitoring center cloud platform, and after the monitoring center cloud platform receives the high early-warning signal, carrying out maintenance control on the power utilization system and continuously monitoring the electric energy data;
If the real-time parameter early warning coefficient YJ in the operation process of the power utilization system does not exceed the real-time parameter early warning coefficient threshold value, judging that the real-time parameter early warning of the data in the operation process of the power utilization system is normal, generating a low early warning signal and sending the low early warning signal to a monitoring center cloud platform;
meanwhile, an electric energy management strategy evaluation signal is generated and sent to an electric energy management strategy evaluation unit, after the electric energy management strategy evaluation unit receives the electric energy management strategy evaluation signal, electric energy management strategy evaluation is carried out on electric energy data of the electric energy utilization system in early warning, whether the electric energy control efficiency of the current electric energy utilization system meets actual requirements is deduced, the situation that the electric energy utilization system cannot be timely regulated when the electric energy data are abnormal is avoided, the operation efficiency of the electric energy utilization system is reduced, and the fact that the electric energy data are abnormal does not influence the integral operation of the electric energy utilization system is ensured;
The method comprises the steps that the overlapping time of the electric energy supply buffer time corresponding to the electric energy acquisition candidate strategy when electric energy data of the electric system are early-warned and the electric energy system early-warning operation time is obtained, meanwhile, the maximum control speed ratio corresponding to the electric energy data floating span growth speed after the electric energy acquisition candidate strategy is executed is obtained, the overlapping time of the electric energy supply buffer time corresponding to the electric energy acquisition candidate strategy when the electric energy data of the electric system is early-warned and the electric energy system early-warning operation time is obtained, the maximum control speed ratio corresponding to the electric energy data floating span growth speed corresponding to the electric energy acquisition candidate strategy is executed is marked as electric energy scheduling time data and electric energy control speed data when the electric energy data of the electric energy system is early-warned, and the electric energy scheduling time data and the maximum control speed ratio threshold are compared respectively:
If the overlapping time of the electric energy supply buffer time corresponding to the electric energy acquisition alternative strategy and the electric energy system early warning operation time exceeds the overlapping time threshold value during the electric energy system electric energy data early warning, or the maximum control speed ratio corresponding to the electric energy data floating span growth speed after the electric energy acquisition alternative strategy is executed is not over the maximum control speed ratio threshold value during the electric energy system electric energy data early warning, judging that the electric energy management strategy of the electric energy system is evaluated to be low-efficiency, generating a management strategy adjustment signal and sending the management strategy adjustment signal to a monitoring center cloud platform, and after the monitoring center cloud platform receives the management strategy adjustment signal, readjusting the electric energy management strategy of the electric energy system, particularly controlling the electric energy scheduling time consumption of the electric energy system and accelerating the electric energy data control speed;
If the overlapping time of the electric energy supply buffer time corresponding to the electric energy acquisition alternative strategy and the electric energy system early warning operation time does not exceed the overlapping time threshold value during the electric energy data early warning of the electric energy system, and the maximum control speed ratio corresponding to the electric energy data floating span growth speed after the electric energy acquisition alternative strategy is executed is over the maximum control speed ratio threshold value during the electric energy data early warning of the electric energy system, judging that the electric energy management strategy of the electric energy system is high-efficient in evaluation, generating a management strategy high-efficiency signal and sending the management strategy high-efficiency signal to the monitoring center cloud platform.
Embodiment 2 referring to fig. 2, a fault indicator synchronization accuracy intelligent detection method includes the following specific management steps:
Step one: data acquisition and monitoring, data acquisition and monitoring analysis are carried out on the electric energy consumption, electric energy data acquisition is carried out on the electric energy system monitored in real time, an electric energy data set is constructed according to the acquired electric energy data, data comparison processing is carried out on the electric energy data set, and a modeling analysis set is obtained after the completion;
Step two: data modeling analysis prediction, modeling analysis prediction is carried out on a modeling analysis set, and a predicted normal signal or a predicted fault signal is generated through modeling analysis early warning;
Step three: real-time parameter early warning of data, real-time parameter early warning of electric energy data of the power utilization system, acquisition of real-time early warning parameters, substitution of the real-time early warning parameters into a formula to acquire real-time parameter early warning coefficients in the operation process of the power utilization system, and comparison of coefficient threshold values to generate high early warning signals or low early warning signals;
Step four: and (3) carrying out electric energy management strategy evaluation when the electric energy data of the power utilization system are early-warned, acquiring electric energy scheduling duration data and electric energy control speed data, and deducing whether the electric energy management strategy evaluation is qualified or not according to data comparison.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
When the system is used, the data acquisition monitoring analysis unit performs data acquisition monitoring analysis on the electric energy consumption, performs electric energy data acquisition on the electric energy system monitored in real time, constructs an electric energy data set according to the acquired electric energy data, performs data comparison processing on the electric energy data set, and obtains a modeling analysis set after completion; the data modeling analysis prediction unit performs modeling analysis prediction on the modeling analysis set, generates a predicted normal signal or a predicted fault signal through modeling analysis early warning, and when the prediction is normal, the data real-time parameter early warning unit performs real-time parameter early warning on the power consumption system electric energy data, acquires real-time early warning parameters, substitutes a formula to acquire real-time parameter early warning coefficients in the power consumption system operation process, generates a high early warning signal or a low early warning signal according to coefficient threshold comparison, and transmits the high early warning signal or the low early warning signal to the monitoring center cloud platform; the power management strategy evaluation unit performs power management strategy evaluation on power data of the power utilization system during early warning, acquires power scheduling duration data and power control speed data, and deduces whether the power management strategy evaluation is qualified or not according to data comparison.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
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