CN104037943A - Method and system for monitoring voltage and capable of improving power grid voltage quality - Google Patents

Abstract

The invention provides a method for monitoring voltage and capable of improving power grid voltage quality. The method for monitoring the voltage and capable of improving the power grid voltage quality includes: obtaining real time voltage data and voltage statistic data of a plurality of monitoring points; analyzing history status of voltage quality indexes according to the collected voltage statistic data of all the monitoring points; performing online prediction on future trends of the voltage quality indexes according to the current collected real time voltage data of all the monitoring points. A system for monitoring the voltage and capable of improving the power grid voltage quality, which is used to achieve the method, comprises a data acquisition unit, a history time period voltage quality index analysis unit, a future time period voltage quality index prediction unit and a data storage unit. The method and the system for monitoring the voltage and capable of improving the power grid voltage quality achieve deep analysis for the voltage quality and timely judgment for the future trends of the power grid voltage quality, and improve the effective degree of power grid voltage quality monitoring.

Description

A kind of voltage monitoring method and system that improve grid voltage quality

Technical field

The present invention relates to grid voltage quality monitoring technical field, relate in particular to a kind of voltage monitoring method and system that improve grid voltage quality.

Background technology

Along with the sustained and rapid development of national economy and improving constantly of living standards of the people, the very fast trend increasing of Electricity Demand can not change in a long time.The development of electrical network, the continuous growth of supply load, if voltage fluctuation surpasses allowed band and the time will produce very big harm when longer: for power supply grid, low-voltage can affect the ability of generation and supply electric equipment, affects power supply reliability; For power consumption equipment, spread of voltage, affects useful life, even burns, and increases line loss.Above-mentioned a variety of causes also brings harmful effect can to the normal electricity consumption of power consumer, therefore strengthens line voltage Real-Time Monitoring and data management analysis, and for improving, quality of voltage is particularly important.

At present the monitoring of quality of voltage and analysis aspect are had to some patents, if " 200910164280.9 (a kind of Network Voltage Stability on-line monitoring method based on voltage stability local indexes) " is according to the topological structure of electric parameter in the node adjacent area of point being monitored and interdependent node electric current and voltage phasor, calculate point being monitored single power consumption and transmit equivalent system parameters, voltage stability to monitoring point judges early warning, effectively realizes the on-line real time monitoring of Network Voltage Stability." 201210034024.X (a kind of method for supervising of quality of voltage) " first gathers the quality of voltage data of each electric pressure of regional power grid, by the analysis to data, adopt different measure, analyze judgement supply power voltage quality index, provide corresponding adjustment scheme, be conducive to tackle Adaptability Analysis occasion." 201310376476.0 (a kind of wide region range self-adapting voltage quality monitoring method) " adopts the voltage deviation rate of voltage and each nominal voltage by calculating, voltage deviation rate result to continuous calculating is carried out intellectual analysis, derive the rated voltage of current system, realize voltage monitoring range self-adapting, in situation without manual intervention, complete voltage quality monitoring." 201310528971.9 (a kind of method of the on-line prediction power system steady state voltage stability limit) " sample dimension reduction method based on Lasso, the screening sample method of Self-Organizing Feature Maps and error back propagation type neural net are carried out off-line training and on-line prediction to the power system steady state voltage stability limit, effectively improve off-line training efficiency and the on-line prediction effect of error back propagation type neural net.Above-mentioned patent is not analysed in depth grid voltage quality index, more grid voltage quality is not made to timely judgement in following trend, is therefore difficult to grid voltage quality to carry out effective monitoring.

Summary of the invention

The deficiency existing for prior art, the invention provides a kind of voltage monitoring method and system that improve grid voltage quality.

Technical scheme of the present invention is:

A voltage monitoring method that improves grid voltage quality, comprises the following steps:

Step 1: real-time voltage data and the voltage statistic data of obtaining a plurality of monitoring points;

Described voltage statistic data comprise voltage day statistics, voltage moon statistics, voltage season statistics, the voltage year statistical indicator and the indicator-specific statistics data of departments at different levels of monitoring point;

Data when wherein, the voltage moon, statistics comprised of that month voltage statistic value, last month voltage statistic value, of that month typical case day, last month data during typical case day, of that month voltage reliability data, last month voltage reliability data, of that month power-failure counting value and last month power-failure counting value; The annual statistical indicator of voltage comprises voltage year qualification rate, the annual super upper limit rate of voltage and the annual super lower limit rate of voltage; The indicator-specific statistics data of departments at different levels comprise day qualification rate statistics, month qualification rate statistics, the season qualification rate statistics and year qualification rate statistics of departments at different levels;

Step 2: according to the voltage statistic data of each monitoring point collecting, the account of the history of quality of voltage index is analyzed;

Step 2-1: the monitoring point of selecting to need analysis;

Step 2-2: select to need the quality of voltage index of analysis, quality of voltage index comprises rate of qualified voltage, voltage standard is poor and voltage probability density;

Step 2-3: select year quarterly, year monthly, season monthly or the moon analysis mode per diem, according to selected analysis mode, select concrete historical time section;

Step 2-4: calculate selected monitoring point in the quality of voltage desired value of this historical time section according to selected analysis mode and historical time section;

Step 2-5: the quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure;

Step 2-6: according to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine the monitoring point of quality of voltage index error, report to the police, so that electric power personnel carry out diagnostic process to the quality of voltage abnormal conditions of this monitoring point;

Step 3: according to the real-time voltage data of current each monitoring point collecting, the future trend of quality of voltage index is carried out to on-line prediction;

Step 3-1: the monitoring point of selecting to need prediction;

Step 3-2: the quality of voltage index of selecting to need prediction;

Step 3-3: select year quarterly, year monthly, season monthly or the moon prediction mode per diem, according to the prediction mode of selecting, select concrete future time section;

Step 3-4: when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

Step 3-5: the future trend on-line prediction model of setting up quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict;

Step 3-6: the future trend of the quality of voltage index of selected monitoring point is predicted according to the future trend on-line prediction model of the quality of voltage index of setting up;

Step 3-7: the on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure;

Step 3-8: according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning, so that electric power personnel are to existing the monitoring point of quality of voltage potential problem to carry out advanced processing.

The future trend on-line prediction model of setting up quality of voltage index in step 3-5 carries out as follows:

Step 3-5-1: settling time series model, time series models be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, time series models are output as the quality of voltage desired value of the future time section that will predict;

Step 3-5-2: set up gray model, gray model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, gray model output is the quality of voltage desired value of the future time section that will predict;

Step 3-5-3: set up combination forecasting, the input of combination forecasting comprises the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, combination forecasting is output as the weighting sum of the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, i.e. the quality of voltage desired value of the future time section of combination forecasting prediction;

Step 3-5-4: set up BP neural network model, the quality of voltage desired value of the future time section that is input as combination forecasting prediction of BP neural network model, BP neural network model is output as the error of quality of voltage desired value of the future time section of combination forecasting prediction;

Step 3-5-5: the future trend on-line prediction model of setting up quality of voltage index, the input of this model comprises the error of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, on-line prediction model is output as the error sum of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, be predicting the outcome of quality of voltage index.

The mode of the monitoring point that the selection described in the mode of the monitoring point that the selection described in step 2-1 need to be analyzed and step 3-1 need to be predicted includes and selects single monitoring point, selects a plurality of monitoring points, once selects all relevant monitoring points of circuit, once selects all monitoring points of transformer station and once select all monitoring points of county power supply administration.

Described quality of voltage index comprises rate of qualified voltage, voltage standard is poor and voltage probability density.

Described in step 2-3, according to selected analysis mode, select concrete historical time section, when analysis mode is year quarterly time, historical time section is chosen as year; When analysis mode is year monthly time, historical time section is chosen as year; When analysis mode is season monthly time, historical time section is chosen as time and season; When analysis mode is the moon per diem time, historical time section is chosen as time and month.

The prediction mode according to selecting described in step 3-3 is selected concrete future time section, when prediction mode be year quarterly time, and the residue season that the affiliated time that predicts the outcome is the current year; When monitoring mode is year monthly time, the residue month that the time under predicting the outcome is the current year; When monitoring mode is season monthly time, the residue month that the time under predicting the outcome is current season; When monitoring mode is the moon per diem time, the residue number of days that the time under predicting the outcome is this month.

The quality of voltage achievement data to disappearance described in step 3-4 supplements the cubic spline interpolation method that adopts.

The grid voltage quality monitoring system that realizes the monitoring method of described raising grid voltage quality, comprises data capture unit, historical time section quality of voltage index analysis unit, future time section quality of voltage index prediction unit and data storage cell;

Data capture unit is for obtaining real-time voltage data and the voltage statistic data of monitoring point;

Historical time section quality of voltage index analysis unit is for the statistics of each monitoring point of obtaining according to data capture unit, account of the history to quality of voltage index is analyzed, and comprising: select the monitoring point of needs analysis and the quality of voltage index that needs are analyzed; Select year quarterly, year monthly, season monthly or the moon analysis mode per diem select concrete historical time section according to selected analysis mode; According to selected analysis mode and historical time section, calculate selected monitoring point in the quality of voltage desired value of this historical time section; The quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error;

Future time section quality of voltage index prediction unit is used for according to the real-time voltage data of current each monitoring point obtaining, the future trend of quality of voltage index is carried out to on-line prediction, comprising: select the monitoring point of needs prediction and the quality of voltage index of needs prediction; Select year quarterly, year monthly, season monthly or the moon prediction mode per diem select concrete future time section according to the prediction mode of selecting; When the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented; Set up the future trend on-line prediction model of quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict; According to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted; The on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure; According to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine that early warning is carried out in the monitoring point of future time section quality of voltage index error;

Data storage cell for the future trend on-line prediction model parameter, monitoring point of storing the real-time voltage data of monitoring point and voltage statistic data, quality of voltage index in every quality of voltage desired value of each historical time section and monitoring point the quality of voltage index prediction value in future time section.

Described historical time section quality of voltage index analysis unit comprises that historical time section quality of voltage desired value computing module and historical time section quality of voltage index analysis show and alarm module;

Historical time section quality of voltage desired value computing module is for selecting to need the monitoring point of analyzing, quality of voltage index and the analysis mode that needs analysis, according to selected analysis mode, select concrete historical time section, and then calculate selected monitoring point in the quality of voltage desired value of this historical time section;

The demonstration of historical time section quality of voltage index analysis and alarm module are for showing the quality of voltage desired value of selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error.

Described future time section quality of voltage index prediction unit comprises that monitoring point historical data complementary module, time series models set up module, gray model and set up module, combination forecasting and set up module, BP Establishment of Neural Model module, quality of voltage index prediction module, quality of voltage index prediction result and show and warning module;

Monitoring point historical data complementary module is for selecting to need the monitoring point of prediction, quality of voltage index and the prediction mode that need to predict, according to the prediction mode of selecting, select concrete future time section, when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

It is input for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture that time series models are set up module, and the quality of voltage desired value of the future time section that will predict of take is the time series models of output;

It is the gray model of output for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture as inputting, take the quality of voltage desired value of the future time section that will predict that gray model is set up module;

Combination forecasting is set up module, and for setting up, to take the quality of voltage desired value of future time section of time series models predictions and the quality of voltage desired value of the future time section of Grey Model be that input, the quality of voltage desired value of future time section of the time series models of take prediction and the weighting sum of the quality of voltage desired value of the future time section of Grey Model are the combination forecasting of exporting;

BP Establishment of Neural Model module is that the error of quality of voltage desired value of the future time section of input, the combination forecasting of take prediction is the BP neural network model of output for take the quality of voltage desired value of future time section of combination forecasting prediction;

For setting up, to take the error of quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output be input to quality of voltage index prediction module, the future trend on-line prediction model that the error sum of the quality of voltage desired value of the future time section of the combination forecasting prediction of the quality of voltage desired value of the future time section of the combination forecasting of take prediction and the output of BP neural network model is the quality of voltage index of output, and according to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted, obtain predicting the outcome of quality of voltage index,

The demonstration of quality of voltage index prediction result and warning module are for showing the on-line prediction result of the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure, and according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning.

Beneficial effect:

For current grid voltage quality monitoring method, quality of voltage do not analysed in depth and the future trend of grid voltage quality do not made to the problem that timely judgement causes being difficult to grid voltage quality to carry out effective monitoring, the present invention proposes a kind of monitoring method based on improving grid voltage quality, comprise data acquisition, monitoring point historical juncture quality of voltage Monitoring Indexes, the following quality of voltage index prediction constantly in the monitoring point based on combination forecasting and BP neural network model.Realized the in-depth analysis of quality of voltage and the timely judgement to grid voltage quality future trend, improved the significant degree of grid voltage quality monitoring.

Accompanying drawing explanation

Fig. 1 is the structured flowchart of monitoring system of the raising grid voltage quality of the specific embodiment of the invention;

Fig. 2 is the flow chart of monitoring method of the raising grid voltage quality of the specific embodiment of the invention;

Fig. 3 is monitoring result curve monthly of poor year of the monitoring point voltage standard of the specific embodiment of the invention monitoring method that improves grid voltage quality;

Fig. 4 is monitoring result rod figure monthly of poor year of the monitoring point voltage standard of monitoring method of raising grid voltage quality of the specific embodiment of the invention;

Fig. 5 is the year monitoring result curve monthly of two monitoring point rate of qualified voltage of monitoring method of the raising grid voltage quality of the specific embodiment of the invention;

Fig. 6 is the monitoring result monthly of year of two monitoring point rate of qualified voltage of the specific embodiment of the invention a kind of monitoring method of improving grid voltage quality;

Fig. 7 is the flow chart that the account of the history to quality of voltage index of the specific embodiment of the invention is analyzed;

Fig. 8 be the specific embodiment of the invention the future trend of quality of voltage index is carried out to the flow chart of on-line prediction;

Fig. 9 is the flow chart of the future trend on-line prediction model of setting up quality of voltage index of the specific embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is elaborated.

In present embodiment, improve the grid voltage quality monitoring system of the monitoring method of grid voltage quality, as shown in Figure 1, comprise data capture unit, historical time section quality of voltage index analysis unit, future time section quality of voltage index prediction unit and data storage cell;

Data capture unit is for obtaining real-time voltage data and the voltage statistic data of monitoring point;

Historical time section quality of voltage index analysis unit is for the statistics of each monitoring point of obtaining according to data capture unit, account of the history to quality of voltage index is analyzed, and comprising: select the monitoring point of needs analysis and the quality of voltage index that needs are analyzed; Select year quarterly, year monthly, season monthly or the moon analysis mode per diem select concrete historical time section according to selected analysis mode; According to selected analysis mode and historical time section, calculate selected monitoring point in the quality of voltage desired value of this historical time section; The quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error;

Future time section quality of voltage index prediction unit is used for according to the real-time voltage data of current each monitoring point obtaining, the future trend of quality of voltage index is carried out to on-line prediction, comprising: select the monitoring point of needs prediction and the quality of voltage index of needs prediction; Select year quarterly, year monthly, season monthly or the moon prediction mode per diem select concrete future time section according to the prediction mode of selecting; When the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented; Set up the future trend on-line prediction model of quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict; According to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted; The on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure; According to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine that early warning is carried out in the monitoring point of future time section quality of voltage index error;

Data storage cell for the future trend on-line prediction model parameter, monitoring point of storing the real-time voltage data of monitoring point and voltage statistic data, quality of voltage index in every quality of voltage desired value of each historical time section and monitoring point the quality of voltage index prediction value in future time section.

Historical time section quality of voltage index analysis unit comprises that historical time section quality of voltage desired value computing module and historical time section quality of voltage index analysis show and alarm module;

Historical time section quality of voltage desired value computing module is for selecting to need the monitoring point of analyzing, quality of voltage index and the analysis mode that needs analysis, according to selected analysis mode, select concrete historical time section, and then calculate selected monitoring point in the quality of voltage desired value of this historical time section;

The demonstration of historical time section quality of voltage index analysis and alarm module are for showing the quality of voltage desired value of selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error.

Future time section quality of voltage index prediction unit comprises that monitoring point historical data complementary module, time series models set up module, gray model and set up module, combination forecasting and set up module, BP Establishment of Neural Model module, quality of voltage index prediction module, quality of voltage index prediction result and show and warning module;

Monitoring point historical data complementary module is for selecting to need the monitoring point of prediction, quality of voltage index and the prediction mode that need to predict, according to the prediction mode of selecting, select concrete future time section, when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

It is input for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture that time series models are set up module, and the quality of voltage desired value of the future time section that will predict of take is the time series models of output;

It is the gray model of output for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture as inputting, take the quality of voltage desired value of the future time section that will predict that gray model is set up module;

Combination forecasting is set up module, and for setting up, to take the quality of voltage desired value of future time section of time series models predictions and the quality of voltage desired value of the future time section of Grey Model be that input, the quality of voltage desired value of future time section of the time series models of take prediction and the weighting sum of the quality of voltage desired value of the future time section of Grey Model are the combination forecasting of exporting;

BP Establishment of Neural Model module is that the error of quality of voltage desired value of the future time section of input, the combination forecasting of take prediction is the BP neural network model of output for take the quality of voltage desired value of future time section of combination forecasting prediction;

For setting up, to take the error of quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output be input to quality of voltage index prediction module, the future trend on-line prediction model that the error sum of the quality of voltage desired value of the future time section of the combination forecasting prediction of the quality of voltage desired value of the future time section of the combination forecasting of take prediction and the output of BP neural network model is the quality of voltage index of output, and according to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted, obtain predicting the outcome of quality of voltage index,

The demonstration of quality of voltage index prediction result and warning module are for showing the on-line prediction result of the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure, and according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning.

In present embodiment, improve the voltage monitoring method of grid voltage quality, its flow process as shown in Figure 2, comprises the following steps:

Step 1: real-time voltage data and the voltage statistic data of obtaining a plurality of monitoring points;

Real-time voltage data comprise 5 minutes real-time voltage data;

Voltage statistic data comprise voltage day statistics, voltage moon statistics, voltage season statistics, the voltage year statistical indicator and the indicator-specific statistics data of departments at different levels of monitoring point;

Data when wherein, the voltage moon, statistics comprised of that month voltage statistic value, last month voltage statistic value, of that month typical case day, last month data, of that month electric departments at different levels during typical case day indicator-specific statistics data press reliability data, last month voltage reliability data, of that month power-failure counting value and last month power-failure counting value; The annual statistical indicator of voltage comprises voltage year qualification rate, the annual super upper limit rate of voltage and the annual super lower limit rate of voltage; The indicator-specific statistics data of departments at different levels comprise day qualification rate statistics, month qualification rate statistics, the season qualification rate statistics and year qualification rate statistics of departments at different levels; Departments at different levels comprise transformer station, power supply station, Zi Kong district, county power supply administration and regional power office;

Step 2: according to the voltage statistic data of each monitoring point collecting, the account of the history of quality of voltage index is analyzed, as shown in Figure 7;

Step 2-1: the monitoring point of selecting to need analysis;

Selection needs the mode of the monitoring point of analysis to comprise the single monitoring point of selection, select a plurality of monitoring points, once select all relevant monitoring points of circuit, once select all monitoring points of transformer station and once select all monitoring points of county power supply administration.

Step 2-2: select to need the quality of voltage index of analysis, quality of voltage index comprises rate of qualified voltage, voltage standard is poor and voltage probability density;

Step 2-3: select year quarterly, year monthly, season monthly or the moon analysis mode per diem, according to selected analysis mode, select concrete historical time section;

When analysis mode is year quarterly time, historical time section is chosen as year; When analysis mode is year monthly time, historical time section is chosen as year; When analysis mode is season monthly time, historical time section is chosen as time and season; When analysis mode is the moon per diem time, historical time section is chosen as time and month.

Step 2-4: calculate selected monitoring point in the quality of voltage desired value of this historical time section according to selected analysis mode and historical time section;

Rate of qualified voltage=(1-voltage overtime/total operating statistic time) * 100%;

when s is that when day, voltage standard was poor, n is the quantity of sampled point in a day, for per day voltage; When s is that when the moon, voltage standard was poor, n is the number of days in January; for monthly average voltage; When s is that when year, voltage standard was poor, n is 12; for annual voltage;

Voltage density variation=∫ (g (x)-h (x)) dx, x represents magnitude of voltage, and g (x) is virtual voltage probability density distribution, and h (x) is that the expectation voltage that historical voltage data obtains distributes.

Step 2-5: the quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure;

Present embodiment shows that with the form of form the voltage standard of selected monitoring point is poor in Table 1:

The quality of voltage desired value of the selected monitoring point of table 1

Time	Monitoring point numbering	Monitoring point title	Voltage indexes title	Voltage indexes value	Evaluate
						In January, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.32	Better
In February, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.66	Better
						In March, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.01	Better
In April, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.16	Better
						In May, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.07	Better
In June, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.41	Better
						In July, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	2.12	Better

In August, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	3.22	Generally
						In September, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	0.96	Better
In October, 2011	02010101010000001	No. 1 LAN monitoring point	Voltage standard is poor	39.34	Poor

As shown in Figure 3, the fraction of the year that the curve in this figure being 2011, the voltage standard of the moon is poor for the quality of voltage desired value that present embodiment shows selected monitoring point with the form of curve; The quality of voltage desired value that shows selected monitoring point with the form of excellent figure as shown in Figure 4.

Step 2-6: according to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine the monitoring point of quality of voltage index error, report to the police, so that electric power personnel carry out diagnostic process to the quality of voltage abnormal conditions of this monitoring point;

Each voltage gradation standard deviation requirement set point is: good: [0,1); Better: [1,3); General: [3,10); Poor: [10,20); Poor [20 ,+∞).

According to the above results, what can be perfectly clear finds out that monitoring point is at the poor index extreme difference of the voltage standard in October, 2011, carry out alarm indication, instruct electric power personnel to monitoring point in October, 2011 quality of voltage situation investigate, realize the in-depth analysis to quality of voltage;

Step 3: according to the real-time voltage data of current each monitoring point collecting, the future trend of quality of voltage index is carried out to on-line prediction, as shown in Figure 8;

Step 3-1: the monitoring point of selecting to need prediction;

Selection needs the mode of the monitoring point of prediction to comprise the single monitoring point of selection, select a plurality of monitoring points, once select all relevant monitoring points of circuit, once select all monitoring points of transformer station and once select all monitoring points of county power supply administration.

Step 3-2: the quality of voltage index of selecting to need prediction;

Step 3-3: select year quarterly, year monthly, season monthly or the moon prediction mode per diem, according to the prediction mode of selecting, select concrete future time section;

When prediction mode is year quarterly time, the residue season that the time under predicting the outcome is the current year; When monitoring mode is year monthly time, the residue month that the time under predicting the outcome is the current year; When monitoring mode is season monthly time, the residue month that the time under predicting the outcome is current season; When monitoring mode is the moon per diem time, the residue number of days that the time under predicting the outcome is this month;

Step 3-4: when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

The method that data filling adopts is cubic spline interpolation algorithm, cubic spline functions S (x) ∈ C ²[a, b], a=x ₀< x ₁< ... < x _n=b, y _j=f (x _j), S (x) is at each minizone [x _j, x _j+1] on be cubic polynomial: S _j(x)=a _jx ³+ b _jx ²+ c _jx+d _jj=0,1 ..., n-1

A wherein _j, b _j, c _j, d _jundetermined, and it is met:

S(x _j)＝y _j j＝0,1,…,n

$\underset{x\&RightArrow;x_j}{\lim }S\left(x\right)=S\left(x_j\right),j=1,...,n-1$

$\underset{x\&RightArrow;x_j}{\lim }{S}^{\&prime;}\left(x\right)=S^{\&prime;}\left(x_j\right),j=1,...,n-1$

$\underset{x\&RightArrow;x_j}{\lim }{S}^{\&prime;\&prime;}\left(x\right)=S^{\&prime;\&prime;}\left(x_j\right),j=1,...,n-1$

X wherein _jrepresent j node in interval [a, b], y _jrepresent node x _jcorresponding functional value, S (x _j) represent that cubic spline functions is at node x _jcorresponding functional value, represent that S (x) is at node x _jlimiting value, represent that S (x) is at node x _jthe limiting value of first derivative, represent that S (x) is at node x _jthe limiting value of second dervative, S'(x _j) represent that S (x) is at node x _jfirst derivative, S " (x _j) represent that S (x) is at node x _jsecond dervative.

Step 3-5: the future trend on-line prediction model of setting up quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict, as shown in Figure 9;

Step 3-5-1: settling time series model, time series models be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, time series models are output as the quality of voltage desired value of the future time section that will predict;

In present embodiment, time series models adopt ARIMA (p, d, q) model, p wherein, and d, q represents respectively the exponent number of exponent number, difference order and the moving average model(MA model) of autoregression model.By Mathematical Modeling, carry out this sequence of approximate description, its representation is:

Wherein, X _tfor known variables, for hysteresis operator, a _jrepresent Parameters of Autoregressive Models coefficient, b _jrepresent moving average model(MA model) parameter, ε _trepresent independent identically distributed sequence of random variables.

Step 3-5-2: set up gray model, gray model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, gray model output is the quality of voltage desired value of the future time section that will predict;

(1) by primary voltage quality index value sequence X ⁽⁰⁾=[x ⁽⁰⁾(1), x ⁽⁰⁾(2) ..., x ⁽⁰⁾(n)] obtain X ⁽¹⁾=[x ⁽¹⁾(1), x ⁽¹⁾(2) ..., x ⁽¹⁾(n)], x wherein ⁽⁰⁾(i) > 0 (i=1,2 ..., n) represent quality of voltage desired value; $x^{\left(1\right)}\left(t\right)=\underset{k=1}{\overset{t}{\mathrm{\&Sigma;}}}{x}^{\left(0\right)}\left(k\right),t=\mathrm{1,2},...,n;$

(2) the cumulative matrix B of structure and constant term vector Y _n,

$B=\left[\begin{array}{cc}-\frac{1}{2}[x^{\left(1\right)}\left(1\right)+x^{\left(1\right)}\left(2\right)]& 1\\ -\frac{1}{2}[x^{\left(1\right)}\left(2\right)+x^{\left(1\right)}\left(3\right)]& 1\\ ...& 1\\ -\frac{1}{2}[x^{\left(1\right)}(n-1)+x^{\left(1\right)}\left(n\right)]& 1\end{array}\right],Y_n={[x^{\left(0\right)}\left(2\right),...,x^{\left(0\right)}\left(n\right)]}^T$

(3) by least square method, solve grey parameter:

(4) albinism differential equation corresponding to GM (1,1) model is:

(5) by grey parameter substitution time respective function: ${\hat{x}}^{\left(1\right)}(t+1)=(x^{\left(0\right)}\left(1\right)-\frac{u}{a})e^{-\mathrm{at}}+\frac{u}{a},t=\mathrm{1,2},...,n,$ Cumulative reduction obtains ${\hat{x}}^{\left(0\right)}(t+1)={\hat{x}}^{\left(1\right)}(t+1)-{\hat{x}}^{\left(1\right)}\left(t\right);$

(6) right ${\hat{x}}^{\left(1\right)}(t+1)=(x^{\left(0\right)}\left(1\right)-\frac{u}{a})e^{-\mathrm{at}}+\frac{u}{a},t=\mathrm{1,2},...,n$ Reduction differentiate obtains predictive equation:

${\hat{x}}^{\left(0\right)}(t+1)=-a(x^{\left(0\right)}\left(1\right)-\frac{u}{a})e^{-\mathrm{at}}$

Step 3-5-3: set up combination forecasting, the input of combination forecasting comprises the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, combination forecasting is output as the weighting sum of the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, i.e. the quality of voltage desired value of the future time section of combination forecasting prediction;

Combination forecasting:

${\hat{x}}_t=w_1{\hat{x}}_{t1}+w_2{\hat{x}}_{t2},$

T=1 wherein, 2 ..., n, the quality of voltage index actual observed value of t phase is x _t, w ₁for the weight coefficient of time series models, w ₂for the weight coefficient of gray model, be the quality of voltage index prediction value of t phase time series models, it is the quality of voltage index prediction value of t phase gray model.Determining as follows of weight coefficient:

$\left\{\begin{array}{c}{w}_j=\left\{\begin{array}{cc}1\&times;|1-j|& n\&le;10\\ \frac{e_j^{-1}}{\underset{j=1}{\overset{2}{\mathrm{\&Sigma;}}}{e}_j^{-1}}& n>10\end{array}\right.\\ w_1+w_2=1\end{array}\right.$

In formula, e _jthe error mean square that is j model (j=1 represents time series models, and j=2 represents gray model) is poor, $e_j=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(x_j-{\hat{x}}_j)}^2.$

Step 3-5-4: set up BP neural network model, the quality of voltage desired value of the future time section that is input as combination forecasting prediction of BP neural network model, BP neural network model is output as the error of quality of voltage desired value of the future time section of combination forecasting prediction;

(1) determine the network number of plies: the three-layer network that adopts single hidden layer;

(2) determine each layer of neuronic number of network: selecting input layer number is 4, and output layer neuron number is 1, the start node number of setting hidden layer is 2;

(3) sample is selected and data preliminary treatment: the quality of voltage achievement data obtaining is divided into groups, one group of quality of voltage achievement data is used for composing training sample, another group quality of voltage achievement data forms test samples, for fear of between data because order of magnitude difference is compared with causing neural network forecast error larger greatly, need to be normalized input voltage quality index sample data: x wherein _tfor raw sample data, X _max, X _minbe respectively original variable X _tin maximum and minimum value; S _tfor X _tvalue after conversion;

Model training: getting hidden layer excitation function is logarithm S type function, and output layer excitation function is pure linear function, choosing training function is that momentum gradient declines and adaptive learning speed training function, learning function is momentum gradient decline learning function.With training sample, according to the step of neural network algorithm, network is carried out to repetition training, until network convergence is in certain standard.Otherwise, repeating to change the initial weight of network, topology of networks even, until training result is satisfied, obtains the error of quality of voltage desired value of the future time section of combination forecasting prediction.

Step 3-5-5: the future trend on-line prediction model of setting up quality of voltage index, the input of this model comprises the error of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, on-line prediction model is output as the error sum of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, be predicting the outcome of quality of voltage index.

With weighted array forecast model, quality of voltage index sample data is predicted, and obtained quality of voltage index prediction value with quality of voltage index prediction error e, with neural network model, obtain the predicted value of quality of voltage index prediction deviation e obtain final quality of voltage index prediction result.

Step 3-6: the future trend of the quality of voltage index of selected monitoring point is predicted according to the future trend on-line prediction model of the quality of voltage index of setting up;

Step 3-7: the on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure;

The rate of qualified voltage that present embodiment shows selected monitoring point with the form of form is in Table 2:

The rate of qualified voltage of the selected monitoring point of table 2

Time	Monitoring point numbering	Monitoring point title	Voltage indexes title	Voltage indexes value	Evaluate
						In January, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	96.88％	Generally
In February, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	96.78％	Generally
						In March, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	97.08％	Generally
In April, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	97.98％	Generally
						In May, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	96.58％	Generally
In June, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	97.98％	Generally
						In July, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	97.98％	Generally
In August, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	96.58％	Generally
						In September, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	94.62％	Generally
In October, 2011	02010101010000002	No. 2 LAN monitoring points	Rate of qualified voltage	98.47％	Better
						In January, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	97.08％	Generally
In February, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	96.88％	Generally
						In March, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	96.78％	Generally
In April, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	96.58％	Generally
						In May, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	96.58％	Generally
In June, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	97.98％	Generally
						In August, 2011	02010101010000004	No. 4 LAN monitoring points	Rate of qualified voltage	70.56％	Poor

Step 3-8: according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning, so that electric power personnel are to existing the monitoring point of quality of voltage potential problem to carry out advanced processing.

Each grade presses qualification rate target setting value to be: good: (99.5%, 100%]; Better: (98.0%, 99.5%]; General: (90.0%, 98.0%]; Poor: (80.0%, 90.0%]; Poor (0,90.0%].

According to above-mentioned demonstration result, what can be perfectly clear finds out that No. 4 LAN monitoring points are at the index extreme difference of the rate of qualified voltage in August, 2011, carry out early warning demonstration, instruct electric power personnel to investigate in advance the quality of voltage situation of No. 4 LAN monitoring points, improved the significant degree of grid voltage quality monitoring.

Claims (10)

1. a voltage monitoring method that improves grid voltage quality, is characterized in that, comprises the following steps:

Step 1: real-time voltage data and the voltage statistic data of obtaining a plurality of monitoring points;

Described voltage statistic data comprise voltage day statistics, voltage moon statistics, voltage season statistics, the voltage year statistical indicator and the indicator-specific statistics data of departments at different levels of monitoring point;

Data when wherein, the voltage moon, statistics comprised of that month voltage statistic value, last month voltage statistic value, of that month typical case day, last month data during typical case day, of that month voltage reliability data, last month voltage reliability data, of that month power-failure counting value and last month power-failure counting value; The annual statistical indicator of voltage comprises voltage year qualification rate, the annual super upper limit rate of voltage and the annual super lower limit rate of voltage; The indicator-specific statistics data of departments at different levels comprise day qualification rate statistics, month qualification rate statistics, the season qualification rate statistics and year qualification rate statistics of departments at different levels;

Step 2: according to the voltage statistic data of each monitoring point collecting, the account of the history of quality of voltage index is analyzed;

Step 2-1: the monitoring point of selecting to need analysis;

Step 2-2: select to need the quality of voltage index of analysis, quality of voltage index comprises rate of qualified voltage, voltage standard is poor and voltage probability density;

Step 2-3: select year quarterly, year monthly, season monthly or the moon analysis mode per diem, according to selected analysis mode, select concrete historical time section;

Step 2-4: calculate selected monitoring point in the quality of voltage desired value of this historical time section according to selected analysis mode and historical time section;

Step 2-5: the quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure;

Step 2-6: according to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine the monitoring point of quality of voltage index error, report to the police, so that electric power personnel carry out diagnostic process to the quality of voltage abnormal conditions of this monitoring point;

Step 3: according to the real-time voltage data of current each monitoring point collecting, the future trend of quality of voltage index is carried out to on-line prediction;

Step 3-1: the monitoring point of selecting to need prediction;

Step 3-2: the quality of voltage index of selecting to need prediction;

Step 3-3: select year quarterly, year monthly, season monthly or the moon prediction mode per diem, according to the prediction mode of selecting, select concrete future time section;

Step 3-4: when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

Step 3-5: the future trend on-line prediction model of setting up quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict;

Step 3-6: the future trend of the quality of voltage index of selected monitoring point is predicted according to the future trend on-line prediction model of the quality of voltage index of setting up;

Step 3-7: the on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure;

Step 3-8: according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning, so that electric power personnel are to existing the monitoring point of quality of voltage potential problem to carry out advanced processing.

2. the voltage monitoring method of raising grid voltage quality according to claim 1, is characterized in that: the future trend on-line prediction model of setting up quality of voltage index in step 3-5 carries out as follows:

Step 3-5-1: settling time series model, time series models be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, time series models are output as the quality of voltage desired value of the future time section that will predict;

Step 3-5-2: set up gray model, gray model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, gray model output is the quality of voltage desired value of the future time section that will predict;

Step 3-5-3: set up combination forecasting, the input of combination forecasting comprises the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, combination forecasting is output as the weighting sum of the quality of voltage desired value of future time section of time series models prediction and the quality of voltage desired value of the future time section of Grey Model, i.e. the quality of voltage desired value of the future time section of combination forecasting prediction;

Step 3-5-4: set up BP neural network model, the quality of voltage desired value of the future time section that is input as combination forecasting prediction of BP neural network model, BP neural network model is output as the error of quality of voltage desired value of the future time section of combination forecasting prediction;

Step 3-5-5: the future trend on-line prediction model of setting up quality of voltage index, the input of this model comprises the error of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, on-line prediction model is output as the error sum of the quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output, be predicting the outcome of quality of voltage index.

3. the voltage monitoring method of raising grid voltage quality according to claim 1, is characterized in that: the mode of the monitoring point that the selection described in the mode of the monitoring point that the selection described in step 2-1 need to be analyzed and step 3-1 need to be predicted includes and selects single monitoring point, selects a plurality of monitoring points, once selects all relevant monitoring points of circuit, once selects all monitoring points of transformer station and once select all monitoring points of county power supply administration.

4. the voltage monitoring method of raising grid voltage quality according to claim 1, is characterized in that: described quality of voltage index comprises rate of qualified voltage, voltage standard is poor and voltage probability density.

5. the voltage monitoring method of raising grid voltage quality according to claim 1, is characterized in that: described in step 2-3, according to selected analysis mode, select concrete historical time section, when analysis mode is year quarterly time, historical time section is chosen as year; When analysis mode is year monthly time, historical time section is chosen as year; When analysis mode is season monthly time, historical time section is chosen as time and season; When analysis mode is the moon per diem time, historical time section is chosen as time and month.

6. the voltage monitoring method of raising grid voltage quality according to claim 1, it is characterized in that: the prediction mode according to selecting described in step 3-3 is selected concrete future time section, when prediction mode is year quarterly time, the residue season that the time under predicting the outcome is the current year; When monitoring mode is year monthly time, the residue month that the time under predicting the outcome is the current year; When monitoring mode is season monthly time, the residue month that the time under predicting the outcome is current season; When monitoring mode is the moon per diem time, the residue number of days that the time under predicting the outcome is this month.

7. the voltage monitoring method of raising grid voltage quality according to claim 1, is characterized in that: the quality of voltage achievement data to disappearance described in step 3-4 supplements the cubic spline interpolation method that adopts.

8. the grid voltage quality monitoring system that realizes the monitoring method of raising grid voltage quality claimed in claim 1, is characterized in that: comprise data capture unit, historical time section quality of voltage index analysis unit, future time section quality of voltage index prediction unit and data storage cell;

Data capture unit is for obtaining real-time voltage data and the voltage statistic data of monitoring point;

Historical time section quality of voltage index analysis unit is for the statistics of each monitoring point of obtaining according to data capture unit, account of the history to quality of voltage index is analyzed, and comprising: select the monitoring point of needs analysis and the quality of voltage index that needs are analyzed; Select year quarterly, year monthly, season monthly or the moon analysis mode per diem select concrete historical time section according to selected analysis mode; According to selected analysis mode and historical time section, calculate selected monitoring point in the quality of voltage desired value of this historical time section; The quality of voltage desired value that shows selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error;

Future time section quality of voltage index prediction unit is used for according to the real-time voltage data of current each monitoring point obtaining, the future trend of quality of voltage index is carried out to on-line prediction, comprising: select the monitoring point of needs prediction and the quality of voltage index of needs prediction; Select year quarterly, year monthly, season monthly or the moon prediction mode per diem select concrete future time section according to the prediction mode of selecting; When the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented; Set up the future trend on-line prediction model of quality of voltage index, this model be input as the quality of voltage desired value in the scope during this period of time from current time to certain historical juncture, this model is output as the quality of voltage desired value of the future time section that will predict; According to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted; The on-line prediction result that shows the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure; According to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine that early warning is carried out in the monitoring point of future time section quality of voltage index error;

Data storage cell for the future trend on-line prediction model parameter, monitoring point of storing the real-time voltage data of monitoring point and voltage statistic data, quality of voltage index in every quality of voltage desired value of each historical time section and monitoring point the quality of voltage index prediction value in future time section.

9. the monitoring system of raising grid voltage quality according to claim 8, is characterized in that: described historical time section quality of voltage index analysis unit comprises that historical time section quality of voltage desired value computing module and historical time section quality of voltage index analysis show and alarm module;

Historical time section quality of voltage desired value computing module is for selecting to need the monitoring point of analyzing, quality of voltage index and the analysis mode that needs analysis, according to selected analysis mode, select concrete historical time section, and then calculate selected monitoring point in the quality of voltage desired value of this historical time section;

The demonstration of historical time section quality of voltage index analysis and alarm module are for showing the quality of voltage desired value of selected monitoring point with the form of form, curve and excellent figure; According to the quality of voltage desired value of selected monitoring point, each voltage gradation quality index set point, determine to report to the police in the monitoring point of quality of voltage index error.

10. the monitoring system of raising grid voltage quality according to claim 8, is characterized in that: described future time section quality of voltage index prediction unit comprises that monitoring point historical data complementary module, time series models set up module, gray model and set up module, combination forecasting and set up module, BP Establishment of Neural Model module, quality of voltage index prediction module, quality of voltage index prediction result and show and warning module;

Monitoring point historical data complementary module is for selecting to need the monitoring point of prediction, quality of voltage index and the prediction mode that need to predict, according to the prediction mode of selecting, select concrete future time section, when the quality of voltage achievement data of selected monitoring point in historical time section exists disappearance, the quality of voltage achievement data of disappearance is supplemented;

It is input for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture that time series models are set up module, and the quality of voltage desired value of the future time section that will predict of take is the time series models of output;

It is the gray model of output for the quality of voltage desired value of setting up in the scope during this period of time of take from current time to certain historical juncture as inputting, take the quality of voltage desired value of the future time section that will predict that gray model is set up module;

Combination forecasting is set up module, and for setting up, to take the quality of voltage desired value of future time section of time series models predictions and the quality of voltage desired value of the future time section of Grey Model be that input, the quality of voltage desired value of future time section of the time series models of take prediction and the weighting sum of the quality of voltage desired value of the future time section of Grey Model are the combination forecasting of exporting;

BP Establishment of Neural Model module is that the error of quality of voltage desired value of the future time section of input, the combination forecasting of take prediction is the BP neural network model of output for take the quality of voltage desired value of future time section of combination forecasting prediction;

For setting up, to take the error of quality of voltage desired value of the quality of voltage desired value of future time section of combination forecasting prediction and the future time section of the combination forecasting prediction of BP neural network model output be input to quality of voltage index prediction module, the future trend on-line prediction model that the error sum of the quality of voltage desired value of the future time section of the combination forecasting prediction of the quality of voltage desired value of the future time section of the combination forecasting of take prediction and the output of BP neural network model is the quality of voltage index of output, and according to the future trend on-line prediction model of the quality of voltage index of setting up, the future trend of the quality of voltage index of selected monitoring point is predicted, obtain predicting the outcome of quality of voltage index,

The demonstration of quality of voltage index prediction result and warning module are for showing the on-line prediction result of the quality of voltage index future trend of selected monitoring point with the form of form, curve and excellent figure, and according to the on-line prediction result of the quality of voltage index future trend of selected monitoring point, each voltage gradation quality index set point, determine the following monitoring point of quality of voltage index error constantly, carry out early warning.