CN105787606A - Power dispatching online trend early warning system based on ultra short term load prediction - Google Patents
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Abstract
The invention relates to the technical field of applying an online safety analysis result to load prediction, and particularly relates to a power dispatching online trend early warning system based on ultra short term load prediction. Particularly, through statistics, analysis and prediction on load data, a power grid future trend is calculated, and a power grid short term trend early warning and accident post-treatment plan is formed. The system of the invention comprises the following operation steps of short term load prediction based on electricity consumption large data analysis, trend tendency analysis through ultra short term load obtained based on an ARIMA algorithm, power grid risk assessment and warning, equipment maintenance schedule management, data access panoramic surveillance, treatment in the accident and power supply restoration after the accident. Linkage between the online trend early warning and dispatching plan change information can be realized, and a trend analysis early warning and assistant decision-making system with integration of a province and regions can be formed.
Description
Technical field
The present invention relates to and safety on line is analyzed result be applied in the technical field of load prediction, particularly relate to a kind of online tendency early warning system of the power scheduling based on ultra-short term, particular by load data being added up, analyze and predicting, calculate electrical network trend in future, form electrical network short-term trend early warning and accident post processing prediction scheme.
Background technology
At present, each province adjust electricity net safety stable analysis progressively by off-line to changing development online.The safety on line analysis system comprising static state, transient state, dynamic, voltage, frequency, small interference stability calculating and calculation of short-circuit current based on D5000 platform is used widely.But, when electrical network is in abnormal condition, still needs to rely on artificial experience and carry out judging and processing, and when dispatcher needs to process magnanimity information at short notice, cognitive disorder easily occurs.The development making dispatching of power netwoks cannot be broken away from dispatcher and think factor negative effect, and degree of intelligence is not high.
For reducing intelligent scheduling interference from human factor, improving and dispatch ageing and quick-reaction capability, power scheduling trend cognition technology arises at the historic moment.On existing on-line analysis early warning technology basis, warning function for current operating conditions is extended to ultra-short term, effectively make up on-line early warning and trend analysis function between 96 Security Checkings is blank a few days ago, management and running personnel are helped to grasp the development trend of electrical network in following a period of time, accomplish to come with preparation, realize the comprehensive comprehensive assessment of safety and stability Multiple Time Scales comprehensively.
Summary of the invention
For above-mentioned the deficiencies in the prior art part, the present invention proposes a kind of online tendency early warning system of the power scheduling based on ultra-short term, purpose is by load data being added up, analyze and predicting, calculate electrical network trend in future, form electrical network short-term trend early warning and accident post processing prediction scheme.
In order to reach foregoing invention purpose, the present invention is achieved by the following technical solutions:
A kind of online tendency early warning system of the power scheduling based on ultra-short term, including following operating procedure:
(1) based on the short-term load forecasting of the big data analysis of power consumption；
(2) super short period load drawn based on ARIMA algorithm carries out trend trend analysis；
(3) power grid risk assessment alarm；
(4) overhaul of the equipments progress control；
(5) data access panoramaization monitors；
(6) accident processes；
(7) restore electricity after accident.
The described short-term load forecasting based on the big data analysis of power consumption, including: use Time Series Method to carry out short-term load forecasting；Time series method is a kind of quantitative forecasting technique, is widely used as a kind of conventional predicting means in data mining；Two tasks to time series modeling, one is analyze how current-period data is subject to the data influence of former phases, and two is variable regularity on the time changes.
Described time series algorithm is ARIMA model, ARIMA model full name is ARMA model (AutoRegressiveIntegratedMovingAverageModel, brief note ARIMA), it is the famous Time Series Forecasting Methods proposed the beginning of the seventies by Bock think of (Box) and Charles Jenkins (Jenkins), so being also called Box-Jenkins model, Bock think of-Jenkins method；Wherein (p, d, q) be called difference ARMA model to ARIMA, and AR is autoregression, and p is autoregression item；MA is rolling average, and q is rolling average item number, and d is the difference number of times that time series is done when becoming steady；The basic thought of ARIMA model is: the data sequence formed predicting object to elapse in time is considered as a random sequence, carrys out this sequence of approximate description with certain mathematical model；This model just can predict future value from seasonal effect in time series past value and present value after identified；Modern Statistical Methods, econometric model have been able to help enterprise that future is predicted to a certain extent；
ARIMA model, refers to and nonstationary time series is converted into stationary time series, then only to its lagged value and the present worth of stochastic error and lagged value, dependent variable is returned the model set up；In the identification process of ARIMA model, mainly use two instruments: auto-correlation function, be called for short ACF；Partial autocorrelation function, is called for short PACF；And the relevant figure of each of which, namely ACF, PACF trace designs relative to the length of lag；For a sequences y, its kth rank autocorrelation coefficient (rK) it is defined as its k rank auto-covariance variance divided by it；
In formula: y is sequence；rKFor the autocorrelation coefficient of k rank；N represents the upper bound, and t represents lower bound；
Formula (1) is the function about k, also referred to as auto-correlation function ACF (k)；Partial autocorrelation function PACF (k) has measured the dependency relation after delayed item in the middle of elimination affects between two lagged variables；
ARIMA (p, d, q) model be ARMA after the differential transformation of d rank (p, q) model, ARMA (p, q) general type of model is formula (2):
yt=c+ φ1yt-1+...+φpyt-p+εt+θ1εt-1+...+θqεt-q(2)；
In formula: c is noise average；φ1,φ2...φpFor auto-regressive parameter；θ1, θ2...θqFor rolling average parameter；εt,εt-1...εt-qFor white-noise process；
Therefore, electrical network super short period load can be predicted by ARIMA model, and then carry out next step trend analysis.
The described super short period load drawn based on ARIMA algorithm carries out trend trend analysis, including: online trend analysis early warning system carries out future-state Load flow calculation based on the prediction of lower 15 points of ultra-short term, ultra-short term wind-powered electricity generation and photovoltaic and real-time generation schedule, then utilizes the existing DSA hardware and software environment applied to carry out trend analysis；First calculate current section and calculate following section again, after online data is integrated, start the calculating of current section, carry out trend data inspection and future-state Load flow calculation simultaneously；After current section has calculated, utilize an existing group of planes and computing function to carry out the calculating of following 15 minutes sections, analyze electrical network evolving trend；Meanwhile, by i.e. Real-time Collection, comparative apparatus power flow changing, carry out the sudden change of supervision equipment trend, all kinds of grid disturbance of real-time perception；By show on giant-screen before and after a certain equipment moment catastrophe and modern yesterday synchronization rate of change, help dispatcher analyzes the change main cause of the trend of a certain element.
Described trend analysis functional module is arranged in on-line security and stability analysis, and first, the ultra-short term drawn by ARIMA algorithm inputs as the prediction module in the application of operation plan class；Then, prediction module coordinate the repair schedule in the application of operation plan class, generation schedule module are pushed in the following Load flow calculation functional module in on-line security and stability analysis；Meanwhile, following Load flow calculation functional module has also considered the online tideway integrating from data integration function module, and AC line plan and inter-provincial interconnection plan etc. the data of short-term trading management are comprehensively analyzed；It follows that following Load flow calculation functional module output trend trend is as the input of trend analysis functional module；Finally, the trend analysis result input of trend analysis functional module is shown and early warning in applying to electrical network monitor in real time with intelligent alarm, operating analysis and evaluation, management and running aid decision, completes the calculating of trend analysis, operation and output overall process.
Described power grid risk assessment alerts, including: by electric network swim, overhaul of the equipments, real-time weather, safety on line analysis, send out for introducing risk evaluating systems such as electric equilibrium, by constantly analysing in depth the data of electrical network inside the province, undertaken classifying by venture influence factor, be layered, the artificial nerve network model of classification and system；Operation of power networks risk assessment flow process includes information gathering, Risk Identification, risk assessment and deciding grade and level, Risk-warning；Information collection function is for collecting and process the various information that " Risk Identification " needs with " risk assessment " link；Namely the various risks factor affecting electric power netting safe running, risk indicator are carried out classification quantitative by Risk Identification, set up the rating score being index with percentage ratio.
Described overhaul of the equipments progress control, including: realizing each electric pressure, all kinds of repair apparatus are shown respectively and inquire about；Utilize time schedule Gantt chart real-time exhibition repair schedule；Employing equipment actual switch state access way grasps equipment state in real time；Automatically obtain electric network swim and control temporary section.
Described data access panoramaization monitors, including: the operation system of the multiple different types of data of system access, interface type enriches, and contains much information, it is achieved that data screening optimization, and has the space continuing extension；System access remote measurement, remote signalling point 180,000, trend sudden change calculates telemetry station more than 1200 every time, within every 15 seconds, refreshes once according to system, and 76778 dynamic telemetry points calculate, and calculates storage data every day and reaches 4.5 hundred million.
Described accident processes, including: when electrical network has an accident, for ensureing the stabilization of power grids, regulation and control personnel need to consult a large amount of information of first closing as system stability reference, such as consult protocol information, prediction scheme information, load condition, line parameter circuit value, accident treatment historical information etc.；But these contain much information, do not have incidence relation each other, and inquiry is wasted time and energy；Collect these relevant informations for this by platform is unified, and set up the incidence relation between information；When electrical network has an accident, according to accident information content, the analysis Query Result of accident relevant information can directly be pushed in face of regulation and control personnel by platform, provides data supporting for regulation and control personnel to the process of accident.
Restore electricity after described accident, including: the process of restoring electricity needed regulation and control personnel to be reported one by one by the form of phone in the past；After online trend analysis early warning system platform is set up, dispatcher can pass through platform and issue the operational order restored electricity, and the operational order that great majority restore electricity by directly quoting in conventional accident treatment history, can shorten overall recovery time.
Advantages of the present invention effect is:
The current electric network data application of China rests on basic aspect, and except routine work manages, other senior application are little.The present invention proposes that safety on line is analyzed result and is applied in load prediction field, major network safety on line analysis is blended with the application such as emergency commanding platform, intelligent prediction scheme system, power grid risk assessment, forms electrical network short-term trend early warning and accident post processing prediction scheme.Achieve online trending early warning and the linkage of operation plan transition information, constitute and economize ground integrated trend analysis early warning and assistant decision support system.
The basis of short-term load forecasting is the big data analysis technique of user power utilization amount.By somewhere history power consumption, analysis load characteristic, set up the time model of load.Utilities Electric Co. is by monitoring the operation big data variation of power consumption, perceiving regional load sudden change, and then load prediction information is fed back to a line traffic department, calculating thus triggering the online trend of traffic department, effective early warning before accident, and shorten and have a power failure and emergency repair time after accident.
Accompanying drawing explanation
Fig. 1 is present system block diagram；
Fig. 2 is power grid risk assessment model.
Detailed description of the invention
The present invention is a kind of online tendency early warning system of the power scheduling based on ultra-short term, specifically includes following operating procedure:
1, based on the short-term load forecasting of the big data analysis of power consumption.
Time Series Method is used to carry out short-term load forecasting.Time series method is a kind of quantitative forecasting technique, is widely used as a kind of conventional predicting means in data mining.Two tasks to time series modeling, one is analyze how current-period data is subject to the data influence of former phases, and two is variable regularity on the time changes.The time series algorithm that the present invention selects is ARIMA model.ARIMA model full name is ARMA model (AutoRegressiveIntegratedMovingAverageModel, brief note ARIMA), it is the famous Time Series Forecasting Methods proposed the beginning of the seventies by Bock think of (Box) and Charles Jenkins (Jenkins), so being also called Box-Jenkins model, Bock think of-Jenkins method.Wherein (p, d, q) be called difference ARMA model to ARIMA, and AR is autoregression, and p is autoregression item；MA is rolling average, and q is rolling average item number, and d is the difference number of times that time series is done when becoming steady.The basic thought of ARIMA model is: the data sequence formed predicting object to elapse in time is considered as a random sequence, carrys out this sequence of approximate description with certain mathematical model.This model just can predict future value from seasonal effect in time series past value and present value after identified.Modern Statistical Methods, econometric model have been able to help enterprise that future is predicted to a certain extent.
ARIMA model, refers to and nonstationary time series is converted into stationary time series, then only to its lagged value and the present worth of stochastic error and lagged value, dependent variable is returned the model set up.In the identification process of ARIMA model, mainly use two instruments: auto-correlation function, be called for short ACF；Partial autocorrelation function, is called for short PACF；And the relevant figure of each of which, namely ACF, PACF trace designs relative to the length of lag.For a sequences y, its kth rank autocorrelation coefficient (rK) it is defined as its k rank auto-covariance variance divided by it.
In formula: y is sequence；rKFor the autocorrelation coefficient of k rank.N represents the upper bound, and t represents lower bound.
Formula (1) is the function about k, also referred to as auto-correlation function ACF (k).Partial autocorrelation function PACF (k) has measured the dependency relation after delayed item in the middle of elimination affects between two lagged variables.
ARIMA (p, d, q) model be ARMA after the differential transformation of d rank (p, q) model, ARMA (p, q) general type of model is formula (2):
yt=c+ φ1yt-1+...+φpyt-p+εt+θ1εt-1+...+θqεt-q(2)；
In formula: c is noise average；φ1,φ2...φpFor auto-regressive parameter；θ1, θ2...θqFor rolling average parameter；εt,εt-1...εt-qFor white-noise process.
Therefore, electrical network super short period load can be predicted by ARIMA model, and then carry out next step trend analysis.
2, the ultra-short term drawn based on ARIMA algorithm carries out trend analysis.
Online trend analysis early warning system carries out future-state Load flow calculation based on the prediction of lower 15 points of ultra-short term, ultra-short term wind-powered electricity generation and photovoltaic and real-time generation schedule, then utilizes the existing DSA hardware and software environment applied to carry out trend analysis.First calculate current section and calculate following section again, after online data is integrated, start the calculating of current section, carry out trend data inspection and future-state Load flow calculation simultaneously.After current section has calculated, utilize an existing group of planes and computing function to carry out the calculating of following 15 minutes sections, analyze electrical network evolving trend.Meanwhile, by i.e. Real-time Collection, comparative apparatus power flow changing, carry out the sudden change of supervision equipment trend, all kinds of grid disturbance of real-time perception.By show on giant-screen before and after a certain equipment moment catastrophe and modern yesterday synchronization rate of change, help dispatcher analyzes the change main cause of the trend of a certain element.
As it is shown in figure 1, trend analysis functional module is arranged in on-line security and stability analysis.First, the ultra-short term drawn by ARIMA algorithm inputs as the prediction module in the application of operation plan class.Then, prediction module coordinate the repair schedule in the application of operation plan class, generation schedule module are pushed in the following Load flow calculation functional module in on-line security and stability analysis.Meanwhile, following Load flow calculation functional module has also considered the online tideway integrating from data integration function module, and AC line plan and inter-provincial interconnection plan etc. the data of short-term trading management are comprehensively analyzed.It follows that following Load flow calculation functional module output trend trend is as the input of trend analysis functional module.Finally, the trend analysis result input of trend analysis functional module is shown and early warning in applying to electrical network monitor in real time with intelligent alarm, operating analysis and evaluation, management and running aid decision, completes the calculating of trend analysis, operation and output overall process.
3, power grid risk assessment alarm.
By electric network swim, overhaul of the equipments, real-time weather, safety on line analysis, send out for introducing risk evaluating systems such as electric equilibrium, by constantly analysing in depth the data of electrical network inside the province, undertaken classifying by venture influence factor, be layered, the artificial nerve network model of classification and system.As in figure 2 it is shown, operation of power networks risk assessment flow process includes information gathering, Risk Identification, risk assessment, risk deciding grade and level, Risk-warning.Information collection function is for collecting and process the various information that " Risk Identification " needs with " risk assessment " link.Namely the various risks factor affecting electric power netting safe running, risk indicator are carried out classification quantitative by Risk Identification, set up the rating score being index with percentage ratio.
4, overhaul of the equipments progress control.
Realizing each electric pressure, all kinds of repair apparatus are shown respectively and inquire about；Utilize time schedule Gantt chart real-time exhibition repair schedule；Employing equipment actual switch state access way grasps equipment state in real time；Automatically obtain electric network swim and control temporary section.
5, data access panoramaization monitors
The operation system of the multiple different types of data of system access, interface type enriches, and contains much information, it is achieved that data screening optimization, and has the space continuing extension.System access remote measurement, remote signalling point 180,000, trend sudden change calculates telemetry station more than 1200 every time, within every 15 seconds, refreshes once according to system, and 76778 dynamic telemetry points calculate, and calculates storage data every day and reaches 4.5 hundred million.
6, accident processes.
When having an accident in electrical network, for ensureing the stabilization of power grids, regulation and control personnel need to consult a large amount of information of first closing as system stability reference, such as consult protocol information, prediction scheme information, load condition, line parameter circuit value, accident treatment historical information etc..But these contain much information, do not have incidence relation each other, and inquiry is wasted time and energy.Collect these relevant informations for this by platform is unified, and set up the incidence relation between information.When electrical network has an accident, according to accident information content, the analysis Query Result of accident relevant information can directly be pushed in face of regulation and control personnel by platform, provides data supporting for regulation and control personnel to the process of accident.
7, restore electricity after accident.
The process of restoring electricity needed regulation and control personnel to be reported one by one by the form of phone in the past.After online trend analysis early warning system platform is set up, dispatcher can pass through platform and issue the operational order restored electricity, and the operational order that great majority restore electricity by directly quoting in conventional accident treatment history, can shorten overall recovery time.
Claims (9)
1. based on the online tendency early warning system of the power scheduling of ultra-short term, it is characterized in that: include following operating procedure:
(1) based on the short-term load forecasting of the big data analysis of power consumption；
(2) super short period load drawn based on ARIMA algorithm carries out trend trend analysis；
(3) power grid risk assessment alarm；
(4) overhaul of the equipments progress control；
(5) data access panoramaization monitors；
(6) accident processes；
(7) restore electricity after accident.
2. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: the described short-term load forecasting based on the big data analysis of power consumption, including:
Time Series Method is used to carry out short-term load forecasting；Time series method is a kind of quantitative forecasting technique, is widely used as a kind of conventional predicting means in data mining；Two tasks to time series modeling, one is analyze how current-period data is subject to the data influence of former phases, and two is variable regularity on the time changes.
3. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 2, it is characterized in that: described time series algorithm is ARIMA model, ARIMA model full name is ARMA model (AutoRegressiveIntegratedMovingAverageModel, brief note ARIMA), it is the famous Time Series Forecasting Methods proposed the beginning of the seventies by Bock think of (Box) and Charles Jenkins (Jenkins), so being also called Box-Jenkins model, Bock think of-Jenkins method；Wherein (p, d, q) be called difference ARMA model to ARIMA, and AR is autoregression, and p is autoregression item；MA is rolling average, and q is rolling average item number, and d is the difference number of times that time series is done when becoming steady；The basic thought of ARIMA model is: the data sequence formed predicting object to elapse in time is considered as a random sequence, carrys out this sequence of approximate description with certain mathematical model；This model just can predict future value from seasonal effect in time series past value and present value after identified；Modern Statistical Methods, econometric model have been able to help enterprise that future is predicted to a certain extent；
ARIMA model, refers to and nonstationary time series is converted into stationary time series, then only to its lagged value and the present worth of stochastic error and lagged value, dependent variable is returned the model set up；In the identification process of ARIMA model, mainly use two instruments: auto-correlation function, be called for short ACF；Partial autocorrelation function, is called for short PACF；And the relevant figure of each of which, namely ACF, PACF trace designs relative to the length of lag；For a sequences y, its kth rank autocorrelation coefficient (rK) it is defined as its k rank auto-covariance variance divided by it；
In formula: y is sequence；rKFor the autocorrelation coefficient of k rank；N represents the upper bound, and t represents lower bound；
Formula (1) is the function about k, also referred to as auto-correlation function ACF (k)；Partial autocorrelation function PACF (k) has measured the dependency relation after delayed item in the middle of elimination affects between two lagged variables；
ARIMA (p, d, q) model be ARMA after the differential transformation of d rank (p, q) model, ARMA (p, q) general type of model is formula (2):
yt=c+ φ1yt-1+...+φpyt-p+εt+θ1εt-1+...+θqεt-q(2)；
In formula: c is noise average；φ1,φ2...φpFor auto-regressive parameter；θ1, θ2...θqFor rolling average parameter；εt,εt-1...εt-qFor white-noise process；
Therefore, electrical network super short period load can be predicted by ARIMA model, and then carry out next step trend analysis.
4. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: the described super short period load drawn based on ARIMA algorithm carries out trend trend analysis, including:
Online trend analysis early warning system carries out future-state Load flow calculation based on the prediction of lower 15 points of ultra-short term, ultra-short term wind-powered electricity generation and photovoltaic and real-time generation schedule, then utilizes the existing DSA hardware and software environment applied to carry out trend analysis；First calculate current section and calculate following section again, after online data is integrated, start the calculating of current section, carry out trend data inspection and future-state Load flow calculation simultaneously；After current section has calculated, utilize an existing group of planes and computing function to carry out the calculating of following 15 minutes sections, analyze electrical network evolving trend；Meanwhile, by i.e. Real-time Collection, comparative apparatus power flow changing, carry out the sudden change of supervision equipment trend, all kinds of grid disturbance of real-time perception；By show on giant-screen before and after a certain equipment moment catastrophe and modern yesterday synchronization rate of change, help dispatcher analyzes the change main cause of the trend of a certain element；
Described trend analysis functional module is arranged in on-line security and stability analysis, and first, the ultra-short term drawn by ARIMA algorithm inputs as the prediction module in the application of operation plan class；Then, prediction module coordinate the repair schedule in the application of operation plan class, generation schedule module are pushed in the following Load flow calculation functional module in on-line security and stability analysis；Meanwhile, following Load flow calculation functional module has also considered the online tideway integrating from data integration function module, and AC line plan and inter-provincial interconnection plan etc. the data of short-term trading management are comprehensively analyzed；It follows that following Load flow calculation functional module output trend trend is as the input of trend analysis functional module；Finally, the trend analysis result input of trend analysis functional module is shown and early warning in applying to electrical network monitor in real time with intelligent alarm, operating analysis and evaluation, management and running aid decision, completes the calculating of trend analysis, operation and output overall process.
5. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: described power grid risk assessment alerts, including:
By electric network swim, overhaul of the equipments, real-time weather, safety on line analysis, send out for introducing risk evaluating systems such as electric equilibrium, by constantly analysing in depth the data of electrical network inside the province, undertaken classifying by venture influence factor, be layered, the artificial nerve network model of classification and system；Operation of power networks risk assessment flow process includes information gathering, Risk Identification, risk assessment and deciding grade and level, Risk-warning；Information collection function is for collecting and process the various information that " Risk Identification " needs with " risk assessment " link；Namely the various risks factor affecting electric power netting safe running, risk indicator are carried out classification quantitative by Risk Identification, set up the rating score being index with percentage ratio.
6. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: described overhaul of the equipments progress control, including:
Realizing each electric pressure, all kinds of repair apparatus are shown respectively and inquire about；Utilize time schedule Gantt chart real-time exhibition repair schedule；Employing equipment actual switch state access way grasps equipment state in real time；Automatically obtain electric network swim and control temporary section.
7. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: described data access panoramaization monitors, including:
The operation system of the multiple different types of data of system access, interface type enriches, and contains much information, it is achieved that data screening optimization, and has the space continuing extension；System access remote measurement, remote signalling point 180,000, trend sudden change calculates telemetry station more than 1200 every time, within every 15 seconds, refreshes once according to system, and 76778 dynamic telemetry points calculate, and calculates storage data every day and reaches 4.5 hundred million.
8. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: process in described accident, including:
When having an accident in electrical network, for ensureing the stabilization of power grids, regulation and control personnel need to consult a large amount of information of first closing as system stability reference, such as consult protocol information, prediction scheme information, load condition, line parameter circuit value, accident treatment historical information etc.；But these contain much information, do not have incidence relation each other, and inquiry is wasted time and energy；Collect these relevant informations for this by platform is unified, and set up the incidence relation between information；When electrical network has an accident, according to accident information content, the analysis Query Result of accident relevant information can directly be pushed in face of regulation and control personnel by platform, provides data supporting for regulation and control personnel to the process of accident.
9. a kind of online tendency early warning system of the power scheduling based on ultra-short term according to claim 1, is characterized in that: restore electricity after described accident, including:
The process of restoring electricity needed regulation and control personnel to be reported one by one by the form of phone in the past；After online trend analysis early warning system platform is set up, dispatcher can pass through platform and issue the operational order restored electricity, and the operational order that great majority restore electricity by directly quoting in conventional accident treatment history, can shorten overall recovery time.
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| CN107527121A (en) * | 2017-09-18 | 2017-12-29 | 云南电网有限责任公司信息中心 | A kind of method of the information system running status diagnosis prediction of power network |
| CN110472772A (en) * | 2019-07-09 | 2019-11-19 | 长沙能川信息科技有限公司 | A kind of disconnecting switch overheat method for early warning and a kind of disconnecting switch overheat early warning system |
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| CN110390151A (en) * | 2019-07-12 | 2019-10-29 | 中国南方电网有限责任公司超高压输电公司广州局 | The converter station valve hall running environment method for early warning based on time series and laterally compared |
| CN110766236A (en) * | 2019-10-31 | 2020-02-07 | 云南电网有限责任公司昆明供电局 | Power equipment state trend prediction method based on statistical analysis and deep learning |
| CN110796567A (en) * | 2019-11-08 | 2020-02-14 | 上海电力大学 | Production early-newspaper application framework based on regulation cloud |
| CN110796567B (en) * | 2019-11-08 | 2023-12-05 | 上海电力大学 | Production early report application framework based on regulation and control cloud |
| CN110991910A (en) * | 2019-12-06 | 2020-04-10 | 广东电网有限责任公司 | Electric power system risk prediction method and device |
| CN110991910B (en) * | 2019-12-06 | 2022-04-12 | 广东电网有限责任公司 | Electric power system risk prediction method and device |
| CN113837420A (en) * | 2020-06-23 | 2021-12-24 | 三菱电机(中国)有限公司 | Power consumption prediction method, power consumption prediction system, and computer-readable storage medium |
| CN112668249A (en) * | 2020-07-17 | 2021-04-16 | 国网山东省电力公司电力科学研究院 | Online construction method and system for major repair technical modification scheme of primary equipment of power grid |
| CN112488418B (en) * | 2020-12-14 | 2023-09-26 | 中国南方电网有限责任公司 | Full topology load prediction method and device and computer equipment |
| CN112488418A (en) * | 2020-12-14 | 2021-03-12 | 中国南方电网有限责任公司 | Full-topology load prediction method and device and computer equipment |
| CN112988978A (en) * | 2021-04-27 | 2021-06-18 | 河南金明源信息技术有限公司 | Case trend analysis system in key field of public welfare litigation |
| CN112988978B (en) * | 2021-04-27 | 2024-03-26 | 河南金明源信息技术有限公司 | Case trend analysis system in important field of public service litigation |
| CN113723677A (en) * | 2021-08-26 | 2021-11-30 | 国网河北省电力有限公司 | Electric power system risk prediction system based on big data analysis |
| CN114091792A (en) * | 2022-01-21 | 2022-02-25 | 华电电力科学研究院有限公司 | Hydro-generator degradation early warning method, equipment and medium based on stable working conditions |
| CN115333100A (en) * | 2022-10-12 | 2022-11-11 | 四川中电启明星信息技术有限公司 | Roof photovoltaic power generation power cooperative control method and system |
| CN116979529A (en) * | 2023-09-22 | 2023-10-31 | 国网山西省电力公司太原供电公司 | Power load evaluation method, system, equipment and storage medium |
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| CN117097768A (en) * | 2023-10-18 | 2023-11-21 | 江苏百维能源科技有限公司 | Intelligent ammeter secure communication transmission system and method based on big data |
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