CN112016748A - Dynamic analysis and quantitative evaluation method for running state of stability control device - Google Patents
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Abstract
The invention discloses a dynamic analysis and quantitative evaluation method for the running state of a stability control device, which belongs to the technical field of power grid safety and comprises the following steps: s1, constructing a digital virtual model completely mapped with the stability control device by using a digital twinning technology; s2, the stability control device and the digital virtual model output data form a twin database of the stability control device; s3, realizing comprehensive perception of the running state of the stability control device through multi-dimensional data real-time acquisition and data preprocessing technology on the basis of a twin database of the stability control device; s4, constructing a fault feature library and a safety index system of the stability control device according to the comprehensive perception result of the operation state of the stability control device, and carrying out dynamic analysis and quantitative evaluation on the operation state of the stability control device. The method of the invention utilizes a digital twin simulation means to realize dynamic analysis and quantitative evaluation of the operation state of the power grid stability control device, thereby ensuring safe and stable operation of the power grid under the condition that the power supply structure, the load type and the network topology are deeply changed.
Description
Technical Field
The invention relates to the technical field of power grid safety, in particular to a dynamic analysis and quantitative evaluation method for the running state of a stability control device.
Background
The ultrahigh voltage direct current projects in multiple cross-regions of China are put into operation in succession, and the characteristics of strong direct current and weak alternating current of a power grid are further highlighted; the grid-connected capacity of new energy such as wind power, photovoltaic and the like is continuously increased, the long-distance trans-regional power transmission scale is further improved, the grid pattern and the power structure are greatly changed, the operation characteristics of the power grid are deeply changed, the safe operation of the power grid faces a large risk, and the three aspects are intensively embodied: firstly, the impact of the fault on the system is global, and the vulnerability of the power grid is increased. Secondly, the power supply structure is deeply changed, and the power grid regulation capability is seriously reduced. Thirdly, the power electronization characteristics are highlighted, and the stable form of the power grid is more complicated. The recognition method, the defense concept and the control technology formed based on the traditional alternating current system lag behind the operation practice of the extra-high voltage alternating current and direct current power grid, the security control technology for guaranteeing the safety of the power grid is not suitable for the new characteristics of the operation of the power grid, the operation control concept of the modern power system needs to be reviewed again, a comprehensive defense system for the safety of the large power grid is reconstructed, and the safe operation of the power grid is guaranteed.
Disclosure of Invention
In view of the above-mentioned drawbacks of the background art, an object of the present invention is to provide a method for dynamically analyzing and quantitatively evaluating an operating state of a stability control device, which dynamically analyzes and quantitatively evaluates a safety situation of the stability control device by constructing a fault feature library and a safety index system of the stability control device, so as to solve the problems in the background art.
The embodiment of the invention provides a dynamic analysis and quantitative evaluation method for the running state of a stability control device, which comprises the following steps:
s1, constructing a digital virtual model completely mapped with the stability control device by using a digital twinning technology;
s2, the stability control device and the digital virtual model output data form a twin database of the stability control device;
s3, realizing comprehensive perception of the running state of the stability control device through multi-dimensional data real-time acquisition and data preprocessing technology on the basis of a twin database of the stability control device;
s4, constructing a fault feature library and a safety index system of the stability control device according to the comprehensive perception result of the operation state of the stability control device, and carrying out dynamic analysis and quantitative evaluation on the operation state of the stability control device.
In a preferred embodiment of the present invention, the step S3 includes:
s31, carrying out multi-dimensional data real-time acquisition on the digital virtual model of the stability control device;
s32, preprocessing the multi-dimensional data acquired in real time;
and S33, analyzing and predicting the running state of the stability control device based on the preprocessed multidimensional data.
In a preferred embodiment of the present invention, the step S31 includes acquiring and analyzing time domain and frequency domain characteristic data of the stability control device data in a large disturbance and steady-state operation state, and the specific steps are as follows:
s311, respectively carrying out multi-dimensional data acquisition aiming at the large disturbance and the steady state running state of the stability control device, and establishing a related database;
s312, respectively sequencing the data in the database in a time sequence mode aiming at the two conditions of large disturbance and steady-state operation states, and analyzing the time domain characteristics of the data;
and S313, respectively carrying out frequency spectrum estimation on the data time sequences in the large disturbance and steady operation states by utilizing fast Fourier transform, and analyzing the frequency domain characteristics of the data time sequences.
In a preferred embodiment of the present invention, the step S32 includes:
s321, combing original data by using a bad data pre-screening method aiming at packet loss, errors and repeated conditions in data acquisition, and removing incomplete and wrong bad data;
s322, selecting a proper data signal pre-filter to process the original data aiming at noise data possibly doped in the original data to obtain a most representative data set;
s323, selecting a data time length for running state analysis and prediction, wherein the data time length is constrained in a control time step of the stability control device, and the data time length can sufficiently and accurately reflect the corresponding running state of the device.
In a preferred embodiment of the present invention, the step S33 includes:
s331, analyzing and calculating the running states of the stability control device of the historical and current time nodes based on the preprocessed multidimensional data, and establishing a device running state database based on a time sequence;
and S332, predicting the future state by using an exponential smoothing method on the basis of the device operation state database.
In a preferred embodiment of the invention, the exponential smoothing method is as follows:
setting an exponential smoothing initial value
For the earliest m data y in the data time series1,y2,...,ymAverage value of (i), i.e.
The first exponential smoothing calculation formula is:
wherein the content of the first and second substances,
and
respectively representing the first exponential smoothing values of the t-th phase and the t-1 th phase, alpha is a smoothing coefficient,
is the predicted value of the t +1 th stage, the predicted value of the t +1 th stage
Equal to the first exponential smoothing value of period t
The range of the smoothing coefficient is 0<α<1。
In a preferred embodiment of the invention, constructing a fault feature library of the stability control device comprises the following steps:
s411, dividing the fault into different fault types according to various fault characteristics in the operation process of the stabilizing device to form a historical database;
s412, aiming at each fault type, training a corresponding digital feature model by using a machine learning algorithm on the basis of historical data of the fault type;
and S413, combining the expert processing records to form a basis for accurately judging the future fault state of the stability control device, enriching and updating the feature library aiming at different new-state faults, and finally establishing a fault feature library of the stability control device.
In a preferred embodiment of the invention, the construction of the safety index system of the stability control device comprises the following steps:
s421, determining a safe operation domain of the stability control device according to the safe operation characteristics of the stability control device and by combining constraint conditions such as upper and lower limits of various operation parameters;
and S422, according to the safe operation domain of the stability control device, providing parameter indexes capable of accurately and precisely representing the safety situation of the device, and constructing a set of safety index system.
The invention has the following advantages:
the dynamic analysis and quantitative evaluation of the running state of the power grid stability control device are realized by using a digital twin simulation means, so that the safe and stable running of the power grid under the condition that the power supply structure, the load type and the network topology are deeply changed is ensured.
Based on historical and current operation data of the stability control device, and in combination with data mining and modeling analysis, quantitative evaluation of the safety situation of the current stability control device, timely diagnosis of past problems and accurate prediction of future operation states can be realized.
Drawings
The drawings of the invention are illustrated as follows:
fig. 1 is a flowchart of a dynamic analysis and quantitative evaluation method for an operation state of a stability control device according to an embodiment of the present invention.
FIG. 2 is a flow chart of establishing a fault feature library of the stability control device in the embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
In order to better adapt to new changes of the characteristics of the power grid, the focusing power grid safety and stability control device is used as a core element in a risk prevention and control system, and technical breakthroughs in 3 dimensions of target, time and space are needed. In the target dimension, for various stability problems possibly occurring in the whole process of fault evolution, a stability control device needs to have stronger robustness, can adaptively adjust the output of the device according to different scenes, improves the stability margin and the anti-interference capability of a system, inhibits disturbance impact, blocks chain reaction and effectively prevents the system from collapsing; in the time dimension, aiming at the time scale characteristics of different stable forms, the input, operation and exit time of the stability control device can be automatically adjusted, and the intelligent control of the dynamic process of the system is realized; in the space dimension, information exchange is carried out between stability control devices in different regions and different voltage levels aiming at high intensity and large range of disturbance impact, and an intelligent control framework combining distribution autonomy and wide area coordination is formed. However, the existing power grid security control system and control theory are difficult to meet the technical requirements of the stability control device due to the problems of multiple types of system stability control devices, wide regions, large control quantity and multi-band, multi-time scale and multi-stability protection range.
Therefore, an embodiment of the present invention provides a method for dynamically analyzing and quantitatively evaluating an operation state of a stability control device, as shown in fig. 1, including the following steps:
s1, constructing a digital virtual model completely mapped with the stability control device by using a digital twinning technology, and realizing accurate real-time description of the stability control device of the physical entity in a computer digital space;
s2, the stability control device and the digital virtual model output data form a twin database of the stability control device, so that the high-efficiency fusion of physical data and virtual data is realized, and a foundation is laid for the comprehensive perception and quantitative evaluation of the subsequent operation state of the stability control device;
s3, on the basis of a twin database of the stability control device, comprehensive perception of the running state of the stability control device and monitoring and management of the whole life cycle are realized through multi-dimensional data real-time acquisition and data preprocessing technology;
s4, constructing a fault feature library and a safety index system of the stability control device according to the comprehensive perception result of the operation state of the stability control device, and carrying out dynamic analysis and quantitative evaluation on the operation state of the stability control device.
Wherein, the dynamic analysis and quantitative evaluation of the running state of the stability control device comprises the following steps: the method comprises the steps of dynamically analyzing and quantitatively evaluating the current operation safety situation of the stability control device, and accurately predicting the future operation state of the stability control device.
The dynamic analysis and quantitative evaluation of the running state of the power grid stability control device are realized by using a digital twin simulation means, so that the safe and stable running of the power grid under the condition that the power supply structure, the load type and the network topology are deeply changed is ensured.
Based on historical and current operation data of the stability control device, and in combination with data mining and modeling analysis, quantitative evaluation of the safety situation of the current stability control device, timely diagnosis of past problems and accurate prediction of future operation states can be realized.
In a preferred embodiment of the present invention, the step S3 includes:
s31, carrying out multi-dimensional data real-time acquisition on the digital virtual model of the stability control device;
s32, preprocessing the multi-dimensional data acquired in real time;
and S33, analyzing and predicting the running state of the stability control device based on the preprocessed multidimensional data.
Specifically, the multidimensional data includes the following. The construction of the digital twin of the stability and control device is based on a twin database, and the twin database of the stability and control device mainly comprises related data of a physical entity of the device and a digital virtual model. The relevant data of the physical device mainly comprises various types, multiple scales and multiple granularity data sources such as geometric characteristics (such as shapes, sizes, tolerances and the like), physical characteristics (including a structure dynamics model, a thermodynamic model, a stress analysis model and a fatigue damage model) and the like; the related data of the digital virtual model is mainly divided into behavior and rule levels, and mainly comprises operation data of the virtual model, model data required by operation, simulation data, evaluation, optimization, prediction and other data.
Specifically, the real-time acquisition includes the following. For data (geometric, physical and other characteristics) which can be directly measured by a stability control device, advanced sensor technology can be used for realizing real-time data acquisition and transmission, and for data (behavior, rule and other characteristics) which cannot be directly measured, intelligent algorithms such as machine learning and the like can be used for conjecturing numerical values of the data.
In a preferred embodiment of the present invention, the step S31 includes acquiring and analyzing time domain and frequency domain characteristic data of the stability control device data in a large disturbance and steady-state operation state, and the specific steps are as follows:
s311, respectively carrying out multi-dimensional data acquisition aiming at the large disturbance and the steady state running state of the stability control device, and establishing a related database;
s312, respectively sequencing the data in the database in a time sequence mode aiming at the two conditions of large disturbance and steady-state operation states, and analyzing the time domain characteristics of the data;
and S313, respectively carrying out frequency spectrum estimation on the data time sequences under the large disturbance and steady operation states by utilizing Fast Fourier Transform (FFT), and analyzing the frequency domain characteristics of the data time sequences.
Preferably, aiming at the time delay generated in the data acquisition and transmission process of the stability control device, the probability statistical algorithm is utilized to analyze the time delay characteristic; and based on the data delay characteristics obtained by analysis, effectively compensating the data delay by utilizing algorithms such as Smith compensation and the like.
In a preferred embodiment of the present invention, the step S32 includes:
s321, combing original data by using a bad data pre-screening method aiming at packet loss, errors and repeated conditions in data acquisition, and removing incomplete and wrong bad data;
s322, selecting a proper data signal pre-filter to process the original data aiming at noise data possibly doped in the original data to obtain a most representative data set;
s323, selecting a data time length for running state analysis and prediction, wherein the data time length is constrained in a control time step of the stability control device, and the data time length can sufficiently and accurately reflect the corresponding running state of the device.
In a preferred embodiment of the present invention, the step S33 includes:
s331, analyzing and calculating the running states of the stability control device of the historical and current time nodes based on the preprocessed multidimensional data, and establishing a device running state database based on a time sequence;
and S332, predicting the future state by using an exponential smoothing method on the basis of the device operation state database.
In a preferred embodiment of the invention, the exponential smoothing method is as follows:
setting an exponential smoothing initial value
For the earliest m data y in the data time series1,y2,...,ymAverage value of (i), i.e.
The first exponential smoothing calculation formula is:
wherein the content of the first and second substances,
and
respectively representing the first exponential smoothing values of the t-th phase and the t-1 th phase, alpha is a smoothing coefficient,
is the predicted value of the t +1 th stage, the predicted value of the t +1 th stage
Equal to the first exponential smoothing value of period t
The range of the smoothing coefficient is 0<α<1。
The establishment of the fault feature library is the premise of dynamic analysis and quantitative evaluation of the security situation, and the safety index system of the stability control device is the important basis for the dynamic analysis and quantitative evaluation of the security situation.
As shown in fig. 2, in the preferred embodiment of the present invention, a fault feature library of the stability control device is constructed to provide a judgment basis for dynamic analysis of the operation safety situation of the stability control device. The specific steps are as follows:
s411, dividing the fault into different fault types according to various fault characteristics in the operation process of the stabilizing device to form a historical database;
s412, aiming at each fault type, training a corresponding digital feature model by using a machine learning algorithm on the basis of historical data of the fault type;
and S413, combining the expert processing records to form a basis for accurately judging the future fault state of the stability control device, enriching and updating the feature library aiming at different new-state faults, and finally establishing a fault feature library of the stability control device.
In the preferred embodiment of the invention, a safety index system of the stability control device is constructed, and a judgment standard is provided for quantitative evaluation and accurate prediction of the subsequent operation state of the stability control device. The specific implementation steps are as follows:
s421, determining a safe operation domain of the stability control device according to the safe operation characteristics of the stability control device and by combining constraint conditions such as upper and lower limits of various operation parameters;
and S422, according to the safe operation domain of the stability control device, providing parameter indexes capable of accurately and precisely representing the safety situation of the device, and constructing a set of safety index system.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered thereby.
Claims (8)
1. A dynamic analysis and quantitative evaluation method for the running state of a stability control device is characterized by comprising the following steps:
s1, constructing a digital virtual model completely mapped with the stability control device by using a digital twinning technology;
s2, the stability control device and the digital virtual model output data form a twin database of the stability control device;
s3, realizing comprehensive perception of the running state of the stability control device through multi-dimensional data real-time acquisition and data preprocessing technology on the basis of a twin database of the stability control device;
s4, constructing a fault feature library and a safety index system of the stability control device according to the comprehensive perception result of the operation state of the stability control device, and carrying out dynamic analysis and quantitative evaluation on the operation state of the stability control device.
2. The method of claim 1, wherein the step S3 includes:
s31, carrying out multi-dimensional data real-time acquisition on the digital virtual model of the stability control device;
s32, preprocessing the multi-dimensional data acquired in real time;
and S33, analyzing and predicting the running state of the stability control device based on the preprocessed multidimensional data.
3. The method of claim 2, wherein the step S31 includes the time domain and frequency domain characteristic data collection and analysis of the stability control device data under the large disturbance and steady state operation conditions, and includes the following specific steps:
s311, respectively carrying out multi-dimensional data acquisition aiming at the large disturbance and the steady state running state of the stability control device, and establishing a related database;
s312, respectively sequencing the data in the database in a time sequence mode aiming at the two conditions of large disturbance and steady-state operation states, and analyzing the time domain characteristics of the data;
and S313, respectively carrying out frequency spectrum estimation on the data time sequences in the large disturbance and steady operation states by utilizing fast Fourier transform, and analyzing the frequency domain characteristics of the data time sequences.
4. The method of claim 2, wherein the step S32 includes:
s321, combing original data by using a bad data pre-screening method aiming at packet loss, errors and repeated conditions in data acquisition, and removing incomplete and wrong bad data;
s322, selecting a proper data signal pre-filter to process the original data aiming at noise data possibly doped in the original data to obtain a most representative data set;
s323, selecting a data time length for running state analysis and prediction, wherein the data time length is constrained in a control time step of the stability control device, and the data time length can sufficiently and accurately reflect the corresponding running state of the device.
5. The method of claim 2, wherein the step S33 includes:
s331, analyzing and calculating the running states of the stability control device of the historical and current time nodes based on the preprocessed multidimensional data, and establishing a device running state database based on a time sequence;
and S332, predicting the future state by using an exponential smoothing method on the basis of the device operation state database.
6. The method of claim 5, wherein the exponential smoothing method is as follows:
setting an exponential smoothing initial value
For the earliest m data y in the data time series1,y2,...,ymAverage value of (i), i.e.
The first exponential smoothing calculation formula is:
wherein the content of the first and second substances,
and
respectively representing the first exponential smoothing values of the t-th phase and the t-1 th phase, alpha is a smoothing coefficient,
is the predicted value of the t +1 th stage, the predicted value of the t +1 th stage
Equal to the first exponential smoothing value of period t
The range of the smoothing coefficient is 0<α<1。
7. The method of any one of claims 1-6, wherein constructing a fault signature library of a stability control device comprises the steps of:
s411, dividing the fault into different fault types according to various fault characteristics in the operation process of the stabilizing device to form a historical database;
s412, aiming at each fault type, training a corresponding digital feature model by using a machine learning algorithm on the basis of historical data of the fault type;
and S413, combining the expert processing records to form a basis for accurately judging the future fault state of the stability control device, enriching and updating the feature library aiming at different new-state faults, and finally establishing a fault feature library of the stability control device.
8. The method of claim 7, wherein constructing a stability control device safety index system comprises the steps of:
s421, determining a safe operation domain of the stability control device according to the safe operation characteristics of the stability control device and by combining constraint conditions such as upper and lower limits of various operation parameters;
and S422, according to the safe operation domain of the stability control device, providing parameter indexes capable of accurately and precisely representing the safety situation of the device, and constructing a set of safety index system.
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| CN112615364A (en) * | 2020-12-02 | 2021-04-06 | 国网重庆市电力公司电力科学研究院 | Novel wide-area intelligent cooperative control method for power grid stability control device |
| CN113270886A (en) * | 2021-05-08 | 2021-08-17 | 国网宁夏电力有限公司电力科学研究院 | Method and system for evaluating adaptability of power grid stability control strategy of direct current group transmission end |
| CN113689119A (en) * | 2021-08-25 | 2021-11-23 | 国网上海市电力公司 | Power distribution network power supply reliability evaluation method, equipment and medium based on digital twins |
| CN114022030A (en) * | 2021-11-23 | 2022-02-08 | 国网重庆市电力公司电力科学研究院 | Dynamic detection analysis and risk assessment method for bus state of transformer substation |
| CN114114956A (en) * | 2021-11-10 | 2022-03-01 | 南方电网科学研究院有限责任公司 | Real-time digital simulation system based on external stability control simulation model |
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