CN114091705A - Power system instability analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a method and a device for analyzing the instability of a power system, electronic equipment and a storage medium, wherein the method comprises the steps of acquiring power data of the power system; inputting the acquired power data into a preset transient instability analysis model, and determining whether the power system is unstable or not; and if the power system is determined to be instable, judging the type of the instability, and positioning the instable area based on the shape clustering algorithm of the original time sequence. The method and the device can automatically judge whether the operation of the power system is unstable or not without the help of the actual experience of related workers, save manpower and material resources and improve the real-time performance and accuracy of judgment.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a method and a device for analyzing instability of a power system, electronic equipment and a storage medium.
Background
In actual operation of a power system, the power system is interfered by various disturbances and emergencies, and cannot continuously and stably operate. In some cases, a part of a device or a region in a power system may be unstable. I.e. unstable operation of the power system.
When the power system is not stably operated, damage and the like to devices in the power system may be caused. Therefore, it is very important to judge whether the operation of the power system is unstable or not in time. However, in the existing scheme, relevant workers are required to determine whether the operation of the power system is unstable or not based on actual experience, so that not only is manpower wasted, but also the judgment is influenced by human factors, and the accuracy is low.
Disclosure of Invention
The invention provides a method and a device for analyzing the instability of a power system, electronic equipment and a storage medium, and aims to solve the problem that the existing instability judgment is low in accuracy. The method and the device can automatically judge whether the operation of the power system is unstable or not without the help of the actual experience of related workers, save manpower and material resources and improve the real-time performance and accuracy of judgment.
The invention is realized by the following technical scheme:
a power system instability analysis method includes:
acquiring power data of a power system;
inputting the acquired power data into a preset transient instability analysis model, and determining whether the power system is unstable or not;
and if the power system is determined to be instable, judging the type of the instability, and positioning the instable area based on the shape clustering algorithm of the original time sequence.
Preferably, the shape clustering algorithm based on the original time sequence of the present invention specifically locates the unstable region including:
and (3) clustering based on a time series clustering algorithm by taking the Euclidean distance as a distance measurement mode of pairwise curves and adopting a hierarchical clustering mode to position unstable areas.
Preferably, the method of the present invention further comprises:
and inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model, and determining whether the power system generates low-frequency oscillation and/or a low-frequency oscillation region.
Preferably, the low-frequency oscillation damping ratio analysis model is built based on a Prony algorithm, and the principle of estimating model parameters by using the minimum sum of squared errors is adopted.
Preferably, the step of inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model and determining whether the power system generates low-frequency oscillation and the region of the low-frequency oscillation includes the following substeps:
determining the frequency, amplitude, phase and attenuation factor of each signal component in the power system based on power data;
calculating the damping ratio of each signal component based on the frequency, amplitude, phase and attenuation factor of each signal component;
determining whether the power system generates low-frequency oscillation based on the damping ratio of each signal component;
if it is determined that low frequency oscillation occurs, the area of low frequency oscillation is located.
Preferably, the acquired power data of the present invention includes: time information, voltage information, current information of the power system, configuration signals of the power system.
In a second aspect, the present invention provides an apparatus for analyzing instability of an electric power system, including an obtaining unit and an analyzing unit;
the acquisition unit is used for acquiring power data of a power system;
the analysis unit inputs the acquired power data into a preset transient instability analysis model, judges whether the power system is instable, determines the type of instability if the power system is instable, and positions an instable area based on a shape clustering algorithm of an original time sequence.
Preferably, the analysis unit of the present invention further inputs the acquired power data to a preset low-frequency oscillation damping ratio analysis model to determine whether the power system has low-frequency oscillation and/or a low-frequency oscillation region.
In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of the present invention when executing the computer program.
In a fourth aspect, the invention proposes a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to the invention.
The invention has the following advantages and beneficial effects:
1. the method realizes the automatic judgment of the instability of the power system based on the shape clustering algorithm, reduces the interference of human experience, and improves the real-time performance and the accuracy of the judgment.
2. The method determines the low-frequency oscillation area by using the low-frequency oscillation damping ratio analysis model, further improves the judgment accuracy, and further improves the safety and reliability of the operation of the power system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic flow chart of an analysis method of an electrical power system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a power system instability analysis method according to another embodiment of the present invention.
Fig. 3 is a schematic block diagram of a power system instability apparatus according to an embodiment of the present invention.
Fig. 4 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
In actual operation of an electric power system, the electric power system is often disturbed by various disturbances and emergencies, and cannot continuously and stably operate, and under some conditions, a certain part of devices or a certain area in the electric power system can be unstable, namely, the electric power system is unstable to operate, so that it is very important to judge whether the operation of the electric power system is unstable or not in time. However, in the prior art, whether the operation of the power system is unstable or not cannot be determined in time, and the accuracy is poor.
As shown in fig. 1, the method of the present embodiment includes:
The electric power data that this embodiment obtained can be the data that electric power system gathered, and current electric power system is in the in-process of building, just has set up many equipment that carry out data acquisition, can carry out electric power system's electric power data acquisition through these data acquisition equipment, and the electric power data of gathering specifically includes: time information, voltage information, current information, configuration information of the power system, and the like, at various locations of the power system.
And 120, inputting the acquired power data into a preset transient instability analysis model to obtain a first analysis result.
The first analysis result obtained in this embodiment includes: whether or not a power system is unstable, the type of instability, and the area of instability.
In the embodiment, a transient instability analysis model is adopted to realize the automatic analysis of whether the power system is unstable, the judgment is carried out without the help of the experience of related workers, and whether the power system is unstable or not, the type of the instability and the instability area can be quickly and accurately determined, so that the related workers can timely obtain the instability state of the power system to further adjust the instability state. The method provided by the embodiment not only saves manpower, but also improves the accuracy and reliability of judgment.
It should be noted that the power system instability types mainly include: 1. power angle instability: the method can be divided into single unit pair system instability, whole power plant pair system instability, certain district cluster pair main network instability, power angle instability among units in the isolated network and the like. 2. Voltage instability: the method can be divided into single node voltage instability, certain station voltage instability (multiple node voltage instability of a 500kV bus and a lower transmitting area thereof), regional transient voltage instability (multiple station voltage instability) and isolated network voltage instability. 3. Frequency instability: the method can be divided into isolated network frequency instability, sheet region frequency instability and main network frequency instability. Various instability problems can cause damage to the power system, such as: the power angle instability and the voltage instability often cause relatively serious consequences instantly, so that instrument damage, equipment failure and accidents are caused, personal safety is endangered, the product quality is seriously influenced by the frequency instability, and the safe and stable operation of the generator is endangered. It should be noted that, in practical applications, there are other types of instability, and the embodiment specifically describes the above obvious type of instability as an example.
The transient instability model adopted by the embodiment is used for determining whether the power system is unstable or not; and if the power system is unstable, judging the type of the instability, and positioning the unstable area based on the shape clustering algorithm of the original time sequence.
The shape clustering algorithm based on the original time sequence of the embodiment of the present embodiment specifically locates the unstable region, including:
and (3) clustering based on a time series clustering algorithm by taking the Euclidean distance as a distance measurement mode of pairwise curves and adopting a hierarchical clustering mode to position unstable areas.
It should be noted that, in the time series clustering algorithm, specifically, intervals with the same fault characteristics are collected together by performing curve clustering on mass data, so that the positioning and analysis of the fault range are realized. The clustering belongs to an unsupervised classification method in machine learning, and is used for automatically grouping objects with the maximum similarity with other objects in a group under the condition of no manual intervention, so that the method can be used for searching for frequently-occurring patterns in a data set, can also be used for carrying out abnormity detection on the data set, finds abnormal patterns which are accidentally generated in the data set, and is very suitable for the situation of searching for the fault range of a large-scale power system when the fault condition is not known in advance.
Time series clustering is classified into shape-based, feature-based and model-based clustering methods according to different clustering objects.
In the shape-based approach, the shapes of the two time series are matched as closely as possible by non-linear stretching and shrinking of the time axis. This method is also referred to as raw data-based method because it generally processes raw time-series data directly. Shape-based clustering algorithms typically employ traditional clustering methods that are compatible with static data while appropriately modifying their distance/similarity metrics.
In the feature-based approach, the original time series is converted into low-dimensional feature vectors; conventional clustering algorithms are then applied to the extracted feature vectors, in which equal length feature vectors are typically computed from each time series, followed by a euclidean distance metric.
In a model-based approach, the original time series are converted into model parameters (a parametric model for each time series), and then appropriate model distances and clustering algorithms (typically conventional clustering algorithms) are selected and applied to the extracted model parameters.
In a physical sense, the shape-based clustering method is obviously the most intuitive way to test whether each to-be-detected quantity of the power system can keep synchronization continuously. Next, a hierarchical clustering method is adopted, and the data set is divided at different levels according to the distance between clustering clusters, so as to form a tree-shaped clustering structure.
In the shape clustering algorithm based on the original time sequence adopted in this embodiment, methods such as dynamic time warping, euclidean distance, cross-correlation distance, minimum variance matching, histogram-based and sequence-weighted model matching, triangle similarity measurement and the like may be selected for the distance measurement of each two curves, wherein the most commonly used effective distance measurement methods are Dynamic Time Warping (DTW) and euclidean distance. The euclidean distance is the sum of the distances between the respective corresponding points in the two sequences calculated directly, while DTW allows points of one sequence to correspond to adjacent points of the other sequence, in which the minimum of the distances is sought. Compared with the simple and intuitive Euclidean distance, the DTW method is more suitable for the sequence with stretching or compression in time. Based on the more urgent time requirement, the sampling data of the power system has the lowest cost and better performance by adopting the Euclidean distance.
K-means, K-medoid and hierarchical clustering are three clustering algorithms based on Euclidean distance. Hierarchical clustering is a tree-shaped clustering structure which is aggregated from bottom to top or split from top to bottom, the number of clustering clusters can be inquired on any hierarchy, each sample in a data set is regarded as a clustering cluster, and then the clustering clusters are combined step by step according to inter-cluster distances, and finally the expected clustering number is achieved. The mode of K-means, K-medoid, requires the value of K to be determined. The value of K is not well determined because the power system is relatively responsible. The K value has a large influence on the clustering result, so that the scheme provided by the embodiment of the invention selects hierarchical clustering in order to adapt to the complex situation of the power system.
Example 2
The present embodiment further defines the method for analyzing the instability of the power system proposed in embodiment 1, and as shown in fig. 2, the method of the present embodiment further includes:
and step 230, inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model to obtain a second analysis result. Step 210 and step 220 are the same as step 110 and step 120 of embodiment 1, and are not described herein again.
The second analysis result of this embodiment specifically includes: the power system is in the region of low frequency oscillations and low frequency oscillations.
It should be noted that the low-frequency oscillation refers to that the electric power system generates relative swing of the rotors of the generators running in parallel under disturbance, continuously oscillates in the absence of damping, and generates corresponding oscillation on the power transmission line. The oscillation frequency is usually 0.1-2.5 Hz. It is generally considered that the low-frequency oscillation is caused by the fact that the negative damping in the power system counteracts the positive damping in the aspects of system machinery and the like, so that the system disturbance is amplified continuously under the condition that the total damping is small, and the low-frequency oscillation of power is caused, namely the low-frequency oscillation is a type of unstable operation of the power system.
The low-frequency oscillation can be divided into low-frequency oscillation of a local system and global low-frequency oscillation according to the occurrence range. Generally, the oscillation frequency is 0.7Hz or higher, which is low-frequency oscillation of a local system, and mainly oscillation between a single machine and a large system. The frequency below 0.7Hz is global low-frequency oscillation, wherein the oscillation frequency between 0.4Hz and 0.7Hz is usually the mutual oscillation between the clusters, and the frequency of all the clusters participating in the whole system is usually below 0.3 Hz. When a simple judgment is made first, the sweep range of the low-frequency oscillation can be judged by using the interval to which the oscillation frequency of the low-frequency oscillation belongs.
The low-frequency oscillation damping ratio analysis model of the embodiment is built based on a Prony algorithm. The Prony method (namely the Prony algorithm) adopts the principle of estimating the parameters of the model by using the minimum sum of squared errors, and because the fitting result of the Prony method has a great relationship with the parameter selection, the sampling frequency and the time length in the main parameters do not need to be artificially selected, and the simulation data can be directly read, but the order of the model needs to be carefully determined. The system order in the dynamic process of the power system is very high, so any model for fitting can only be an approximate model with reduced order. The currently selected order-fixing method is as follows: and selecting an initial order which is close to half of the number (N/2) of the data points, selecting 5 values around the N/2 in order to avoid the situation that the Prony method cannot solve at the N/2, selecting the initial order with a small fitting percentage error, and decomposing to obtain components with the number equal to the order. And then, selecting main components with larger amplitudes by adopting an algorithm, taking the number of the main components as a final model order, carrying out Prony analysis again to obtain the frequency, the amplitude, the phase and the attenuation factor of each signal component, and then calculating the damping ratio based on a preset formula. The main idea is as follows: after the frequency, the amplitude, the phase and the attenuation factor of each signal component of the active curve and the voltage curve are calculated, the damping ratio is calculated and is compared with the lowest standard, the dynamic stability of the system at the moment is judged, and the low-frequency oscillation area is determined based on the damping ratio of each signal component.
That is, step 230 of this embodiment specifically includes the following sub-steps:
determining the frequency, amplitude, phase and attenuation factor of each signal component in the power system based on the power data;
calculating a damping ratio of each signal component based on the frequency, amplitude, phase and attenuation factor;
determining whether the power system generates low-frequency oscillation or not based on the damping ratio of each signal component;
if low frequency oscillation occurs, the area of low frequency oscillation is located.
The method of the embodiment can timely and accurately determine whether the power system has instability or low-frequency oscillation, and if the power system has instability or low-frequency oscillation, the method can quickly determine the instability or low-frequency oscillation area so as to facilitate timely processing by related workers, avoid loss caused by untimely processing, and improve the operation safety of the power system.
Example 3
In this embodiment, an apparatus for analyzing a power system instability is provided, as shown in fig. 3, the apparatus of this embodiment includes:
the acquiring unit 31 is configured to acquire power data of the power system.
The analysis unit 32 is configured to input the acquired power data into a preset transient instability analysis model to obtain a first analysis result.
The first analysis result obtained in this embodiment includes whether the power system is unstable, the type of instability, and the unstable area.
The transient instability analysis module of the embodiment is used for judging whether the power system has instability or not based on the acquired power data; if yes, determining the type of instability, and positioning the instability region based on the shape clustering algorithm of the original time sequence.
The shape clustering algorithm based on the original time sequence of this embodiment specifically locates the unstable region, including:
and (3) clustering based on a time series clustering algorithm by taking the Euclidean distance as a distance measurement mode of pairwise curves and adopting a hierarchical clustering mode to position unstable areas.
The analysis unit 32 of the present embodiment is further configured to:
and inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model to obtain a second analysis result.
The second analysis result obtained in this embodiment includes whether the power system has low-frequency oscillation or not and at least one of the low-frequency oscillation regions.
The low-frequency oscillation damping ratio analysis model of the embodiment is built based on the Prony algorithm.
In the low-frequency oscillation damping ratio analysis model of the embodiment, the principle of the minimum sum of squared errors is adopted as the model parameter estimation principle.
The low frequency oscillation damping ratio analysis model of the present embodiment is used for:
determining the frequency, amplitude, phase and attenuation factor of each signal component in the power system based on the power data;
calculating a damping ratio of each signal component based on the frequency, amplitude, phase and attenuation factor;
determining whether the power system generates low-frequency oscillation or not based on the damping ratio of each signal component;
and if the low-frequency oscillation occurs, determining the area of the low-frequency oscillation.
Example 4
This embodiment proposes an electronic device, configured to perform the method for analyzing the power system instability as proposed in embodiment 1 or 2, where as shown in fig. 4, the electronic device of this embodiment may include:
a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may call logical commands in the memory 430 to perform the following method: acquiring power data of a power system; inputting the power data into a preset transient instability analysis model to obtain a first analysis result; wherein the first analysis result comprises at least one of whether the power system is unstable, the type of the unstable and the unstable area.
In addition, the logic commands in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic commands are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of commands for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes: acquiring power data of a power system; inputting the power data into a preset transient instability analysis model to obtain a first analysis result; wherein the first analysis result comprises at least one of whether the power system is unstable, the type of the unstable and the unstable area.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes commands for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for analyzing power system instability is characterized by comprising the following steps:
acquiring power data of a power system;
inputting the acquired power data into a preset transient instability analysis model, and determining whether the power system is unstable or not;
and if the power system is determined to be instable, judging the type of the instability, and positioning the instable area based on the shape clustering algorithm of the original time sequence.
2. The method for analyzing the instability of the power system according to claim 1, wherein the positioning of the unstable region based on the shape clustering algorithm of the original time series specifically comprises:
and (3) clustering based on a time series clustering algorithm by taking the Euclidean distance as a distance measurement mode of pairwise curves and adopting a hierarchical clustering mode to position unstable areas.
3. The method according to claim 1, further comprising:
and inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model, and determining whether the power system generates low-frequency oscillation and/or a low-frequency oscillation region.
4. The method for analyzing the instability of the power system according to claim 1, wherein the low-frequency oscillation damping ratio analysis model is built based on a Prony algorithm, and a principle of estimating model parameters by using the minimum sum of squared errors is adopted.
5. The method for analyzing the instability of the power system according to claim 1, wherein the step of inputting the acquired power data into a preset low-frequency oscillation damping ratio analysis model and determining whether the power system has low-frequency oscillation or not and the region of the low-frequency oscillation comprises the following sub-steps:
determining the frequency, amplitude, phase and attenuation factor of each signal component in the power system based on power data;
calculating the damping ratio of each signal component based on the frequency, amplitude, phase and attenuation factor of each signal component;
determining whether the power system generates low-frequency oscillation based on the damping ratio of each signal component;
if it is determined that low frequency oscillation occurs, the area of low frequency oscillation is located.
6. The method according to claim 1, wherein the acquired power data includes: time information, voltage information, current information of the power system, configuration signals of the power system.
7. The electric power system instability analysis device is characterized by comprising an acquisition unit and an analysis unit;
the acquisition unit is used for acquiring power data of a power system;
the analysis unit inputs the acquired power data into a preset transient instability analysis model, judges whether the power system is instable, determines the type of instability if the power system is instable, and positions an instable area based on a shape clustering algorithm of an original time sequence.
8. The power system instability analysis device according to claim 7, wherein the analysis unit further inputs the acquired power data into a preset low-frequency oscillation damping ratio analysis model to determine whether the power system has low-frequency oscillation and/or a low-frequency oscillation region.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method according to any of claims 1-6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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