CN117371300A - Method and device for testing reliability of power system containing wind power and electronic equipment - Google Patents
Method and device for testing reliability of power system containing wind power and electronic equipment Download PDFInfo
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Abstract
The invention discloses a reliability test method and device for a power system containing wind power and electronic equipment. Wherein the method comprises the following steps: acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data; predicting target power of a preset power system containing wind power in a preset time period according to historical wind power data; determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period to obtain wind power interval predicted power; and testing the preset power system according to the wind power interval predicted power to obtain a reliability test result of the preset power system. The invention solves the technical problem of low accuracy of the test result when the reliability test is carried out on the power system containing wind power in the related technology.
Description
Technical Field
The invention relates to the field of power grids, in particular to a method and a device for testing reliability of a power system containing wind power and electronic equipment.
Background
In increasingly severe environmental situations, carbon emissions reduction has become a common consensus among countries around the world. In order to achieve the aim of double carbon, the construction of a novel power system mainly based on new energy becomes a future development trend. On the energy production side, the development of new energy sources represented by wind power and photovoltaic will become a necessary potential. With the continuous incorporation of distributed power sources into a power system, load power supply is changed from original upper power grid power supply to main power supply with the distributed power sources, and the upper power grid is auxiliary power supply. Meanwhile, the distributed power sources such as photovoltaic and fans are easily influenced by environmental factors, and the output has intermittence and uncertainty. The traditional reliability evaluation method is mainly suitable for the traditional medium-voltage distribution network, and cannot reflect the influence of the change on the reliability of the power system. Therefore, in a two-carbon background, new reliability assessment methods are urgently needed for new power systems.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a reliability test method and device for a power system containing wind power and electronic equipment, which at least solve the technical problem of low accuracy of test results when the reliability test is carried out on the power system containing wind power in the related technology.
According to an aspect of the embodiment of the invention, there is provided a method for testing reliability of a power system including wind power, including: acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data; predicting target power of a preset power system containing wind power in a preset time period according to historical wind power data; determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period to obtain wind power interval predicted power; and testing the preset power system according to the wind power interval predicted power to obtain a reliability test result of the preset power system.
Optionally, predicting the target power of the wind-powered electricity generation-containing predetermined power system within a predetermined period of time according to the historical wind power data includes: extracting wind power characteristics of historical wind power data; and predicting the target power of the preset power system in a preset time period according to the wind power characteristics.
Optionally, predicting the target power of the predetermined power system for a predetermined period of time according to the wind power characteristics includes: obtaining a wind power sequence vector with a preset dimension according to wind power characteristics; and inputting the wind power sequence vector with the preset dimension into a convolutional neural CNN-long-short-term memory LSTM deep neural network model to obtain target power of a preset power system in a preset time period, wherein the CNN-LSTM deep neural network model comprises a CNN layer and an LSTM layer, the CNN layer is used for reducing the preset dimension of the wind power sequence vector, and the LSTM layer is used for predicting the target power of the preset power system in the preset time period according to the wind power sequence vector with the reduced preset dimension.
Optionally, determining the target upper limit parameter and the target lower limit parameter of the target power in the preset time period to obtain the wind power interval predicted power includes: dividing a predetermined time period into a plurality of target time periods; determining a target sub-upper limit parameter and a target sub-lower limit parameter of target power in any one of a plurality of target time periods; and obtaining the wind power interval predicted power according to the target sub-upper limit parameter and the target sub-lower limit parameter.
Optionally, determining the target upper limit parameter and the target lower limit parameter of the target power in the preset time period to obtain the wind power interval predicted power includes: determining a predetermined confidence level based on a predetermined power system, wherein the coverage rate of a predetermined power interval and the average bandwidth of the predetermined power interval; determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period according to preset confidence coefficient, preset power interval coverage rate and preset power interval average bandwidth; and obtaining the wind power interval predicted power based on the target upper limit parameter and the target lower limit parameter.
Optionally, determining the target upper limit parameter and the target lower limit parameter of the target power in the predetermined period according to the predetermined confidence, the predetermined power interval coverage rate and the predetermined power interval average bandwidth includes: determining a first weight parameter item corresponding to the coverage rate of the preset power interval and a second weight parameter item corresponding to the average bandwidth of the preset power interval; determining an objective function based on a preset confidence coefficient, a preset power interval coverage rate, a preset power interval average bandwidth, a first weight parameter item corresponding to the preset power interval coverage rate and a second weight parameter item corresponding to the preset power interval average bandwidth; and solving the objective function based on a preset rule, and determining an objective upper limit parameter and an objective lower limit parameter of the objective power in a preset time period.
Optionally, testing the predetermined power system according to the wind power interval predicted power to obtain a reliability test result of the predetermined power system, including: determining a reliability indicator for testing a predetermined power system; determining a first reliability index of the predetermined power system under the reliability index in the case of setting the output power of the predetermined power system as the target upper limit parameter, and determining a second reliability index of the predetermined power system under the reliability index in the case of setting the output power of the predetermined power system as the target lower limit parameter; and obtaining a reliability test result of the preset power system according to the first reliability index and the second reliability index.
According to an aspect of the embodiment of the present invention, there is provided a reliability test device for a wind-powered electricity generation-containing power system, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data; the prediction module is used for predicting the target power of a preset power system containing wind power in a preset time period according to the historical wind power data; the determining module is used for determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period to obtain wind power interval predicted power; the testing module is used for testing the preset power system according to the wind power interval predicted power to obtain a reliability testing result of the preset power system.
According to an aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the wind power system reliability test method of any one of the above.
According to an aspect of an embodiment of the present invention, there is provided a computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform any one of the above-described methods for testing reliability of a wind-powered electricity containing power system.
In the embodiment of the invention, historical wind power data is acquired, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data, so that target power of a preset power system containing wind power in a preset time period is predicted according to the acquired historical wind power data, target upper limit parameters and target lower limit parameters of the target power in the preset time period are determined, wind power interval test power is obtained according to the target upper limit parameters and the target lower limit parameters, and a preset power system is tested according to the wind power interval test power, so that a reliability test result of the preset power system is obtained. According to the embodiment of the invention, the influence caused by the random change of wind power can be accurately determined through the historical data related to wind power, so that the technical problem of low accuracy of a test result when the reliability test is carried out on the power system containing wind power in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method for testing reliability of a wind-powered electrical power system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method provided by an alternative embodiment of the present invention;
FIG. 3 is a schematic diagram of a wind power interval prediction result provided by an alternative embodiment of the present invention;
FIG. 4 is a schematic illustration of a probability interval of no-load provided by an alternative embodiment of the present invention;
FIG. 5 is a block diagram of a power system reliability test apparatus including wind power according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a method for testing reliability of a power system including wind power, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.
FIG. 1 is a flow chart of a method for testing reliability of a wind-powered electricity containing power system according to an embodiment of the invention, as shown in FIG. 1, the method comprising the steps of:
step S102, acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data;
step S104, predicting target power of a preset power system containing wind power in a preset time period according to historical wind power data;
step S106, determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period to obtain a wind power interval prediction result;
and S108, testing the preset power system according to the wind power interval prediction result to obtain a reliability test result of the preset power system.
Through the steps, historical wind power data are obtained, wherein the historical wind power data comprise historical wind power data, historical wind speed data, historical wind direction data and historical load data, so that target power of a preset power system containing wind power in a preset time period is predicted according to the obtained historical wind power data, target upper limit parameters and target lower limit parameters of the target power in the preset time period are determined, wind power interval test power is obtained according to the target upper limit parameters and the target lower limit parameters, and the preset power system is tested according to the wind power interval test power, so that a reliability test result of the preset power system is obtained. According to the embodiment of the invention, the influence caused by the random change of wind power can be accurately determined through the historical data related to wind power, so that the technical problem of low accuracy of a test result when the reliability test is carried out on the power system containing wind power in the related technology is solved.
The predetermined historical wind power data can be wind power data of a predetermined power system containing wind power, or can be historical wind power data in a historical time period simulated according to the predetermined power system containing wind power, so that the target power can be predicted still under the condition that the predetermined power system containing wind power is a newly built wind power system, and a reliability test result of the predetermined power system is determined.
As an alternative embodiment, the target power of the wind-powered electricity containing predetermined power system within the predetermined time period is predicted from the historical wind power data by: extracting wind power characteristics of historical wind power data; and predicting the target power of the preset power system in a preset time period according to the wind power characteristics. By extracting the wind power characteristics of the historical wind power data, the dimension of the original input data can be reduced, the original various historical wind power data are arranged, and the characteristics of the original input various historical wind power data are recombined so as to be convenient for subsequent use.
As an alternative embodiment, the target power of the predetermined power system in the predetermined time period is predicted according to the wind power characteristics, and the wind power sequence vector with the predetermined dimension is obtained according to the wind power characteristics. The features of the extracted data are arranged into sequence vectors with the same dimension so as to input the model for operation. The dimensions why are specifically sorted, autonomous settings can be made according to the model, and specifically the application and scenario. The wind power sequence vector with the preset dimension is input into a convolutional neural CNN-long-short-term memory LSTM deep neural network model to obtain target power of a preset power system in a preset time period, wherein the CNN-LSTM deep neural network model comprises a CNN layer and an LSTM layer, the CNN layer is used for reducing the preset dimension of the wind power sequence vector, the dimension of the processing vector can be reduced, the processing speed is increased, the CNN layer can prevent data from being over-fitted, and the LSTM layer is used for predicting the target power of the preset power system in the preset time period according to the wind power sequence vector with the reduced preset dimension. The LSTM layer comprises a basic LSMT layer and a final full-connection layer, and the LSTM layer comprises an input gate, a forgetting gate and an output gate, so that the characteristics of the sequence type can be better processed, and a more accurate prediction effect is achieved.
As an optional embodiment, determining an upper limit target parameter and a lower limit target parameter of the target power within a predetermined period of time to obtain a wind power interval prediction power, where the upper limit target parameter is an upper limit fluctuation value of the target power when predicting a feasible power interval of the target power according to the target power, i.e. an upper limit value in the power interval can be obtained by predicting the target power according to the upper limit target parameter and the target power, and the lower limit target parameter is a lower limit fluctuation value of the target power after predicting the feasible power interval of the target power according to the target power, i.e. a lower limit value in the power interval can be obtained by predicting the target power according to the lower limit target parameter and the target power. In a simple way, the target power can be directly multiplied by the target upper limit parameter and the target lower limit parameter to obtain the upper limit value and the lower limit value of the predicted power, and the wind power section predicted power is obtained through the upper limit value and the lower limit value. Therefore, a feasible power interval of the preset power system can be obtained according to the target upper limit parameter and the target lower limit parameter, and the wind power interval prediction probability is obtained. When determining the target upper limit parameter and the target lower limit parameter of the target power in the preset time period, various modes can be adopted, a subjective analysis method can be adopted, an objective analysis method can be adopted, and a specific method can be determined according to actual application and scenes.
As an alternative embodiment, determining the target upper limit parameter and the target lower limit parameter of the target power in the predetermined period of time to obtain the wind power interval predicted power may be as follows: based on a predetermined power system, a predetermined confidence level, a predetermined power interval coverage rate, and a predetermined power interval average bandwidth are determined. According to the preset confidence coefficient, the coverage rate of a preset power interval and the average bandwidth of the preset power interval, determining a target upper limit parameter and a target lower limit parameter of target power in a preset time period, and obtaining the wind power interval predicted power based on the target upper limit parameter and the target lower limit parameter. The wind power section prediction power obtained by the method can simultaneously give consideration to the coverage rate of the preset power section and the average bandwidth of the preset power section, and the reliability and the accuracy of the obtained section are improved.
As an alternative embodiment, when determining the target upper limit parameter and the target lower limit parameter of the target power in the predetermined period according to the predetermined confidence, the predetermined power interval coverage rate and the predetermined power interval average bandwidth, the constraint condition of the target function may be determined by constructing the target function, and solving the target function under the constraint condition by using the target algorithm, for example, the target function may be constructed based on the average bandwidth of the prediction interval and the coverage rate of the prediction interval, where the constraint condition is that the coverage rate of the prediction interval is as large as possible, the average bandwidth of the prediction interval is as small as possible, and a trade-off is made between the two to obtain the target upper limit parameter and the target lower limit parameter after the best trade-off is made. The following means may be employed: and determining a first weight parameter item corresponding to the coverage rate of the preset power interval and a second weight parameter item corresponding to the average bandwidth of the preset power interval, wherein the first weight parameter item and the second weight parameter item are used for balancing the average bandwidth and the coverage rate of the prediction interval. Determining an objective function based on a preset confidence coefficient, a preset power interval coverage rate, a preset power interval average bandwidth, a first weight parameter item corresponding to the preset power interval coverage rate and a second weight parameter item corresponding to the preset power interval average bandwidth, solving the objective function based on a preset rule, and determining a first weight parameter value and a second weight parameter value after the average bandwidth and the coverage rate of a prediction interval are balanced, wherein the obtained first weight parameter value and second weight parameter value are the target upper limit parameter and the target lower limit parameter of target power in a preset time period. In the process, when the target upper limit parameter and the target lower limit parameter are determined, a quantum genetic algorithm can be adopted to solve an objective function under a constraint condition. The method has the advantages that the calculated upper limit target parameter and the calculated lower limit target parameter meet the preset rule by determining the preset rule of the objective function and the objective function, the requirement can be met, the result obtained by solving can be more accurate by constructing the objective function and solving, and the effects of high calculation speed and strong global optimizing capability can be achieved by solving through the quantum genetic algorithm.
As an alternative embodiment, in order to determine the target upper limit parameter and the target lower limit parameter more accurately, the predetermined time period may be divided into a plurality of target time periods, and the target sub-upper limit parameter and the target sub-lower limit parameter of the target power in each target time period in the plurality of target time periods are determined respectively, that is, the target sub-upper limit parameter and the target sub-lower limit parameter of multiple segments are solved respectively, and the large time period is divided into small time periods, so that accuracy and reliability of the solved target sub-upper limit parameter and the solved target sub-lower limit parameter of each segment can be increased.
As an alternative embodiment, when testing the predetermined power system according to the predicted power of the wind power section to obtain the reliability test result of the predetermined power system, the following manner may be adopted: determining a reliable index for testing a preset power system, wherein the reliable index can be a power system load loss probability index and represents the probability that the system power does not meet the load electricity demand; the expected index of insufficient power can be used for representing an expected value of less power supply of the system due to forced shutdown of the unit; the average power failure frequency index can be used for representing the average power failure times in unit time; etc. The method can be set autonomously according to actual application and scene. Alternatively, multiple reliable indicators can be selected for common judgment. After the reliability index is determined, a first reliability index of the predetermined power system under the reliability index is determined in the case of setting the output power of the predetermined power system to an upper limit value in the wind power section predicted power, and a second reliability index of the predetermined power system under the reliability index is determined in the case of setting the output power of the predetermined power system to a lower limit value in the wind power section predicted power. And obtaining a reliability test result of the preset power system according to the first reliability index and the second reliability index. According to the reliability index of the target power in the predicted limit state, the reliability of the power system can be preset when the wind power interval predicted power is applied to the maximum degree.
Based on the foregoing embodiments and optional embodiments, an optional implementation is provided, and is specifically described below.
The invention provides a reliability evaluation method for a wind-power-containing power system based on CNN-LSTM, which can remarkably improve the reliability evaluation efficiency of the wind-power-containing power system. FIG. 2 is a schematic flow chart of a method provided by an alternative embodiment of the present invention, as shown in FIG. 2, and the following details of the alternative embodiment of the present invention are described below:
s1, historical wind power data are obtained;
the historical wind power data comprises wind power output historical data and power system historical load data, wherein the wind power output historical data comprises the following components: wind power, wind speed, wind direction and historical load data of the electric power system;
s2, carrying out data preprocessing on input original data;
wherein, the preprocessing mode can be to extract the corresponding features of the data to obtain n feature vectors { x } 1 ,x 2 ,x 3 ,…x n }。
S3, building a CNN-LSTM neural network model to predict wind power, wherein the CNN-LSTM neural network model comprises a CNN layer and an LSTM layer;
s3.1, extracting implicit features of the feature vectors by a CNN layer through a convolutional neural network;
the convolution operation is shown as follows:
wherein: y is k-1 Is the input of the kth convolution layer; * Is convolution operation; w (W) i k The weight of the ith convolution kernel of the kth convolution layer;is the offset of the kth convolutional layer.
The size of the feature vector input by the model is 1 multiplied by 3, the moving step length is 2, the convolution kernel moves in a right translation mode, and convolution operation is carried out. And obtaining the characteristic map by a maximum pooling mode (the size of a pooling layer is 1 multiplied by 2). The dimension of the feature vector can be reduced after the convolution layer and the pooling layer are stacked for a plurality of times.
S3.2, predicting the power of the wind power system in a preset time period by the LSTM layer according to the feature vector after dimension reduction;
the basic LSTM layer in the LSTM layer can extract long-distance characteristics of the dimension-reduced characteristic vector, and then the long-distance characteristics are converted and input into the full-connection layer to output predicted power.
S4, equally-spaced division is carried out on the predicted power, and the upper power limit and the lower power limit of the power in each interval are determined;
the method for determining the upper power limit and the lower power limit of the power in each interval can be implemented by multiplying the output two weight parameters with the predicted power respectively through a QGA optimization model to determine the upper power limit and the lower power limit of the power in each interval.
S4.1, selecting an optimization objective function;
constructing an objective function which simultaneously considers the interval reliability and the accuracy, and defining F as follows:
in the formula, PICE= |PINC-PICP|, and PINC is the rated confidence level. PICP is the coverage of the prediction interval, PINAW is the average bandwidth of the prediction interval, U t (α) ,L t (α) The upper and lower limits of the interval prediction, gamai,the weight coefficients of the coverage rate of the prediction interval and the average bandwidth of the prediction interval are respectively, N is the number of samples, K is the Boolean quantity, and N is the algorithm iteration number.
S4.2, determining a rule for solving an objective function, solving the objective function through a QGA optimization model, and outputting weight parameters, namely weight parameters corresponding to the coverage rate of an output prediction interval and weight parameters of the average bandwidth of the prediction interval;
the rule for solving the objective function is that the coverage rate of the prediction interval is increased, the average bandwidth of the prediction interval is reduced, and the objective function is solved through the QGA optimization model, and proper weight is output. It should be noted that, when solving according to the QGA optimization model, initializing QGA parameters, and setting the initial population scale as N; measuring individuals of the initial population N to obtain a state P (t); recording fitness for each state and recording the optimal individual and corresponding fitness; using quantum hybridization, variation and rotation gate pair to update population individuals and measuring the states of the new population individuals; recording the optimal individual and the fitness value, if the optimal individual and the fitness value are larger than the current optimal value, updating the optimal value, otherwise, keeping the current value; and obtaining the optimal output weight when the iteration is finished, otherwise, continuing to search for the iteration.
It should be noted that, the wind power section prediction result determined by the method is shown in fig. 3, fig. 3 is a schematic diagram of the wind power section prediction result provided by the alternative embodiment of the method, and as can be seen from fig. 3, while ensuring that the wind power time sequence change is tracked, the prediction result has very high prediction section coverage rate and very narrow section average bandwidth, and a smaller section average bandwidth means that the higher the prediction precision is, the smaller the uncertainty degree is, and the better the prediction effect is.
And S4.2, determining the upper power limit and the lower power limit of the power in each interval according to the weight parameter corresponding to the coverage rate of the output prediction interval and the weight parameter of the average bandwidth of the prediction interval, and obtaining the predicted wind power interval.
S5, building an IEEE-RTS79 electric power reliability test system on the basis of wind power interval prediction, and calculating reliability after wind power is connected into an electric power system.
S5.1, evaluating the reliability of the power system by taking a reliability index;
the LOLP is the probability of losing load of the power system and represents the probability that the system power does not meet the load electricity demand:
wherein N is the number of cycles; t is the total simulation time of the system; x is X i The system state at the moment i is divided into a fault state and a normal state; t is t i For system state X i Duration of (2); f (F) LOLP As a test function of LOLP, if the system is at X i State switch load, F LOLP 1 if the system is at X i No load is cut off in state, F LOLP Is 0.
EPNS is a power shortage expectation, representing an expectation that the system will be powered less by a forced outage of the unit:
wherein F is EPNS As a test function of EPNS, if the system is at X i State switch load, F EPNS 1 if the system is in Xi No load is cut off in state, F EPNS Is 0.
SAIFI is the average power outage frequency per unit time:
wherein C is L The load number is inscribed in the simulation time; t is the total simulation time of the system; t is t i For system state X i Is not shown, is not shown.
S5.2, according to the wind power plant power interval prediction model, assuming that the output power of the wind power plant system respectively takes the upper limit or the lower limit of the interval prediction model, simulating to obtain a reliability result, and thus obtaining the reliability index of the wind power plant in the access mode.
S6, in order to verify the superiority of the reliability assessment method of the wind-power-containing power system based on the CNN-LSTM in the reliability field of the power system, the wind-power-containing power system shown in the figure 1 is utilized for simulation analysis. And selecting historical data of the same wind power plant, and comparing the reliability obtained by adopting the two methods.
Scheme 1: the reliability of the original power system is not accessed with wind power;
scheme 2: and predicting wind power by adopting a CNN-LSTM neural network model, then accessing an IEEE-RTS79 system, and calculating reliability.
And adopting a reliability index and performing reliability analysis on the two by LOLP. FIG. 4 is a schematic diagram of a load loss probability interval provided by an alternative embodiment of the present invention, as shown in FIG. 4, after wind power is connected to a power system, the load loss probability of the system is reduced by 31.8% -41.7% compared with that of the original system, i.e. the reliability of the system is improved by 31.8% -41.7%.
By the alternative embodiments, at least the following advantages can be achieved:
(1) The method is used in the field of reliability of the power system, can be suitable for reliability evaluation stages of various typical power scenes, and provides a beneficial reference for improving wind power grid connection benefits;
(2) The influence of uncertain characteristics of renewable energy sources on the safety and stability of the power system is researched, the renewable energy sources are efficiently combined into the power system, the running reliability is improved, and therefore more reasonable planning and scheduling are conducted.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the various embodiments of the present invention.
Example 2
According to an embodiment of the present invention, there is further provided an apparatus for implementing the above method for testing reliability of a power system including wind power, and fig. 5 is a block diagram of a structure of an apparatus for testing reliability of a power system including wind power according to an embodiment of the present invention, as shown in fig. 5, the apparatus includes: the acquisition module 502 predicts the module 504, determines the module 506 and tests the module 508, which is described in detail below.
The obtaining module 502 is configured to obtain predetermined historical wind power data, where the historical wind power data includes historical wind power data, historical wind speed data, historical wind direction data, and historical load data; the prediction module 504, connected to the obtaining module 502, is configured to predict a target power of a predetermined power system including wind power within a predetermined period of time according to the historical wind power data; the determining module 506 is connected to the predicting module 504, and is configured to determine a target upper limit parameter and a target lower limit parameter of the target power within a predetermined period of time, so as to obtain a wind power interval predicted power; the testing module 508 is connected to the determining module 506, and is configured to test the predetermined power system according to the wind power interval predicted power, so as to obtain a reliability testing result of the predetermined power system.
Here, the above-mentioned obtaining module 502 predicts the module 504, the determining module 506 and the testing module 508 correspond to the steps S102 to S108 in the method for implementing the reliability test of the power system including wind power, and the plurality of modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in the above-mentioned embodiment 1.
Example 3
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including: a processor; a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the wind power system reliability test method of any of the above.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the wind-powered electricity generation-containing power system reliability test method of any one of the above.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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 Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. The method for testing the reliability of the power system containing wind power is characterized by comprising the following steps of:
acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data;
predicting target power of a preset power system containing wind power in a preset time period according to the historical wind power data;
determining a target upper limit parameter and a target lower limit parameter of the target power in the preset time period to obtain wind power interval predicted power;
and testing the preset power system according to the wind power interval predicted power to obtain a reliability test result of the preset power system.
2. The method of claim 1, wherein predicting a target power for a predetermined power system including wind power over a predetermined period of time based on the historical wind power data comprises:
extracting wind power characteristics of the historical wind power data;
and predicting the target power of the preset power system in a preset time period according to the wind power characteristics.
3. The method of claim 2, wherein predicting the target power of the predetermined power system over a predetermined period of time based on the wind power signature comprises:
obtaining a wind power sequence vector with a preset dimension according to the wind power characteristics;
and inputting the wind power sequence vector with the preset dimension into a convolutional neural CNN-long and short term memory LSTM depth neural network model to obtain target power of the preset power system in a preset time period, wherein the CNN-LSTM depth neural network model comprises a CNN layer and an LSTM layer, the CNN layer is used for reducing the preset dimension of the wind power sequence vector, and the LSTM layer is used for predicting the target power of the preset power system in the preset time period according to the wind power sequence vector with the reduced preset dimension.
4. The method according to claim 1, wherein determining the target upper limit parameter and the target lower limit parameter of the target power in the predetermined period of time to obtain the wind power interval predicted power comprises:
dividing the predetermined time period into a plurality of target time periods;
determining a target sub-upper limit parameter and a target sub-lower limit parameter of target power in any one of the target time periods;
and obtaining the wind power interval predicted power according to the target sub-upper limit parameter and the target sub-lower limit parameter.
5. The method according to claim 1, wherein determining the target upper limit parameter and the target lower limit parameter of the target power in the predetermined period of time to obtain the wind power interval predicted power comprises:
determining a preset confidence coefficient based on the preset power system, wherein the preset power interval coverage rate and the preset power interval average bandwidth;
determining the target upper limit parameter and the target lower limit parameter of the target power in the preset time period according to the preset confidence coefficient, the preset power interval coverage rate and the preset power interval average bandwidth;
and obtaining the wind power interval prediction power based on the target upper limit parameter and the target lower limit parameter.
6. The method of claim 5, wherein determining the target upper limit parameter and the target lower limit parameter for the target power for the predetermined period of time based on the predetermined confidence level, the predetermined power interval coverage, the predetermined power interval average bandwidth, comprises:
determining a first weight parameter item corresponding to the coverage rate of the preset power interval and a second weight parameter item corresponding to the average bandwidth of the preset power interval;
determining an objective function based on the preset confidence coefficient, the preset power interval coverage rate, the preset power interval average bandwidth, a first weight parameter item corresponding to the preset power interval coverage rate and a second weight parameter item corresponding to the preset power interval average bandwidth;
and solving the target function based on a preset rule, and determining the target upper limit parameter and the target lower limit parameter of the target power in the preset time period.
7. The method according to any one of claims 1 to 6, wherein testing the predetermined power system according to the wind power section predicted power to obtain a reliability test result of the predetermined power system comprises:
determining a reliability indicator for testing the predetermined power system;
determining a first reliability index of the predetermined power system under the reliability index in the case of setting the output power of the predetermined power system as a target upper limit parameter, and determining a second reliability index of the predetermined power system under the reliability index in the case of setting the output power of the predetermined power system as a target lower limit parameter;
and obtaining the reliability test result of the preset power system according to the first reliability index and the second reliability index.
8. The utility model provides a contain electric power system reliability test device of wind-powered electricity generation which characterized in that includes:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring preset historical wind power data, wherein the historical wind power data comprises historical wind power data, historical wind speed data, historical wind direction data and historical load data;
the prediction module is used for predicting the target power of a preset power system containing wind power in a preset time period according to the historical wind power data;
the determining module is used for determining a target upper limit parameter and a target lower limit parameter of the target power in the preset time period to obtain wind power interval predicted power;
and the testing module is used for testing the preset power system according to the wind power interval predicted power to obtain a reliability testing result of the preset power system.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the wind power containing power system reliability test method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the wind power system reliability test method according to any one of claims 1 to 7.
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