CN111489028A - Thundercloud trajectory tracking-based photovoltaic power prediction method under lightning condition - Google Patents

Thundercloud trajectory tracking-based photovoltaic power prediction method under lightning condition Download PDF

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CN111489028A
CN111489028A CN202010272463.9A CN202010272463A CN111489028A CN 111489028 A CN111489028 A CN 111489028A CN 202010272463 A CN202010272463 A CN 202010272463A CN 111489028 A CN111489028 A CN 111489028A
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童充
徐箭
李文博
廖思阳
徐洋
柏筱飞
吴博文
华夏
吴越涛
蔡云峰
冯家欢
金晟
殷伟
付柳笛
马亮
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Wuhan University WHU
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

A photovoltaic power prediction method under a lightning condition based on thundercloud track tracking is characterized in that the time when thundercloud arrives and leaves the sky above a photovoltaic power station is predicted based on the thundercloud track tracking technology, and the relation between the size of a cloud body and the wind intensity is judged by combining momentum theorem, so that the duration time of the thundercloud above the photovoltaic power station is known; and then, using the BP neural network to predict irradiance by taking temperature and humidity as input according to meteorological factors and irradiance historical information, and finally obtaining a predicted value of photovoltaic power through a photovoltaic power conversion formula. The invention simplifies the nonlinear relation between weather factors and irradiance through a photovoltaic power conversion formula, solves the problem of photovoltaic power prediction effectiveness under extreme climate conditions such as thunder and lightning through a cloud track tracking technology, improves prediction precision, provides a reliable predicted value for a power dispatching department, facilitates the dispatching department to carry out power grid tide optimization and dispatching in advance under the influence of extreme climate conditions such as thunder and lightning, and improves the safety and stability of a power system.

Description

Thundercloud trajectory tracking-based photovoltaic power prediction method under lightning condition
Technical Field
The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a thundercloud trajectory tracking prediction and BP neural network-based photovoltaic power prediction method under a lightning climate condition.
Background
With global warming, fossil energy on earth is reduced day by day, new energy is certainly the trend of future power generation, and the installed capacity of photovoltaic power generation is gradually improved in recent years. The fluctuation and instability are caused by meteorological conditions, wherein cloud layer changes above a photovoltaic power station are the main reasons for causing photovoltaic output changes, particularly under the condition of thunder and lightning climate, the phenomenon of photovoltaic severe fluctuation can occur due to obvious changes of thickness of thunderclouds and other surrounding clouds, and the fluctuation is characterized by large amplitude change fall, short time difference before and after change, large impact on a power grid can be brought, small-proportion photovoltaic investment can influence the quality of electric energy, large-proportion photovoltaic investment can influence the safe and stable operation of a power system, so that a timely and accurate thundercloud tracking technology is used for photovoltaic prediction under the condition of thunder and lightning, and the timeliness and the accuracy of the photovoltaic prediction are greatly improved.
At present, in the aspect of photovoltaic power prediction, direct prediction and indirect prediction are divided. In the aspect of direct prediction, the photovoltaic power is directly predicted by using the existing power, irradiance and meteorological information, and the method combines a large amount of historical information and summarizes statistical rules by a machine learning method to finally obtain a more accurate photovoltaic power predicted value.
In the prior art, a typical direct prediction scheme is photovoltaic power prediction based on an inverse error propagation neural network according to target photovoltaic power station meteorological and photovoltaic power historical information. The method comprises the following specific steps: 1. screening historical data, and selecting meteorological data related to photovoltaic power; 2. training a neural network: the method comprises the steps of taking meteorological data as input of a neural network, taking photovoltaic power data as output of the neural network, and correcting weights of an input layer and a hidden layer, the hidden layer and an output layer of the neural network and thresholds of nerve sources of all layers according to reverse error propagation to obtain a trained neural network; 3. photovoltaic power prediction: and (3) taking the meteorological information of the predicted time period as the input of the trained neural network, and obtaining the output result which is the photovoltaic power of the predicted time period.
The prior art direct photovoltaic power prediction scheme has the following disadvantages:
(1) because meteorological information only has direct physical connection with irradiance, and photovoltaic power and irradiance are connected through a photoelectric conversion model, namely, there is no direct physical connection between the meteorological information and the photovoltaic power, the prediction method adopts a simple statistical method, the physical meaning of a prediction result cannot be explained, and the complexity of a nonlinear function established in statistical prediction is increased.
(2) Under the condition of thunder weather, the appearance and disappearance of thunderclouds above a photovoltaic power station are main reasons causing severe fluctuation of photovoltaic, the existing method cannot effectively distinguish two phenomena that the thunderclouds appear above the photovoltaic power station and do not appear above the photovoltaic power station, the prediction result cannot reflect the characteristics of short-time irradiance or steep rise and steep fall of photovoltaic power under the condition of thunder weather, and the prediction is often invalid under the condition of thunder.
(3) The existing method generally uses all meteorological information as input of prediction in a unified way, a large amount of data occupies a large amount of memory of a computer, and a large amount of time is consumed when the historical data is used for machine learning.
Aiming at the defects, the irradiance is predicted through meteorological factors, the irradiance prediction is carried out according to the temperature and the humidity, the irradiance is suitable for the meteorological factors to be directly related, so that the irradiance prediction method has certain physical significance, compared with the method of directly establishing the nonlinear relation between the meteorological factors and the photovoltaic power, the predicted nonlinear relation is simplified, and the machine can obtain a more accurate nonlinear relation by needing less historical data in the learning process. Based on a thundercloud track tracking technology, two phenomena of thundercloud appearing above a photovoltaic power station and thundercloud not appearing above the photovoltaic power station are effectively distinguished, the time when the thundercloud arrives at and leaves the space above the photovoltaic power station is predicted, the duration time of the thundercloud above the photovoltaic power station is further known, the amplitude value of irradiance is predicted by combining meteorological and irradiance historical data when the thundercloud arrives at the space above the photovoltaic power station, the change degree of steep rise and decline of the irradiance under a meteorological condition of thunder and the duration time after the irradiance falls are effectively predicted, and the effectiveness and the accuracy of the irradiance under a climatic condition of an extreme end of thunder are improved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention discloses a photovoltaic power prediction method under a lightning condition based on thundercloud trajectory tracking.
The technical problems of the invention are mainly solved by the following technical scheme:
a photovoltaic power prediction method under a lightning condition based on thundercloud trajectory tracking is characterized by comprising the following steps:
step 1, determining the latitude and longitude of a target photovoltaic power station, and collecting thundercloud track prediction data and irradiance prediction data from a historical database;
the thundercloud track prediction data refers to the time and longitude and latitude of cloud-ground flash in a set range around a target photovoltaic power station; the irradiance prediction data refers to values of temperature, humidity and irradiance during cloud-to-ground flashing in a set range around a target photovoltaic power station;
step 2, preprocessing the thundercloud track prediction data and the irradiance prediction data collected in the step 1;
step 3, taking the temperature and humidity in the irradiance prediction data preprocessed in the step 2 during the cloud flash generation period as the input of a neural network, and taking the irradiance data as the output of the neural network to train a BP neural network;
step 4, predicting the moment when the thundercloud arrives and leaves the sky of the target photovoltaic power station and the duration time of the thundercloud in the target photovoltaic power station based on the current actually measured wind speed and the thundercloud track prediction data preprocessed in the step 2;
and 5, predicting the irradiance amplitude of the thundercloud above the target photovoltaic power station and the photovoltaic power amplitude of the target photovoltaic power station.
The present invention further includes the following preferred embodiments.
In step 1, the set range around the target photovoltaic power station is a range with a radius of 3000km-9000km with the target photovoltaic power station as a center.
In a preferred embodiment of the present application, the set range around the target photovoltaic power station is a range with a radius of 5600km with the target photovoltaic power station as a center.
Taking the time of cloud-to-ground flashing at the position farthest from the center of the photovoltaic power station in a set range as the initial time of data acquisition;
the cloud-ground flash positioning spatial resolution is 0.0001 degree longitude and 0.0001 degree latitude respectively, and the time resolution is 0.1 × 10-6And second.
Obtaining the values of temperature, humidity and irradiance during each cloud-to-ground flash in a set range and a set time period around the target photovoltaic power plant by: inquiring the date recorded by the cloud flashing within the set range and the set time of the target photovoltaic power station in the historical record, observing the irradiance change curve of the photovoltaic power station corresponding to the date, intercepting the irradiance change curve which is firstly steeply reduced so that the irradiance is equal to or lower than the set irradiance threshold value and is continuously set for a set time period, then steeply raised, and recording the values of the temperature, the humidity and the irradiance under the continuous time period.
Wherein, the steep drop and the steep rise refer to irradiance per square meter being greater than or equal to an irradiance change threshold in a set time. The steep drop and rise refer to irradiance change being more than or equal to 400W/m within 30min2/h。
The irradiance threshold is set to mean that the average value of irradiance clear index is less than 0.28.
The duration setting time period means that the duration is more than 90 min.
In step 2, the processing of the thundercloud trajectory prediction data includes:
calculating the time difference and the latitude and longitude difference of cloud-ground flashes at adjacent moments, and recording the first time difference as t according to the time sequence1The corresponding longitude difference is denoted as Sψ1And the corresponding latitude difference is recorded as Sφ1Similarly, the nth time difference is denoted as tnTo, forThe difference in longitude is denoted as SψnAnd the corresponding latitude difference is recorded as Sφn。
In step 2, the processing of irradiance prediction data comprises:
the temperature and the humidity during the cloud-ground flash generation are used as the input of a BP neural network, the irradiance data is used as the output of the BP neural network, and a data set is formed by corresponding two-to-one mapping relation;
in step 4, predicting the time when the thundercloud arrives and leaves the sky of the target photovoltaic power station and the duration of the thundercloud at the target photovoltaic power station comprises the following steps:
step 4.1, thundercloud track prediction is carried out, preprocessed thundercloud track prediction data, namely the time difference and the longitude and latitude difference of cloud-ground lightning at adjacent moments are sequentially subjected to a kinematic fitting expression of the thundercloud moving track in the longitude direction and the latitude direction, the middle moment in the cloud-ground lightning duration in the cloud-ground lightning event is calculated and recorded as tIntermediate time；
Substituting the thundercloud track prediction data preprocessed in the step 2, namely the time difference and the latitude and longitude difference of cloud-ground lightning at adjacent moments into a kinematic fitting expression of the thundercloud moving track in the longitude direction and the latitude direction in sequence;
t1，t2，…，tn；Sψ1，Sψ2，…，Sψn；Sφ1，Sφ2，…，Sφnsubstituting into fitting expressions in longitude and latitude directions, and respectively fitting initial velocities v in the longitude and latitude directions by using a linear fitting method0And an acceleration a; finally, obtaining a fitting expression:
the fitted expression in the longitudinal direction is:
Figure BDA0002443600590000041
the fitted expression in the latitudinal direction is:
Figure BDA0002443600590000042
tnthe time difference between the nth cloud-to-ground flash of the same thundercloud and the next adjacent cloud-to-ground flash of the same thundercloud, SψnIs cloud layer tnDistance moved in the longitudinal direction within a time period, vψ0Is the initial velocity of the cloud layer moving in the longitudinal direction, aψAcceleration of movement of the cloud layer in the longitudinal direction; sφ1Is cloud layer tnThe distance moved in the latitudinal direction over the time period,
Figure BDA0002443600590000043
is the initial velocity of the movement of the cloud layer in the latitudinal direction, aφAcceleration of movement of the cloud layer in the latitudinal direction;
respectively subtracting the longitude and latitude of the central position of the photovoltaic power station and the recorded longitude and latitude of the last cloud-ground flash generation, and respectively substituting the difference values into S in the expressionψnAnd SφnIn (1), find tnT in two fitting expressionsnThe calculation results respectively represent the coincidence time of the thundercloud longitude center and the photovoltaic power station longitude center and the coincidence time of the thundercloud latitude center and the photovoltaic power station latitude center, and t in the two fitting expressions is solvednThe average value of the calculation results is regarded as the middle moment of the irradiance value in the minimum duration time of the amplitude under the influence of the thundercloud, and is recorded as tIntermediate time。
Step 4.2, predicting duration t of thundercloud above the photovoltaic power stationDuration of time；
Calculating the time constant tau of each cloud-ground lightning event of the target photovoltaic power station in the historical database according to the following formula:
Figure BDA0002443600590000051
wherein v is a vertical cloud layer wind speed measured value in the historical database; v. of0The initial velocity of the cloud layer is vψ0And
Figure BDA0002443600590000053
by step 4.1 fittingCalculating to obtain; a is the acceleration of the cloud layer, and isψAnd aφThe synthetic vector is obtained by fitting calculation in the step 4.1; v. ofCloudThe cloud layer moving speed is calculated according to the cloud layer acceleration a and each cloud-to-ground flash occurrence and ending time obtained from a historical database;
and obtaining a statistical ratio p by averaging according to the following formula according to the historical data information:
Figure BDA0002443600590000052
wherein w is variable representation of the number of cloud-ground lightning events in the historical database, m is the total number of cloud-ground lightning events in the historical database, and is determined by querying the historical database, and tauwCalculating a time constant of the w-th cloud-to-ground flash event in the historical database; t is tDuration wThe duration of the w-th cloud-to-ground flash event is obtained through historical database query; p is a statistical ratio;
according to the current actually measured wind speed v and v in the fitting result0And a, calculating to obtain a time constant tau of the forecast cloud-ground lightning event, eliminating a negative value, and obtaining duration t of the forecast thundercloud above the photovoltaic power station according to a statistical ratio pDuration of time：
tDuration of time＝p×τ。
And 4.3, calculating and predicting the time when the thundercloud arrives and leaves the space above the photovoltaic power station.
T obtained in step 4.2Duration of timeT obtained by combining step 4.1Intermediate timeRespectively obtaining the predicted time t when the thundercloud arrives and leaves the sky of the photovoltaic power stationsAnd te：
Wherein:
Figure BDA0002443600590000061
Figure BDA0002443600590000062
in step 5, forecasting the time when the thundercloud reaches and leaves the sky of the target photovoltaic power station, which is obtained by forecasting in step 4, and forecasting the irradiance of the thundercloud when the thundercloud passes through the target photovoltaic power station by taking forecast data of the temperature and the humidity at the corresponding time as the input of the BP neural network forecasting model trained in step 3; then, calculating a photovoltaic power predicted value under the lightning condition according to the predicted value of the irradiance;
converting the irradiance predicted value into a photovoltaic power predicted value according to the following formula:
Ps＝ηPVSE[1-0.005(T+0.03E+25)]
wherein: psFor photovoltaic power generation power prediction value ηPVConverting the efficiency of the photovoltaic cell; s is the area of the photovoltaic cell panel; e is an irradiance predicted value; t is the air temperature.
The invention has the following beneficial technical effects:
the photovoltaic power prediction method based on thundercloud tracking is provided according to irradiance characteristics under the thunder condition, the problem of effectiveness of photovoltaic power prediction under the thunder extreme climate condition is solved, the prediction precision of the photovoltaic power under the thunder extreme climate condition is improved, reliable prediction values are provided for power dispatching departments, the power grid tide transfer and dispatching can be conveniently carried out in advance by the dispatching departments under the influence of the thunder extreme climate condition, the influence of the thunder weather on the power grid is reduced, and the safety and the stability of a power system are improved.
Drawings
Fig. 1 is a schematic flow chart of a photovoltaic power prediction method based on thundercloud trajectory tracking under a lightning condition according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and examples of the specification.
As shown in the attached figure 1, the invention discloses a photovoltaic power prediction method under a lightning condition based on thundercloud trajectory tracking. In the present invention, first, a hypothesis condition and a fitting expression are determined:
determining the hypothetical conditions: (1) the researched object is thundercloud flying from a distant place, and cannot be suddenly generated above a photovoltaic power station. (2) Approximately considering the longitude and latitude of the cloud-ground flash point as the position of the rain-accumulating cloud center; (3) the wind thrust and the cloud speed meet the momentum theorem; (4) and considering that the moving track of the cloud layer is uniformly accelerated linear motion with the initial speed not being 0 in the warp direction and the weft direction respectively, and fitting multiple groups of data to obtain corresponding results.
Determining a fitting expression: the kinematic fitting expressions of the moving track of the cloud layer in the warp direction and the weft direction are as follows:
the meridian direction fitting expression is:
Figure BDA0002443600590000071
the weft direction fitting expression is as follows:
Figure BDA0002443600590000072
wherein, tnTime difference S between adjacent moments when cloud-to-ground flash occurs on the same cloud layerψnIs cloud layer tnDistance moved in the longitudinal direction within a time period, vψ0Is the initial velocity of the cloud layer moving in the longitudinal direction, aψAcceleration of movement of the cloud layer in the longitudinal direction; sφ1Is cloud layer tnThe distance moved in the latitudinal direction over the time period,
Figure BDA0002443600590000073
is the initial velocity of the movement of the cloud layer in the latitudinal direction, aφIs the acceleration of the movement of the cloud in the latitudinal direction.
As shown in fig. 1, the present invention specifically comprises the following steps:
step 1, determining the longitude and latitude of a target photovoltaic power station, and acquiring thundercloud track prediction data in a set range and a set time period around the photovoltaic power station from a historical database according to the longitude and latitude of the target photovoltaic power station; inquiring the date recorded by cloud-to-ground flashing in a set range and a set time period around the target photovoltaic power station in a historical database, and acquiring irradiance prediction data of the target photovoltaic power station;
screening thundercloud track prediction data, namely recording the cloud-ground flash generation time and longitude and latitude in a certain range around a photovoltaic power station on the premise of knowing the accurate longitude and latitude range of a target photovoltaic power station, wherein the cloud-ground flash positioning spatial resolution is 0.0001 degree longitude and 0.0001 degree latitude respectively, and the time resolution is 0.1 × 10-6And second.
The set range around the target photovoltaic power station is a range with the target photovoltaic power station as the center and the radius of 3000km-9000 km. In the preferred embodiment of the application, a range with the radius of 5600km is selected as a set range around the target photovoltaic power station for prediction.
Screening for irradiance prediction history data: inquiring the date recorded by cloud field flashing within a certain range around the photovoltaic power station in the historical record, observing the irradiance change curve of the photovoltaic power station corresponding to the date, and intercepting the curve with steep drop firstly and then lasting for a period of time at a position with a very low amplitude, wherein the average value of the irradiance clear index of the period of time is less than 0.28, and then recording the values of temperature, humidity and irradiance of the period of time.
Wherein, the steep drop and the steep rise refer to irradiance per square meter being greater than an irradiance change threshold within a set time. In the preferred embodiment of the application, the steep drop and rise refer to irradiance change of more than or equal to 400W/m within 30min2H is used as the reference value. The irradiance threshold is set to mean that the average value of irradiance clear index is less than 0.28. The duration setting time period means that the duration is more than 90 min.
The calculation formula of the irradiance clear index is as follows:
Figure BDA0002443600590000081
true solar time ═ Beijing time- (120-local longitude)/15 + E
Figure BDA0002443600590000082
Wherein: k is a radical ofexIs a clear index; i is the actual irradiance; i isexThe irradiance in clear sky which is not influenced by cloud layer and atmosphere is calculated; isc is the solar constant, which is about 1367; i isONThe intensity of solar radiation of an atmospheric layer circumscribed plane; thetaZIs the solar zenith angle; solar declination angle; phi is the local geographical latitude; omega is the solar time angle; n is the accumulated day; s and F respectively represent the hours and minutes of the real sun; and E is the time difference generated by the movement and the rotation speed change of the earth around the sun, and the time difference unit is min.
And 2, preprocessing the thundercloud track prediction data and the irradiance prediction data acquired in the step 1.
Step 2.1, processing the thundercloud trajectory prediction data: according to the thundercloud track prediction screening data, calculating to obtain the time difference and the latitude and longitude difference of the cloud-ground lightning at the adjacent moments, and according to the time sequence, recording the first time difference as t1The corresponding longitude difference is denoted as Sψ1And the corresponding latitude difference is recorded as Sφ1Similarly, the nth time difference is denoted as tnThe corresponding longitude difference is denoted as SψnAnd the corresponding latitude difference is recorded as Sφn。
Step 2.2, processing irradiance prediction data: and taking the same time as a basis, taking the temperature and the humidity at the lightning occurrence time as the input of the neural network, taking the irradiance data as the output of the neural network, and correspondingly forming a data set by using a two-to-one mapping relation.
Step 3, training the BP neural network: and (3) taking the temperature and the humidity of the lightning occurrence moment in the historical data as the input of the neural network, and taking the irradiance data as the output of the neural network to train the BP neural network.
Step 4, predicting the time when the thundercloud arrives and leaves the sky of the target photovoltaic power station and the duration time of the thundercloud in the target photovoltaic power station;
in the technical solution of the present application, the method for predicting a thundercloud trajectory may adopt a time series method and an artificial intelligence method in the prior art, and may also adopt the preferred embodiments introduced below in the present application.
The following thundercloud trajectory prediction method is preferred in the present application, and is not intended to limit the spirit of the present invention, but to better illustrate the technical solution of the present invention. All existing thundercloud trajectory prediction methods in the prior art can be applied to the method and can obtain beneficial technical effects.
Step 4.1, thundercloud trajectory prediction:
will t1，t2，…，tn；Sψ1，Sψ2，…，Sψn；Sφ1，Sφ2，…，SφnSubstituting into the fitting expressions of the longitude and latitude directions in the step 1.2, and respectively fitting the initial velocities v in the longitude and latitude directions by using a linear fitting method0And an acceleration a. Finally, obtaining a fitting expression:
the fitted expression in the longitudinal direction is:
Figure BDA0002443600590000091
the fitted expression in the latitudinal direction is:
Figure BDA0002443600590000101
respectively subtracting the longitude and latitude of the central position of the photovoltaic power station and the recorded longitude and latitude of the last cloud-ground flash generation, and respectively substituting the difference values into S in the expressionψnAnd SφnIn (1), find tnEach equation has two results, the result that the median of each equation is negative is respectively discarded, the two results that the median is positive are finally reserved, the two results that the median is positive respectively represent the time when the thundercloud longitude center and the photovoltaic power station longitude center coincide and the time when the thundercloud latitude center and the photovoltaic power station latitude center coincide, the average value is calculated to be the middle moment of the irradiance numerical value in the minimum continuous time period under the influence of the thundercloud, and the average value is recorded as tIntermediate time。
Step 4.2, forecasting the duration of the thundercloud above the photovoltaic power station:
Ft＝mwind power(v-v0)
mWind power＝ρWind powerQS
Q＝v*1＝v
Obtaining:
F＝ρwind powerv(v-v0)S
Because:
Figure BDA0002443600590000102
a refers to the total acceleration of thundercloud, is aψAnd aφVector sum of
mCloud＝ρCloudV∝hr2
V is the volume of thundercloud
S∝hr
Obtaining:
Figure BDA0002443600590000103
satisfies the following conditions:
Figure BDA0002443600590000104
vcloudRefers to the moving speed of cloud layer, tau is a set parameter, has no practical physical meaning, and tDuration of timeIn direct proportion, wherein:
τ∝tduration of time
Wherein: s is the contact area of wind and the cloud layer; q is the flow of wind; v is a vertical cloud layer wind speed measured value in a certain direction; v. of0The initial velocity of the cloud in the same direction as the wind speed is vψ0And
Figure BDA0002443600590000114
the synthetic vector is obtained by fitting calculation in the step 4.1; f is thrust; m isWind powerIs the air mass; rhoWind powerFor the air density, the value of m is 1.293 g/LCloudIs the mass of the cloud; h is the height of the cloud; r is the generalized radius of the cloud top view;
according to historical data information, the data can be obtained by statistical analysis and mean calculation:
Figure BDA0002443600590000111
wherein, w is variable representation of the number of the historical data, m is the specific number of the historical data, and the specific value of m is determined according to the number of the inquired historical data; p is the statistical ratio
According to the measured wind speed v and v in the fitting result0And a, calculating to obtain tau, eliminating a negative value, and obtaining the duration t of the thundercloud above the photovoltaic power station according to a statistical result pDuration of time：
tDuration of time＝p×τ
Step 4.3, the moment when the thundercloud arrives and leaves the space above the photovoltaic power station:
t obtained in step 4.2Duration of timeT obtained by combining step 4.1Intermediate timeRespectively obtaining the time t when the thundercloud arrives and leaves the space above the photovoltaic power stationsAnd te。
Wherein:
Figure BDA0002443600590000112
Figure BDA0002443600590000113
step 5, predicting the irradiance amplitude of the thundercloud above the target photovoltaic power station and the photovoltaic power amplitude of the target photovoltaic power station:
predicting the moments when the thundercloud arrives and leaves the sky above the target photovoltaic power station, which are obtained by predicting in the step 4, and taking forecast data of the temperature and the humidity at the corresponding moments as the input of the BP neural network prediction model trained in the step 3 to predict the irradiance of the thundercloud during the period when the thundercloud passes through the target photovoltaic power station; then, calculating a photovoltaic power predicted value under the lightning condition according to the predicted value of the irradiance;
converting the irradiance predicted value into a photovoltaic power predicted value according to the following formula:
Ps＝ηPVSE[1-0.005(T+0.03E+25)]
wherein: psFor photovoltaic power generation power prediction value ηPVConverting the efficiency of the photovoltaic cell; s is the area of the photovoltaic cell panel; e is an irradiance predicted value; t is the air temperature.
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 by the claims.

Claims (13)

1. A photovoltaic power prediction method under a lightning condition based on thundercloud trajectory tracking is characterized by comprising the following steps:
step 1, determining the latitude and longitude of a target photovoltaic power station, and collecting thundercloud track prediction data and irradiance prediction data from a historical database;
the thundercloud track prediction data refers to the time and longitude and latitude of cloud-ground flash in a set range around a target photovoltaic power station; the irradiance prediction data refers to values of temperature, humidity and irradiance during cloud-to-ground flashing in a set range around a target photovoltaic power station;
step 2, preprocessing the thundercloud track prediction data and the irradiance prediction data collected in the step 1;
step 3, taking the temperature and humidity in the irradiance prediction data preprocessed in the step 2 during the cloud flash generation period as the input of a neural network, and taking the irradiance data as the output of the neural network to train a BP neural network;
step 4, predicting the moment when the thundercloud arrives and leaves the sky of the target photovoltaic power station and the duration time of the thundercloud in the target photovoltaic power station based on the current actually measured wind speed and the thundercloud track prediction data preprocessed in the step 2;
and 5, predicting the irradiance amplitude of the thundercloud above the target photovoltaic power station and the photovoltaic power amplitude of the target photovoltaic power station.
2. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1, characterized in that:
in step 1, the set range around the target photovoltaic power station is a range with a radius of 3000km-9000km with the target photovoltaic power station as a center.
3. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 2, characterized in that:
the set range around the target photovoltaic power station is a range with the target photovoltaic power station as the center and the radius of 5600 km.
4. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1 or 2, characterized in that:
taking the time of cloud-to-ground flashing at the position farthest from the center of the photovoltaic power station in a set range as the initial time of data acquisition;
the cloud-ground flash positioning spatial resolution is 0.0001 degree longitude and 0.0001 degree latitude, and the time resolution is 0.1 × 10-6And second.
5. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1 or 2, characterized in that:
obtaining values of temperature, humidity and irradiance during each cloud-to-ground flash within a set range around a target photovoltaic power plant by: inquiring the date recorded by the cloud flashing within the set range and the set time of the target photovoltaic power station in the historical record, observing the irradiance change curve of the photovoltaic power station corresponding to the date, intercepting the irradiance change curve which is firstly steeply reduced so that the irradiance is equal to or lower than the set irradiance threshold value and is continuously set for a period of time, then steeply raised, and recording the values of temperature, humidity and irradiance under the continuous period of time;
wherein, the steep drop and the steep rise refer to that the change of irradiance is larger than or equal to an irradiance change threshold value in a set time.
6. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 5, wherein:
the steep drop and rise refer to irradiance change being more than or equal to 400W/m within 30min2。
Setting an irradiance threshold value means that the average value of irradiance clear index is less than 0.28;
the duration setting time period means that the duration is more than 90 min.
7. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1 or 6, characterized in that:
in step 2, the processing of the thundercloud trajectory prediction data includes:
calculating the time difference and the latitude and longitude difference of cloud-ground flashes at adjacent moments, and recording the first time difference as t according to the time sequence1The corresponding longitude difference is denoted as Sψ1And the corresponding latitude difference is recorded as Sφ1Similarly, the nth time difference is denoted as tnThe corresponding longitude difference is denoted as SψnAnd the corresponding latitude difference is recorded as Sφn。
8. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 7, wherein:
in step 2, the processing of irradiance prediction data comprises:
and (3) taking the temperature and the humidity during the cloud-to-ground flash generation as the input of the BP neural network, taking the irradiance data as the output of the BP neural network, and corresponding the irradiance data to form a data set according to a two-to-one mapping relation.
9. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1 or 8, characterized in that:
in step 4, predicting the time when the thundercloud arrives and leaves the sky of the target photovoltaic power station and the duration of the thundercloud at the target photovoltaic power station comprises the following steps:
step 4.1, thundercloud track prediction is carried out, preprocessed thundercloud track prediction data, namely the time difference and the longitude and latitude difference of cloud-ground lightning at adjacent moments are sequentially subjected to a kinematic fitting expression of the thundercloud moving track in the longitude direction and the latitude direction, the middle moment in the cloud-ground lightning duration in the cloud-ground lightning event is calculated and recorded as tIntermediate time；
Step 4.2, predicting duration t of thundercloud above the photovoltaic power stationDuration of time；
And 4.3, calculating and predicting the time when the thundercloud arrives and leaves the space above the photovoltaic power station.
10. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 9, wherein:
in step 4.1, the thundercloud trajectory prediction data preprocessed in step 2, namely the time difference and the latitude and longitude difference of cloud-ground flashes at adjacent moments are substituted into a kinematic fitting expression of the thundercloud movement trajectory in the warp direction and the weft direction in sequence;
t1，t2，…，tn；Sψ1，Sψ2，…，Sψn；Sφ1，Sφ2，…，Sφnsubstituting into fitting expressions in longitude and latitude directions, and respectively fitting initial velocities v in the longitude and latitude directions by using a linear fitting method0And an acceleration a; finally, obtaining a fitting expression:
the fitted expression in the longitudinal direction is:
Figure FDA0002443600580000031
the fitted expression in the latitudinal direction is:
Figure FDA0002443600580000032
tnthe time difference between the nth cloud-to-ground flash of the same thundercloud and the next adjacent cloud-to-ground flash of the same thundercloud, SψnIs cloud layer tnDistance moved in the longitudinal direction within a time period, vψ0Is the initial velocity of the cloud layer moving in the longitudinal direction, aψAcceleration of movement of the cloud layer in the longitudinal direction; sφ1Is cloud layer tnThe distance moved in the latitudinal direction over the time period,
Figure FDA0002443600580000033
is the initial velocity of the movement of the cloud layer in the latitudinal direction, aφAcceleration of movement of the cloud layer in the latitudinal direction; v. of0The initial velocity of the cloud layer is vψ0And
Figure FDA0002443600580000034
the resultant vector of (a); a is the acceleration of the cloud layer, and isψAnd aφThe resultant vector of (a);
respectively subtracting the longitude and latitude of the central position of the target photovoltaic power station from the recorded longitude and latitude of the last cloud-ground flash generation, and respectively substituting the difference into S in the expressionψnAnd SφnIn (1), find tnT in two fitting expressionsnThe calculation results respectively represent the coincidence time of the thundercloud longitude center and the photovoltaic power station longitude center and the coincidence time of the thundercloud latitude center and the photovoltaic power station latitude center, and t in the two fitting expressions is solvednThe average value of the calculation results is regarded as the middle moment of the irradiance value in the minimum duration time of the amplitude under the influence of the thundercloud, and is recorded as tIntermediate time。
11. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 10, wherein:
in step 4.2, the duration of the thundercloud over the photovoltaic power station is predicted, which comprises the following steps:
calculating the time constant tau of each cloud-ground lightning event of the target photovoltaic power station in the historical database according to the following formula:
Figure FDA0002443600580000041
wherein v is a vertical cloud layer wind speed measured value in the historical database; v. of0The initial velocity of the cloud layer is vψ0And
Figure FDA0002443600580000043
the synthetic vector is obtained by fitting calculation in the step 4.1; a is the acceleration of the cloud layer, and isψAnd aφThe synthetic vector is obtained by fitting calculation in the step 4.1; v. ofCloudThe cloud layer moving speed is calculated according to the cloud layer acceleration a and each cloud-to-ground flash occurrence and ending time obtained from a historical database;
and obtaining a statistical ratio p by averaging according to the following formula according to the historical data information:
Figure FDA0002443600580000042
wherein w is variable representation of the number of cloud-ground lightning events in the historical database, m is the total number of cloud-ground lightning events in the historical database, and is determined by querying the historical database, and tauwCalculating a time constant of the w-th cloud-to-ground flash event in the historical database; t is tDuration wThe duration of the w-th cloud-to-ground flash event is obtained through historical database query; p is a statistical ratio;
according to the current actually measured wind speed v and v in the fitting result0And a, calculating to obtain a time constant tau of the forecast cloud-ground lightning event, eliminating a negative value, and obtaining duration t of the forecast thundercloud above the photovoltaic power station according to a statistical ratio pDuration of time：
tDuration of time＝p×τ。
12. The thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 11, wherein:
step 4.3, calculating the moment when the thundercloud arrives and leaves the sky above the photovoltaic power station, wherein the moment comprises the following contents:
t obtained in step 4.2Duration of timeT obtained by combining step 4.1Intermediate timeRespectively obtaining the predicted time t when the thundercloud arrives and leaves the sky of the photovoltaic power stationsAnd te：
Wherein:
Figure FDA0002443600580000051
Figure FDA0002443600580000052
13. the thundercloud trajectory tracking-based photovoltaic power prediction method under lightning conditions according to claim 1 or 12, wherein:
in step 5, forecasting the time when the thundercloud reaches and leaves the sky of the target photovoltaic power station, which is obtained by forecasting in step 4, and forecasting the irradiance of the thundercloud when the thundercloud passes through the target photovoltaic power station by taking forecast data of the temperature and the humidity at the corresponding time as the input of the BP neural network forecasting model trained in step 3; then, calculating a photovoltaic power predicted value under the lightning condition according to the predicted value of the irradiance;
converting the irradiance predicted value into a photovoltaic power predicted value according to the following formula:
Ps＝ηPVSE[1-0.005(T+0.03E+25)]
wherein: psFor photovoltaic power generation power prediction value ηPVConverting the efficiency of the photovoltaic cell; s is the area of the photovoltaic cell panel; e is an irradiance predicted value; t is the air temperature.
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