CN110361090B - Future illuminance prediction method based on relevance of photovoltaic array sensor - Google Patents
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Abstract
The invention relates to the technical field of photovoltaic power generation, and provides a future illuminance prediction method based on photovoltaic array sensor relevance, which comprises the following steps of: calculating the sunlight illumination of all sensors at each moment according to the solar altitude and the solar radiation degree of the position of the photovoltaic array; acquiring a low-frequency coefficient of sunlight illumination of a nearby sensor at the current moment and a high-frequency coefficient of historical sunlight illumination data of the nearby sensor through wavelet analysis, and then generating sunlight illumination data with harmonic waves of the nearby sensor through wavelet inverse transformation; performing relevance calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensors to obtain the data of the lead-lag relation between the central sensor and the nearby sensors; and taking the lead-lag relation data as the moving time length, and performing time shift processing on the sunlight illumination data with harmonic waves of the nearby sensor to obtain the predicted future illumination data of the nearby sensor.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a future illuminance prediction method based on relevance of a photovoltaic array sensor.
Background
Photovoltaic power generation is a common power generation mode in the field of new energy, has the problem of intermittence, provides challenges for the operation of a micro-grid and the stability of a power system, and simultaneously has influence on the price of power on the internet in a power trading scene, so that the illuminance prediction of the photovoltaic power generation becomes a key factor.
The conventional photovoltaic power generation illuminance prediction method establishes a prediction model mainly through a solar irradiance transfer equation, a component operation equation and the like, and then obtains corresponding future illuminance data by taking the solar irradiance as an input vector. However, the predictive model established by this method has certain limitations. In addition, because the sensors in the same photovoltaic array have different degrees of relevance, when the cloud layer moves, the change of the illuminance can be transferred based on the relevance between different sensors, and the relevance between the sensors is not considered in the conventional photovoltaic power generation illuminance prediction method, so that the problem of low prediction accuracy exists.
Disclosure of Invention
The invention provides a future illuminance prediction method based on the relevance of a photovoltaic array sensor, aiming at overcoming the defect of low prediction accuracy caused by the fact that the relevance between sensors is not considered in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a future illuminance prediction method based on photovoltaic array sensor relevance comprises the following steps:
s1: calculating the sunlight illumination Y of all sensors at each moment according to the solar altitude h and the solar radiation X of the photovoltaic array at each moment;
s2: acquiring a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient of historical sunlight illumination data of the nearby sensor by wavelet analysis on the sunlight illumination of the nearby sensor at each moment, and then generating sunlight illumination data H (t) with harmonic waves of the nearby sensor by wavelet inverse transformation;
s3: performing relevance calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensors to obtain leading-lagging relation data M between the central sensor and the nearby sensors;
s4: and (3) performing time shift processing on the sunlight illumination data H (t) with harmonic waves of the nearby sensors by taking the lead-lag relation data M between the central sensor and the nearby sensors as the moving time length to obtain future illumination data h (t) of the nearby sensors.
In the technical scheme, the sensor at the center of the photovoltaic array is used as a center sensor, and other sensors are used as nearby sensors for processing. In the process of processing the sunlight illumination data of the nearby sensors, effective information of the sunlight illumination of the nearby sensors at each moment is extracted by adopting wavelet analysis: in the field of signal processing, wavelet analysis has the advantages of multi-resolution analysis and good time-frequency characteristics, and can extract effective information from signals mixed with noise and harmonic waves. In addition, considering that different sensors of the same photovoltaic array have different degrees of relevance, and the relevance among the different sensors forms a transfer relation along with the change of the illumination intensity, in the technical scheme, the sunlight illumination intensity of the central sensor and the sunlight illumination intensity of the nearby sensors are subjected to relevance calculation, and then the sunlight illumination intensity data of the sensors are subjected to time shift processing, so that the future illumination intensity data based on the relevance of the photovoltaic array sensors are finally obtained.
Preferably, in step S1, the calculation formula of the solar irradiance X is as follows:
X＝0.69243h+0.41526h2-0.00511h3
when the sunlight illuminance Y of the sensor corresponding to the total solar radiation of the visible light wave band at each moment needs to be calculated:
Y＝255.613X+362.649
when the sunlight illumination Y of the sensor corresponding to the total sunlight of the full wave band at each moment needs to be calculated:
Y＝-402.591+100.466X+0.00971536X2。
preferably, the specific steps of the step S2 are as follows:
s2.1: selecting a base wavelet as a basis of wavelet analysis, and performing expansion and translation on the selected base wavelet to obtain a wavelet sequence:
wherein a is a scale factor, b is a translation factor, and ψ (-) represents the selected base wavelet;
s2.2: extracting a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient (W) of the historical sunlight illumination of the nearby sensor through continuous wavelet transformψf) (a, b) the calculation formula is as follows:
wherein, f (t) represents an energy limited signal, namely sunlight illumination data of a nearby sensor at the current moment.
S2.3: and generating a sunlight illumination signal H (t) with harmonic waves of a nearby sensor by performing inverse wavelet transform on the low-frequency coefficient, the high-frequency coefficient and the wavelet sequence, wherein the calculation formula is as follows:
wherein, cψIs a tolerable condition for wavelets.
Preferably, the specific steps of the step S3 are as follows:
s3.1: forming the historical illuminance data of the central sensor into an illuminance sequence F1(t) normalizing the central sensor illuminance sequence to obtain a normalized central sensor illuminance sequence f1(t)；
S3.2: illumination sequence f of central sensor after normalization1(t) at a time point t0Sampling a sequence of samples X of n time lengths for a sampling start timenThe calculation formula is as follows:
Xn＝[f1(t0),f1(t0+1),...,f1(t0+n-1)]；
s3.3: composing historical illuminance data of nearby sensors into illuminance sequence F2(t) normalizing the intensity of the light to obtain a normalized illuminance sequence f of the nearby sensor2(t)；
S3.4: sequence f of illuminance of sensor in vicinity subjected to normalization processing2(t) at a time point t0+ m is the sampling start time, sampling n time length sample sequence Yn_mThe calculation formula is as follows:
Yn_m＝[f2(t0+m),f2(t0+m+1),...,f2(t0+m+n-1)]；
s3.5: sample sequence X according to central sensor illuminancenAnd a sequence of illuminance samples Y of nearby sensorsn_mAnd performing correlation calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensor to obtain the lead-lag relation data M between the central sensor and the nearby sensor.
Preferably, in step S3, the calculation formula for normalizing the illuminance sequence of the central sensor or the nearby sensor is as follows:
wherein, Forder(t) represents the illuminance sequence of the sensor, Forder(t)maxRepresenting the maximum illumination element in the sequence of illuminations, Forder(t)minRepresenting the minimum illuminance element, f, in the sequence of illuminancesorderAnd (t) represents the illumination sequence of the sensor after normalization.
Preferably, in the step S3.5, the specific step of calculating the correlation between the sunlight illuminance of the central sensor and the sunlight illuminance of the nearby sensor is: by calculating the time length of the movement as m time sample sequence XnAnd sample sequence Yn_mPearson's correlation coefficient rmObtaining a set of Pearson correlation coefficient sequences { r) with different moving time lengthsmObtaining a maximum correlation coefficient element, namely leading-lag relation data M between the central sensor and a nearby sensor;
the Pearson correlation coefficient rmThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
representing a sample sequence XnIs determined by the average value of (a) of (b),
represents a sample sequence Yn_mAverage value of (d);
the calculation formula of the lead-lag relationship data M is as follows:
M＝g(max{rm})
wherein g (-) is a subscript function, max { rmDenotes a sequence of Pearson correlation coefficients { r } over a set of different lengths of time of movementmThe largest correlation coefficient element in (j).
Preferably, in step S4, the future illuminance data h (t) of the proximity sensor is calculated by the following formula:
h(t)＝H(t+M)。
compared with the prior art, the technical scheme of the invention has the beneficial effects that: effective information is extracted from sunlight illumination data containing noise and harmonic aliasing through wavelet analysis, correlation calculation is carried out on the sunlight illumination corresponding to different sensors of the same photovoltaic array, future sunlight illumination data of the sensors are further predicted, and the accuracy of prediction of the future sunlight illumination data can be effectively improved.
Drawings
Fig. 1 is a flowchart of a future illuminance prediction method based on association of a photovoltaic array sensor according to this embodiment.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Fig. 1 is a flowchart of a future illuminance prediction method based on the association of the photovoltaic array sensor according to the present embodiment.
In this embodiment, the sensor located at the center of the photovoltaic array is used as the center sensor, and the other sensors are used as the nearby sensors for processing.
The embodiment provides a future illuminance prediction method based on relevance of a photovoltaic array sensor, which comprises the following steps:
s1: and calculating the sunlight illumination Y of all the sensors at each moment according to the solar altitude h and the solar radiation degree X of the photovoltaic array at each moment.
The calculation formula of the solar radiation degree X is as follows:
X＝0.69243h+0.41526h2-0.00511h3
when the sunlight illuminance Y of the sensor corresponding to the total solar radiation of the visible light wave band at each moment needs to be calculated:
Y＝255.613X+362.649
when the sunlight illumination Y of the sensor corresponding to the total sunlight of the full wave band at each moment needs to be calculated:
Y＝-402.591+100.466X+0.00971536X2。
s2: acquiring a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient of historical sunlight illumination data of the nearby sensor by wavelet analysis on the sunlight illumination of the nearby sensor at each moment, and then generating sunlight illumination data H (t) with harmonic waves of the nearby sensor by wavelet inverse transformation;
s2.1: selecting a base wavelet as a basis of wavelet analysis, and performing expansion and translation on the selected base wavelet to obtain a wavelet sequence:
wherein a is a scale factor, b is a translation factor, and ψ (-) represents the selected base wavelet;
S2.2：extracting a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient (W) of the historical sunlight illumination of the nearby sensor through continuous wavelet transformψf) (a, b) the calculation formula is as follows:
wherein, f (t) represents an energy limited signal, namely sunlight illumination data of a nearby sensor at the current moment.
S2.3: and generating a sunlight illumination signal H (t) with harmonic waves of a nearby sensor by performing inverse wavelet transform on the low-frequency coefficient, the high-frequency coefficient and the wavelet sequence, wherein the calculation formula is as follows:
wherein, cψIs a tolerable condition for wavelets.
S3: and performing relevance calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensors to obtain the lead-lag relation data M between the central sensor and the nearby sensors. The method comprises the following specific steps:
s3.1: forming the historical illuminance data of the central sensor into an illuminance sequence F1(t) normalizing the central sensor illuminance sequence to obtain a normalized central sensor illuminance sequence f1(t)；
S3.2: illumination sequence f of central sensor after normalization1(t) at a time point t0Sampling a sequence of samples X of n time lengths for a sampling start timenThe calculation formula is as follows:
Xn＝[f1(t0),f1(t0+1),...,f1(t0+n-1)]；
s3.3: composing historical illuminance data of nearby sensors into illuminance sequence F2(t) normalizing the intensity of the light to obtain a normalized illuminance sequence f of the nearby sensor2(t)；
S3.4: sequence f of illuminance of sensor in vicinity subjected to normalization processing2(t) at a time point t0+ m is the sampling start time, sampling n time length sample sequence Yn_mThe calculation formula is as follows:
Yn_m＝[f2(t0+m),f2(t0+m+1),...,f2(t0+m+n-1)]；
s3.5: sample sequence X according to central sensor illuminancenAnd a sequence of illuminance samples Y of nearby sensorsn_mAnd performing correlation calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensors:
by calculating the time length of the movement as m time sample sequence XnAnd sample sequence Yn_mPearson's correlation coefficient rmObtaining a set of Pearson correlation coefficient sequences { r) with different moving time lengthsmAnd acquiring a maximum correlation coefficient element, namely the lead-lag relationship data M between the central sensor and the nearby sensors, wherein the specific formula is as follows:
the time length of the movement is m times of the sample sequence XnAnd sample sequence Yn_mPearson's correlation coefficient rmThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
representing a sample sequence XnIs determined by the average value of (a) of (b),
represents a sample sequence Yn_mAverage value of (d);
the calculation formula of the lead-lag relationship data M between the center sensor and the nearby sensors is:
M＝g(max{rm})
wherein g (-) is a subscript function, max { rmDenotes a sequence of Pearson correlation coefficients { r } over a set of different lengths of time of movementmThe largest correlation coefficient element in (j).
Specifically, the calculation formula for performing normalization processing on the illuminance sequence of the central sensor or the nearby sensor in this step is as follows:
wherein, Forder(t) represents the illuminance sequence of the sensor, Forder(t)maxRepresenting the maximum illumination element in the sequence of illuminations, Forder(t)minRepresenting the minimum illuminance element, f, in the sequence of illuminancesorderAnd (t) represents the illumination sequence of the sensor after normalization.
S4: taking the lead-lag relation data M between the central sensor and the nearby sensors as the moving time length, and performing time shift processing on the sunlight illumination data H (t) with harmonic waves of the nearby sensors to obtain the future illumination data h (t) of the nearby sensors, wherein the formula is as follows:
h(t)＝H(t+M)。
in the embodiment, the sunlight illumination of the nearby sensor and the central sensor at each moment is obtained by calculation according to the solar altitude h and the solar irradiance X at each moment of the position of the photovoltaic array, then the calculated sunlight illumination data is subjected to wavelet analysis, effective information is extracted from the calculated sunlight illumination data, the correlation among different sensors in the same photovoltaic array is reflected by adopting a Pearson correlation coefficient, the element with the largest correlation coefficient is selected from a Pearson correlation coefficient sequence to serve as a time shift length, the extracted sunlight illumination data with the harmonic waves of the nearby sensor is subjected to time shift processing, and the future sunlight illumination data based on the correlation of the photovoltaic array sensor is obtained by calculation.
In the wavelet analysis, the sunlight illumination data is used as an energy limited signal and belongs to an energy limited space, so that a square integrable condition can be met, meanwhile, in the embodiment, a base wavelet is selected as an extrusion of the wavelet analysis, the base wavelet is subjected to expansion and translation to obtain a corresponding wavelet sequence, a low-frequency coefficient of the sunlight illumination data of the nearby sensor at the current moment and a high-frequency coefficient of historical illumination data of the nearby sensor are extracted through continuous wavelet transformation, and finally, the sunlight illumination data with harmonic waves of the nearby sensor are generated through wavelet inverse transformation, so that effective information can be extracted from the sunlight illumination data of the nearby sensor.
In the data generation process with the lead-lag relation, the illumination sample sequence X of the central sensor is calculatednAnd a sequence of illuminance samples Y of nearby sensorsn_mPearson's correlation coefficient between rmObtaining a set of Pearson correlation coefficient sequences { r) with different moving time lengthsmAnd finally, performing time shifting processing on the sunlight illumination data with harmonic waves of the nearby sensors by taking the lead-lag relation data M as the time shifting time length, so that the finally obtained future sunlight illumination data of the nearby sensors has the relevance among different sensors in the photovoltaic array, the prediction precision can be effectively improved, and the method can be better applied to safety decision and transaction decision.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (7)
1. A future illuminance prediction method based on photovoltaic array sensor relevance is characterized by comprising the following steps:
s1: calculating the sunlight illumination Y of all sensors at each moment according to the solar altitude h and the solar radiation X of the photovoltaic array at each moment;
s2: acquiring a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient of historical sunlight illumination data of the nearby sensor by wavelet analysis on the sunlight illumination of the nearby sensor at each moment, and then generating sunlight illumination data H (t) with harmonic waves of the nearby sensor by wavelet inverse transformation;
s3: performing relevance calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensors to obtain leading-lagging relation data M between the central sensor and the nearby sensors;
s4: and (3) performing time shift processing on the sunlight illumination data H (t) with harmonic waves of the nearby sensors by taking the lead-lag relation data M between the central sensor and the nearby sensors as the moving time length to obtain future illumination data h (t) of the nearby sensors.
2. The method of predicting future illuminance according to claim 1, wherein: in the step S1, the formula for calculating the solar irradiance X is as follows:
X＝0.69243h+0.41526h2-0.00511h3
when the sunlight illuminance Y of the sensor corresponding to the total solar radiation of the visible light wave band at each moment needs to be calculated:
Y＝255.613X+362.649
when the sunlight illumination Y of the sensor corresponding to the total sunlight of the full wave band at each moment needs to be calculated:
Y＝-402.591+100.466X+0.00971536X2。
3. the method of predicting future illuminance according to claim 2, wherein: the specific steps of the step S2 are as follows:
s2.1: selecting a base wavelet as a basis of wavelet analysis, and performing expansion and translation on the selected base wavelet to obtain a wavelet sequence:
wherein a is a scale factor, b is a translation factor, and ψ (-) represents the selected base wavelet;
s2.2: extracting a low-frequency coefficient of the sunlight illumination of the nearby sensor at the current moment and a high-frequency coefficient (W) of the historical sunlight illumination of the nearby sensor through continuous wavelet transformψf) (a, b) the calculation formula is as follows:
wherein, f (t) represents an energy limited signal, namely sunlight illumination data of a nearby sensor at the current moment;
s2.3: and generating a sunlight illumination signal H (t) with harmonic waves of a nearby sensor by performing inverse wavelet transform on the low-frequency coefficient, the high-frequency coefficient and the wavelet sequence, wherein the calculation formula is as follows:
wherein, cψIs a tolerable condition for wavelets.
4. The method of predicting future illuminance according to claim 3, wherein: the specific steps of the step S3 are as follows:
s3.1: forming the historical illuminance data of the central sensor into an illuminance sequence F1(t) normalizing the central sensor illuminance sequence to obtain a normalized central sensor illuminance sequence f1(t)；
S3.2: illumination sequence f of central sensor after normalization1(t) at a time point t0Sampling a sequence of samples X of n time lengths for a sampling start timenThe calculation formula is as follows:
Xn＝[f1(t0),f1(t0+1),...,f1(t0+n-1)]；
s3.3: composing historical illuminance data of nearby sensors into illuminance sequence F2(t) normalizing the intensity of the light to obtain a normalized illuminance sequence f of the nearby sensor2(t)；
S3.4: sequence f of illuminance of sensor in vicinity subjected to normalization processing2(t) at a time point t0+ m is the sampling start time, sampling n time length sample sequence Yn_mThe calculation formula is as follows:
Yn_m＝[f2(t0+m),f2(t0+m+1),...,f2(t0+m+n-1)]；
s3.5: sample sequence X according to central sensor illuminancenAnd a sequence of illuminance samples Y of nearby sensorsn_mAnd performing correlation calculation on the sunlight illumination of the central sensor and the sunlight illumination of the nearby sensor to obtain the lead-lag relation data M between the central sensor and the nearby sensor.
5. The method of predicting future illuminance according to claim 4, wherein: in step S3, the calculation formula for normalizing the illuminance sequence of the center sensor or the vicinity sensor is as follows:
wherein, Forder(t) represents the illuminance sequence of the sensor, Forder(t)maxRepresenting the maximum illumination element in the sequence of illuminations, Forder(t)minRepresenting the minimum illuminance element, f, in the sequence of illuminancesorderAnd (t) represents the illumination sequence of the sensor after normalization.
6. The method of predicting future illuminance according to claim 4, wherein: in the step S3.5, the specific step of performing correlation calculation on the sunlight illuminance of the central sensor and the sunlight illuminance of the nearby sensor is: by calculating the time length of the movement as m time sample sequence XnAnd sample sequence Yn_mPearson's correlation coefficient rmObtaining a set of Pearson correlation coefficient sequences { r) with different moving time lengthsmObtaining a maximum correlation coefficient element, namely leading-lag relation data M between the central sensor and a nearby sensor;
the Pearson correlation coefficient rmThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
representing a sample sequence XnIs determined by the average value of (a) of (b),
represents a sample sequence Yn_mAverage value of (d);
the calculation formula of the lead-lag relationship data M is as follows:
M＝g(max{rm})
wherein g (-) is a subscript function, max { rmDenotes a sequence of Pearson correlation coefficients { r } over a set of different lengths of time of movementmThe largest correlation coefficient element in (j).
7. The method of predicting future illuminance according to claim 6, wherein: in the step S4, the calculation formula of the future illuminance data h (t) of the proximity sensor is:
h(t)＝H(t+M)。
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