CN110568327A - Photovoltaic system direct current fault arc detection method based on machine learning - Google Patents
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Abstract
The invention belongs to the field of photovoltaic electrical fault detection, and particularly relates to a photovoltaic system direct-current fault arc detection method based on machine learning training. The method comprises the following steps: the method comprises the steps that an inverter collects real-time current signals of a direct current side of a photovoltaic system; analyzing to obtain frequency domain characteristics and time domain characteristics of the target; judging whether a fault arc occurs or not through the three models; and if the photovoltaic system is judged to generate the direct-current fault arc, starting an alarm mechanism. The invention adopts a detection algorithm based on machine learning, which can improve the detection accuracy and is practical under the conditions of small current and large current; the false detection operation caused by the fact that the threshold setting cannot adapt to all conditions can be avoided, and the false detection rate is effectively reduced.
Description
Technical Field
The invention belongs to the field of photovoltaic electrical fault detection, and particularly relates to a photovoltaic system direct-current fault arc detection method based on machine learning.
background
arcing is a gas discharge phenomenon, which refers to the momentary spark produced by a current passing through some insulating medium (e.g., air). Arc discharge is a self-sustaining discharge that is distinguished from other types of discharge by a very low sustaining voltage. At present, it is difficult to define the arc discharge strictly, and the arc discharge is a discharge with a reduced cathode potential and a large current density, and generally has a negative volt-ampere characteristic, simply from the electrical characteristic of the discharge.
In a photovoltaic system, once a fault electric arc is generated, if a timely and effective protective measure is not taken, a continuous direct current electric arc can generate a high temperature of more than 3000 ℃, and then a fire disaster is caused. In recent years, fire accidents caused by fault arcs occur in European and American countries, and equipment damage is caused to different degrees. The united states electrical code (NEC) of 2011 stipulates that a photovoltaic system should be equipped with a detection device and a circuit breaker for detecting a fault arc. The Underwriters Laboratories (UL) also introduced corresponding development testing methods and mechanisms.
At present, most researchers put forward a detection method for passive detection aiming at the characteristics of the arc, and the defect is that under some large current conditions, the arc characteristics are not obvious, and false detection is easily caused. The false detection can cause shutdown of the whole photovoltaic system once occurring, and unnecessary loss is brought.
disclosure of Invention
The invention aims to provide a photovoltaic system direct-current fault arc detection method based on machine learning, which is obvious in arc characteristic performance and not easy to cause false detection.
the invention provides a photovoltaic system direct current fault arc detection method based on machine learning, which is realized by a photovoltaic system; the photovoltaic system comprises a photovoltaic array, a combiner box, an inverter and an alternating current power grid; the output end of the photovoltaic array is connected with the input end of the combiner box, the output end of the combiner box is connected with the input end of the inverter, the output end of the inverter is connected with an alternating current power grid, a current collecting device is arranged between the combiner box and a circuit connected with the inverter, and a switch is arranged between the photovoltaic array and the combiner box; the detection method comprises the following steps:
(1): the inverter acquires a real-time current signal obtained by a photovoltaic array on the direct current side of the photovoltaic system through a current acquisition device; the sampling time is T1 seconds, and the interval time between two adjacent samplings is T2 seconds;
(2): analyzing the real-time current signal acquired in the step (1) to obtain the frequency domain characteristic and the time domain characteristic of the real-time current signal;
When the time domain characteristics show that direct current fault arc occurs, the current average value a1 is suddenly reduced, and the degree of reduction is determined by the specific power level of the photovoltaic system; the current average value a1 is calculated asWherein a1 is the average value of the current, AiThe value of the sampled current is N, and the number of the samples is N;
When the time domain characteristics show that a direct current fault arc occurs, the current variance a2 suddenly increases, and the degree of the increase is determined by specific photovoltaic system configuration parameters; the current variance a2 is calculated asWherein a2 is the current variance, Aithe value of the sampled current is a1, the average value of the current is a, and N is the number of samples;
A specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbThen, the variance a3 of the wavelet coefficient of the wavelet decomposition suddenly increases, and the degree of the increase is determined by the specific power level of the photovoltaic system; the variance a3 is calculated aswherein a3 is the variance, diIs the coefficient after wavelet decomposition of the current signal,the average value of N sampling numbers is obtained, and N is the sampling number; f. ofafor the lower limit of the selected frequency band interval, fbIs the upper limit of the selected frequency band interval;
a specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbthen, the energy a4 of the wavelet coefficient of the wavelet decomposition is suddenly increased, and the degree of the increase is determined by the specific power level of the photovoltaic system; the calculation formula of the energy a4 is a4 ═ d2in the formula, d is speciallyand (4) coefficients of wavelet decomposition under a fixed frequency band. f. ofaFor the lower limit of the selected frequency band interval, fbIs the upper limit of the selected frequency band interval;
a specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbThen, the absolute value a5 of the maximum value minus average value of the wavelet coefficient d of the wavelet decomposition is suddenly increased, and the degree of the increase is determined by the specific power level of the photovoltaic system; the absolute value a5 is calculated asIn the formula (d)maxFor the maxima of the wavelet decomposition coefficients at a particular frequency band,The coefficient average value of wavelet decomposition under a specific frequency band is obtained; f. ofaFor the lower limit of the selected frequency band interval, fbis the upper limit of the selected frequency band interval;
(3): and (3) respectively identifying whether the direct-current fault arc occurs in 3 models of a random forest, a support vector machine and a decision tree by using the time-frequency characteristics and the time-domain characteristics of the fault arc characteristics in the step (2). The Flag1 value was read and the Flag value was calculated.
(3.1) if flag is 0; and judging that the direct current fault arc is not generated in the current detection.
(3.2) if flag is 1; the method judges that the detected direct-current fault arc is possible to occur, and shortens the sampling time.
(3.3) if flag is 2; and judging that the direct current fault arc is generated at the current time.
(4): if the photovoltaic system is judged to generate the direct-current fault arc in the step (3), Flag +1, and judgment is carried out:
(4.1) if Flag is 2 at this time; and judging the occurrence of the direct-current fault arc. The breaker acts to break the circuit and send out alarm information.
(4.2) if Flag is 1 at this time; delaying 200ms for next detection.
If the direct current fault arc is not detected, the Flag value is still judged:
(4.3) if Flag is 1 at this time; setting Flag1 to 1, and storing the value of Flag1 for waiting for the next detection;
If Flag is 0 at this time; and clearing 0 for Flag1, and waiting for the next detection.
In the invention, the current collecting device arranged between the combiner box and the inverter of the photovoltaic system is a coil-in induction type real-time current collecting device which is used for collecting real-time current signals of the direct current side of the photovoltaic system in the step (1).
Compared with the prior art, the invention has the beneficial effects that:
(1) The detection accuracy can be improved by adopting a detection mode based on machine learning, and the method is practical under the conditions of low current and large current;
(2) The method can adapt to various conditions of the photovoltaic system during actual operation, and the false detection rate is reduced.
Drawings
FIG. 1 is a schematic diagram of real-time current collection locations for detecting a DC fault arc in a photovoltaic system.
fig. 2 is a flow chart of steps of a photovoltaic system dc fault arc detection method based on machine learning detection.
Reference numbers in the figures: the photovoltaic array is used as 1, the combiner box is used as 2, the inverter is used as 3, the alternating current power grid is used as 4, and the real-time current acquisition device is used as 5.
Detailed Description
the present invention will be described in detail with reference to the following embodiments, which are provided for illustration and not for limitation.
example 1:
The example takes the experimental data of a certain 10kW roof photovoltaic power station as an example to carry out photovoltaic direct current fault arc detection.
As shown in fig. 1, a photovoltaic array 1 outputs a dc current, a plurality of dc branches are connected in parallel in a combiner box 2 for combining, the total dc current is input into an inverter 3, the inverter converts the dc current into an ac current and transmits the ac current to an ac power grid 4, and the inverter controls the inverter to send out a detection signal.
As shown in fig. 2, the invention provides a photovoltaic system dc fault arc detection method based on machine learning detection, which mainly adopts the following technical scheme to determine whether a dc fault arc exists in a photovoltaic system, and specifically includes the following steps:
(1): collecting real-time current signals obtained by a photovoltaic array on the direct current side of a photovoltaic system; the sampling time was 0.1 seconds and the interval between two samplings was 0.5 seconds. The Flag1 value was read and the Flag value was calculated.
(2): and (3) analyzing the real-time current signals acquired in the step (2) and calculating the time-frequency domain characteristics.
1) Characteristic value 1: the average value a1 of the current is calculated asWherein a1 is the average value of the current, Aifor the sampling current value, N is the number of samples, and in this example, N is 10000.
2) characteristic value 2: the current variance a2 is calculated asWherein a2 is the current variance, AiFor the sampled current value, a1 is the current average value, N is the number of samples, and N is 10000 in this example.
3) characteristic value 3: under the frequency band of 40kHz-80kHz, the variance a3 of the wavelet coefficient of the wavelet decomposition is calculated asWherein a3 is the variance, diis the coefficient after wavelet decomposition of the current signal,the average value of N samples, where N is 10000, is defined as N.
4) Characteristic value 4: the energy a4 of wavelet coefficient of wavelet decomposition in the frequency band of 40kHz-80kHz is calculated as a4 ═ d2where d is the coefficient of the lower wavelet decomposition.
5) characteristic value 5: in the frequency band of 40kHz-80kHz, the absolute value of the maximum value minus the average value of the wavelet coefficient d of the wavelet decompositionthe calculation formula isin the formula (d)maxIs the maximum value of the wavelet decomposition coefficient under the frequency band of 40kHz-80kHz,The average value of the coefficients of the wavelet decomposition under the frequency band of 40kHz-80 kHz.
The calculation results are shown in table 1 below.
TABLE 1 Fault and Normal Current example calculations
| Current of arc | Normal current of | |
| Characteristic value 1 | 4.0891 | 4.4271 |
| Characteristic value 2 | 0.0024 | 0.0015 |
| Characteristic value 3 | 2.98E-04 | 2.78E-04 |
| Characteristic value 4 | 0.3768 | 0.3521 |
| Characteristic value 5 | 0.0776 | 0.0543 |
(3): and (3) respectively identifying whether the direct-current fault arc occurs in 3 models of a random forest, a support vector machine and a decision tree by using the time-frequency characteristics and the time-domain characteristics of the fault arc characteristics in the step (2). The Flag1 value was read and the Flag value was calculated.
1. if flag is equal to 0; and judging that the direct current fault arc is not generated in the current detection.
2. If flag is 1; the method judges that the detected direct-current fault arc is possible to occur, and shortens the sampling time.
3. if flag is 2; and judging that the direct current fault arc is generated at the current time.
(4): if the photovoltaic system is judged to generate the direct-current fault arc in the step (3), Flag +1, and judgment is carried out:
1. if Flag is 2 at this time; and judging the occurrence of the direct-current fault arc. The breaker acts to break the circuit and send out alarm information.
2. if Flag is 1 at this time; delaying 200ms for next detection.
If the direct current fault arc is not detected, the Flag value is still judged:
1. If Flag is 1 at this time; set Flag1 to 1 and store the value of Flag1 for the next check.
2. If Flag is 0 at this time; and clearing 0 for Flag1, and waiting for the next detection.
Claims (2)
1. A photovoltaic system direct current fault arc detection method based on machine learning is characterized in that the detection method is realized through a photovoltaic system; the photovoltaic system comprises a photovoltaic array, a combiner box, an inverter and an alternating current power grid; the output end of the photovoltaic array is connected with the input end of the combiner box, the output end of the combiner box is connected with the input end of the inverter, the output end of the inverter is connected with an alternating current power grid, a current collecting device is arranged between the combiner box and a circuit connected with the inverter, and a switch is arranged between the photovoltaic array and the combiner box; the detection method comprises the following steps:
(1): the inverter acquires a real-time current signal obtained by a photovoltaic array on the direct current side of the photovoltaic system through a current acquisition device; the sampling time is T1 seconds, and the interval time between two adjacent samplings is T2 seconds;
(2): analyzing the real-time current signal acquired in the step (1) to obtain the frequency domain characteristic and the time domain characteristic of the real-time current signal;
When the time domain characteristics show that direct current fault arc occurs, the current average value a1 is suddenly reduced, and the degree of reduction is determined by the specific power level of the photovoltaic system; the current average value a1 is calculated asWherein a1 is the average value of the current, AiThe value of the sampled current is N, and the number of the samples is N;
When the time domain characteristics show that a direct current fault arc occurs, the current variance a2 suddenly increases, and the degree of the increase is determined by specific photovoltaic system configuration parameters; the current variance a2 is calculated aswherein a2 is the current variance, AiThe value of the sampled current is a1, the average value of the current is a, and N is the number of samples;
A specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbThen, the variance a3 of the wavelet coefficient of the wavelet decomposition suddenly increases, and the degree of the increase is determined by the specific power level of the photovoltaic system; the variance a3 is calculated asWherein a3 is the variance, diIs the coefficient after wavelet decomposition of the current signal,Is N numberThe average value of the number of samples, N is the number of samples; f. ofaFor the lower limit of the selected frequency band interval, fbis the upper limit of the selected frequency band interval;
A specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbThen, the energy a4 of the wavelet coefficient of the wavelet decomposition is suddenly increased, and the degree of the increase is determined by the specific power level of the photovoltaic system; the calculation formula of the energy a4 is a4 ═ d2Wherein d is the coefficient of wavelet decomposition under a specific frequency band; f. ofaFor the lower limit of the selected frequency band interval, fbIs the upper limit of the selected frequency band interval;
A specific frequency band f when the frequency domain characteristics show that a direct current fault arc occursa-fbThen, the absolute value a5 of the maximum value minus average value of the wavelet coefficient d of the wavelet decomposition is suddenly increased, and the degree of the increase is determined by the specific power level of the photovoltaic system; the absolute value a5 is calculated asIn the formula (d)maxFor the maxima of the wavelet decomposition coefficients at a particular frequency band,Is the average value of the coefficients of wavelet decomposition under a specific frequency band, faFor the lower limit of the selected frequency band interval, fbIs the upper limit of the selected frequency band interval;
(3): identifying whether the direct-current fault arc occurs in 3 models of a random forest, a support vector machine and a decision tree by using the time-frequency characteristics and the time-domain characteristics of the fault arc characteristics in the step (2); reading a Flag1 value, and calculating a Flag value;
(3.1) if flag is 0; judging that the detected direct-current fault arc does not occur at this time;
(3.2) if flag is 1; judging that the detected direct-current fault arc is possible to occur at this time, and shortening the sampling time;
(3.3) if flag is 2; judging the occurrence of the direct current fault arc detected at this time;
(4): if the photovoltaic system is judged to generate the direct-current fault arc in the step (3), Flag +1, and judgment is carried out:
(4.1) if Flag is 2 at this time; judging the occurrence of direct-current fault arc; the breaker acts to break the circuit and send out alarm information;
(4.2) if Flag is 1 at this time; delaying for 200ms to carry out next detection;
If the direct current fault arc is not detected, the Flag value is still judged:
(4.3) if Flag is 1 at this time; setting Flag1 to 1, and storing the value of Flag1 for waiting for the next detection;
If Flag is 0 at this time; and clearing 0 for Flag1, and waiting for the next detection.
2. The photovoltaic system direct-current fault arc detection method based on machine learning as claimed in claim 1, wherein the current collection device provided between the combiner box and the inverter of the photovoltaic system is a series coil induction type real-time current collection device, which is used for realizing the collection of the real-time current signal on the direct-current side of the photovoltaic system in the step (1).
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| CN112305308A (en) * | 2020-10-21 | 2021-02-02 | 阳光电源股份有限公司 | Direct-current arc detection method and device and string inverter |
| CN112462176A (en) * | 2020-11-13 | 2021-03-09 | 丰郅(上海)新能源科技有限公司 | Device and method for supporting detection of photovoltaic system direct current arc fault |
| WO2021043027A1 (en) * | 2019-09-03 | 2021-03-11 | 复旦大学 | Machine learning-based direct current fault arc detection method for photovoltaic system |
| CN112904156A (en) * | 2021-01-19 | 2021-06-04 | 重庆邮电大学 | Fault arc detection method based on frequency domain classification |
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