Island detection method suitable for new energy pooling station

Abstract

The invention discloses an island detection method suitable for a new energy pooling station. The island detection method comprises the following steps: firstly, synchronously sampling voltage and current signals of a grid-connected point of the new energy pooling station; preprocessing the obtained voltage and current signals to obtain a fundamental wave voltage amplitude and a fundamental wave current amplitude; calculating a power factor angle according to the fundamental wave voltage amplitude and the fundamental wave current amplitude; constructing an island detection judgment basis based on the calculated power factor angle, and judging whether an island phenomenon occurs or not; if it is judged that the island phenomenon possibly occurs, voltage harmonic distortion rate detection is carried out; if it is determined that the island phenomenon occurs, an island alarm signal is sent out, and corresponding protection actions are adopted. According to the island detection method suitable for the new energy pooling station, two-parameter judgment of the power factor angle and the voltage harmonic distortion rate is integrated, detection blind areas are effectively reduced, accuracy is improved, extra signals do not need to be injected, electric energy quality is guaranteed, and the island detection method is widely applicable to various new energy power stations.

Description

Technical Field

[0001] The present invention relates to the field of new energy power technology, and in particular to an island detection method suitable for a new energy collection station. Background Art

[0002] In the field of renewable energy power, renewable energy collection stations collect and integrate the power of various renewable energy power generation units (such as solar photovoltaic power stations, wind farms, etc.) and connect them to the power grid. As the proportion of renewable energy in the power system continues to increase, the island phenomenon has become a key issue that needs to be solved urgently. The island phenomenon refers to the situation where the renewable energy collection station continues to supply power to the local load it carries after the power grid is disconnected from the renewable energy collection station due to a fault or planned maintenance, forming an isolated power system.

[0003] Traditional island detection methods mainly include passive detection methods and active detection methods. Passive detection methods such as over / under voltage detection and over / under frequency detection are based on the principle of monitoring whether the voltage and frequency of the power grid are beyond the normal range to determine whether an island has occurred. However, this method has a detection blind spot. When an island occurs, if the voltage and frequency changes are within the normal range, the island cannot be detected. Active detection methods such as frequency offset method and sliding mode frequency offset method can improve detection performance, but they will inject harmonics or disturbance signals into the power grid, affecting the power quality, and may interfere with each other when multiple inverters are running in parallel, reducing the accuracy and reliability of detection.

[0004] Therefore, a new island detection method is needed, which can not only improve the accuracy and reliability of detection, but also avoid adverse effects on power quality, and is suitable for the complex operating environment of new energy collection stations. Summary of the invention

[0005] The purpose of the present invention is to provide an island detection method suitable for a new energy collection station, which solves the problems of low detection accuracy, influence on power quality and poor adaptability of traditional island detection methods in the application of new energy collection stations.

[0006] To achieve the above object, the present invention provides an island detection method applicable to a new energy collection station, comprising the following steps:

[0007] S1, synchronously sampling the voltage and current signals of the grid connection point of the new energy collection station;

[0008] S2, preprocessing the voltage and current signals obtained in S1 to obtain the fundamental voltage amplitude and the fundamental current amplitude;

[0009] S3, calculating the power factor angle according to the fundamental voltage amplitude and the fundamental current amplitude;

[0010] S4, constructing an islanding detection judgment basis based on the power factor angle calculated in S3 to determine whether an islanding phenomenon occurs;

[0011] S5. If it is determined that islanding may occur, voltage harmonic distortion rate detection is performed;

[0012] S6. If it is determined that an islanding phenomenon occurs, an islanding alarm signal is issued and corresponding protective actions are taken.

[0013] Preferably, in S1, a voltage transformer and a current transformer are used to adjust the sampling frequency f in real time. s , synchronously sample the voltage u(t) and current signals i(t) of the grid connection point of the new energy collection station, and calculate the instantaneous value expressions of voltage and current based on the sampled data;

[0014] Among them, the instantaneous value expression of voltage u(t) is:

[0015] u(t)=U m sin(ωt+θ u );

[0016] Where U m is the voltage amplitude, ω is the angular frequency, θ u is the initial phase of voltage;

[0017] The instantaneous value expression of current i(t) is:

[0018] i(t)=I m sin(ωt+θ i );

[0019] In the formula, I m is the current amplitude, ω is the angular frequency, θ i is the initial phase of the current.

[0020] Preferably, in S2, discrete Fourier transform DFT is used to pre-process the voltage and current signals obtained in S1 to obtain the fundamental voltage amplitude U 1 and fundamental current amplitude I 1 :

[0021] Let u n and i n are the discrete voltage and current signal sequences, n=0,1,...,N-1 is the position of each sampling point in the sequence, N is the number of sampling points in one cycle, then the expression of the fundamental voltage amplitude is:

[0022]

[0023] The expression of fundamental current amplitude is:

[0024]

[0025] Preferably, in S3, according to the fundamental voltage amplitude U 1 and fundamental current amplitude I 1 , the power factor angle is obtained by calculating the phase difference between the voltage and current signals

[0026] Assume that the fundamental phase angle of the voltage signal is but:

[0027]

[0028] Assume that the fundamental phase angle of the current signal is but:

[0029]

[0030] Finally, the power factor angle is

[0031]

[0032] Preferably, in S4, based on the power factor angle calculated in S3 Constructing the judgment basis for island detection to determine whether an island phenomenon occurs includes the following steps:

[0033] S41. When the new energy collection station is connected to the grid and operating normally, the power factor angle is calculated through multiple measurements. Fundamental voltage amplitude U 1 , fundamental current amplitude I 1 and sampling frequency f s , build a training data set;

[0034] Define the eigenvector The corresponding label is y, y = 0 in normal operation, and y = 1 if an island situation is known to exist;

[0035] S42, constructing a neural network including an input layer, several hidden layers and an output layer, wherein the number of nodes in the input layer corresponds to the dimension of the feature vector, and the output layer is a node, indicating whether the island phenomenon occurs, 0 if it does not occur, and 1 if it occurs;

[0036] S43, use the ReLU function in the hidden layer to make the neural network fit the nonlinear function, and use the Sigmoid function in the output layer to map the output to the [0,1] interval, indicating the probability of islanding;

[0037] S44, using the training data set to train the neural network, and using the cross-validation method to evaluate and tune the model;

[0038] S45, real-time acquisition of power factor angle And other related parameters, construct the current feature vector X test , X test Input into the trained neural network to get the output Y of the model pred :

[0039] If Y pred =1, it is determined that islanding may occur, and the process goes to S5 to detect the voltage harmonic distortion rate;

[0040] If Y pred =0, it is determined that the current state is normal grid-connected operation and continues monitoring.

[0041] Preferably, in S41, before constructing the training data set, the power factor angle measured and calculated is also included. Fundamental voltage amplitude U 1 , fundamental current amplitude I 1 and sampling frequency f s Perform preprocessing operations, including removing outliers and normalizing.

[0042] Preferably, in S41, the box plot method is used to remove outliers, comprising the following steps:

[0043] S411. Calculate the first quartile Q 1 : After sorting the data from small to large, if the number of data is m, when m is an odd number, Q 1 It is data; when m is an even number, Q 1 It is The data and The average value of the data;

[0044] S412. Calculate the third quartile Q 3 :Same operation is performed on the sorted data. When m is an odd number, Q 3 It is data; when m is an even number, Q 3 It is Data and The average value of the data;

[0045] S413. Calculate the interquartile range IQR = Q 3 -Q 1 ;

[0046] S414, determine the lower limit value lower = Q of the abnormal value range 1 -1.5×IQR and upper limit value upper=Q 3 +1.5×IQR;

[0047] S415, traverse each data point in the data set, mark the data point that is less than lower or greater than upper as an outlier and delete it from the data set.

[0048] Preferably, in S41, the Z-score normalization method is used to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. Specifically, for a set of data x, the normalized result x norm The calculation formula is as follows:

[0049]

[0050] in, is the mean value of the data set x, s is the standard deviation of the data set x, and the calculation formula is:

[0051]

[0052] where x i represents the i-th data point, and d is the total number of data points.

[0053] Preferably, in S5, if it is determined that an islanding phenomenon may occur, the voltage harmonic distortion rate THD is calculated. u Detection:

[0054] Let U h is the hth harmonic voltage amplitude, U 1 is the fundamental voltage amplitude, then:

[0055]

[0056] If THD u Exceeds the set harmonic distortion threshold THD uth , it is determined that an island phenomenon occurs.

[0057] Preferably, in S6, if it is determined that an islanding phenomenon occurs, an islanding alarm signal is issued and a corresponding protection action is taken, that is, a control instruction is sent to the control unit of the new energy collection station to control the new energy power generation equipment to stop running or reduce the output power;

[0058] The control instructions are sent through a communication bus, which uses optical fiber communication or wireless communication.

[0059] Therefore, the present invention adopts the above-mentioned island detection method applicable to a new energy collection station, and the beneficial effects are as follows:

[0060] (1) The present invention comprehensively considers the two parameters of power factor angle and voltage harmonic distortion rate. When an island occurs, the power balance relationship between the new energy collection station and the local load is broken, which will cause a significant change in the power factor angle. At the same time, due to the nonlinear characteristics of the new energy power generation equipment and the change in load, the voltage harmonic distortion rate will also change. By jointly judging these two parameters, compared with the traditional single parameter detection method, it can effectively reduce the detection blind area and improve the accuracy of island detection.

[0061] (2) Unlike active detection methods, the present invention does not need to inject additional disturbance or harmonic signals into the power grid. It only performs island detection based on the monitoring and analysis of voltage and current signals. It will not cause adverse effects on the power quality of the power grid, thus ensuring the normal operation of other electrical equipment in the power system and meeting the strict requirements of modern power systems for power quality.

[0062] (3) The present invention is based on the basic processing and analysis of voltage and current signals and does not rely on a specific type of renewable energy generation technology or equipment. Regardless of whether the collection station is composed of solar energy, wind energy or other renewable energy generation forms, as long as the voltage and current signals of the grid connection point can be obtained, the present method can be used for island detection. It has wide adaptability and can be applied to renewable energy collection stations of different scales and structures.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is an overall flow chart of an embodiment of an island detection method applicable to a new energy collection station of the present invention.

[0065] Figure 2 The present invention is a flowchart of an island detection method embodiment applicable to a new energy collection station for determining whether an island phenomenon occurs. DETAILED DESCRIPTION

[0066] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0067] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0068] As shown in the figure, an island detection method applicable to a new energy collection station includes the following steps:

[0069] S1, synchronously sampling the voltage and current signals of the grid connection point of the new energy collection station;

[0070] By using voltage transformer and current transformer, the sampling frequency f is adjusted in real time.s For example, at 10kHz, the voltage u(t) and current signals i(t) of the grid connection point of the new energy collection station are synchronously sampled, and the instantaneous value expressions of the voltage and current are calculated based on the sampled data;

[0071] Among them, the instantaneous value expression of voltage u(t) is:

[0072] u(t)=U m sin(ωt+θ u );

[0073] Where U m is the voltage amplitude, ω is the angular frequency, θ u is the initial phase of voltage;

[0074] The instantaneous value expression of current i(t) is:

[0075] i(t)=I m sin(ωt+θ i );

[0076] In the formula, I m is the current amplitude, ω is the angular frequency, θ i is the initial phase of the current.

[0077] S2, preprocessing the voltage and current signals obtained in S1 to obtain the fundamental voltage amplitude and the fundamental current amplitude;

[0078] The voltage and current signals obtained in S1 are preprocessed using discrete Fourier transform DFT to obtain the fundamental voltage amplitude U 1 and fundamental current amplitude I 1 :

[0079] Let u n and i n are the discrete voltage and current signal sequences, n=0,1,...,N-1 is the position of each sampling point in the sequence, N is the number of sampling points in one cycle, then the expression of the fundamental voltage amplitude is:

[0080]

[0081] The expression of fundamental current amplitude is:

[0082]

[0083] S3, calculating the power factor angle according to the fundamental voltage amplitude and the fundamental current amplitude;

[0084] According to the fundamental voltage amplitude U 1 and fundamental current amplitude I 1 , the power factor angle is obtained by calculating the phase difference between the voltage and current signals

[0085] Assume that the fundamental phase angle of the voltage signal is but:

[0086]

[0087] Assume that the fundamental phase angle of the current signal is but:

[0088]

[0089] Finally, the power factor angle is

[0090]

[0091] like Figure 2 As shown, S4, constructing an island detection judgment basis based on the power factor angle calculated in S3 to determine whether an island phenomenon occurs, including the following steps:

[0092] S41, measured and calculated power factor angle Fundamental voltage amplitude U 1 , fundamental current amplitude I 1 and sampling frequency f s Perform preprocessing operations, including removing outliers and normalization. Specifically, the box plot method is used to remove outliers, which includes the following steps:

[0093] S411. Calculate the first quartile Q 1 : After sorting the data from small to large, if the number of data is m, when m is an odd number, Q 1 It is data; when m is an even number, Q 1 It is Data and The average value of the data;

[0094] S412. Calculate the third quartile Q 3 :Same operation is performed on the sorted data. When m is an odd number, Q 3 It is data; when m is an even number, Q 3 It is Data and The average value of the data;

[0095] S413. Calculate the interquartile range IQR = Q 3 -Q 1 ;

[0096] S414, determine the lower limit value lower = Q of the abnormal value range1 -1.5×IQR and upper limit value upper=Q 3 +1.5×IQR;

[0097] S415, traverse each data point in the data set, mark the data point that is less than lower or greater than upper as an outlier and delete it from the data set.

[0098] For example, for a set of power factor angle data (Unit: degree): 10, 12, 20, 15, 25, 40, 30, 100. First sort to get 10, 12, 15, 20, 25, 30, 40, 100. m = 9 is an odd number. Calculate to get Q 1 It is data, that is Q 3 It is data, that is Then IQR = 37.5-13.5 = 24, lower limit lower = 13.5-1.5×24 = -22.5, upper limit upper = 37.5 = 1.5×24 = 73.5, so the data 100 is an outlier and is removed from the data set.

[0099] Then use the Z-score normalization method to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. Specifically, for a set of data x, the normalized result x norm The calculation formula is as follows:

[0100]

[0101] in, is the mean value of the data set x, s is the standard deviation of the data set x, and the calculation formula is:

[0102]

[0103] where x i represents the i-th data point, and d is the total number of data points.

[0104] For example, for a set of voltage amplitude data (unit: volt): 220, 222, 225, 218, 230, calculate the mean The standard deviation s≈4.32, for data 225, the normalized result is:

[0105]

[0106] Through the above-mentioned data cleaning and normalization processing, the collected data such as power factor angle, voltage amplitude, current amplitude, etc. can be made more standardized and reasonable, providing a high-quality data foundation for subsequent model training for islanding phenomenon judgment based on artificial intelligence or support vector machine and other methods.

[0107] When the new energy collection station is connected to the grid and operating normally, the power factor angle is calculated through multiple measurements. Fundamental voltage amplitude U 1 , fundamental current amplitude I 1 and sampling frequency f s , build a training data set;

[0108] Define the eigenvector The corresponding label y indicates whether an islanding phenomenon occurs. In normal operation, y=0, and if an islanding phenomenon is known to exist, y=1.

[0109] S42. Construct a neural network including an input layer, several hidden layers and an output layer. The number of nodes in the input layer corresponds to the dimension of the feature vector (four features: power factor angle, fundamental voltage amplitude, fundamental current amplitude and sampling frequency), that is, the number of nodes in the input layer is 4. The hidden layer can be set to two layers. The first hidden layer contains nodes, and the second hidden layer contains nodes. Each node is fully connected to the previous layer. The output layer is a node, indicating whether the islanding phenomenon occurs, 0 if it does not occur, and 1 if it occurs.

[0110] S43. Use the ReLU function f(x)=max(0,x) in the hidden layer to make the neural network fit the nonlinear function, and use the Sigmoid function in the output layer The output is mapped to the interval [0,1], indicating the probability of islanding.

[0111] S44. Use the training data set to train the neural network, adopt the back-propagation algorithm to calculate the gradient and update the weights and biases of the neural network, and continuously optimize the parameters of the neural network by setting hyperparameters, including the learning rate and the number of training rounds, such as setting the learning rate to 0.001 and the number of training rounds to 1000, so that the cross entropy loss function of the model on the training data gradually decreases, achieving better classification results.

[0112] The model is evaluated and tuned by cross-validation method. In this embodiment, the k-fold cross-validation method (k=5) is used to evaluate and tune the model. The training data set is divided into subsets, each subset is used as the training set, and the remaining subset is used as the validation set. The accuracy, recall rate and other indicators of the model on the validation set are calculated, and the hyperparameters are adjusted to obtain the model with the best performance.

[0113] S45, real-time acquisition of power factor angle And other related parameters, construct the current feature vector X test , X test Input into the trained neural network to get the output Y of the model pred :

[0114] If Y pred =1, it is determined that islanding may occur, and the process goes to S5 to detect the voltage harmonic distortion rate;

[0115] If Y pred =0, it is determined that the current state is normal grid-connected operation and continues monitoring.

[0116] S5. If it is determined that islanding may occur, the voltage harmonic distortion rate THD is calculated. u Detection, specifically:

[0117] The hth harmonic voltage amplitude U is obtained by Fourier analysis h , let U 1 is the fundamental voltage amplitude, then the voltage harmonic distortion rate THD u :

[0118]

[0119] If THD u Exceeds the set harmonic distortion threshold THD uth 5%, it is determined that islanding occurs.

[0120] S6. If it is determined that an islanding phenomenon occurs, an islanding alarm signal is issued and corresponding protective actions are taken, that is, a control instruction is sent to the control unit of the new energy collection station. The control instruction is sent through a communication bus, which uses optical fiber communication or wireless communication to control the new energy power generation equipment to stop running or reduce the output power to ensure the safety of the power system.

[0121] During the entire detection process, the detection data is stored in real time in a local storage unit (such as a hard disk) or uploaded to a remote monitoring center so that operation and maintenance personnel can perform subsequent analysis and troubleshooting.

[0122] Therefore, the present invention adopts the above-mentioned island detection method suitable for new energy collection stations, and performs detection through comprehensive power factor angle and voltage harmonic distortion rate, thereby avoiding detection blind spots and not injecting additional signals, thereby ensuring the quality of electric energy. At the same time, it is applicable to various forms of new energy power generation and improves adaptability.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An island detection method applicable to a new energy collection station, characterized in that: The following steps are involved: S1, synchronously sampling the voltage and current signals of the grid connection point of the new energy collection station; S2, preprocessing the voltage and current signals obtained in S1 to obtain the fundamental voltage amplitude and the fundamental current amplitude; S3, calculating the power factor angle according to the fundamental voltage amplitude and the fundamental current amplitude; S4, constructing an islanding detection judgment basis based on the power factor angle calculated in S3 to determine whether an islanding phenomenon occurs; S5. If it is determined that islanding may occur, voltage harmonic distortion rate detection is performed; S6. If it is determined that an islanding phenomenon occurs, an islanding alarm signal is issued and corresponding protective actions are taken.

2. The island detection method applicable to a new energy collection station according to claim 1, characterized in that: In S1, the voltage transformer and current transformer are used to adjust the sampling frequency f in real time. s , synchronously sample the voltage u(t) and current signals i(t) of the grid connection point of the new energy collection station, and calculate the instantaneous value expressions of voltage and current based on the sampled data; Among them, the instantaneous value expression of voltage u(t) is: u(t)=U m sin(ωt+θ u ); Where U m is the voltage amplitude, ω is the angular frequency, θ u is the initial phase of voltage; The instantaneous value expression of current i(t) is: i(t)=I m sin(ωt+θ i ); In the formula, I m is the current amplitude, ω is the angular frequency, θ i is the initial phase of the current.

3. The island detection method applicable to a new energy collection station according to claim 2, characterized in that: In S2, discrete Fourier transform DFT is used to preprocess the voltage and current signals obtained in S1 to obtain the fundamental voltage amplitude U1 and fundamental current amplitude I1: Let u n and i n are the discrete voltage and current signal sequences, n=0,1,...,N-1 is the position of each sampling point in the sequence, N is the number of sampling points in one cycle, then the expression of the fundamental voltage amplitude is: The expression of fundamental current amplitude is:

4. The island detection method applicable to a new energy collection station according to claim 3 is characterized in that: In S3, the power factor angle is obtained by calculating the phase difference between the voltage and current signals according to the fundamental voltage amplitude U1 and the fundamental current amplitude I1. Assume that the fundamental phase angle of the voltage signal is but: Assume that the fundamental phase angle of the current signal is but: Finally, the power factor angle is 5. The island detection method applicable to a new energy collection station according to claim 4, characterized in that: In S4, based on the power factor angle calculated in S3 Constructing the judgment basis for island detection to determine whether an island phenomenon occurs includes the following steps: S41. When the new energy collection station is connected to the grid and operating normally, the power factor angle is calculated through multiple measurements. Fundamental voltage amplitude U1, fundamental current amplitude I1 and sampling frequency f s , build a training data set; Define the eigenvector The corresponding label is y, y = 0 in normal operation, and y = 1 if an island situation is known to exist; S42, constructing a neural network including an input layer, several hidden layers and an output layer, wherein the number of nodes in the input layer corresponds to the dimension of the feature vector, and the output layer is a node, indicating whether the island phenomenon occurs, 0 if it does not occur, and 1 if it occurs; S43, use the ReLU function in the hidden layer to make the neural network fit the nonlinear function, and use the Sigmoid function in the output layer to map the output to the [0,1] interval, indicating the probability of islanding; S44, using the training data set to train the neural network, and using the cross-validation method to evaluate and tune the model; S45, real-time acquisition of power factor angle And other related parameters, construct the current feature vector X test , X test Input into the trained neural network to get the output Y of the model pred : If Y pred =1, it is determined that islanding may occur, and the process goes to S5 to detect the voltage harmonic distortion rate; If Y pred =0, it is determined that the current state is normal grid-connected operation and continues monitoring.

6. The island detection method applicable to a new energy collection station according to claim 5, characterized in that: In S41, before constructing the training data set, the power factor angle Fundamental voltage amplitude U1, fundamental current amplitude I1 and sampling frequency f s Perform preprocessing operations, including removing outliers and normalizing.

7. The island detection method applicable to a new energy collection station according to claim 6, characterized in that: In S41, the box plot method is used to remove outliers, including the following steps: S411. Calculate the first quartile Q1: After sorting the data from small to large, if the number of data is m, when m is an odd number, Q1 is the first quartile. data; when m is an even number, Q1 is the Data and The average value of the data; S412, calculate the third quartile Q3: perform the same operation on the sorted data. When m is an odd number, Q3 is the data; when m is an even number, Q3 is the Data and The average value of the data; S413, calculate the interquartile range IQR = Q3-Q1; S414, determine the lower limit value lower = Q1-1.5 × IQR and the upper limit value upper = Q3 + 1.5 × IQR of the abnormal value range; S415, traverse each data point in the data set, mark the data point that is less than lower or greater than upper as an outlier and delete it from the data set.

8. The island detection method applicable to a new energy collection station according to claim 7, characterized in that: In S41, the Z-score normalization method is used to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. Specifically, for a set of data x, the normalized result x norm The calculation formula is as follows: in, is the mean value of the data set x, s is the standard deviation of the data set x, and the calculation formula is: where x i represents the i-th data point, and d is the total number of data points.

9. The island detection method applicable to a new energy collection station according to claim 8, characterized in that: In S5, if it is determined that islanding may occur, the voltage harmonic distortion rate THD is calculated. u Detection: Let U h is the hth harmonic voltage amplitude, U1 is the fundamental voltage amplitude, then: If THD u Exceeds the set harmonic distortion threshold THD uth , it is determined that an island phenomenon occurs.

10. The island detection method applicable to a new energy collection station according to claim 9, characterized in that: In S6, if it is determined that an islanding phenomenon occurs, an islanding alarm signal is issued and corresponding protection actions are taken, that is, a control instruction is sent to the control unit of the new energy collection station to control the new energy power generation equipment to stop operating or reduce the output power; The control instructions are sent through a communication bus, which uses optical fiber communication or wireless communication.

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