A circuit for judging faults based on current signals

Abstract

The present invention relates to the field of power system distribution, and particularly to a fault judgment circuit based on current signals, including: a topological network transmission grid, on which a beat frequency generator, an instrument transformer, a signal acquisition unit, and an execution unit are provided; the signal acquisition unit, which receives the signal emitted by the beat frequency generator of the upstream node through the instrument transformer and is also used to receive the signal output by the instrument transformer of the corresponding node; the signal processing unit, which is used to perform noise reduction processing on the signals received by the signal acquisition unit; the data self-learning processing system, which is connected to the signal processing unit to calculate the data after noise reduction by the signal processing unit and is connected to the beat frequency generator of each node to control the parameters of the signal output by the beat frequency generator; the execution unit, which is arranged on the corresponding node and is connected to the data self-learning processing system, and the data self-learning processing system controls the execution unit according to the data of the signal processing unit to open or close the circuit between the corresponding nodes. The present invention can achieve self-repair when the circuit fails.

Description

Technical Field

[0001] The present invention relates to the field of power system distribution, and particularly to a fault judgment circuit based on current signals. Background Art

[0002] The distribution network refers to the power network that receives electric energy from the transmission network or regional power plants and distributes it locally through distribution facilities or by voltage levels to various users. It is composed of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensators, and some auxiliary facilities, etc., and plays an important role in distributing electric energy in the power network. With the increasing demand for electricity in all walks of life, especially the rapid rise of industrial and civil loads in areas with relatively fast economic development, the number of distribution systems and their distribution equipment has increased significantly, the network structure of the distribution system has become more and more complex, and the power supply operation mode has changed more and more frequently. 110 kV is a relatively key link in the entire distribution network for the high-voltage distribution system, and there are increasingly high requirements for the stability of the distribution network operation, the effectiveness of regulation, and the reliability of fault section judgment. Therefore, accurately determining the fault section of a more complex distribution network and quickly handling it during a fault have become an important part of the work of the distribution network.

[0003] Currently, topology is often used in regional distribution networks. Topology is an important means for distribution network analysis. At present, distribution automation can only correctly handle centralized equipment, and some cannot cooperate correctly with sectionalizing switches, etc. When a fault occurs, its self-recovery ability cannot meet the actual needs. Summary of the Invention

[0004] The purpose of the present invention is to provide a fault judgment circuit based on current signals to solve the above problems, and when a fault occurs in the line, the power supply can be quickly restored.

[0005] To achieve the above purpose, the present invention provides the following solution:

[0006] A fault judgment circuit based on current signals, comprising:

[0007] A topological network transmission grid, on whose nodes there are beat frequency generators, current transformers, signal acquisition units, and execution units;

[0008] The signal acquisition unit is connected to the current transformer of the corresponding node, receives the signal emitted by the beat frequency generator of the upstream node through the current transformer, and is also used to receive the signal output by the current transformer of the corresponding node;

[0009] A signal processing unit is connected to the signal acquisition unit and is used to perform noise reduction processing on the signal received by the signal acquisition unit;

[0010] A data self-learning processing system, connected to the signal processing unit to calculate the data after noise reduction by the signal processing unit, and connected to the beat frequency generators of each node to control the parameters of the output signals of the beat frequency generators;

[0011] An execution unit, arranged on the corresponding node, connected to the data self-learning processing system, and the data self-learning processing system controls the execution unit according to the data of the signal processing unit to open or close the circuit between the corresponding nodes;

[0012] The topological network power grid is one of a ring network topology circuit or a hybrid topology circuit.

[0013] Preferably, the output signals of the beat frequency generator include time, phase, amplitude, and frequency.

[0014] Preferably, the signal processing unit is a wavelet transform noise reduction processor.

[0015] Preferably, the nodes of the topological network power grid are numbered, and the corresponding beat frequency generators, current transformers, signal acquisition units, and execution units of the nodes are also numbered corresponding to the nodes;

[0016] The data self-learning processing system processes the time, phase, amplitude, and frequency output signals of the beat frequency generator after noise reduction by the signal processing unit and the voltage and current signals of each node;

[0017] After being trained by the data self-learning processing system, when a fault occurs, through the numbers corresponding to the nodes, beat frequency generators, current transformers, signal acquisition units, and execution units, the corresponding beat frequency generator is controlled to change the detection signal, and combined with the time, phase, amplitude, frequency, voltage, and current signals on the downstream nodes, the execution unit that can restore the corresponding fault location is controlled to operate, realizing the closing of the control circuit.

[0018] Preferably, the data self-learning processing system is set as follows:

[0019] Create a Sequential model setting, set the number of input channels, the number of input channels is the same as the number of parameters to be detected, the number of input channels is 8, namely time, phase, amplitude, frequency, voltage, current, and numbering parameter

[0020] Add a one-dimensional convolutional layer;

[0021] Add a pooling layer, and the pooling window size is 2;

[0022] Perform secondary convolution and pooling, and add a one-dimensional convolutional layer again;

[0023] Add a max pooling layer again, and the pooling window size is 2;

[0024] Flatten layer, which flattens the output of the convolutional layer into a one-dimensional vector;

[0025] Fully connected layer, setting the corresponding number of neurons;

[0026] Output layer, number of neurons: set according to the number of fault categories, and the activation function is softmax.

[0027] Preferably, in the added one-dimensional convolutional layer, the convolutional layer uses 32 convolutional kernels of size 3 and adopts the ReLU activation function.

[0028] Preferably, in the re-added one-dimensional convolutional layer, the convolutional layer uses 64 convolutional kernels of size 3, and the activation function is ReLU.

[0029] Preferably, in the fully connected layer, the number of neurons is 128, and the activation function is ReLU.

[0030] The present invention has the following technical effects:

[0031] By setting beat frequency detectors, current transformers, signal acquisition units, execution units, and signal acquisition units on each node, the present invention can collect voltage and current information in the circuit, and can also detect the information transmitted from the beat frequency detector of the upstream node to the relevant information of the downstream node through the components on each node. After the signal is denoised by the signal processing unit, and then through the self-learning of the data self-learning processing system, various input signals are used to determine whether there is a fault between nodes, and then the corresponding execution unit is controlled to open or close the circuit to achieve the self-healing of faults in the power grid power supply process. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of the fault recovery circuit of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0035] To make the above objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] A current signal-based fault judgment circuit includes:

[0037] A topological network power transmission grid, on whose nodes there are beat frequency detectors, transformers, signal acquisition units, and execution units;

[0038] A signal acquisition unit, connected to the transformer of the corresponding node, receives the signal emitted by the beat frequency detector of the upstream node through the transformer, and is also used to receive the signal output by the transformer of the corresponding node;

[0039] A signal processing unit, connected to the signal acquisition unit, is used to perform noise reduction processing on the signals received by the signal acquisition unit;

[0040] A data self-learning processing system, connected to the signal processing unit to calculate the data after noise reduction by the signal processing unit, and connected to the beat frequency detectors of each node to control the parameters of the signals output by the beat frequency detectors;

[0041] An execution unit, arranged on the corresponding node, is connected to the data self-learning processing system, and the data self-learning processing system controls the execution unit according to the data of the signal processing unit to open or close the circuit between the corresponding nodes;

[0042] The topological network power transmission grid is one of a ring network topology circuit or a hybrid topology circuit.

[0043] In the present invention, by arranging beat frequency detectors, transformers, signal acquisition units, execution units, and signal acquisition units on each node, voltage and current information in the circuit can be collected, and the information transmitted from the beat frequency detector of the upstream node to the relevant information of the downstream node can also be detected by the components on each node. After the signal is noise-reduced by the signal processing unit, through the self-learning of the data self-learning processing system, various input signals are used to judge whether there is a fault between each node, and then the corresponding execution unit is controlled to open or close the circuit to achieve self-healing of the power grid supply process faults.

[0044] In a further optimized solution, the output signal of the beat frequency detector includes time, phase, amplitude, and frequency. The transformer includes a PT transformer and a CT transformer. The PT transformer is used to detect the voltage of the circuit on the node, and the CT transformer is used to detect the current of the circuit on the node.

[0045] In a further optimized solution, the signal processing unit is a wavelet transform noise reduction processor.

[0046] In a further optimized solution, the nodes of the topological network power transmission grid are numbered, and the corresponding beat frequency detectors, transformers, signal acquisition units, and execution units of the nodes are also numbered corresponding to the nodes;

[0047] The data self-learning processing system processes the time, phase, amplitude, and frequency output signals of the beat frequency detector after noise reduction by the signal processing unit, as well as the voltage and current signals of each node.

[0048] After being trained by the data self-learning processing system, when a fault occurs, the corresponding beat frequency detector is controlled to transform the detection signal through the numbers corresponding to the nodes, beat frequency detector, mutual inductor, signal acquisition unit, and execution unit, and the execution unit that can restore the corresponding fault position is controlled to operate by combining the time, phase, amplitude, frequency, voltage, and current signals on the downstream nodes, realizing the conduction of the control circuit.

[0049] For a further optimized solution, the data self-learning processing system is set as follows:

[0050] Create a Sequential model setting, set the number of input channels, and the number of input channels is the same as the number of parameters to be detected.

[0051] Add a one-dimensional convolutional layer;

[0052] Add a pooling layer with a pooling window size of 2;

[0053] Perform secondary convolution and pooling, and add a one-dimensional convolutional layer again;

[0054] Add a max pooling layer again with a pooling window size of 2;

[0055] Flatten layer, flatten the output of the convolutional layer into a one-dimensional vector;

[0056] Fully connected layer, set the corresponding number of neurons; the number of neurons is twice the number of neurons in the output layer. The number of neurons in the output layer is set according to the number of fault categories, and the activation function is softmax.

[0057] For a further optimized solution, in adding the one-dimensional convolutional layer, the convolutional layer uses 32 convolutional kernels of size 3 and adopts the ReLU activation function.

[0058] For a further optimized solution, in adding the one-dimensional convolutional layer again, the convolutional layer uses 64 convolutional kernels of size 3 and the activation function is ReLU.

[0059] For a further optimized solution, the number of neurons in the fully connected layer is 128 and the activation function is ReLU.

[0060] Train the model:

[0061] Number of training epochs: 100;

[0062] Batch size: 64;

[0063] Ratio of validation set: 0.1;

[0064] After being trained by a neural network, the data self-learning processing system is connected to the corresponding node of the power grid. When a fault occurs in the circuit, the data self-learning processing system can determine the fault point through the input parameters, control the output parameters of the fault point beat frequency oscillator, and further verify the fault function. When the fault is determined, the execution unit with the corresponding number is controlled to change the open circuit and closed circuit between the nodes, so as to realize the self-repair of the power grid fault.

[0065] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0066] The above-described embodiments are only for describing the preferred mode of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solution of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A fault judgment circuit based on current signal, characterized in that: include: The topological network transmission network has frequency beaters, mutual inductors, signal acquisition units, and execution units at its nodes; The signal acquisition unit is connected to the mutual inductor of the corresponding node, receives the signal transmitted by the beat frequency device of the upstream node through the mutual inductor, and is also used to receive the signal output by the mutual inductor of the corresponding node; A signal processing unit, connected to the signal acquisition unit, and configured to perform noise reduction processing on the signal received by the signal acquisition unit; A data self-learning processing system, connected to the signal processing unit to calculate the data after noise reduction of the signal processing unit, and connected to the beater of each node to control the parameters of the beater output signal; An execution unit is arranged on a corresponding node and connected to the data self-learning processing system. The data self-learning processing system controls the execution unit to disconnect or connect the corresponding nodes according to the data of the signal processing unit; The topology network transmission network is one of a ring network topology circuit or a hybrid topology circuit; The output signal of the beat frequency generator includes time, phase, amplitude and frequency.

2. The current signal-based fault judgment circuit according to claim 1, characterized in that: The signal processing unit is a wavelet transform noise reduction processor.

3. The current signal-based fault judgment circuit according to claim 2, characterized in that: The nodes of the topological network transmission network are numbered, and the beat frequency devices, mutual inductors, signal acquisition units, and execution units corresponding to the nodes are also numbered corresponding to the nodes; Processing the time, phase, amplitude and frequency output signals of the beat frequency generator after noise reduction by the signal processing unit and the voltage and current signals of each node by the data self-learning processing system; After being trained by the data self-learning processing system, when a fault occurs, the corresponding numbers of the nodes, frequency beaters, mutual inductors, signal acquisition units, and execution units are used to control the corresponding frequency beaters to transform the detection signal, and the timing, phase, amplitude and frequency, voltage, and current signals on the downstream nodes are combined to control the execution unit that can restore the corresponding fault position to operate, thereby realizing the path of the control circuit.

4. The current signal-based fault judgment circuit according to claim 3, characterized in that: The data self-learning processing system is configured as follows: Create a Sequential model setting and set the number of input channels. The number of input channels should be the same as the number of parameters to be tested. Add a 1D convolutional layer; Add a pooling layer with a pooling window size of 2; Second convolution and pooling, add one-dimensional convolution layer again; Add the maximum pooling layer again with a pooling window size of 2; The flattening layer flattens the output of the convolutional layer into a one-dimensional vector; Fully connected layer, set the corresponding number of neurons; Output layer, number of neurons: set according to the number of fault categories, activation function is softmax.

5. The current signal-based fault judgment circuit according to claim 4, characterized in that: In the added one-dimensional convolution layer, the convolution layer uses 32 convolution kernels of size 3 and adopts the ReLU activation function.

6. The current signal-based fault judgment circuit according to claim 4, characterized in that: In the one-dimensional convolution layer added again, the convolution layer uses 64 convolution kernels of size 3, and the activation function is ReLU.

7. The current signal-based fault judgment circuit according to claim 4, characterized in that: The number of neurons in the fully connected layer is 128, and the activation function is ReLU.

Images