Test method of secondary equipment, equipment, storage medium and product

Abstract

The invention discloses a secondary equipment test method, equipment, a storage medium and a product, and the method comprises the steps: building a simulation test model according to configuration information when the configuration information set for an operation scene of a power system is received, generating original test data of the power system in a normal working state based on the simulation test model, and transmitting the original test data to the power system; calculating component data of the power system in a fault state according to the configuration information, combining the component data with the original test data to obtain target test data containing fault features, and testing the secondary equipment in the power system according to the target test data. The method comprises the following steps: acquiring configuration information of a secondary device to generate a digital test message conforming to a communication protocol configured for the secondary device, acquiring a protection action signal of a protection device in the secondary device within a preset time threshold according to the digital test message and the configuration information, and generating a test result for the secondary device according to the protection action signal, and the accuracy and the reliability of the secondary equipment test are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of automated testing, and in particular to a testing method, equipment, storage medium and product for secondary equipment. Background Art

[0002] The secondary equipment in the power system is one of the devices that ensure the stability and safety of the power system. Accurate verification of the performance of the secondary equipment is usually important for the reliable operation of the power system.

[0003] Traditional secondary equipment testing methods for power systems usually rely on field testing or laboratory testing based on a single scenario. These methods mainly verify the functions, response time and fault handling capabilities of secondary equipment by manually setting test conditions and using specific test instruments, which have low accuracy and reliability for comprehensive testing of secondary equipment. Summary of the invention

[0004] The present invention provides a secondary equipment testing method, equipment, storage medium and product, which are used to improve the accuracy and reliability of secondary equipment testing.

[0005] In a first aspect, an embodiment of the present invention provides a method for testing a secondary device, including:

[0006] Upon receiving configuration information for setting an operation scenario of the power system, constructing a simulation test model according to the configuration information, and generating original test data of the power system under normal working state based on the simulation test model;

[0007] Calculating component data of the power system under a fault state according to the configuration information, combining the component data with the original test data to obtain target test data containing fault characteristics;

[0008] Testing the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment;

[0009] According to the digital test message and the configuration information, collecting the protection action signal of the protection device in the secondary device within a preset time threshold;

[0010] A test result is generated for the secondary device according to the protection action signal.

[0011] In a second aspect, an embodiment of the present invention further provides a testing device for a secondary device, comprising:

[0012] An original test data generating module is used to construct a simulation test model according to the configuration information when receiving the configuration information of the operation scenario of the power system, and generate original test data of the power system under normal working state based on the simulation test model;

[0013] A target test data acquisition module, used to calculate the component data of the power system under the fault state according to the configuration information, and combine the component data with the original test data to obtain the target test data containing the fault characteristics;

[0014] A digital test message generation module, used to test the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment;

[0015] A protection action signal acquisition module, used to acquire the protection action signal of the protection device in the secondary device within a preset time threshold according to the digital test message and the configuration information;

[0016] The test result generating module is used to generate a test result for the secondary device according to the protection action signal.

[0017] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0018] one or more processors;

[0019] A storage device for storing one or more programs;

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement the secondary device testing method provided by the first aspect of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the secondary device testing method provided in the first aspect of the present invention.

[0022] In a fifth aspect, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the secondary device testing method provided in the first aspect of the present invention is implemented.

[0023] In an embodiment of the present invention, when receiving configuration information for setting the operation scenario of the power system, a simulation test model is constructed according to the configuration information, original test data of the power system under normal working state is generated based on the simulation test model, component data of the power system under fault state is calculated according to the configuration information, the component data and the original test data are combined to obtain target test data containing fault characteristics, the secondary equipment in the power system is tested according to the target test data to generate a digital test message that conforms to the communication protocol configured for the secondary equipment, and the protection action signal of the protection device in the secondary equipment is collected within a preset time threshold according to the digital test message and the configuration information, and the test result is generated for the secondary equipment according to the protection action signal. The real performance of the power system under different operating states is effectively simulated, and a comprehensive test of the secondary equipment in the power system is realized, and the compatibility of the communication protocol and the response capability of the protection device can be quickly checked and verified, thereby ensuring the safety and stability of the entire power system and improving the accuracy and reliability of the secondary equipment test. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a method for testing a secondary device provided in Embodiment 1 of the present invention;

[0025] Figure 2 A structural block diagram of a testing device for secondary equipment provided in Embodiment 2 of the present invention;

[0026] Figure 3 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] See also Figure 1 , shows a flow chart of a secondary device testing method provided by the first embodiment of the present invention, the method can be executed by a secondary device testing device, the secondary device testing device can be implemented in the form of hardware and / or software, and the secondary device testing device can be configured in a computer device. Figure 1 As shown, the method includes:

[0031] Step 101: upon receiving configuration information for setting an operation scenario of a power system, constructing a simulation test model according to the configuration information, and generating original test data of the power system under normal working conditions based on the simulation test model.

[0032] In this embodiment, when receiving the configuration information of the operating scenario setting of the power system, a simulation test model is constructed to provide a simulated environment for reproducing and analyzing the working performance of the power system under ideal conditions. The basis for evaluating the performance and stability of the power system in actual operation helps to identify key parameters such as voltage and current of the power system under normal conditions, and provides a benchmark for subsequent fault analysis and performance testing. The original test data obtained can be further used to compare the data under fault conditions, so as to more accurately evaluate and improve the protection performance and response capability of secondary equipment.

[0033] Exemplarily, the configuration information includes the protection type of the secondary device (such as overcurrent protection, distance protection, etc.), protection setting parameters (such as overcurrent start value, time delay, etc.), test type parameters (such as single-phase ground fault, two-phase short circuit, etc.), sampling period parameters (such as 4ms), fault start time parameters (such as 100ms), fault duration parameters (such as 200ms), fault type parameters (such as A-phase ground fault) and fault impedance parameters (such as fault impedance 10Ω); the simulation test model includes a transient response model and a steady-state response model. The transient response model is used to simulate the change process of electrical parameters at the moment of the fault, and the steady-state response model is used to simulate the steady-state electrical parameters during the duration of the fault. Taking overcurrent protection as an example, the transient response model can adopt an RC circuit model, and the steady-state response model can adopt a constant current source model.

[0034] Based on the constructed simulation test model, the original test data of the power system under normal working conditions is generated, including the original voltage sampling data and the original current sampling data. Taking the 50Hz power frequency power system as an example, the sine wave voltage data with an amplitude of 100V and a frequency of 50Hz (i.e., the original voltage sampling data) and the sine wave current data with an amplitude of 5A and a frequency of 50Hz (i.e., the original current sampling data) can be generated as the original test data.

[0035] In one embodiment of the present invention, step 101 may include the following steps:

[0036] Step 1011: Establish a test condition including a startup characteristic and a return characteristic according to the test type parameters.

[0037] In this embodiment, a clear test condition is defined at the beginning of the simulation test to ensure that the tested power system can reflect its dynamic characteristics of startup and return.

[0038] The protection type in the configuration information divides the protection function of the power system into distance protection function area, overcurrent protection function area and differential protection function area. Through the setting of different function areas, it is ensured that the protection can be started in time under different fault conditions to avoid equipment damage. The protection start value, action time value and return coefficient value of each function area are determined according to the protection setting parameters in the configuration information. Reasonable start value and action time value can prevent premature or late protection action, thereby protecting the stable operation of the power system. The setting of the return coefficient value helps the power system to quickly resume normal operation after the fault is removed, reducing the downtime.

[0039] Taking distance protection as an example, the protection start value of the distance protection function area is determined to be 80% of the line impedance, the action time value is 0.5 seconds, and the return coefficient value is 1.2 according to the protection setting parameters. According to the test type parameters, a test condition including the start characteristic and the return characteristic is established. For example, when testing the start characteristic of the distance protection, the fault point is set to gradually increase from 0% of the line length to 120% of the line length, with a step size of 5%.

[0040] Step 1012: construct a simulation test model according to the protection function area and test conditions of the power system, and divide the simulation test model into a frequency-variable parameter line sub-model, a nonlinear transformer sub-model and a composite load sub-model.

[0041] In this embodiment, in this embodiment, a test condition including a startup characteristic and a return characteristic is established according to the test type parameter, and a simulation test model is constructed by combining the constructed test condition with the protection function area to accurately simulate the electrical characteristics and protection behavior of the power system under a specific operating scenario, and the simulation test model is further divided into a frequency-varying parameter line sub-model, a nonlinear transformer sub-model and a composite load sub-model from two major directions: a transient response model and a steady-state response model.

[0042] The frequency-variable parameter line sub-model includes skin effect compensation term and distributed parameter characteristic term. The skin effect compensation term uses Carson formula to calculate the frequency characteristics of line resistance and inductance. The distributed parameter characteristic term uses π-type equivalent circuit to simulate the distributed parameter characteristics of long-distance transmission lines.

[0043] The nonlinear transformer sub-model includes the flux current characteristic terms and uses the piecewise linearization method to simulate the saturation characteristics of the transformer core.

[0044] The composite load sub-model configures constant impedance, constant current and constant power according to a preset ratio, such as 40% constant impedance, 30% constant current and 30% constant power.

[0045] Step 1013: construct a conductance matrix and a susceptance matrix based on the frequency-variable parameter line sub-model, the nonlinear transformer sub-model and the composite load sub-model, combine the conductance matrix and the susceptance matrix to form a node admittance matrix, and obtain the transient voltage vector under the normal working state of the power system by solving the node admittance matrix.

[0046] In this embodiment, a node admittance matrix is ​​constructed so that the electrical network of the entire power system can be represented in the form of a mathematical matrix. The coupling relationship between power systems is effectively described, and by solving the node admittance matrix, the transient response of each node voltage (i.e., transient voltage vector) is obtained, which provides a basis for understanding the dynamic characteristics of the power system. The conductance matrix represents the conductance relationship between each node in the network, and the susceptance matrix represents the susceptance relationship between each node.

[0047] Step 1014: Under normal working conditions of the power system, the three-phase system is decomposed into positive-sequence components, negative-sequence components and zero-sequence components, an inter-sequence impedance matrix including a coupling coefficient is established, and a steady-state voltage vector and a steady-state current vector between the positive-sequence component, the negative-sequence component and the zero-sequence component are calculated based on the inter-sequence impedance matrix.

[0048] In this embodiment, decomposing the three-phase system into positive sequence components, negative sequence components and zero sequence components is a step to analyze the commonalities and differences of the three-phase system. By calculating the inter-sequence impedance matrix, the interaction between the sequence components can be decoupled to obtain the steady-state voltage vector and the steady-state current vector, and the symmetry and asymmetry of the power system can be deeply analyzed, which can accurately describe the voltage and current distribution of the power system under steady-state conditions.

[0049] Step 1015: Calculate the difference between the transient voltage vector and the steady-state voltage vector as the initial voltage iteration vector, take the steady-state current vector as the initial current iteration vector, iteratively calculate the initial voltage iteration vector and the initial current iteration vector, introduce the fundamental voltage component and the harmonic voltage component in the iterative calculation process, and obtain the converged voltage data and current data.

[0050] In this embodiment, iterative calculation is introduced, emphasizing the approach to the real power system state through repeated corrections. This technology uses the difference between the transient voltage vector and the steady-state voltage vector, the steady-state current vector, and the fundamental and harmonic components in the dynamic process to iterate more accurate voltage and current data, which can effectively reveal the performance of the power system in the frequency response and lay a data support for the subsequent performance test of secondary equipment and the evaluation of protection devices.

[0051] Specifically, the voltage of the power system is represented as the superposition of the fundamental voltage component and the harmonic voltage component, the harmonic voltage component includes the third harmonic voltage component, the fifth harmonic voltage component and the seventh harmonic voltage component, and the current of the power system is represented as the superposition of the fundamental current component and the harmonic current component, the harmonic current component includes the third harmonic current component, the fifth harmonic current component and the seventh harmonic current component. Considering the harmonic components in the power system, the iterative calculation adopts the Newton-Raphson method.

[0052] An iterative calculation equation is established based on the initial voltage iteration vector and the initial current iteration vector. The iterative calculation equation superimposes each harmonic voltage component with the initial voltage iteration vector to obtain a voltage iteration value, and superimposes each harmonic current component with the initial current iteration vector to obtain a current iteration value. The key to the iterative calculation equation is to continuously adjust the fundamental and harmonic components of the voltage and current so that the voltage and current iteration values ​​gradually approach the true values, thereby accurately describing the voltage and current characteristics of the power system.

[0053] Perform Fourier analysis on the voltage iteration value and the current iteration value to obtain the amplitude and phase of each harmonic voltage component and the amplitude and phase of each harmonic current component. This Fourier analysis result provides an important basis for the subsequent convergence criterion calculation and helps determine whether the power system has reached the preset convergence standard. Calculate the sum of the voltage data difference and current data difference between two adjacent iterations as the iteration convergence criterion.

[0054] A relaxation factor is introduced to perform weighted adjustment on the voltage iteration value and the current iteration value. When the iterative convergence criterion is greater than the previous iterative convergence criterion, the relaxation factor is reduced. When the iterative convergence criterion is less than the previous iterative convergence criterion, the relaxation factor is increased. This dynamic adjustment mechanism helps the power system to ensure accuracy while speeding up the iterative convergence and avoiding unnecessary fluctuations in the calculation process.

[0055] When the iterative convergence criterion is less than a preset convergence threshold and the difference between two adjacent iterative convergence criteria is less than a preset accuracy requirement, the iterative calculation is determined to converge, and finally the converged voltage data and current data are obtained. In this case, the power system outputs the converged voltage data and current data, which accurately reflect the voltage and current conditions in the power system and can effectively describe its fundamental wave and harmonic components of each order.

[0056] Step 1016: Adaptively sample the converged voltage data and current data according to the sampling period parameters to generate original test data under normal working conditions of the power system.

[0057] In this embodiment, the continuous voltage data and current data are discretized into voltage sampling data and current sampling data that meet the processing requirements of the digital equipment according to the sampling period parameters, while ensuring that the sampling process can adapt to the dynamic characteristics and fault characteristics of the power system. Through adaptive sampling, the data volume can be optimized and excessive redundant information can be avoided while ensuring the accuracy of the original test data.

[0058] For example, the sampling period can be set to 1 ms, and linear interpolation is used to interpolate between each sampling point to generate raw test data with a higher sampling rate. The raw test data including initial voltage sampling data and initial current sampling data is generated. The raw test data includes time series of three-phase voltage and three-phase current, and each phase includes 1000 sampling points.

[0059] Step 102: Calculate component data of the power system under fault status according to the configuration information, combine the component data with the original test data, and obtain target test data containing fault characteristics.

[0060] In this embodiment, by calculating the component data under the fault state of the power system and combining it with the original test data under the normal working state, target test data containing fault characteristics are generated. These target test data can accurately simulate the dynamic response characteristics of the power system in different fault scenarios, and the generated target test data reflects both the information of the normal state and the information of the fault characteristics. Through this step, the protection action performance of the secondary equipment can be tested more realistically, and its reliability and accuracy under actual fault conditions can be verified, providing a basis for the performance evaluation and optimization of the secondary equipment. The component data includes transient component data and steady-state component data.

[0061] Specifically, a transient response calculation model under the fault state of the power system is established according to the fault type parameter and the fault impedance parameter. When the fault type parameter is a single-phase ground fault, it is assumed that the fault occurs in phase A and the fault impedance is 5Ω. The initial fault voltage amplitude is calculated according to the phase A voltage (assuming it is 10kV) at the time of the fault under the power system fault state, and the fault loop attenuation coefficient α=200s under the power system fault state is calculated according to the fault impedance parameter 5Ω. -1 and the damping angular frequency ω

[0062] =314rad / s, the single-phase grounding fault voltage under the power system fault state is calculated using an exponentially decayed cosine function containing the fault loop attenuation coefficient α and the damping angular frequency ω, and the transient component data under the power system fault state is obtained. When the fault type parameter is a phase-to-phase fault, such as an AB phase short circuit, the phase-to-phase coupling coefficient k=0.5 is calculated, and the two-phase voltage difference exponentially decayed cosine function containing the phase-to-phase coupling coefficient is used to calculate the phase-to-phase fault voltage under the power system fault state, and the transient component data under the power system fault state is obtained.

[0063] A steady-state response calculation model for the power system under a fault state is established according to the fault start time parameters and the fault duration parameters, the steady-state fault voltage amplitude under the power system fault state is calculated based on the fault type parameters and the fault impedance parameters, the impedance angle and the phase difference are calculated according to the fault impedance parameters, the steady-state fault voltage under the power system fault state is calculated using a finite-time sinusoidal function including the fault start time parameters and the fault duration parameters, the steady-state fault current under the power system fault state is calculated using a finite-time sinusoidal function including the impedance angle and the phase difference, and the steady-state component data under the power system fault state is obtained.

[0064] The transient component data and the steady-state component data are superimposed on the original test data by vector superposition, and the original test data is phase corrected to obtain the positive-sequence component, negative-sequence component and zero-sequence component under the fault state of the power system. The positive-sequence component is obtained by superimposing the positive-sequence phasors of the three-phase quantities, and the negative-sequence and zero-sequence components are similar. The positive-sequence component, negative-sequence component and zero-sequence component are converted into three-phase fault characteristic data to generate target test data containing fault characteristics.

[0065] Among them, the fault types usually include short circuit fault, ground fault, open circuit fault, etc. Different fault types will have different effects on the voltage and current waveforms. The fault impedance describes the relationship between the current and voltage at the fault point.

[0066] Transient component data refers to the rapidly changing part of the voltage and current waveform of the power system at the beginning of a fault, which usually contains high-frequency components. When calculating transient component data, it is necessary to determine the changing pattern of the system's voltage and current waveforms at the moment of the fault according to the fault type, and calculate the corresponding current and voltage transient waveforms based on the fault impedance. The calculation method of transient component data includes measuring the current and voltage waveforms in the power system, filtering and fast Fourier transform analysis of these waveforms to extract transient signals.

[0067] Steady-state component data refers to the low-frequency components in the voltage and current waveforms when the power system gradually stabilizes after a fault occurs. The calculation of steady-state component data is based on the fault start time parameter and the fault duration parameter. These two parameters determine the time window of the start and end of the fault and are used to analyze how the power system transitions to a new steady state after the fault. In the specific operation, the fault start time point is first determined, and the steady-state waveforms of the voltage and current of the power system after the fault are calculated based on this time point. The steady-state waveform is similar to the waveform under normal operating conditions, usually manifested as a low-frequency sine wave, and the amplitude and phase of the voltage and current waveforms may change, but there is no obvious instantaneous change.

[0068] Step 103: Test the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment.

[0069] The communication protocol configured for secondary devices usually refers to the standard format used for data exchange between devices in the power system. Common protocols include IEC 61850, Distributed Network Protocol (DNP) 3, Modbus, etc. When generating digital test messages, the target test data will be encoded into a format that conforms to these communication protocols to ensure that the data can be correctly decoded and used by various secondary devices (such as protection devices, monitoring systems, measuring equipment, etc.). In this process, the data will be packetized, verified, and packaged into a format that conforms to the protocol specifications for transmission and analysis in the power system.

[0070] In this embodiment, the digital test message that conforms to the communication protocol configured by the secondary device is generated to verify the communication and response capabilities of the secondary device under fault conditions. The test uses the generated target test data to simulate various operating conditions in the actual operating environment to ensure that the secondary device can correctly parse, respond and process the communication information in the actual fault situation, thereby improving the reliability and safety of the power system.

[0071] Specifically, the target test data is discretized and sampled according to the sampling period parameters. The sampling period can be set to 1ms, and the voltage and current signals in the target test data are sampled at equal intervals to obtain discrete voltage data and discrete current data. For example, for a 50Hz AC signal, 20 sampling points can be collected in each period. The discrete voltage data and discrete current data are filtered by a low-pass filter. A Butterworth low-pass filter with a cutoff frequency of 500Hz can be selected to obtain the filtered voltage data and filtered current data. The filtered voltage data and filtered current data are amplitude calibrated and phase compensated. The amplitude calibration can be achieved by multiplying the correction coefficient, and the phase compensation can be achieved by introducing an appropriate time delay. For example, the correction coefficient of the voltage channel is 1.02, and the phase compensation is 2 degrees; the correction coefficient of the current channel is 0.98, and the phase compensation is -1 degree. The calibrated voltage data and current data are resampled by a linear interpolation method. The sampling rate can be increased to 4800Hz, that is, 96 points per period, to obtain a resampled test sampling sequence.

[0072] A digital test message is constructed according to the test sampling sequence, and the sampling counter value is encapsulated in the message header of the digital test message as a synchronization identification field, for example, starting from 0 and increasing; the fault start time parameter is encapsulated as a timestamp field, accurate to microseconds; the data validity flag is encapsulated as a quality bit field, with 0 indicating valid and 1 indicating invalid. In the data segment of the digital test message, the voltage channel data, current channel data and phase identification of the test sampling sequence are encapsulated in the order of voltage channel and current channel, and each channel data contains amplitude and phase information. For example, the voltage amplitude of phase A is 220V and the phase is 0 degrees; the voltage amplitude of phase B is 220V and the phase is -120 degrees; the voltage amplitude of phase C is 220V and the phase is 120 degrees. The current amplitude of phase A is 5A and the phase is 0 degrees; the current amplitude of phase B is 5A and the phase is -120 degrees; the current amplitude of phase C is 5A and the phase is 120 degrees. Calculate the cyclic redundancy check (CRC) value of the digital test message and add it to the end of the digital test message. The check value can be calculated using the cyclic redundancy check 32-bit (CRC-32) algorithm. Set the digital test message sending time interval based on the sampling period parameter to generate the message sequence number. For example, if the sampling period is 1ms, the message sending interval is also set to 1ms. The message sequence number starts to increase from 1. The data format of the digital test message conforms to the sampling value message format in the communication protocol configured by the secondary device.

[0073] Step 104 : collecting protection action signals of protection devices in secondary equipment within a preset time threshold according to the digital test message and configuration information.

[0074] In this embodiment, by collecting the protection action signal of the protection device in the secondary device within a preset time threshold, it is verified whether the protection response of the secondary device under fault conditions meets expectations. Specifically, the digital test message is generated based on the configuration and communication protocol of the secondary device, and the secondary device is stimulated to perform protection action through the digital test message. According to the configuration information, the power system collects the action signal of the protection device within the set time to confirm whether the secondary device can correctly respond to the fault condition within the specified time. This process ensures that key performance parameters such as the response time and action accuracy of the protection device are correctly evaluated.

[0075] Exemplarily, the generated digital test message is input to the secondary device under test through a communication interface (such as an Ethernet interface). At the same time, the monitoring time threshold is set based on the protection setting parameter of the configuration parameter, such as 300ms. The action state of the small red protection device of the secondary device is monitored within the monitoring time threshold, and the protection action signal output by the secondary device, such as a trip signal, is collected.

[0076] Step 105: Generate test results for the secondary equipment according to the protection action signal.

[0077] In this embodiment, by collecting protection action signals, it is confirmed whether the secondary equipment responds as expected and takes correct protection measures. By analyzing these protection action signals, the response time, action accuracy and reliability of the secondary equipment under actual fault conditions can be determined, and test results can be generated to ensure that the function of the protection device meets the requirements for safe operation. Effectively verify the performance of the secondary equipment, ensure that the power system can take protection measures in a timely and accurate manner when a fault occurs, and improve the safety and stability of the power system.

[0078] Specifically, the protection action signal is sampled. When the protection device in the secondary equipment triggers the protection action, the protection action signal will change. The change of the protection action signal is recorded through sampling technology to obtain the sampling data. The sampling data is the discrete value of the current signal in a specific time interval during the operation of the protection device in the secondary equipment, and records the change of the current amplitude over time; the sampled data is subjected to wavelet transform denoising to obtain the denoised data. Since the protection action signal is usually affected by noise, the use of wavelet transform can effectively remove high-frequency noise, thereby extracting the real change of the protection action signal. The denoised data will more clearly reflect the actual situation of the protection action signal and remove the interference signal introduced by the environment or equipment; the fault input time, protection action time and protection return time of the protection device in the secondary equipment are extracted from the denoised data, and the current signal corresponding to the protection action time is the action current signal. The fault input time refers to the moment when the fault starts; the protection action time refers to the moment when the protection device detects the fault and starts the protection operation; the protection return time refers to the moment when the protection device returns to normal after the fault is eliminated; the action current signal reflects the intensity and change of the current when the protection device is in action.

[0079] The time difference between the protection action time and the fault input time is calculated to obtain the measured action time, and the theoretical action time is determined according to the preset protection setting time. The measured action time is compared with the theoretical action time to obtain the first evaluation parameter. The first evaluation parameter is the time error rate, which can reflect the accuracy and deviation of the response time of the protection device.

[0080] The action current signal is converted into a complex form to obtain a complex form current signal, the action current amplitude and phase angle information are extracted from the complex form current signal, and the second evaluation parameter is calculated according to the action current amplitude and the preset protection current constant value, and the second evaluation parameter is the current error rate. The current error rate reflects the accuracy of the current intensity of the protection device when the action is triggered.

[0081] The current signal amplitude corresponding to the protection return moment is obtained, and the third evaluation parameter is calculated based on the current signal amplitude and the action current amplitude. The third evaluation parameter is the return coefficient. A higher return coefficient usually indicates that the protection device has successfully recovered and exited the protection state, while a lower return coefficient may indicate an abnormal recovery process. This return coefficient is compared with the preset standard return coefficient to determine whether it meets the expected standard.

[0082] The preset evaluation model in the power system is loaded. The evaluation model includes an input layer, a hidden layer and an output layer.

[0083] The first evaluation parameter, the second evaluation parameter and the third evaluation parameter are used as evaluation factors to construct the performance level of the protection device in the secondary equipment in the power system. The membership of the evaluation factors to each performance level is calculated to obtain a membership matrix. The row vectors of the membership matrix correspond to the evaluation factors, and the column vectors correspond to the performance levels. The performance levels include excellent, good, qualified and unqualified. The membership degree indicates the strength of the relationship between the evaluation factors and each performance level, and the degree of membership from a certain evaluation factor to a certain performance level is expressed by a numerical value.

[0084] The membership matrix is ​​received at the input layer, and the weight coefficients are set for the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter of the membership matrix in the hidden layer to obtain a weight vector. The importance of each evaluation factor may be different, so the weight coefficients are assigned to the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter to reflect their relative importance in the final test results. The weight vector and the membership matrix are fuzzy synthesized to obtain a comprehensive evaluation result vector. In the output layer, the performance level of the protection device in the secondary equipment is determined according to the maximum membership in the comprehensive evaluation result vector, and the test results are generated for the secondary equipment according to the performance level. The higher the performance level, the better the performance of the protection device. The test results can explain in detail the performance of the protection device on different indicators, providing a basis for equipment maintenance, fault diagnosis, and optimization adjustment.

[0085] Exemplarily, the training process of the evaluation model may include the following:

[0086] An evaluation model with a three-layer neural network structure is constructed. The three-layer neural network structure includes an input layer, a hidden layer, and an output layer. The membership matrix obtained by calculating the membership of each performance level using the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter is used as the input feature of the evaluation model. The input features are subjected to maximum and minimum normalization processing to obtain standardized features, and the standardized features are input into the input layer.

[0087] In the hidden layer, the membership matrix is ​​fuzzy synthesized to obtain the comprehensive evaluation result vector. The output of the hidden layer is linearly transformed using the modified linear unit (ReLU) activation function with buffer parameters to obtain the test results of the secondary device. The activation function has a non-zero gradient in the negative range, avoiding the problem of the traditional ReLU having a zero gradient on the negative semi-axis. In specific implementation, a small positive number a (such as 0.01) can be set. When the input x<0, the activation function outputs ax; when x≥0, it outputs x. This can retain a small amount of information in the negative range, which is beneficial to the learning of neurons.

[0088] Based on the test results of the secondary device, the loss value including the cross entropy term and the regularization term is calculated, and the learning rate decay coefficient is determined according to the continuous multiple iterations of the loss value; the cross entropy loss is used to measure the difference between the test results predicted by the evaluation model and the true label. The regularization term is used to control the complexity of the model and prevent overfitting. L2 regularization can be used, which is half of the sum of the squares of the weights of each connection. The two terms are balanced by adjusting the regularization coefficient λ. During the training process, the learning rate needs to be adjusted dynamically. The rate of decrease of the loss function value for multiple consecutive iterations can be calculated. When the rate of decrease is lower than a certain threshold, the learning rate is multiplied by a decay coefficient (such as 0.9). For example, if the average rate of decrease for 5 consecutive iterations is less than 1%, the current learning rate is multiplied by 0.9. In this way, a large learning rate can be maintained for rapid convergence in the early stage of training, and a small learning rate can be used for fine adjustment in the later stage.

[0089] The evaluation model is trained using mini-batch stochastic gradient descent, and the learning rate of each iteration is dynamically adjusted based on the learning rate decay coefficient; the evaluation model is trained using mini-batch stochastic gradient descent, and all training samples are randomly divided into multiple mini-batches, and each iteration uses a mini-batch to update the parameters. The mini-batch size can be set to 32 or 64, etc. For each mini-batch, forward propagation calculates the loss, backward propagation calculates the gradient, and then the parameters of the evaluation model are updated based on the current learning rate. This process is repeated until the preset number of iterations is reached or the loss drops below a certain threshold.

[0090] A cross-validation method (such as K-fold cross-validation) is used to apply the trained evaluation model to the validation sample set. The data set is randomly divided into K subsets (such as K=5), and one of them is selected as the validation set each time. The remaining K-1 are used as training sets to retrain the evaluation model. The consistency index (such as accuracy, F1 score, etc.) between the test results of the secondary equipment output by the evaluation model and the expert evaluation labels is calculated. Repeat K times and take the average value as the final performance indicator.

[0091] The weight sensitivity analysis method can be used to calculate the sensitivity of each connection weight in the evaluation model to the loss function to obtain the weight importance index (i.e., calculate the absolute value of the partial derivative of the loss function for each connection weight). The larger the absolute value of the partial derivative, the more significant the impact of the connection weight on the model output. Set a threshold (such as 10% of the average value of the absolute value of all partial derivatives), remove the connection weights below the threshold, and obtain a trained evaluation model.

[0092] In an embodiment of the present invention, when receiving configuration information for setting the operation scenario of the power system, a simulation test model is constructed according to the configuration information, original test data of the power system under normal working state is generated based on the simulation test model, component data of the power system under fault state is calculated according to the configuration information, the component data and the original test data are combined to obtain target test data containing fault characteristics, the secondary equipment in the power system is tested according to the target test data to generate a digital test message that conforms to the communication protocol configured for the secondary equipment, and the protection action signal of the protection device in the secondary equipment is collected within a preset time threshold according to the digital test message and the configuration information, and the test result is generated for the secondary equipment according to the protection action signal. The real performance of the power system under different operating states is effectively simulated, and a comprehensive test of the secondary equipment in the power system is realized, and the compatibility of the communication protocol and the response capability of the protection device can be quickly checked and verified, thereby ensuring the safety and stability of the entire power system and improving the accuracy and reliability of the secondary equipment test.

[0093] Embodiment 2

[0094] Figure 2 A structural diagram of a testing device for secondary equipment provided in the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the device comprises:

[0095] The original test data generating module 201 is used to construct a simulation test model according to the configuration information when receiving the configuration information of the operation scenario of the power system, and generate the original test data of the power system under the normal working state based on the simulation test model;

[0096] A target test data acquisition module 202 is used to calculate the component data of the power system under the fault state according to the configuration information, and combine the component data with the original test data to obtain target test data containing fault characteristics;

[0097] A digital test message generation module 203, used to test the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment;

[0098] A protection action signal acquisition module 204, configured to acquire a protection action signal of a protection device in the secondary device within a preset time threshold according to the digital test message and the configuration information;

[0099] The test result generating module 205 is used to generate a test result for the secondary device according to the protection action signal.

[0100] In one embodiment of the present invention, the configuration information includes protection type, protection setting parameter, test type parameter, sampling period parameter, fault start time parameter, fault duration parameter, fault type parameter and fault impedance parameter of the secondary device; the original test data generation module 201 includes:

[0101] A test condition establishment module, used to establish a test condition including a start-up characteristic and a return characteristic according to the test type parameters;

[0102] A simulation test construction module is used to construct a simulation test model according to the protection function area of ​​the power system and the test condition, and divide the simulation test model into a frequency-variable parameter line sub-model, a nonlinear transformer sub-model and a composite load sub-model; the frequency-variable parameter line sub-model includes a skin effect compensation item and a distributed parameter characteristic item, the nonlinear transformer sub-model includes a flux current characteristic item, and the composite load sub-model configures a constant impedance quantity, a constant current quantity and a constant power quantity according to a preset ratio;

[0103] A transient voltage vector acquisition module is used to construct a conductance matrix and an admittance matrix based on the frequency-variable parameter line sub-model, the nonlinear transformer sub-model and the composite load sub-model, combine the conductance matrix and the admittance matrix to form a node admittance matrix, and obtain the transient voltage vector of the power system under normal working state by solving the node admittance matrix;

[0104] A steady-state voltage and current vector acquisition module is used to decompose the three-phase system into a positive-sequence component, a negative-sequence component and a zero-sequence component under the normal working state of the power system, establish an inter-sequence impedance matrix including a coupling coefficient, and calculate a steady-state voltage vector and a steady-state current vector between the positive-sequence component, the negative-sequence component and the zero-sequence component based on the inter-sequence impedance matrix;

[0105] A voltage and current data acquisition module, used to calculate the difference between the transient voltage vector and the steady-state voltage vector as an initial voltage iteration vector, take the steady-state current vector as an initial current iteration vector, perform iterative calculation on the initial voltage iteration vector and the initial current iteration vector, introduce fundamental voltage components and harmonic voltage components in the iterative calculation process, and obtain converged voltage data and current data;

[0106] The original test data acquisition module is used to adaptively sample the converged voltage data and current data according to the sampling period parameters to generate the original test data of the power system under normal working conditions; the original test data includes initial voltage sampling data and initial current sampling data.

[0107] In one embodiment of the present invention, the voltage and current data acquisition module includes:

[0108] A voltage and current representation module, used for representing the voltage of the power system as a superposition of a fundamental voltage component and a harmonic voltage component, wherein the harmonic voltage component includes a third harmonic voltage component, a fifth harmonic voltage component and a seventh harmonic voltage component, and representing the current of the power system as a superposition of a fundamental current component and a harmonic current component, wherein the harmonic current component includes a third harmonic current component, a fifth harmonic current component and a seventh harmonic current component;

[0109] A voltage and current iteration value acquisition module, used to establish an iterative calculation equation based on the initial voltage iteration vector and the initial current iteration vector, wherein the iterative calculation equation superimposes each of the harmonic voltage components with the initial voltage iteration vector to obtain a voltage iteration value, and superimposes each of the harmonic current components with the initial current iteration vector to obtain a current iteration value;

[0110] An iteration judgment module, used for performing Fourier analysis on the voltage iteration value and the current iteration value to obtain the amplitude and phase of each harmonic voltage component and the amplitude and phase of each harmonic current component, and calculating the sum of the voltage data difference and the current data difference between two adjacent iterations as an iteration convergence criterion;

[0111] A relaxation factor introduction module, used for introducing a relaxation factor to perform weighted adjustment on the voltage iteration value and the current iteration value, reducing the relaxation factor when the iteration convergence criterion is greater than the previous iteration convergence criterion, and increasing the relaxation factor when the iteration convergence criterion is less than the previous iteration convergence criterion;

[0112] The iterative convergence module is used to determine that the iterative calculation converges when the iterative convergence criterion is less than a preset convergence threshold and the difference between two adjacent iterative convergence criteria is less than a preset accuracy requirement, and finally obtain converged voltage data and current data.

[0113] In one embodiment of the present invention, the component data includes transient component data and steady-state component data; the target test data acquisition module 202 includes:

[0114] A transient component data acquisition module is used to establish a transient response calculation model under the fault state of the power system according to the fault type parameter and the fault impedance parameter, calculate the initial fault voltage amplitude based on the phase voltage at the time of fault occurrence under the fault state of the power system, calculate the fault loop attenuation coefficient and the damping angular frequency under the fault state of the power system according to the fault impedance parameter, when the fault type parameter is a single-phase grounding fault, use an exponentially decayed cosine function containing the fault loop attenuation coefficient and the damping angular frequency to calculate the single-phase grounding fault voltage under the fault state of the power system, when the fault type parameter is an inter-phase fault, calculate the inter-phase coupling coefficient and use the exponentially decayed cosine function of the two-phase voltage difference containing the inter-phase coupling coefficient to calculate the phase-to-phase fault voltage under the fault state of the power system, and obtain transient component data under the fault state of the power system;

[0115] A steady-state component data acquisition module, used to establish a steady-state response calculation model under the fault state of the power system according to the fault start time parameter and the fault duration parameter, calculate the steady-state fault voltage amplitude under the fault state of the power system based on the fault type parameter and the fault impedance parameter, calculate the impedance angle and phase difference according to the fault impedance parameter, calculate the steady-state fault voltage under the fault state of the power system using a finite-time sine function including the fault start time parameter and the fault duration parameter, calculate the steady-state fault current under the fault state of the power system using a finite-time sine function including the impedance angle and the phase difference, and obtain steady-state component data under the fault state of the power system;

[0116] A target test data generation module is used to superimpose the transient component data and the steady-state component data onto the original test data by means of vector superposition, perform phase correction on the original test data, calculate the positive-sequence component, negative-sequence component and zero-sequence component under the fault state of the power system, convert the positive-sequence component, the negative-sequence component and the zero-sequence component into three-phase fault characteristic data, and generate target test data containing fault characteristics.

[0117] In one embodiment of the present invention, the digital test message generating module 203 includes:

[0118] a test sampling sequence acquisition module, configured to discretize and sample the target test data according to the sampling period parameter to obtain discrete voltage data and discrete current data, filter the discrete voltage data and the discrete current data using a low-pass filter to obtain filtered voltage data and filtered current data, perform amplitude calibration and phase compensation on the filtered voltage data and the filtered current data, and resample the filtered voltage data and the filtered current data using a linear interpolation method to obtain a test sampling sequence;

[0119] A digital test message construction module is used to construct a digital test message according to the test sampling sequence, encapsulate the sampling counter value as a synchronization identification field, encapsulate the fault start time parameter as a timestamp field, and encapsulate the data validity flag as a quality bit field in the message header of the digital test message, encapsulate the voltage channel data, current channel data and phase identification of the test sampling sequence in the data segment of the digital test message, calculate the cyclic redundancy check value of the digital test message, set the digital test message sending time interval based on the sampling period parameter to generate a message sequence number, and the data format of the digital test message conforms to the sampling value message format in the communication protocol configured by the secondary device.

[0120] In one embodiment of the present invention, the test result generating module 205 includes:

[0121] A time extraction module is used to sample the protection action signal to obtain sampled data, perform wavelet transform noise reduction processing on the sampled data to obtain noise-reduced data, and extract the fault input time, protection action time and protection return time of the protection device in the secondary device from the noise-reduced data, and the current signal corresponding to the protection action time is the action current signal;

[0122] A time error rate calculation module is used to calculate the time difference between the protection action time and the fault input time to obtain the measured action time, determine the theoretical action time according to the preset protection setting time, and compare the measured action time with the theoretical action time to obtain a first evaluation parameter, wherein the first evaluation parameter is the time error rate;

[0123] a current error rate calculation module, configured to convert the action current signal into a complex form to obtain a complex form current signal, extract the action current amplitude and phase angle information from the complex form current signal, and calculate a second evaluation parameter according to the action current amplitude and a preset protection current constant value, wherein the second evaluation parameter is the current error rate;

[0124] A return coefficient calculation module, used for obtaining the current signal amplitude corresponding to the protection return moment, and calculating a third evaluation parameter according to the current signal amplitude and the action current amplitude, wherein the third evaluation parameter is the return coefficient;

[0125] A model loading module, used to load a preset evaluation model; the evaluation model includes an input layer, a hidden layer and an output layer;

[0126] A membership matrix acquisition module is used to use the first evaluation parameter, the second evaluation parameter and the third evaluation parameter as evaluation factors, construct the performance level of the protection device in the secondary device in the power system, calculate the membership of the evaluation factors to each performance level to obtain a membership matrix, wherein the row vectors of the membership matrix correspond to the evaluation factors, and the column vectors correspond to the performance levels, and the performance levels include excellent, good, qualified and unqualified;

[0127] A test result acquisition module is used to receive the membership matrix at the input layer, set weight coefficients for the first evaluation parameter, the second evaluation parameter and the third evaluation parameter of the membership matrix in the hidden layer to obtain a weight vector, perform fuzzy synthesis operation on the weight vector and the membership matrix to obtain a comprehensive evaluation result vector, determine the performance level of the protection device in the secondary equipment according to the maximum membership in the comprehensive evaluation result vector in the output layer, and generate a test result for the secondary equipment based on the performance level.

[0128] In one embodiment of the present invention, the test result generating module 205 further includes:

[0129] A model building module, used to build an evaluation model including a three-layer neural network structure, wherein the three-layer neural network structure includes an input layer, a hidden layer, and an output layer;

[0130] An input feature standardization module, used to use the membership matrix obtained by calculating the membership of each performance level using the first evaluation parameter, the second evaluation parameter and the third evaluation parameter as the input feature of the evaluation model, perform maximum and minimum normalization processing on the input feature to obtain a standardized feature, and input the standardized feature into the input layer;

[0131] A hidden layer transformation module, used for performing fuzzy synthesis calculation on the membership matrix in the hidden layer to obtain a comprehensive evaluation result vector, and using an activation function with a buffer parameter to perform linear transformation on the output of the hidden layer to obtain a test result of the secondary device, wherein the activation function has a non-zero gradient in a negative value interval;

[0132] A loss value calculation module, used to calculate a loss value including a cross entropy term and a regularization term based on the test result of the secondary device, and determine a learning rate attenuation coefficient according to a continuous multiple iteration descent rate of the loss value;

[0133] A model updating module, used for training the evaluation model using a small batch stochastic gradient descent method, and dynamically adjusting the learning rate of each iteration based on the learning rate decay coefficient;

[0134] A model verification module, used to apply the trained evaluation model to a verification sample set by a cross-validation method, and calculate a consistency index between the test result of the secondary device output by the evaluation model and the expert evaluation label;

[0135] The model training completion module is used to calculate the sensitivity of each connection weight in the evaluation model to the loss function to obtain a weight importance index, remove the connection weights whose weight importance index is lower than a preset threshold, and obtain a trained evaluation model.

[0136] The testing device for secondary equipment provided by the embodiment of the present invention can execute the testing method for secondary equipment provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the testing method for secondary equipment.

[0137] Embodiment 3

[0138] See also Figure 3 , showing a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. The computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The computer device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0139] like Figure 3 As shown, the computer device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the computer device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] A number of components in the computer device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the computer device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the test method of the secondary device.

[0142] In some embodiments, the test method for the secondary device may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the test method for the secondary device described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the test method for the secondary device in any other appropriate manner (e.g., by means of firmware).

[0143] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0145] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0148] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0149] Embodiment 4

[0150] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the test method for a secondary device provided by any embodiment of the present invention is implemented.

[0151] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0152] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0153] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for testing a secondary device, characterized in that: include: Upon receiving configuration information for setting an operation scenario of the power system, constructing a simulation test model according to the configuration information, and generating original test data of the power system under normal working state based on the simulation test model; Calculating component data of the power system under a fault state according to the configuration information, combining the component data with the original test data to obtain target test data containing fault characteristics; Testing the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment; According to the digital test message and the configuration information, collecting the protection action signal of the protection device in the secondary device within a preset time threshold; A test result is generated for the secondary device according to the protection action signal.

2. The method according to claim 1, characterized in that The configuration information includes protection type of secondary equipment, protection setting parameters, test type parameters, sampling period parameters, fault start time parameters, fault duration parameters, fault type parameters and fault impedance parameters; The step of constructing a simulation test model according to the configuration information and generating original test data of the power system under normal working state based on the simulation test model includes: Establishing a test condition including a start-up characteristic and a return characteristic according to the test type parameters; A simulation test model is constructed according to the protection function area of ​​the power system and the test condition, and the simulation test model is divided into a frequency-variable parameter line sub-model, a nonlinear transformer sub-model and a composite load sub-model; the frequency-variable parameter line sub-model includes a skin effect compensation item and a distributed parameter characteristic item, the nonlinear transformer sub-model includes a flux current characteristic item, and the composite load sub-model configures a constant impedance quantity, a constant current quantity and a constant power quantity according to a preset ratio; constructing a conductance matrix and a susceptance matrix based on the frequency-variable parameter line sub-model, the nonlinear transformer sub-model and the composite load sub-model, combining the conductance matrix and the susceptance matrix to form a node admittance matrix, and obtaining a transient voltage vector of the power system under normal working state by solving the node admittance matrix; Decomposing the three-phase system into a positive-sequence component, a negative-sequence component and a zero-sequence component under normal working conditions of the power system, establishing an inter-sequence impedance matrix including a coupling coefficient, and calculating a steady-state voltage vector and a steady-state current vector between the positive-sequence component, the negative-sequence component and the zero-sequence component based on the inter-sequence impedance matrix; Calculating the difference between the transient voltage vector and the steady-state voltage vector as an initial voltage iteration vector, taking the steady-state current vector as an initial current iteration vector, performing iterative calculation on the initial voltage iteration vector and the initial current iteration vector, introducing fundamental voltage components and harmonic voltage components during the iterative calculation process, and obtaining converged voltage data and current data; The converged voltage data and current data are adaptively sampled according to the sampling period parameters to generate original test data under the normal working state of the power system; the original test data includes initial voltage sampling data and initial current sampling data.

3. The method according to claim 2, characterized in that The iterative calculation of the initial voltage iteration vector and the initial current iteration vector, introducing the fundamental voltage component and the harmonic voltage component in the iterative calculation process, and obtaining the converged voltage data and current data, includes: The voltage of the power system is represented as a superposition of a fundamental voltage component and a harmonic voltage component, wherein the harmonic voltage component includes a third harmonic voltage component, a fifth harmonic voltage component, and a seventh harmonic voltage component; the current of the power system is represented as a superposition of a fundamental current component and a harmonic current component, wherein the harmonic current component includes a third harmonic current component, a fifth harmonic current component, and a seventh harmonic current component; An iterative calculation equation is established based on the initial voltage iteration vector and the initial current iteration vector, wherein the iterative calculation equation superimposes each of the harmonic voltage components with the initial voltage iteration vector to obtain a voltage iteration value, and superimposes each of the harmonic current components with the initial current iteration vector to obtain a current iteration value; Performing Fourier analysis on the voltage iteration value and the current iteration value to obtain the amplitude and phase of each harmonic voltage component and the amplitude and phase of each harmonic current component, and calculating the sum of the voltage data difference and the current data difference between two adjacent iterations as an iteration convergence criterion; Introducing a relaxation factor to perform weighted adjustment on the voltage iteration value and the current iteration value, reducing the relaxation factor when the iteration convergence criterion is greater than the previous iteration convergence criterion, and increasing the relaxation factor when the iteration convergence criterion is less than the previous iteration convergence criterion; When the iterative convergence criterion is less than a preset convergence threshold and the difference between two adjacent iterative convergence criterions is less than a preset accuracy requirement, it is determined that the iterative calculation is converged, and finally the converged voltage data and current data are obtained.

4. The method according to claim 2, characterized in that: The component data includes transient component data and steady-state component data; the component data under the fault state of the power system is calculated according to the configuration information, and the component data is combined with the original test data to obtain target test data containing fault characteristics, including: A transient response calculation model for the power system under a fault state is established according to the fault type parameter and the fault impedance parameter, an initial fault voltage amplitude is calculated based on the phase voltage at the moment of fault occurrence under the power system fault state, a fault loop attenuation coefficient and a damping angular frequency under the power system fault state are calculated according to the fault impedance parameter, when the fault type parameter is a single-phase grounding fault, an exponentially decayed cosine function including the fault loop attenuation coefficient and the damping angular frequency is used to calculate the single-phase grounding fault voltage under the power system fault state, when the fault type parameter is an inter-phase fault, an inter-phase coupling coefficient is calculated, and an exponentially decayed cosine function of a two-phase voltage difference including the inter-phase coupling coefficient is used to calculate the inter-phase fault voltage under the power system fault state, so as to obtain transient component data under the power system fault state; Establishing a steady-state response calculation model for the power system under a fault state according to the fault start time parameter and the fault duration parameter, calculating a steady-state fault voltage amplitude under the power system under a fault state based on the fault type parameter and the fault impedance parameter, calculating an impedance angle and a phase difference according to the fault impedance parameter, calculating a steady-state fault voltage under the power system under a fault state using a finite-time sine function including the fault start time parameter and the fault duration parameter, calculating a steady-state fault current under the power system under a fault state using a finite-time sine function including the impedance angle and the phase difference, and obtaining steady-state component data under the power system under a fault state; The transient component data and the steady-state component data are superimposed on the original test data by means of vector superposition, the original test data is phase corrected, the positive-sequence component, the negative-sequence component and the zero-sequence component under the fault state of the power system are calculated, the positive-sequence component, the negative-sequence component and the zero-sequence component are converted into three-phase fault characteristic data, and target test data containing fault characteristics are generated.

5. The method according to claim 4, characterized in that The testing of the secondary equipment in the power system according to the target test data to generate a digital test message that complies with the communication protocol configured for the secondary equipment includes: Discretely sampling the target test data according to the sampling period parameter to obtain discrete voltage data and discrete current data, filtering the discrete voltage data and the discrete current data using a low-pass filter to obtain filtered voltage data and filtered current data, performing amplitude calibration and phase compensation on the filtered voltage data and the filtered current data, and resampling the filtered voltage data and the filtered current data using a linear interpolation method to obtain a test sampling sequence; A digital test message is constructed according to the test sampling sequence, and a sampling counter value is encapsulated in a message header of the digital test message as a synchronization identification field, the fault start time parameter is encapsulated as a timestamp field, and a data validity flag is encapsulated as a quality bit field; voltage channel data, current channel data and phase identification of the test sampling sequence are encapsulated in a data segment of the digital test message, a cyclic redundancy check value of the digital test message is calculated, and a message sequence number is generated by setting the digital test message sending time interval based on the sampling period parameter, and the data format of the digital test message conforms to the sampling value message format in the communication protocol configured by the secondary device.

6. The method according to any one of claims 1 to 5, characterized in that: Generating a test result for the secondary device according to the protection action signal includes: Sampling the protection action signal to obtain sampling data, performing wavelet transform noise reduction processing on the sampling data to obtain noise-reduced data, extracting the fault input time, protection action time and protection return time of the protection device in the secondary device from the noise-reduced data, and the current signal corresponding to the protection action time is the action current signal; Calculate the time difference between the protection action time and the fault input time to obtain the measured action time, determine the theoretical action time according to the preset protection setting time, compare the measured action time with the theoretical action time to obtain a first evaluation parameter, the first evaluation parameter is the time error rate; The action current signal is converted into a complex form to obtain a complex form current signal, the action current amplitude and phase angle information are extracted from the complex form current signal, and a second evaluation parameter is calculated according to the action current amplitude and a preset protection current constant value, wherein the second evaluation parameter is a current error rate; Acquire the current signal amplitude corresponding to the protection return moment, and calculate a third evaluation parameter according to the current signal amplitude and the action current amplitude, wherein the third evaluation parameter is a return coefficient; Loading a preset evaluation model; the evaluation model includes an input layer, a hidden layer and an output layer; The first evaluation parameter, the second evaluation parameter and the third evaluation parameter are used as evaluation factors to construct the performance level of the protection device in the secondary device in the power system, and the membership of the evaluation factors to each performance level is calculated to obtain a membership matrix, wherein the row vectors of the membership matrix correspond to the evaluation factors, and the column vectors correspond to the performance levels, and the performance levels include excellent, good, qualified and unqualified; The membership matrix is ​​received at the input layer, weight coefficients are set for the first evaluation parameter, the second evaluation parameter and the third evaluation parameter of the membership matrix in the hidden layer to obtain a weight vector, fuzzy synthesis operation is performed on the weight vector and the membership matrix to obtain a comprehensive evaluation result vector, and the performance level of the protection device in the secondary equipment is determined in the output layer according to the maximum membership in the comprehensive evaluation result vector, and a test result is generated for the secondary equipment based on the performance level.

7. The method according to claim 6, characterized in that Generating a test result for the secondary device according to the protection action signal also includes: Constructing an evaluation model including a three-layer neural network structure, wherein the three-layer neural network structure includes an input layer, a hidden layer, and an output layer; The membership matrix obtained by calculating the membership of each performance level using the first evaluation parameter, the second evaluation parameter and the third evaluation parameter is used as the input feature of the evaluation model, the input feature is subjected to maximum and minimum normalization processing to obtain a standardized feature, and the standardized feature is input into the input layer; In the hidden layer, the membership matrix is ​​subjected to fuzzy synthesis calculation to obtain a comprehensive evaluation result vector, and the output of the hidden layer is subjected to linear transformation using an activation function with a buffer parameter to obtain a test result of the secondary device, wherein the activation function has a non-zero gradient in a negative value interval; Calculate a loss value including a cross entropy term and a regularization term based on the test result of the secondary device, and determine a learning rate attenuation coefficient according to a continuous multiple iteration decrease rate of the loss value; The evaluation model is trained using a mini-batch stochastic gradient descent method, and the learning rate of each iteration is dynamically adjusted based on the learning rate decay coefficient; The trained evaluation model is applied to a validation sample set by a cross-validation method, and a consistency index between the test result of the secondary device output by the evaluation model and the expert evaluation label is calculated; The sensitivity of each connection weight in the evaluation model to the loss function is calculated to obtain a weight importance index, and the connection weights whose weight importance index is lower than a preset threshold are removed to obtain a trained evaluation model.

8. A computer device, characterized in that: The computer device comprises: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the secondary device testing method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the secondary device testing method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method for testing a secondary device according to any one of claims 1 to 7 is implemented.

Images