A testing method, device, storage medium and product for secondary equipment

Abstract

The present invention discloses a testing method, equipment, storage medium and product for secondary equipment. The method comprises: upon receiving configuration information for setting an operation scenario of an electric power system, constructing a simulation test model according to the configuration information, generating original test data of the electric power system under normal working conditions based on the simulation test model, calculating component data of the electric power system under fault conditions 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 electric 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, collecting protection action signals of protection devices in the secondary equipment within a preset time threshold according to the digital test message and the configuration information, generating test results for the secondary equipment according to the protection action signals, thereby improving the accuracy and reliability of the secondary equipment test.

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] 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 secondary equipment is usually important for the reliable operation of the power system.

[0003] Traditional testing methods for secondary equipment in power systems typically rely on field testing or single-scenario laboratory testing. These methods primarily rely on manually setting test conditions and utilizing specific test instruments to verify the functionality, response time, and fault handling capabilities of secondary equipment. This results in low accuracy and reliability for comprehensive secondary equipment testing. 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 conditions based on the simulation test model;

[0007] Calculating component data of the power system under a fault state according to the configuration information, and 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] collecting protection action signals of protection devices in the secondary equipment within a preset time threshold according to the digital test message and the configuration information;

[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, configured to, upon receiving configuration information for setting an operation scenario of the power system, construct a simulation test model according to the configuration information, and generate original test data of the power system under normal working conditions based on the simulation test model;

[0013] a target test data acquisition module, configured to calculate component data of the power system under a 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;

[0014] a digital test message generating module, configured 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, configured to acquire protection action signals of protection devices in the secondary equipment within a preset time threshold according to the digital test message and the configuration information;

[0016] A 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, 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, which includes a computer program. When the computer program is executed by a processor, it implements the secondary device testing method provided in the first aspect of the present invention.

[0023] In an embodiment of the present invention, upon receiving configuration information for setting an operating scenario for a power system, a simulation test model is constructed based on the configuration information. Original test data for the power system under normal operating conditions is generated based on the simulation test model. Component data for the power system under fault conditions is calculated based on the configuration information. The component data and the original test data are combined to obtain target test data containing fault characteristics. Secondary equipment in the power system is tested based on the target test data to generate digital test messages that comply with the communication protocol configured for the secondary equipment. Based on the digital test messages and the configuration information, protection action signals of protection devices in the secondary equipment are collected within a preset time threshold. Test results are generated for the secondary equipment based on the protection action signals. This effectively simulates the actual performance of the power system under different operating conditions, enables comprehensive testing of secondary equipment in the power system, and can quickly verify and validate the compatibility of communication protocols and the responsiveness of protection devices, thereby ensuring the safety and stability of the entire power system and improving the accuracy and reliability of secondary equipment testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a method for testing a secondary device provided in Example 1 of the present invention;

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

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

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description 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 numbers used in this way can be interchanged 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] See also Figure 1 , shows a flow chart of a method for testing a secondary device provided by the first embodiment of the present invention. The method can be executed by a testing device for a secondary device. The testing device for a secondary device can be implemented in the form of hardware and / or software, and the testing device for a secondary 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 under a normal operating state of the power system based on the simulation test model.

[0032] In this embodiment, upon receiving configuration information for the power system's operating scenario, a simulation test model is constructed to provide a simulated environment for reproducing and analyzing the power system's performance under ideal conditions. This serves as a foundation for evaluating the power system's performance and stability in actual operation, helping to identify key parameters such as voltage and current in the power system under normal conditions, and providing a benchmark for subsequent fault analysis and performance testing. The resulting raw test data can be further compared with data under fault conditions to more accurately evaluate and improve the protection performance and response capabilities of secondary equipment.

[0033] For example, the configuration information includes the protection type of the secondary device (e.g., overcurrent protection, distance protection), protection setting parameters (e.g., overcurrent pickup value, time delay, etc.), test type parameters (e.g., single-phase ground fault, two-phase short circuit, etc.), sampling period parameters (e.g., 4ms), fault start time parameters (e.g., 100ms), fault duration parameters (e.g., 200ms), fault type parameters (e.g., phase A ground fault), and fault impedance parameters (e.g., fault impedance 10Ω). The simulation test model includes a transient response model and a steady-state response model. The transient response model simulates the electrical parameter changes at the moment of the fault, while the steady-state response model simulates the steady-state electrical parameters during the duration of the fault. Taking overcurrent protection as an example, the transient response model can use an RC circuit model, while the steady-state response model can use a constant current source model.

[0034] Based on the constructed simulation test model, raw test data for the power system under normal operating conditions is generated, including raw voltage sampling data and raw current sampling data. Taking a 50Hz power frequency power system as an example, sinusoidal voltage data with an amplitude of 100V and a frequency of 50Hz (i.e., raw voltage sampling data) and sinusoidal current data with an amplitude of 5A and a frequency of 50Hz (i.e., raw current sampling data) can be generated as raw 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 startup characteristics and return characteristics 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 types in the configuration information divide the power system's protection functions into distance protection, overcurrent protection, and differential protection zones. By setting these zones, timely protection is ensured in various fault scenarios, preventing equipment damage. The protection start value, action time, and return coefficient values ​​for each zone are determined based on the protection setting parameters in the configuration information. Proper start value and action time values ​​prevent premature or delayed protection action, thereby maintaining stable power system operation. Setting the return coefficient value helps the power system quickly resume normal operation after a fault is resolved, reducing downtime.

[0039] Taking distance protection as an example, the protection setting parameters determine the distance protection function zone's protection pickup value to be 80% of the line impedance, the operating time to be 0.5 seconds, and the return coefficient to be 1.2. Based on the test type parameters, a test condition is established that includes both pickup and return characteristics. For example, when testing the pickup characteristics of distance protection, the fault point is set to increase gradually from 0% 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, a test condition including startup characteristics and return characteristics is established according to the test type parameters, 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. 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 a skin effect compensation term and a distributed parameter characteristic term. The skin effect compensation term uses the Carson formula to calculate the frequency characteristics of the line resistance and inductance, and the distributed parameter characteristic term uses a π-type equivalent circuit to simulate the distributed parameter characteristics of long-distance transmission lines.

[0043] The nonlinear transformer sub-model includes flux and current characteristic terms and uses a 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. By solving the node admittance matrix, the transient voltage vector under the normal working state of the power system is obtained.

[0046] In this embodiment, a node admittance matrix is ​​constructed, allowing the entire power system's electrical network to be represented in the form of a mathematical matrix. This effectively describes the coupling relationships between power systems. By solving this node admittance matrix, the transient response of each node voltage (i.e., the transient voltage vector) is obtained, providing a foundation for understanding the dynamic characteristics of the power system. The conductance matrix represents the conductance relationship between each node in the network, while the susceptance matrix represents the susceptance relationship between each node.

[0047] Step 1014: Under normal operating 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 components, the negative-sequence components, and the zero-sequence components are calculated based on the inter-sequence impedance matrix.

[0048] In this embodiment, decomposing the three-phase system into positive-sequence, negative-sequence, and zero-sequence components is a step in analyzing the commonalities and differences between three-phase systems. By calculating the inter-sequence impedance matrix, the interactions between the sequence components can be decoupled, resulting in steady-state voltage and current vectors. This allows for in-depth analysis of the symmetry and asymmetry of the power system and a precise description of the voltage and current distribution 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, use the steady-state current vector as the initial current iteration vector, perform iterative calculations on the initial voltage iteration vector and the initial current iteration vector, introduce the fundamental voltage component and the harmonic voltage component during the iterative calculation process, and obtain converged voltage data and current data.

[0050] This embodiment introduces iterative calculation, emphasizing the importance of repetitive corrections to approximate the actual power system state. This technique leverages the difference between the transient and steady-state voltage vectors, the steady-state current vector, and the fundamental and harmonic components during dynamic processes to iteratively generate more accurate voltage and current data. This effectively reveals the power system's frequency response behavior, providing data support for subsequent secondary equipment performance testing and protection device evaluation.

[0051] Specifically, the power system voltage is represented as a superposition of the fundamental voltage component and the harmonic voltage components, with the harmonic voltage components comprising the third, fifth, and seventh harmonic voltage components. The power system current is represented as a superposition of the fundamental current component and the harmonic current components, with the harmonic current components comprising the third, fifth, and seventh harmonic current components. To account for the harmonic components present in the power system, the iterative calculation employs 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 this 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] Fourier analysis is performed on the iterative voltage and current values ​​to obtain the amplitude and phase of each harmonic voltage component and the amplitude and phase of each harmonic current component. These Fourier analysis results provide an important basis for subsequent convergence criterion calculations, helping to determine whether the power system has met the preset convergence standard. The sum of the difference between the voltage data and the current data between two adjacent iterations is calculated as the iterative convergence criterion.

[0054] A relaxation factor is introduced to perform weighted adjustment on the voltage and current iteration values. 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 the preset convergence threshold and the difference between the convergence criteria of two adjacent iterative calculations is less than the preset accuracy requirement, the iterative calculation is determined to have converged, and the converged voltage and current data are finally obtained. In this case, the power system outputs the converged voltage and current data. These voltage and current data 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 the normal working state of the power system.

[0057] In this embodiment, continuous voltage and current data are discretized into voltage and current sampled data that meet the processing requirements of digital equipment based on 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 maintaining the accuracy of the original test data.

[0058] For example, the sampling period can be set to 1ms, and linear interpolation is used between each sampling point to generate raw test data with a higher sampling rate. Raw test data containing 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, with 1000 sampling points per phase.

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

[0060] In this embodiment, target test data containing fault characteristics is generated by calculating component data under power system fault conditions and combining it with original test data under normal operating conditions. This target test data accurately simulates the dynamic response characteristics of the power system under different fault scenarios. The generated target test data reflects both normal state information and fault characteristics. This step allows for more realistic testing of the protection performance of secondary equipment, verifying its reliability and accuracy under actual fault conditions, and providing a basis for secondary equipment performance evaluation and optimization. Component data includes transient component data and steady-state component data.

[0061] Specifically, a transient response calculation model for the power system under fault conditions is established based on the fault type parameters and the fault impedance parameters. 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 based on the phase A voltage (assuming it is 10kV) at the time of the fault under the power system fault condition, and the fault loop attenuation coefficient α = 200s under the power system fault condition is calculated based on the fault impedance parameter 5Ω. -1 and damping angular frequency ω

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

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

[0064] The transient component data and 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. 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] Fault types typically include short circuit faults, ground faults, and open circuit faults. Different fault types have different effects on the voltage and current waveforms. Fault impedance describes the relationship between current and voltage at the fault point.

[0066] Transient component data refers to the rapidly changing portions of the power system's voltage and current waveforms at the initial stage of a fault, typically containing high-frequency components. Calculating transient component data requires determining how the system's voltage and current waveforms change at the moment of the fault, based on the fault type. The corresponding current and voltage transient waveforms are then calculated based on the fault impedance. Calculating transient component data involves measuring the current and voltage waveforms in the power system and performing filtering and fast Fourier transform analysis on these waveforms to extract transient signals.

[0067] Steady-state component data refers to the low-frequency components in the voltage and current waveforms as the power system gradually stabilizes after a fault occurs. Steady-state component data is calculated based on the fault start time parameter and the fault duration parameter. These two parameters determine the time window between 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. Specifically, the fault start time point is first determined, and then the steady-state waveforms of the power system voltage and current after the fault are calculated based on this time point. The steady-state waveform is similar to the waveform under normal operating conditions, typically appearing as a low-frequency sine wave. The amplitude and phase of the voltage and current waveforms may change, but there are no obvious instantaneous changes.

[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 protocols configured for secondary devices typically refer to the standard formats used for data exchange between devices in power systems. Common protocols include IEC 61850, Distributed Network Protocol (DNP) 3, and Modbus. When generating digital test messages, the target test data is encoded into a format that complies with these communication protocols to ensure that the data can be correctly decoded and used by various secondary devices (such as protection devices, monitoring systems, and measuring equipment). During this process, the data is packetized, verified, and packaged into a format that complies with protocol specifications for transmission and analysis within the power system.

[0070] In this embodiment, digital test messages conforming to the communication protocol configured for the secondary equipment are generated to verify the secondary equipment's communication and response capabilities under fault conditions. The test utilizes the generated target test data to simulate various operating conditions in a real-world operating environment, ensuring that the secondary equipment can correctly interpret, respond to, and process communication information in actual fault conditions, 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 parameter. The sampling period can be set to 1ms. The voltage and current signals in the target test data are sampled at equal intervals to obtain discrete voltage and current data. For example, for a 50Hz AC signal, 20 sampling points can be collected per cycle. The discrete voltage and current data are filtered using a low-pass filter. A Butterworth low-pass filter with a cutoff frequency of 500Hz is selected to obtain the filtered voltage and current data. The filtered voltage and current data are then amplitude-calibrated and phase-compensated. Amplitude calibration is achieved by multiplying by a correction coefficient, while phase compensation is achieved by introducing an appropriate time delay. For example, the correction coefficient for the voltage channel is 1.02, with a phase compensation of 2 degrees; the correction coefficient for the current channel is 0.98, with a phase compensation of -1 degree. The calibrated voltage and current data are resampled using linear interpolation. The sampling rate can be increased to 4800Hz, or 96 points per cycle, to obtain the resampled test sampling sequence.

[0072] A digital test message is constructed based on the test sampling sequence. The message header of the digital test message contains the sampling counter value as a synchronization identifier field, for example, starting from 0 and incrementing. 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 identifier of the test sampling sequence are encapsulated in the order of voltage channel and current channel. 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; and 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; and 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 append it to the end of the digital test message. The checksum can be calculated using a 32-bit cyclic redundancy check (CRC-32) algorithm. Set the digital test message transmission interval based on the sampling period parameter to generate a message sequence number. For example, if the sampling period is 1ms, then the message transmission interval is also set to 1ms. The message sequence number starts at 1 and increments. The data format of the digital test message conforms to the sampled value message format in the communication protocol configured for the secondary device.

[0073] Step 104 : Based on the digital test message and the configuration information, the protection action signal of the protection device in the secondary equipment is collected within a preset time threshold.

[0074] In this embodiment, the protection action signals of the protection devices in the secondary equipment are collected within a preset time threshold to verify whether the secondary equipment's protection response under fault conditions meets expectations. Specifically, digital test messages are generated based on the secondary equipment's configuration and communication protocol, and these digital test messages trigger the secondary equipment to perform protective actions. Based on the configuration information, the power system collects the protection device's action signals within a set timeframe to confirm whether the secondary equipment can correctly respond to the fault condition within the specified timeframe. This process ensures that key performance parameters such as the protection device's response time and action accuracy are accurately evaluated.

[0075] Exemplarily, the generated digital test message is input to the secondary device under test via a communication interface (e.g., an Ethernet interface). Simultaneously, a monitoring time threshold is set based on the protection setting parameters of the configuration parameters, such as 300ms. Within this monitoring time threshold, the operating status of the secondary device's small red protection device is monitored, and protection action signals, such as trip signals, output by the secondary device are collected.

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

[0077] In this embodiment, protection action signals are collected to confirm whether the secondary equipment responds as expected and takes the correct protective 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, generating test results to ensure that the protection device meets the requirements for safe operation. This effectively verifies the performance of the secondary equipment, ensuring that the power system can take timely and accurate protective measures when a fault occurs, thereby improving the safety and stability of the power system.

[0078] Specifically, the protection action signal is sampled. When a protection device in a secondary device triggers a protection action, the protection action signal changes. Sampling technology records the changes in the protection action signal and generates sampled data. The sampled data is the discrete value of the current signal within a specific time interval during the operation of the protection device in the secondary device, recording the change in current amplitude over time. Wavelet transform is then used to reduce noise on the sampled data to obtain de-noised data. Since protection action signals are often affected by noise, wavelet transform can effectively remove high-frequency noise, thereby extracting the true changes in the protection action signal. The de-noised data more clearly reflects the actual state of the protection action signal and eliminates interference signals introduced by the environment or equipment. The fault activation time, protection action time, and protection return time of the protection device in the secondary device are extracted from the de-noised data. The current signal corresponding to the protection action time is the action current signal. The fault activation time refers to the moment when the fault begins; the protection action time refers to the moment when the protection device detects the fault and initiates the protection operation; and 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 changes of the current when the protection device is actuated.

[0079] The time difference between the protection action moment and the fault input moment 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 degree of the protection device response time.

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

[0081] The current signal amplitude corresponding to the protection return moment is obtained. A third evaluation parameter, the return coefficient, is calculated based on the current signal amplitude and the operating current amplitude. A high return coefficient generally indicates that the protection device has successfully recovered and exited the protection state, while a low 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, second, and third evaluation parameters are used as evaluation factors to construct the performance levels of the protection devices in the secondary equipment within the power system. The membership of the evaluation factors to each performance level is calculated to create a membership matrix. The row vectors of the membership matrix correspond to the evaluation factors, and the column vectors correspond to the performance levels, which are classified as excellent, good, acceptable, and unacceptable. The membership level indicates the strength of the relationship between the evaluation factors and each performance level, and the numerical value represents the degree of membership from a given evaluation factor to a given performance level.

[0084] The input layer receives the membership matrix, and in the hidden layer, weight coefficients are assigned to the first, second, and third evaluation parameters of the membership matrix to obtain a weight vector. The importance of each evaluation factor may vary, so weight coefficients are assigned to the first, second, and third evaluation parameters to reflect their relative importance in the final test results. A fuzzy synthesis operation is performed on the weight vector and the membership matrix to obtain a comprehensive evaluation result vector. In the output layer, the performance level of the protection device in the secondary equipment is determined based on the maximum membership in the comprehensive evaluation result vector. Test results are generated for the secondary equipment based on the performance level. A higher performance level indicates better performance of the protection device. The test results can provide detailed information on the performance of the protection device on different indicators, providing a basis for equipment maintenance, fault diagnosis, and optimization adjustments.

[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 a comprehensive evaluation result vector. The output of the hidden layer is linearly transformed using a rectified linear unit (ReLU) activation function with a buffer parameter 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, a loss value consisting of a cross-entropy term and a regularization term is calculated. The learning rate decay coefficient is determined based on the rate of decrease of the loss value over multiple consecutive iterations. The cross-entropy loss measures the difference between the test results predicted by the evaluation model and the true labels. The regularization term is used to control model complexity and prevent overfitting. L2 regularization can be used, which is half the sum of the squares of the connection weights. The regularization coefficient λ is adjusted to balance these two terms. During training, the learning rate needs to be adjusted dynamically. The rate of decrease of the loss function value over multiple consecutive iterations can be calculated. When the rate of decrease falls below a certain threshold, the learning rate is multiplied by a decay coefficient (such as 0.9). For example, if the average rate of decrease over five consecutive iterations is less than 1%, the current learning rate is multiplied by 0.9. This allows for rapid convergence by maintaining a higher learning rate in the early stages of training, while using a smaller learning rate for fine-tuning in the later stages.

[0089] The evaluation model is trained using mini-batch stochastic gradient descent, with the learning rate dynamically adjusted for each iteration based on a learning rate decay factor. The evaluation model is trained using mini-batch stochastic gradient descent, randomly dividing all training samples into multiple mini-batches, and updating the parameters using one mini-batch per iteration. The mini-batch size can be set to 32 or 64, for example. For each mini-batch, forward propagation calculates the loss, backward propagation calculates the gradient, and then the evaluation model parameters 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 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 dataset is randomly divided into K subsets (such as K=5). One of them is selected as the validation set each time, and 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. The average value of this is repeated K times and used as the final performance indicator.

[0091] We can use weight sensitivity analysis to calculate the sensitivity of each connection weight in the evaluation model to the loss function to obtain a weight importance index (i.e., calculate the absolute value of the partial derivative of the loss function with respect to 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. A threshold is set (e.g., 10% of the average of the absolute values ​​of all partial derivatives), and connections with weights below the threshold are removed to obtain a trained evaluation model.

[0092] In an embodiment of the present invention, upon receiving configuration information for setting an operating scenario for a power system, a simulation test model is constructed based on the configuration information. Original test data for the power system under normal operating conditions is generated based on the simulation test model. Component data for the power system under fault conditions is calculated based on the configuration information. The component data and the original test data are combined to obtain target test data containing fault characteristics. Secondary equipment in the power system is tested based on the target test data to generate digital test messages that comply with the communication protocol configured for the secondary equipment. Based on the digital test messages and the configuration information, protection action signals of protection devices in the secondary equipment are collected within a preset time threshold. Test results are generated for the secondary equipment based on the protection action signals. This effectively simulates the actual performance of the power system under different operating conditions, enables comprehensive testing of secondary equipment in the power system, and can quickly verify and validate the compatibility of communication protocols and the responsiveness of protection devices, thereby ensuring the safety and stability of the entire power system and improving the accuracy and reliability of secondary equipment testing.

[0093] Example 2

[0094] Figure 2 A schematic diagram of a test device for secondary equipment according to the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the device includes:

[0095] The original test data generating module 201 is configured to, upon receiving configuration information for setting an operation scenario of the power system, construct a simulation test model according to the configuration information, and generate original test data of the power system under normal working conditions based on the simulation test model;

[0096] a target test data acquisition module 202 for calculating component data of the power system under a fault state according to the configuration information, and combining the component data with the original test data to obtain target test data containing fault characteristics;

[0097] A digital test message generating module 203 is configured 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 is configured to acquire protection action signals of protection devices in the secondary equipment within a preset time threshold according to the digital test message and the configuration information;

[0099] The test result generating module 205 is configured 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, configured to establish a test condition including a startup characteristic and a return characteristic according to the test type parameters;

[0102] a simulation test construction module, configured to construct a simulation test model according to the protection functional area of ​​the power system and the test operating condition, and divide the simulation test model into a frequency-varying parameter line sub-model, a nonlinear transformer sub-model, and a composite load sub-model; the frequency-varying parameter line sub-model includes a skin effect compensation term and a distributed parameter characteristic term, the nonlinear transformer sub-model includes a flux current characteristic term, 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, configured to 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 a transient voltage vector under normal working conditions of the power system by solving the node admittance matrix;

[0104] a steady-state voltage and current vector acquisition module, configured to decompose a three-phase system into a positive-sequence component, a negative-sequence component, and a zero-sequence component under normal operating conditions 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, configured to calculate a difference between the transient voltage vector and the steady-state voltage vector as an initial voltage iteration vector, use the steady-state current vector as an initial current iteration vector, perform iterative calculations on the initial voltage iteration vector and the initial current iteration vector, introduce fundamental voltage components and harmonic voltage components during the iterative calculation process, and obtain converged voltage 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 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.

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

[0108] a voltage and current representation module, configured to represent 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 to represent 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 iterative value acquisition module, configured to establish an iterative calculation equation based on the initial voltage iterative vector and the initial current iterative vector, wherein the iterative calculation equation superimposes each of the harmonic voltage components with the initial voltage iterative vector to obtain a voltage iterative value, and superimposes each of the harmonic current components with the initial current iterative vector to obtain a current iterative value;

[0110] an iteration judgment module, configured to 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, and calculate 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, configured to introduce a relaxation factor to perform weighted adjustment on the voltage iteration value and the current iteration value, reduce the relaxation factor when the iterative convergence criterion is greater than the previous iterative convergence criterion, and increase the relaxation factor when the iterative convergence criterion is less than the previous iterative convergence criterion;

[0112] The iterative convergence module is used to determine that the iterative calculation is converged 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, configured to establish a transient response calculation model for the power system under a fault state based on the fault type parameter and the fault impedance parameter, calculate the fault initial voltage amplitude based on the phase voltage at the moment of fault occurrence under the power system fault state, calculate the fault loop attenuation coefficient and the damping angular frequency under the power system fault state based on the fault impedance parameter, and when the fault type parameter is a single-phase grounding fault, calculate the single-phase grounding fault voltage under the power system fault state using an exponentially decayed cosine function containing the fault loop attenuation coefficient and the damping angular frequency; and when the fault type parameter is an inter-phase fault, calculate the inter-phase coupling coefficient using an 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 power system fault state, thereby obtaining transient component data under the power system fault state;

[0115] a steady-state component data acquisition module, configured to establish a steady-state response calculation model for the power system under a fault state based on the fault start time parameter and the fault duration parameter, calculate a steady-state fault voltage amplitude under the power system fault state based on the fault type parameter and the fault impedance parameter, calculate an impedance angle and a phase difference based on the fault impedance parameter, calculate a steady-state fault voltage under the power system fault state using a finite-time sinusoidal function including the fault start time parameter and the fault duration parameter, and calculate a steady-state fault current under the power system fault state using a finite-time sinusoidal function including the impedance angle and the phase difference, thereby obtaining steady-state component data under the power system fault state;

[0116] a target test data generation module, configured 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 complies with 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, configured 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 equipment from the noise-reduced data, wherein the current signal corresponding to the protection action time is the action current signal;

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

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

[0124] a return coefficient calculation module, configured to obtain the current signal amplitude corresponding to the protection return moment, and calculate a third evaluation parameter based on the current signal amplitude and the action current amplitude, wherein the third evaluation parameter is the return coefficient;

[0125] A model loading module is 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, configured to use the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter as evaluation factors to construct a performance level of the protection device in the secondary device in the power system, calculate the membership of the evaluation factors to each of the performance levels 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, wherein 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 is used to build an evaluation model comprising 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 normalization module, configured to use a 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 input features of the evaluation model, perform maximum and minimum normalization processing on the input features to obtain standardized features, and input the standardized features into the input layer;

[0131] a hidden layer transformation module, configured to perform a fuzzy synthesis calculation on the membership matrix in the hidden layer to obtain a comprehensive evaluation result vector, and to perform a linear transformation on the output of the hidden layer 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;

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

[0133] A model updating module is used to train the evaluation model using a mini-batch stochastic gradient descent method and dynamically adjust the learning rate of each iteration based on the learning rate decay coefficient;

[0134] A model validation module is used to apply the trained evaluation model to a validation sample set using a cross-validation method, and calculate a consistency index between the test results 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 corresponding functional modules and beneficial effects for executing the testing method for secondary equipment.

[0137] Example 3

[0138] See also Figure 3 , shows 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, workstations, 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 claimed herein.

[0139] like Figure 3 As shown, the computer device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the 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 into the random access memory (RAM) 13. Various programs and data required for the operation of the computer device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Various 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 magnetic 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 via a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors that run 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 secondary device testing method.

[0142] In some embodiments, the test method for the secondary device can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can 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 can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the test method for the secondary device by any other appropriate means (for example, by means of firmware).

[0143] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, 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 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 can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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 clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0149] Example 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] The computer program product may be implemented by writing computer program code for performing the operations of the present invention in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0152] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0153] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection 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 conditions based on the simulation test model; Calculating component data of the power system under a fault state according to the configuration information, and 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; collecting protection action signals of protection devices in the secondary equipment within a preset time threshold according to the digital test message and the configuration information; 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 conditions 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 functional area of ​​the power system and the test operating 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 term and a distributed parameter characteristic term, the nonlinear transformer sub-model includes a flux current characteristic term, 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 under a normal working state of the power system by solving the node admittance matrix; Decomposing a three-phase system into a positive-sequence component, a negative-sequence component, and a zero-sequence component under a normal operating state 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 a difference between the transient voltage vector and the steady-state voltage vector as an initial voltage iteration vector, using the steady-state current vector as an initial current iteration vector, performing iterative calculations on the initial voltage iteration vector and the initial current iteration vector, introducing fundamental components and harmonic components during the iterative calculation process, and obtaining converged voltage data and current data; Adaptively sampling the converged voltage data and current data according to the sampling period parameters to generate 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; The iterative calculation of the initial voltage iteration vector and the initial current iteration vector, introducing the fundamental component and the harmonic component in the iterative calculation process to obtain 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 iterative voltage value and the iterative current 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 iterative convergence criterion is greater than the previous iterative convergence criterion, and increasing the relaxation factor when the iterative convergence criterion is less than the previous iterative convergence criterion; 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, it is determined that the iterative calculation is converged, and finally converged voltage data and current data are obtained.

3. 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 power system fault state 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: establishing a transient response calculation model for the power system under a fault state according to the fault type parameter and the fault impedance parameter, calculating a fault initial voltage amplitude based on the phase voltage at the moment of fault occurrence under the power system fault state, calculating a fault loop attenuation coefficient and a damping angular frequency under the power system fault state according to the fault impedance parameter, when the fault type parameter is a single-phase grounding fault, using an exponentially decaying cosine function including the fault loop attenuation coefficient and the damping angular frequency to calculate the single-phase grounding fault voltage under the power system fault state, when the fault type parameter is an inter-phase fault, calculating an inter-phase coupling coefficient, and using an exponentially decaying cosine function of a two-phase voltage difference including the inter-phase coupling coefficient to calculate the phase-to-phase fault voltage under the power system fault state, thereby obtaining 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, and 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, to obtain 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 is generated.

4. The method according to claim 3, 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. The voltage channel data, current channel data and phase identifier 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. The data format of the digital test message complies with the sampling value message format in the communication protocol configured by the secondary device.

5. The method according to any one of claims 1 to 4, 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 sampled data, performing wavelet transform noise reduction processing on the sampled data to obtain noise-reduced data, and 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, wherein the current signal corresponding to the protection action time is the action current signal; Calculating the time difference between the protection action moment and the fault input moment to obtain a measured action time, determining a theoretical action time according to a preset protection setting time, and comparing the measured action time with the theoretical action time to obtain a first evaluation parameter, the first evaluation parameter being a time error rate; Converting the operating current signal into a complex form to obtain a complex current signal, extracting operating current amplitude and phase angle information from the complex current signal, and calculating a second evaluation parameter based on the operating current amplitude and a preset protection current constant, where 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; Using the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter as evaluation factors, constructing a performance level of the protection device in the secondary device in the power system, and calculating the membership of the evaluation factors to each of the performance levels 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.

6. The method according to claim 5, characterized in that Generating a test result for the secondary device according to the protection action signal further includes: Constructing an evaluation model comprising a three-layer neural network structure, wherein the three-layer neural network structure includes an input layer, a hidden layer, and an output layer; Calculating the membership of each performance level using the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter to obtain a membership matrix as input features of the evaluation model, performing maximum and minimum normalization processing on the input features to obtain standardized features, and inputting the standardized features into the input layer; In the hidden layer, a fuzzy synthesis calculation is performed on the membership matrix to obtain a comprehensive evaluation result vector, and an activation function with a buffer parameter is used to linearly transform 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; Calculating a loss value including a cross entropy term and a regularization term based on the test result of the secondary device, and determining 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; Applying the trained evaluation model to a validation sample set using a cross-validation method, and calculating a consistency index between the test results of the secondary device output by the evaluation model and the expert evaluation labels; 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.

7. 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 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for testing a secondary device according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes 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 6 is implemented.

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