Power grid fault measurement system and method based on water electrolysis hydrogen production

Abstract

The invention relates to the technical field of power grid fault detection, in particular to a power grid fault measurement system and method based on water electrolysis hydrogen production. The power input module is used for receiving power signals provided by an external power grid, is equipped with voltage and current sensors and is used for monitoring voltage, current and frequency of a power system in real time; the fault detection module specifically comprises a harmonic analysis algorithm, a harmonic filtering unit and an asymmetric fault monitoring algorithm; the hydrogen production unit is connected with the power input module and is used for carrying out electrolytic reaction on water according to the received treated power so as to generate hydrogen; and the control center receives an output result of the asymmetric fault monitoring algorithm in the fault detection module and adjusts the working state of the hydrogen production unit so as to cope with the influence caused by the power grid fault. According to the invention, the power supply utilization efficiency and the reaction stability in the water electrolysis hydrogen production process are improved, so that the hydrogen yield is increased, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid fault detection, and in particular to a power grid fault measurement system and method based on water electrolysis to produce hydrogen. Background Art

[0002] Water electrolysis hydrogen production technology is a method of decomposing water into hydrogen and oxygen. It is widely used in the energy industry, especially in the fields of renewable energy and green energy. As a clean energy carrier, hydrogen is of great significance for achieving a low-carbon transformation of the energy structure. However, the efficiency and stability of water electrolysis hydrogen production greatly depend on the quality of the power supply system, especially the stable supply of electricity.

[0003] In the prior art, power fluctuations and faults in the power grid, such as voltage instability, frequency fluctuations, harmonic interference, and asymmetric loads, often affect the operating efficiency and life of electrolysis equipment. In particular, when the power grid is unstable or fails, the electrolysis equipment may not be able to effectively adjust the operating parameters, resulting in a decrease in hydrogen production or even equipment damage.

[0004] Current grid fault detection technologies are usually limited to basic monitoring, such as simple measurements of voltage and current, and cannot fully reflect the specific impact of the grid status on the hydrogen production process by water electrolysis. In addition, existing fault response measures are often slow to respond and lack targeted automatic adjustment strategies, making it difficult for hydrogen production facilities to maintain optimal operating conditions when facing grid fluctuations and faults.

[0005] Therefore, there is an urgent need to develop a system that can monitor and respond to changes in grid quality in real time to ensure efficient and stable operation of the water electrolysis hydrogen production process. Summary of the invention

[0006] Based on the above objectives, the present invention provides a power grid fault measurement system and method based on water electrolysis to produce hydrogen.

[0007] A power grid fault measurement system based on water electrolysis hydrogen production includes a power input module, a fault detection module, a hydrogen production unit, a control center and a communication module, wherein: The power input module is used to receive power signals provided by the external power grid and is equipped with voltage and current sensors to monitor the voltage, current and frequency of the power system in real time; The fault detection module specifically includes: Harmonic analysis algorithm: Based on discrete Fourier transform (DFT), it extracts and analyzes harmonic components from power signals, identifies and quantifies harmonics in real time, and promptly detects harmonic levels that exceed the standard; Harmonic filtering unit: Adaptive filtering technology is used to dynamically adjust filtering parameters according to the output of the harmonic analysis algorithm to reduce harmonic interference and protect the hydrogen production unit from harmonic influence; Asymmetric fault monitoring algorithm: uses vector combination and phase angle analysis methods to detect and evaluate phase imbalance in the power grid and quickly identify single-phase grounding and two-phase short circuit asymmetric faults; The hydrogen production unit is connected to the power input module and performs water electrolysis reaction according to the processed power received to produce hydrogen. The unit is equipped with inverter technology to adapt to the power quality received from the power input module; The control center receives the output result of the asymmetric fault monitoring algorithm in the fault detection module and adjusts the working state of the hydrogen production unit to cope with the impact of the power grid fault; The communication module is used to feed back the decision of the control center and the status information of the hydrogen production unit, as well as the grid fault and its processing status to the grid operator or maintenance personnel.

[0008] Preferably, the power input module further comprises a power interface, which is designed to be connected to an external power grid and receive an AC power signal provided by the external power grid through a circuit configuration including a switch and a protection device.

[0009] Preferably, the discrete Fourier transform DFT extracting and analyzing harmonic components from the power signal specifically includes: For a signal sequence consisting of N sample points , whose DFT is defined as: ,in, is the kth point in the frequency domain, representing the complex form of the frequency component, j is the imaginary unit, N is the total number of samples, k is the frequency index, ; In harmonic analysis, DFT is used to identify and quantify the harmonic components in power grid signals: Signal sampling: The power signal of the power grid By presetting the sampling frequency f s Convert to discrete signal ; Apply DFT: to the sampled signal Perform DFT calculation to obtain the amplitude and phase of each frequency component; Harmonic Identification: By Analyzing As a result, the fundamental wave is identified; Quantizing Harmonics: Calculating the amplitude of each harmonic frequency component , and compared with the threshold value of the harmonic.

[0010] Preferably, the adaptive filtering technology specifically includes: Calculate the amplitude of each harmonic frequency component , using adaptive infinite impulse response filters to compensate and suppress harmonic components by dynamically adjusting filter parameters; A minimum mean square error algorithm is implemented to optimize the adaptive adjustment of the filter parameters and dynamically update the filter coefficients by minimizing the error between the filtered signal and the ideal signal.

[0011] Preferably, the output of the adaptive infinite impulse response filter depends not only on the input signal but also on the past output, and the expression is: ,in, is the input signal, is the output signal, b i is the forward filter coefficient, a j is the feedback filter coefficient, M and Z are the number of forward and feedback filter coefficients, respectively.

[0012] Preferably, the asymmetric fault monitoring algorithm in the fault detection module adopts vector combination and phase angle analysis methods to detect and evaluate the phase imbalance in the power grid, calculates the vector and phase angle of the three-phase voltage or current signal received from the power input module, and calculates and detects whether there is an imbalance between the phases by comparing the vector length and angle of each phase voltage or current, and determines the fault type based on the deviation of the vector angle.

[0013] Preferably, the asymmetric fault monitoring algorithm calculation includes: Get three-phase current I from the power input module A ,I B and I C Real-time data; Calculate the phase vector: For each sampling point, calculate the instantaneous value of the three-phase current and the phase vector V of the three-phase current A ,V B and V C : ;in is the phase factor used to convert the real current value into a phase vector on the complex plane; Phase imbalance detection: Calculate the balance of the three-phase current vectors using the modulus of the vector sum: ; Fault type identification: Use the angle and modulus of the vector sum to identify the specific asymmetric fault type.

[0014] Preferably, the control center further comprises a machine learning model, which receives asymmetric fault input data from the fault detection module, including phase imbalance, actual phase angle difference and voltage deviation, and uses the input data to predict and calculate the optimal voltage and current output settings of the hydrogen production unit, and the recommended rate of hydrogen production; The machine learning model uses a decision tree to dynamically adjust the output parameters of the inverter according to the severity and type of asymmetric faults, and optimize the operating efficiency of the hydrogen production unit in real time. It also adjusts the electrolysis current density and electrolysis time during the hydrogen generation process to adapt to changes in grid conditions.

[0015] Preferably, the decision tree specifically includes: The input features of the decision tree model are defined as follows: Phase imbalance: It indicates the imbalance degree of voltage or current between three-phase electricity; Actual phase angle difference: The phase angle difference between the three phases, used to identify the type and severity of asymmetry; Voltage deviation: the deviation between each phase voltage and its ideal value, which is an absolute value or a relative value; Decision tree model training: A decision tree model is trained using historical data, which includes different types of asymmetric faults and their impact on the performance of the hydrogen production unit. During the training process, the model learns how to predict the optimal hydrogen production unit parameter adjustment based on the input asymmetric fault characteristics, including inverter output voltage, inverter output current, hydrogen production rate, electrolysis current density, and electrolysis time; Real-time prediction and adjustment: When the system receives data from the fault detection module in real time, the decision tree model instantly calculates and outputs corresponding adjustment suggestions, using the input phase imbalance, actual phase angle difference and voltage deviation to predict and recommend the optimal output voltage and current settings of the inverter, while providing adjustment suggestions on hydrogen production rate, electrolysis current density and electrolysis time; According to the output of the decision tree model, the control system dynamically adjusts the inverter settings: adjusts the inverter voltage output ΔV and current output ΔI to ensure that the hydrogen production and quality are maximized under the current grid state, adjusts the hydrogen production rate and electrolysis current density, and adjusts the electrolysis time to adapt to the impact of asymmetric load on the electrolysis process; The decision tree construction process is as follows: Select the best split features and split points: Entropy or Gini impurity is the criterion for determining how to select split features and split points. For each node, the decision tree algorithm traverses all features and their split points to calculate the information gain or the reduction of Gini impurity. The entropy calculation formula is: , where p i is the proportion of target values ​​of the i-th category in the set S; Information gain calculation: , where A is the feature considered and S v is a subset of feature A under value v, is the entropy of the subset, and the Gini impurity calculation formula is: ; Recursively build the tree: For each node, use the above formula to select the best split feature and point to divide the data set into two subsets; Recursively apply the same method to each subset until the stopping condition is met; To prevent overfitting, the tree needs to be pruned using cost complexity pruning, in which a parameter is used to balance the depth of the tree and the training error.

[0016] A method for measuring a power grid fault based on hydrogen production by electrolysis of water is implemented by the above-mentioned power grid fault measurement system based on hydrogen production by electrolysis of water, and comprises the following steps: S1: Power reception and monitoring, connected to the external power grid, receiving AC power signals through the power interface, using voltage and current sensors to monitor and record the voltage, current and frequency data of the power system in real time; S2: Fault data processing and analysis: Use harmonic analysis algorithm to process received power signals, digitize continuous power signals, calculate their spectrum data using discrete Fourier transform, identify and analyze harmonic components in power signals, calculate the amplitude and phase of each harmonic frequency component, and compare them with preset harmonic thresholds to monitor and quantify harmonic levels that exceed the standards in real time; S3: Harmonic filtering and compensation. If the harmonic exceeds the safety threshold, the adaptive infinite impulse response filter in the harmonic filtering unit is used to dynamically adjust the filtering parameters according to the output of the harmonic analysis algorithm to compensate and suppress the harmonic components. S4: Asymmetric fault monitoring and response, applying the asymmetric fault monitoring algorithm, by calculating the vector and phase angle of the three-phase current or voltage, to detect and evaluate the phase imbalance in the power grid, and identify whether there is an asymmetric fault based on the comparison of the vector length and angle; S5: Control and adjustment. According to the output result of S4, the working state of the hydrogen production unit is adjusted to cope with the impact of power grid failure.

[0017] Beneficial effects of the present invention: The present invention integrates advanced fault detection modules and adaptive control algorithms to monitor the power quality of the power grid in real time and quickly respond to detected power grid faults (such as asymmetric faults and harmonic interference). The control center predicts and adjusts the output parameters of the inverter through a decision tree algorithm to ensure that the hydrogen production unit can maintain optimal operating conditions when a power grid fault occurs. This dynamic adjustment mechanism greatly improves the power utilization efficiency and reaction stability in the process of hydrogen production by electrolysis of water, thereby increasing the yield of hydrogen and reducing energy consumption.

[0018] By implementing fault detection and response, the present invention enables the water electrolysis hydrogen production facility to detect and respond to power grid faults in the early stages, significantly improving the reliability of the system. In particular, when dealing with asymmetric loads and harmonic problems in the power grid, it can avoid equipment damage or operating failures due to unstable power supply, thereby ensuring the continuity and safety of the entire hydrogen production process. In addition, adaptive adjustment of the inverter output also helps optimize the working state of the electrolyzer, avoiding safety risks caused by abnormal voltage or current.

[0019] The intuitive decision path and prediction results provided by the decision tree algorithm of the present invention not only enable operators to understand and track the adjustment logic of the system, but also provide a scientific basis for daily maintenance, reduce operating risks, and improve maintenance efficiency. In addition, the intelligent design of the system helps to identify potential failure risks in advance and provide support for the formulation of preventive maintenance strategies, thereby reducing the incidence of unexpected downtime and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a schematic diagram of functional modules of a measurement system according to an embodiment of the present invention; Figure 2 The figure is a flow chart of a measuring method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below in conjunction with specific embodiments.

[0023] like Figure 1 As shown, a power grid fault measurement system based on water electrolysis hydrogen production includes a power input module, a fault detection module, a hydrogen production unit, a control center and a communication module, wherein: The power input module is used to receive power signals provided by the external power grid and is equipped with voltage and current sensors to monitor the voltage, current and frequency of the power system in real time; The fault detection module specifically includes: Harmonic analysis algorithm: Based on discrete Fourier transform (DFT), it extracts and analyzes harmonic components from power signals, identifies and quantifies harmonics in real time, and promptly detects harmonic levels that exceed standards; Harmonic filtering unit: Adaptive filtering technology is used to dynamically adjust filtering parameters according to the output of the harmonic analysis algorithm to reduce harmonic interference and protect the hydrogen production unit from harmonic influence; Asymmetric fault monitoring algorithm: uses vector combination and phase angle analysis methods to detect and evaluate phase imbalance in the power grid and quickly identify single-phase grounding and two-phase short circuit asymmetric faults; The hydrogen production unit is connected to the power input module and performs water electrolysis reaction according to the processed power received to produce hydrogen. The unit is equipped with inverter technology to adapt to the power quality received from the power input module; The hydrogen production unit includes an electrolyzer and an inverter, which is connected to the power input module. The power input module receives and processes the power signal provided by the external power grid, and transmits the optimized power to the hydrogen production unit after harmonic filtering and asymmetric fault processing. The inverter technology in the hydrogen production unit is used to convert the received AC power into DC power suitable for water electrolysis, ensuring a stable supply of current and voltage. The output parameters (voltage and current) of the inverter are dynamically adjusted according to the power quality data received from the fault detection module to adapt to changes in power quality and maximize hydrogen yield and energy efficiency. The electrolyzer uses the adjusted DC power to perform water electrolysis reaction to produce hydrogen.

[0024] The control center receives the output result of the asymmetric fault monitoring algorithm in the fault detection module and adjusts the working state of the hydrogen production unit to cope with the impact of the power grid fault; The communication module is used to feed back the decision of the control center and the status information of the hydrogen production unit, as well as the grid fault and its processing status to the grid operator or maintenance personnel.

[0025] The power input module also includes a power interface, which is designed to be connected to an external power grid and receive an AC power signal provided by the external power grid through a circuit configuration including a switch and a protection device.

[0026] The harmonic analysis algorithm in the fault detection module processes the power signal received by the power input module by using discrete Fourier transform (DFT) to extract and analyze the harmonic components in the power signal. The continuous power signal is first digitized, and then DFT is applied to calculate its spectrum to separate and identify harmonics of different frequencies. The amplitude and phase of each harmonic are further calculated and compared with the preset harmonic threshold to monitor and quantify the harmonic level that exceeds the standard in real time.

[0027] Discrete Fourier Transform DFT extracts and analyzes harmonic components from power signals, including: For a signal sequence consisting of N sample points , whose DFT is defined as: ,in, is the kth point in the frequency domain, representing the complex form of the frequency component, j is the imaginary unit, N is the total number of samples, k is the frequency index, ; In harmonic analysis, DFT is used to identify and quantify the harmonic components in power grid signals: Signal sampling: The power signal of the power grid By presetting the sampling frequency f s Convert to discrete signal ; Apply DFT: to the sampled signal Perform DFT calculation to obtain the amplitude and phase of each frequency component; Harmonic Identification: By Analyzing As a result, identify the fundamental wave (50Hz or 60Hz); Quantizing Harmonics: Calculating the amplitude of each harmonic frequency component , and compared with the threshold value of the harmonic.

[0028] The judgment of harmonic exceeding the standard is based on The algorithm compares the harmonics with the set safety threshold. When the amplitude of one or more harmonics exceeds the threshold, the algorithm reports this information to the system control center, triggering necessary response measures, such as adjusting the load, starting the harmonic filtering device, etc.

[0029] In the water electrolysis hydrogen production system, the analysis of harmonic components needs to focus on the impact of low-order harmonics (such as the third, fifth, and seventh harmonics), which have a more direct impact on the efficiency and life of the electrolyzer. Compared with high-order harmonics, low-order harmonics are more likely to produce thermal effects and electrochemical instabilities in the electrolysis reaction. Therefore, the harmonic analysis algorithm should be specially designed to identify and quantify these key harmonic components, and take corresponding measures to reduce their impact. The k value corresponds to an integer multiple of the fundamental frequency of 50Hz, such as k=3,5,7, etc. These are the harmonics that most affect the system performance in the hydrogen production process.

[0030] Adaptive filtering technology specifically includes: Calculate the amplitude of each harmonic frequency component , using adaptive infinite impulse response filters to compensate and suppress harmonic components by dynamically adjusting filter parameters; A minimum mean square error algorithm is implemented to optimize the adaptive adjustment of the filter parameters and dynamically update the filter coefficients by minimizing the error between the filtered signal and the ideal signal.

[0031] The output of the adaptive infinite impulse response filter depends not only on the input signal, but also on the past output, as expressed by: ,in, is the input signal, is the output signal, b i is the forward filter coefficient, a jis the feedback filter coefficient, M and Z are the number of forward and feedback filter coefficients, respectively.

[0032] The minimum mean square error algorithm is used to dynamically update the filter coefficients to minimize the mean square value of the error signal. The steps are as follows: Initialization parameters: select step size μ (learning rate), initialize filter coefficient b i and a j ; Iterative process: For each sample n, do the following: 1. Calculate output: Calculate output using current filter coefficients .

[0033] 2. Calculation error: Error The expected signal (i.e. ideal signal output) and actual output The difference: .

[0034] 3. Update filter coefficients: Update the forward coefficient b i : ; Update feedback coefficient a j : ; where μ needs to be carefully selected to ensure the stability and convergence of the system.

[0035] The filter parameters are updated in real time based on the data obtained from the harmonic analysis to ensure the optimization of the power quality during the electrolysis of water to produce hydrogen. The system will continuously monitor the quality of the output signal and adjust the filter coefficients based on the difference between the actual output and the expected output to achieve the best harmonic suppression effect.

[0036] The asymmetric fault monitoring algorithm in the fault detection module uses vector combination and phase angle analysis methods to detect and evaluate phase imbalance in the power grid. First, the vector and phase angle of the three-phase voltage or current signal received from the power input module are calculated. By comparing the vector length and angle of each phase voltage or current, it is calculated to detect whether there is an imbalance between the phases, such as a single-phase voltage is significantly lower than the other two phases or the two-phase voltages are close and the third phase deviates from these values. In addition, the fault type is determined based on the deviation of the vector angle. For example, a single-phase grounding fault is usually manifested as a large difference in the vector angle between one phase and the other two phases, while a two-phase short circuit fault is manifested as the vectors of the two phases are almost coincident and the third phase is deviated. The algorithm can process signals in real time to ensure rapid identification and accurate evaluation of power grid faults.

[0037] The asymmetric fault monitoring algorithm calculation includes: Get three-phase current I from the power input module A ,IB and I C For real-time data, the sampling frequency should be high enough to ensure the time resolution and accuracy of the signal; Calculate the phase vector: For each sampling point, calculate the instantaneous value of the three-phase current and the phase vector V of the three-phase current A ,V B and V C : ;in is the phase factor used to convert the real current value into a phase vector on the complex plane; Phase imbalance detection: Calculate the balance of the three-phase current vectors using the modulus of the vector sum: , for a fully balanced three-phase system, Should be close to zero; Fault type identification: Use the angle and modulus of the vector sum to identify specific asymmetric fault types, including: Single-phase ground fault: If the vector amplitude of one phase is much smaller than that of the other two phases, and Significantly different from zero; Two-phase short circuit fault: If the vector directions of two phases are close or coincident, and the vector of the third phase makes a large angle with the vector sum of the two phases.

[0038] The control center also includes a machine learning model based on which the machine learning model receives asymmetric fault input data from the fault detection module, including phase imbalance, actual phase angle difference, and voltage deviation, and uses the input data to predict and calculate optimal voltage and current output settings for the hydrogen production unit, as well as a recommended rate of hydrogen production; The machine learning model uses a decision tree to dynamically adjust the output parameters of the inverter according to the severity and type of asymmetric faults, and optimize the operating efficiency of the hydrogen production unit in real time. It also adjusts the electrolysis current density and electrolysis time during the hydrogen generation process to adapt to changes in grid conditions and ensure the continuous production and stable quality of hydrogen. This algorithm enables the hydrogen production unit to not only respond to the current grid status but also predict future changes, thereby achieving higher operational flexibility and overall robustness of the system.

[0039] The decision tree specifically includes: The input features of the decision tree model are defined as follows: Phase imbalance: It indicates the imbalance degree of voltage or current between three-phase electricity; Actual phase angle difference: The phase angle difference between the three phases, used to identify the type and severity of asymmetry; Voltage deviation: the deviation between each phase voltage and its ideal value, which is an absolute value or a relative value; Decision tree model training: A decision tree model is trained using historical data, which includes different types of asymmetric faults and their impact on the performance of the hydrogen production unit. During the training process, the model learns how to predict the optimal hydrogen production unit parameter adjustment based on the input asymmetric fault characteristics, including inverter output voltage, inverter output current, hydrogen production rate, electrolysis current density, and electrolysis time; Real-time prediction and adjustment: When the system receives data from the fault detection module in real time, the decision tree model instantly calculates and outputs corresponding adjustment suggestions, using the input phase imbalance, actual phase angle difference and voltage deviation to predict and recommend the optimal output voltage and current settings of the inverter, while providing adjustment suggestions on hydrogen production rate, electrolysis current density and electrolysis time; Based on the output of the decision tree model, the control system dynamically adjusts the inverter settings: adjusts the inverter's voltage output ΔV and current output ΔI to ensure that the hydrogen production and quality are maximized under the current grid status, adjusts the hydrogen production rate and electrolysis current density, and adjusts the electrolysis time to adapt to the impact of asymmetric load on the electrolysis process.

[0040] The decision tree construction process is as follows: Select the best split features and split points: Entropy or Gini impurity is the criterion for determining how to select split features and split points. For each node, the decision tree algorithm traverses all features and their split points to calculate the information gain or the reduction of Gini impurity. The entropy calculation formula is: , where p i is the proportion of target values ​​of the i-th category in the set S; Information gain calculation: , where A is the feature considered and S v is a subset of feature A under value v, is the entropy of the subset, and the Gini impurity calculation formula is: ; Recursively build the tree: For each node, use the above formula to select the best split feature and point to divide the data set into two subsets; Recursively apply the same method to each subset until the stopping condition is met (such as the number of samples under the node is less than a certain threshold, or the preset tree depth is reached).

[0041] To prevent overfitting, the tree needs to be pruned using cost complexity pruning, in which a parameter is used to balance the depth of the tree and the training error.

[0042] The decision tree is trained as follows: The decision tree model needs to learn from historical data and prepare a set of historical fault data and the corresponding optimal hydrogen production unit response parameters. Each data point includes: Input characteristics: x1: phase imbalance; x2: actual phase angle difference; x3: voltage deviation; Output response: y1: inverter output voltage adjustment (ΔV); y2: inverter output current adjustment (ΔI); y3: hydrogen production rate; y4: electrolysis current density; y5: electrolysis time; Decision tree splitting: Decision trees minimize the impurity of the target variable within a node by selecting the best splitting feature and splitting point at each node. For example, the decision tree selects x1 (phase imbalance) as the splitting feature at the first node and divides the data into two parts according to the size of the imbalance. Each branch continues to split until the stopping condition is met (such as the maximum depth of the tree or the minimum number of samples in the node). Decision rules and output prediction: Once the decision tree is built, for new input data, the tree will classify the data along the path from the root to the leaves until it reaches a leaf node. Each leaf node stores the average of the best response output corresponding to the historical data of the input features based on the path. If the new input data falls on a specific leaf node, the node recommends the following response: ; ; Hydrogen production rate increased by 5%; electrolysis current density adjusted by 20%; electrolysis time extended by 10%.

[0043] like Figure 2 As shown, a method for measuring a power grid fault based on hydrogen production by electrolysis of water is implemented by the above-mentioned power grid fault measurement system based on hydrogen production by electrolysis of water, and includes the following steps: S1: Power reception and monitoring, connected to the external power grid, receiving AC power signals through the power interface, using voltage and current sensors to monitor and record the voltage, current and frequency data of the power system in real time; S2: Fault data processing and analysis: Use harmonic analysis algorithm to process received power signals, digitize continuous power signals, calculate their spectrum data using discrete Fourier transform, identify and analyze harmonic components in power signals, calculate the amplitude and phase of each harmonic frequency component, and compare them with preset harmonic thresholds to monitor and quantify harmonic levels that exceed the standards in real time; S3: Harmonic filtering and compensation. If the harmonic exceeds the safety threshold, the adaptive infinite impulse response filter in the harmonic filtering unit is used to dynamically adjust the filtering parameters according to the output of the harmonic analysis algorithm to compensate and suppress the harmonic components. S4: Asymmetric fault monitoring and response, applying the asymmetric fault monitoring algorithm, by calculating the vector and phase angle of the three-phase current or voltage, to detect and evaluate the phase imbalance in the power grid, and identify whether there is an asymmetric fault based on the comparison of the vector length and angle; S5: Control and adjustment. According to the output result of S4, the working state of the hydrogen production unit is adjusted to cope with the impact of power grid failure.

Claims

1. A power grid fault measurement system based on water electrolysis to produce hydrogen, characterized in that: It includes power input module, fault detection module, hydrogen production unit, control center and communication module, among which: The power input module is used to receive power signals provided by the external power grid and is equipped with voltage and current sensors to monitor the voltage, current and frequency of the power system in real time; The fault detection module specifically includes: Harmonic analysis algorithm: Based on discrete Fourier transform (DFT), it extracts and analyzes harmonic components from power signals, identifies and quantifies harmonics in real time, and promptly detects harmonic levels that exceed the standard; Harmonic filtering unit: Adaptive filtering technology is used to dynamically adjust filtering parameters according to the output of the harmonic analysis algorithm to reduce harmonic interference and protect the hydrogen production unit from harmonic influence; Asymmetric fault monitoring algorithm: uses vector combination and phase angle analysis methods to detect and evaluate phase imbalance in the power grid and quickly identify single-phase grounding and two-phase short circuit asymmetric faults; The hydrogen production unit is connected to the power input module and performs water electrolysis reaction according to the processed power received to produce hydrogen. The unit is equipped with inverter technology to adapt to the power quality received from the power input module; The control center receives the output result of the asymmetric fault monitoring algorithm in the fault detection module and adjusts the working state of the hydrogen production unit to cope with the impact of the power grid fault; The communication module is used to feed back the decision of the control center and the status information of the hydrogen production unit, as well as the grid fault and its processing status to the grid operator or maintenance personnel.

2. A power grid fault measurement system based on water electrolysis for hydrogen production according to claim 1, characterized in that: The power input module also includes a power interface, which is designed to be connected to an external power grid and receive an AC power signal provided by the external power grid through a circuit configuration including a switch and a protection device.

3. A power grid fault measurement system based on water electrolysis hydrogen production according to claim 1, characterized in that: The discrete Fourier transform DFT extracts and analyzes harmonic components from the power signal specifically including: For a signal sequence consisting of N sample points , whose DFT is defined as: ,in, is the kth point in the frequency domain, representing the complex form of the frequency component, j is the imaginary unit, N is the total number of samples, k is the frequency index, ; In harmonic analysis, DFT is used to identify and quantify the harmonic components in power grid signals: Signal sampling: The power signal of the power grid By presetting the sampling frequency f s Convert to discrete signal ; Apply DFT: to the sampled signal Perform DFT calculation to obtain the amplitude and phase of each frequency component; Harmonic Identification: By Analyzing As a result, the fundamental wave is identified; Quantizing Harmonics: Calculating the amplitude of each harmonic frequency component , and compared with the threshold value of the harmonic.

4. A power grid fault measurement system based on water electrolysis hydrogen production according to claim 3, characterized in that: The adaptive filtering technology specifically includes: Calculate the amplitude of each harmonic frequency component , using adaptive infinite impulse response filters to compensate and suppress harmonic components by dynamically adjusting filter parameters; A minimum mean square error algorithm is implemented to optimize the adaptive adjustment of the filter parameters and dynamically update the filter coefficients by minimizing the error between the filtered signal and the ideal signal.

5. A power grid fault measurement system based on water electrolysis hydrogen production according to claim 4, characterized in that: The output of the adaptive infinite impulse response filter depends not only on the input signal, but also on the past output, and the expression is: ,in, is the input signal, is the output signal, b i is the forward filter coefficient, a j is the feedback filter coefficient, M and Z are the number of forward and feedback filter coefficients, respectively.

6. A power grid fault measurement system based on water electrolysis for hydrogen production according to claim 1, characterized in that: The asymmetric fault monitoring algorithm in the fault detection module adopts vector combination and phase angle analysis methods to detect and evaluate phase imbalance in the power grid, calculates the vector and phase angle of the three-phase voltage or current signal received from the power input module, and calculates and detects whether there is an imbalance between the phases by comparing the vector length and angle of each phase voltage or current, and determines the fault type based on the deviation of the vector angle.

7. A power grid fault measurement system based on water electrolysis for hydrogen production according to claim 6, characterized in that: The asymmetric fault monitoring algorithm calculation includes: Get three-phase current I from the power input module A ,I B and I C Real-time data; Calculate the phase vector: For each sampling point, calculate the instantaneous value of the three-phase current and the phase vector V of the three-phase current A ,V B and V C : ;in is the phase factor used to convert the real current value into a phase vector on the complex plane; Phase imbalance detection: Calculate the balance of the three-phase current vectors using the modulus of the vector sum: ; Fault type identification: Use the angle and modulus of the vector sum to identify the specific asymmetric fault type.

8. A power grid fault measurement system based on water electrolysis for hydrogen production according to claim 7, characterized in that: The control center also includes a machine learning model that receives asymmetric fault input data from the fault detection module, including phase imbalance, actual phase angle difference, and voltage deviation, and uses the input data to predict and calculate optimal voltage and current output settings for the hydrogen production unit, as well as a recommended rate of hydrogen production; The machine learning model uses a decision tree to dynamically adjust the output parameters of the inverter according to the severity and type of asymmetric faults, and optimize the operating efficiency of the hydrogen production unit in real time. It also adjusts the electrolysis current density and electrolysis time during the hydrogen generation process to adapt to changes in grid conditions.

9. A power grid fault measurement system based on water electrolysis hydrogen production according to claim 8, characterized in that: The decision tree specifically includes: The input features of the decision tree model are defined as follows: Phase imbalance: It indicates the imbalance degree of voltage or current between three-phase electricity; Actual phase angle difference: The phase angle difference between the three phases, used to identify the type and severity of asymmetry; Voltage deviation: the deviation between each phase voltage and its ideal value, which is an absolute value or a relative value; Decision tree model training: A decision tree model is trained using historical data, which includes different types of asymmetric faults and their impact on the performance of the hydrogen production unit. During the training process, the model learns how to predict the optimal hydrogen production unit parameter adjustment based on the input asymmetric fault characteristics, including inverter output voltage, inverter output current, hydrogen production rate, electrolysis current density, and electrolysis time; Real-time prediction and adjustment: When the system receives data from the fault detection module in real time, the decision tree model instantly calculates and outputs corresponding adjustment suggestions, using the input phase imbalance, actual phase angle difference and voltage deviation to predict and recommend the optimal output voltage and current settings of the inverter, while providing adjustment suggestions on hydrogen production rate, electrolysis current density and electrolysis time; According to the output of the decision tree model, the control system dynamically adjusts the inverter settings: adjusts the inverter voltage output ΔV and current output ΔI to ensure that the hydrogen production and quality are maximized under the current grid state, adjusts the hydrogen production rate and electrolysis current density, and adjusts the electrolysis time to adapt to the impact of asymmetric load on the electrolysis process; The decision tree construction process is as follows: Select the best split features and split points: Entropy or Gini impurity is the criterion for determining how to select split features and split points. For each node, the decision tree algorithm traverses all features and their split points to calculate the information gain or the reduction of Gini impurity. The entropy calculation formula is: , where p i is the proportion of target values ​​of the i-th category in the set S; Information gain calculation: , where A is the feature considered and S v is a subset of feature A under value v, is the entropy of the subset, and the Gini impurity calculation formula is: ； Recursively build the tree: For each node, use the above formula to select the best split feature and point to divide the data set into two subsets; Recursively apply the same method to each subset until the stopping condition is met; To prevent overfitting, the tree needs to be pruned using cost complexity pruning, in which a parameter is used to balance the depth of the tree and the training error.

10. A method for measuring power grid faults based on water electrolysis for hydrogen production, implemented by a power grid fault measurement system based on water electrolysis for hydrogen production according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: Power reception and monitoring, connected to the external power grid, receiving AC power signals through the power interface, using voltage and current sensors to monitor and record the voltage, current and frequency data of the power system in real time; S2: Fault data processing and analysis: Use harmonic analysis algorithm to process received power signals, digitize continuous power signals, calculate their spectrum data using discrete Fourier transform, identify and analyze harmonic components in power signals, calculate the amplitude and phase of each harmonic frequency component, and compare them with preset harmonic thresholds to monitor and quantify harmonic levels that exceed the standards in real time; S3: Harmonic filtering and compensation. If the harmonic exceeds the safety threshold, the adaptive infinite impulse response filter in the harmonic filtering unit is used to dynamically adjust the filtering parameters according to the output of the harmonic analysis algorithm to compensate and suppress the harmonic components. S4: Asymmetric fault monitoring and response, applying the asymmetric fault monitoring algorithm, by calculating the vector and phase angle of the three-phase current or voltage, to detect and evaluate the phase imbalance in the power grid, and identify whether there is an asymmetric fault based on the comparison of the vector length and angle; S5: Control and adjustment. According to the output result of S4, the working state of the hydrogen production unit is adjusted to cope with the impact of power grid failure.

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