Short circuit fault monitoring system and method based on Internet of Things

Abstract

The invention relates to the technical field of the Internet of Things, in particular to a short circuit fault monitoring system and method based on the Internet of Things. According to the method, fault mode identification is carried out based on current characteristic waveforms, prediction is carried out based on historical data, current waveform data are captured in real time by using a high-speed sampling current sensor, and abnormal changes in the current waveforms are identified and marked; an RNN deep learning model is constructed, features are extracted from waveforms, the features are compared with known fault modes, potential short circuit faults are recognized, the constructed deep learning model conducts reasoning based on real-time data, abnormal waveform features are quickly recognized, and therefore quick response is achieved, the fault detection time is greatly shortened, and the fault detection efficiency is improved. According to the method, short-circuit faults in a high-speed circuit are effectively handled, power flow data of multiple nodes are processed in real time by adopting a distributed data aggregation and analysis technology, a Bayesian network model is constructed, potential short-circuit faults are identified when power flow is abnormal, and specific nodes and paths where the faults occur are positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a circuit short circuit fault monitoring system and method based on the Internet of Things. Background Art

[0002] The current circuit short-circuit fault monitoring system mainly relies on current sensors, fuses or circuit breakers. Such devices identify short-circuit faults by detecting sudden increases in current, and then cut off the circuit for line protection. The monitoring of power systems mainly relies on single-point current or voltage monitoring. Generally, sensors are arranged at multiple nodes of the circuit to monitor the flow of electricity in real time. When an abnormality is detected, the protection mechanism is triggered, thereby realizing the monitoring of circuit short-circuit faults.

[0003] Although the traditional circuit protection methods mentioned above are simple and effective, they still have disadvantages. It can be seen that the several fault monitoring methods mentioned above mainly focus on the change of current amplitude and ignore the characteristics of current waveform. However, in complex circuits, potential short-circuit faults often do not cause instantaneous and large changes in current, but are manifested through subtle abnormalities in the waveform. If short-circuit faults are identified only by sudden increases in current, it will undoubtedly lead to missed reports or failure to provide early warnings. At the same time, the circuit short-circuit fault monitoring system relies on the action of physical components (such as fuses), which require a certain response time when detecting current abnormalities. In some high-speed circuits or dynamic load environments, short-circuit faults can develop into serious problems in a very short time, and the response speed of the above monitoring methods cannot meet the requirements. The power-off of current sensors, fuses or circuit breakers only responds after the fault occurs, and potential fault risks cannot be identified in advance.

[0004] As for the monitoring method, the traditional monitoring method cannot fully cover all nodes in the entire power system, it is difficult to reflect the overall situation, and it is easy to cause false alarms or missed alarms; also because the monitoring points are limited, when an abnormality is detected, it is necessary to conduct step-by-step investigation to determine the fault location, which can easily delay the timing of fault handling. Therefore, there is an urgent need for a circuit short circuit fault monitoring system and method based on the Internet of Things to solve such problems. Summary of the invention

[0005] To this end, the present invention provides a circuit short circuit fault monitoring system and method based on the Internet of Things, which is used to overcome the characteristics of the prior art that it cannot effectively monitor the current waveform, relies too much on the action of physical components (such as fuses), and has a certain delay in reaction; the single-point current and voltage monitoring method of the power system cannot fully cover all nodes in the entire power system, which is easy to cause false alarms or missed alarms, and easily delay the timing of fault handling.

[0006] To achieve the above objectives, on the one hand, the present invention provides a circuit short circuit fault monitoring system based on the Internet of Things, comprising:

[0007] Real-time waveform capture module, which can capture the current waveform data in the circuit in real time and quickly detect waveform anomalies;

[0008] The fault identification module uses deep learning algorithms to identify faults based on real-time waveform data and predict potential fault trends based on historical data;

[0009] Multi-node power flow monitoring module, which performs real-time monitoring of power flow at multiple key nodes in the power system and supports fault location;

[0010] Distributed data aggregation and anomaly detection module aggregates power flow data from multiple nodes, uses Bayesian networks to perform global anomaly detection, and accurately locates fault locations;

[0011] The management module executes fault response according to the detection results of the distributed data aggregation and anomaly detection module.

[0012] Furthermore, the real-time waveform capture module includes:

[0013] High-speed sampling current sensors are deployed at key locations in the circuit to continuously capture instantaneous waveform changes of the current;

[0014] The edge processing unit performs preliminary analysis and anomaly marking on the collected waveform data, and transmits key data to the fault identification module for further processing;

[0015] The fault identification module includes:

[0016] The RNN deep learning model extracts features from the data transmitted by the real-time waveform capture module to identify possible short-circuit faults. The model identifies anomalies through real-time reasoning and feeds the results back to the monitoring center.

[0017] The fault prediction engine uses historical waveform data to predict possible fault trends and issues an early alarm when a waveform similar to historical fault characteristics is detected;

[0018] The multi-node power flow monitoring module includes:

[0019] Multi-node monitoring equipment, distributed at different locations in the power system, collects power flow data at each node in real time and transmits the data to the circuit short-circuit fault monitoring system through the Internet of Things;

[0020] Data transmission and synchronization: data synchronization between nodes;

[0021] The distributed data aggregation and anomaly detection module includes:

[0022] The data aggregation platform integrates the data received from multiple nodes to form a global power flow map;

[0023] The Bayesian network model analyzes abnormal phenomena in power flow based on global data, quickly identifies short-circuit faults, and locates faulty nodes and paths;

[0024] The management modules include:

[0025] The fault response system automatically triggers the preset response mechanism, including power off and alarm, based on the analysis results of the fault identification module and Bayesian network.

[0026] On the other hand, the present invention also provides a circuit short circuit fault monitoring method based on the Internet of Things, comprising:

[0027] Step S1, data acquisition, the real-time waveform capture module continuously collects current waveform data, the edge processing unit performs preliminary processing on it, and sends abnormal data to the fault identification module;

[0028] Step S2, data analysis, the fault identification module conducts in-depth analysis on the real-time data, identifies short-circuit faults, and predicts possible future fault trends, and outputs the results to the management module;

[0029] Step S3, global monitoring, a multi-node power flow monitoring module tracks the status of each node in the power system in real time;

[0030] Step S4, fault detection and location, the distributed data aggregation and anomaly detection module aggregates the data of multiple nodes, performs global analysis through the Bayesian network model, identifies anomalies and locates the fault point;

[0031] Step S5, response and management, the management module executes emergency response measures according to the detected fault information.

[0032] Furthermore, in step S1, the data collection method is:

[0033] Introducing adaptive sampling rate f s (t), dynamically adjust the sampling rate according to the change of current waveform, where f s (t) represents the sampling rate at time t, f base represents the basic sampling rate, α represents the sampling rate gain coefficient, which controls the amplitude of the adaptive sampling rate, β represents the current change sensitivity coefficient, which controls the degree of response of the sampling rate to the current change rate, It represents the instantaneous rate of change of the current I(t), reflecting the sudden change of the current waveform. γ represents the nonlinear enhancement index of the rate of change. When the current changes drastically, the sampling rate is increased to capture more details. When the current changes steadily, the sampling rate is reduced to reduce redundant data.

[0034] The current signal I(t) is converted into f by an A / D converter. s (t) is sampled at a dynamic sampling rate to generate a discrete current signal sequence I[n], where n is a discrete time index, I[n] is a discrete sampling data point, which indicates the current value collected at the nth moment, and η[n] indicates sampling noise, which is processed by introducing a filter;

[0035] Perform high-dimensional feature extraction on discrete sampling points I[n] and construct feature vector F(n) for preliminary anomaly detection. Where F(n) represents the feature vector, which contains a variety of signal features. and They represent the gradient and second-order gradient of the current respectively, H{I[n]} represents the signal envelope after Hilbert transform, P(n) represents the average power feature in a specific window, indicating the power change of the current, E(n) represents the energy feature, indicating the energy accumulation of the signal in a specific time window.

[0036] Furthermore, in step S1, the data collection method further includes:

[0037] Use the dynamic threshold detection algorithm to perform preliminary abnormal marking on the feature vector F(n), Θ(n) = μ + κ·σ, where Θ(n) represents the dynamic threshold, which is dynamically calculated based on the mean μ and standard deviation σ of the feature vector, and κ represents the sensitivity coefficient;

[0038] When any component of F(n) exceeds the threshold Θ(n), it is considered that there may be an abnormality and the data is marked as a potential fault signal;

[0039] The waveform data marked as abnormal is transmitted to the fault identification module in priority;

[0040] The regular waveform data that is not marked as abnormal is cached and compressed and stored using a compression algorithm based on wavelet transform:

[0041] Where W(n) represents the compressed signal representation, ψ i (n) represents the wavelet basis function, a i Represents the wavelet coefficient, which indicates the characteristics of the signal at each scale, and N represents the number of levels of signal decomposition in the wavelet transform. The data acquisition module not only realizes high-precision current waveform capture, but also performs preliminary processing and abnormal marking while collecting, significantly improving the real-time performance, response speed and detection accuracy of the system.

[0042] Furthermore, the data analysis method in step S2 is:

[0043] Perform adaptive multi-scale wavelet transform on the current signal I[n] collected in real time to extract the time-frequency characteristics of the signal:

[0044] Where W I (a, b) represents the wavelet transform coefficient of the current signal I[n], a represents the scale parameter, which controls the frequency resolution of the signal, b represents the translation parameter, which controls the time resolution of the signal, ψ represents the mother wavelet function, which is used to extract the local features of the signal, and ψ * Represents the conjugate function of the mother wavelet, used for the inverse transform of the wavelet transform;

[0045] By adaptively adjusting the wavelet scale a and translation parameter b, the characteristics of different current waveforms can be flexibly adapted, especially in multi-band signals, and fault signals can be effectively separated from normal signals.

[0046] On the basis of wavelet transform, the multidimensional feature matrix F(a,b) is further extracted, which contains time-frequency features, energy features and instantaneous phase information. Where W I (a, b) represents the time-frequency characteristics, ε I (a) represents the energy characteristics, indicating the energy distribution at each scale, P I (a) represents the average power feature, which represents the power distribution in a specific time window, φ I (a,b)=arg(W I (a, b)) represents the instantaneous phase feature, capturing the phase change of the signal;

[0047] The multi-dimensional feature matrix F(a, b) is analyzed using the deep residual network ResNet. The residual structure of ResNet can effectively solve the gradient disappearance problem in deep networks. The network structure is:

[0048] Ο=ResNet θ (F(a,b)), where Ο represents the output fault classification result, that is, whether the current signal has a short circuit fault, ResNet θ Represents a deep residual network with parameter θ; the structure of the residual network maintains high-precision recognition as the network depth increases;

[0049] The network is trained using the weighted cross entropy loss function:

[0050] Where L represents the loss function value, C represents the number of fault and non-fault classifications, and w i Represents the category weight, which is adjusted for the sample imbalance problem, y i and Represents the true label and the predicted probability; the weighted loss function is used to balance the impact of sample imbalance.

[0051] Furthermore, the data analysis method in step S2 also includes:

[0052] Use long short-term memory network LSTM to perform time series modeling on historical fault data:

[0053] in Indicates the predicted fault status at the future time t+k, LSTM φ represents an LSTM network with parameter φ, F t To F t-m Represents the multidimensional feature matrix from the current time t to the mth time in the past;

[0054] Combined with the historical multi-dimensional feature matrix, it predicts short-term failures and gives early warnings based on long-term trends to help prevent potential system risks;

[0055] The Bayesian neural network BNN is used to evaluate the uncertainty of the prediction results and quantify the confidence interval of the prediction. The output of BNN includes the predicted value and the predicted variance: where μ t+k , μ t+k and are the mean and variance of the predictions, respectively.

[0056] Furthermore, the global monitoring method in step S3 is:

[0057] Install monitoring equipment at key nodes of the power system, including transformers, switches, and busbars, to measure current, voltage, and power parameters in real time;

[0058] Monitoring equipment at each node continuously collects real-time data on power flow;

[0059] The collected real-time data is transmitted to the central processing unit through the IoT network, where the data from different nodes are processed synchronously;

[0060] The central processing unit aggregates data from different nodes to form a real-time global view of the entire power system.

[0061] Furthermore, the fault detection and location method in step S4 is:

[0062] Data from different nodes are processed locally on their respective collection devices and then transmitted to the data aggregation platform through a distributed computing architecture;

[0063] After data aggregation, the aggregated data is preprocessed to eliminate noise and irrelevant fluctuations, and an adaptive noise filter is used for data cleaning;

[0064] Construct a Bayesian network based on power system nodes, where the nodes represent the various state variables in the power system, including voltage, current, and power, and the edges represent the causal relationship between the state variables. The Bayesian network is used to display the dependency relationship between the nodes in the power system.

[0065] Use Bayesian networks to reason about real-time data and calculate the probability of failure of the system in its current state;

[0066] Bayesian Update:

[0067] Where P(V i |D clean (t)) represents the given cleaned data D clean (t) later, node V i The posterior probability, P(D clean (t)|V i ) indicates that at node V i Data D is observed in the state clean The likelihood function of (t), P(V i ) represents the node V i The prior probability, P(D clean (t)) represents the marginal probability of the data;

[0068] By updating the Bayesian network in real time, the failure probability of each node in the power system is re-evaluated at each moment, and abnormal situations are identified in advance;

[0069] For each node V i , when the posterior probability P(V i |D clean (t)) exceeds a predetermined threshold, the system marks the node as abnormal.

[0070] Furthermore, the fault detection and location method in step S4 also includes:

[0071] When multiple node anomalies are detected, the causal chain analysis of the Bayesian network is used to identify possible fault sources. The fault source node is usually the first node to have an abnormality or the node that has the greatest impact on downstream nodes. The fault source location algorithm is:

[0072] When the fault source is confirmed, the alarm mechanism is immediately triggered to notify maintenance personnel to take emergency measures.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention performs fault mode recognition based on current characteristic waveforms and performs prediction based on historical data, utilizes high-speed sampling current sensors to capture current waveform data in real time, and recognizes and marks abnormal changes in the current waveform.

[0075] The present invention constructs an RNN deep learning model, extracts features from the waveform, and compares it with known fault modes to identify potential short-circuit faults. The constructed deep learning model performs reasoning based on real-time data, quickly identifies abnormal waveform features, thereby achieving rapid response, greatly shortening fault detection time, and effectively dealing with short-circuit faults in high-speed circuits.

[0076] The present invention analyzes and studies historical waveform data to predict potential fault trends in circuits; when waveform features are similar to historical short-circuit fault features, an alarm is issued in advance; the probability of fault occurrence is reduced, and the service life of circuit equipment is extended.

[0077] The present invention arranges monitoring devices at multiple nodes of the power system. The monitoring devices are interconnected through the Internet of Things, and the flow of electricity between the nodes is monitored in real time. The abnormal changes of any node can be captured from a global perspective, thereby reducing monitoring blind spots.

[0078] The present invention uses distributed data aggregation and analysis technology to process the power flow data of multiple nodes in real time, constructs a Bayesian network model, identifies potential short-circuit faults when power flow is abnormal, and locates the specific nodes and paths where the faults occur.

[0079] The present invention uses a multi-node monitoring method to simultaneously analyze data from different nodes. When an abnormality occurs in a certain node, the relevant data is immediately compared with the power flow data of other nodes to quickly locate the source of the fault. Compared with the step-by-step investigation of the traditional system, this method greatly improves the fault response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic flow chart of a circuit short circuit fault monitoring method based on the Internet of Things of the present invention;

[0081] Figure 2 It is a schematic diagram of the structure of the circuit short circuit fault monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0082] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0083] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0084] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0085] Example 1

[0086] See also Figure 1-Figure 2 The present invention provides a circuit short circuit fault monitoring system based on the Internet of Things, comprising:

[0087] Real-time waveform capture module, which can capture the current waveform data in the circuit in real time and quickly detect waveform anomalies;

[0088] The real-time waveform capture module includes:

[0089] High-speed sampling current sensors are deployed at key locations in the circuit to continuously capture instantaneous waveform changes of the current;

[0090] The edge processing unit performs preliminary analysis and anomaly marking on the collected waveform data, and transmits key data to the fault identification module for further processing;

[0091] The fault identification module uses deep learning algorithms to identify faults based on real-time waveform data and predict potential fault trends based on historical data;

[0092] The fault identification module includes:

[0093] The RNN deep learning model extracts features from the data transmitted by the real-time waveform capture module to identify possible short-circuit faults. The model identifies anomalies through real-time reasoning and feeds the results back to the monitoring center.

[0094] The fault prediction engine uses historical waveform data to predict possible fault trends and issues an early alarm when a waveform similar to historical fault characteristics is detected;

[0095] Multi-node power flow monitoring module, which performs real-time monitoring of power flow at multiple key nodes in the power system and supports fault location;

[0096] The multi-node power flow monitoring module includes:

[0097] Multi-node monitoring equipment, distributed at different locations in the power system, collects power flow data at each node in real time and transmits the data to the circuit short-circuit fault monitoring system through the Internet of Things;

[0098] Data transmission and synchronization: data synchronization between nodes;

[0099] Distributed data aggregation and anomaly detection module aggregates power flow data from multiple nodes, uses Bayesian networks to perform global anomaly detection, and accurately locates fault locations;

[0100] The distributed data aggregation and anomaly detection module includes:

[0101] The data aggregation platform integrates the data received from multiple nodes to form a global power flow map;

[0102] The Bayesian network model analyzes abnormal phenomena in power flow based on global data, quickly identifies short-circuit faults, and locates faulty nodes and paths;

[0103] The management module executes fault response according to the detection results of the distributed data aggregation and anomaly detection module;

[0104] The management modules include:

[0105] The fault response system automatically triggers the preset response mechanism, including power off and alarm, based on the analysis results of the fault identification module and Bayesian network.

[0106] The present invention also provides a circuit short circuit fault monitoring method based on the Internet of Things, comprising the following steps:

[0107] Step S1, data acquisition, the real-time waveform capture module continuously collects current waveform data, the edge processing unit performs preliminary processing on it, and sends abnormal data to the fault identification module;

[0108] Introducing adaptive sampling rate f s (t), dynamically adjust the sampling rate according to the change of current waveform, where f s (t) represents the sampling rate at time t, f base represents the basic sampling rate, α represents the sampling rate gain coefficient, which controls the amplitude of the adaptive sampling rate, β represents the current change sensitivity coefficient, which controls the degree of response of the sampling rate to the current change rate, It represents the instantaneous rate of change of the current I(t), reflecting the sudden change of the current waveform. γ represents the nonlinear enhancement index of the rate of change. When the current changes drastically, the sampling rate is increased to capture more details. When the current changes steadily, the sampling rate is reduced to reduce redundant data.

[0109] The current signal I(t) is converted into f by an A / D converter. s (t) is sampled at a dynamic sampling rate to generate a discrete current signal sequence I[n], where n is a discrete time index, I[n] is a discrete sampling data point, which indicates the current value collected at the nth moment, and η[n] indicates sampling noise, which is processed by introducing a filter;

[0110] Perform high-dimensional feature extraction on discrete sampling points I[n] and construct feature vector F(n) for preliminary anomaly detection. Where F(n) represents the feature vector, which contains a variety of signal features. and They represent the gradient and second-order gradient of the current, H{I[n]} represents the signal envelope after Hilbert transformation, P(n) represents the average power feature in a specific window, indicating the power change of the current, E(n) represents the energy feature, indicating the energy accumulation of the signal in a specific time window;

[0111] Use the dynamic threshold detection algorithm to perform preliminary abnormal marking on the feature vector F(n), Θ(n) = μ + κ·σ, where Θ(n) represents the dynamic threshold, which is dynamically calculated based on the mean μ and standard deviation σ of the feature vector, and κ represents the sensitivity coefficient;

[0112] When any component of F(n) exceeds the threshold Θ(n), it is considered that there may be an abnormality and the data is marked as a potential fault signal;

[0113] The waveform data marked as abnormal is transmitted to the fault identification module in priority;

[0114] The regular waveform data that is not marked as abnormal is cached and compressed and stored using a compression algorithm based on wavelet transform:

[0115] Where W(n) represents the compressed signal representation, ψ i (n) represents the wavelet basis function, a i Represents the wavelet coefficient, which indicates the characteristics of the signal at each scale, and N represents the number of levels of signal decomposition in the wavelet transform; the data acquisition module not only realizes high-precision current waveform capture, but also performs preliminary processing and abnormal marking while collecting, significantly improving the real-time performance, response speed and detection accuracy of the system;

[0116] Specifically, an adaptive sampling rate is used in data acquisition, and the sampling frequency is adjusted according to the dynamic changes of the current waveform, increasing the sampling density when the fluctuation is severe and reducing redundant data when it is stable. The dynamic adjustment mechanism enables the system to accurately capture subtle anomalies in the current waveform. In addition, through real-time feature extraction and anomaly marking, potential faults can be preliminarily identified in the acquisition stage, shortening the response time of fault detection.

[0117] Step S2, data analysis, the fault identification module conducts in-depth analysis on the real-time data, identifies short-circuit faults, and predicts possible future fault trends, and outputs the results to the management module;

[0118] Perform adaptive multi-scale wavelet transform on the current signal I[n] collected in real time to extract the time-frequency characteristics of the signal:

[0119] Where W I (a, b) represents the wavelet transform coefficient of the current signal I[n], a represents the scale parameter, which controls the frequency resolution of the signal, b represents the translation parameter, which controls the time resolution of the signal, ψ represents the mother wavelet function, which is used to extract the local features of the signal, and ψ * Represents the conjugate function of the mother wavelet, used for the inverse transform of the wavelet transform;

[0120] By adaptively adjusting the wavelet scale a and translation parameter b, the characteristics of different current waveforms can be flexibly adapted, especially in multi-band signals, and fault signals can be effectively separated from normal signals.

[0121] On the basis of wavelet transform, the multidimensional feature matrix F(a,b) is further extracted, which contains time-frequency features, energy features and instantaneous phase information. Where W I (a, b) represents the time-frequency characteristics, ε I (a) represents the energy characteristics, indicating the energy distribution at each scale, P I (a) represents the average power feature, which represents the power distribution in a specific time window, φ I (a,b)=arg(W I (a, b)) represents the instantaneous phase feature, capturing the phase change of the signal;

[0122] The multi-dimensional feature matrix F(a, b) is analyzed using the deep residual network ResNet. The residual structure of ResNet can effectively solve the gradient disappearance problem in deep networks. The network structure is:

[0123] Ο=ResNet θ (F(a,b)), where Ο represents the output fault classification result, that is, whether the current signal has a short circuit fault, ResNet θRepresents a deep residual network with parameter θ; the structure of the residual network maintains high-precision recognition as the network depth increases;

[0124] The network is trained using the weighted cross entropy loss function:

[0125] Where L represents the loss function value, C represents the number of fault and non-fault classifications, and w i Represents the category weight, which is adjusted for the sample imbalance problem, y i and Represents the true label and predicted probability; balances the impact of sample imbalance through a weighted loss function;

[0126] Use long short-term memory network LSTM to perform time series modeling on historical fault data:

[0127] in Indicates the predicted fault status at the future time t+k, LSTM φ represents an LSTM network with parameter φ, F t To F t-m Represents the multidimensional feature matrix from the current time t to the mth time in the past;

[0128] Combined with the historical multi-dimensional feature matrix, it predicts short-term failures and gives early warnings based on long-term trends to help prevent potential system risks;

[0129] The Bayesian neural network BNN is used to evaluate the uncertainty of the prediction results and quantify the confidence interval of the prediction. The output of BNN includes the predicted value and the predicted variance: where μ t+k , μt+k and are the mean and variance of the prediction, respectively;

[0130] Specifically, in the data analysis stage, adaptive multi-scale wavelet transform is used to extract the time-frequency characteristics of the signal, combined with a deep residual network (ResNet) for fault pattern recognition; a multi-level analysis method enables the system to maintain high-precision fault recognition capabilities when faced with complex current waveforms; and by combining historical data for long short-term memory network (LSTM) modeling, possible future fault trends are predicted in advance.

[0131] Step S3, global monitoring, a multi-node power flow monitoring module tracks the status of each node in the power system in real time;

[0132] Install monitoring equipment at key nodes of the power system, including transformers, switches, and busbars, to measure current, voltage, and power parameters in real time;

[0133] Monitoring equipment at each node continuously collects real-time data on power flow;

[0134] The collected real-time data is transmitted to the central processing unit through the IoT network, where the data from different nodes are processed synchronously;

[0135] The central processing unit aggregates data from different nodes to form a real-time global view of the entire power system;

[0136] Specifically, in terms of global monitoring, monitoring equipment is deployed at key nodes to achieve comprehensive real-time monitoring of the power system. Data is synchronized and transmitted through the Internet of Things and aggregated in the central processing unit to form a global view of the power system.

[0137] Step S4, fault detection and location, the distributed data aggregation and anomaly detection module aggregates the data of multiple nodes, performs global analysis through the Bayesian network model, identifies anomalies and locates the fault point;

[0138] Data from different nodes are processed locally on their respective collection devices and then transmitted to the data aggregation platform through a distributed computing architecture;

[0139] After data aggregation, the aggregated data is preprocessed to eliminate noise and irrelevant fluctuations, and an adaptive noise filter is used for data cleaning;

[0140] Construct a Bayesian network based on power system nodes, where the nodes represent the various state variables in the power system, including voltage, current, and power, and the edges represent the causal relationship between the state variables. The Bayesian network is used to display the dependency relationship between the nodes in the power system.

[0141] Use Bayesian networks to reason about real-time data and calculate the probability of failure of the system in its current state;

[0142] Bayesian Update:

[0143] Where P(V i |D clean (t)) represents the given cleaned data D clean (t) later, node V i The posterior probability, P(D clean (t)|V i ) indicates that at node V i Data D is observed in the state clean The likelihood function of (t), P(V i ) represents the node V i The prior probability, P(D clean (t)) represents the marginal probability of the data;

[0144] By updating the Bayesian network in real time, the failure probability of each node in the power system is re-evaluated at each moment, and abnormal situations are identified in advance;

[0145] For each node V i , when the posterior probability P(V i |D clean (t)) exceeds the predetermined threshold, the system marks the node as abnormal;

[0146] When multiple node anomalies are detected, the causal chain analysis of the Bayesian network is used to identify possible fault sources. The fault source node is usually the first node to have an abnormality or the node that has the greatest impact on downstream nodes. The fault source location algorithm is:

[0147] When the fault source is confirmed, the alarm mechanism is immediately triggered to notify maintenance personnel to take emergency measures;

[0148] Specifically, during the fault detection and location process, distributed data aggregation and Bayesian network models are used to perform a global analysis of the aggregated data of multiple nodes. The failure probability of each node is calculated through the reasoning ability of the Bayesian network, abnormal situations are identified in advance, and the location of the fault source is determined using causal chain analysis. Through real-time updates, the system can dynamically adjust and re-evaluate the status of each node, and can quickly locate and respond when a fault occurs.

[0149] Step S5, response and management, the management module executes emergency response measures according to the detected fault information.

[0150] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A circuit short circuit fault monitoring system based on the Internet of Things, characterized in that: include: Real-time waveform capture module, real-time capture of current waveform data in the circuit; The fault identification module uses deep learning algorithms to identify faults based on real-time waveform data and predict potential fault trends based on historical data; Multi-node power flow monitoring module, which performs real-time monitoring of power flow at multiple key nodes in the power system and supports fault location; Distributed data aggregation and anomaly detection module aggregates power flow data from multiple nodes, uses Bayesian networks to perform global anomaly detection, and accurately locates fault locations; The management module executes fault response according to the detection results of the distributed data aggregation and anomaly detection module.

2. According to the circuit short circuit fault monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The real-time waveform capture module includes: High-speed sampling current sensors are deployed at key locations in the circuit to continuously capture instantaneous waveform changes of the current; The edge processing unit performs preliminary analysis and anomaly marking on the collected waveform data, and transmits key data to the fault identification module for further processing; The fault identification module includes: The RNN deep learning model extracts features from the data transmitted by the real-time waveform capture module to identify possible short-circuit faults. The model identifies anomalies through real-time reasoning and feeds the results back to the monitoring center. The fault prediction engine uses historical waveform data to predict possible fault trends and issues an early alarm when a waveform similar to historical fault characteristics is detected; The multi-node power flow monitoring module includes: Multi-node monitoring equipment, distributed at different locations in the power system, collects power flow data at each node in real time and transmits the data to the circuit short-circuit fault monitoring system through the Internet of Things; Data transmission and synchronization: data synchronization between nodes; The distributed data aggregation and anomaly detection module includes: The data aggregation platform integrates the data received from multiple nodes to form a global power flow map; The Bayesian network model analyzes abnormal phenomena in power flow based on global data, quickly identifies short-circuit faults, and locates faulty nodes and paths; The management modules include: The fault response system automatically triggers the preset response mechanism, including power off and alarm, based on the analysis results of the fault identification module and Bayesian network.

3. A circuit short circuit fault monitoring method based on the Internet of Things, characterized in that: A circuit short circuit fault monitoring system based on the Internet of Things according to any one of claims 1 to 2 is used, comprising the following steps: Step S1, data acquisition, the real-time waveform capture module continuously collects current waveform data, the edge processing unit performs preliminary processing on it, and sends abnormal data to the fault identification module; Step S2, data analysis, the fault identification module conducts in-depth analysis on the real-time data, identifies short-circuit faults, and predicts possible future fault trends, and outputs the results to the management module; Step S3, global monitoring, a multi-node power flow monitoring module tracks the status of each node in the power system in real time; Step S4, fault detection and location, the distributed data aggregation and anomaly detection module aggregates the data of multiple nodes, performs global analysis through the Bayesian network model, identifies anomalies and locates the fault point; Step S5, response and management, the management module executes emergency response measures according to the detected fault information.

4. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 3 is characterized in that: In step S1, the data collection method is: Introducing adaptive sampling rate f s (t), dynamically adjust the sampling rate according to the change of current waveform, where f s (t) represents the sampling rate at time t, f base represents the basic sampling rate, α represents the sampling rate gain coefficient, which controls the amplitude of the adaptive sampling rate, β represents the current change sensitivity coefficient, which controls the degree of response of the sampling rate to the current change rate, It represents the instantaneous rate of change of current I(t), reflecting the sudden change of current waveform, and γ represents the nonlinear enhancement index of the rate of change; The current signal I(t) is converted into f by an A / D converter. s (t) is sampled at a dynamic sampling rate to generate a discrete current signal sequence I[n], where n is a discrete time index, I[n] is a discrete sampling data point, indicating the current value collected at the nth moment, and η[n] represents the sampling noise; Perform high-dimensional feature extraction on discrete sampling points I[n] and construct feature vector F(n) for preliminary anomaly detection. Where F(n) represents the feature vector, which contains a variety of signal features. and They represent the gradient and second-order gradient of the current respectively, H{I[n]} represents the signal envelope after Hilbert transform, P(n) represents the average power feature in a specific window, indicating the power change of the current, E(n) represents the energy feature, indicating the energy accumulation of the signal in a specific time window.

5. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 4 is characterized in that: In step S1, the data collection method further includes: Use the dynamic threshold detection algorithm to perform preliminary abnormal marking on the feature vector F(n), Θ(n) = μ + κ·σ, where Θ(n) represents the dynamic threshold, which is dynamically calculated based on the mean μ and standard deviation σ of the feature vector, and κ represents the sensitivity coefficient; When any component of F(n) exceeds the threshold Θ(n), it is considered that there may be an abnormality and the data is marked as a potential fault signal; The waveform data marked as abnormal is transmitted to the fault identification module in priority; The regular waveform data that is not marked as abnormal is cached and compressed and stored using a compression algorithm based on wavelet transform: Where W(n) represents the compressed signal representation, ψ i (n) represents the wavelet basis function, a i It represents the wavelet coefficient, which indicates the characteristics of the signal at each scale, and N represents the number of levels of signal decomposition in the wavelet transform.

6. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 5 is characterized in that: The data analysis method in step S2 is: Perform adaptive multi-scale wavelet transform on the current signal I[n] collected in real time to extract the time-frequency characteristics of the signal: Where W I (a, b) represents the wavelet transform coefficient of the current signal I[n], a represents the scale parameter, which controls the frequency resolution of the signal, b represents the translation parameter, which controls the time resolution of the signal, ψ represents the mother wavelet function, which is used to extract the local features of the signal, and ψ * Represents the conjugate function of the mother wavelet, used for the inverse transform of the wavelet transform; On the basis of wavelet transform, the multidimensional feature matrix F(a,b) is further extracted, which contains time-frequency features, energy features and instantaneous phase information. Where W I (a, b) represents the time-frequency characteristics, ε I (a) represents the energy characteristics, indicating the energy distribution at each scale, P I (a) represents the average power feature, which represents the power distribution in a specific time window, φ I (a,b)=arg(W I (a, b)) represents the instantaneous phase feature, capturing the phase change of the signal; Use the deep residual network ResNet to analyze the multi-dimensional feature matrix F(a,b). The network structure is: Ο=ResNet θ (F(a,b)), where Ο represents the output fault classification result, that is, whether the current signal has a short circuit fault, ResNet θ represents a deep residual network with parameters θ; The network is trained using the weighted cross entropy loss function: Where L represents the loss function value, C represents the number of fault and non-fault classifications, and w i Represents the category weight, which is adjusted for the sample imbalance problem, y i and Represents the true label and predicted probability.

7. A circuit short circuit fault monitoring method based on the Internet of Things according to claim 6, characterized in that: The data analysis method in step S2 also includes: Use long short-term memory network LSTM to perform time series modeling on historical fault data: in Indicates the predicted fault status at the future time t+k, LSTM φ represents an LSTM network with parameter φ, F t To F t-m Represents the multidimensional feature matrix from the current time t to the mth time in the past; The Bayesian neural network BNN is used to evaluate the uncertainty of the prediction results and quantify the confidence interval of the prediction. The output of BNN includes the predicted value and the predicted variance: where μ t+k , and are the mean and variance of the predictions, respectively.

8. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 7 is characterized in that: The global monitoring method in step S3 is: Install monitoring equipment at key nodes of the power system, including transformers, switches, and busbars, to measure current, voltage, and power parameters in real time; Monitoring equipment at each node continuously collects real-time data on power flow; The collected real-time data is transmitted to the central processing unit through the IoT network, where the data from different nodes are processed synchronously; The central processing unit aggregates data from different nodes to form a real-time global view of the entire power system.

9. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 8, characterized in that: The fault detection and location method in step S4 is: Data from different nodes are processed locally on their respective collection devices and then transmitted to the data aggregation platform through a distributed computing architecture; After data aggregation, the aggregated data is preprocessed to eliminate noise and irrelevant fluctuations, and an adaptive noise filter is used for data cleaning; Construct a Bayesian network based on power system nodes, where the nodes represent the various state variables in the power system, including voltage, current, and power, and the edges represent the causal relationship between the state variables. The Bayesian network is used to display the dependency relationship between the nodes in the power system. Use Bayesian networks to reason about real-time data and calculate the probability of failure of the system in its current state; Bayesian Update: Where P(V i |D clean (t)) represents the given cleaned data D clean (t) later, node V i The posterior probability, P(D clean (t)|V i ) indicates that at node V i Data D is observed in the state clean The likelihood function of (t), P(V i ) represents node V i The prior probability, P(D clean (t)) represents the marginal probability of the data; For each node V i , when the posterior probability P(V i |D clean (t)) exceeds a predetermined threshold, the system marks the node as abnormal.

10. The circuit short circuit fault monitoring method based on the Internet of Things according to claim 9, characterized in that: The fault detection and location method in step S4 also includes: When multiple node anomalies are detected, the causal chain analysis of the Bayesian network is used to identify possible fault sources. The fault source location algorithm: When the fault source is confirmed, the alarm mechanism is immediately triggered to notify maintenance personnel to take emergency measures.

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