559 results about "SCADA" patented technology

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

The invention discloses a power equipment fault prediction system based on big data analysis. The method comprises the following steps: acquiring initial equipment multi-dimensional data; constructing a dynamic topology network of the power equipment, including a dependency relationship between the equipment and a fault propagation path, performing embedded learning on the dynamic topology network by using a GNN graph neural network, and extracting equipment collaboration features in the initial equipment multi-dimensional data; a multi-task learning framework is constructed in combination with the equipment cooperation features to predict the equipment fault probability and the remaining service life, and an equipment health index is obtained; and acquiring environmental parameters, dynamically adjusting a fault judgment threshold based on the equipment health index and the environmental parameters, generating a prediction result, integrating the prediction result with an SCADA system, and triggering graded early warning. And the influence of environmental factors on the operation state of the equipment is fully considered. Under different environmental conditions, the equipment fault risk can be judged timely and accurately.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a control method and system for an intelligent calculation center comprehensive energy supply device, and the method comprises the steps: collecting the operation data of energy supply equipment and an environment sensor in real time through an SCADA system, and carrying out the preprocessing; based on the collected data and simulation data generated by a simulation environment, performing offline mixed training on the reinforcement learning model to generate an energy scheduling strategy; inputting a real-time state into the trained reinforcement learning model to generate a preliminary scheduling instruction, performing security verification and interpretability analysis by using a large language model, and optimizing a strategy; and fusing the preliminary scheduling instruction with the suggestion of the large language model, generating a final scheduling command through the energy router control unit, and issuing the final scheduling command to the energy supply equipment for execution. The problems that in the prior art, black box decision making and simulation are not accurate, and experience is difficult to solidify are solved, and the purposes of decision making transparency, simulation high fidelity and experience structuring are achieved.

The invention relates to the technical field of new energy power data governance, in particular to a multi-source fusion cleaning and closed-loop quality control method and device for wind power data and a medium, and the method comprises the steps: generating an initial data set through collecting SCADA time sequence data and at least one type of external meteorological data of a wind turbine generator; based on the initial data set, executing physical consistency check, grouping adaptive statistical detection, unsupervised machine learning detection and time sequence residual analysis in parallel, and generating four independent exception judgment vectors; inputting the four abnormal judgment vectors into a preset fusion decision model, and outputting a comprehensive abnormal label; obtaining a repaired data set according to the comprehensive abnormal label; and calculating a multi-dimensional quality score representing data quality based on the repaired data set, and triggering a quality control closed-loop operation according to the multi-dimensional quality score and a comparison test result. Through the arrangement, multi-dimensional fusion detection and self-adaptive intelligent repair can be realized, and integrated cleaning of quality quantitative evaluation and closed-loop verification can be realized.

The invention discloses a wind turbine generator state monitoring method driven by network fusion of a knowledge graph and a space-time diagram. The method comprises the following steps: S1, collecting time sequence SCADA data and wind power text data of a wind turbine generator; s2, constructing the wind power text data into a wind power operation and maintenance knowledge graph; s3, generating time sequence diagram data by querying a wind power operation and maintenance knowledge graph and combining the preprocessed SCADA data; s4, carrying out normal behavior modeling on the time sequence diagram data by utilizing the time-space diagram neural network model, predicting each node in the diagram data in the healthy operation time period of the unit in the normal behavior modeling process, and calculating the maximum absolute value of the difference between the predicted value and the actual value of each node as the maximum absolute value of the difference between the predicted value and the actual value; the maximum absolute error is used as a health threshold value of each node to perform state monitoring; in the state monitoring stage, the matching condition of the mapping information of each node in the graph structure in the monitoring result of the real-time data and the fault transmission chain is compared, the health state of the unit is judged, and the fault early warning time is determined. According to the invention, the accuracy and robustness of the monitoring result are enhanced.

The invention discloses a wind driven generator temperature monitoring method based on a graph space-time attention network, and the method comprises the following steps: carrying out the preprocessing of collected SCADA data, and obtaining a data set; each sensor is used as a node, the top-k neighbor relation of each node is calculated, edge weights among the nodes are calculated through a Gaussian kernel function, a weighted adjacency matrix is constructed based on the neighbor relation and the edge weights of all the nodes, and therefore a multi-sensor time sequence diagram of the wind driven generator is obtained, time sequence characteristics are given to the nodes by time sequence data collected by different timestamps, and a multi-sensor time sequence diagram of the wind driven generator is obtained. Forming a space-time diagram structure, and outputting time sequence characteristics of each sensor; a graph attention network is introduced into wind driven generator temperature state monitoring, and global and connectivity characteristics of a multi-sensor network are modeled. According to the method, the generator temperature prediction precision is remarkably improved by fusing a graph space-time two-dimensional attention mechanism and key variable screening, redundant variables are eliminated by adopting the maximum information coefficient MIC, and noise interference is reduced.

The invention relates to the technical field of monitoring and data acquisition systems, and discloses an SCADA (supervisory control and data acquisition) system based on a large language model, an implementation method and equipment, and the system comprises a user interaction terminal which receives a natural language instruction input by a user and displays a dynamic user interface; the natural language processing module is used for preliminarily analyzing the natural language instruction to obtain a user intention and key information; the large language model is used for deeply analyzing the user intention and the key information and generating a task description or a task execution plan; the MCP arrangement engine is used for mapping the task description or the task execution plan to a predefined function to obtain executable structured data; and the dynamic UI generator is used for generating a dynamic user interface based on the executable structured data and a preset UI instruction. According to the method, the usability and the data analysis capability of the SCADA system are improved through natural language interaction, and meanwhile, the reliability and the safety of the system are ensured by utilizing an MCP and a predefined function mechanism.

The invention relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention discloses an industrial equipment operation state monitoring and abnormity early warning method and system based on acoustic characteristics. The system comprises a multi-channel acoustic data acquisition module, an acoustic data preprocessing and enhancing module, a robustness acoustic feature extraction module, a deep learning abnormal mode recognition module, an abnormal early warning and auxiliary positioning module, an intelligent diagnosis and early warning enhancing module and a data storage and management module. The method comprises the following steps: collecting multichannel acoustic data, and extracting time domain, frequency domain and time-frequency domain robustness features after noise reduction, screening and framing preprocessing; identifying equipment abnormity through a deep learning model; a microphone array is combined to analyze the sound source direction and position abnormal equipment, and early warning information is generated; and integrating SCADA / DCS process data, and generating graded early warning and operation and maintenance suggestions. The method can adapt to a complex noise environment, accurately recognize early abnormity, provide intelligent diagnosis, support predictive maintenance and reduce fault risks.

The invention discloses a sewage plant intelligent inspection robot system and method based on multi-modal perception, and the system employs a cloud-edge-end collaborative architecture, and comprises an intelligent inspection robot, an edge calculation unit and a cloud platform. The intelligent inspection robot integrates a visual perception unit, a voiceprint perception unit, an environment perception unit and a mechanical contact unit, and synchronously acquires multi-modal data; the edge calculation unit operates the lightweight model to carry out preliminary anomaly recognition; the cloud platform realizes equipment high-confidence diagnosis, residual life prediction and maintenance work order generation by combining SCADA / DCS real-time process data through a feature level fusion diagnosis model based on a cross-modal attention mechanism; the robot has self-adaptive inspection capability and can respond to real-time abnormal events to adjust paths. According to the invention, the transformation of sewage plant equipment operation and maintenance from passive maintenance to active prediction is realized, and the intelligent inspection level and the operation and maintenance efficiency are remarkably improved.

The invention discloses a power grid industrial control system security protection scheme based on a block chain, and belongs to the field of power grid automation security. Aiming at the problems of high centralized authentication risk, weak physical layer protection and the like of a traditional industrial control system, a trusted execution environment and intelligent contract response system is constructed through fusion of a block chain distributed account book and a hardware security module (HSM). According to the scheme, equipment physical identity binding and vibration monitoring are achieved through an HSM, the consensus efficiency is optimized through an improved PBFT algorithm, instruction legality, parameter compliance and execution logic rationality are dynamically verified in combination with an intelligent contract, a security baseline is generated in real time, and the instruction stream deviation degree is monitored. The method can dynamically balance the security and real-time performance of the instruction, reduce manual intervention, improve the abnormal response efficiency to a second level, is suitable for industrial control systems such as SCADA (Supervisory Control And Data Acquisition) and the like, remarkably improves the protection capability in scenes such as DDoS attack resistance and physical tampering detection, and provides an extensible intelligent protection normal form for power grid security.

The invention discloses a hydropower station operation state intelligent prediction method and system based on multi-source data fusion. The method comprises the steps that heterogeneous data including but not limited to water level, flow, generating capacity, equipment operation parameters, environmental meteorological data, hydrological data and the like are collected in real time from multiple data sources of a sensor network, an SCADA system, a meteorological station and a hydrological station of a hydropower station; the method comprises the following steps of: preprocessing collected original data, extracting time sequence features, fusing multi-source data, taking a fused multi-dimensional feature vector as input, and learning and training through an encoder-decoder architecture of a model so as to capture a complex dynamic mode of an operation state of a hydropower station; and inputting the data acquired and preprocessed in real time into the trained AI prediction model, and generating prediction results of key operation parameters, equipment states, residual life and potential risks of the hydropower station. The method can more accurately capture the complex nonlinear relation in the operation of the hydropower station, and significantly improves the prediction precision of key parameters and risks.

The invention discloses a power distribution network source load space-time distribution collaborative prediction system based on a deep learning model, and particularly relates to the technical field of power system operation and power distribution automation. The system accesses and aligns multi-source data under a unified time base, constructs graph priori carrying version weight in combination with operation topology, and outputs multi-step prediction of each node. Dynamic updating is introduced, wherein switching, reconstruction and power flow information is obtained through SCADA / power distribution automation, and graph priori is updated by event driving according to planned / instantaneous dual modes; performing time matching of the operation and the work order / instruction and historical plan probability weighting to obtain a planning coefficient, and setting a hysteresis threshold for discrimination; events such as tripping and protection action are directly judged to be instantaneous; and the undefined but continuously existing persons are conservatively classified according to the threshold duration. And the state duration threshold is self-adaptive along with the planning coefficient and the topological disturbance intensity. The system is matched with consistency auditing and gray release, and stable operation is supported.

The invention relates to the technical field of wind power energy storage, and discloses a collaborative peak regulation method and system based on a wind power energy storage system, and the method comprises the steps: collecting the detail data and SCADA data of a power generation fan, carrying out the time sequence alignment, constructing a prediction error model, calculating a standardized prediction error, constructing a hybrid prediction model, and dynamically adjusting the model parameters; a time-function double-layer decomposition strategy is constructed to calculate an energy storage attenuation value, a multi-objective optimization model is constructed in combination with the energy storage attenuation value, cross-day balance is carried out, and a charging and discharging strategy is optimized; constructing a three-channel reward function, dynamically adjusting the weight coefficient of the reward function, and automatically adjusting the weight according to the real-time state of the power grid to realize multi-target dynamic balance; the method comprises the following steps: constructing a hybrid hierarchical control execution framework, optimizing an edge layer, obtaining a wind power fluctuation rate, judging whether to trigger super capacitor pre-charging, performing model prediction control MPC optimization, realizing credible synchronization of energy storage SOC data in combination with an intelligent contract, and performing health state early warning.

A system for AI-supported cybersecurity and regulatory compliance in energy distribution networks, consisting of: a hardware-embedded data acquisition module configured to intercept, capture, and time-stamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) via multiple communication protocols without operational latency; an FPGA-based deep packet inspection unit coupled with the data acquisition module, wherein the FPGA firmware is configured to perform line rate filtering, protocol decomposition and metadata extraction of the acquired data and forwards preprocessed packet data to an AI processing unit; an AI processing unit consisting of a multi-core central processing unit (CPU), a dedicated AI accelerator selected from a graphics processing unit (GPU) or a tensor processing unit (TPU), and a volatile memory buffer; a response orchestration module that is communicatively coupled with network management devices and operations controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation measures, including network isolation of compromised segments, enforcement of protocol encryption, and privilege revocation; and an immutable audit logging subsystem configured to record all detected events, compliance assessments, and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

The invention provides a pipeline water hammer monitoring method, device and equipment, and relates to the technical field of oil and gas pipeline monitoring. The method is applied to an SCADA system and comprises the steps that a detected trigger signal of fluid control equipment in an oil conveying pipeline is responded, a pressure signal and a flow signal in the oil conveying pipeline are obtained, and microsecond-level timestamps, corresponding to the trigger signal, of the pressure signal and the flow signal serve as time references; determining physical sparse characteristics according to the pressure signal and the flow signal; and determining whether an event corresponding to the trigger signal is a water hammer based on the physical sparse feature. According to the invention, the accuracy of water hammer positioning is improved.

The invention relates to an expert question and answer auxiliary interaction method and system based on a large water affair model, and relates to the technical field of intelligent water affair. The method comprises the following steps: firstly, constructing a problem semantic vector fusing a current intention and a historical background by deeply analyzing user interaction data and a historical session; then, SCADA time sequence data, a static knowledge graph and a historical processing record are retrieved from an external multi-source data stream by taking the vector as an index, and are converted into a situation feature vector reflecting the running state constraint at the current moment through semantic mapping and time sequence feature engineering. By jointly inputting a problem semantic vector and a situation feature vector into a pre-trained water affair field large model, the system can perform verification reasoning under strict working condition constraints, and encapsulates a reasoning result into a structured expert reply based on a visual template. The technical problems that in the prior art, a general model is prone to generating illusion, the retrieval result accuracy is low, and end-to-end intelligent diagnosis cannot be provided are effectively solved.

The invention discloses an intelligent identification power distribution network fault prediction method fusing relay protection data, and belongs to the technical field of a power transmission and distribution network of a power system, and the method comprises the following steps: S1, collecting and fusing multi-source heterogeneous data; s2, constructing feature engineering and a dynamic knowledge graph; s3, multi-modal deep learning model training is carried out; s4, carrying out incremental learning and adaptation on a model based on transfer learning; and S5, fault early warning and decision support are carried out. The relay protection action event sequence and the fault recording data are fused with traditional SCADA measurement data, standing book information and historical records, the limitation that only SCADA or a single data source is used in a traditional method is broken through, the protection action event reflects the most direct response of the system to abnormity, and fault symptoms can be captured earlier and more accurately.

The invention discloses a power grid power flow real-time calculation and state estimation method and system based on a PMU, and belongs to the technical field of electric measurement and electric fault positioning. The method comprises the following steps: synchronously measuring electrical variables, including a voltage phasor and a current phasor, of a power grid node through PMU equipment to obtain synchronous phasor data, and verifying time and topology consistency; the power grid observability grade is judged based on PMU configuration node distribution corresponding to the verified electric measurement data; directly generating a real-time state estimation result through linear calculation based on PMU electric measurement data if the whole domain is observable; and if a part is observable, fusing PMU and SCADA data, carrying out nonlinear state estimation through a weighted least square method, distributing a high weight for the PMU electrical measurement data, and dynamically adjusting the SCADA weight to suppress bad data. According to the method, through observability adaptive judgment, the precision and efficiency of state estimation are improved, a data basis is provided for power grid fault positioning, and the method is suitable for practical application scenes of various PMU coverage degrees.

The invention provides multi-station additive cluster accurate feeding equipment based on a PLC and SCADA, the multi-station additive cluster accurate feeding equipment based on the PLC and the SCADA comprises a material distributing assembly and a stirring assembly which are fixed at the top of a support, the stirring assembly is located on one side of a feeding assembly, a plurality of material distributing pieces distributed at equal intervals are rotationally arranged in the feeding assembly, and the stirring assembly is located on the other side of the feeding assembly; a plurality of material distributing pieces are arranged on the base, material distributing barrels are arranged in the material distributing pieces, material distributing grooves are formed in the surfaces of the material distributing barrels, scraping assemblies are arranged in the material distributing grooves in a sliding mode, the scraping assemblies are attached to the inner walls of the material distributing grooves, and one ends of the scraping assemblies penetrate through one ends of the material distributing barrels and are fixedly connected with the output ends of the material distributing pieces; according to the multi-station additive cluster accurate feeding equipment based on the PLC and the SCADA, a linkage mode is adopted, the inner wall of the material distributing groove can be cleaned in time in the material taking process, the phenomenon that high-viscosity materials are left is avoided, and the risk that the residual materials go bad or are mixed into a new batch is reduced to a certain extent.

The invention provides a power distribution communication network topology optimization system based on the Internet of Things, and relates to the technical field of power Internet of Things, which obtains and fuses topology and fault state data from a power grid SCADA system and slice performance and bearer network state data from a 5G network management system in real time. The system can construct a fusion state vector comprehensively describing the power grid-communication network joint operation situation. The vector is input into an intelligent decision-making model based on imitation-reinforcement learning training so as to generate an optimal action instruction in real time. The instructions are accurately analyzed into dynamic adjustment commands for 5G network slice resources and routing policy change instructions for key service flows. According to the concept, the problems that in an existing scheme, a communication network perceives the power grid state blindly, and slice resource configuration is rigid are solved, communication resources can be instantly and intelligently inclined and reconfigured according to the real-time and specific requirements of the power grid, and the stability and safety of power grid operation are improved.

The invention relates to the technical field of fan blade health monitoring, in particular to a vibration-illumination double dynamic compensation fan blade cracking early warning method and system. The method comprises the following steps: triggering and capturing a video key frame at a key angle through a rotation angle sensor, and carrying out image coarse alignment by utilizing SIFT feature matching; a corrected motion vector field is generated by combining physical parameters of the rotating speed and the angular acceleration, vibration time sequence compensation is carried out, and illumination time sequence compensation is completed through Gamma correction by taking a clear frame as a reference; an improved DeepSORT algorithm is adopted to associate crack tracks with crack mask contour similarity, evolution characteristics are extracted and fused with SCADA working condition data, an expansion rate model is established, and the remaining life is predicted; early warning grades are divided according to the service life and the expansion rate, four-dimensional operation and maintenance data are fused to output differential operation and maintenance strategies, and model parameters are optimized through feedback data. According to the invention, the accuracy and early warning reliability of crack detection in a complex environment are effectively improved.

The invention provides a limited power compensation method and system based on multi-scale time convolution and a Transform network, and the method comprises the steps: S1, obtaining the historical data of a wind turbine generator SCADA, and constructing a high-quality feature data set through feature engineering and abnormal value processing; s2, respectively inputting the feature data set into a multi-scale TCN module and a Transform module, and extracting local time sequence features and global dependency features of different time granularities in parallel; s3, adaptively fusing the multi-scale features and the global features through an attention mechanism; and S4, predicting future available power by using the full-connection network. The method effectively solves the problems that a traditional statistical method has high requirements for data stability and a recurrent neural network is insufficient in long-range dependence modeling capability, and remarkably improves the accuracy and robustness of wind power prediction in a power limiting scene.

The invention discloses a secondary equipment hidden fault identification method and system based on transient state-steady state data space-time alignment. The method comprises the following steps: collecting transient state recording data and steady state SCADA (Supervisory Control And Data Acquisition) data; unified time service is carried out on transient state and steady state data, and a time mark error is corrected; performing Lagrange interpolation resampling on the transient data to generate a virtual sequence aligned with a time axis of the steady-state data; constructing a cost matrix for the resampled transient sequence and the resampled steady-state sequence, and backtracking a shortest path to obtain an aligned fusion sequence; inputting the fusion sequence into a multi-scale feature fusion network, and outputting a fault type and confidence; the system comprises a data acquisition module, a clock drift correction module, a resampling alignment module, a dynamic time warping module and a fault feature extraction module. According to the method, the fusion problem caused by sampling rate difference, clock drift and dimension difference of transient and steady data is solved, millisecond-level space-time alignment is realized, and the precision of data fusion is remarkably improved.

The invention discloses a smart power grid intrusion detection method and system based on event triggering. Establishing a federal learning architecture and defining an optimization target and an event triggering mechanism; training an intrusion detection model according to the SCADA collected data to serve as a global model for federal learning architecture maintenance; the server side selects the client side based on a preset selection proportion and sends the global model to the selected client side; the client receives the global model and loads local SCADA acquisition data to execute the global model to perform intrusion detection on the smart power grid; the client verifies the local model parameters obtained by the current round of learning through an event trigger mechanism, and determines whether to upload the local model parameters to the server in the current round; and the server side aggregates the local model parameters uploaded by the client side and updates the global model parameters to enter the next round of learning. According to the method, through combination of federal learning and an event triggering mechanism, the performance of intelligent power grid intrusion detection is improved, the communication overhead is reduced, and the learning efficiency is improved.

Disclosed are a method for distribution of PHASE to monitor the operating state of a power system by using heterogeneous data obtained from measurement of SCADA and a time synchronized PMU and a method for processing defect data in mixed DSE by using same. The method for distribution of PHASE to monitor the operating state of a large scale power system includes the steps of: defining an extended state variable and an extended state variable set for each region; performing a SCADA-based DSE by using a SCADA measurement value for each region and a covariance matrix thereof, and parallelly performing a PMU-based DSE by using a PMU measurement value for each region and a covariance matrix thereof; and mixing the estimation results of the SCADA-based and the PMU-based DSE algorithms so as to perform a phasor-aided normalized residual test and a general normalized residual test.

The invention provides a monitoring method for a clearance value of a fan blade, a fan control method and a medium, and the monitoring method comprises the steps: obtaining an actual strain parameter, meteorological data and SCADA data when a radar signal fails; inputting the meteorological data and the SCADA data into a preset digital twin system to obtain a predicted strain parameter; when the difference value between the predicted strain parameter and the actual strain parameter is greater than an error threshold value, adjusting the system parameter of the digital twin system until the error value is less than or equal to the error threshold value; and obtaining the clearance value based on the adjusted digital twin system.