93 results about "SCADA" patented technology

The application provides a PLC information security analysis model construction method based on variable association, which comprises the following steps: analyzing PLC code to obtain PLC variables, combining variable pairs according to network blocks, splicing variable pairs in a global range to obtain global variable dependency relations, constructing a weighted PLC program dependency graph according to the global variable dependency relations and SCADA log features, cleaning the relationship edges of the weighted PLC program dependency graph, and enhancing the dependency strength of the dependency edges of the cleaned weighted PLC program dependency graph to generate a PLC information security analysis model. The method can simultaneously act on two levels of structure optimization and weight optimization, and realize stable reconstruction of PLC dependency relations. Not only can the method effectively remove redundant dependency edges, but also can maintain the stability of key dependency relations under the conditions of data drift and noise interference, and significantly improve the accuracy and robustness of the PLC information security analysis model.

The utility model provides a kind of wisdom gas field station safety dynamic monitoring system, belong to wisdom gas supervision technical field, the utility model carries out data acquisition by integrating high point monitoring unit, low point monitoring unit and industrial control unit, solve the problem that SCADA system and video monitoring platform cannot be linked, simultaneously can carry out all-around data acquisition to field station.The utility model technical scheme includes high point monitoring unit, low point monitoring unit and industrial control unit;High point monitoring unit, low point monitoring unit and industrial control unit are connected POE switch respectively, POE switch is connected core switch, core switch is also connected storage server and digital intelligence collaborative platform respectively;Industrial control unit includes serial port equipment and serial port server, serial port equipment is connected serial port server;Serial port server is connected with POE switch.

The application discloses a fan control parameter online optimization method and system based on digital twinning, which inputs real-time SCADA data into a fast digital twinning model, performs millisecond-level state estimation to meet the real-time response requirement of the control system. At the same time, the slow digital twinning model is driven by buffer data for deep calibration, a high-fidelity state log containing physical mechanisms is generated, and the knowledge distillation technology is used to migrate the physical knowledge of the slow model to the fast model for periodic correction of the fast model. Finally, the modified fast model is used for rolling optimization under the model predictive control framework to output the optimal control sequence. The scheme effectively overcomes the defects of the traditional physical model that the calculation time is too long, and the pure data-driven model that is easy to drift and lacks physical interpretability, while ensuring the millisecond-level response speed, and improving the physical consistency of model prediction and the robustness of long-term operation.

This disclosure provides an LSTM-attention fusion method and system for predicting and warning gearbox oil temperature. The method includes: collecting SCADA data from a wind turbine gearbox, performing data cleaning, normalization, and feature selection; constructing an offline LSTM-attention fusion network comprising an input layer, a second LSTM layer, an attention layer, and a fully connected output layer, and training it using a health dataset to obtain the optimal prediction model; collecting monitoring data in real time, preprocessing it, and inputting it into the optimal model to obtain the predicted oil temperature value; calculating the residual between the predicted and actual values, and using the EWMA algorithm to calculate dynamic upper and lower limits of the residual control as warning thresholds based on the residuals; and determining the fault based on the comparison between the residuals and the thresholds. Through the hierarchical collaboration of LSTM and attention, the prediction accuracy under varying operating conditions and the sensitivity to subtle fault characteristics are improved. Combined with dynamic threshold warning, the robustness and interpretability of the system are enhanced.

Systems and methods of executing secure write operations within combined supervisory control and data acquisition (SCADA) and programmable logic controller (PLC) systems including receiving an operator interaction at a user interface of a SCADA system, generating an encoded secure write instruction based on the operator interaction in a tamper-resistant secure format at the SCADA system, transmitting the encoded secure write instruction from the SCADA system to a PLC, extracting an operation instruction comprising an operation and a plurality of operation parameters by decoding the encoded secure write instruction at the PLC, and one of executing the operation instruction and rejecting the operating instruction at the PLC.

The application discloses a kind of high-voltage substation intelligent monitoring and protection method and system based on cloud edge coordination architecture, and relates to power system control technical field, which comprises: S100: data acquisition;S200: preliminary detection of fault;S300: fault type identification;S400: protection decision;S500: cloud coordination;S600: system recovery.Adopt data hierarchical processing mechanism, guarantee the real-time of key protection function, simultaneously utilize cloud computing capacity to realize advanced analysis.Compared with traditional SCADA system, the cost of the application is reduced by more than 60%, deployment is more flexible, scalability is stronger, and supports fault prediction and preventive maintenance, significantly improves the safety and reliability of high-voltage substation.

This invention discloses a method for monitoring wind turbine blade deformation, comprising the following steps: S1, opening equalizing holes on the blade baffle and installing flow meters inside the holes to acquire airflow data during blade operation and transmitting the data to the main control system; S2, collecting SCADA data from the turbine generator set and transmitting the SCADA data to the main control system; S3, obtaining airflow data at different speeds and positions during normal blade operation based on the turbine generator set SCADA data and airflow valve data, and establishing corresponding data for different speeds and rotational positions during normal blade operation; S4, setting threshold values ​​for airflow valve flow data at different speeds and positions of the blade; S5, monitoring the airflow data and SCADA data of the airflow valve in real time, and issuing an alarm when the airflow data exceeds the set threshold. This method deeply integrates the internal and external airflow of the blade with operating parameters, utilizing the internal gas flow of the blade to reflect the deformation state, thus adapting to the complex operating environment of wind turbine blades while also considering accuracy, economy, and practicality.

This application discloses a method, apparatus, electronic device, and medium for anomaly detection in photovoltaic module data based on an integrated unsupervised algorithm. The method may include: establishing and training corresponding base learners based on multiple unsupervised algorithms using a SCADA dataset; determining the anomaly score and weight for each base learner; and performing anomaly detection based on the anomaly score and weight. This invention can improve the automation level of detection, reduce manual workload, promptly detect and handle anomalies, effectively prevent the escalation of faults, extend the service life of photovoltaic modules, thereby improving the power generation efficiency of the entire system and reducing operation and maintenance costs.

The application discloses a kind of linkage automation control systems for sewage treatment whole process, it is related to sewage treatment control technical field, sequentially include: acquisition online process variable, executor feedback, laboratory reference value and upstream event data, form state candidate set and credible evidence package;Based on credible evidence package, working condition identification, effective domain determination and candidate control generation;According to the risk level of execution object, the correlation of associated state variable Relevant credibility and model effective domain state, multi-level authority mapping and degradation are carried out to candidate control vector;Candidate set value with execution qualification is written into the shadow label area of existing PLC / SCADA, and according to the receipt execution rollback and event window update.The system can reduce the error writing under abnormal working condition while retaining the existing control system, can improve the continuity of control chain, execution robustness and operation adaptability and facilitate engineering implementation.

The application provides a rectifier transformer cooperative voltage regulation management method and a rectifier transformer for aluminum foil formation, relates to the fields of SCADA distributed power grid control and power conversion technology, and the method acquires multi-modal slow change characteristic data to perform dissipation evaluation calculation, generates a global fatigue damping coefficient representing cumulative attenuation; meanwhile, based on photovoltaic side electrical transient fluctuation and node load capacity, local transient voltage compensation scalar is calculated; the two are input to the joint mapping logic to output a dynamic compensation weight matrix, and the transient cooperative control instruction for indicating the bottom converter to execute trigger angle bias is generated according to the matrix; and the damping attenuation degradation reconstruction strategy triggered by communication timeout is configured. The application realizes accurate voltage stabilization and group control load balancing in AC / DC conversion network, effectively overcomes the limitation of traditional single-point anti-disturbance which does not take into account the physical attenuation of equipment, and ensures the absolute stability and high robustness of DC power supply under the condition of input power fluctuation.

The multi-level data correlation fusion photovoltaic and energy storage flexible system short-term load forecasting method constructs a multi-source heterogeneous data acquisition and preprocessing system to collect and process uncertain load data such as photovoltaic output power and building electricity load. Dynamic weighted fusion and correlation analysis are used to calculate the dynamic correlation weight between multi-source data, and multi-scale feature extraction is performed to construct a fusion data set. A multi-modal attention fusion forecasting model is developed, and a spatio-temporal joint attention mechanism is designed to realize explicit modeling of long-range correlation and nonlinear interaction features in high-dimensional data. An intelligent load forecasting and decision support platform is deployed, and the forecasting model is integrated into the energy management system. Combined with dynamic pricing and energy storage control strategies, comprehensive reports are generated, and real-time interaction and policy issuance are realized through the OPC UA protocol and the SCADA system. The present application effectively improves the accuracy of load forecasting and the stability of system operation, and realizes the optimization management and efficient utilization of energy.

This invention belongs to the field of power system restoration technology, and relates to a method and apparatus for dynamic partitioning of new energy power systems during black start based on Voronoi diagrams. The method includes: mapping black start power sources to Voronoi generators and mapping loads to be restored to Voronoi spatial points; acquiring generator capacity information and generating reliable capacity for new energy black start power sources; generating adaptive weights based on capacity information, reliable capacity, and load density; adjusting the diffusion speed of generators in different directions according to the adaptive weights and performing weighted Voronoi diffusion to obtain preliminary partitions; performing partition constraint verification during diffusion and controlling the corresponding generator to exit diffusion when the constraint reaches the upper limit; triggering update calculation based on new energy output information fed back by SCADA and outputting the partition results. The technical solution of this application can realize online partition updating and executability determination.

This invention relates to the field of wind power technology, and more particularly to an intelligent method for acquiring wind farm impedance by combining simulation and measured data. The steps include: constructing a simulation sample set using a wind turbine with parameters similar to those of the actual wind turbines; constructing a labeled measured sample set and an unlabeled sample set using the actual wind turbines; building an artificial neural network to form a pre-trained model; fine-tuning the pre-trained model using a hybrid loss function to form a wind turbine impedance identification model; acquiring the phase angle at each wind turbine using a SCADA system; inputting the steady-state operating data and scanning frequency of each wind turbine into the wind turbine impedance identification model to obtain the dq impedance of each wind turbine in its own coordinate system; performing coordinate transformation using the phase angle at the wind turbine to convert the dq impedance to the admittance in the global xy coordinate system; and calculating the overall wind farm impedance based on the extended connection matrix and combined with the wind turbine admittance. This invention combines simulation and measured data, effectively improving the accuracy of the obtained impedance.

This invention provides a visualization-based automatic dispatching method and system for operators. The method acquires real-time operating status parameters of power grid equipment in the target dispatch area through a SCADA system, and collects the working status and process of operators through an operation monitoring terminal. The dispatch center performs status diagnosis on the operating status parameters of the power grid equipment based on a preset status diagnosis model, obtaining fault status types and levels, and synchronizes the operating status parameters, status diagnosis results, and operator working status to a GIS visualization platform. The GIS visualization platform displays the operating status parameters of the power grid equipment in the target dispatch area, status diagnosis results, and operator working status in real time. The dispatch center automatically determines the target operators based on the status diagnosis results and operator working status. This invention achieves real-time power grid status diagnosis, dynamic matching of operators, and visualization, improving dispatching efficiency and accuracy.

This invention discloses a wind turbine fault detection method and system that integrates seasonal features and an attention mechanism. The method includes: acquiring historical SCADA data of the wind turbine for preprocessing and introducing seasonal labels to construct a standardized input vector; inputting the standardized input vector into an attention-enhanced bidirectional long short-term memory network to output predicted values ​​of normal behavior for each key component of the wind turbine; performing seasonal standardization mapping on the original residual sequence based on the corresponding seasonal labels to obtain a deseasonalized standardized residual sequence; calculating the small offsets of each key component in the standardized residual sequence using an exponentially weighted moving average operator; and issuing a fault alarm for the corresponding key component when the offset statistic exceeds the detection threshold of that component. This invention integrates seasonal features and a deep attention mechanism, using bidirectional long short-term memory to model component thermal response and seasonally standardized residuals to eliminate environmental interference, significantly improving the accuracy of early fault diagnosis for key wind turbine components.

The application discloses a main bearing damage diagnosis method based on flow-heat coupling dynamic topology and a physical information graph autoencoder, and comprises the following steps: one-dimensional residual feature extraction is performed on multi-source SCADA time series data to obtain time series feature vectors of nodes; a Nusselt number is calculated, edge weights of a directed adjacency matrix are dynamically calculated based on the Nusselt number, a temperature gradient gating function and a thermal resistance parameter, and a dynamic flow-heat topology graph is constructed in combination with the time series feature vectors; a graph encoder is used to perform spatial aggregation on neighbor node features along a directed edge to obtain a predicted temperature of the main bearing at the next moment; a total loss function is calculated based on mean square errors of the predicted temperature and an actual temperature and physical consistency loss, and training is constrained to obtain a state reconstruction model; a residual error between the predicted temperature output by the model and the actual temperature, and a decay factor calculated based on a bearing fatigue life equation are used to dynamically adjust a warning threshold, and damage warning is triggered when the residual error exceeds the threshold.

A system and method for capturing, monitoring, and utilizing fugitive combustible gases from natural gas compressors and engines. The system comprises collection points for capturing gases from multiple sources, a filtration system, a control valve, a programmable logic controller for monitoring and control, and an engine air intake system for utilizing the gases as supplementary fuel. Advanced AI-driven monitoring capabilities transform sensor and emissions data into predictive maintenance insights, enabling early detection of equipment failures through pattern analysis. The system integrates FTIR analyzers, electrochemical sensors, and SCADA connectivity to provide comprehensive emissions monitoring and regulatory compliance documentation while optimizing operational efficiency through probability-based failure predictions.

The application relates to an automatic production line intelligent improvement data conversion comprehensive application practical training system, which comprises an intelligent storage unit, an intelligent identification and transfer unit, an intelligent assembly and detection unit, an AMR carrying robot unit, a control unit, an SCADA unit, a WMS storage management unit, an MES production management unit, a robot data acquisition unit and a digital twin unit. The system can embody a typical automatic production line, students can contact advanced intelligent manufacturing technology, and corresponding use, debugging and maintenance and repair skills of intelligent manufacturing can be cultivated.

The application discloses an AI-based SCADA data request behavior anomaly detection method and system, which first performs multi-modal serialization and time sequence alignment on original request data flow and physical state data flow, and extracts deep context features of communication behavior and physical state in parallel by using a double-tower Transformer structure. Further, through a cross-modal gating interaction mechanism, consistency and inconsistency features between communication behavior and physical state are explicitly decoupled and enhanced, and joint context encoding containing physical-information causal logic is generated. Finally, based on joint reconstruction error analysis and dynamic threshold determination of communication and state sequences, accurate identification of abnormal behavior is realized. The method effectively improves the detection accuracy and robustness of the industrial control system when facing deep camouflage threats by constructing a logical constraint model of physical state on communication request.

This application discloses a method, apparatus, device, and storage medium for generating SCADA configuration screens based on generative AI, relating to the field of industrial control technology. The method includes: collecting natural language design requirements using an intent understanding module and the perception layer in an AI Agent cognitive architecture; obtaining state acquisition data and integrating it before inputting it to the cognitive layer; the inference engine and large language model in the cognitive layer decompose the configuration design task of the integrated data, generating a configuration design operation instruction set and inputting it to the execution layer; the execution layer processes the operation instruction set to obtain each component to be laid out in the configuration screen and its configuration data; the layout engine determines the component layout result in the configuration canvas; the variable binding engine binds and maps each component to the real-time data points of the SCADA system; and the target configuration screen is generated by combining the Shell command security module and the security layered architecture to improve the efficiency of generating SCADA configuration screens.

This invention discloses a wind turbine blade icing detection method based on a lightweight gated spatiotemporal Transformer, belonging to the field of wind turbine blade icing detection technology. The method first collects multivariate data from the wind farm's SCADA system and preprocesses it, then inputs it into a core model for identification. This model mainly consists of two core components: a spatiotemporal gated network and a lightweight Transformer encoder. The former utilizes asymmetric convolution and bidirectional GRU to extract spatial correlations and bidirectional temporal dependencies, and adaptively enhances key spatiotemporal features and suppresses noise through parallel feature and temporal attention and gated fusion mechanisms. The latter perfectly balances global dependency modeling and parameter compression through gated multi-head attention and gated linear units. This invention maintains high detection accuracy while reducing the number of parameters, possessing practical engineering value and real-time deployment capability at the edge.

The application discloses a kind of based on topological-quantity measurement consistency analysis distribution network topology real-time checking and early warning method, comprising the following steps: first, with high-precision synchronous measurement data as time reference, time alignment is carried out to multi-source measurement data and SCADA topological data;Second, construct the quantification analysis model based on weighted least square method, calculate the consistency index of whole network to determine the topological correctness;When inconsistent, the branch state suspicious degree is calculated by fusing state estimation residual and voltage space correlation, abnormal positioning and early warning are realized;Finally, generate backup topology set and evaluate its consistency, select the optimal topology and output for correction.The application solves the problem of distribution network partition basic distortion caused by uneven quality of distribution network measurement data, realizes the quantification analysis and real-time topology checking of the internal consistency of topology and real-time measurement data under the online operation state of distribution network, and provides a reliable foundation for subsequent application of distribution network.

The invention discloses a steel pipe production process material tracing system and method. The system comprises a longitudinal submerged arc welded pipe production line logistics tracking mechanism. A steel plate jet printing system and a steel pipe jet printing system of an automatic jet printing system of the tracking mechanism are used for jet printing of steel plate numbers and steel pipe numbers correspondingly. The automatic identification system automatically acquires plate number / pipe number images of the steel plates / steel pipes by using industrial cameras arranged beside each process, performs positioning and identification on plate number / pipe number information in an acquired video stream through an image processing algorithm, and uploads the identified plate number / pipe number to an MES and SCADA database; the communication network uses an optical fiber ring network to provide nearby access for the mark spraying equipment, the industrial camera and the edge computing unit, a camera triggering sensor is arranged, and steel plate / steel pipe in-place triggering recognition is achieved. The system has the beneficial effects that the automatic identification of plate numbers / pipe numbers is realized through the automatic identification system, and the plate numbers / pipe numbers are transmitted to the SCADA real-time database and the MES relational database, so that the digital control of the steel pipe production process is realized.

This invention discloses a parallel processing and debugging method for stateful measurement load balancing and a power SCADA system. It achieves fixed binding between stateful measurement points and threads through a global variable marking-caching association algorithm combined with dynamic balancing optimization logic. A load rebalancing mechanism addresses thread data skew, and a new architecture identification module adapts to centralized and distributed coexistence scenarios. A three-layer register model is constructed using bit-based shared memory control technology, and a measurement point clustering intelligent debugging module is built using a hook-based embedding mechanism and keyword indexing to achieve rapid location and visual debugging of abnormal measurement point logs. Finally, data availability and business real-time performance are ensured through the linkage of a real-time database and a relational database, along with a hierarchical alarm service. This invention is compatible with existing SCADA data processing pipeline architectures and can significantly improve the stability, efficiency, and debugging convenience of stateful measurement data processing, making it suitable for real-time processing scenarios involving massive amounts of power measurement data.

The application discloses a coal mine underground power feeding switch remote monitoring system based on SCADA, relates to the technical field of coal mine electrical equipment monitoring, and comprises a main control room host computer system, an engineer station and an operator station are arranged in the main control room host computer system, wherein the engineer station is used for acquiring the number, parameter type and corresponding parameter data of the underground power feeding switch, and displaying on a configuration picture, the operator station is connected with the engineer station, and sends a close-open control instruction to the underground power feeding switch. The application constructs a main channel and a side channel, introduces a tentacle detection mechanism, uses a packaging conveying unit and a tentacle unit, realizes pre-detection of a transmission channel before data packet transmission, collects the signal strength, error code rate and transmission time delay of the next transmission channel in real time, compares with a preset threshold value dynamically, and completes channel switching before the data packet enters a poor link section.

The application discloses a long-term wind power multi-site prediction method based on BiLSTM-Transformer, comprising the following steps: obtaining original SCADA data of multiple wind turbines, extracting and processing multi-dimensional feature vectors in the data to form a unified feature vector sequence; adopting a leak-proof sliding window protocol, defining an input review window length and a future prediction time interval, and dividing the unified feature vector sequence into a training sample set and a verification sample set in chronological order according to timestamps; inputting each input feature tensor in the training sample set into a pre-constructed BiLSTM-Transformer model for training, verifying by using the verification sample set, and obtaining an optimal model; inputting a to-be-predicted input feature tensor into the optimal model, and outputting a wind power prediction sequence of a to-be-predicted wind turbine within a future prediction window. The application improves the accuracy of cross-month prediction and the generalization ability across sites.

The application provides an industrial control network anomaly detection method based on frequency domain feature and process logic association, which comprises the following steps: first, collecting industrial control network traffic, extracting communication interval timing features, and after denoising preprocessing, obtaining frequency domain main frequency features through discrete Fourier transform; a mathematical mapping relationship between the main frequency and the process control period is established, a dynamic process baseline is constructed by weighted iteration, and an abnormal threshold is adaptively set; the abnormality is determined through frequency domain drift and process logic fluctuation double check, and finally the detection real-time is optimized relying on the sliding window mechanism. The application does not need protocol deep analysis, can adapt to various industrial control protocols, accurately identifies process tampering type hidden attacks, has low false positive rate and detection delay <100 ms, is suitable for security protection of various industrial control systems such as SCADA, DCS and PLC, and effectively fills the blind area of industrial control hidden attack detection.