2513results about How to "Safe and stable operation" patented technology

The invention discloses a real-time fault monitoring Internet of Things system for a chemical production equipment cluster, relates to the technical field of equipment fault monitoring control, and is used for solving the problem of poor fault monitoring precision of the chemical production equipment cluster. Comprising a data acquisition module, a comparative analysis module, an equipment abnormity determination module and a fault trend prediction module. According to the method, data integrity is ensured by collecting data of different frequencies in a layering manner, complementing and correcting abnormal values, a baseline model is established, multi-dimensional features of normal states of equipment are extracted through an auto-encoder, operation features are quantified through a Gaussian mixture model, deviation judgment is achieved, an isolated forest and principal component analysis are combined, and the accuracy of deviation judgment is improved. According to the method, an abnormal state is comprehensively judged according to the deviation degree, the isolated score and the principal component deviation degree, an alarm signal is generated, meanwhile, ARIMA and LSTM models are adopted for predicting short-term and long-term trends in a fused mode, a comprehensive predicted value is formed, control measures are automatically executed when the trends are abnormal, and the operation safety of equipment is improved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses an aviation converter fault detection method based on an information fusion algorithm, and the method comprises the steps: collecting the operation data of an aviation converter, and constructing a data set; performing time information extraction and space information extraction on the data in the data set, and performing feature fusion on time features and space features based on a variational auto-encoder to obtain spatio-temporal data; constructing a Bayesian deep learning model, and training and optimizing the Bayesian deep learning model; inputting the spatio-temporal data into the trained and optimized Bayesian deep learning model for processing, outputting a numerical value representing a fault occurrence probability, and taking a variance based on the probability numerical value as uncertainty estimation; presetting a probability threshold, and when the output probability is greater than the threshold, determining that the converter has a fault; otherwise, judging that the operation state is normal; a more accurate and reliable diagnosis result is provided for fault detection, and the method has high adaptability and robustness in practical application.

The invention discloses an electrical switch cabinet detection method based on the Internet of Things, and particularly relates to the technical field of electrical switch cabinets. A sensor is installed inside or outside a switch cabinet, temperature, humidity, current, voltage and gas concentration are collected, data are transmitted to an edge computing node for preprocessing by using a wireless communication technology, the preprocessed data are uploaded to a cloud platform, storage and trend analysis are performed at a cloud end, and the data are analyzed based on big data analysis and an artificial intelligence algorithm. According to the invention, the system can predict potential faults, a user can check the operation state and the prediction result of the equipment in real time through the cloud platform, and carries out the emergency shutdown or equipment scheduling operation remotely according to the early warning information, thereby effectively solving a reliability problem caused by communication interruption or data delay in an existing Internet of Things system, and improving the safety and intelligent level of the monitoring of the switch cabinet.

The invention relates to the technical field of synchronous resonance suppression, and discloses a synchronous resonance suppression method and system for a wind power plant. The method comprises the following steps: performing group vibration characteristic decomposition processing on system impedance characteristics and key frequency monitoring points to obtain a fan group disturbance sensitivity index and an oscillation source positioning result; performing impedance reconstruction processing on the disturbance sensitivity index and the grading early warning threshold parameter to obtain a fan group cooperative damping parameter and a power dynamic distribution coefficient; and performing resonance link tracking processing on the oscillation source positioning result, the fan group disturbance sensitivity index and the power dynamic distribution coefficient to obtain a key branch impedance compensation value and a reactive power regulation coefficient, and performing closed-loop correction processing to obtain a suppression control instruction and a dynamic correction parameter. According to the invention, through establishing a complete technical scheme of resonance characteristic identification, risk assessment, group vibration analysis and cooperative control, accurate identification and effective suppression of a resonance problem in a wind power plant range are realized.

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention discloses a power grid fault early warning system based on a topology change scene, and relates to the technical field of power grid fault early warning. The topological graph construction module constructs a network topological graph; for each element in the network topological graph, the abnormity positioning module determines whether the element is abnormal or not according to the contrast parameter and the actual parameter; the topology reconstruction module is used for obtaining a setting topological graph according to the element abnormity, and substituting the setting topological graph into the Bayesian compressed sensing model to obtain a target network topological graph; the fault probability calculation module is used for calculating the fault probability of each node; and the setting value determining module is used for determining the setting value of each node according to the fault probability of each node. Network topology reconstruction is carried out through the Bayesian compressed sensing model, the adaptability and flexibility of the early warning system are improved, the efficiency and reliability of power grid fault early warning are improved by calculating the fault probability of each node and quantifying the fault risk of each node in a power grid, and safe and stable operation of a power system is guaranteed.

The invention relates to the field of multi-energy complementary clean energy systems, in particular to a water-wind-light-storage multi-energy complementary clean energy base capacity configuration method and system, and the method comprises the following steps: collecting the related basic data of a hydropower station, wind power, photovoltaic, pumped storage and power transmission lines in a research area; analyzing and determining the wind and light electricity abandoning rate, the annual utilization hours of the power transmission channel comprehensive internet access channel, the comprehensive utilization demand of the reservoir and the pumping power limitation of the pumped storage power station; constructing a water-wind-light-storage multi-energy complementary clean energy base capacity configuration double-layer nested optimization model; and obtaining the optimal capacity configuration scheme of the clean energy base and the output process of each power supply under the capacity configuration scheme. Technical support can be provided for planning, development and construction of a watershed or regional water-wind-light-storage clean energy base, the wind-light-electricity absorption capacity of a hydropower station and a pumped storage power station is effectively improved, the wind-light-abandoning rate of a power grid is reduced, safe and stable operation of the power grid is guaranteed, and the energy conservation and emission reduction target of China is smoothly achieved.

The invention discloses an insulation detection system supporting a direct-current high-voltage energy storage system, which belongs to the technical field of insulation detection and comprises a data acquisition module, a state analysis module, a detection module and an alarm module. Wherein the data acquisition module is used for acquiring leakage current data and environmental parameter data in real time; the state analysis module is used for calculating an insulation state evaluation value according to the leakage current data and the environmental parameter data; the detection module is used for selecting an alternating current superposition method, a direct current leakage current method or a resistance voltage division method to perform insulation detection according to the insulation state evaluation value; and the alarm module is used for judging whether the detection result is abnormal or not and giving an alarm when the detection result is abnormal. By comprehensively monitoring the direct current leakage current and the environmental parameters and dynamically selecting the optimal insulation detection method, efficient and accurate evaluation of the insulation state of the direct current high-voltage energy storage system is achieved, insulation faults are effectively prevented, and safe and stable operation of the system is guaranteed.

The invention discloses a multi-scale feature fusion fault prediction method for a high-speed rail bearing. The method comprises the following steps: acquiring a fault vibration signal of the high-speed rail bearing through a vibration signal comparison technology; noise and interference signals of the high-speed rail bearing fault original vibration signals are removed through an adaptive filter; performing multi-level decomposition on the vibration signal by using wavelet packet decomposition, extracting sub-band signals of different frequency bands, calculating instantaneous frequency and energy distribution of the sub-band signals by using Hilbert-Huang transform, and forming a time-frequency-energy three-dimensional feature map; carrying out weight distribution on each feature dimension in the three-dimensional feature map by adopting a self-adaptive weighting algorithm; constructing a training data set through the time-frequency-energy three-dimensional feature map and the fault type label; and training the long short-term memory network LSTM by using the training data set, constructing a fault prediction model, and performing fault prediction on the high-speed rail bearing. According to the invention, accurate prediction of the high-speed rail bearing fault is realized, and the accuracy of fault detection is improved.

The invention relates to the technical field of power grid coordination control, and provides a coordination control method for a smart power grid, which comprises the following steps: S1, collecting voltage, current, frequency and load data in real time by deploying sensors and monitoring equipment, S2, processing the data by using a big data analysis technology, extracting load trend and fault early warning information, and carrying out early warning on the load trend and the fault early warning information. Real-time data collection is realized by deploying sensors and monitoring equipment, data analysis and load prediction are performed by using big data and a machine learning technology, an optimized objective function is constructed, fine coordination and a global optimal solution are realized by using a multi-agent system and a distributed optimization algorithm, and meanwhile, energy privacy is protected. The reliability of the power grid is improved through intelligent fault detection and a quick response mechanism; according to the technical scheme, interaction between the power grid and users is enhanced, demand side management is achieved, an efficient safety monitoring system is established, multi-level protection measures are taken, safe and stable operation of the power grid is ensured, and the problem that optimization and coordination are not comprehensive in the prior art is solved.

The invention provides an electric heating gas energy storage collaborative charge state disturbance suppression optimization method, and relates to the technical field of information, and the method comprises the steps: carrying out the filtering processing of a second-level disturbance signal in the real-time operation data of a power grid through an adaptive Kalman filtering algorithm, so as to improve the signal-to-noise ratio; performing time-frequency domain feature extraction on the filtered disturbance signal by using a wavelet analysis method, and judging the type and severity of the disturbance signal in combination with a preset disturbance mode library; and according to the type and characteristics of the disturbance signal. According to the method, an electric energy storage collaborative optimization control model is constructed by adopting a robust optimization theory, the uncertainty of disturbance signals and electric energy storage characteristics is considered, an optimal power instruction is solved, in the control process, the state of an electric energy storage unit is dynamically evaluated, a strategy is adjusted, online optimization is carried out according to power grid index changes, the control effect is continuously improved, and the control efficiency is improved. Through cooperative optimization control of electric energy storage, second-level disturbance of the power grid is effectively suppressed, and safe and stable operation of the power grid is guaranteed.

The invention relates to the technical field of lithium batteries, and discloses a lithium battery life decline quantitative prediction and health state intelligent evaluation method, which comprises the following steps: S1, multi-source data acquisition: arranging strain gauge sensors at battery electrode plates and a shell to acquire stress-strain data, integrating a gas sensor in a battery module to acquire gas data, and calculating the data of the stress-strain data; battery data and environment data are acquired by a high-precision synchronous acquisition system through nanosecond clock signals and are processed and stored, and the data quality is monitored at the same time; s2, data preprocessing: generating an extended data set through data transformation and combination and GAN-based data, and processing abnormal data by using a method based on statistics and machine learning; according to the invention, through multi-source data fusion acquisition, a stress-strain sensor and a plurality of gas sensors are added to monitor the internal mechanical change and gas component concentration of the battery, so that the problem that the accuracy and practicability of the model are greatly reduced due to a traditional method based on an electrochemical model is improved.

The invention discloses a neural network adaptive iterative learning control method for a flexible joint robot, and the method comprises the steps: S1, building a constrained flexible joint robot, and initializing a system state, sampling time and control parameters; s2, compensating input saturation constraint; s3, calculating a system tracking error, and constructing an expected error trajectory; and S4, defining an error variable, and designing a neural network adaptive iterative learning controller. According to the method, the uncertainty of the flexible joint robot is estimated and compensated through the neural network, the error convergence precision is improved, by constructing the quadratic fraction type symmetric constraint Lyapunov function, it is guaranteed that the system output is always within the constraint range, the transient performance of the system is improved, and the reliability of the system is improved. According to the method, the flexible joint robot can safely and stably run in an iterative change task, when the number of iterations is increased, the tracking error is consistently converged to zero, and the method has the advantages of less required priori knowledge, no need of an accurate system model, high control precision and the like.

The invention discloses a wind driven generator oil leakage fault early warning method, which comprises the following steps: arranging vibration, temperature and oil sensors at a monitoring part, collecting multi-dimensional data for comprehensive analysis, analyzing a vibration signal by using a frequency spectrum to judge a mechanical fault, and combining a temperature trend and an oil parameter abnormal condition to realize early warning of an oil leakage fault of a wind driven generator. And carrying out classified early warning on the oil leakage fault by adopting a support vector machine algorithm. Furthermore, a Bayesian network algorithm is used to carry out accurate reasoning on the oil leakage position. According to the invention, the early warning result and the oil leakage position are uploaded to the monitoring center, and the processing suggestion is generated, so that intelligent and precise early warning of the oil leakage fault of the wind driven generator is realized. According to the multi-sensor fusion method, the accuracy and timeliness of oil leakage fault diagnosis are remarkably improved, the equipment loss and the maintenance cost are effectively reduced, and the method has important significance for guaranteeing safe and stable operation of the wind driven generator.

According to the 20KV electric brake switch multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition, all internal faults of a 20KV generator brake switch are monitored online in real time through the voiceprint recognition technology, and the industry blank of real-time online monitoring of the 20KV generator brake switch is filled up; a voiceprint collector, a temperature sensor, a vibration sensor, an SF6 gas monitoring device, a position node relay and the like are installed in a proper area, and multi-modal data are integrated into the same system, so that comprehensive analysis of the working condition of the 20KV generator brake switch is realized; and deep learning and fault judgment process logic design are carried out on each data parameter, so that the multi-mode working condition abnormity comprehensive diagnosis of the 20KV generator brake switch is realized, and the timeliness of working condition abnormity or fault judgment of the 20KV generator brake switch equipment is greatly improved.

The invention discloses a water-wind-light complementary strategy optimization method and system based on a large-scale water-wind-light cluster, and belongs to the technical field of power system optimization scheduling. The method comprises the steps of performing generalization processing on a water-wind-light complementary power system optimization object of a large-scale water-wind-light cluster through a power grid aggregation model and power supply cluster division to obtain a generalized optimization object; establishing constraint conditions of the water-wind-light complementary power system, wherein the constraint conditions comprise power transmission channel constraint, wind-light output constraint and water-electricity constraint; constructing a complementary strategy optimization model of the water-wind-light complementary power system by taking maximization of the dry-period supply conservation capacity of the cascade water-wind-light system and maximization of the water-wind-light annual energy output as objective functions and combining constraint conditions; and solving the complementary strategy optimization model of the water-wind-light complementary power system to obtain a complementary strategy of the water-wind-light complementary power system. According to the method, the optimization modeling accuracy and the calculation efficiency of the large-scale system are improved, and the multi-target optimization of the water-wind-light complementary power generation based on the large-scale water-wind-light cluster is realized.

The invention provides a safety protection algorithm design method for an industrial control system, which is used for determining a safety protection target and range, determining a key component and a business process and defining a protection boundary. And then risk assessment and threat modeling are carried out, potential threat and attack vectors are identified, and asset vulnerability and risk influence are assessed. Designing a security protection algorithm framework, selecting proper security technology and algorithm, and determining architecture and module composition. Identity authentication modules, data encryption and integrity protection modules, intrusion detection and defense modules and the like are designed in detail, and authentication modes, key management modules, integrity verification modules, intrusion detection algorithms, response strategies and the like are designed. And then realizing and integrating an algorithm, and programming in the system to realize and guarantee the cooperative work among the modules. And the performance and the security of the algorithm are optimized through a function and security test. And finally, deploying the algorithm to the industrial control system, continuously monitoring the operation state and the security situation of the industrial control system, dynamically adjusting the strategy and parameters, and effectively guaranteeing the security of the industrial control system.

The invention relates to the technical field of electrical cabinet heat dissipation control, in particular to an intelligent electrical cabinet with a stable heat dissipation function and a heat dissipation control method thereof. The method comprises the steps of firstly obtaining load power data, temperature data and heat dissipation power; further predicting future power change trend parameters according to the change characteristics of the historical load power data; further obtaining heat dissipation effect parameters according to the fluctuation characteristics of the historical temperature data; further obtaining predicted heat dissipation power according to the power change trend parameter and the heat dissipation effect parameter at the current moment in combination with the heat dissipation power at the current moment; and finally, controlling the heat dissipation of the intelligent electrical cabinet based on the predicted heat dissipation power. According to the method, the change trend of the load power possibly borne by the electrical cabinet is predicted, the heat dissipation effect of the current electrical cabinet is evaluated, the predicted heat dissipation power is obtained, heat dissipation of the intelligent electrical cabinet is controlled, the heat dissipation power is adjusted in time, the heat dissipation stability is improved, and stable and safe operation of the electrical cabinet is guaranteed.

The invention relates to a real-time monitoring method for asynchronous vibration stress of a blade based on a blade tip timing principle, which comprises the following steps of: obtaining at least six-order modal shape of the blade under the action of prestress by using finite element analysis, and then obtaining the actual vibration displacement of the blade to at least one blade tip timing probe by using a blade tip timing system; the change relation of the actual vibration displacement of the blade tip along with time is obtained, the actual vibration frequency of the blade, the actual vibration amplitude of the blade and the actual vibration pitch diameter of the blade corresponding to the actual vibration displacement of the blade tip are calculated, the actual modal shape of the blade is determined, and then the modal shape corresponding to the actual modal shape is used; and the maximum stress value and the position of the maximum stress under the asynchronous vibration condition of the blade are determined. According to the invention, the real-time monitoring of the vibration frequency, the vibration amplitude and the circumferential pitch diameter number of the blade can be realized, and the method is of great significance to the health early warning of the engine blade, the research of a fluid-solid coupling mechanism and the research of a vibration suppression method.

The invention discloses a high-proportion new energy power grid island division method considering frequency constraint, and belongs to the technical field of power system stability analysis and control, and the method comprises the steps: calculating a coherence coupling matrix of a generator and a new energy station through point voltage phase angle information; constructing a homology information undirected graph and calculating a normalized Laplacian matrix; performing characteristic value analysis, and determining the number of coherent generator groups and the coordinates of the generator and the new energy station; determining the composition of a coherent generator group through inverse mapping, and obtaining a multi-type power supply grouping result; establishing a coherence grouping constraint and a frequency security constraint according to a multi-type power supply grouping result; and constructing an island division optimal section search model and solving to obtain an optimal splitting section. By the adoption of the method, coherence grouping of multiple types of power supplies is achieved, and the optimal island division scheme is obtained by constructing and solving the island division optimal section search model considering the new energy supporting capacity and the frequency constraint.

The invention belongs to the technical field of transformer substation small animal driving, and particularly discloses a comprehensive small animal driving device and method.The comprehensive small animal driving device comprises a device shell, a driving module, a power supply module, a detection module and a control module, and the detection module is arranged on the device shell and used for judging whether small animals invade or not based on infrared detection; the detection module is arranged on the device shell and sends a detection signal to the control module, and the driving module is arranged on the device shell, is connected with the control module and is used for driving invading small animals from multiple aspects of vision, hearing, smell and touch; the power supply module is arranged on the device shell, connected with the driving module, the detection module and the control module and used for supplying power to all the modules. According to the comprehensive driving device and method for the small animals, multiple driving modes are adopted for cooperative operation, the small animals are driven from multiple angles and multiple aspects, and therefore the reliability of driving operation is guaranteed.

The invention provides a power station photovoltaic module defect detection method and device based on unsupervised learning. The method comprises the steps that firstly, a thermal imaging image to be detected and a visible light image in a target area provided with a photovoltaic module are collected; cutting a thermal imaging photovoltaic module image from the thermal imaging image, and cutting a visible light photovoltaic module image from the visible light image; afterwards, a teacher network model and a visible light photovoltaic student network model in the photovoltaic defect detection model are used for processing the visible light photovoltaic module image, and position information of the visible light abnormal photovoltaic module is determined and returned; and the teacher network model and the thermal imaging photovoltaic student network model in the photovoltaic defect detection model are used to process the thermal imaging photovoltaic module image, and the position information of the thermal imaging abnormal photovoltaic module is determined and returned. Wherein the photovoltaic defect detection model is obtained by training in a label-free supervision mode.

The invention discloses a PSO-based multi-conditional constraint gas storage injection-production scheme optimization method and computer equipment, and relates to the technical field of energy storage, and the PSO-based multi-conditional constraint gas storage injection-production scheme optimization method mainly comprises the following steps: according to core data, logging data, seismic data and static fluid data of a gas storage, obtaining a PSO-based multi-conditional constraint gas storage injection-production scheme; a gas storage three-dimensional geologic model and a gas storage fluid model are constructed, fitting and inversion are performed by using a numerical simulation method and historical production data, a gas storage injection-production well is selected as an injection-production optimization object, an optimization model, a gas storage operation objective function and a multi-constraint condition are constructed, and an optimal injection-production allocation scheme is obtained by using a particle swarm algorithm. According to the multi-condition constraint gas storage injection-production scheme optimization method based on PSO and the computer equipment, the single-well injection-production gas quantity can be optimized, the gas storage production allocation scheme design is achieved, and long-term stable and safe operation of the gas storage is guaranteed.

The invention provides a power grid frequency modulation cooperative control method considering a tie line section, and relates to the field of power system automatic scheduling, and the method comprises the steps: obtaining a load characteristic parameter, a transmission capacity parameter and a stability margin parameter of a section spanned by a tie line; solving the multi-objective optimization model by adopting an improved particle swarm optimization algorithm to obtain an optimal tie line electric power distribution curve meeting various constraint conditions; discretizing the optimal tie line electric power distribution curve, and converting the discretized optimal tie line electric power distribution curve into an active power instruction value of a tie line control system; and the tie line control system adjusts the electric power of the tie line in real time according to the active power instruction value. According to the invention, the reasonable distribution of the electric power of the tie line is realized, the frequency modulation demand and transmission capacity limitation of each section are balanced, and the safety and stability of power grid operation are improved. Through a differential scheduling strategy and a real-time optimization mechanism, load fluctuation can be effectively coped with, and safe and stable operation of a power grid is guaranteed.

The present invention relates to the technical field of distribution network planning and construction, and discloses a power balancing method for power system source-grid-load-storage coordination, including the following steps: S10: superimpose total load, the total load includes conventional load and electric energy substitution load, perform superimposition calculation on them, adopt a method combining natural growth and special load when predicting electric energy substitution load, and fit the total load curve after the total load is superimposed; S20: net load curve; S30: curve after natural peak shaving; S40: curve after grid side peak regulation; S50, balance curve; S60, power demand. The present invention establishes a clear and highly operable power balancing process for new power system source-grid-load-storage coordination, clarifies the relationship between the source-grid-load-storage interaction after a large number of new energy sources are connected to the power grid, and can improve the utilization efficiency of the power grid on the basis of ensuring the security of the power grid while fully absorbing a large number of new energy sources.

The invention relates to a method and a system for solving and reconstructing a feasible region of a network-following hybrid converter grid-connected system. The method comprises the following steps of: acquiring parameters of the hybrid converter grid-connected system and parameters of an alternating-current power grid; the hybrid converter grid-connected system is simplified into a plurality of equivalent grid-following or grid-constructing converters through grouping and equivalent aggregation; theoretically solving a capacity feasible region meeting the overload capacity constraint and a static voltage stability feasible region meeting the static voltage stability constraint; discretizing the intersection of the capacity feasible region and the static voltage stability feasible region; judging whether the operation condition meets the broadband oscillation constraint point by point, if so, determining that the condition belongs to the operation feasible region of the hybrid converter grid-connected system, and finally obtaining the operation feasible region of the grid-following and grid-constructing hybrid converter grid-connected system; and carrying out expansion and reconstruction of an operation feasible region by configuring a network construction converter. According to the method, the difference between different converters and all possible working conditions of the converters are considered, and safe and stable operation of the converter grid-connected system can be guaranteed.

The invention relates to the technical field of circuit breaker service life evaluation, in particular to a high-voltage circuit breaker residual service life intelligent evaluation system, which comprises an operation data acquisition module for acquiring operation data of a high-voltage circuit breaker; the degradation degree feature extraction module is used for extracting degradation degree features from the processed operation data; the residual life calculation module is used for constructing a residual life prediction model by utilizing the degradation degree characteristics and adopting a generalized regression neural network algorithm, and calculating the residual life of the residual life prediction model; the early warning and maintenance strategy module is used for formulating a predictive maintenance strategy of the high-voltage circuit breaker based on the residual life calculation result in combination with the residual life probability density function and the expected loss rate of the circuit breaker, and balancing the economical efficiency and the reliability; according to the method, the degradation process of the circuit breaker is reflected in real time, potential faults of equipment are predicted in time, and delay and inaccuracy caused by traditional experience judgment and regular inspection are avoided.

The invention relates to an overhead line system ice melting method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring an infrared image and point cloud data of an overhead line system, which are acquired at the same time; carrying out image identification on the infrared image, and determining a first icing thickness of the overhead line system; analyzing the point cloud data, and determining a second icing thickness of the contact network; the first icing thickness and the second icing thickness are fused, and the target icing thickness of the contact network is determined; and based on the target icing thickness, determining an ice melting parameter, and sending the ice melting parameter to the control equipment, so that the control equipment executes an overhead line system ice melting operation based on the ice melting parameter. According to the method, the infrared image of the overhead line system and the icing thickness analysis result of the point cloud data are fused, the accuracy of determining the icing thickness of the overhead line system is improved, meanwhile, even if one data source has an error, other data sources can still continue to provide effective information, the robustness of the system is enhanced, and the reliability of the system is improved. Therefore, a reasonable ice melting strategy is formulated based on the accurate icing thickness.