922 results about "Power system security" patented technology

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention provides a method and a system for detecting and defending cross-domain threats of a power system. The method comprises the following steps: after carrying out anomaly identification on operation monitoring data of a physical domain node in a target power grid region to obtain an anomaly identification result and carrying out denial of service attack identification according to network flow data of an information domain node to obtain an attack identification result, carrying out abnormal event association analysis on the anomaly identification result and the attack identification result to obtain an attack cross-domain anomaly identification result; according to key nodes and key risk propagation paths in a cross-domain attack chain generated based on a graph theory algorithm, generating an attack tracing atlas, and according to vulnerability information of the key nodes in the atlas, obtaining a corresponding power system topological graph and a corresponding communication network topological graph; and iteratively generating an active defense rule based on a game theory algorithm and a reinforcement learning algorithm, and issuing the active defense rule to the node. According to the method, the cross-domain attack risk is accurately perceived in real time and adaptive security defense is executed through cross-domain abnormal event association analysis, so that the comprehensiveness and reliability of security protection of the power system are improved.

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention relates to the field of power system safety monitoring, and discloses a power transmission line vibration monitoring and early warning method for dealing with extreme weather, which comprises the following steps: arranging a multi-level sensor network to monitor vibration and environmental conditions of a power transmission line; monitoring data are processed and transmitted in real time through edge calculation and a 5G network; performing trend prediction on the vibration data by using a multi-modal deep learning model; an early warning threshold value is adjusted through a distributed self-adaptive dynamic threshold value control method; and when abnormal vibration or a high-risk trend is detected, generating and issuing early warning information. Through a multi-level sensor network, an intelligent early warning algorithm and dynamic threshold control, efficient monitoring of power transmission line vibration and accurate early warning in extreme weather are realized, the real-time performance of data transmission and processing is effectively improved, the accuracy of vibration trend prediction is enhanced, the false alarm rate and the missing report rate are reduced, and the method is suitable for popularization and application. Therefore, the method has higher adaptability and stability in a complex environment.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are improved.

The invention discloses a bird repelling method and related equipment for a power distribution iron tower, and the method employs a multi-dimensional sensor for cooperative operation, and collects infrared, sound, images and radar reflection signals in real time. Through multi-dimensional data fusion, the appearance, sound wave and behavior characteristics of the bird flock are deeply mined, the bird flock type is accurately identified, and the problem of insufficient detection of a single sensor is solved. Based on the sensitivity difference of different bird flocks to vision, hearing and ultrasonic waves, bird repelling modes such as strong light stroboflash, sound or ultrasonic waves are dynamically matched from the preset bird repelling strategy library, the adaptability of birds to a single bird repelling means is avoided, and the bird repelling success rate is remarkably increased. And a closed-loop optimization mechanism is formed by iteratively optimizing a strategy library matching rule by using bird repelling effect feedback data, so that the system can adapt to the environment and bird behavior changes, and the long-term bird repelling effect is enhanced. Compared with the prior art, line fault risks caused by bird activities are effectively reduced, and safe and reliable operation of a power system is powerfully guaranteed.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention relates to the field of cable fault identification, in particular to a high-voltage cable fault identification method for on-line monitoring of sheath current, which comprises the following steps of: firstly, monitoring in real time through a sheath current sensor, a fault traveling wave collector and a cable temperature sensor at key positions along a line, collecting cable line parameters, analyzing normal operation data, and determining the fault of a high-voltage cable according to the normal operation data; establishing a sheath current and temperature characteristic model, calculating the sheath current in real time and comparing the model, if the sheath current exceeds a threshold value, preliminarily judging a fault, and then calculating a fault area through traveling waves; temperature change data are obtained in a fault area, and an average temperature highest point area is found through calculation to accurately calibrate a fault position. According to the method, cable operation conditions are comprehensively mastered by multiple means, faults can be found in time, fault areas and positions can be accurately determined, fault types can be accurately judged according to multi-aspect information, the accuracy and timeliness of high-voltage cable fault recognition are effectively improved, safe and stable operation of a power system is powerfully guaranteed, and the working efficiency is improved. The operation and maintenance cost is reduced; and the cable management level is improved.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

The invention relates to the technical field of information, and provides an intelligent power consumption behavior inspection method and device based on multi-source data and a storage medium, and the method comprises the steps: processing a mass power consumption data flow collected by a sensor and an intelligent electric meter in real time through employing a distributed calculation framework, completing the integration and standardization of the multi-source data, and generating a dynamically updated power consumption monitoring original data set; extracting a multi-dimensional feature vector from time series data and historical electricity utilization records containing the basic identifier, the time information, the real-time change feature and the abnormal mark, and outputting a classification result of the abnormal behavior; finally confirmed abnormal behavior data are extracted, a real-time monitoring instrument panel is generated through the visualization technology, and the abnormal behaviors of power utilization inspection of the power supply enterprise are dynamically displayed. According to the invention, multi-source heterogeneous data are effectively integrated, intelligent analysis and real-time monitoring of power consumption behaviors are realized, the accuracy and efficiency of power consumption inspection of power supply enterprises are improved, and powerful support is provided for safe and stable operation of a power system.

The invention provides a method for monitoring and analyzing electrical equipment through linkage of a camera and a monitoring system, and the method comprises the steps: firstly, enabling a patrol system to define a key region needing to be monitored, and enabling the camera to carry out the continuous monitoring in a non-patrol task period; and then acquiring a video stream for analysis, extracting key features by using a computer vision technology, identifying the abnormality of the electrical equipment by means of a trained deep learning model, and immediately alarming and recording related information once the abnormality is found. And when the electrical equipment monitoring system perceives a potential problem, the alarm signal triggers the inspection host, and the camera is controlled to jump to a preset position to collect a field condition. And after the collected data is stored, the authenticity and severity of the abnormity are deeply evaluated by using the intelligent analysis algorithm again. And finally, an analysis result is fed back to an operator to assist in decision making, and the operation process and result are recorded in detail so as to facilitate review and performance optimization. According to the method, all-around, accurate and efficient monitoring analysis of the electrical equipment can be realized, the operation and maintenance level is improved, and safe and stable operation of a power system is guaranteed.

The invention discloses a method for discriminating inertia and inertia response characteristics in a power system frequency dynamic state. The method comprises the following steps: firstly, collecting multi-source heterogeneous data such as power grid nodes and meteorology to construct a dynamic data lake, and after data fusion, standardization and space-time alignment, extracting key features such as frequency fluctuation entropy to form a high-dimensional feature space; secondly, abstracting a power grid topology into a dynamic graph structure, learning a frequency disturbance propagation path by using a graph convolutional network, quantifying a node inertia contribution weight, and identifying a weak link; then, the inertia demand is decomposed into transient inertia, dynamic inertia and steady inertia, resource allocation is optimized through a multi-agent game model, and frequency stability of different time scales is guaranteed. And finally, constructing a framework based on meta reinforcement learning, performing off-line pre-training, performing on-line fine adjustment on network parameters, and generating a self-adaptive inertia control instruction. The method effectively overcomes the defects in the prior art, and is of great significance to guarantee safe and efficient operation of a power system.

The invention belongs to the field of bird repelling and equipment state monitoring, and particularly discloses a bird inhabitation multi-mode repelling and state monitoring device and method. The device is combined with a photosensitive device for day and night environment discrimination, is integrated with an intelligent hardware device of a multi-mode driving unit and a state monitoring module, and is suitable for long-term bird prevention operation in unattended scenes such as power transmission lines and transformer substations; further, accurate detection of bird approaching is realized by fusing various sensors, efficient repelling is performed by combining a multi-channel interference means of touch, hearing and vision, meanwhile, the running state of the anti-bird structure is monitored in real time, and data is remotely transmitted back, so that long-term, stable, protection and monitoring integration in an unattended scene is realized, and the safety and reliability of the system are improved. The threat of bird damage to safe operation of a power system is effectively reduced.

The invention discloses a reclosing locking dynamic adjustment method and system based on voltage mutual inductance, and relates to the technical field of reclosing locking dynamic adjustment, and the method comprises the following steps: collecting the voltage waveform of a power system, and carrying out the power failure prediction if the system does not recover the power supply; acquiring cable equipment data and power grid load data when the power system breaks down; constructing a power system anomaly evaluation model based on machine learning according to the cable equipment data and the power grid load data; obtaining a node area power system abnormity evaluation coefficient with the voltage waveform higher than the stable voltage, and carrying out fault positioning on the node area based on a preset power system safety and stability coefficient; and judging whether locking operation is carried out or not, if the locking operation is carried out, acquiring the voltage waveform after maintenance and the abnormal evaluation coefficient of the power system to judge whether the fault is eliminated or not, and closing the locking of the reclosing. According to the invention, through reclosing locking dynamic adjustment, the problems that the locking time is too long and the fault type cannot be accurately identified are solved.

The invention discloses a resource cooperation elastic planning method considering new energy uncertainty under typhoon disasters, and the method comprises the steps: building a time-varying fault rate of a line through typhoon disaster disturbance and a line fault rate model, recognizing a fragile line, and building an evolution model considering cascading faults under the typhoon disasters; the method comprises the following steps: generating a wind and light output scene by utilizing a mixed Copula function and combining multivariate normal distribution, and then screening out a wind and light scene set by adopting a synchronous return band reduction method; the flexibility of the system is improved through flexible resource collaborative planning such as wind power photovoltaic energy storage and line reinforcement; and solving the constructed model through column and constraint generation and a Big-M method to obtain a collaborative planning scheme of various flexible resources. By utilizing the method, the problem of safe and reliable operation of a power system caused by typhoon disasters and uncertainty of new energy output can be solved.

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention belongs to the field of electrical equipment monitoring, and discloses an electrical equipment operation monitoring method and system, electronic equipment and a storage medium, and the method comprises the following steps: obtaining real-time monitoring data in an electrical equipment operation process; segmenting the real-time monitoring data by using the sliding window to obtain a plurality of data windows; calculating the change trend of each operation parameter in each data window to obtain change rate characteristics; constructing a state monitoring model based on the historical operation data, and evaluating the equipment operation state based on the change rate characteristics to obtain a window evaluation result; and judging an abnormal window based on a window evaluation result, extracting operation data of the abnormal window, and judging a fault type and a fault position. By fusing multi-dimensional parameter analysis and dynamic window processing, real-time monitoring and accurate fault diagnosis of the operation state of the electrical equipment are realized, the maintenance efficiency and reliability of the equipment are improved, and the method is of great significance to guarantee safe and stable operation of a power system.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of power system safety early warning, in particular to a power grid risk intelligent early warning and analysis method, system and equipment, and the method comprises the steps: obtaining multi-source heterogeneous data of a power grid, constructing an association relationship and an index through an association key, and constructing a directed graph knowledge base; constructing a risk knowledge graph based on the directed graph knowledge base, and setting a risk rule, so as to carry out risk event and risk level early warning when the target equipment is overhauled; and for the maintenance of the target equipment, determining a comprehensive risk representation index from the equipment risk, the user influence and the load transfer capability, and determining a risk early warning level and generating an early warning list based on the comprehensive risk representation index. Thus, through fusion of multi-source data, an intelligent risk level determination model is established, and efficient, accurate and intelligent risk early warning is realized.

The invention relates to an electric power system transient state and voltage stability collaborative prediction method based on a space-time diagram neural network, and belongs to the field of electric power system stability evaluation. The method comprises the following steps: constructing a power grid information adjacency matrix according to a topological structure and electrical parameters of a power system; based on the power grid information adjacency matrix, extracting topological features and time features of the power system from the PMU data; and fusing the extracted topological features and time features, and outputting prediction results of transient state and voltage stability of the power system. According to the method, the accuracy and robustness of stability prediction of the power system can be effectively improved, the real-time requirement is met, and technical support is provided for safe and stable operation of the power system.

The invention relates to the technical field of partial discharge detection of power equipment, and discloses a KNN algorithm-based photoacoustic feature combined particle identification method and system, optical and acoustic signals caused by particles in different shapes under alternating voltage are obtained through experiments, and mathematical modeling is performed on particle motion discharge based on particle motion and discharge theories; and then, time sequence distribution characteristics of optical and ultrasonic signals triggered by the particles are mastered, a particle type identification algorithm based on a KNN model is constructed, and particle shape identification and mixed particle component identification are realized. In conclusion, the method combines an optical detection method and an ultrasonic method to form photoacoustic combined detection, excavates combined feature information and establishes a particle discharge combined detection method, and has important significance on application of a particle discharge optical detection method and multi-parameter combined detection; and the method has outstanding value for maintaining safe and stable operation of power equipment and even a power system.

The invention discloses an improved DBSCAN abnormal node identification method based on space-time fusion features in the technical field of power systems, and the method comprises the steps: extracting five types of spatial topology features, including node proximity centrality, betweenness centrality and hierarchy depth, through constructing a power distribution area adjacency matrix; according to the method, 16 time sequence features such as a voltage fluctuation index, a current peak-valley difference and power integration are combined to form space-time dual feature representation, principal component analysis (PCA) is adopted to carry out dimension reduction processing on fusion features, and more than 90% of variance information is reserved to reduce calculation complexity. According to the method, the propagation rule of electricity stealing disturbance in a power grid is effectively described through spatio-temporal feature fusion, the problem of dimension disasters is solved through PCA dimension reduction, adaptation of DBSCAN to a complex topology scene is improved by means of dynamic parameter optimization, compared with a traditional detection technology, the false detection rate is reduced by 50% or above, the calculation efficiency is improved by 80%, and the method is suitable for large-scale popularization and application. The method can be widely applied to the fields of high-loss transformer area electricity larceny screening, abnormal electricity consumption behavior monitoring and the like, and provides technical support for safe operation of an electric power system.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention relates to a controllable turn-off converter valve for a high voltage direct current (HVDC) system, each converter valve arm comprises a valve arm voltage-sharing device and a plurality of cascaded controllable turn-off converter modules, and each controllable turn-off converter module comprises an assembly voltage-sharing device and a plurality of cascaded controllable turn-off converter assemblies. Each controllable turn-off current conversion assembly is configured to comprise at least one voltage-sharing circuit and at least one controllable switching device, and two ends of each controllable switching device are provided with one voltage-sharing circuit in parallel. Therefore, the controllable turn-off converter valve can realize voltage sharing at each level, the requirement of the controllable turn-off converter valve on the consistency of controllable switching devices is reduced, a large number of controllable switching devices can be connected in series in the same converter valve arm, and reliable conversion of extra-high voltage direct current transmission at the voltage level of + / -800 kilovolts and above is realized. The problem of commutation failure of the extra-high-voltage direct-current type converter valve is solved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of power system safety monitoring, in particular to an electricity utilization potential safety hazard supervision method and system based on an electricity utilization data cloud platform. Multi-dimensional electricity utilization data are collected in real time and preprocessed, and the preprocessed data are subjected to multi-source fusion in combination with a data fusion algorithm; carrying out hidden danger detection on the fused data by utilizing a multi-layer intelligent learning model; a dynamic scoring mechanism is introduced, multi-dimensional scoring is performed on a detection result, hidden danger risk levels are divided, a multi-level early warning mechanism is triggered based on the risk levels, and automatic emergency response is implemented; potential electricity utilization safety hazards can be recognized in time, early warning is achieved, and lagged detection and processing of a traditional method are avoided. By means of intelligent hidden danger detection and dynamic risk assessment, the monitoring accuracy and efficiency are improved, human intervention is reduced, and the timeliness of emergency response is improved.

The invention discloses an electric power operation supervision method and system based on AI behavior recognition and semantic analysis, and belongs to the technical field of electric power system safety, and the method comprises the steps: arranging a control architecture and collection equipment of an electric power system, and continuously collecting voiceprint data and video stream data; verifying the identity of an operator through voiceprint recognition, carrying out semantic analysis on a voice instruction based on an electric power standard term library, and judging the consistency between the instruction content and a task issued by a system; identifying the position of an operator through visual positioning, performing spatial matching with the interval number of the target equipment, and detecting the normalization of an operation behavior through video analysis; and generating a voice interaction prompt according to instruction fuzziness, triggering hierarchical alarm through multi-modal data fusion, and synchronously recording and storing violation operation data. The power system supervision efficiency is improved, authorization operation is ensured, human errors are reduced, safety is guaranteed through visual monitoring, alarm is triggered through multi-mode fusion, violation is responded in time, evidence is recorded, and traceable safety management is supported.