5861 results about "Hydropower" patented technology

The invention provides a hydropower station auxiliary machine equipment real-time monitoring method and system based on the Internet of Things, and relates to the technical field of information, and the method comprises the steps: constructing a pressure oil pump fault diagnosis model for a temperature and vibration related feature subset, and training a temperature and vibration feature sample of a preset fault type, establishing a mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; on the basis of the pressure oil pump fault diagnosis model, typical faults of bearing abrasion and poor gear meshing of the pressure oil pump are classified and recognized, and the temperature and vibration fault diagnosis result of the pressure oil pump is determined through the mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; and according to the fault diagnosis results of the temperature and vibration of the pressure oil pump, carrying out comprehensive decision making on a plurality of diagnosis results, according to the preset confidence of different diagnosis results, giving corresponding weight coefficients, and generating a final health state evaluation result of the pressure oil pump. The operation reliability and the maintenance efficiency of the pressure oil pump are improved.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a hydropower station gate detection method and system, and relates to the technical field of hydropower station gate detection, and the method comprises the steps: collecting environment parameters, building a dynamic environment model, and determining a real-time impact factor on a gate state; synchronously collecting gate vibration and sound wave data to obtain a composite signal; constructing a short-term and long-term trend model, and identifying a potential abnormal trend; automatically adjusting the sampling frequency of the sensor according to an abnormal detection result; performing fault classification on the multi-parameter signals by using an event pattern recognition algorithm; and generating an automatic response strategy according to a fault classification result. According to the hydropower station gate detection method provided by the invention, the accuracy and efficiency of hydropower station gate detection are improved through multi-dimensional parameter fusion and adaptive sampling adjustment. Through monitoring of various parameters, the system can comprehensively analyze the state of the gate, the misjudgment risk caused by a single signal is reduced, and comprehensive recognition of faults is enhanced.

The invention discloses a hydropower station equipment state monitoring method and system, and relates to the technical field of hydropower station equipment monitoring, and the method comprises the steps: collecting the hydropower station equipment state through multi-layer data, and dynamically adjusting the collection frequency according to the change of the equipment state; performing signal quality evaluation on the collected data to generate fusion data; inputting the fused data into a trend analysis module, and generating a trend model of the equipment state in combination with historical data and real-time data; according to an analysis result of the trend model, combining the plurality of abnormal states into an event mode, and comparing the event mode with a known fault mode to determine a risk level; and generating a corresponding early warning signal based on the risk level, and performing corresponding response processing on early warning events of different levels. According to the hydropower station equipment state monitoring method provided by the invention, through multi-level data acquisition and dynamic frequency adjustment, the accuracy of data acquisition and the resource utilization rate are effectively improved, so that the system can adapt to different equipment states.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention relates to the technical field of hydropower station operation and maintenance management, in particular to an operation and maintenance management method and system for a hydropower station, and aims to improve operation and maintenance efficiency and equipment reliability. Then dynamic feature extraction operation is executed, a state feature set containing equipment operation stability, environment coupling and abnormal fluctuation correlation features is generated, then abnormal state matching processing is conducted on the state feature set through a preset abnormal detection strategy network, the abnormal level and influence range parameters are determined, and the abnormal state is obtained. And finally, generating an equipment operation and maintenance optimization strategy set according to the parameters, including an equipment control instruction sequence and a maintenance resource scheduling scheme, and feeding back to a hydropower station control center to trigger operation and maintenance operation, thereby realizing intelligentization and precision of hydropower station operation and maintenance management, and ensuring safe and stable operation of the hydropower station.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The invention discloses a hydropower station workshop deformation monitoring method, which comprises the following steps of collecting multi-dimensional monitoring data and point cloud data of a hydropower station workshop, performing adaptive filtering and noise reduction on the multi-dimensional monitoring data, performing spatial registration on the multi-dimensional monitoring data and the point cloud data in a unified three-dimensional coordinate system, constructing a multi-channel time sequence deep learning model, and performing multi-channel time sequence deep learning on the multi-channel time sequence deep learning model; a dynamic early warning threshold model is constructed based on material characteristics, equipment operation parameters and historical monitoring data, risk assessment is carried out, a three-level response mechanism is triggered according to a risk assessment result, a sensor reliability evaluation index is established based on deviation analysis of monitoring data and an early warning result, and a data fusion weight is dynamically optimized. Adjusting the decision boundary of the classification model, and carrying out visualization and traceability analysis on the multi-dimensional monitoring data; the invention further discloses a hydropower station plant deformation monitoring system. According to the invention, a multi-dimensional and three-dimensional risk assessment system is established through data monitoring and real-time analysis, and the structural safety of the hydropower house is guaranteed to the maximum extent.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

An optical storage system designed to compensate for the water hammer effect in hydropower turbines and its cooperative frequency modulation method are provided. The method involves detecting the water hammer effect and utilizing a pre-established system model integrating a hydropower unit, photovoltaic system, and hybrid energy storage system. Through model predictive control combined with a whale optimization algorithm, the governor parameters of the hydropower unit are optimized. The system then obtains the photovoltaic active power output and the battery's state of charge (SOC) within the hybrid energy storage system. Based on these parameters, a collaborative control strategy for the photovoltaic and hybrid energy storage systems is determined and executed. This strategy enables effective control of the photovoltaic system and / or hybrid energy storage to compensate for reverse power adjustments in the hydropower unit, addressing the water hammer effect and ensuring stable power grid operation.

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

The present invention relates to the technical field of electricity markets. Disclosed are a method for coordinated transmission and distribution dispatching of power grids in an electricity market environment, and a system. The method comprises: collecting transmission and distribution data of power grids, and performing economic dispatch modeling for a hybrid system incorporating hydropower and thermal power; performing linear processing on nonlinear terms in a model; and using Benders decomposition to accelerate the solution of the model, and optimizing coordinated transmission and distribution dispatching of the power grids. In the method of the present invention, by means of performing economic dispatch modeling for the hybrid system incorporating hydropower and thermal power, the power system's ability to integrate renewable energy is enhanced, improving the resource utilization efficiency; by means of converting complex nonlinear terms in an original model into linear expressions, the model is easier to solve, providing rapid response capabilities for power grid dispatching; and by using a Benders decomposition method to accelerate the solution, a problem is decomposed into a master problem and a sub-problem, which are solved independently, thus improving the overall solving efficiency, scalability and flexibility.

The invention relates to the field of intelligent monitoring and fault prediction, and discloses a hydropower station computer monitoring system fault prediction method based on big data analysis, and the method comprises the following steps: collecting multi-dimensional sensor data of key equipment of a hydropower station, and carrying out the preprocessing of the data; a Morse function is constructed, the gradient and a Hessian matrix of the Morse function are calculated, topological features of the system are extracted, and a Morse-Smal complex is used for dividing system states to distinguish stable, transitional and fault states; based on the topological features and the time sequence data, using a Transform model to carry out fault prediction, and calculating the probability of equipment fault occurrence; and according to the calculated fault discrimination score value and in combination with a dynamic threshold adjustment strategy, if the score value exceeds a preset threshold, triggering an alarm mechanism of the SCADA monitoring system to realize real-time fault early warning. Through comprehensive data analysis and a deep learning model, the accuracy and real-time performance of equipment fault prediction are improved, and stable operation and safe maintenance of hydropower station equipment are ensured.

The invention provides a robot path planning method for mixed-flow water turbine runner blade defect inspection. The robot path planning method comprises the following steps that S1, feasibility analysis of robot defect inspection is conducted on a blade, and an effective detection area is determined; s2, extracting a three-dimensional mesh model of the blade surface from the effective detection area, and establishing bidirectional mapping between a three-dimensional space and a two-dimensional UV parameter space by projecting the three-dimensional mesh model to the two-dimensional UV parameter space; s3, generating a boundary offset path of the two-dimensional UV parameter space and a robot arch scanning path; and S4, based on the bidirectional mapping, processing the boundary offset path and the arch scanning path of the robot, and generating a path back projection and three-dimensional detection track. Through accurate curved surface parameterization, an efficient path generation algorithm and an accurate three-dimensional mapping strategy, the method aims at improving the detection efficiency and quality of the runner blade of the mixed-flow water turbine, and technical guarantee is provided for safe and stable operation of a hydropower station.

The invention discloses a hydropower station feeder line fault detection system and method, and particularly relates to the technical field of feeder line detection. A hydropower station feeder line fault traveling wave signal and an amplitude change in a propagation process are collected in real time, an established theoretical propagation model is combined, a traveling wave attenuation rule is calculated, the reasonability of a signal transmission path is judged, frequency spectrum characteristics are extracted through frequency analysis for a signal with an unreasonable path, and the signal is matched with a preset fault traveling wave characteristic library. According to signal path rationality and source authenticity, fault point positioning accuracy is predicted, traveling wave propagation speed and line impedance are dynamically adjusted, deviation caused by clock synchronization errors is corrected, positioning precision is improved, and the problem of inaccurate positioning caused by synchronization signal interference in the prior art is effectively solved. And the stability and economic benefits of a hydropower station power supply system are greatly improved.

The invention provides a watershed cascade water, wind, light and storage integrated scheduling operation simulation method considering long-short term nesting correction, and the method comprises the steps: constructing a watershed cascade water, wind, light and storage integrated medium-long term optimization scheduling model, and solving the model, thereby obtaining a water, wind, light and medium-long term scale electric quantity complementation scheduling scheme; and constructing a long-short-term nested correction model, and performing refined correction on each power supply output process of the watershed cascade water, wind, light and storage integrated multi-energy complementary base to obtain a water, wind, light and short-term scale electric power complementary scheduling scheme. On the basis of medium and long term optimization scheduling, a long-short term nested correction model is further constructed, and the random volatility of short-term wind and light output and the adjusting capacity of a short-term hydropower station and a pumped storage power station are fully considered. Effective undertaking of long-term absorption and short-term grid connection of the water, wind, light and storage integrated multi-energy complementary base under high-proportion new energy permeation is achieved, and the refined requirement of a multi-energy complementary dispatching system for evaluating indexes such as the new energy absorption rate is met.

The invention relates to the technical field of side slope deformation prediction, and discloses a side slope three-dimensional deformation prediction method, which comprises the following steps of: 1, carrying out InSAR three-dimensional time sequence deformation inversion to generate three-dimensional time sequence deformation of a side slope high-coherence point; 2, decomposing the three-dimensional time sequence deformation by using a grey wolf optimization algorithm and a variational mode decomposition model to obtain a trend term, a periodic term and a residual error; 3, establishing a deep learning network prediction model considering the environmental impact factors of the region where the slope is located; and step 4, generating a slope time sequence deformation prediction value. The method comprises the following steps: resolving a three-dimensional deformation sequence by using a synthetic aperture radar time sequence image; constructing a variational mode signal decomposition model based on a grey wolf optimization algorithm; and establishing a deep learning network prediction model considering the environmental impact factors. By means of the method, the difficult problem of the hydropower station dam area slope three-dimensional real-time deformation prediction technology can be solved, environmental physical factors such as the reservoir water level, the gradient and the direction are fully considered, and rapid, real-time and accurate three-dimensional deformation prediction of the slope can be achieved.

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 relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

The invention relates to the technical field of hydropower, and discloses an intelligent alarm method of a hydropower station computer monitoring system, comprising the following steps: S100, preprocessing historical operation data of hydropower station equipment to obtain a first data set; s200, establishing a data model based on the first data set; s300, acquiring first associated information based on physical structures of various devices of the hydropower station; s400, acquiring second associated information based on the operation data of various devices of the hydropower station; s500, constructing an equipment association model based on the first association information, the second association information and the data model; s600, monitoring operation data of the target equipment in real time, and dynamically acquiring operation state information of the target equipment based on the equipment association model; and when the operation state information is abnormal, triggering an alarm based on a preset alarm strategy. The problems of high false alarm rate and the like in the prior art are solved.

The invention provides an intelligent speed regulation control method and system for hydroelectric generating set multi-source data fusion, and relates to the technical field of hydropower station automation control, and the method comprises the steps: S1, collecting multi-source heterogeneous data, including mechanical quantity, hydraulic parameters and electrical quantity, in the operation process of a hydroelectric generating set; s2, performing standardized preprocessing on the multi-source heterogeneous data to obtain preprocessed data; s3, on the basis of the preprocessed data, global deep state feature information and operation trend prediction information of the hydroelectric generating set are extracted through a multi-layer data fusion algorithm, and a fused speed regulation decision basis is obtained; s4, generating a speed regulation control instruction for regulating the rotating speed of the hydroelectric generating set based on the fused speed regulation decision basis; and S5, performing speed regulation control on the hydroelectric generating set according to the speed regulation control instruction. Through multi-layer data fusion and a dynamic self-adaptive control strategy, comprehensive perception and accurate regulation and control of the operation state of the hydroelectric generating set are realized.

The invention relates to the field of water conservancy and hydropower engineering, in particular to a multi-objective optimized hydropower ecological scheduling decision-making system and method, and the system comprises a data collection module, an ecological model module, an intelligent decision-making engine module, a scheduling execution module, an effect evaluation module and a knowledge base module. The data acquisition module collects multi-source data, the ecological model module generates a training data set, the intelligent decision engine carries out ecological process modeling and probability prediction based on a deep learning architecture and spatial-temporal feature extraction, and generates a scheduling decision, the scheduling execution module controls hydropower engineering operation, and the effect evaluation module monitors ecological and economic effects. The knowledge base module stores historical experience and provides optimization suggestions, and the system improves the simulation accuracy of the ecological system, especially when the flow changes suddenly; and through uncertainty quantification, the system reliability and the ecological safety guarantee rate are enhanced, and high efficiency, accuracy and sustainability of ecological scheduling of the hydropower engineering are realized.

A hydraulic engineering video monitoring intelligent stationing optimization design method based on a three-dimensional model realizes video monitoring intelligent stationing planning design based on a mode of combined development of a three-dimensional modeling platform and a BIM lightweight engine. The method comprises five stages of camera adaptive arrangement, monitoring area three-dimensional visualization, monitoring coverage check, design optimization and design result display output. According to the method, the overall planning of the video monitoring intelligent stationing of the water conservancy and hydropower engineering is enhanced, the advantages and disadvantages of the video monitoring stationing are scientifically and quantitatively evaluated, and a quantifiable video monitoring intelligent stationing planning scheme is provided. According to the method, the requirements of the current video monitoring distribution point and the monitoring coverage rate are combined, a monitoring model optimization algorithm is utilized, the monitoring coverage rate or the camera configuration is taken as an optimization target, the monitoring coverage rate is improved, the monitoring blind area range is reduced, monitoring redundancy setting is avoided, and the monitoring camera configuration is optimized.

The invention relates to a hydroelectric power generation prediction method and system based on a VMD-LSTM-KAN model. The method comprises the following steps that historical power generation data and historical meteorological data of a target hydropower station are collected and preprocessed; carrying out VMD variational mode decomposition on the preprocessed historical power generation data, and decomposing the historical power generation data into a plurality of mode components from time sequence data; combining the plurality of modal components and historical power generation data into a multi-dimensional output matrix; fusing the preprocessed historical meteorological data with the multi-dimensional output matrix to generate an input data set; constructing a prediction model based on the LSTM network, performing nonlinear optimization on an output layer of the LSTM network in combination with a KAN network, and training the constructed prediction model through an input data set; and predicting a modal component prediction value and a power generation prediction value of the target hydropower station in a future time period through the trained prediction model.

The invention belongs to the technical field of automatic assembly, and discloses an automatic assembly system and method for a large nut of a water turbine ball valve, and the system comprises a mechanical arm, an end effector and an intelligent nut tightening control system. The end effector integrates an electromagnet adsorption mechanism, a servo drive rotating mechanism, a floating compensation mechanism and a visual recognition mechanism, nuts of different specifications are grabbed in a self-adaptive mode through an electromagnet, submillimeter-level accurate positioning is achieved through a laser galvanometer stereo camera, and the assembling posture is automatically adjusted in combination with the floating compensation mechanism. The intelligent control system adopts a staged tightening strategy, performs real-time monitoring through a torque sensor, and has a dual protection mechanism. The hydraulic turbine ball valve large nut assembling machine achieves full-automatic assembling of hydraulic turbine ball valve large nuts, has the advantages of being high in assembling efficiency, high in positioning precision, high in adaptability, reliable in quality and the like, solves the problems that manual assembling is large in labor intensity, many in potential safety hazards, poor in quality consistency and the like, and remarkably improves the assembling quality and efficiency of key parts of a hydropower station.

The invention discloses a wind-light-water-storage combined body spot market operation optimization scheduling strategy, and belongs to the technical field of electric power system operation and control. Aiming at the problems of fluctuation of wind-solar power generation and uncertainty of prediction errors, the invention proposes that a wind-solar group is combined with cascade hydropower and an energy storage pump station thereof, and a wind-solar-water storage combination body is constructed to participate in a multi-time-scale power market; by constructing an optimal scheduling model considering the source-price uncertainty, the comprehensive benefit maximization of the complex in the day-ahead market, the real-time market and the medium-and-long-term contract decomposition is realized; an information gap decision theory and a conditional value-at-risk method are adopted to ensure that the joint profit can still be ensured when the wind and light prediction error is expanded; meanwhile, profits are distributed fairly through a Shapley method, and the benefits of all subjects are improved; and the market competitiveness and executive force of wind and light power generation are effectively improved, the absorption and utilization of new energy are promoted, the flexibility and toughness of the system are high, and powerful support is provided for constructing a green, low-carbon and efficient modern energy system.

The invention relates to a method for cooperatively monitoring hydropower station slope deformation through SBAS-InSAR and TLS, and the method comprises the steps: extracting hydropower station slope deformation information through employing a short baseline set technology based on GB-InSAR time sequence radar data; intercepting two groups of point cloud data in the abnormal deformation area; sampling and denoising under the two groups of point clouds; point cloud registration: aligning point clouds in a three-dimensional space to effectively reduce pose errors so as to ensure the consistency of geometric relationships; carrying out point cloud difference and carrying out interpolation on a difference result; and comparing and analyzing the two deformation monitoring results of the landslide abnormal area. According to the embodiment of the invention, comprehensive monitoring with higher precision can be realized through time sequence analysis and multi-dimensional data analysis, the reliability and accuracy of slope disaster early warning are remarkably improved, and the complexity of earth surface movement and deformation modes is revealed.

The invention discloses a cascade hydropower station output prediction method and system, and the method comprises the steps: collecting various historical data of a cascade hydropower station, analyzing the load condition of the hydropower station, and obtaining the influence relation of upstream and downstream outputs; carrying out clustering analysis on factors which have the maximum influence on the output of the hydropower station according to seasonal changes, and determining an optimal scheduling period for output prediction; constructing a load prediction model, and predicting the future output condition of the cascade hydropower station in the optimal scheduling period; and predicting a future flow water level, and associating the future flow water level with the output prediction result to obtain a cascade hydropower station operation scheduling result. According to the method, the historical condition of the output load of the cascade power station is specifically analyzed, and the load rate change trend of the cascade power station is effectively predicted through clustering analysis and establishment of the hydropower station load prediction model, so that an important reference is provided for operation and scheduling of the hydropower station, and the stability and reliability of power supply are ensured.