17774 results about "Generating unit" patented technology

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention provides an intelligent detection method and system for the health state of a wind generating set in an intelligent wind field, and relates to the technical field of intelligent wind field multi-source monitoring. The method comprises the following steps: firstly, collecting multi-source operation data such as a transmission chain, structural parts and environment working conditions, and performing time reference unification; performing noise reduction, calibration, compensation and time window segmentation on the original data to form a preprocessed data set; extracting and aligning features in multiple domains to construct fusion feature representation; establishing a health baseline model based on historical normal samples and working condition variables to generate a self-adaptive alarm threshold value; inputting a health discrimination model to obtain an anomaly index, and generating an early warning event according to a trigger condition; and comprehensively fusing the features, the health base line and the judgment result to calculate a health index and output early warning information, thereby realizing accurate detection and risk early warning of the whole life cycle and the whole working condition.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

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.

The invention discloses a thermal power plant multi-target economic dispatch optimization method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: constructing an initial data set based on power grid operation parameters, generator set state parameters and environment monitoring data of a thermal power plant; determining characteristic parameters of the current operation scheduling demand according to the initial data set; based on the characteristic parameters, real-time target weight coefficients of the economic index, the environmental protection index and the scheduling flexibility index are determined through a preset dynamic weight distribution model; according to the real-time target weight coefficient, generating an initial candidate scheduling scheme set meeting the safety constraint of the generator set; performing main target directional optimization on the initial candidate scheduling scheme set to obtain a Pareto optimal solution set meeting a preset convergence condition; and generating a scheduling recommendation scheme based on the Pareto optimal solution set. According to the method, the problem of multi-target conflict caused by multi-target optimization imbalance in economic dispatching of the thermal power plant can be relieved.

The invention provides an equipment health management method and system for a wind generating set. The method belongs to the technical field of wind power generation, and comprises the following steps: acquiring multi-source data of a wind generating set, preprocessing the acquired multi-source data, and integrating the preprocessed multi-source data into a uniform data format by using a data fusion technology; based on domain knowledge and data characteristics, key features reflecting the health state of the equipment are extracted, and a feature subset with the highest prediction value for the health state of the equipment is screened out through a feature selection algorithm. Key components of the wind generating set are monitored in real time through a high-precision sensor network, and comprehensive collection of data is realized in combination with multi-source information such as a historical fault database and meteorological data.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

Embodiments of the present invention provide a runner defect intelligent diagnosis and evaluation method based on a knowledge graph, comprising the following steps: collecting basic information including data, pictures, standards, and regulations related to design, manufacturing, testing, acceptance, operation, and maintenance of a runner of a hydro turbine generator set; by means of analysis and screening, reviewing and retaining data having high reliability and practicality; on the basis of the data, pictures, standards, and regulations and a similar case, extracting, from the data, defect diagnosis related service knowledge as well as entities, relations, and attributes, and constructing a defect diagnosis concept graph and a defect diagnosis entity graph; and sorting defect diagnosis knowledge points, performing semantic representation on runner defect diagnosis knowledge, and establishing a diagnosis rule knowledge base. According to the present invention, a repetition semantic matching model is used to perform text and extraction rule matching for different defects so that a relation instance of defect representation features can be obtained, the defect of a runner is evaluated on the basis of the relation instance and an accurate diagnosis result is obtained, so as to guide production.

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention discloses a hybrid power combine harvester and a control method and a controller thereof, and relates to the technical field of agricultural machinery. The harvester comprises a hybrid power system which comprises a power battery and a range extender composed of an engine and a generator; a working system including a plurality of working members and a plurality of working motors that enable each working member to be driven by the motor; the walking system comprises walking wheels and walking motors used for driving the corresponding walking wheels; the control system comprises a whole vehicle controller and a plurality of motor controllers corresponding to the operation motors, the walking motors and the generators respectively. Wherein the hybrid power system responds to a power control strategy of the control system and provides power for corresponding motors in the operation system and the walking system so as to drive the harvester to walk and operate. The full-electric-drive hybrid power scheme of the harvester is achieved, and the efficiency problem caused by multi-stage mechanical transmission of a traditional harvester is avoided.

The invention provides a multi-region hierarchical PID parameter adaptive adjustment method and device, and the method comprises the steps: collecting the load deviation of a generator set in real time, and comparing the load deviation with a preset first threshold value and a preset second threshold value (the first threshold value is greater than the second threshold value); when the load deviation absolute value is larger than a first threshold value, entering an emergency adjusting area, when the load deviation absolute value is larger than a second threshold value and smaller than or equal to the first threshold value, entering a rapid adjusting area, and when the load deviation absolute value is smaller than or equal to the second threshold value, entering a high-precision adjusting area. And the proportional gain is dynamically adjusted according to the current adjusting area and the load deviation, and the output control pulse width is calculated by combining the adjusted proportional gain, the load deviation, the control period and the maximum output of the unit. In the high-precision adjusting area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient, and precise control over the generator set is achieved. The adaptability, the stability and the adjusting precision of the control system can be improved, and the operation performance of the generator set is optimized.

The invention discloses a carbon capture and energy storage and thermal power generation coupled energy comprehensive utilization system, which comprises a thermal power generation system, an energy storage system and a carbon capture system, and is characterized in that a compression energy storage unit of the energy storage system is coupled to a feed water heat regeneration unit of the thermal power generation system; an expansion energy release unit of the energy storage system is coupled to a steam turbine generator set of the thermal power generation system, and a steam-water cycle working medium in the thermal power generation system serves as a heat storage and cold storage medium of the carbon dioxide energy storage system. The carbon capture system is coupled between the thermal power generation system and the carbon dioxide energy storage system, captures carbon dioxide gas in the flue gas, inputs the carbon dioxide gas into the carbon dioxide energy storage system to serve as a circulating working medium, and heats the circulating working medium in the expansion energy release unit through waste heat of the flue gas. According to the scheme, the operation efficiency of the thermal power generation system and the operation efficiency of the carbon dioxide energy storage system can be improved, the overall energy utilization efficiency of the system can be improved, and meanwhile the emission amount of gas pollutants is reduced.

Systems and methods to generate units of work within a collaboration environment based on audio are described herein. Exemplary implementations may: manage environment state information maintaining a collaboration environment; obtain audio information characterizing audio content of the audio recording sessions; generate one or more units of work for the users based on the audio content of the audio recording sessions by storing information defining the one or more units of work as part of the environment state information; store the audio information as part of the environment state information; and / or perform other operations.

The invention discloses a centralized unloading type power generation robot which comprises a mute box type diesel generator set and further comprises at least two sets of lifting supporting rod assemblies, and the lifting supporting rod assemblies are arranged on the two sides of the mute box type diesel generator set so as to lift the mute box type diesel generator set in the loading and unloading process. According to the centralized unloading type power generation robot, by arranging the lifting supporting rod assembly, the problem that a traditional generator set loading and unloading vehicle depends on hoisting equipment is solved, automatic lifting and unloading of the loading and unloading vehicle can be achieved in the environment without the hoisting equipment, the transportation efficiency is improved, the transportation cost is reduced, and it is ensured that the equipment can be rapidly transferred to the needed place to be put into use.

The invention relates to the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The invention provides a gust-oriented wind generating set anti-overspeed control method and device and a medium, and belongs to the field of wind generating set control. The method comprises the following steps: acquiring real-time wind speed data and generator speed data; determining whether to enter a gust mode of the wind generating set according to the real-time wind speed data; under the condition that the wind generating set enters the gust mode, the minimum pitch angle of operation of the wind generating set is adjusted to be the minimum pitch angle of operation of the gust mode; calculating generator speed acceleration according to the generator speed data; determining whether to enter an acceleration anti-overspeed control strategy according to the generator rotating speed data and the generator rotating speed acceleration; and under the condition that it is determined that the acceleration anti-overspeed control strategy is entered, the preset variable-pitch rate is superposed on the normal variable-pitch rate. The pitch angle of the wind generating set in operation is increased when the wind generating set enters the gust mode, so that the attack angle margin of the blade can be kept in a safe range, and after the acceleration overspeed prevention control strategy is entered, the preset variable pitch rate is superposed on the normal variable pitch rate.

According to the power dispatching method and system based on carbon emission, power demand and carbon emission data are obtained in real time through sensors installed on a power user side and a thermal power generating unit; a dynamic clustering algorithm is adopted to analyze the data, and a graph neural network model is used for prediction; inputting a prediction result into a multi-agent reinforcement learning system by using an adaptive scheduling engine to generate decision data; and an optimized power generation combination plan is generated by combining a complex hybrid optimization algorithm, and the carbon quota is dynamically adjusted. According to the invention, the efficiency of power dispatching is improved, the flexibility of carbon emission management is enhanced, and the effective coupling of the power system and the carbon emission dispatching system is realized.

The invention relates to the field of power systems, in particular to a multi-energy complementary power supply network cooperative control method and system. The method comprises the following steps: collecting dynamic characteristic data of wind power, photovoltaic power, energy storage and load through a sensor group, and generating a multi-energy state characteristic matrix with a timestamp; and matching the feature matrix with a dynamic response feature library, and outputting a dynamic feature parameter set. Based on the set, a multi-objective optimization model is constructed, second-level frequency deviation suppression, minute-level tie line power stabilization and hour-level economic dispatching are covered, and a cooperative control parameter set is generated. And generating a thermal power generating unit compensation amount, an energy storage charging and discharging sequence and an interruptible load priority list. After execution, power grid deviation data are collected, a dynamic deviation value is calculated, and when a threshold value is exceeded, feature library parameters are corrected and loop optimization is carried out. According to the invention, the problem of insufficient cooperative control of the multi-energy system in the prior art is solved, and the stability and economy of the power grid are improved.

The invention discloses a cooling system, method and device of a wind generating set, and relates to the technical field of wind power generation. The cooling system comprises a cooling loop, the cooling loop comprises a cooling pump station, a cooling medium, a connecting assembly and a plurality of cooling branches connected in parallel, the connecting assembly communicates between the cooling pump station and the cooling branches, the cooling pump station drives the cooling medium to circularly flow in the cooling loop, and each cooling branch comprises N heat exchange assemblies connected in series, n is an integer greater than or equal to 2; wherein the outlet of the cooling pump station is respectively connected with the inlet of each cooling branch, and the inlet of the cooling pump station is respectively connected with the outlet of each cooling branch. In other words, when the cooling system is designed, the heat exchange assemblies are connected in series, so that the number of parts can be reduced, the cost is reduced, and the economical efficiency is improved.

A virtual synchronization method for an energy storage system and a radial-flow water turbine generator set, said method comprising: analyzing characteristics of a radial-flow water turbine generator set, and designing a suitable energy storage system so as to smooth the power fluctuation of the radial-flow water turbine generator set (S1); acquiring characteristic parameters of the radial-flow water turbine generator set, on the basis of the characteristic parameters, designing a virtual rotor model, and establishing a machinery dynamics equation (S2); on the basis of the virtual rotor model, designing a virtual synchronization controller, constructing a closed-loop control system, and implementing charging and discharging control for the energy storage system (S3); establishing a simulation model for coupling the energy storage system and the radial-flow water turbine generator set, and optimizing parameters of the controller (S4); and, on a test platform of an actual radial-flow water turbine generator set, implementing a proposed virtual synchronous control policy, and carrying out experiment verification and result analysis (S5).

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention discloses an application of a water supply main and bypass switching control method in unit wide load peak regulation, which comprises the following steps: step 1, load monitoring and prediction: monitoring unit load change in real time, collecting historical load data, and utilizing an advanced prediction algorithm to predict future load of a unit; the load change of a unit is monitored in real time, the future load is predicted, the water supply system is adjusted in advance, stable operation of the unit is ensured, the optimal switching strategy is intelligently selected according to the performance characteristics of the main and bypass water supply system and the current operation condition of the unit, and the switching efficiency is improved; smooth switching between the main path and the bypass path is achieved, fluctuation and loss in the switching process are reduced, the water supply system is optimized and adjusted in real time after switching is completed, efficient and stable operation of a unit in the wide-load peak shaving process is ensured, the water supply system can effectively switch the water supply main path and the bypass path, and the water supply efficiency is improved. And the generator set can stably operate in the wide-load peak regulation operation.

The floating type offshore wind turbine based on the tuned mass damper comprises a floating type foundation and a wind generating set, the wind generating set comprises a stand column, and the tuned mass damper is arranged in the stand column; the floating foundation comprises a buoy and a mounting platform, a base plate is slidably connected into the buoy, one end of the base plate is connected with the buoy through a damper spring, and the other end of the base plate is connected with the mounting platform through a supporting column. A submersible pump is arranged on the surface, close to the buoy, of the mounting platform, communicates with seawater through a first pipeline and communicates with the buoy through a second pipeline. And the buoys are distributed in a circumferential array along the mounting platform. The wind-resistant strength is improved, the cost is low, resetting is fast, the resetting effect is good, and offshore wind energy resources can be better utilized; the tuned mass damper is installed, vibration is reduced through compression and reset of the damping spring, and safety is improved; an air speed sensor is arranged, offshore air speed and air volume can be monitored, and the controller can conveniently control whether seawater is extracted or not.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

A mobile body control device includes: an observation trajectory acquisition unit that acquires, a trajectory along which the subject walks; a basic trajectory acquisition unit that acquires, a basic trajectory that matches the observation trajectory; a subject predicted trajectory acquisition unit that acquires a trajectory along which the subject walks after the current time point based on the specific basic trajectory; a prediction reliability calculation unit that calculates a prediction reliability based on uncertainty added along the subject predicted trajectory; a target trajectory acquisition unit that acquires, a trajectory formed by a target position of a mobile body set ahead of the subject in a traveling direction of the subject; and a control-command-sequence-generating unit that generates a control command sequence using an evaluation function that uses as input the subject predicted trajectory, prediction reliability, target trajectory, and a control trajectory.

The invention provides a power plant deep peak regulation and power generation optimization method and system based on steam extraction energy storage, and relates to the technical field of power grids, the method comprises the steps that high-temperature and high-pressure steam is generated through a steam turbine, the steam enters a steam extraction system, and the steam extraction system is used for extracting part of steam exhausted by the steam turbine when the load of the power grid is low; the extracted steam is conveyed into the energy storage device to be stored; based on historical power grid load data and real-time load prediction data, dynamic optimization adjustment of generated power is carried out by combining characteristics of a generator set of a power plant, and deep peak regulation operation of the generator set in a low-load operation state is realized by controlling a power generation dispatching control module of the power plant. The output power of each unit is adjusted through an optimization algorithm; when the load of the power grid is low, part of steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the load of the power grid is peak, the stored steam flows back to the steam turbine again to be reused.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.