4263 results about "Solar power" patented technology

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention relates to the technical field of power supply, in particular to a new energy station power prediction method and system based on a weather forecast large model, and the method comprises the following steps: detecting a weather forecast difference value and output fluctuation, marking an abnormal time period, calculating a voltage and frequency deviation to generate a correction threshold value, and matching phase deviation to generate a smooth reference. And analyzing inertia and response rate, applying dynamic attenuation, and collecting tie line power to adjust, predict and generate an output result. According to the method, abnormal time periods are screened through reverse correlation of meteorological difference and output fluctuation, error conduction is blocked, irradiance prediction is corrected through a voltage frequency integral quantity closed loop, power grid response is improved, actually measured wind speed rolling mean value is matched with AGC phase offset, output smoothness is optimized, inertia time delay and frequency modulation rate dynamic matching is performed, and attenuation constraints are adjusted. The frequency risk is reduced, the tie line fluctuation and deviation superimposed effect is quantified, the attenuation range is dynamically adjusted, the power transmission robustness is enhanced, and wind and light curtailment is reduced.

The invention relates to an intelligent charging method and system based on light and wind complementation and intelligent energy storage. According to the method, dynamic optimized charging is realized through multi-source data collaborative decision, and the method comprises the following steps: firstly, collecting real-time data such as light-wind power generation, energy storage SOC, charging load and commercial power supply, analyzing a wind-solar power generation complementary relationship and calculating a real-time complementary coefficient; a dynamic weight distribution scheme of light wind and commercial power is generated in combination with the commercial power state, and an energy storage charging and discharging instruction is generated by fusing peak and valley electricity prices and energy storage efficiency optimization; and based on the adjusted energy storage output power, comprehensively considering the charging demand and the vehicle battery state, and dynamically distributing the power of the charging pile through the charging priority. By adopting the method, energy complementation, economic scheduling and charging demand management can be combined, the utilization rate of renewable energy sources is effectively improved, and the charging cost is reduced.

The invention provides a device and a method for extracting water from air operated under all working conditions. The device comprises a solar power supply unit, a salt-carrying hydrogel adsorption-desorption unit, an electric heating desorption unit, a water vapor condensation and collection unit and a PID control unit. The solar power supply system comprises a solar photovoltaic panel, an MPPT (maximum power point tracking) type photovoltaic inverter and a storage battery, the salt-carrying hydrogel adsorption-desorption unit comprises hydrophilic porous hydrogel and a LiCl / CaCl2 composite adsorption material, and the electric heating desorption unit comprises a copper heat-conducting plate loaded with the adsorption material and an electric heating sheet. The water vapor condensation and collection unit is composed of four finned radiators and two semiconductor chilling plates. The PID control unit is composed of a temperature and humidity sensor and a control circuit, wherein the temperature and humidity sensor is connected to the radiators and the front side of the adsorption bed. Water vapor in the environment is enriched through an efficient salt-loaded adsorbent packed bed, desorption is driven in two modes of solar photo-thermal and electric heating, desorbed high-humidity airflow passes through a fin type cooling fin connected with a semiconductor and then is condensed into liquid water, and the process of taking water from air is completed through a collector below. According to the device, low-energy-consumption air water taking under the humidity of 15%-100% can be achieved by controlling the operation time of all the units, the device is portable and compact, the automation degree is high, and high-water-yield air water taking is achieved by using solar photo-thermal and power generation as energy input.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

A fault alarm device for solar power generation belongs to the field of solar fault monitoring. The system includes an edge monitoring module, a multimodal data fusion module, a fault classification module, a trend prediction module, a dynamic adjustment module and an alarm response module. The present invention improves the accuracy of fault identification by integrating multidimensional features such as photovoltaic panel current, voltage, power, ambient temperature and light intensity by using a deep learning fusion model. By introducing an attention mechanism and dynamically adjusting the alarm threshold, it effectively distinguishes environmental fluctuations from actual faults, reduces the false alarm rate and missed alarm rate, and can adapt to the complex and changeable photovoltaic system operating environment without manual intervention.

The invention belongs to the technical field of wind-solar power generation power monitoring, and particularly relates to a new energy wind-solar power data monitoring system and method. According to the method, power fluctuation characteristics and equipment state correlation indexes are extracted through abnormal value detection and time sequence analysis, so that the precision and efficiency of data processing are improved, and in the aspect of prediction, dynamic comparison between a historical operation data set and the current power fluctuation characteristics is utilized, and environmental parameter change trends are combined. A power prediction curve and a confidence interval are generated, so that the system can recognize potential abnormal operation in advance, decision support time is provided for operation and maintenance personnel, in addition, the system has corresponding fault diagnosis and optimization capabilities, when the prediction deviation degree exceeds a preset threshold value, an early warning mechanism is automatically triggered, a fault source is positioned through a fault diagnosis process, and the fault diagnosis efficiency is improved. And finally, according to a fault diagnosis result, a power prediction curve is corrected and optimized to form a closed-loop optimization mechanism, and the prediction precision and the operation efficiency of the system are continuously improved.

The invention provides a solar power generation power real-time prediction method, system and device based on multi-mode deep learning and a storage medium. Wherein the prediction precision is improved by fusing multi-dimensional features of earth surface radiation, equipment temperature and environmental data, mutation fluctuation decomposition is performed on long and short wave components of earth surface solar radiation, and a frequency domain sub-modal sequence is extracted; constructing a correlation model of a photovoltaic module temperature field and hot spot infrared data, and generating a dynamic feature map reflecting hot spot interference space distribution; establishing a multi-mode time sequence prediction model, and synchronously processing frequency domain features of radiation sub-modes, spatial correlation of hot spot feature maps and time sequence dependence of temperature and humidity data; and integrating the prediction results of the sub-modals through a frequency domain superposition reconstruction technology. According to the technical scheme provided by the invention, through multi-modal spatial-temporal feature joint modeling and dynamic interference decoupling, the power prediction precision in extreme weather and hot spot abnormal scenes is remarkably improved, and meanwhile, the dynamic adaptive capacity of a prediction system to complex environment disturbance is enhanced.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention discloses a slope multi-modal monitoring and early warning system and method based on digital twinning, and relates to the technical field of slope monitoring, and the system comprises a data acquisition module, a data processing module, a digital twinning modeling module, a deformation prediction module, an early warning visualization module and a solar power supply module. According to the invention, slope multi-source monitoring data is obtained through the data acquisition module, and after the slope multi-source monitoring data is preprocessed by the data processing module, a slope digital twinborn model is constructed and calibrated by the digital twinborn modeling module; deformation prediction is realized by combining a space-time prediction model which contains a space correlation and time sequence analysis module and is optimized by multi-scale neighborhood aggregation, a DTW loss function and transfer learning, and a future-state digital twin is generated through an early warning visualization module and risks are visualized. And the solar power supply module is matched to ensure the operation of a scene without an external power supply and the flexible deployment of the assembly connection module, so that a multi-mode, high-precision and sustainable slope monitoring and early warning scheme is formed.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention discloses a deep foundation pit excavation deformation real-time monitoring system based on relative measurement. The deep foundation pit excavation deformation real-time monitoring system comprises a relative measurement sensor group, a data acquisition and preprocessing module, a wireless data transmission module, a cloud data processing and analysis module, an intelligent risk assessment module, a visual alarm module and a solar power supply and energy storage management module. The relative measurement sensor group collects deformation data of the deep foundation pit, the data acquisition and preprocessing module performs primary processing on the data, the wireless data transmission module stably transmits the data to the cloud, the cloud data processing and analysis module performs deformation prediction, and the intelligent risk assessment module performs risk grading. The visual alarm module gives an alarm in real time and carries out emergency notification according to different risk levels, and the solar power supply and energy storage management module guarantees continuous operation of the system. The system solves the problems of measurement precision, data transmission, risk assessment, power supply stability and the like of an existing monitoring system, provides powerful support for safe construction of the deep foundation pit, and has remarkable benefits.

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.

The invention relates to the technical field of comprehensive application of renewable energy sources and hydrogen energy, in particular to a wind-solar-hydrogen multi-energy complementary intelligent scheduling platform. According to the technical scheme, the wind-light-hydrogen multi-energy complementary intelligent scheduling platform comprises a wind-light power generation unit which monitors wind power and photovoltaic power fluctuation in real time; the hybrid energy storage unit comprises a super capacitor and a flywheel for energy storage and respectively responds to millisecond-level and second-level power fluctuations; the hydrogen energy subsystem comprises an electrolytic hydrogen production device, a hydrogen storage tank and a fuel cell and responds to minute-level power scheduling; and the multi-time scale controller is configured to execute millisecond-level, second-level and minute-level cooperative control and dynamically match the wind-light fluctuation and the hydrogen energy response speed. According to the method, through multi-time scale dynamic cooperative control, the matching problem between the wind-solar power generation volatility and the hydrogen energy response lag is effectively solved, and seamless connection between second-level high-frequency fluctuation suppression and minute-level energy scheduling is achieved.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention discloses an intelligent scheduling method for video equipment based on solar power supply, and relates to the technical field of energy management and video. Through multi-dimensional data acquisition, intelligent prediction and dynamic scheduling technologies, the energy management challenge faced by solar power supply video equipment in remote areas is effectively solved, the power generation amount, equipment power consumption and environmental change data are acquired in real time by using an Internet of Things sensor, and the energy management efficiency is improved by combining time sequence analysis and a machine learning algorithm. The power generation amount and power consumption fluctuation are accurately predicted, an energy supply and demand matching model is established based on a dynamic planning algorithm, a power distribution strategy is optimized, operation of high-priority equipment is guaranteed preferentially when the power generation amount is insufficient, meanwhile, the working mode of the equipment is dynamically adjusted through a reinforcement learning algorithm, and energy consumption optimization and self-adaptive matching are achieved. The energy utilization efficiency and the system stability are improved, the adaptive capacity to dynamic environment changes is enhanced, and continuous and stable operation of video equipment under complex conditions is ensured.

The invention provides a power system net load prediction method based on multi-mode decomposition and reconstruction, and aims to solve the problems of violent net load fluctuation, frequent sudden change, difficult prediction and the like under a new energy high-permeability background, and the prediction precision and the trend response capability are improved by fusing a physical mechanism and a deep learning modeling means. Firstly, historical net load data and environmental parameters are collected, and historical wind and light net generating capacity is accurately estimated based on a wind and light generating capacity dynamic coupling calculation method. Afterwards, an improved complete set empirical mode decomposition algorithm is used for conducting multi-mode decomposition on historical net loads, an effective mode function is screened in combination with the energy proportion and correlation, the effective mode function and a residual term jointly reconstruct a net load sequence, and noise interference is eliminated; and after the reconstructed sequence generates a sample through a sliding window, inputting the sample into a hybrid neural network model composed of a convolutional neural network and a long-short-term memory network, and performing training in combination with wind-solar power generation. And finally, multi-step accurate prediction of the future net load is realized.

The invention belongs to the technical field of solar power generation, and particularly relates to a back contact BC solar cell structure and a preparation method thereof. A tunneling SiO2 layer and a phosphorus-doped N + poly layer with high crystallization rate and low parasitic absorption are arranged in a back N region of the cell; the P region is sequentially provided with an i: a-Si layer, a P: a-Si sub-layer and a P-uc-SiOxCy sub-layer; the front surface of the cell is provided with a laminated gradient silicon nitride antireflection passivation film; the back N region adopts the SiO2 layer and the N + poly field structure with high crystallization rate and low parasitic absorption to replace an i: a-Si + N: a: Si field structure of a traditional HBC to improve the current density, and the back N region adopts the suede light trapping structure to replace a polishing structure to improve the current density. A low-temperature silicon nitride antireflection film is additionally arranged on the upper layer of the TCO transparent conductive film to improve passivation and current density, silicon nitride coverage antireflection is carried out on the isolation regions of the P region and the N region to improve passivation and current density, the back efficiency of the cell is improved, and the double-sided rate is improved.

The invention provides a multi-source energy storage self-power supply method and system for an iron tower, equipment and a storage medium, and belongs to the technical field of iron tower power supply, and the method comprises the steps: inputting historical load data of the iron tower into a time sequence model, and obtaining a load demand of the iron tower; inputting meteorological data of the geographic position of the iron tower into the power prediction model to obtain wind power and photovoltaic power; determining a charging strategy of the energy storage battery based on the load demand, the wind power, the photovoltaic power and the charge state of the energy storage battery; and determining a self-power-supply control strategy according to the charging strategy of the energy storage battery, and selecting any one of a wind-solar power generation device, the energy storage battery or a power grid to supply power to the iron tower based on the self-power-supply control strategy. According to the multi-source energy storage self-powered method and system for the iron tower, the equipment and the storage medium, the overall operation cost of the iron tower is effectively reduced.

The invention discloses a wind and light storage station power reporting and tracking method based on energy storage rapid power response. The method comprises the following steps: acquiring wind and light power probability prediction data, power grid real-time operation state data, station operation state data and power grid examination allowable power deviation data in a current scheduling period; generating a dynamic power reporting interval of the next scheduling period based on the wind-solar power probability prediction data, the power grid real-time operation state data, the station operation state data and the power grid examination allowable power deviation data; and based on the dynamic power reporting interval and the real-time operation data of the wind and light storage station, a flexible control strategy is adopted to control the charging and discharging behaviors of an energy storage system in the station. By generating the dynamic power reporting interval with self-adaptive change and adopting the flexible control strategy, the operation condition of the energy storage system can be obviously optimized, the fluctuation of the wind and light power can be effectively stabilized, and the economy and reliability of the operation of the station can be improved on the premise of ensuring that the wind and light storage station tracks the power grid assessment requirement.

The invention relates to an electricity-hydrogen comprehensive energy system planning method considering multi-time scale uncertainty. The method comprises the following steps: establishing an electricity-hydrogen comprehensive energy system model; based on the electricity-hydrogen comprehensive energy system model, considering the uncertainty of wind and light output and load requirements, and constructing a planning mathematical model of the electricity-hydrogen comprehensive energy system by using a multi-time scale uncertainty modeling method fusing opportunity constraint and stochastic optimization; based on the electricity-hydrogen comprehensive energy system model, establishing an electricity market model as a lower-layer optimization model of a planning mathematical model; and solving the planning mathematical model by adopting a two-stage optimization method to obtain a planning scheme. Compared with the prior art, a multi-time-scale planning mathematical model of the electricity-hydrogen comprehensive energy system is established, and planning of the electricity-hydrogen comprehensive energy system is achieved.

The invention provides a transient current capability improvement control system and method of a wind and light power generation system. The system comprises a self-synchronizing voltage source control module, a junction temperature prediction module, a transient current improvement control module, a current instruction calculation module and a transient current precise control module. According to the invention, online observation of the junction temperature of the wind-solar converter power device can be realized, the maximum short-circuit current which can be output can be accurately predicted, the DC bus voltage, the switching frequency and the modulation strategy are adjusted when the power grid has a fault, the junction temperature of the power device is reduced, and the transient current endurance capability of the power device is improved. And the transient current is accurately controlled to limit the utilization of the transient capability of the power device.

The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.

The invention relates to a large-scale new energy base scheduling method based on uncertainty of an electric power spot market, and the method comprises the steps: collecting historical data of spot market node marginal electricity price and wind-solar power generation power, and generating a day-ahead market decision scene set comprising the spot market electricity price and new energy unit output through a Monte Carlo sampling method; establishing a two-stage stochastic optimization model for the large new energy base to participate in the day-ahead market by considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating a day-ahead market bidding strategy, a thermal power generating unit start-stop plan and a wind turbine generator maintenance plan in combination with the uncertain day-ahead market decision scene set; and according to high-precision short-period price prediction and renewable energy power prediction values, a real-time market bidding strategy and a charging and discharging strategy of energy storage equipment are calculated, and profit maximization in a large-scale new energy base market environment is realized. The problem of low economic benefit during energy storage scheduling of a large-scale new energy base in an electric power spot market is solved.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention relates to the technical field of intelligent power grids, in particular to a micro-grid energy storage optimization method and system based on big data analysis, and the method comprises the steps: obtaining the comprehensive prediction power generation through the prediction segmentation time, a wind power generator set and a solar power generator set, and obtaining the analysis power based on the prediction segmentation time and the comprehensive prediction power generation, an energy storage battery parameter set is obtained, if the analysis electric quantity is larger than or equal to a storage electric quantity threshold value, a target storage battery pack is retrieved from the energy storage battery packs through the energy storage battery parameter set, active equalizing charging is conducted on the target storage battery pack, and an updated storage battery pack is obtained; and after it is confirmed that a preset target energy supply battery pack is retrieved in the energy storage battery packs by using the analysis electric quantity and the energy storage battery parameter set, power is supplied by using the target energy supply battery pack, and an updated power supply battery pack is obtained. According to the method, the energy storage of the micro-grid can be accurately and intelligently optimized.

The invention discloses a solar power generation integrated management system, equipment and method based on AI intelligence, and particularly relates to the technical field of photovoltaic power generation. By analyzing the light-induced attenuation data of the photovoltaic module, judging whether the light-induced attenuation of the photovoltaic module has reversibility or not by using a convolutional neural network; based on the light intensity change data of the cloud layer edge light intensity abrupt change area, the influence degree of the cloud edge nonlinear shielding and reflection gain effect on the photovoltaic array power fluctuation is evaluated; and determining the working state of the photovoltaic module according to the judgment result of whether the light-induced attenuation is reversible and the evaluation result of the nonlinear shielding and reflection gain effect. When the assembly is in an abnormal working state, the non-linear attenuation influence degree of the aerosol retention layer on the light transmittance is evaluated by analyzing the light transmittance change. And whether an operation and maintenance scheme is started or not is judged according to the evaluation result, so that intelligent diagnosis and fine management of the operation state of the photovoltaic module are realized, and the operation efficiency and reliability of solar power generation are improved.

The invention discloses a park comprehensive energy optimization scheduling method based on artificial intelligence, and relates to the technical field of energy scheduling, and the method comprises the steps: employing a Temporal Fusion Transform model to carry out the time series modeling of electric, thermal and cold loads and wind-solar power generation output in a park, capturing the load change trend, generating the probability distribution of prediction errors, and carrying out the prediction of the prediction errors; load characteristics and wind-solar power generation volatility in the park are dynamically adapted, and prediction errors are remarkably reduced; in addition, in combination with real-time monitoring and an event-driven mechanism, abnormity of a load or equipment state is rapidly responded, energy storage discharge is dynamically adjusted, standby equipment is started or a load plan is modified, and stable operation of the system is ensured. Real-time energy assessment and transaction further efficiently utilize complementary energy, and can quickly make up for the energy gap, thereby enhancing the flexibility and economy of park comprehensive energy management.