7226 results about "Power station" patented technology

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

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 station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

The invention discloses a photovoltaic power station intelligent remote operation and maintenance method and system based on relay protection, and the method comprises the steps: collecting the multi-mode characteristic quantity of a photovoltaic array region relay protection device in real time through a distributed optical fiber sensing network, so as to construct a dynamic protection characteristic matrix; performing feature decoupling on the dynamic protection feature matrix by adopting a space-time convolution adversarial encoder to generate a fault feature vector set; inputting the fault feature vector set into a physically constrained LSTM-GAN hybrid prediction model, and predicting a multi-dimensional failure probability tensor of the key relay protection node in a future preset duration; generating an optimal operation and maintenance strategy decision tree based on the multi-dimensional failure probability tensor; and analyzing the operation and maintenance strategy decision tree through a remote execution gateway driven by the digital twinborn, and synchronously updating the topological connection relationship of the digital twinborn bodies of the power station. According to the embodiment of the invention, the operation and maintenance efficiency and reliability of photovoltaic power station protection can be improved.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention provides a power grid evolution behavior modeling method and system based on dynamic digital twinning, and relates to the technical field of intelligent power grid digital twinning. According to the method, interaction characteristics of power station output, user load response and an energy storage scheduling strategy are captured in real time through a digital twin interface, behavior coupling factors containing complementarity and conflict weight are formed, and a power grid response relation and a multi-scale demand elastic curved surface are constructed by using an implicit tensor fusion technology and nonlinear projection. And through the steps of virtual behavior anchor point disturbance and the like, offset is separated to identify an unbalance region, an evolution trajectory is generated, an optimal collaborative path is extracted, and the weight of a behavior coupling factor is dynamically adjusted, so that rapid convergence of a global collaborative stability domain is realized. The method covers a behavior coupling factor generation module and the like, supports various operations, effectively solves many problems in power grid resource interaction, and improves dynamic adaptability and cooperation efficiency of power grid evolution modeling.

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.

The embodiment of the invention provides a new energy optical power correction method and system based on weather abrupt change prediction. The method comprises the following steps: identifying a cloud layer type contained in a target area, and obtaining a cloud layer type coverage rate; if the cumulonimbus coverage rate exceeds a first threshold value, predicting a cloud layer movement path in combination with the cloud layer movement vector field to obtain a first cloud layer coverage change sequence; if the layer cloud coverage rate exceeds a second threshold value, an irradiance attenuation coefficient is predicted based on the cloud layer thickness in the meteorological data and the solar altitude angle, and a second cloud layer coverage change sequence is obtained in combination with the ground aerosol optical thickness and the corrected irradiance attenuation coefficient; and monitoring real-time operation data of the power station, combining the first / second cloud layer coverage change sequence, predicting a matching relationship between a cloud layer moving direction and a power station power reduction area by adopting a double-flow Transform architecture, calculating a power fluctuation range corresponding to each power station power reduction area, dynamically adjusting power grid operation parameters of a target area, and determining the power grid operation parameters of the target area. The power grid stability is ensured.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

Provided in the present application is an energy optimization method for a multi integrated energy system (MIES) including shared energy storage. A unified shared energy storage power station is established by the MIES, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs. In the present application, the optimization method for an MIES including shared energy storage is provided, i.e., a two-stage three-layer optimization model is established: the upper layer is a shared energy storage power station capacity configuration model, and the lower layer is an MIES optimization scheduling model. In the present application, an uncertain fuzzy set is constructed on the basis of a Wasserstein distance and moment information, such that first-order moment information is merged into a Wasserstein distance fuzzy set, thereby making the establishment of the fuzzy set more accurate. By means of the present application, an MIES including shared energy storage can be constructed, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs.

The invention discloses a time-space characteristic analysis method for meteorological new energy load big data, and belongs to the technical field of new energy power management, and the method comprises the steps of data fusion alignment, physical causal modeling, extreme weather attribution and time-space characteristic analysis. According to the method, time-space characteristic analysis is performed by combining physical causal modeling and extreme weather attribution, the prediction precision of new energy load fluctuation under daily weather is improved, and load abnormity caused by extreme weather can be effectively identified and coped with; a double-flow space-time causal graph network model is adopted to perform physical causal modeling, and through physical constraint expression of wind power and photovoltaic characteristics, causal modeling of load change association between power stations and a dynamic feature fusion prediction mechanism of the wind power and photovoltaic characteristics, the load prediction accuracy, stability and scheduling practicability are improved; extreme weather attribution is carried out by adopting an attribution method combined with an improved disturbance response ratio, the actual influence of the extreme weather on the new energy load is accurately identified, and the interference degree of different types of weather events on the load is quantified.

The invention belongs to the technical field of power station boiler waste heat recycling energy-saving equipment and relates to a plate-and-shell type coal economizer. The plate-and-shell type coal economizer comprises a corrugated plate heat exchange core body (1), a heat exchanger shell (2), a first and a second water inlet / outlet water tank (3, 3') and a first and a second exhaust gas inlet / outlet pipeline (4, 4'), wherein the corrugated plate heat exchange core body (1) is mounted in the heat exchanger shell (2), the first and second water inlet / outlet water tanks (3, 3') are respectively arranged at the upper and lower ends of the front side of the corrugated plate heat exchange core body (1), and the first and second exhaust gas inlet / outlet pipelines (4, 4') are respectively arranged at the upper and lower sides of the corrugated plate heat exchange core body (1). The plate-and-shell type coal economizer solves the problem that the length of a water passage needs to be increased to solve the problem of unapparent water heating due to the great difference between an exhaust gas density and a water density, and meanwhile, a corrugated passage at the exhaust gas side enables the exhaust gas flow turbulivity to be increased and the heat transfer efficiency to be improved; the blockage due to ash accumulation happens rarely, even if the ash is accumulated, the cleaning is easy to finish.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention discloses an optical storage charging station cross-site heterogeneous data fusion health state early warning method, which adopts transfer learning and domain adaptation technologies, measures the data distribution difference between a source site and a target site through the maximum mean value difference, realizes knowledge transfer and improves the generalization ability of a model. And on the basis of an Attention-LSTM feature fusion mechanism, feature weights are adaptively allocated, and the fault feature extraction capability is enhanced. And designing a fault mode sharing module, coding the fault mode of the source site into a knowledge base, and rapidly matching similar faults of the target site. A model compression technology is introduced, lightweight deployment is realized through knowledge distillation and a parameter quantification method, and operation of edge equipment is effectively supported. And furthermore, an online learning mechanism is adopted to dynamically update model parameters to ensure that the model adapts to the environment change of the target site. According to the method, the accuracy and robustness of cross-site fault prediction are remarkably improved, the model deployment time is shortened, and the method is widely applied to intelligent operation and maintenance of the optical storage charging and discharging integrated power station.

The invention relates to the technical field of new energy intelligent decentralized control, and discloses a distributed photovoltaic power station intelligent monitoring method and system, and the method comprises the steps: collecting electrical state parameters, and carrying out the normalization processing; performing anomaly detection and island detection based on a multi-dimensional deviation fusion mechanism; generating a power support parameter through a self-adaptive reactive power injection mechanism; under the island mode, loading voltage frequency double-droop combined control; and after the main network is recovered, grid-connected reconnection is realized based on a phase progressive phase locking mechanism. Compared with the prior art that multi-dependence centralized master station judgment and scheduling cannot realize fault isolation and independent operation in time especially under the condition that main network communication is abnormal or frequency fluctuation occurs in a far-end island edge node, the technical problem that fault isolation and independent operation cannot be realized in time in the prior art is solved. Autonomous monitoring of an individual power station level and stable operation of an island are realized, and the stability of the system in a weak network or island operation scene is improved.

The invention relates to a single battery early-stage thermal runaway early warning method based on operation parameter fusion and isolated forest algorithm optimization, and the method achieves the closed loop of early perception, precise recognition and real-time early warning of the thermal runaway of a battery through the dual-engine drive of LSTM time sequence feature enhancement and isolated forest dynamic optimization. The hysteresis limitation of traditional BMS threshold value alarm is broken through, and an early warning node is advanced before the thermal runaway chain reaction is started; the problem of abnormal monomer positioning in a large-scale battery cluster is solved, and high-efficiency detection of large-order-of-magnitude monomer / cabinet hierarchy is realized; an active safety protection system of the energy storage power station is constructed, and a landing technical support is provided for intelligent operation and maintenance.

The invention provides an energy storage power station and power grid collaborative complementary regulation method and system, and relates to the technical field of smart power grids, comprising the steps of obtaining disturbance components by performing multi-scale decomposition on power grid frequency and voltage data, and constructing a distributed model prediction control framework to optimize energy storage resources in groups; and designing a self-adaptive dynamic programming controller to evaluate the system state and output the optimal charging and discharging power, and responding to the topological change of the power grid in real time. According to the invention, collaborative optimization configuration of energy storage resources can be realized, the power grid stability is improved, the service life of energy storage equipment is prolonged, and the system operation cost is reduced.

The invention relates to the technical field of energy storage power station operation and maintenance, and particularly provides an energy storage power station risk assessment method and system, and the method comprises the steps: obtaining an operation state data set of an energy storage power station, the operation state data set comprises a plurality of equipment monitoring sequences, and each equipment monitoring sequence comprises the electrical parameter and environmental parameter monitoring data of energy storage equipment; according to the method, an electrical risk feature set and an environmental risk feature set are accurately extracted from operation state data by adopting risk feature extraction, real-time dynamic assessment is performed on extracted risk features based on a dynamic risk assessment model, and a comprehensive risk assessment result of an equipment monitoring sequence is generated; according to the comprehensive risk assessment result, an optimized regulation and control strategy of the energy storage power station is intelligently generated, and the optimized regulation and control strategy is fed back to an energy storage power station control system in real time so as to trigger targeted risk reduction operation, so that closed-loop management of risk assessment and regulation and control strategies is realized, and the risk assessment efficiency is improved. And the safe operation level and the risk response capability of the energy storage power station are obviously improved.

The invention discloses a wind and light storage adjustment inertia and frequency coordination control method, and belongs to the technical field of new energy grid-connected power generation. The method is used for inertia support and frequency cooperative control of a new energy power station containing wind power generation, photovoltaic power generation, energy storage and a phase modifier, and comprises the steps of system minimum inertia demand evaluation, wind and light storage and adjustment inertia time constant evaluation, calculation of minimum inertia support time needed by the system and a wind and light storage and adjustment inertia distribution strategy. According to the wind and light storage and adjustment inertia and frequency coordination control method, the inertia supporting capacity of a new energy grid-connected system is effectively improved, wind, storage, photovoltaic and phase modifier inertia supporting tasks are effectively distributed, and the system frequency safety setting requirement is flexibly met. By calculating the minimum inertial supporting time, inertial supporting and exiting are carried out in time, power back-filling in the frequency recovery stage is avoided, and the frequency modulation burden of the system is relieved.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

System and method embodiments are described for solar panel protection against severe environmental impacts. A tracker controller may be configured to rotate a solar tracker comprising a group of solar panels to a safe position to diminish hail impact during a hailstorm. Such a rotation may be implemented using one or more motors to drive arc gears coupled to a pair of supporting purlins that support the group of solar panels. The tracker may comprise a bumper rail placed on the top when the tracker is at the safe position for hail impact absorption. Implementing solar panel protection provides a practical solution to protect solar panels from damage. Thus, the need for costly repair or replacement may be avoided, minimized, or decreased. As a result, the economic efficiency of solar power plant operations may be improved significantly.

The invention provides a blast suppression method and system for an underground cavity of a pumped storage power station. The method comprises the following steps: configuring and deploying a multispectral gas sensor array; carrying out gas explosion limit calculation and risk assessment; calculating the risk level of explosion by using the prediction model; performing graded explosion suppression operation by using a graded explosion suppression execution module; combustible gas is guided to enter an inerting area by utilizing a space flow guide optimization module; and the ignition source blocking module is used for cooperative control to block an ignition source. The technical problems of low mixed gas explosion risk prediction and monitoring precision, low explosion suppression efficiency, slow response speed and relatively high operation and maintenance cost are solved.

The invention discloses a new energy output characteristic quantification method for a high-capacity energy storage power station, and belongs to the technical field of power systems, and the method comprises the steps: constructing a new energy output characteristic index system through the time characteristic analysis and output distribution characteristic analysis of the historical output data of new energy power generation, and carrying out the quantitative calculation of each index in the index system, the fluctuation and randomness characteristic parameters of new energy output are obtained based on the calculation result, the grid-connected scheduling strategy of the energy storage power station is adjusted based on the numerical values of the characteristic parameters, and the fluctuation and randomness of new energy power generation can be quantitatively analyzed from multiple time scales and multiple statistical dimensions. More comprehensive data support can be provided for energy storage scheduling, the change rule of new energy output in different operation scenes can be accurately represented, and a reliable basis is provided for power grid planning and energy storage configuration.

The invention relates to the technical field of energy storage power station fire monitoring, and discloses a fire detection and early warning method and system. The method comprises the steps that real-time monitoring signals are acquired and preprocessed, and after a preliminary abnormal index is determined, changes of temperature and gas concentration are analyzed and evaluated to obtain a fire risk pre-judgment score; if the preset risk threshold value is exceeded, potential fire source area distribution is judged according to a pre-established dynamic fire feature database, fire source positioning coordinates are obtained, and consistency is verified; abnormal points of the temperature and the gas concentration are classified, a fire initial symptom set is determined, the dynamic fire feature database is updated, judgment is conducted again, and the accuracy of early warning signals is determined; and if the accuracy is higher than the preset accuracy threshold, generating a comprehensive risk assessment report, obtaining a fire identification result, extracting an optimized feature vector from the fire identification result, and generating a fire source region distribution map and an abnormal early warning signal. According to the invention, fire source positioning can be realized, the false alarm and missing alarm rate is obviously reduced, and the emergency response speed is improved.

The invention relates to the technical field of energy storage lithium batteries, and discloses a remote monitoring operation and maintenance management device for energy storage lithium batteries, which is characterized in that a battery temperature acquisition module, a battery voltage acquisition module, a battery current acquisition module and an environment temperature acquisition module are respectively used for acquiring the surface temperature, voltage and current of a battery cell of an energy storage lithium battery group and the environment temperature of the battery; the state of charge calculation module calculates the state of charge (SOC) of the energy storage lithium battery group; the battery fault diagnosis module performs battery abnormity detection to identify a fault battery; and the early warning module generates alarm information when the SOC is not in a preset range and detects that a fault battery exists in the energy storage lithium battery group. According to the method, more data are collected through the intelligent sensor, multiple calculation modes are established to calculate the SOC of the battery, the calculation result can accurately reflect the actual working state of the energy storage power station, real-time fault detection is carried out on the energy storage lithium battery, and the use safety of the energy storage lithium battery is guaranteed.