10097 results about "Power capacity" patented technology

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention discloses an AGV scheduling system optimization method based on dynamic priority adjustment and real-time load balance, and the method comprises the steps: dynamically monitoring the emergency degree and arrival time of cargos, enabling a system to adjust the task priority in real time, guaranteeing the priority processing of emergency cargos, and giving consideration to the scheduling efficiency of common cargos; information such as current positions, loads and electric quantity of the AGVs is collected, weighted load values are calculated, weight factors are dynamically adjusted according to task types, load balance is achieved, and the situation that part of the AGVs are overloaded and part of the AGVs are insufficient in load is avoided; and finally, scheduling optimization is carried out by comprehensively considering factors such as the adaptation degree between the task and the AGV, the path distance, the predicted completion time and the load balance, and the overall performance of the AGV system is fully exerted. The flexibility, efficiency and resource utilization rate of the AGV scheduling system are effectively improved, and the AGV scheduling system is suitable for various scenes such as logistics storage and production and manufacturing.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

The present application relates to a multi-terminal linkage energy scheduling method and apparatus. The method comprises: acquiring multi-terminal data, wherein the multi-terminal data comprises power supply side data, power grid side data, load side data, and energy storage side data; on the basis of the acquired multi-terminal data, predicting multi-terminal electricity amount related information within a future preset time period by means of a prediction algorithm, wherein the multi-terminal electricity amount related information comprises photovoltaic power generation related information, power grid power generation related information, power consumption information of a load side device, and electricity charging and discharging related information of an energy storage device; on the basis of an energy efficiency target, acquiring energy regulation information by means of the multi-terminal electricity amount related information; and controlling the energy storage device and / or the load side device on the basis of the energy regulation information. According to the solution of the present application, by means of a multi-party linkage mechanism, a scheduling plan formed on the basis of a preset scheduling target is more suitable for actual requirements and more accurate, and the energy storage device and the load side device can be automatically controlled on the basis of the scheduling plan.

The invention provides a distributed BMS battery active equalization type safety management method and system, and the method comprises the steps: obtaining the state information and performance change trend corresponding to each single battery through real-time monitoring, constructing a target battery pack topological structure model, combining a reinforcement learning algorithm, generating an intelligent agent model, and employing the intelligent agent model and a fuzzy logic controller to achieve the active equalization type safety management of a battery pack. Determining an energy flow management scheme; based on the feedback data and historical data of the energy flow management scheme, using a deep belief network to construct an anomaly detection model, based on the anomaly detection model, generating a risk assessment report, and constructing a security record platform based on the block chain technology to obtain security record information; optimizing each model parameter based on the risk assessment report and the security record information, and generating a security management system in combination with a transfer learning algorithm and a quantum computing method; and high-precision monitoring, intelligent electric quantity balance regulation and control, anomaly detection, risk management and continuous optimization of the distributed BMS battery system are realized.

The invention relates to the technical field of SOC estimation, in particular to an SOC estimation method and system for a lithium battery pack. The method comprises the following steps: collecting basic parameters and voltage data of a lithium battery pack, generating steady-state voltage data by using singular value decomposition and time domain reconstruction, further performing multi-level feature mapping and performance reconstruction to obtain basic discharge data, calculating battery energy efficiency and deducing normal power consumption based on the data, and calculating the energy efficiency of the lithium battery pack. Discharging simulation is carried out to evaluate the temperature rise condition, discharging capacity attenuation is predicted in combination with working environment parameters, relative errors are calculated, finally, the charge state is corrected, electric quantity self-calibration is carried out, and intelligent state estimation of the lithium battery pack is achieved. According to the invention, a more efficient SOC estimation method is realized.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention discloses a power distribution network voltage control method based on distributed photovoltaic active-reactive cooperation, and the method comprises the steps: analyzing the influence of reactive / active injection on voltage stability through building a sensitivity matrix, deducing a critical control gain constraint condition, and guaranteeing the static stability of the voltage of a power distribution network; a photovoltaic inverter active / reactive cooperative control general model suitable for different scenes is constructed based on an intra-day scheduling and real-time control two-stage framework, and control curve parameters of each node photovoltaic inverter are optimized by taking minimization of network loss cost, active reduction cost, inverter voltage regulation cost and voltage deviation as targets in the intra-day scheduling stage; in the real-time control stage, active power reduction and reactive power regulation are dynamically coordinated based on an optimal control curve, reactive power margin is preferentially utilized to suppress voltage rise, and power flow distribution is directly regulated through active power reduction when reactive power capacity is saturated. According to the method, the safety and economy of the power grid are considered, and theoretical support and a practical solution are provided for voltage control of the high-permeability photovoltaic power distribution network.

The invention relates to the technical field of inverter control, and discloses an inverter output power control method and device, equipment and a storage medium. The method comprises the following steps: performing power frequency fundamental wave extraction and multi-band band-pass filtering on acquired voltage and current sampling data; performing phase compensation, first coordinate transformation and positive and negative sequence decoupling operation on the filtered fundamental wave principal component data; on the basis of a synchronous phase angle and a power grid fundamental wave frequency obtained by positive and negative sequence decoupling operation, carrying out second coordinate transformation, moving average and harmonic power delay difference operation on three-phase current and voltage data of an alpha-beta coordinate system; performing multi-level power calculation, power capacity distribution, error characteristic evaluation and decoupling operation on the inversion fundamental component and the inversion harmonic component subjected to the difference operation; and performing output signal modulation on the inversion power control quantity of the decoupling operation based on the fundamental wave synchronization phase angle, and generating a power control result. According to the invention, the control performance of the inverter on the output power in actual operation is improved.

A charging system comprises a local energy storage disposed at a charging site; an electric coupling configured to provide an electrical interconnect between the charging site and an electric vehicle (EV); and one or more controllers configured to: (i) detect a condition related to availability of electrical energy at the charging station, (ii) in response to the detecting of the condition: (a) estimate a number of EVs to which a charge remaining at the local energy storage is to be distributed within a certain time interval, (b) initiate a charging session for transferring electric energy from the local energy storage to the EV, via the electric coupling, and (c) limit an amount of electrical energy in the charging session in view of at least the estimated number of EVs.

The invention discloses a vehicle network interaction regulation and control method and system based on charging pile data. The method comprises the steps that electric quantity data of each adjustable vehicle at the current collection moment, power supply load data of each node in a power distribution network, position data of each charging pile and charging power data, charging load data and environment data at each collection moment are collected, and the charging load of each charging pile is predicted; obtaining load prediction data of each charging pile, calculating to obtain a vehicle-network interaction suitability index of each charging pile, and determining the power supply load prediction data of each node based on the vehicle-network interaction suitability indexes of all the charging piles, the electric quantity data of all the adjustable and controllable vehicles and the determined matching degree among the power supply load prediction data of each node. And executing a vehicle network interaction regulation and control strategy corresponding to the matching result. According to the charging pile data-based vehicle network interaction regulation and control method and system provided by the embodiment of the invention, the efficiency of vehicle network interaction regulation and control is improved.

A power transmission network carbon emission accounting method based on a full life cycle theory. The method comprises: acquiring a bill of materials required in a production stage of a power transmission network device and carbon emission factors of materials corresponding to the bill of materials, constructing a carbon emission accounting model and calculating carbon emissions; acquiring transportation modes and carbon emission coefficients of related materials in the transportation and construction stages, constructing a model and calculating carbon emissions; establishing a model, which takes into consideration indirect carbon emissions generated due to line losses and carbon emissions generated during a replacement process related to conventional consumables and device failure, and calculating carbon emissions; constructing a carbon emission accounting model in the decommissioning and recycling stages, and calculating carbon emissions and carbon emission reductions generated in the stages; and performing accounting on carbon emissions throughout the full life cycle of a power transmission network, and calculating the carbon emission intensity. A carbon emission reduction effect of device recycling in a decommissioning stage is taken into consideration, the carbon emission intensity throughout the full life cycle of the power transmission network is calculated by means of a net power transmission capacity, and practical significance is given to a carbon emission target.

The invention relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.

The invention relates to the technical field of electronic equipment battery management, and discloses a battery charging management method and an electric quantity accurate display method based on electronic equipment. The dynamic adjusting module generates a multi-dimensional strategy through a staged charging control unit (including a fast, balanced and trickle charging sub-unit), a parameter transition processing layer and a global charging state pooling layer; the environment adaptation module adjusts the charging power and the temperature threshold according to the environment data, and has the functions of environment feature extraction, self-adaptive compensation and anti-interference switching; and the strategy fusion module outputs a control instruction in a weighted manner, and is additionally provided with an exception handling and wireless charging optimization mechanism. When the electric quantity is displayed, the collected data is dynamically calibrated to compensate polarization errors, and the capacity is fused with a matching historical curve to generate a high-precision electric quantity percentage which is dynamically rendered and displayed. According to the scheme, the charging safety, efficiency and electric quantity display precision are improved.

The invention provides a hybrid vehicle energy management method, device and equipment and a medium, and the method comprises the steps: obtaining the real-time traffic information and configuration information of a vehicle, and calculating the demand power and demand torque of the vehicle according to the real-time traffic information and configuration information; calculating energy consumption of the battery according to the battery remaining capacity, the capacity information and the temperature information to obtain energy consumption information of the battery; power parameters of the vehicle are obtained, and the actual power and the actual torque of the vehicle are calculated according to the energy consumption information and the power parameters; actual energy consumption of the vehicle is calculated according to the actual power and the actual torque, and an energy distribution strategy is made according to the actual energy consumption; and constructing a dynamic programming algorithm to perform power distribution on an engine and a motor of the vehicle according to the energy distribution strategy. According to the technical scheme of the embodiment, through real-time interaction of the Internet of Vehicles, the vehicle can complete efficient energy distribution according to the processing information, and energy consumption and environmental pollution of the hybrid power vehicle are reduced.

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

To provide a power amount adjustment system capable of reducing power consumption according to requests to reduce the power consumption due to tight power supply and demand.SOLUTION: In a power amount adjustment system 100, when a server 4 acquires tight power supply and demand information requiring a reduction in power consumption in a specific time period, the server is configured to change and adjust a start time of defrosting operation when the specific time period overlaps with a time period during which pulldown is scheduled to be performed.SELECTED DRAWING: Figure 1

The invention provides a battery dynamic equalization method and system based on cloud computing, relates to the field of battery management, and solves the technical problem of insufficient utilization of battery equalization resources in the prior art. The method comprises the following steps: a cloud calculates a fault probability index of each monomer based on acquired operation data and historical equilibrium data of each monomer in a battery pack, and marks the monomer of which the fault probability index is smaller than a preset fault threshold as a normal monomer; according to the battery state data and the power grid load data of the normal monomers, calculating the dynamic balance priority of each normal monomer; distributing equalization current and equalization electric quantity of each normal monomer based on the dynamic equalization priority to obtain an equalization strategy, and sending the equalization strategy to a local end; and the local end executes the equalization strategy, and when a re-triggering condition of the equalization strategy is met, an equalization calculation request instruction is sent to the cloud end, and the calculation process is repeated. The method is used in the dynamic balance control process of the battery pack under the scenes of energy storage, power and the like.

The invention relates to the field of battery car charger detection, in particular to a charger health state assessment method and system, and the method comprises a data collection step, a predicted electric quantity calculation step, a health state assessment step and a fault feature analysis step. A sliding time window algorithm is used for dynamically calculating electric quantity loss, a self-adaptive dynamic threshold system based on an operation state and an environment working condition is constructed, fault characteristics are analyzed in combination with a nonlinear fusion model and an equipment aging factor, and accurate evaluation of the health state of the charger and rapid fault positioning are achieved. And the evaluation comprehensiveness, accuracy, threshold dynamic nature and fault analysis precision are improved.

The invention relates to a battery SOC intelligent monitoring and management method based on a precise electric quantity algorithm, and belongs to the technical field of battery power generation management, and the method comprises the steps: obtaining the voltage, current, temperature and historical data of a battery in real time, and calculating an initial SOC value through an ampere-hour integral method; when the SOC is greater than or equal to 90% in a charging state, generating a first correction factor based on a voltage hysteresis characteristic and a temperature coefficient, and dynamically reducing an ampere-hour integral weight; in the discharge state and when SOC is smaller than or equal to 20%, a second correction factor is generated in combination with the polarization voltage and the temperature attenuation coefficient, and the Kalman filtering weight is increased; and fusing the correction factors through a gradient descent optimization algorithm, performing closed-loop feedback, and outputting a calibrated SOC value. According to the method, through dynamic weight distribution, a nonlinear compensation model and a closed-loop self-optimization mechanism, the problems that an extreme SOC interval is large in estimation error and a traditional method is poor in adaptability are solved, and the precision and reliability of battery management are remarkably improved.

The invention discloses a system and method for estimating the charging capacity of an energy storage lithium battery, and particularly relates to the technical field of energy storage battery management, and the method comprises the following steps: collecting dynamic parameters in the charging process of the energy storage lithium battery, and forming a dynamic data set; calculating an environmental disturbance influence index and a charging response consistency factor based on time sequence analysis and feature decoupling; constructing a two-dimensional working condition mapping matrix, identifying a current working condition area and generating a scene label; determining parameter weights of the plurality of estimation models by adopting a probabilistic reasoning mode; according to the scene state, selecting a single model output result or fusing a plurality of model output results for estimation; according to the method, the environment disturbance influence index and the charging response consistency factor are constructed, so that the complex working condition is accurately identified; scene labels are automatically generated based on two-dimensional working condition mapping and a clustering algorithm, and a plurality of estimation models are dynamically selected or fused in combination with model confidence, so that the accuracy, robustness and intelligent level of estimation are improved.

The invention discloses an orderly power utilization system for a charging pile. The orderly power utilization system comprises a cloud platform, an orderly power utilization gateway and an orderly power utilization terminal, the cloud platform is used for judging the residual power of the power transformation box through the orderly power utilization gateway; the orderly power utilization gateway is used for collecting and controlling the charging power of the charging pile through the orderly power utilization terminal; the cloud platform is provided with a basic guarantee layer and a competition layer, and the basic guarantee layer is used for distributing the lowest power to each vehicle connected with the charging pile, so that the basic charging requirement is ensured; and the competition layer calculates priority scores according to the current electric quantity, the charging remaining time and the charging power of the charged vehicles, and distributes the remaining power of the power transformation box step by step according to the priority scores of the vehicles from large to small under the condition that the total power capacity of the charging pile is not exceeded. The competition layer calculates the priority score through the current electric quantity, the charging remaining time and the charging power of each vehicle, and then distributes the remaining power of the power transformation box according to the priority score, thereby adapting to the requirements of complex scenes, and improving the charging efficiency.

The invention relates to the technical field of information, in particular to a control method and system for an intelligent photovoltaic charging pile in the technical field of automobile charging piles, and the method comprises the steps: obtaining the charging demands and residual electric quantity information of a plurality of electric automobiles, dynamically adjusting the charging power of each automobile through combining with an electric power distribution algorithm, and guaranteeing the reasonable distribution of electric power resources; acquiring real-time load data through a power grid load monitoring system, predicting a future power grid load change trend in combination with a historical load curve, and judging whether a charging strategy needs to be adjusted or not; according to historical data and real-time data, cooperative working parameters of photovoltaic power generation and storage battery power storage are optimized by adopting a machine learning algorithm, and the overall efficiency of the system is improved; a cooperative working mode of photovoltaic power generation, storage battery power storage and power grid power supply is dynamically adjusted through an intelligent optimization algorithm; if the illumination fluctuation is large, a standby power supply is started to supplement power supply, it is ensured that the charging process is not affected, meanwhile, the energy storage efficiency is optimized, and power resource waste is reduced.

The invention relates to the technical field of energy storage inverters, in particular to an energy storage inverter parallel operation stability control method, which comprises the steps of training a load prediction model based on historical load data and environmental data of a first region, and obtaining a load prediction demand and a total load demand of the first region in a first time; based on the historical power generation data of the renewable energy sources in the first region, total power generation output in the first time is predicted; when the total power generation output is greater than the total load demand, redundant electric quantity is stored through an energy storage inverter; when the total power generation output is smaller than the total load demand, the energy storage inverter discharges to provide supplementary power; the working state of the energy storage inverter is obtained in real time, and when one or more energy storage inverters break down, the fault energy storage inverter is disconnected from the parallel connection group by adopting a fault isolation mechanism; obtaining the health degree of the remaining energy storage inverters, and distributing output power for each energy storage inverter; and the power distribution strategy of the energy storage inverter is adjusted in real time according to the load prediction demand.

The invention discloses a distributed power supply distribution optimization method and system based on a virtual power plant, and the method comprises the steps: generating estimated power distribution data according to a power distribution region power characteristic value, climate and holiday and festival information, and generating estimated power distribution data in combination with a target historical power generation record and the like; then judging a peak power consumption period through the estimated power consumption distribution, obtaining the total electric energy demand quantity, and determining the cache capacity of the energy storage equipment according to the total electric energy demand quantity and the estimated power generation distribution data, and when the difference value between the cache capacity and the flow of the transmission bottleneck point of the power distribution network is lower than a preset margin threshold value; and generating a power plant supplementary electric quantity proportion by using the power distribution proportion adjustment parameter and the real-time load data, further adjusting a power plant scheduling frequency and distributed power supply access density proportion, and finally generating a power distribution scheduling scheme of a target power distribution period. The method can effectively balance the load of the power grid, improves the power distribution efficiency, adapts to the access of a distributed power supply and renewable energy sources, and achieves the intelligent dispatching and optimized operation of the power grid.

A method for configuring reactive power capacity of a power grid to which offshore wind power is connected on a large scale includes the following steps. Static voltage stability index of substations in a regional power grid to which the offshore wind power is connected on the large scale and at multiple points and relative dynamic voltage drop area index for an N-2 fault are calculated. Thus, weak nodes in voltage stability are located, thereby implementing a prediction of a risk to the voltage stability of the regional power grid. These nodes are used as candidate reactive power compensation configuration nodes. An objective function is established as the minimum total cost of reactive power compensation devices at the candidate reactive power compensation configuration nodes. A multi-type reactive power optimization configuration model including static voltage stability constraints, voltage stability constraints under faulty state, and other constraints is solved.

The invention provides a photovoltaic wind power energy storage intelligent management method and system for a building. The method comprises the following steps: acquiring operation parameters, environmental conditions and historical fault data records of system equipment, and preprocessing the acquired data; establishing health assessment indexes by using the comprehensive wind and light energy storage equipment performance model, and determining the health degrees of different equipment; the fault abnormity level is judged according to the fault alarm signal sending time and frequency of the system equipment; through combination of an autoregressive differential moving average model and a long-short-term memory model, the photovoltaic and wind power comprehensive electric quantity is predicted, and the loss abnormity level is judged; and in combination with the health degree, the fault abnormity level and the loss abnormity level of the equipment, making a patrol inspection and daily maintenance work plan, and sending out reminding and early warning. By analyzing and diagnosing key equipment and core performance of the system and early warning key events, energy waste and equipment damage caused by improper maintenance can be effectively avoided.