4290 results about "Frequency modulation" patented technology

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses a wind-light-hydrogen multi-energy complementary system cooperative operation optimization and comprehensive benefit evaluation method, which relates to the technical field of electric energy storage, and comprises the following steps: constructing a hybrid power flow model containing electricity, hydrogen and heat coupling, taking whole life cycle cost and new energy consumption as double targets, introducing grid-connected and off-grid risk constraints, and optimizing equipment capacity configuration; a scheduling baseline and an intra-day dynamic correction strategy are formulated in the day-ahead stage, and hybrid energy storage cooperative control is combined to improve the response speed and flexibility of the system to wind and light fluctuation; a peak regulation and frequency modulation mode is switched according to the power grid state, a power response model is established, fluctuation is stabilized through hydrogen energy storage, the stability of the power grid is enhanced, and auxiliary service benefits are created; the performance of the system is evaluated from the three dimensions of efficiency, economy and interactivity, dynamic weight and Monte Carlo simulation optimization are utilized, system parameters are continuously optimized through a feedback mechanism, and the self-evolution ability is formed. The problem that system instability and economy are difficult to take into account due to wind and light fluctuation is solved.

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

The invention relates to a self-adaptive intelligent power control method for an electric energy router in a photovoltaic power generation system. According to the method, key parameters of photovoltaic, energy storage and load are monitored in real time, a virtual power pool is constructed to quantify a multi-source power margin and dynamically distribute a priority sequence, energy storage output is dynamically adjusted in combination with a feed-forward compensation control strategy, frequency modulation deviation is periodically checked, weight optimization redistribution is triggered, and energy storage output is dynamically adjusted. Cooperative control of photovoltaic load reduction frequency modulation and DC bus voltage stability is realized, the multi-source cooperative frequency modulation response capability and the energy utilization rate are effectively improved, voltage fluctuation caused by power abrupt change is suppressed, and both the system operation economy and the power supply reliability are considered.

The invention discloses an energy storage frequency modulation instruction prediction method and system based on a signal sequence folding rate. An original frequency modulation instruction signal sequence is obtained; obtaining optimization parameters for each signal in the original frequency modulation instruction signal sequence, wherein the optimization parameters comprise a first folding rate Z1, a first average value Q1 and a first variance C1; generating an auxiliary signal sequence based on the optimization parameters, and obtaining a combined signal sequence; predicting by using a neural network based on the auxiliary signal sequence and the combined signal sequence to obtain an intermediate prediction result; performing decoupling operation on the intermediate prediction result to obtain a final prediction result; the prediction result of the frequency modulation instruction is more accurate, the size of the frequency modulation instruction is predicted in advance for energy storage regulation, and the response accuracy and the frequency modulation income are improved.

The invention relates to the technical field of signal parameter measurement, and provides a parameter measurement method for a frequency modulation monitoring antenna, which realizes self-adaptive monitoring and accurate measurement of a complex electromagnetic environment through multi-dimensional data acquisition, intelligent analysis and dynamic optimization, and deeply integrates environment perception, signal processing and parameter optimization. A complete technical chain is formed, so that the parameter measurement method can have self-learning and self-adjusting capabilities, can cope with environmental changes and interference challenges in real time, remarkably improves the monitoring efficiency and the measurement precision, and provides a systematic solution for the application of a frequency modulation monitoring antenna in a complex scene.

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

The invention relates to the technical field of signal analysis, and discloses a primary frequency modulation large disturbance signal peak value analysis method, which comprises the following steps: collecting a primary frequency modulation large disturbance signal and carrying out filtering processing of multi-band noise separation; performing multi-scale decomposition on the filtered disturbance signal, extracting a dynamic entropy of a multi-scale signal sequence, and performing dynamic entropy denoising on the disturbance signal to obtain a denoised disturbance signal; and calculating a potential peak position of the denoised disturbance signal, and carrying out multi-condition verification on the potential peak position to obtain peak information for frequency modulation disturbance analysis. Based on spectrum energy distribution, specific wavelet bases are selected for different noise frequency bands for noise reduction, the wavelet noise reduction performance is improved, the energy distribution complexity and the structure complexity of the disturbance signals on different scales are extracted, the dynamic characteristics of the disturbance signals are captured more comprehensively, the noise reduction precision is improved, and the noise reduction efficiency is improved. And the accuracy of wind power integration fault identification and equipment life monitoring in the peak value analysis process is improved.

The invention discloses an AI computing power equipment intelligent energy consumption management system and method based on big data, and relates to the technical field of data center energy consumption management, the method comprises the following steps: obtaining real-time operation data of AI computing power equipment, the real-time operation data comprising load characteristic parameters, energy consumption characteristic parameters, environment characteristic parameters and equipment state parameters; obtaining a load discrimination value according to the load characteristic parameter; obtaining an energy consumption discriminant value according to the energy consumption characteristic parameters; acquiring an environment discrimination value according to the environment characteristic parameters; acquiring a state discrimination value according to the equipment state parameter; fusing the load discriminant value, the energy consumption discriminant value, the environment discriminant value and the state discriminant value to generate a comprehensive energy efficiency index; judging whether the comprehensive energy efficiency index exceeds a preset dual-threshold decision interval or not; if the upper limit threshold value is exceeded, triggering graded consumption reduction response, including dynamic frequency modulation, task migration or equipment dormancy; and if so, determining that the current operation mode is in a high-energy-efficiency state and maintaining the current operation mode.

The invention discloses a collaborative optimization scheduling method and system considering energy storage frequency modulation reserve. The method comprises the following steps: acquiring basic data of a power system; based on the basic data, a collaborative optimization scheduling model considering energy storage frequency modulation reserve is established, and the collaborative optimization scheduling model takes the minimum sum of the coal consumption cost, the unit start-stop cost and the wind curtailment cost of the thermal power generating unit as a scheduling target under multiple constraint conditions; the multiple constraint conditions comprise a lithium battery power constraint, a charging and discharging state constraint, an SOC constraint, a frequency modulation response constraint, a frequency safety constraint, a frequency modulation standby constraint, a flywheel energy recovery constraint, a thermal power generating unit operation constraint and a power balance constraint; solving the collaborative optimization scheduling model to obtain an optimal scheduling scheme of the power system; by comprehensively utilizing the advantages of a traditional unit, flywheel energy storage and lithium battery energy storage, the safety and operation economy of the system during frequency disturbance are improved.

The invention relates to the technical field of mechanical fault diagnosis, and discloses a gearbox fault diagnosis method and system based on lightweight variational Bayesian learning, and the method comprises the steps: collecting a to-be-diagnosed vibration signal of a gearbox, carrying out the preprocessing of the to-be-diagnosed vibration signal, and obtaining an original vibration signal and the fault feature frequency of the original vibration signal; determining amplitude modulation-frequency modulation sparse combination representation of the original vibration signal according to the fault characteristic frequency of the original vibration signal, and constructing a joint probability model according to classification distribution containing sparse vectors and the amplitude modulation-frequency modulation sparse combination representation of the original vibration signal; performing variational Bayesian inference solution on the joint probability model by using the non-overlapping sub-sequence of the original vibration signal and natural gradient optimization to obtain posterior probability estimation of a sparse coefficient; and determining an activation component according to the posterior probability estimation of the sparse coefficient and the sparse precision parameter, and matching the activation component with a pre-established multi-scale amplitude modulation-frequency modulation sparse dictionary to obtain a fault type and a confidence coefficient thereof.

The invention provides an adaptive time-frequency fusion sequence recommendation method based on a Mama framework, and relates to the technical field of sequence recommendation. Comprising the following steps: S1, carrying out embedding expansion processing on a user behavior sequence, and constructing a unified sequence input representation containing user embedding, article embedding and position embedding; s2, sending the sequence input representation into a frequency kernel guided filtering module, and extracting periodic preference features in the frequency domain representation; s3, inputting the extracted features into a selective state space module, constructing frequency modulation parameters, completing frequency domain state modeling, and generating frequency domain feature representation; s4, the sequence input representation is input into a Mamba module, time sequence modeling operation is executed, and time domain feature representation is generated; and S5, inputting the time-frequency feature representations obtained in S3 and S4 into an adaptive fusion module, calculating a fusion weight, completing feature fusion, and inputting the fused features into a prediction layer to generate a final recommendation result. The frequency domain and time domain features are modeled in a combined mode, and the accuracy of recommendation results is improved.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention relates to the technical field of energy storage frequency modulation, in particular to an energy storage frequency modulation instruction prediction method and system, and the method comprises the steps: decomposing a frequency modulation instruction sequence into a plurality of subsequences; determining the sub-sequence with the maximum frequency change in all the sub-sequences as a target sub-sequence, and generating a target virtual sequence to replace the target sub-sequence; predicting the target virtual sequence and other subsequences by adopting a GRU network to obtain a plurality of corresponding prediction results; and superposing all the prediction results to obtain a target frequency modulation instruction prediction result. The method has the beneficial effects that the frequency modulation instruction sequence is decomposed into a plurality of subsequences by using the variational mode decomposition algorithm, and then the subsequences are processed and predicted through the frequency method and the virtual sequence method, so that the frequency modulation instruction is predicted in advance, an energy storage system can respond more quickly, the response time difference is reduced, and the frequency modulation performance index is improved; and higher economic benefits are created for a power plant.

The invention relates to the technical field of power system source-grid-load-storage collaborative planning, and discloses a source-grid-load-storage collaborative planning method, system and device and a medium, and the method comprises the steps: obtaining the structure, operation and economic parameters of a power system; dividing extreme scenes and establishing a frequency response model; setting a frequency security constraint based on the extreme scene; a three-layer optimization planning model is constructed, the three-layer optimization planning model comprises an objective function and constraint conditions, and the third layer comprises frequency security constraints; the model is solved to determine a planning scheme. According to the method, the minimization change point detection technology is utilized, artificial truncation of the wind-light load output features in sample selection is avoided, and the extreme scene is accurately extracted. The constructed frequency response model comprises nine frequency modulation resources and comprehensively reflects the frequency modulation capability of the novel power system. According to the provided three-layer iterative planning method, the construction investment is properly increased, so that the frequency safety of a power supply, a power transmission line, energy storage and a demand side response scheme in an extreme scene is ensured, and the frequency instability risk and the economic loss are avoided.

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

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 discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

The invention belongs to the technical field of optical storage cooperative control, and discloses a new energy grid-connected optimization control method based on an energy storage system, which comprises the following steps: collecting a power grid impedance fluctuation amplitude, a harmonic distortion rate and environment temperature data, obtaining an initial SOC value, correcting the SOC value, calculating an adjustable power margin and a response delay parameter of a photovoltaic unit, and calculating the optimal power grid impedance fluctuation amplitude and the optimal power grid impedance fluctuation amplitude. Generating an efficiency responsibility table; constructing a response cluster, generating a frequency modulation task package, and further generating a three-dimensional efficiency mapping table; the inertia demand is dynamically calculated in combination with the power grid frequency change rate, a responsibility transfer strategy is automatically activated in a high-altitude and high-temperature scene, and an efficiency instruction set is generated; analyzing response deviation by simulating typical disturbance of a high-altitude weak power grid scene, positioning a deviation source in combination with historical data, and dynamically adjusting an efficiency weight correction factor; and updating a responsibility allocation rule of the efficiency responsibility table, disrupting the low-efficiency cluster, and recombining the high-adaptability unit.

The invention discloses a method for grabbing an object by a robot, which comprises the following steps: acquiring surface vibration, transient deformation and three-dimensional deformation data of the object through multi-point pulse excitation, and generating stress wave characteristic mapping data; analyzing internal propagation characteristics of the object according to the stress wave characteristic mapping data, identifying a stress transmission channel, determining a material boundary position and a deformation sensitive area, and generating internal structure characteristic data; calculating a deformation risk coefficient and a stability contribution degree according to the internal structure characteristic data, selecting a grabbing position, and generating grabbing strategy data; and according to the stress wave characteristic mapping data and the grabbing strategy data, the inherent frequency and the local deformation characteristic are identified, a force loading sequence is designed, the vibration state and the deformation amount are monitored, and the force loading sequence is dynamically adjusted. According to the method, the deformation behavior of the object can be accurately predicted, the optimal grabbing strategy is determined by analyzing the internal structure characteristics of the object, dynamic force control is achieved based on frequency modulation, and the instability problem in the grabbing process of the deformed object is effectively solved.

The invention discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.

The invention discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

The invention discloses a state prediction method for a centralized flywheel energy storage system, and belongs to the technical field of power system energy storage, and the method comprises the steps: obtaining the real-time operation parameters of each flywheel unit in the centralized flywheel energy storage system at the current moment; synchronously inputting the real-time operation parameters into a short-time high-frequency processing channel and a long-time trend processing channel for domain division processing, and generating a real-time abnormal probability value and an accumulated degradation index; generating a comprehensive prediction state index based on the real-time abnormal probability value and the accumulated degradation index; and dynamically adjusting an operation control strategy of the energy storage system according to the comprehensive prediction state index. According to the method, the short-time high-frequency processing and long-time trend processing dual-channel domain analysis technology is adopted, the abnormal evaluation parameters are dynamically corrected in combination with the electromechanical coupling model, minute-level accurate prediction of the flywheel health state can be achieved, the service life of equipment is prolonged, and the frequency modulation stability of a power grid is guaranteed.

The invention relates to the field of power system frequency stability control, and discloses a virtual synchronous machine energy storage frequency modulation method and system based on model prediction and adaptive control, and the method comprises the steps: constructing a prediction model of a virtual synchronous machine, wherein a virtual inertia coefficient and a virtual damping coefficient are included; designing a cost function, wherein the cost function is a weighted sum of squares of the frequency increment and the input power increment; solving the optimal control sequence by using quadratic programming, and further correcting the input power of the VSG in real time; and in combination with the dynamic characteristics of the energy storage device, the virtual inertia coefficient and the damping coefficient are adaptively adjusted according to the frequency deviation and the frequency change rate. The method focuses on the combination of model prediction control and adaptive control, is applied to the energy storage frequency modulation of the virtual synchronous machine, and aims to effectively solve the problem of frequency stability caused by the access of high-proportion renewable energy to a power system.

The invention relates to the technical field of big data, and discloses a resource pool planning method based on a computing power network, comprising the following steps: S1, global resource modeling; s2, intention-driven resource slicing; s3, intelligent scheduling and load balancing; s4, predictive fault-tolerant self-healing is carried out; s5, energy efficiency optimization resource recovery; according to the method, a safe and efficient distributed computing power network resource awareness framework is constructed by fusing a dynamic key agreement protocol and a federated learning mechanism; according to the new method, the resource discovery accuracy, the state updating timeliness and the abnormal node recognition capability are remarkably improved, and meanwhile, the privacy leakage risk in cross-domain collaboration is effectively avoided through the national secret algorithm encryption, block chain auditing and zero-knowledge certification technologies; according to the method, greenization and high efficiency of resource allocation are realized through a carbon perception dynamic scheduling strategy and a multi-objective optimization model; tasks are preferentially scheduled to green energy nodes, energy consumption of idle nodes is reduced in combination with a dynamic frequency modulation technology, and system energy consumption and carbon emission are reduced.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

The invention discloses a stator blade shape-following frequency modulation design method based on a bending vibration mode and application, belongs to the field of vibration design of stator blades of aero-engines and gas turbines, and is suitable for vibration frequency modulation design of non-cantilever type stator structure blades (such as fans and gas compressor stators) with inner rings and outer rings in the aero-engines or the gas turbines. According to the method, firstly, static frequency and dynamic frequency modal analysis is carried out through finite element modeling, and dangerous resonance points and corresponding vibration modes in a working rotating speed range are recognized by combining a Campbell diagram; based on a vibration mode amplitude normalization result, setting a vibration degree parameter and a scale factor, selecting a high response node and carrying out directed disturbance, and updating a structure model to improve a target vibration mode frequency; and through frequency amplification verification and iterative optimization, geometric reconstruction and frequency modulation confirmation are finally completed in combination with aerodynamic performance and processing requirements. The method can realize rapid and accurate frequency modulation of a specific bending vibration mode, avoids introduction of new resonance, and has the advantages of high efficiency, strong adaptability and the like.