1184results about "Energy industry" patented technology

The invention discloses a real-time monitoring and early warning method and system for a variable-frequency energy-saving motor, and the method comprises the steps: collecting the operation parameters of the motor in real time through a multi-source sensor group disposed on the variable-frequency energy-saving motor; determining a current motor working condition according to the motor operation parameters, and determining a real-time load rate of the motor based on a preset load working condition correlation model; acquiring historical operation data of the variable-frequency energy-saving motor, and generating a dynamic threshold curve according to the historical operation data; generating a dynamic threshold according to the motor operation parameters and the dynamic threshold curve, performing health degree calculation on the variable-frequency energy-saving motor based on the dynamic threshold, and determining a life prediction value of the variable-frequency energy-saving motor according to the health degree; and when the life prediction value of the variable-frequency energy-saving motor is lower than a preset value, automatically triggering a frequency reduction instruction, switching to a field-oriented vector control mode, and synchronously generating an alarm and equipment health file of the variable-frequency energy-saving motor.

An electronic device comprises a heat source and a heat distribution structure coupled to the heat source to distribute heat generated by the heat source during operation of the electronic device.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention provides an intelligent building energy-saving processing control method and system, and relates to the technical field of industrial automation. The method comprises the following steps: dividing a factory into a plurality of areas, and collecting operation data and energy consumption data of production equipment; constructing a digital twin model comprising an equipment energy consumption physical model and a production process logic model, and establishing an initial energy consumption benchmark; generating a cross-regional scheduling scheme through a hierarchical control architecture, inputting the scheduling scheme into a digital twinborn model for simulation, dynamically adjusting an energy consumption reference and scheduling parameters according to a simulation result, and verifying whether an energy-saving index meets a preset threshold value or not; deploying the optimized scheduling scheme to a physical factory, controlling equipment start-stop and operation parameters through an automatic control system, and updating a digital twin model and an energy consumption benchmark based on real-time feedback data; accurate management and control, efficient scheduling and dynamic optimization of intelligent building production energy consumption are realized, the energy consumption cost is effectively reduced, and the service life of equipment is prolonged.

The invention discloses an on-site monitoring system for the performance of an elastic loose type anchorage system suitable for deep-sea buoys, and particularly relates to the field of deep-sea monitoring. The on-site monitoring system auxiliary comprises a central processing module, wherein the output end of the central processing module is separately connected with an information display module,an information storage module, an auxiliary recovery module and a driving module, and the input end of the central processing module is separately connected with a power supply module, a monitoring module, a correction module and a positioning module. According to the on-site monitoring system for the performance of the elastic loose type anchorage system suitable for the deep-sea buoys, the auxiliary recovery module is arranged, air bags are installed at a plurality of node positions on the anchorage system, a booster pump in a pressurization unit is used, outside air is conveyed into a floating ball unit through a pipeline unit, so that the retractable air bags are enabled to bulge upwards to float, the whole anchor system is directly floated in sections, people conveniently recycle theanchorage system greatly, the workload during the recycling process is reduced, and the practicability is high.

The invention provides a compressor energy-saving operation control method and system based on reinforcement learning, and belongs to the technical field of compressor control. The method comprises the steps that multi-dimensional data in the operation process of a compressor are collected through a multi-parameter sensor network; preprocessing the multi-dimensional data to obtain target feature data; inputting the target characteristic data into a state prediction model, and predicting an operation parameter prediction value in a future control period; splicing and fusing the target feature data and the operation parameter predicted value, and constructing state representation of the reinforcement learning model; inputting the state representation into a target reinforcement learning model based on a near-end strategy optimization framework to obtain an optimal control action; safety verification is conducted on the optimal control action based on preset compressor safety operation constraints, and an execution instruction is determined; and adjusting operation parameters of the compressor based on the execution instruction. According to the compressor energy-saving operation control method and system based on reinforcement learning, the energy-saving performance and the operation stability of the compressor are improved.

The invention relates to the technical field of heat energy recovery, in particular to an efficient multistage waste heat recovery and reutilization combined heat and power generation system which comprises a heat output calculation module, a temperature difference mode analysis module, a heat exchange efficiency analysis module, an optimal parameter identification module, a condenser management module and a condensate water recovery control module. According to the method, temperature, flow and pressure data of multiple positions are collected in real time, the heat fluctuation trend under multiple input conditions is accurately predicted, a basis is provided for follow-up optimization decision making, and through dynamic prediction of a temperature difference mode, the heat exchanger inlet and outlet temperature and the mutual relation between multiple equipment working parameters are combined, so that the heat exchange efficiency is improved. Real-time evaluation of the heat exchange efficiency is achieved, construction of a working model of a heat exchanger is combined, optimal working parameters under multiple temperature gradients are accurately recognized, optimization of working parameters of a condenser and condensate water recovery equipment is combined, the energy utilization rate of the combined heat and power generation system is increased, energy consumption is reduced, and resource waste is reduced.

The invention provides a large reciprocating compressor continuous stepless regulation system and method based on intelligent control, and the method comprises the steps: building a continuous stepless regulation model according to a load change trend and process fluctuation characteristics, and determining an initial parameter set of a regulation instruction through a preset load range regulation threshold value and a dynamic response speed requirement; according to the non-equilibrium stress adjustment scheme, dynamically adjusting operation parameters of a compressor execution mechanism, monitoring the abrasion state of key components through a real-time feedback control technology, and determining operation data of the components after stress equilibrium; and the operation data of the parts after stress balance are obtained, the operation parameters of the compressor are subjected to secondary calibration through an intelligent control system in combination with an energy efficiency improvement target, and the optimized optimal pressure ratio state parameters are obtained.

An example monitoring system for data collection includes a data collector including a plurality of sensor. The system includes a data storage to store a collector route template for the plurality of sensors with a sensor collection routine defining how the plurality of sensors. The system includes a data acquisition and analysis circuit to receive detection signals and evaluate the detection values with respect to a rule, and further, based on the evaluation of the detection values with respect to the rule, to modify the sensor collection routine.

A power distribution apparatus, comprising: a plurality of switches each operable to switch a respective power source of a plurality of power sources between a first input node of a first load and a second input node of a second load; a first power meter configured to determine a first power being delivered to the first input node from the plurality of power sources via the plurality of switches; a second power meter configured to determine the power consumption of the first load; and a control module configured to control the switches to distribute power between the first and second loads in dependence on the determined first power and the determined power consumption of the first load.

Systems related to concentrated solar combination heating and power generation; solar heating; industrial heat driven power generation; thermal storage systems and heat exchanger and power generation systems therefore, including any of the above with optional supplemental fuel production, and associated methods, are generally described.

The utility model provides a full-bridge LLC resonant converter control circuit, which is characterized in that a full-bridge LCC resonant converter is provided with a frequency conversion controller, a phase shift controller and a mode switching module, the input end of the mode switching module is connected with an input voltage detection module, and when the input voltage of the full-bridge LCC resonant converter is lower than a preset threshold value, the phase shift controller is connected with the mode switching module. When the input voltage of the full-bridge LCC resonant converter is larger than or equal to a preset threshold value, the variable frequency controller controls a switching tube in the full-bridge LCC resonant converter to work through switching of the switch, and when the input voltage of the full-bridge LCC resonant converter is larger than or equal to the preset threshold value, the phase shift controller controls the switching tube in the full-bridge LCC resonant converter to work through switching of the switch. The full-bridge LLC resonant converter has the advantages that the variable-frequency controller and the phase-shift controller are arranged in the control circuit at the same time, switching of control modes can be achieved based on judgment of the magnitude of input voltage and through a simple switching circuit, the full-bridge LLC resonant converter has the advantages of variable-frequency control and phase-shift control, and the problems of voltage fluctuation and instability are solved.

The invention belongs to the technical field of industrial control, and particularly relates to a brushless direct current motor high-efficiency control method and system based on an FOC algorithm, and the method comprises the steps: superposing high-frequency sine disturbance in a voltage instruction, extracting a response component, achieving the online identification of the equivalent impedance of a motor, and dynamically solving and updating the estimated values of resistance and inductance; and correcting d-axis and q-axis voltage feed-forward items in real time by using the updated parameters, and correspondingly adjusting a current loop proportion and an integral gain. The method can adapt to the time-varying characteristics of motor parameters in an industrial field, so that the accuracy of feed-forward compensation and the stability of current loop bandwidth are effectively guaranteed, and the robustness and the control precision of a control system under complicated industrial working conditions such as variable temperature and variable load are remarkably improved; and the requirements of high-end industrial equipment on high performance and high reliability of the motor driving system are met.

The invention relates to the technical field of industrial automation, and discloses a self-adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment, and the system comprises a state monitoring module which is used for constructing an operation state matrix of the pneumatic equipment; the pressure control module is used for generating a dynamic pressure control vector of the pneumatic equipment; the risk judgment module is used for calculating a real-time operation risk coefficient of the pneumatic equipment; the hierarchical regulation and control module is used for establishing a hierarchical regulation and control strategy set of the pneumatic equipment; the control feedback module is used for generating a self-adaptive control parameter sequence of the dynamic voltage regulation instruction and the state early warning instruction; and the scheme output module is used for integrating the dynamic pressure regulating instruction, the state early warning instruction, the hierarchical regulation and control strategy set and the self-adaptive control parameter sequence and outputting a self-adaptive dynamic pressure regulating and energy saving scheme of the pneumatic equipment. The energy utilization efficiency and the operation stability of the pneumatic equipment can be improved.

The invention discloses an energy and low carbon cooperative regulation and control method and system based on reinforcement learning, and relates to the technical field of energy management and low carbon control, and the method comprises the steps: building an initial data set through real-time collection of energy consumption data, carbon emission data and key operation parameters in industrial production; preprocessing the data by adopting a multivariable correlation analysis and data standardization technology; constructing a dynamic optimization model based on a reinforcement learning algorithm, and adaptively adjusting operation parameters of production equipment by taking energy utilization efficiency maximization and carbon emission minimization as targets; and a closed-loop feedback mechanism is established, the regulation and control effect is monitored in real time, model parameters are optimized, and continuous efficient low-carbon operation of the system is ensured. The technical problem that energy efficiency and environmental protection are difficult to collaboratively optimize in a traditional industrial energy management method is solved, and the method is suitable for intelligent low-carbon production in high-energy-consumption industries such as chemical engineering and metallurgy.

The invention discloses a control method and system for energy efficiency optimization of a workshop air conditioning system of a constant-temperature and constant-humidity factory. The control method comprises the steps that a load prediction model is trained, and the constant-temperature and constant-humidity factory is partitioned to set importance weights for all air conditioners; based on a reinforcement learning algorithm, multi-objective optimization is carried out on an air conditioning system composed of all air conditioners, the reinforcement learning algorithm takes adjustment of operation parameters of all the air conditioners as actions, and the actions of each iteration and environment parameters, detected in real time, of a constant-temperature and constant-humidity factory workshop are input into a load prediction model; the predicted cold load, the predicted heat load and the predicted dehumidification amount of each air conditioner are obtained, and the performance coefficient of each air conditioner is calculated; and weighting the performance coefficient of each air conditioner according to the importance weight, and constructing a reward function for multi-objective optimization by combining a weighting result and the environment parameters, detected in real time, of the constant-temperature and constant-humidity factory workshop. According to the invention, the optimal optimization of the constant-temperature and constant-humidity factory among the lowest energy consumption, the most stable control and the highest precision is realized.

A steam single-screw compressor testing system is characterized in that the steam single-screw compressor testing system is composed of a steam pocket (5) and a liquid-gas separator (3), an electric heater (12) and an inverted-T-shaped steam distribution mixer (13) are installed in the steam pocket (5), and the steam pocket (5) is communicated with a steam single-screw compressor (1) through a steam return pipeline (6); the steam single-screw compressor (1) is communicated with a gas inlet of the liquid-gas separator (3) through a high-pressure steam outlet pipeline (2), high-pressure steam separated by the liquid-gas separator (3) is communicated with a gas inlet of the inverted-T-shaped steam distribution mixer (13) installed in the steam pocket through a low-pressure steam pipeline (4), and the bottom of the inverted-T-shaped steam distribution mixer (13) is located below the waterline of the steam pocket (5). And small holes are fully distributed, so that high-temperature steam and water in the steam pocket are uniformly mixed for heat transfer, and heat energy is recycled. The air conditioner is simple in structure, the compressor is started without electric heating, and energy and water resources are saved.

The invention provides an intelligent valve management and control platform based on the Internet of Things, and relates to the technical field of industrial automation control, and the intelligent valve management and control platform based on the Internet of Things comprises a data collection module, an edge calculation module, an Internet of Things communication module, a cloud platform management module, an intelligent diagnosis module and a self-adaptive control module. Through a mode of combining multi-source sensor data acquisition, edge calculation and cloud intelligent analysis, real-time monitoring of a valve state, accurate prediction of a fault and self-adaptive optimization of a control strategy are realized, compared with a traditional valve control system, the problems of response lag, high maintenance cost, data isolation and the like are effectively solved, and the system is suitable for large-scale popularization and application. The method can be widely applied to multiple industrial fields such as petroleum, chemical engineering and water affairs, the automation level, production efficiency and safety of industrial production are remarkably improved, the operation cost of enterprises is reduced, and the method has wide application prospects and huge economic value.

The invention discloses a step disturbance influence resistant micro-grid on-grid and off-grid state detection method and system, and the method comprises the steps: carrying out the filtering and normalization processing of a discrete time sequence, and obtaining a standard signal; identifying mutation points in the standard signal, and constructing a mutation point index set; screening out a point column meeting a time closeness feature from the mutation point index set, and constructing a continuous candidate section; performing stability analysis, monotonicity judgment and fallback trend identification on each continuity candidate section to judge whether the continuity candidate section is a step change section or not; whether a real continuous pulse sequence is formed or not is judged according to the time interval, the amplitude change rate and the amplitude peak-valley characteristic for the remaining sections excluding the step change section in the continuous candidate sections; and outputting the disturbance type of each continuity candidate section. According to the method, the accuracy of disturbance identification and the robustness of system control are improved, and the operation safety and the control decision reliability of the system under the complex disturbance background are guaranteed.

The invention discloses an energy-saving control system and method based on air compressor operation state analysis, and relates to the technical field of air compressor energy-saving control. The system comprises the following components: a data acquisition module, a data processing module, a virtual twin model construction module, an energy-saving control strategy test module, a scheme evaluation and optimization module and an actual control execution module. According to the method, the virtual twin model of the air compressor system is constructed, the operation states under different energy-saving control strategies are simulated in the meta-cosmic space, the energy-saving effects of various strategies can be rapidly and accurately evaluated, the energy-saving control strategies are continuously optimized through the scheme evaluation and optimization module, and the energy-saving effect of the air compressor system is improved. And finally, the optimal energy-saving control strategy is determined and applied to an actual control system, the energy-saving effect and the operation efficiency of the air compressor are improved, and the energy consumption and the operation cost are reduced.

The invention relates to the field of automation control, and discloses an integrated control method and system for an efficient and energy-saving industrial automation product, and the method comprises the steps: firstly obtaining the historical energy consumption data of automation equipment of a target industrial product, recognizing an abnormal point to determine a basic control mode, and constructing an energy consumption optimization process according to the basic control mode; the method comprises the steps of determining an optimal operation mode and generating an energy-saving operation strategy according to energy consumption efficiency of different process links, then evaluating energy consumption performance under different working conditions, finding out energy consumption bottlenecks and occurrence conditions, constructing a dynamic adjustment mechanism, generating an adjustment task list and extracting detailed adjustment tasks, and finally analyzing an energy-saving target and identifying hindering factors. And generating an energy consumption control scheme based on the production feedback data. The control efficiency of the industrial automation system can be improved.

The embodiment of the invention provides an intelligent air compressor control system and method based on deep learning and an optimization algorithm, and the system comprises a pressure sensing device which is configured to obtain pressure feature data; the electric quantity monitoring equipment is configured to obtain power consumption data of the air compressor; the temperature sensing equipment is configured to obtain temperature data of the air compressor; and the processor is configured to periodically determine operation parameters of the air compressor based on the adjusting period. And in one adjusting period, the processor is configured to determine operation parameters of one air compressor through an air compression model based on the pressure characteristic data, the air compressor power consumption data and the air compressor temperature data.

Controlling heating and cooling in a conditioned space utilizes a fluid circulating in a thermally conductive structure in fluid connection with a hydronic-to-air heat exchanger and a ground heat exchanger. Air is moved past the hydronic-to-air heat exchanger, the air having fresh air supply and stale air exhaust. Sensors located throughout the conditioned space send data to a controller. User input to the controller sets the desired set point temperature and humidity. Based upon the set point temperature and humidity and sensor data, the controller sends signals to various devices to manipulate the flow of the fluid and the air in order to achieve the desired set point temperature and humidity in the conditioned space. The temperature of the fluid is kept less than the dew point at the hydronic-to-air heat exchanger and the temperature of the fluid is kept greater than the dew point at the thermally conductive structure.

The present invention provides a DC power supply system and a DC arc detection method and device thereof, which simultaneously collect the AC signal and various DC signals of an inverter; when there is external noise introduced into the AC side of the inverter, the noise can be crosstalked to its DC side through the inverter; and the operation of the inverter itself may also cause noise, and the noise may appear on both sides of it at the same time; therefore, according to the AC signal and the various DC signals, it can be judged whether the DC power supply system has noise interference; if there is noise interference, the arc detection data is denoised, so that the arc detection data can be adjusted according to the actual noise situation of the system; and then according to the data results after denoising, it is judged whether the inverter has arcing; compared with the prior art, it can reduce the false alarm rate and improve the detection rate, and can be applicable to the situation where high-power inverters or inverters with nonlinear loads are prone to harmonic deterioration.

The utility model discloses a heat recovery and utilization system and a micro-pressure steam heat energy unit, and an efficient heat energy recovery and reutilization loop is constructed through equipment such as an evaporator, a screw compressor, a condenser, an expansion valve and a flash tank. The evaporator absorbs heat from heat source water and evaporates a refrigerant into low-pressure gas, then high-temperature hot water is generated through the compression and condensation process, meanwhile, the flash tank is used for converting the high-temperature hot water into micro-pressure steam so as to meet the requirement for low-pressure steam in the system, by efficiently recycling and utilizing waste heat, the dependence of the system on external steam and electric power is reduced, and the energy consumption of the system is reduced. Therefore, energy efficiency is improved, operation cost is reduced, and energy waste and environmental influence are reduced.

The invention relates to an energy efficiency prediction method and system based on a three-phase asynchronous motor. According to the method, an equivalent circuit model based on a motor physical mechanism is constructed, and theoretical current and ideal loss are generated to serve as benchmark references; voltage and current signals of actual operation of the motor are collected in real time, and vector subtraction is carried out on actually measured current and theoretical current to obtain difference current signals; blind source separation processing under physical constraint is applied to the difference signal, independent components representing different physical loss sources are decoupled, and additional power of the independent components is quantified; and the ideal loss, the additional loss and the output power are fused to realize dynamic accurate prediction of the real-time energy efficiency of the motor. According to the scheme, through the synergistic effect of differential driving and a physical mechanism, penetrability diagnosis of loss roots such as power grid interference and mechanical degradation is achieved, and the self-adaptive precision of energy efficiency prediction under variable working conditions is improved more remarkably.

The invention provides an air compressor unit energy-saving scheduling optimization method and system based on SSA-LSTM dynamic load prediction, and relates to the technical field of industrial energy efficiency management. The method comprises the following steps: decomposing gas load data to perform characteristic analysis; firstly, parameters with physical significance are qualitatively screened based on the working principle of the air compressor, and then multi-dimensional parameters are selected as input through Pearson correlation coefficient analysis; in combination with shipyard production data characteristics, LSTM is adopted to carry out dynamic load prediction, SSA optimization hyper-parameters are introduced, and an SSA-LSTM load prediction model is constructed; sSA-LSTM load prediction is taken as key input, constraint conditions of multiple time scales of air compressors are integrated, and an air compressor unit optimization scheduling model is systematically constructed. According to the method, the space-time heterogeneity and the dynamic fluctuation characteristics in the working condition data are effectively captured through the SSA-LSTM model, efficient global tuning is realized, and the prediction precision and the calculation efficiency are remarkably improved; the method is suitable for a high-dynamic, real-time and accurate load prediction industrial scene represented by a shipyard, and provides key support for scheduling decision making.

The invention relates to the technical field of temperature control, in particular to an electric heating steam generator control system and a using method, and the system comprises an operation state monitoring module, a dynamic power distribution module, a temperature pressure compensation module, an abnormity monitoring and early warning module and an operation control optimization module. According to the method, by comprehensively mastering the multi-dimensional operation data of the steam system, the over-limit value of the steam temperature adjustment rate is effectively screened, the heating power requirement is accurately evaluated, adjustment lag is avoided, the dynamic compensation power interval adjustment ensures the steam supply stability, heat loss compensation is optimized, and the heat supply quality is improved; the efficient early warning system recognizes pipeline blockage and pressure abnormity, safety is improved, steam flow distribution is optimized, operation efficiency is improved, and reasonable utilization of energy is achieved.

The invention relates to the technical field of steel plant air compression, and discloses a steel plant air compression system universal energy-saving and stable air supply control method which comprises the following steps that S1, the total air consumption demand Q of a steel plant compressed air pipe network is monitored in real time; s2, executing a load segmentation control strategy based on the total gas consumption demand Q; s3, loading and unloading operation of the power frequency air compressor and rotation speed adjustment of the variable frequency air compressor are controlled in a coordinated mode through a central energy-saving control unit; and S4, a pressure buffer tank is arranged on an output pipeline of the variable-frequency air compressor and used for stabilizing the pressure of the pipe network. The total air consumption demand Q is divided into two sections, the power frequency machine is started and stopped according to the N + 1 principle, and the frequency converter is supplemented for continuous quantity supplement, so that the system can operate only with the minimum necessary unit in any load scene, energy waste caused by over-start and over-stop in a traditional scheme is avoided, and overall reduction of compressed air production energy consumption is realized.