8results about How to "Avoid overvoltage" patented technology

The application belongs to the technical field of black start control of power grid system, and particularly discloses a black start DC excitation regulator and a control method thereof applied to a black start system. The black start DC excitation regulator comprises a rate limiter and a PID controller. The black start DC excitation power source outputs an excitation step signal to a signal input end of the rate limiter. An output signal of the rate limiter is compared with a terminal voltage of a feedback machine, and a difference value is output to a signal input end of the PID controller. An output signal of the PID controller is sent to a generator terminal. The control method comprises the following steps: constructing a black start system; starting initial excitation of the black start DC excitation power source, and adjusting the terminal voltage of the generator through the black start DC excitation regulator; switching to an AC excitation adjustment mode; starting a load, and operating the black start system. The application shortens the excitation time of the black start system with zero start-up and voltage rise, and improves the probability of one-time successful excitation. The application is suitable for black start control of a power grid.

The invention discloses an online evaluation method and system for the grid-connected capacity margin and trend of a Sagomean new energy base. According to the method, on the basis of meteorological data, power grid operation data and equipment state data which are collected in real time, the real-time grid-connected capacity margin of the new energy base is calculated through multi-source data fusion and dynamic modeling, and the short-term change trend of the new energy base is predicted through a time sequence analysis and machine learning algorithm. The system comprises a data acquisition layer, a fusion analysis layer, an evaluation prediction layer and a visual interaction layer, and full-process automation from data to decision is realized. According to the invention, the new energy consumption capability of the Sagomean region can be improved, the safety and economy of power grid operation are enhanced, and a scientific basis is provided for scheduling decision making.

The invention provides a dynamic energy distribution optimization method and system for a photovoltaic energy storage charging station, is applied to the technical field of power supply and distribution control, and aims at the complexity of multi-source heterogeneous data in a photovoltaic energy storage charging scene and based on an industrial big data analysis technology, and comprises the following steps: collecting multi-source data to construct a variable set, and inputting a model to obtain a supply and demand prediction value; generating an electric quantity flow direction matrix by adopting multi-particle strategy global optimization, generating dynamic boundary verification based on a battery health index, repairing an out-of-limit path by utilizing linear programming, and finally generating a control instruction to drive a power device to execute; according to the method, industrial big data value can be excavated to realize global economy and local safety collaborative optimization of energy distribution, system loss is effectively reduced, the service life of an aged battery is prolonged, control failure caused by sudden working conditions is avoided, and the operation stability and response real-time performance of the micro-grid are improved.

The utility model discloses a motorcycle front magnetorheological damper and motorcycle, including working cylinder, guider assembly, piston assembly, floating piston assembly, magnetorheological fluid, outer tube support assembly, ECU control unit and sensor, guider assembly, piston assembly and floating piston assembly are located in the inside of working cylinder, and piston assembly includes built -in solenoid and damping passage, and damping passage penetrates the upper and lower end of piston assembly, and magnetorheological fluid fills the working cavity between guider assembly and floating piston assembly, and, outer tube support assembly is at least partially coated in the outside of working cylinder, and is connected with guider assembly, and ECU control unit and solenoid electric connection are used for the signal adjustment input to solenoid's current according to sensor, like this through solenoid and the combination of magnetorheological fluid, ECU control unit can real -time regulation magnetic field intensity, change the viscosity of magnetorheological fluid to dynamic control damping force.

A source-grid-load coordinated scheduling method based on multi-time scale coupling comprises the following steps: Step 1: performing pre-season scheduling, dividing the year into multiple typical seasons according to the distribution characteristics of water resources in the year, respectively formulating corresponding water resource allocation strategies, comprehensively considering the seasonal variation characteristics of new energy output, formulating the coordinated scheduling arrangement within the season, and outputting the pre-season scheduling plan; Step 2: based on the pre-season scheduling plan output in Step 1, combining the load prediction and new energy output prediction information in the day-ahead scheduling stage, carrying out day-ahead scheduling, and outputting the day-ahead scheduling plan; Step 3: based on the day-ahead scheduling plan output in Step 2, combining the updated weather and load prediction information within the day, carrying out intra-day scheduling, and outputting the intra-day scheduling plan; Step 4: based on the intra-day scheduling plan output in Step 3, combining the real-time monitoring data, carrying out real-time scheduling, and outputting the real-time scheduling plan.

The invention discloses a distributed photovoltaic district energy storage charging and discharging control method based on deep learning, relates to the technical field of power system automation, realizes the conversion of energy storage control from passive response to active defense through situation awareness and dynamic boundary prospective mechanisms, and utilizes a deep learning network in district situation awareness to realize the energy storage control. The power and electric quantity safety boundary of energy storage charging and discharging in the next period is calculated in real time by accurately predicting the area net load power and combining the physical constraint of the area, and the mechanism is used for constructing an operation space dynamically changing along with the future situation, so that any follow-up control decision is limited in the safety space in advance, and the safety boundary of the energy storage charging and discharging in the next period can be calculated in real time. And when the photovoltaic output is predicted to be suddenly increased, the charging power upper limit of energy storage is tightened in advance, and the occurrence of line overload or voltage out-of-limit is actively prevented, so that the probability of potential safety risks is reduced.

The application provides a micro-grid grid-connected and off-grid control system, through constructing a gated loop unit timing prediction model, accurately predicting net load and power distribution network demand in multiple time periods in the future, and simultaneously minimizing power purchase and sale cost, line loss and power supply power shortage by using an improved NSGA-II multi-objective optimization algorithm, so that the economic efficiency and reliability are cooperatively optimized; a hierarchical architecture combining a regional coordination control layer and a micro-grid local control layer is used, so that the global optimization and dynamic response performance are considered; N-1 safety check and virtual synchronous machine pre-synchronization control are introduced, so that the problems of voltage out-of-limit and frequency instability caused by grid-connected and off-grid operation are effectively avoided; through a digital twin three-dimensional dynamic display module, power flow and switch state are directly presented, so that the situation awareness ability of operation and maintenance personnel to complex micro-grid groups is significantly improved; meanwhile, an incremental learning mechanism is used to continuously update the prediction model, so that the system can adapt to long-term changes of load and power generation.

The utility model discloses a kind of oilfield operation pneumatic pressure testing device, it includes main support, pressure generation module and pressure transmission component.Pressure generation module generates pressure by air cylinder, piston and driving rod, pressure transmission component is composed of gas holder, pressure regulating valve and output connector, realize pressure storage and regulation.Gas holder is equipped with partition and spring type check valve, prevent overpressure;Output connector uses corrosion-resistant coating and sealing ring to enhance the reliability of connection.The bottom of main support is equipped with supporting foot and non-slip mat, improve stability.The device is simple in structure, convenient to operate, can efficiently and stably complete pressure testing task, applicable to complex oilfield environment, significantly improve work efficiency and reduce labor intensity.