The invention discloses an analysis model construction method for typical characteristics of the wind power output. Original data are obtained and data pretreatment is carried out, the data are normalized by using multi-temporal-scale wind power volatility and simultaneous rate valuation indexes, and segmented aggregation is carried out to reduce the dimension; on the basis of an improved adaptivefuzzy clustering algorithm, an adaptive function alpha (c) is calculated and an optimal cluster number c is determined, and wind power output characteristics are classified; a variation dispersion coefficient betai is introduced and wind-power output distortion data are rejected; and a partition weighted median probability estimation method is put forward and a wind-power output daily typical characteristic curve/zone band is identified and extracted. According to the invention, the multi-space-scale large-scale wind power output characteristics and monthly, quarterly or yearly multi-time-scale wind-power output characteristics are analyzed and calculated and the wind-power output daily typical characteristic curve/zone band is extracted, so that the power operation staff can master the wind-power output rule well and a technical reference is provided for real-time scheduling of a power system, scheduling plan arrangement, wind farm planning and system backup configuration.

Analysis model construction method for typical characteristics of wind power output

The invention discloses a periodic disturbance dual-loop forecast suppression method of a permanent-magnet synchronous motor. The periodic disturbance dual-loop forecast suppression method comprises the steps of designing a current loop main forecast controller, embedding a current loop disturbance observer for suppressing non-periodic disturbance, constructing a cost function, and solving to obtain an optimal stator given control voltage by minimization; designing a disturbance suppression loop and a disturbance controller which are additionally embedded to a current loop; designing a speed loop main forecast controller, embedding a speed loop disturbance observer for suppressing non-periodic disturbance, constructing another one cost function, and solving to obtain an optimal reference q-axis current; designing a disturbance suppression loop and a disturbance controller which are additionally embedded to a speed loop; determining parameters of the forecast controller and the observeraccording to closed-loop transfer functions of the current loop and the speed loop and according to expected bandwidth and an observer pole; and weighing disturbance suppression capability and noisesensitivity, comprehensively considering high suppression capability on special sub-harmonic and low influence on other frequency signals, and determining a disturbance controller parameter.

Periodic disturbance dual-loop forecast suppression method of permanent-magnet synchronous motor

The invention discloses a wind power prediction method based on a time interval fuzzy operator and approximate weight integration, so as to solve the defects that certain time delay exists when a generator blade is pushed by wind to rotate and output power change is finally reflected currently, data collected from a wind farm are possibly disturbed by noise factors, fluctuation in a short time thus appears and good prediction is hard to realize. The method comprises steps: 1, the power change trend at a precursor time point of each time point is calculated; 2, all data are divided to G groups, and each group of data is corresponding to a center point; 3, G regression forecasting models from model (1) to model (G) are acquired; 4, the approximate weight of each center point is built based on a description vector V and the distance between center points of groups of data; and 5, a wind power prediction result is acquired. The method is applied to the technical field of wind power.

Wind power prediction method based on time interval fuzzy operator and approximate weight integration

The invention provides an air pollutant concentration prediction method and device, electronic equipment and a storage medium. The method comprises the steps of: obtaining environmental monitoring historical data and meteorological historical data in a preset time period; training a pre-created encoding-decoding network structure according to the environment monitoring data and the meteorologicaldata in the preset time period to obtain an encoding-decoding structure model; acquiring environmental monitoring data and meteorological data in a first preset time unit; and according to the environment monitoring data and the meteorological data in the first preset time unit, predicting the air pollutant concentration in a second preset time unit in the future through the encoding-decoding structure model. According to the invention, the encoding-decoding structure is used for air quality prediction, the air pollutant concentration can be predicted without meteorological prediction, the prediction process is simple and rapid, and the efficiency and accuracy are very high. Deep analysis can be carried out on various environmental features and meteorological features, environmental big data is effectively utilized, and the environmental management level is improved.

Air pollutant concentration prediction method and device, electronic equipment and storage medium

The invention aims to solve the problems in the prior art, and innovatively provides a device for zero-voltage switching of an MOS transistor on a BUCK circuit. On the basis of a BUCK circuit, a thirdMOS transistor, a transformer and a diode are added, the third MOS transistor and the transformer are connected in series to form a series circuit which is connected in parallel with two sides of thedrain electrode and the source electrode of a first MOS transistor, and zero-voltage switching is realized when the first MOS transistor is turned on. The invention further provides an implementationmethod for the zero-voltage switching of the MOS transistor on the BUCK circuit. The problem that ZVS cannot be achieved by the MOS transistor of the BUCK circuit in server power supply due to the prior art is effectively solved, and the transmission efficiency of the MOS transistor of the BUCK circuit is effectively improved. The switching loss of the MOS in a high-frequency working state is reduced, the overall power supply efficiency of the power supply system is effectively improved, and the power utilization cost is saved.

Device for zero-voltage switching of MOS transistor on BUCK circuit, and implementation method thereof

The invention discloses a PM2.5 concentration prediction method and device and a medium, and relates to the technical field of pollutant prediction, and the method comprises the steps: building a PM2.5 prediction model based on a CNN and a bidirectional GRU neural network and based on a one-dimensional convolutional neural network CNN and a bidirectional GRU neural network; the meteorological training data tensor is sent to a PM2.5 prediction model for training; the one-dimensional convolutional neural network CNN respectively performs local feature learning and dimension reduction on each input variable time sequence, and forms a low-dimensional feature sequence through convolution and pooling operation in sequence; inputting the feature sequence into a bidirectional GRU neural network, and learning the feature sequence from the time positive sequence and the time negative sequence by the bidirectional GRU neural network; the meteorological test data tensor is sent to a trained PM2.5prediction model for prediction, and a PM2.5 prediction concentration value is obtained. According to the model, the speed and lightweight characteristics of the convolutional neural network and the sequential sensitivity of the RNN are effectively utilized, more data volume is allowed to be checked during training, and the prediction accuracy is improved.

PM2.5 concentration prediction method and device and medium

The invention discloses a high-efficiency all-soft switching circuit applied to a non-isolated Buck DC-DC converter and a modulation method of the high-efficiency all-soft switching circuit. The circuit comprises a synchronous rectification switch tube Q2 used for replacing a common freewheel diode and reducing freewheel loss, an auxiliary capacitor Cr used for voltage clamping at the moment whenthe switch tube is turned off, a series branch composed of an auxiliary switch tube Qr, a diode Dr and an inductor Lr and used for eliminating reverse recovery loss of the diode and realizing zero-voltage switching of a main switch, and a protection diode DP used for protecting the auxiliary switch tube when the load fluctuates violently. For the proposed soft switching circuit, in order to realize soft switching in a wide load range, a feasible modulation method is proposed. Zero-voltage turn-on and approximate-zero-voltage turn-off of all main switches and synchronous rectification switchesare realized, and zero-current turn-on and turn-off of auxiliary switches are realized.

Synchronous rectification Buck converter all-soft switching circuit and control method thereof

The invention discloses a wind power ultra short period prediction method based on a T-S fuzzy model. Firstly, the main factors which influence wind power prediction are obtained through correlation analysis, then the wind speed and power of a historical moment are subjected to preprocessing and normalization, and a training sample set and a prediction sample set are established. Finally, a fuzzy C-means clustering algorithm and a recursive least square method with a forgetting factor are used to carry out structure division and parameter identification of a model, and the T-S fuzzy model of the wind power ultra short period prediction with the wind speed and power as input is obtained. The method has the advantages of simple process, small calculation amount, and an accurate result.

Wind power ultra short period prediction method based on T-S fuzzy model

The invention relates to a neutral-point voltage balance control method for a three-level inverter. The method comprises the following steps: 1, adding an additional neutral-point branch to an original circuit of the three-level inverter, and converting a neutral-point voltage balance control problem of the three-level inverter into a disturbance inhibition problem; step 2, based on a repetitive control thought, the periodic midpoint voltage pulsation with the invariable direct current component in the midpoint voltage of the three-level inverter is suppressed; based on an equivalent input interference thought, the influence of aperiodic midpoint voltage drift in midpoint voltage of a three-level inverter on output is estimated by adopting an internal model, a state feedback controller, afull-dimensional state observer and an interference estimator based on a controlled object, and is mapped to an input end to perform reverse compensation on disturbance.

Neutral-point voltage balance control method of three-level inverter

The invention discloses a noise-like signal-based wind farm potential subsynchronous oscillation identification method. A subsynchronous oscillation frequency range is estimated according to an actualsituation; noise-like signals are extracted from a wind farm; through carrying out wavelet decomposition and reconstruction on the noise-like signals, extraction signals in the needed subsynchronousoscillation frequency range are acquired; and through carrying out mode identification on the extraction signals, wind farm potential subsynchronous oscillation mode information is acquired. Line parameters and a wind farm subsynchronous oscillation mode can be acquired accurately from normal operation data of the wind farm, early warning can be carried out on wind farm potential subsynchronous oscillation risks, and the operation mode of the wind farm can be adjusted timely.

Noise-like signal-based wind farm potential subsynchronous oscillation identification method

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