The invention relates to a rotating speed recovery method of a double-fed induction generator after virtual inertia control, and belongs to the technical field of control in new energy generation. The method includes: building a wind turbine transmission chain motion model, and converting the motion model into the power form; converting the motion model in the power form into the standard form capable of designing an extended state observer; designing the extended state observer, and estimating the mechanical power outputted by a wind turbine; setting parameters of the extended state observer; and designing the acceleration power of the wind turbine based on the mechanical power obtained by estimation and calculating an electromagnetic power reference value of the wind turbine. Compared with the conventional proportion/proportion-integral rotating speed recovery strategy of virtual inertia of the double-fed induction generator, according to the control method, the problem of difficult adjustment of proportion/proportion-integral parameters is avoided, the rotating speed can be rapidly recovered, and secondary falling of system frequency is prevented.

Rotating speed recovery method of double-fed induction generator after virtual inertia control

The invention discloses a fused extreme learning machine-based prediction method and system of future temperature of a battery. The method includes training steps and prediction steps. The system includes a training unit and a prediction unit. According to the method and system, high-precision prediction of the future temperature of the battery can be realized at low computational costs through the training steps and the prediction steps, and dependence on battery models can be greatly reduced in a prediction process; and a future temperature change situation of the battery can be effectively predicted, and a computational amount is greatly reduced, and does not exceed 10% of a computational amount of a traditional neural network. The method and system can be widely applied to battery temperature prediction.

Fused extreme learning machine-based prediction method and system of future temperature of battery

The invention relates to a method for controlling the virtual inertia of a doubly fed induction wind turbine, and the method relates to the wind power technology field and solves a technology problem that the influence of the wind turbine rotating speed and the system stability during the frequency modulation process is reduced is solved. The method comprises: the doubly fed induction wind turbine is provided with a virtual inertia control system containing a virtual inertia control module, a rotating speed time-delay recovery module and a rotating speed protection module; the triggering condition of the rotating speed time-delay recovery module is set as: the judgment condition is that the output power reference value change rate of the doubly fed induction wind turbine is less than 0, and the rotating speed recovery function starts when the output power reference value reduces to an optimal power. According to the invention, the mistaken start of the rotating speed recovery function at the initial stage of working of the virtual inertia control module is avoided.

Method for controlling virtual inertia of doubly fed induction wind turbine

The invention discloses an inertia frequency modulation method of a wind farm. The method comprises the steps of (1) obtaining parameter information of the wind farm and units in the wind farm; (2) judging whether the wind farm has a primary frequency modulation function or not and whether the wind farm enters a frequency modulation mode or not; (3) detecting the inertia adjusting ability of eachfan and counting the total inertia adjusting ability of the whole wind farm; (4) calculating an inertia requirement value of the wind farm; (5) distributing the inertia to single fan; and (6) issuingand outputting a command value. The frequency modulation is carried out by mainly using the inertia of each fan, so that the double requirements of sustainable response speed and energy can be met atthe same time.

Inertia frequency modulation method of wind farm

The invention discloses a wind speed prediction method and prediction system. The prediction method comprises the following steps: acquiring an original wind speed sequence; performing empirical modedecomposition on the wind speed sequence, and obtaining a plurality of inherent modal functions and residual items; classifying all inherent modal functions according to the instantaneous frequency mean value of each inherent modal function, obtaining a plurality of high-frequency modal functions and a plurality of low-frequency modal functions; training the least square support vector machine byadopting the training sample data of each high-frequency modal function to obtain a high-frequency prediction model; training a BP neural network through the training sample data of each low-frequencymode function to obtain a low-frequency prediction model; training the BP neural network through the training sample data of the residual items to obtain a residual prediction model; predicting the wind speed by utilizing all the high-frequency prediction models, the low-frequency prediction models and the residual prediction models. A prediction model is built on the basis of fluctuation characteristics of different components, the random fluctuation of the wind speed sequence can be effectively weakened, and the wind speed can be accurately predicted.

Wind speed prediction method and wind speed prediction system

The invention discloses a method and a device for controlling virtual inertia of a double-fed fan. The method comprises the steps of establishing an electric power system frequency response equation; acquiring an estimated value of a system inertia time constant; acquiring a standard form of an extended state observer; designing the extended state observer; and acquiring an active reference increment through the extended state observer so as to acquire a virtual inertia control equation of the double-fed fan. According to the control method disclosed by the invention, the active reference increment is acquired through the extended state observer, and the virtual inertia control equation of the double-fed fan is acquired, thereby avoiding a problem that a differentiator is difficult to be realized, and being capable of conveniently considering the relative contribution of wind power virtual inertia control for the system total inertia.

Method and device for controlling virtual inertia of double-fed fan

The invention relates to the technical field of wind/photovoltaic power generation, and discloses a wavelet packet-neural network-based wind/photovoltaic power prediction method The method comprises the following steps of: firstly analyzing weather forecast data and history data, and assessing the influences, on the photovoltaic power prediction accuracy, of the weather forecast data and the history data; analyzing the influences, on the wind power prediction accuracy, of factors such as temperature, humidity and atmospheric pressure in the natural environment; carrying out wavelet packet decomposition on the data to obtain different frequency component sequences of a meteorological factor sequence and of a wind/photovoltaic active power sequence to ensure that the representation of change characteristics on various spatial and temporal scales is more accurate, wherein the low-frequency components and the high-frequency components respectively map the interior change rules of predicted objects from two scales of profile and detail; aiming at each sequence component, establishing a plurality of BP networks to carry out prediction; and finally reconstructing each network prediction output to obtain a complete prediction result The method disclosed by the invention has the effects of improving the ability of mapping wind/photovoltaic power change characteristics and effectively improving the wind/photovoltaic power prediction accuracy

Wavelet packet-neural network-based wind/photovoltaic power prediction method

The invention discloses a new energy electric heating value analysis method. The method comprises the steps of firstly calculating total energy consumption required for new energy electric heating according to heating loads, wherein the total energy consumption of the new energy electric heating depends on fire power consumed in a heating process; secondly calculating consumed standard coal corresponding to fire coal, gas and fuel oil, and reflecting resource values by comparison with the energy consumption of the new energy electric heating; and thirdly calculating a pollution load generated by new energy instead of the fire power used for assistance in the electric heating, and a pollution load generated by conventional new energy heating, and performing comparison to obtain an environment value of the new energy electric heating. The economy of the new energy electric heating to new energy enterprises depends on on-grid energy increments and corresponding on-grid prices; the economy of the new energy electric heating to power companies depends on heating prices and fire power on-grid prices; the value of the new energy electric heating to heating enterprises is mainly reflected in heating cost; and by comparing annual costs of the new energy electric heating and other heating, the economic value of the new energy electric heating to the heating enterprises can be obtained.

New energy electric heating value analysis method

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Method and device for controlling virtual inertia of double-fed fan

Wavelet packet-neural network-based wind/photovoltaic power prediction method

New energy electric heating value analysis method