60results about How to "Safe and Stable Economic Operation" patented technology

The invention discloses a method for implementing a power system stabilizer. The method includes steps of acquiring a rotation speed variable quantity via a blocking filter link according to the rotation speed and active power of a rotor of a power generator, and respectively acquiring low-band differential signals, medium-band differential signals and high-band differential signals via a differential filter; parallelly adding the low-band differential signals, the medium-band differential signals and the high-band differential signals together to acquire rotation speed variation comprehensive signals of the power generator; measuring phase-shift characteristics of the rotation speed variation comprehensive signals and the rotation speed and uncompensated phase-shift characteristics of an excitation control system of the power generator by a forced oscillation process, computing required phase-shift characteristics among the rotation speed variation comprehensive signals and the power system stabilizer, designing multi-order lead-lag link phase-shift parameters which are serially multiplied by one another, performing phase compensation to meet the required phase-shift characteristics; finally outputting signals of the power system stabilizer and superposing the signals of the power system stabilizer on a reference voltage of an excitation regulator of the power generator. The method has the advantage that active power low-frequency oscillation of the synchronous generator can be effectively suppressed.

The invention discloses a multi-component composite energy storage system grid combination control method based on power prediction, comprising steps of calculating difference value PV-L of the photovoltaic output power prediction value PV and the load power prediction value PL according to the photovoltaic output power prediction value PV and the load power prediction value PL and, utilizing a wavelet packet composition method to map PV-L to the subspaces of m wavelet packets to obtain signals with different frequencies, using the frequency signals which are close to the responding frequency of a storage battery in the frequency signals as low frequency signals P low which are complimented by the storage battery and using the rest of the frequency signals and the frequency signals which are not absorbed by the battery as high frequency signals P high which are complimented by a super capacitor, using an external loop control method and an inner loop current control method to output the real power of the storage battery and the super capacitor and control the charging and discharging power of the storage battery and the super capacitor by utilizing the state of charges of the super capacitor and the current, the state of charge and maximum charge-discharge power of the storage battery according to the real condition. The advantages of the invention are safe, stable, and economic in operation. Furthermore, the times of charging and discharging of the storage battery are reduced.

The present invention provides an optimal power flow calculating method for equivalent interconnected power network on the basis of consistency of power flow, sensitivity and constraint. The method includes the steps as follows: firstly, performing a calculation by utilizing the optimal power flow calculating method so as to acquire the available capacity of the outside network before equivalent; secondly, keeping the constant available capacity before equivalent and after equivalent and the constant constraint information on the basis of consistency satisfaction of power flow and sensitivity, and deducing the constraint condition of the equivalent network; and finally, establishing a new optimal power flow model on the basis of the network after equivalent and the constrain condition. The optimal power flow calculating method calculates the available capacity of boundary nodes and boundary sections of the outside network and deduces the equivalent constraint condition on the basis of consistency of the available capacity before equivalent and after equivalent so that the accuracy of the equivalent constraint calculating is increased. The calculating accuracy of the method provided by the invention is higher than the current optimal power flow calculating method without regard to constraint, and the method can well simulate the running condition of the actual outside network and provide the internal network with suitable power supporting so as to ensure the safe, stable and economic running of the interconnected power network.

The invention provides a multi-target voltage optimizing method of a wind power plant cluster. The wind power plant cluster is controlled as a whole, the minimum wind power plant grid-connection point voltage deviation, the minimum wind power plant cluster area transmission loss and the maximum wind power plant cluster voltage stabilization margin serve as the target functions, a wind power plant reactive power adjustable capacity constraint and wind power plant grid-connection point voltage upper and lower limit constraints are taken into account, the voltage optimized value of each wind power plant grid-connection point is obtained through optimizing calculation, and optimal control over the voltage of the wind power plant cluster is realized. The multi-target voltage optimizing method can be integrated in a wind power plant cluster control system and provides support for safe, stable and economical operation of the wind power plant cluster.

The invention discloses an ultra-short-term wind power prediction method based on an autoregression moving average model. The ultra-short-term wind power prediction method based on the autoregression moving average model comprises the steps that data are input to enable parameters of the autoregression moving average model to be obtained; input data required by wind power prediction are input into the autoregression moving average model determined according to the parameters of the autoregression moving average model, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term wind power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention relates to a steam turbine cylinder joint surface deformation processing method. The method comprises following steps:1) sending a low-pressure inner upper cylinder to a steam turbine factory for machining and rubbing down; 2) polishing the lower cylinder joint surface of a low-pressure inner lower cylinder based on the machined inner upper half cylinder; 3) cleaning burrs on the machined cylinder surface and traces remained by the machining; 4) hoisting all carrier rings and separator plates in the lower cylinder; 5) cleaning the surface of the lower cylinder; 6) making analysis according to measured results and traces pressed on the lower cylinder joint surface; 7) employing a mechanical polishing tool to polish the lower cylinder joint surface from the thickest position to be polished and gradually and radially enlarging the polishing area, wherein the joint surface is repeatedly polished in the vertical and horizontal directions by a long flat ruler during each polishing process; 8) finely polishing the joint surface when a set scraping value of a depth mark is remained; and 9) repeating the step 8) until the joint surface gap value meets requirements. Compared with the prior art, the cylinder deformation can be completely eliminated through the method, and the maintenance is convenient and reliable.

The invention discloses a photovoltaic generation power prediction method based on a self-learning radial basis function. The photovoltaic generation power prediction method based on the self-learning radial basis function comprises the steps that model training is conducted to enable an SVM model to be obtained; data required by photovoltaic generation power prediction are input into the SVM model obtained through training, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term prediction accuracy is effectively improved by the adoption of a composite data source, and thus high-accuracy short-term photovoltaic generation power prediction is achieved.

The invention discloses a day-ahead plan making method and system considering a gas turbine unit. The method comprises steps of obtaining a load prediction value of a power grid and prediction power of a new energy unit; substituting the load prediction value of the power grid and the prediction power of the new energy unit into a pre-constructed optimization model for calculation to obtain the output power of each unit; making a day-ahead plan of the power grid based on the output power of each unit; wherein the unit comprises a gas unit, a thermal power unit and a new energy unit. an optimization model being constructed on the basis of the load prediction value of the gas unit in multiple application modes, the power grid frequency modulation standby demand, the power grid accident standby demand and the prediction power of the new energy unit as constraints and with the minimum operation cost of each unit and the minimum electricity abandoning power of the new energy unit as the target. The method is advantaged in that a gas turbine unit participating in power grid dispatching is brought into full play, the regulation and control level and utilization efficiency of the gas turbine unit in multiple modes are improved, the wind power consumption level is improved, and safe and stable operation of a power grid is guaranteed.

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on a self-learning composite data source. The ultra-short-term photovoltaic generation power prediction method based on the self-learning composite data source comprises the steps that data are input to enable parameters of an autoregression moving average model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression moving average model determined according to the parameters of the autoregression moving average model, so that a prediction result is obtained, post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the model and estimation of the parameters of the model are conducted again if a predicted error is larger than an allowable maximum error. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact the composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention discloses a new stator structure of high-pressure and hyperhigh-pressure generator, which consists of stator iron core (7) overlapped by stator punching sheets and frame (4), wherein the stator winding (1) is winded by high-pressure cable with round cross section to place the :sugarcoated haws string' in the stator groove (2); one cable is pierced in each groove of the stator. The invention insulates laminate, groove and phase without turn insulating to save cost and simplify operation, which improves the power quality for wind power generating, magnetic levitation train and diving and oil-submersible motors.

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on a composite data source autoregression model. The ultra-short-term photovoltaic generation power prediction method based on the composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression model which is determined according to the parameters of the autoregression model, so that a prediction result is obtained; model training basic data are input, order determination is conducted on the autoregression model AR(p) according to a residual variogram method, and the parameters of the model AR(p) with the determined order are estimated according to a moment estimation method. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention discloses an ultra-short-term forecasting method of wind power plant generated power. The method includes the steps of A, acquiring the actually-measured power data and short-term forecasting power data of a wind power plant; B, preprocessing the acquired data; C, using a wind power plant generated power ultra-short-term forecasting model based on forecasting time duration to obtain wind power plant ultra-short-term forecasting power according to the preprocessed actually-measured power data and short-term forecasting power data; D, determining the optimal forecasting time duration of the wind power plant generated power ultra-short-term forecasting model according to the wind power plant ultra-short-term forecasting power; E, by the wind power plant generated power ultra-short-term forecasting model, performing wind power plant ultra-short-term power forecasting. By the method, the real-time scheduling plan of a power grid is optimized, coordination of conventional-energy and wind power generation is comprehensively arranged, spinning reserve capacity of a power system is reduced, operation cost is lowered, and good application prospect is achieved.

The invention relates to the technical field of power systems, in particular to a non-intrusive residential user load decomposition method based on a residual convolution module. The method comprises the following steps: acquiring training data and preprocessing the data; constructing and training a load decomposition model: inputting a total active power sequence in training data into a residual convolution module, learning active power features by taking a CNN model as a basis in the residual convolution module, adding original input data and feature data learned by the CNN through cross-layer connection, further inputting the obtained data into a GRU network to learn time sequence features, and outputting a predicted value of the active power of the target electric appliance; comparing the predicted value of the active power of the target electric appliance with a true value, and continuously adjusting network parameters of the load decomposition model to obtain a trained load decomposition model; and decomposing the total active power of the user to be decomposed through the trained load decomposition model to obtain an active power decomposition result of the target electric appliance. The method is high in decomposition precision.

The invention discloses a multi-wind-field wind speed space-time prediction method based on graph convolution and a recurrent neural network. Aiming at the problem that a convolutional neural network commonly used in an existing wind speed space-time prediction method is difficult to effectively analyze wind speed data of multiple wind fields presenting non-grid distribution in reality, a graph convolutional long-short-term memory neural network is provided to process the data. The method comprises the following steps: firstly, performing graph modeling on wind speed data of multiple wind fields based on a Pearson correlation coefficient to construct a wind speed graph signal sequence; then, using graph convolution to replace multiplication in the long short-term memory neural network, and constructing a graph convolution long short-term memory neural network; and finally, constructing a multi-wind-field wind speed space-time prediction model based on the graph convolution long-short-term memory neural network and a transfer learning principle. The space-time prediction model has good point prediction and probability prediction performance, it is verified that the accuracy of wind speed point prediction and probability prediction can be improved by fusing historical wind speed information of adjacent wind fields, and a new thought is provided for short-term wind speed prediction of multiple wind fields.

The invention discloses a photovoltaic generation power prediction method based on a self-learning composite data source autoregression model. The photovoltaic generation power prediction method based on the self-learning composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression model determined according to the parameters of the autoregression model, so that a prediction result is obtained; post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the autoregression model AR(p) and estimation of the parameters of the model AR(p) are conducted again if a predicted error is larger than an allowable maximum error. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention discloses an optical selection method, based on total life cycle costs, of transformer station reactive power compensation devices. The method comprises the following several steps: (1) establishing total life cycle cost models of different types of reactive compensation devices, (2) determining values of calculation parameters of various costs of the total life cycle cost models, (3) performing basic calculation and comparison of total life cycle costs on the different types of reactive compensation devices according to the calculation parameters to obtain different advantages of the different types of reactive compensation devices in total life cycle cost, (4) performing sensitivity analyses on the different types of reactive compensation devices, (5) performing benefit evaluation on the different types of reactive compensation devices, and calculating results according to the total life cycle costs to obtain a comparison of cost effectiveness on the condition that a reactive compensation equivalent benefit is considered. The invention provides the optical selection method of the transformer station reactive power compensation device, improves voltage quality to the maximum extent, reduces losses to the maximum extent, and guarantees safe, stable and economic running of a power grid.

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on an autoregression moving average model. The ultra-short-term photovoltaic generation power prediction method based on the autoregression moving average model comprises the steps that data are input to enable parameters of the autoregression moving average model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression moving average model determined according to the parameters of the autoregression moving average model, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention provides a CFD-based transformer respiratory system capacity matching evaluation method. The method comprises the following steps: establishing a full-size three-dimensional model of a to-be-evaluated transformer and a respiratory system thereof; then, dividing a calculation domain, then setting boundary conditions and physical parameters of materials, then setting a theoretical model and a solving algorithm, setting initial conditions, calculating heat productivity and taking the heat productivity as a heat source, and finally, performing simulation to obtain oil conservator oillevel cloud chart data under different load coefficient conditions, thereby obtaining the adaptation condition of a respiratory system and a transformer. The adaptability of the transformer and the respiratory system is rapidly and accurately calculated through the CFD technology, it is guaranteed that the capacities of the transformer and the respiratory system are well matched, serious accidents such as transformer oil overflow after operation are avoided while the capacity utilization rate of the respiratory system is increased, and safe, stable and economical operation of the transformeris guaranteed.

The invention discloses a wind power prediction method based on a self-learning radial basis function support vector machine. The wind power prediction method based on the self-learning radial basis function support vector machine comprises the steps that model training is conducted to enable an SVM model to be obtained; data required by wind power prediction are input into the SVM model obtained through training, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term prediction accuracy is effectively improved by the adoption of a composite data source, and thus high-accuracy short-term wind power prediction is achieved.

The invention discloses an ultra-short-term wind power prediction method based on a composite data source autoregression model. The ultra-short-term wind power prediction method based on the composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by wind power prediction are input into the autoregression model which is determined according to the parameters of the autoregression model, so that a prediction result is obtained, wherein the method for obtaining the parameters of the autoregression model by inputting the data specifically comprises the steps that model training basic data are input, order determination is conducted on the autoregression model AR(p) according to a residual variogram method, and the parameters of the model AR(p) with the determined order are estimated according to a moment estimation method. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term wind power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention provides a solid waste pyrolysis and oil gas catalytic reforming system, and belongs to the technical field of solid waste treatment. Pyrolysis reaction is carried out in a pyrolysis cavity 6 of an external heating type pyrolysis furnace 1, obtained pyrolysis oil gas enters a catalytic reforming reactor 2 to be subjected to catalytic reforming reaction, and obtained fuel gas can be subjected to resource utilization such as internal combustion power generation, boiler power generation and production of high-added-value products; pyrolytic carbon generated by pyrolysis enters a combustion area 17 for combustion, and the generated heat can be used for supporting a catalytic reforming reaction; the flue gas generated by combustion is high-temperature flue gas, and after being subjected to dust removal treatment, the flue gas enters a heat exchange cavity 7 of the external heating type pyrolysis furnace 1 to provide heat for pyrolytic reaction; and the flue gas subjected to heat exchange enters a heat exchanger 4 to perform second heat exchange with air, and the obtained hot air enters a combustion furnace 3 to provide hot air for combustion of pyrolytic carbon.