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Improving the dynamic regulation ability of thermal power units is effective for realizing flexible scheduling in modern power systems. At present, the unit regulation capacity is usually reflected by the load adjustment of the main steam pressure and flow tracking ability, through the calculation of the given and real-time deviation to complete the load, and by pressure adjustment. However, although the calculation involved in this method is easy and the results are intuitive, overshoot and lag can easily occur. The main reason for this is that the process from boiler combustion to turbine works has strong hysteresis and inertia, and the feedback signal of the pressure and flow rate cannot dynamically reflect the change in boiler combustion and steam energy. According to the heat transfer process of the unit, the main steam temperature can directly reflect the energy transfer in the furnace combustion process and then reflect the changing trend of steam energy. Analyzing the changing characteristics of the temperature, pressure, and flow of superheated steam under rapid load regulations makes it possible to calculate the instantaneous energy storage value of the main steam before the regulating valve, and this value was inserted into the coordinate system as a new feedforward signal. Finally, a simulation model was established by using the actual running data of the unit. A simulation experiment under variable working conditions demonstrated that this method could improve the dynamic adjustment of the unit to load and pressure and help the power grid absorb renewable energy. [ABSTRACT FROM AUTHOR]

Abstract The accurate weighting of pseudorange observations is important to improve the convergence time and positioning quality of Precise Point Positioning (PPP). Currently, the weight of a pseudorange observation is mainly determined with empirical stochastic models. However, in a complex environment, due to the inability to adapt for the dynamic changes of the user environment, the empirical stochastic models usually cannot reflect the real error level of pseudorange observations. To address this problem, a resilient adjustment method to weigh pseudorange observations is proposed, which constructs the real-time estimation and inflation model for the variance of pseudorange multipath error and measurement noise to replace the empirical stochastic model to determine the weights of pseudorange observations. A set of static and dynamic Global Positioning System (GPS) test data are used to verify the effectiveness of the proposed method. The test results indicate that the proposed real-time estimation model can provide a better representation of the pseudorange accuracy, and the positioning performance of PPP using the real-time estimation model is better than that with the empirical stochastic model. Compared with the optimal empirical stochastic model, the positioning accuracy of PPP with the real-time estimation model is improved by at least 20%, and the convergence time is reduced by at least 50%.

The accurate weighting of pseudorange observations is important to improve the convergence time and positioning quality of Precise Point Positioning (PPP). Currently, the weight of a pseudorange observation is mainly determined with empirical stochastic models. However, in a complex environment, due to the inability to adapt for the dynamic changes of the user environment, the empirical stochastic models usually cannot reflect the real error level of pseudorange observations. To address this problem, a resilient adjustment method to weigh pseudorange observations is proposed, which constructs the real-time estimation and inflation model for the variance of pseudorange multipath error and measurement noise to replace the empirical stochastic model to determine the weights of pseudorange observations. A set of static and dynamic Global Positioning System (GPS) test data are used to verify the effectiveness of the proposed method. The test results indicate that the proposed real-time estimation model can provide a better representation of the pseudorange accuracy, and the positioning performance of PPP using the real-time estimation model is better than that with the empirical stochastic model. Compared with the optimal empirical stochastic model, the positioning accuracy of PPP with the real-time estimation model is improved by at least 20%, and the convergence time is reduced by at least 50%. [ABSTRACT FROM AUTHOR]

Improving the dynamic regulation ability of thermal power units is effective for realizing flexible scheduling in modern power systems. At present, the unit regulation capacity is usually reflected by the load adjustment of the main steam pressure and flow tracking ability, through the calculation of the given and real-time deviation to complete the load, and by pressure adjustment. However, although the calculation involved in this method is easy and the results are intuitive, overshoot and lag can easily occur. The main reason for this is that the process from boiler combustion to turbine works has strong hysteresis and inertia, and the feedback signal of the pressure and flow rate cannot dynamically reflect the change in boiler combustion and steam energy. According to the heat transfer process of the unit, the main steam temperature can directly reflect the energy transfer in the furnace combustion process and then reflect the changing trend of steam energy. Analyzing the changing characteristics of the temperature, pressure, and flow of superheated steam under rapid load regulations makes it possible to calculate the instantaneous energy storage value of the main steam before the regulating valve, and this value was inserted into the coordinate system as a new feedforward signal. Finally, a simulation model was established by using the actual running data of the unit. A simulation experiment under variable working conditions demonstrated that this method could improve the dynamic adjustment of the unit to load and pressure and help the power grid absorb renewable energy.