Time-delay Correction Control Strategy for HVDC Frequency Regulation Services

With the advancements in voltage source converter(VSC)technology,VSC based high voltage direct current(VSCHVDC)systems provide system operators with a prospective approach to enhance system operating stability and resilience.In addition to long-distance transmission,the VSC-HVDC system can also provide multiple ancillary services,such as frequency regulation,due to its power controllability.However,if a time delay exists in the control signal,the VSC-HVDC system may bring destabilizing influences to the system,which will decrease the system resilience under the disturbance.In order to reduce control deviation caused by time delay,in this paper,a small signal model is first conducted to analyze the impact of time delay on system stability.Then a time-delay correction control strategy for HVDC frequency regulation control is developed to reduce the influence of the time delay.The control performance of the proposed time-delay correction control is verified both in the established small signal model and the runtime simulation in a modified IEEE 39 bus system.The results indicate that the proposed time-delay correction control strategy shows significant improvement in system stability.

＂Mistake＂ And ＂Error＂

This paper discussed a method to judge and to reject the mistake and error of the monitoring strategies for English teaching and learning, and alsopresented a classification of mistake and error according to the following factors： the definition and analysisofmistake and error.With the development of theanalysis theories, more attention has been paid to the characteristic and the principle of mistake and error in the second language teaching and learning. By analyzing the differences between mistake and error, the teachers and learners can get clear of the study of English and improve the ability of using English.

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part II:A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.