[Objective]With the continuous development and improvement of microgrid technology,the use of renewable energy is increasingly valued.The impact of source-load uncertainty on microgrid security as well as economic operation cannot be ignored,and in order to solve the problem of optimal economic operation of microgrid,a hybrid Harris Hawk optimization(HHHO)algorithm is proposed.[Methods]Firstly,an optimal microgrid scheduling model considering source-load uncertainty was developed.Secondly,a HHHO algorithm combining elite strategy and differential learning mechanism was proposed.The elite strategy improved the convergence speed of the algorithm by retaining the elite individuals in the population.The differential learning mechanism optimized the accuracy of the algorithm's search by enhancing the dissemination of valid information.Then,a hybrid scene reduction method based on Latin hypercubic sampling-probabilistic distance was proposed,and the reduced equivalent scene was used as the basis for the economic operation of the microgrid.Finally,the superiority of the proposed HHHO algorithm was verified by test functions and scene simulations.[Results]The test function experimental results showed that the proposed HHHO algorithm had high solution accuracy and fast convergence speed.Simulation results in scenarios such as wind and photovoltaic fluctuations showed that the HHHO algorithm was highly superior and significantly reduced the overall operating cost of the microgrid.[Conclusion]The proposed HHHO algorithm enables optimal economic operation of microgrids and provides theoretical support for long-term economic operation of microgrid.
To improve the dynamic response and disturbance rejection property of the permanent magnet synchronous motor(PMSM)speed regulation system,an improved super-twisting sliding mode linear active disturbance rejection control(STSM-LADRC)strategy was proposed.This strategy optimized the linear extended state observer(LESO)and linear state error feedback(LSEF)control law in LADRC using the sliding mode variable structure principle.First,the STSM-LESO was designed,where the STSM control algorithm improves the LESO in LADRC,enhancing the observing property and thus the controller's disturbance rejection property.Second,the STSM control algorithm replaced the original LSEF control law,improving the controller's dynamic response property.The hyperbolic tangent function was used instead of the sign function in the STSM algorithm to further reduce inherent sliding mode chattering and enhance system stability.The proposed control strategy was then analyzed for stability using Lyapunov theory.Finally,simulations were conducted on the Matlab/Simulink platform for validation.The results showed that the proposed strategy provided better dynamic response and disturbance rejection property compared to traditional LADRC and other controllers.
A star-delta connection winding structure can restrain the low order harmonic of armature magnetic field and improve the operating efficiency of machine. However, the risk of inter turn short circuit in its structure is higher. In this paper, the short-circuit current of inter-turn short circuit occurring at different winding positions is derived. An adaptive weight strategy is used to improve whale optimization algorithm (WOA). The improved WOA is adopted for optimizing the parameters of variational mode decomposition (VMD). VMD based on improved WOA for processing current signal is proposed. Finally, by calculating the fuzzy entropy of current and torque, a fault diagnosis method for inter-turn short circuit fault in star-delta connection winding structure is proposed. The accuracy of this fault diagnosis method can reach over 90%. And it can effectively distinguish that the inter-turn short circuit fault occurs in Y connection part or △ connection part.
With the increase of the proportion of new energy,in the Yunnan power grid with abundant hydropower resources,relying on the primary frequency modulation of traditional units can no longer meet the requirements of frequency stability of Yunnan asynchronous networking system.For the power reverse adjustment caused by the water hammer effect of large hydropower,a wind-storage combined frequency modulation control strategy considering load frequency characteristics is proposed.Firstly,the frequency domain model transfer function of traditional power(turbine),doubly-fed wind turbine,energy storage system and comprehensive load is established.Then,the DC power flow method is used to simplify the electric network,and the analytical formula of frequency response of wind-storage node under load disturbance is obtained.The four-machines system with wind-stroage,water turbine and lood is built in the electromechanical transient simulation sofware PSD-BPA.Through the theoretical analysis of the analytical formula and the simulation verification of the four-machine system,the influence of the proportion of static load model and frequency characteristic parameters and the proportion of energy storage on the system frequency is analyzed.The results show that the wind-storage combined frequency modulation considering the load frequency characteristics can effectively reduce the system frequency deviation.
Aiming at the problem of low accuracy of classification model caused by the unbalanced category of transformer fault samples,a transformer fault diagnosis model based on sample integration learning and snake optimisation algorithm(SO)optimised support vector machine(SVM)is proposed.The model first uses the EasyEnsemble sampler to generate multiple subsets with balanced categories after multiple under-sampling of the samples;then the SVM model optimised by SO with key parameters is trained with the Bagging strategy,and the final fault types are obtained by integrating the results of the classifiers.The validity of the proposed model is verified by the arithmetic example,and the data show that the diagnostic accuracy of SO-SVM's fault diagnosis is improved by 3.44％,6.89％,10.92％,and the AUC value is improved by 0.026 4,0.042 5,and 0.081 2,respectively,compared with the models of RF,SVM and KNN;in the same classifier,the SO-SVM model is more accurate than the SMOTE and ADASYN sample balancing methods,the diagnostic accuracy is improved by 4.59％and 2.87％,respectively,indicating that the SO-SVM model has better fault diagnosis capability for unbalanced samples.
