Operational Optimization Strategy Research On Wind-solar Hybrid Power Generation System

With the situation of energy crisis and the problems of circumstance pollution becoming more and more severity,new energy,represented by wind energy and solar energy,has gradually received widespread attention.Wind energy and solar energy have good complementary characteristics in time and space,compared with single wind power or photovoltaic power generation,wind-solar hybrid power generation system has higher energy efficiency,which is of great practical significance for expanding power supply and realizing energy model transformation in depth.In this dissertation,the current permeability of new energy in power grid as the background,and introduces energy storage devices to integrate wind and solar power reasonably.The dissertation aims at further improving the utilization ratio of new energy and the operation status of power generation system,aiming at improving the operation efficiency of permanent magnet synchronous generator system,improving dynamic responsiveness of PV generation system,optimal control of energy storage system and multi-objective coordinated control of inverters under unbalanced grid.The main research work of this dissertation are as follows:1)A new model predictive direct torque control scheme of efficiency optimization is proposed to suppress the torque ripple of wind generator and running efficiency of the wind energy conversion system based on a direct-drive PMSG.The model predictive direct torque control is combined with the generator loss model of permanent magnet synchronous generator.A predictive value of stator active current which is used to obtain the predictive value of electromagnetic torque is proposed to replace the traditional predictive value of stator current.The stator active current setting value is obtained from the minimum loss point of the wind power generator system.Besides,the torque setting value is obtained from the maximum wind power extraction control adopting the optimal feedback torque method.A cost function for efficiency optimization is designed to select the optimum voltage vector,so as to keep the system running in the state of minimum loss.Simulation studies and experiment researches are carried out on a surface-mounted permanent magnet synchronous generator by doing speed step and torque step.2)A maximum power point tracking control algorithm is developed to determine power efficiency,and to improve the transient nature of the power curve characteristics of a PV cell.Based on the analysis of existing maximum power point tracking methods,for PV systems,the relationship between MPP voltage and the open circuit voltage of PV cell under different conditions is studied;moreover,a novel maximum power point tracking control strategy which can be applied to different irradiance conditions based on a sub-domain gradient optimization principle and maximum power point voltage law is proposed.Simulation studies and experiment researches are also carried out.3)In order to improve grid-connected inverter operation performance,a power oscillation and current harmonic suppression coordinate control strategy that considers the problem of over-current is proposed in this work.Firstly,the internal relationship between power fluctuation suppression and output current balance is analyzed,then combining dynamic regulation of the active and reactive power settings,the peak value of the grid-connected current is limited to a safe range.Secondly,a simplified fourier transform algorithm is proposed for detecting fundamental positive and negative sequence vector components and band-pass filtering the three-phase input signals.By simplifying the transform and introducing a recursion algorithm,the on-line computational complexity is reduced effectively and the controller based on this algorithm is also used as an inner current loop regulator.Finally,the controller was tested using simulations and experiments.4)In the process of stabilizing the randomness and intermittent fluctuation of new energy output,there are some problems,such as over adjustment of volatility and local optimization when using traditional adaptive dynamic programming algorithm,therefore,a control strategy for the battery energy storage system using a deep learning adaptive dynamic algorithm is proposed.Firstly,power fluctuation rate feedback control is used to suppress the power fluctuation from the new energy source first time.Secondly,by introducing an adaptive dynamic algorithm based on a deep belief network,the charging and discharging power of the battery energy storage system with secondary regulation is achieved.Finally,the validity of the methods is verified under centralized and decentralized topologies.It was found the proposed control strategy is highly effective for suppression of unwanted fluctuations.5)On the basis of the existing development platform,supplementing some experimental hardwares,a small wind-solar hybrid experimental device platform is designed to verify the efficiency optimization algorithm for model predictive direct torque control of permanent magnet synchronous generator with loss at wind power side and the maximum power point tracking strategy based on step-size dynamic matching principle and maximum power point voltage law at photovoltaic side.Taking the actual output power curves of wind power and photovoltaic side as objects,the new energy stable output control strategy based on deep learning adaptive dynamic programming algorithm is tested,and the effectiveness and feasibility of the multi-objective cooperative control strategy of grid-connected inverters are verified in combination with the unbalanced voltage conditions.