Analysis And Control On The Probability Small-signal Stability Of The Wind-PV-thermal-bundled Power Transmission Power System

Driven by China’s "14th Five Year Plan" and the "30·60" carbon peak and carbon neutrality requirements,renewable energy,represented by wind and solar energy,is increasingly gaining penetration in the power grid.As renewable energy improves environmental benefits and reduces energy consumption,the characteristics of intermittency and randomness have a significant impact on the safe operation of power grids.Interregional and intraregional weak or even negative damping problems often occur,and the low-frequency oscillation generated by it has also become a bottleneck in restricting the transmission capacity of tie lines and the system’s ability to absorb renewable energy.How to effectively overcome the harm that the volatility,intermittency,and randomness of renewable energy bring to the power system is an important issue.Therefore,it is necessary to systematically and deeply study the lowfrequency oscillation characteristics and stability improvement measures of the windPV-thermal-bundled transmission system under multiple operations.The specific research content of this article is as follows:(1)This paper constructs the wind-pv-thermal-bundled power transmission system model,adds additional control signals to the AC voltage control section of Static Synchronous Compensator(STATCOM)and selects active power PL as the STATCOM-Power Oscillation Damping(POD)input signal.By sampling the daily load curve of a certain area,multiple operation modes of load fluctuations in the power system are simulated.Considering the interaction between controllers,based on Lyapunov stability rules,a mature QR method is used to obtain all eigenvalues of the power system in each operation mode.Determine the system oscillation mode for each working condition based on the participation factor.The objective function of the controller parameter optimization problem is constructed based on probability theory.The particle swarm optimization algorithm is used to optimize the parameters of STATCOM-POD.Finally,the effectiveness of the probabilistic optimization method is verified by eigenvalue analysis and time domain simulation.(2)Considers the impact of wind energy fluctuations on low-frequency oscillations in power system and optimize them using probabilistic optimization methods.Aiming at the problem of excessive sample size of wind energy data,the Latin hypercube sampling(LHS)method is used to sample it,and the relevant data of the system is obtained through Monte Carlo simulation,and the probability optimization objective function is designed.As the proportion of renewable energy in power systems increases,a single controller cannot ensure the stable operation of the system.Therefore,this chapter uses optimization algorithms to coordinate the design of Power System Stabilizer(PSS)and STATCOM-POD.The Grey Wolf algorithm is used to address the limitations of the original algorithm,namely,the lack of population diversity,poor optimization performance caused by linear parameters,and invalid location update processes.Finally,the validity of the proposed algorithm is verified by time-domain simulation and eigenvalue analysis of an IEEE 4-machine 2-area system under different operating conditions.(3)Expand the range of wind power data sampling,it is ensured that the system can operate safely and efficiently in a larger range.Using an improved Grey Wolf optimization algorithm for probabilistic coordinated design of PSS and Unified Power Flow Controller(UPFC)-POD under multiple operation modes.The Grey Wolf algorithm is optimized using Tent mapping equations and nonlinear adjustment strategies to quickly and accurately optimize controller parameters in power systems.Design an objective function that includes damping ratio and voltage to ensure that the overall damping ratio of the optimized power system is improved,and voltage fluctuations can quickly return to normal levels in the event of three-phase short circuit faults,ensuring that the system can operate safely and efficiently over a larger range.Finally,the effectiveness of the probabilistic optimization method is verified through power system probability indices,probability density function diagrams,and time domain simulation waveforms under various operating conditions.