Jun Deng

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate.

The dynamic nonlinear order reduction, preserving the dominant state variables of wind turbine units, is a prerequisite for analyzing the stability of large-scale wind power integration systems under large disturbances. Therefore, this paper proposes a dynamic dimension reduction method for wind turbine models, which is applicable for analyzing large disturbance scenarios while retaining the state variables and could give the quantitative indicators for state variables. The direct drive permanent magnet synchronous generator (PMSG) model is used as the test example, and the singular perturbation reduced-order model (ROM) is obtained by the proposed nonlinear model reduction method. In the modified test system, the performance results have demonstrated high accuracy, computational efficiency, and robustness of the proposed method.

High voltage ride through (HVRT) control strategies for doubly-fed induction wind turbines (DFIG) have mostly focused on the rotor side converter, while the reactive power compensation capability of the grid side converter and the impact of grid side converter current transients have often been ignored on the DC bus voltage and reactive power. Therefore, a control strategy based on grid side current dynamic change characteristics is proposed, which resets the reference values of the grid side active and reactive currents for wind turbine HVRT to ensure partial absorption of reactive power on the grid side. Secondly, the key parameter in the proposed control strategy is optimization to get the most suitable DC bus voltage value with the grey wolf algorithm. Finally, the grid-integrated wind turbine simulation model is built on the MATLAB/SIMULINK and RT-Lab platforms. The simulation test results show that the proposed HVRT control strategy and its parameter optimization method are effective, DFIG can achieve HVRT when the wind turbine voltage rises to 1.3 pu.

With the continuous development of wind power capacity, a large number of wind turbines connected by power electronic devices make the system inertia lower, which leads to the problem of system frequency stability degradation. The virtual synchronous generator (VSG) control can make wind turbines possess inertia and damping. However, the stochastic dynamic behavior of wind generation results in the stochastic changing of operating condition; this paper presents an adaptive subsynchronous oscillation (SSO) damping control method for the wind generation with VSG control. Firstly, the small signal model of the permanent magnet synchronous generator (PMSG) with VSG is built, and the model of state space is derived and built. The active power of PMSG is selected as the variable parameter vector to establish a polytopic linear variable parameter system model. Then, based on the hybrid H₂/H_∞ control method, each vertex state feedback matrix is solved by linear matrix inequality, and a subsynchronous oscillation adaptive damping controller with polytope is obtained. Finally, the 4-machine 2-area system connected to two PMSGs with VSG control is used as the test system for time domain simulation. The simulation results demonstrate that the LPV based adaptive damping controller could provide enough damping under the circumstances of wider changes of wind power outputs.

With the continuous proliferation of renewable energy generation, distributed photovoltaic inverters operating at a maximum power point reduce the inertia of power systems, degrading system frequency stability and potentially causing severe oscillations in systems after being disturbed. The virtual synchronous generator (VSG) control method, which causes photovoltaic inverters to possess inertia and damping, now plays an important role in the field of distributed generation. However, while introducing the advantages of synchronous machines, problems with oscillations are also introduced and the stochastic fluctuation characteristic of photovoltaics results in the stochastic drifting of the operating point. This paper presents an adaptive controller parameter design method for a photovoltaic-VSG (PV-VSG) integrated power system. Firstly, a small-signal model of the PV-VSG is built and a state space model is deduced. Then, the small-signal stability and low frequency oscillation characteristics of the photovoltaic power generation system are analyzed. Finally, considering the limitations of system oscillations and the stochastic drifting of the operating point, a global optimization design method for controller parameters used to improve system stability is proposed. The time domain simulation shows that an optimized PV-VSG could provide sufficient damping in the case of photovoltaic power output changes across a wider range.

This paper proposes oscillation propagation factors to analyze power oscillations caused by the interharmonics of doubly fed induction generators (DFIG) at different points in the power system. First, a dynamic model of the DIFG is built, including the asynchronous generator, its transmission system, converters and the control systems. Then, the state space expression is formed by deducing the input and output matrices. From this, the oscillation propagation factor is proposed and denoted to exhibit the propagation mechanism of interharmonics in the view of frequency domain, by deducing the multi-input-multi-output transfer functions matrix. Along with this, the sensitivity of propagation is calculated for adjusting the parameters to block the oscillation propagating path. Finally, the modified four machine system with two DFIGs and the New-England 39 bus system with two DFIGs is used as a test system to verify the effectiveness of the oscillation propagation factor. From this the simulation results demonstrate that the subsynchronous interharmonics of DFIGs injected into the grid will propagate to the different points of the system and results in oscillation of the power. The oscillation propagation factor could quantize the oscillation magnitude propagating from one point to other point in the wind integrated power system.

Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve a robust damping control effect. Firstly, the operating space is classified into different modes according to the classification of wind power outputs. Multiple power system stabilizers (PSSs) are then designed. Secondly, the method of optimal classification and regression decision tree (CART) is utilized for classifying subspaces of system operating point and it is proposed that the on-line measurements from wide area measurement system (WAMS) are used for tracking the dynamic behaviors of stochastic drifting point and thus guide the updating of appropriate PSSs be switched on adaptively. A 16-generator-68-bus power system integrated with wind power is presented as a test system to demonstrate that the adaptive control scheme by use of the CART can damp multi-mode oscillations effectively when the wind power output changes.

With the continuous increase of wind power grid-connected capacity, the random fluctuation of wind power makes subsynchronous oscillations (SSO) exhibit time-varying amplitudes and frequencies characteristics. This paper proposes an on-line monitoring method to identify subsynchronous oscillation modals (including frequencies, amplitudes and damping factors, etc.) with time-varying amplitudes and frequencies based on sliding window FFT. First, windows and interpolation are used to reduce spectral leakage and fence effect, and decrease the FFT identification errors. Then, through the sliding of the time window, the signal intercepted by each time window is transformed to obtain a dynamic sequence of oscillation frequency and oscillation amplitude, that is, varying frequency and amplitude with time. The dynamic sequence of the damping factor is obtained by analyzing and calculating the time-varying oscillation amplitude. Finally, using the ideal non-stationary signal and the measured signal of the power grid as test case, the comparison results with the Prony and HHT algorithms show that this method not only can eliminate the mode mixing, but also have anti-noise ability. The method can effectively identify the random time-varying oscillation modals and realize on-line modal onitoring and analysis of subsynchronous oscillation modals.