As the wind-integrated power system is a strongly nonlinear system with uncertain operating conditions that vary with time, it is difficult to guarantee the effectiveness of the conventional model-based sub-synchronous damping control (SSDC). To solve this problem, a model-free adaptive control (MFAC) method for static synchronous compensator (STATCOM) is proposed to realize sub-synchronous oscillation (SSO) mitigation. The method is driven by a limited number of online input/output (I/O) data rather than full technical and operational details of the system. First, the structure of the proposed MFAC is described in detail with control algorithms of all modules. Particularly, a new form of one-step-ahead weighted predictive control algorithm of MFAC for SSO mitigation is proposed. Then, the convergence of tracking error along with the stability of the closed-loop system with the improved MFAC are analyzed. Finally, the performance of the proposed MFAC for STATCOM is demonstrated and compared with that of the conventional SSDC under various operating conditions. Time domain simulation results validate the effectiveness of the proposed MFAC and demonstrate its better adaptability than the conventional SSDC.

The most effective countermeasure against forced oscillations (FOs) is to locate and isolate external disturbances from power systems. However, it is challenging for existing location identification schemes to meet real-time computation and communication requirements in central data concentrators. To solve this problem, we propose a distributed cooperative scheme for locating FO sources. Phasor data collectors (PDCs) located at local control centers are utilized to calculate local energy flows embedded in low frequency oscillations (LFOs). We apply an average-consensus algorithm for exchanging the energy flow information among local PDCs which detects the cut-set energy and locate the FO source in a distributed manner. We also propose an adaptive minimum cut-set division method based on graph theory. By implementing the proposed method on the central PDC, the auxiliary control data are updated automatically, which makes the location scheme adaptive to changes in grid and communication network topologies. The effectiveness of the distributed cooperative location scheme is verified using the results for IEEE 39-bus power system.

Equipment safety and system stability are increasingly threatened by sub-synchronous oscillations (SSOs) for the mounting proportion of doubly-fed induction generators (DFIGs) in power systems, which calls for accurate and timely identification of SSO sources. In this article, a sub-synchronous modal energy (SSME)-based approach is proposed for locating SSO sources in power systems with DFIGs. Firstly, the sub-synchronous modal energy balance equation of a power system with DFIGs is derived via generalized Hamiltonian theory. Then, each component of the SSME balance equation is analyzed to illustrate that the flow direction of SSME serves as a suggestive criterion for identifying SSO sources. Considering the SSME of subsystems can be easily obtained online with measurement data, a novel SSO source location method is proposed based on the SSME of subsystem. Finally, the performance of the SSME-based method is demonstrated under both forced and negative-damping SSO scenarios. Simulation results validate the effectiveness of the proposed SSO source location method as well as its improvement in accuracy compared with conventional energy-based method.

The proliferation of renewable generations significantly in power system increases the severity and possibility of subsynchronous oscillations (SSOs) recently. Monitoring SSOs is critical for setting up control strategies and mitigating SSOs effectively. In many SSO events, the oscillation frequency and magnitude are time-varying, which brings great challenges to SSO monitoring. This article proposes an adaptive-reset extended Kalman filter (AREKF) method for accurate estimation of SSO modes. Two improvements are made to the EKF method. The first one is establishing a four-state SSO signal model for the EKF algorithm to track damping factors of SSO modes. The second one is developing an adaptive-reset method to make the EKF algorithm capable of handling the time-varying SSOs by resetting the covariance matrix adaptively. The threshold in the adaptive-reset criterion is automatically tuned with the M ₂ M ₄ estimator. The performance of the AREKF method is demonstrated under various conditions and compared with that of conventional KF-based and phase-locked loop-based methods. Simulation results validate the effectiveness and robustness of the proposed method as well as its superiority over conventional approaches. Real-time experiment results demonstrated the effectiveness of the proposed method in practical applications.

Aluminum (Al) and nutrients are key factors to influence tea (Camellia sinensis L.) productivity and quality, while how they interplay in tea plantations under the pressure of global change and increasing fertilization is little studied. In this study, we selected the tea plantations along an age-chronosequence to study Al fractions using a sequential extraction procedure, and nutrient concentrations in topsoil and subsoil and various plant organs. Our results indicated that Al levels and nutrient concentrations in soils and plants generally increased with planting year (P < 0.05), and soil Al bioavailability was positively correlated with Al concentrations in most plant organs. Significant negative relations among pH and most extractable Al fractions in both soil layers suggested that decreased pH would directly alter soil-plant Al cycling due to exogenous nitrogen (N) fertilizer and atmospheric acid deposition. Topsoil total phosphorus (P) was positively correlated with most Al fractions, and root P was positively correlated with root Al concentration, both of which indicate that P and Al were synchronously absorbed by roots in acid tea soils. In addition, topsoil organic carbon was positively correlated with both active and inert Al fractions, indicating that above-ground organic litters would be the main source of elevated Al levels in older tea plantations. Clearly, Al enrichment in tea leaves with increasing planting year needs to be considered under management practices with heavy N and P fertilizers and increasing atmospheric acid deposition in subtropical China.