This paper presents a linear-quadratic regulator (LQR) for damping of subsynchronous interaction (SSI) in doubly-fed induction generator (DFIG)-based wind farms. The proposed LQR controller employs a full-state observer to estimate all state variables. The output of the LQR is added to control signals of inner current control loops of DFIG converters as supplementary control signals. The supplementary control signals are dynamically limited to avoid saturating the converters and to provide the DFIG with the desired transient response against power system faults. The proposed SSI damping controller is designed for a realistic series compensated wind farm, and its performance is verified using electromagnetic transient (EMT) simulations. The EMT simulations are performed using a detailed DFIG model which includes all nonlinearities and all required transient functions to meet the grid code requirements corresponding to fault-ride-through (FRT). The results show that the proposed SSI controller is able to significantly mitigate the oscillations due to the SSI phenomenon, and to provide excellent transient response against systems faults.

This paper proposes a new and robust control strategy for distributed energy resource (DER) units operating in parallel with a three-phase ac grid that could have unbalance voltages. It is well known that the grid imbalance imposes double-frequency ripples on the dc-side capacitor and low-order harmonics on the ac side of a voltage source converter widely used to interface the DERs with the grid. The proposed controller is able to overcome both problems. This not only improves the power quality and stability aspects of the DER and overall system, but will also help reduce the size of the dc-side capacitor. The proposed controller belongs to the family of current controllers and can in principle be added as an upgrade to the existing current controllers. Mathematical analysis and optimal control design aspects are presented and then the performance of the proposed controller is evaluated by various simulation scenarios and hardware-in-the-loop experiments.

Modern distribution grids are shifting toward resilient and sustainable energy networks through the use of unprecedented number of distributed generation (DG) units and renewable energy sources. However, unlike conventional grids, they are also highly integrative in the sense that, centralized control applications must support critical protective actions to reliably provide power to consumers. To achieve this goal, this paper presents a novel mechanism for coordinated protection and control of DG systems under permanent line faults in distribution networks. First, a centralized fault detector employs voltage phasors and frequency data to identify and isolate the fault within a tolerance time. Upon fault detection, a secondary control algorithm retrieves archived synchrophasor datasets to calculate real and reactive power disturbances caused by the fault isolation. In this mechanism, the secondary controller facilitates voltage/frequency recovery by adapting reference points of local controllers to the post-fault conditions. Coordination is carried out based on the knowledge of the response time of protective devices and communication delays in control links. The proposed approach is a promising paradigm for reliable networked protection and improved situational awareness in smart distribution networks.

This article presents a detailed and rigorous comparison between two vector current control (VCC) approaches for voltage source converters (VSC). A PLL-less VCC (also called the direct power control) has recently been proposed in several major publications with the assumption that it will improve the performance of conventional PLL-based VCC during weak grid conditions. In this article, first, we introduce an enhancement to both the PLL-based and PLL-less VCCs by using an optimal multivariable control design for both of them. Then, we show that both controllers perform equally during normal and weak grid conditions. However, the PLL-less VCC produces errors when the grid frequency variations occur and eventually becomes unstable when the grid is also weakened. Furthermore, the PLL-less VCC cannot produce a clean output current when unbalanced grid disturbances occur. This article proposes a method to address this latter issue. It also proves that the filters used in PLL-less VCC introduce similar dynamics to those of PLL. This article concludes that the PLL-based VCC is still the preferred choice for the grid-interfaced VSCs. Laboratory-scale experimental results are also included to validate the simulation results.

The use of supplementary controllers for mitigating subsynchronous control interaction (SSCI) in DFIG-based wind parks is quite promising due to their low investment costs. These SSCI damping controllers are typically designed and tested using an aggregated wind turbine (WT) model that represents the entire wind park (WP). However, no research has been reported on their implementations in a realistic WP. This paper first presents various implementation schemes for a linear-quadratic regulator (LQR)-based SSCI damping controller, and discusses the corresponding practical challenges. Then, an implementation scheme which obviates the need for high rate data transfer between the WTs and the WP secondary control layer is proposed. In the proposed implementation, the SSCI damping controller receives only the WT outage information updates from the WP controller (WPC), hence it is not vulnerable to the variable communication network latency. The SSCI damping controller parameters are also modified when there is a change in WT outage information for the ultimate performance. The effectiveness of the proposed implementation scheme is confirmed with detailed electromagnetic transient (EMT) simulations, considering different wind speed at each WT and WT outages due to sudden decrease in wind speeds.

Faults are extreme events that canadversely affect the voltages in islanded microgrids. This paper provides a new data-driven methodology for timely prediction of the post-fault voltage stability in hybrid AC/DC microgrids. The proposed method performs a binary classification with delay constraints by processing sequences of the short-time mean squared deviations using a deep learning system. The deep learning system consists of a bidirectional long short-term memory network whose output is a probabilistic voltage instability indicator. When the value of the indicator is non-zero, persistent voltage disturbances are most likely to occur even after the fault clearance. The proposed method enables the microgrid to carry out remedial or preventive actions, such as event-triggered protection and control of distributed energy resources (DERs), which are advantageous to the resilient operation of the microgrids. Extensive and detailed electromagnetic transient (EMT) simulations of a low-voltage hybrid AC/DC microgrid benchmark are analyzed, and the results confirm the effectiveness of the proposed method for online prediction and fast voltage regulation.