This paper presents the impact of Inverter-Based Resources (IBRs) controlled through the Grid-Forming (GFM) scheme on Low-Frequency Oscillations (LFOs) and power system dynamic behavior. These IBRs are integrated at low-inertia buses, where their effect on stability is critical. The study employs the Virtual Synchronous Generator (VSG) control-based GFM scheme for IBRs. The dynamic interaction between the power system and the active and reactive power control loops of VSG-controlled IBRs has a significant impact on the system's LFOs. This impact becomes more pronounced as the VSG control loop parameters are increased, particularly the virtual inertia constant$(H)$, the virtual damping coefficient$(D_{p})$, and the virtual voltage gain coefficient$(K_{q})$. These dynamic interactions can introduce new, weakly damped LFOs, negatively impacting the system's dynamic behavior. To address this challenge, a Supplementary Damping Control (SDC) scheme is proposed for IBRs. This scheme aims to improve LFO damping and mitigate power oscillations of IBRs. The SDC comprises a multi-stage mixed$H_{2}/H_\infty$decentralized damping controller integrated with the IBR's reactive control loop. The parameter variation uncertainty and explicit modelling of disturbance input have been considered in the design process of this SDC scheme. Further, the robustness of the proposed SDC is validated on the IEEE 39-bus system. The system is tested under various operating conditions, such as load increments, changes in network topology, and integration of renewable sources. Eigenvalue analysis is conducted using MATLAB, while dynamic simulations are performed using the Real-Time Digital Simulator (RTDS). Simulation results confirm that the proposed SDC effectively mitigates system LFOs dynamic behavior.

This article presents a backup protection scheme for hybrid ac–dc microgrids. A time-domain technique is proposed to isolate the faulted dc and ac subgrids from the point of common coupling using ac and dc currents, respectively, in case the primary protection fails. Symmetrical component decomposition in a bipolar dc microgrid is used to obtain the mode-0 and mode-1 current at the terminal of the bidirectional interlink converter (BIC). The superimposed component of mode-1 current acts as the decisive discriminator between ac and dc subgrid faults. One cycle moving average of the phase currents at the BIC terminal identifies the ground faults in dc subgrids. Further, the pole-to-pole faults in the dc grids can be detected using two consecutive zero-crossing widths in the phase currents at the ac terminal. The proposed protection scheme is validated on a hybrid ac–dc microgrid, simulated using a real-time digital simulator and a digital signal processor in a hardware-in-loop scenario for different faults on either side of the microgrid.

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. Note to Practitioners—This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

In this article, a machine-learning-based fault detection and classification method is proposed. Two supervised learning-based protection modules are developed for the relays considered in the study—one to detect the fault and discriminate between the symmetrical or unsymmetrical nature of the fault and another to detect the faulty phase(s). A robust set of features using both relay voltage and current signals is utilized for developing the modules. The features are obtained using the multiresolution decomposition based on the empirical wavelet transform. The modules are tested on the unbalanced IEEE 13-node network integrated with inverter-based distributed generation systems capable of reactive power injection in the low-voltage ride-through mode of operation. Varying penetration levels and intermittent output of distributed generation, fault resistance, fault inception time, noise in the signals, and switching events occurring around the fault period are some of the unique operating scenarios considered to demonstrate the consistent performance of the relays. The simulations are performed in power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) and the protection modules are developed in MATLAB. The performance of the protection modules is evaluated using several metrics with respect to various classes of events occurring in the network.

This article presents a unit protection scheme for bipolar dc microgrids, which is resilient to cyber attacks. The proposed method uses current measurement of both ends of the protected line segment for distinguishing the internal faults and cyber attacks. For any change in current, the disturbance index exceeds the threshold value, subsequently, the proposed protection algorithm is triggered. Being a bipolar dc system, the concept of symmetrical component decomposition is used to obtain the bias, unbalance, and balance components during an unbalance condition. The superimposed balance component of both ends of the line on a four-quadrant plane acts as a decisive discriminator between internal and external faults. The correct faulted pole is identified by analyzing the superimposed unbalanced component. By comparing the superimposed bias component with the local pole domain currents, the cyber attack is correctly identified. The proposed protection scheme is validated on a ring-type bipolar dc microgrid, simulated in real-time digital simulator, for different internal and external faults and cyber attacks. The performance of the proposed method is tested for various conditions, including the detection of internal and external high resistance faults, fault type classification, change in load, distinguishing between different faults, and cyber attacks. A comparative assessment of the proposed method with the available techniques confirms its strength.

In this paper an event based adaptive neural network (ANN) observer is designed, where selected neurons are trained by an adaptive law to estimate uncertainties in order to mitigate agent misbehave in the multiagent featured microgrid. Time dependent uncertainties can be directly compensated through sliding mode controller (SMC) but to compensate state dependent uncertainties and agent uncertainties, ANN algorithm is proposed. Novel triggering law is derived for neurons weight update in ANN observer. Event trigger consensus control in multiagent scenario allows all agents (DGs) to achieve desired reference value while reducing the control effort. Robust and fast response is guaranteed by designing integral sliding surface with proportional surface term in SMC reaching law. Proposed method is immune to system sensitivity against both state and time dependent uncertainties. The controller efficacy is demonstrated in MATLAB/Simulink platform.

Wind power plant is source of harmonics and inter-harmonics. Estimation of these harmonics is very important and crucial task in modern distribution generation environment. This paper presents sliding window matrix pencil algorithm to estimate harmonics and inter harmonics produced by doubly fed induction generator based wind power plant. The series of simulation results under variable wind speed is performed to show the effectiveness of proposed algorithm and its performance is also compared with sliding widow ESPRIT.