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As the integration of distributed generation units into the power grid continues to grow, the system becomes increasingly vulnerable to power fluctuations and potential failures, which significantly challenge grid stability. Virtual synchronous generators (VSGs) have emerged as a key technology to enhance grid stability by emulating the behavior of traditional synchronous generators. However, with multiple VSGs operating within independent microgrids, coordinating their control parameters becomes critical to ensuring stable and reliable operation. This article proposes an optimization method based on the gradient descent algorithm to fine‐tune the control parameters of multiple VSGs in independent microgrids. A small‐signal model for microgrids with multiple VSGs is developed to analyze the system's dynamic behavior under small disturbances. The proposed method optimizes VSG control parameters to improve the frequency and voltage dynamic response and enhance the overall system stability. The simulation results demonstrate that the proposed multi‐VSG control parameter optimization method significantly enhances the frequency and voltage dynamic response, as well as the stability of the independent microgrid under small disturbances, offering a practical solution for improving microgrid performance in real‐world applications. This article establishes the small signal model of independent microgrid and then the multi‐VSG control parameters are optimized by the gradient descent method based on the proposed small signal model and the optimal damping ratio of the system eigenvalues. The simulation and optimization results show that the proposed multi‐VSG control parameter optimization method based on the gradient descent method can improve the frequency and voltage dynamic response and the stability of independent microgrid system under small disturbances and has practical application value.

In this study, a novel approach is proposed for fast prediction of dynamic voltage instability occurrence (as a short term phenomenon and/or a long term one) and voltage stability stiffness of the system, against load disturbances. The main contribution of this paper is in introducing a procedure for generating novel features to be applied to a pattern classifier, by which dynamic voltage stability status of a power system can be predicted. The proposed feature generation procedure only needs measured pre-disturbance variables and disturbance severity provided by phasor measurement units as inputs whereas a set of output variables are derived from an unconstrained power flow program. Since the proposed method does not need any measured post disturbance data, the prediction task can be performed just after the disturbance. Thus, corrective actions can be executed in a short time after the disturbance to inhibit voltage instability. Moreover as no measured post-disturbance data are needed, the proposed method can also be employed in preventive procedures for voltage stability enhancement and/or decreasing possibility of voltage instability occurrence. Training a decision tree based classifier with the proposed features and testing the method on a modified version of Nordic32 test system, the simulation results have demonstrated that the proposed method effectively predicts the status of dynamic voltage stability in the test system.

Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system. The solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. Results show that analysing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.

The integration of a flexible alternating current transmission system (FACTS) and a power system stabilizer (PSS) can increase dynamic stability. This paper presents the enhancement of power system dynamic stability through the optimal design of a power system stabilizer and UPFC using an ant lion optimization (ALO) technique to enhance transmission line capacity. The gained damping ratio, eigenvalue and time domain results of the suggested ALO technique were compared with a base case system, ALO-based PSS and ALO-based PSS-UPFC to test the effectiveness of the proposed system in different loading cases. Eigenvalues gained from an ant lion approach-based UPFC with a PSS and a base case system are compared to examine the robustness of the ALO method for various loading conditions. Thus, this paper addresses the mechanism regarding the power system dynamic stability of transmission lines by integrating the optimal size of a PSS and UPFC into the power system. Therefore, the main contribution of this manuscript is the optimal coordination of a power system stabilizer, power oscillation damper and unified power flow using ant lion optimization for the mitigation of low-frequency oscillation.

In this study, a novel dynamic paradigm aimed at solving the power flow equations in both solvable and unsolvable region is proposed. The challenging idea is to reformulate the power flow equations into a set of ordinary differential equations, whose equilibrium points represent the power flow problem solutions. Starting from the Lyapunov theory, the authors demonstrate that this system of dynamic equations is characterised by an exponential asymptotic convergence to equilibrium points. This feature allows us to overcome the inherent limitations of the traditional iterative minimisation algorithms that can fail to converge because of the highly non-linearities of the first-order condition. Extensive simulation studies aimed at demonstrating the effectiveness of the proposed methodology are presented and discussed.

This paper presents a novel method of hydro power plant operation, based on the control of the injectors’ or wicked gates opening time as a function of the upper reservoir level. In this way, a faster power injection, depending on the current water level on the upper reservoir, could be achieved. When this level is higher, the opening time could be shorter; hence, hydropower ramps could be steeper. Due to this control, frequency excursions and load shedding trips are smaller, thus the power quality is enhanced. This method has been tested and validated by computer simulations in a case study located in El Hierro island, Canary Archipelago (Spain). The simulations made show significant improvements, dependent on upper reservoir water level, in power quality.

Event-driven load shedding is an effective countermeasure against voltage collapse in power systems. Conventionally, its optimisation relies on sensitivity-based linear methods, which, however, could suffer from unrealistic assumptions and sub-optimality. In this study, an alternative approach based on parallel-differential evolution (P-DE) is proposed for efficiently and globally optimising the event-driven load shedding against voltage collapse. Working in a parallel structure, the approach consists of candidate buses selection, voltage stability assessment (VSA) and DE optimisation. Compared with conventional methods, it fully considers the non-linearity of the problem and is able to effectively escape from local optima and not limited to system modelling and unrealistic assumptions. Besides, any type of objective functions and VSA techniques can be used. The proposed approach has been tested on the IEEE 118-bus test system considering two cases for preventive control and corrective control, respectively, and compared with the two existing methods. Simulation results have verified its effectiveness and superiority over the compared methods.

Quickly getting back the synchronism of a disturbed interconnected multi area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control (AGC). In this regard, AGC system based on fuzzy logic, i.e., so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system. Apart from that, simultaneous coordination scheme based on particle swarm optimization (PSO) along with real coded genetic algorithm (RCGA) is suggested to coordinate FLAGCs of the all areas. To clarify the high efficiency of aforementioned strategy, two different interconnected multi area power systems, i.e., three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies. The potency of this strategy has been thoroughly dealt with by considering the step load perturbation (SLP) in both the under study power systems. To sum up, the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC (CAGC) in each power system in order to damp out the power system oscillations.

This study investigates the effect of voltage profile and steady-state voltage stability margin when a wind-driven squirrel cage induction generator (SCIG)-based distributed generator (DG) is integrated with radial distribution system (RDS). The study has been carried out considering different configurations of SCIG. The node at which a DG has to be installed is identified based on voltage collapse index (VCI), which is a measure of steady-state voltage stability margin. The computational procedure for studying the voltage profile of RDS without and with DG has been developed. The developed algorithm has been tested on the 33-bus RDS with 1 MW SCIG integrated at the identified node and results are furnished. Finally, a configuration of SCIG having star and delta switching arrangements with permanently connected capacitor across each phase winding of the stator is found to be superior in providing improved system performance over a wide range of wind velocity. A switching criterion for this type of DG is proposed based on VCI for the improved performance of RDS.

In this research, an effective application and performance assessment of the Neuro-Fuzzy Controller (NFC) damping controller is designed to replace a single machine infinite bus (SMIB) power system stabilizer (PSS), and coordinated multi PSSs in large interconnected power systems are presented. The limitation of the conventional PSSs on SMIB and interconnected multi-machine test power systems are exposed and disclosed by the proposed NFC stabilizer. The NFC is a nonlinear robust controller which does not require a mathematical model of the test power system to be controlled, unlike the conventional PSSs’ damping controller. The Proposed NFC is designed to improve the stability of SMIB, an interconnected IEEE 3-machine, 9-bus power system, and an interconnected two-area 10-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The proposed NFC damping controller performance is compared with the conventional PSS damping controller to confirm the capability of the proposed stabilizer and realize an improved system stability enhancement. The conventional PSSs’ design problem is transformed into an optimization problem where an eigenvalue-based objective function is developed and applied to design the SMIB-PSS and the interconnected multi-machine PSSs. The time-domain phasor simulation was done in the SIMULINK domain, and the simulation results show that the transient responses of the system rise time, settling time, peak time, and peak magnitude were all impressively improved by an acceptable amount for all the test system with the proposed NFC stabilizer. Thus, the NFC was able to effectively control the LFOs and produce an enhanced performance compared to the conventional PSS damping controller. Similarly, the result validates the effectiveness of the proposed NFC damping controller for LFO control, which demonstrates more robustness and efficiency than the classical PSS damping controller. Therefore, the application and performance of the NFC has appeared as a promising method and can be considered as a remarkable method for the optimal design damping stabilizer for small and large power systems.