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Real-time monitoring and fault diagnosis of transformers are essential for the stable power system operation. This paper presents an RFID-based transformer fault feature extraction and classification algorithm. Experiments show that monitored current signals are stable while the temperature peak is 356°C. Hilbert decomposition reveals regular current and voltage patterns that can be used as fault indicators. Signal strength classification accuracy reached 80%. At rated load, the transformer temperature soared to 186°C, indicating overheating issues. The monitoring during a sample day showed that overload events were concentrated from 16:00-20:00, which required attention. The approach helps accurately identify transformer fault types from real-time RFID data for proactive maintenance. Compared to reactive repairs after failures, this not only improves employee productivity but also reduces costs. Based on customized RFID deployment, the algorithm contributes to the stability and economy of power infrastructure. [ABSTRACT FROM AUTHOR]

Current power system stabilizer designs, including fuzzy-PID and Lead-Lag compensators, need help with adaptability and complexity in diverse and evolving power system environments. Conventional tuning methods like the Newton–Raphson approach and optimization strategies like particle swarm optimization, genetic algorithms, and cuckoo search algorithms face challenges in achieving optimal performance for Fractional Order Proportional Integral Derivative (FOPID) controllers. There is a critical need for innovative tuning methods that offer enhanced adaptability and performance in complex power system stability analysis. This research contributes to the advancement of power system stability analysis, specifically in SMIB systems, offering insights into optimizing FOPID controllers utilizing the innovative Harris Hawks Optimization (HHO) algorithm. The work expects these findings to broaden the implications for power system control and enhance the stability of Single Machine Infinite Bus (SMIB) systems, thus fostering the resilience and reliability of modern electrical infrastructure. The FOPID controller encompasses fractional order parameters, encompassing proportional, integral, and derivative gain, integral order, and derivative order, each exerting a substantial influence on control responses and stability. This research harnesses HHO, an optimization technique inspired by nature, to fine-tune FOPID parameters. The investigation involves initializing the SMIB model, formulating an objective function to minimize control errors, and employing HHO iteratively to refine the FOPID controller. The outcomes reveal enhanced stability, diminished overshoot, accelerated settling time, and transient response. [ABSTRACT FROM AUTHOR]

In this paper, explore the effectiveness of a new Wide Area Fuzzy Power System Stabilizer (WAFPSS), optimized using the Exponential Distribution Optimization (EDO) algorithm, and applied to an IEEE three-area, six-machine power system model. This research primarily focuses on assessing the stabilizer's capability to dampen inter-area oscillations, a critical challenge in power grid operations. Through extensive simulations, the study demonstrates how the WAFPSS enhances stability and reliability under a variety of operational conditions characterized by different communication delay patterns. The application of the proposed stabilizer on this specific IEEE model provides a detailed insight into its performance in real-world scenarios, illustrating its adaptability and effectiveness in managing dynamic disturbances. The simulation results reveal that the proposed WAFPSS achieves significant reductions in the Integral Time Squared Error (ITSE), with improvements of 94.1%, 97.02%, and 98.18% in three distinct cases, showcasing its superior damping capability and robustness. The findings indicate that the advanced optimization techniques provided by the EDO algorithm significantly improve the stabilizer's response, ensuring robust power system performance. This integration of WAMS with sophisticated control systems using fuzzy logic presents a strategic solution to managing the complexities faced by modern power networks, optimizing their stability in the face of increasing renewable integration and fluctuating demand. [ABSTRACT FROM AUTHOR]

The utilization of Harris Hawks Optimization (HHO) for Multi-Objective Optimal Power Flow (MaO-OPF) challenges presented in this paper is both novel and compelling, as this approach has not been previously applied to these types of optimization problems. HHO, which shares characteristics with ant behavior, demonstrates significant strength in addressing high-dimensional, nonlinear optimization issues within power systems. In this study, HHO is implemented on an IEEE 30-bus power system, optimizing six competing objectives: minimizing total fuel cost, emissions, active power loss, reactive power loss, reducing voltage deviation, and enhancing voltage steady state. The effectiveness of HHO is assessed by comparing its performance to two alternative methods, MOEA/D-DRA and NSGA-III. Experimental results reveal that solutions derived from HHO exhibit superior convergence, enhanced diversity maintenance, and higher quality Pareto-optimal solutions compared to the MOEA/D trail algorithms. The research breaks new ground in the application of the Harris Hawks Optimization (HHO) algorithm to the Multi-Objective Optimal Power Flow (MaO-OPF) problem. The restructuring not only incorporates self-adaptive constraint-handling techniques and dynamic exploration exploitation strategies, but also addresses the more pressing requirements of modern power systems with even better convergence, and both sequential and global computational efficiency than existing skill. This approach proves to be a powerful and effective solution for addressing the complex challenges associated with MaO, enabling power systems to manage multiple conflicting objectives more efficiently. [ABSTRACT FROM AUTHOR]

The presence of oscillation modes in a power system with low damping rates can compromise its proper operation and motivated the development of different damping control projects to improve these damping rates. Central damping controllers using PMU data showed to be able to mitigate oscillation modes of power systems with high damping ratios. However, time delays and vulnerability of communication channels to cyber-attacks can damage the desired operation of this type of damping controller. This paper proposes an optimization problem-based method for designing centralized controllers that are resilient to channel losses and using time delays as variables of the proposed optimization procedure. The bio-inspired algorithms Marine Predators Algorithm, Particle Swarm Optimization and Genetic Algorithms were applied and evaluated in the proposed optimization model. The IEEE 68-bus was used as a test system, and a set of case studies were carried out. The obtained results show that considering time delays as optimization variables can be beneficial to achieve optimal control objectives. Furthermore, the communication failure robustness strategy was effective as observed by the modal analyses and nonlinear time domain simulations of the test system under contingencies. [ABSTRACT FROM AUTHOR]

This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique and employs cascading control theory to significantly improve reliability and robustness. The unique control architecture, termed Fuzzy Fractional Order Proportional Integral (PI) plus Fractional Order Proportional Derivative (PD) plus Integral (Fuzzy FOPI+FOPD+I), integrates advanced control methodologies to achieve superior performance. To optimize the controller parameters, the Teaching–Learning-Based Optimization (TLBO) algorithm is utilized in conjunction with the Integral Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal control behavior. The methodology is validated through comparative analyses with controllers reported in prior studies, highlighting substantial improvements in performance metrics. Key findings demonstrate significant reductions in peak overshoot, peak undershoot, and settling time, emphasizing the proposed controller's effectiveness. Additionally, the robustness of the controller is extensively evaluated under challenging scenarios, including parameter uncertainties and load disturbances. Results confirm its ability to maintain stability and performance across a wide range of conditions, outperforming existing methods. This study presents a notable contribution by introducing an innovative control structure that addresses critical challenges in AVR systems, paving the way for more resilient and efficient power system operations. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques have gained substantial attention in many aspects of contemporary life during the last several years. As part of the digital revolution, the electricity industry is one of the leaders in implementing such appealing and effective technology for various applications. In general, low-frequency oscillations (LFO) are nonthreatening but slow-burning issues that, if not addressed appropriately, might lead to complete network collapse. Due to the significance of prominent ML family members in improving LFO damping in electric power system (EPS) networks, the applicability of the fuzzy c-means (FCM) clustering-based deep learning (DL) technique is modeled in this paper for two typical EPS networks by predicting the critical parameters of the power system stabilizers (PSS). The first network is a single-machine infinite bus (SMIB) network where the synchronous generator is equipped with a PSS. On the other hand, a unified power flow controller (UPFC) coordinated PSS is connected at one terminal of the synchronous generator of the second network. The clustering of the datasets obtained through the whale optimization algorithm (WOA) is performed based on the calculated silhouette values for both power networks. Then, several statistical performance indices (SPI) are evaluated to validate the robustness of the training and testing procedure of the DL method for the prepared data clusters using the FCM clustering technique. The efficacy of the proposed FCM-DL strategy in enhancing LFO damping for the two test networks is assessed based on standard analytical and time-domain analysis. Therefore, the minimum damping ratio (MDR), eigenvalue, rotor angle, and angular frequency with respect to time are simulated and analyzed. The article also includes a comparison of the findings of previous studies to illustrate the potential of the proposed FCM-DL strategy in improving EPS stability by damping out undesirable LFOs. It is worth noting that the developed FCM-DL models can predict the candidate parameters with a coefficient of determination (R2) value of more than 0.9974. During the implementation phase, the proposed strategy achieves competitive MDR, for instance, more than 0.50 and 0.74 MDR for the first and second networks, respectively. [ABSTRACT FROM AUTHOR]

A cyber-physical system is a system that integrates computation and physical processes. Such a system has found numerous applications in power systems. One such application is power hardware-in-the-loop (PHIL) simulation. In the context of PHIL simulation, a hardware device under test (DUT) is typically linked to a digital real-time simulator (DRTS) via a PHIL interface. Over time, several PHIL interfaces have been proposed and explored. Notably, the ideal transformer model (ITM) stands out due to its popularity, primarily for its ease of implementation. Other PHIL interfaces, such as partial circuit duplication (PCD) and damping impedance, can be viewed as extensions of the ITM concept. These PHIL interfaces necessitate a strict impedance ratio between the physical (i.e., the DUT) and the cyber parts (i.e., the system modeled in DRTS) before embarking on a PHIL implementation. This prerequisite can often prove to be a demanding and complex task. This article introduces a novel PHIL interface for PHIL using Nelder–Mead (NM) algorithm, designed to eliminate such constraints. Notably, the proposed PHIL interface offers an expanded stability region when compared with ITM, thus rendering it suitable for a broader range of PHIL applications. The effectiveness of this proposed method has been confirmed by a practical PHIL setup. [ABSTRACT FROM AUTHOR]

This paper presents an analytical examination of the small‐single stability (SSS) criterion of the permanent magnet synchronous generator (PMSG)‐based wind power delivery system via voltage source converter‐based high voltage direct current (VSC‐HVDC). First, a small‐signal model of the PMSG‐based WPDS is developed. Then, the SSS criterion, driven by the phase‐locked loop of the PMSG in the sub‐synchronous timescale, is derived. The derived SSS criterion provides analytical insights into why and how the loading condition, the grid connection, and the control parameters affect the system's SSS. It is unambiguously revealed that increasing loading conditions of the PMSG or/and the grid connection of the WPDS to VSC‐HVDC shall bring about a higher risk of oscillatory instability. Hence, analytical derivation of the SSS criterion helps better understand the instability mechanism in the PMSG‐based WPDS via VSC‐HVDC. In addition, while the derivation of the SSS criterion presupposes identical dynamics among PMSGs, this derived criterion can still be approximately utilized to assess the SSS of the PMSG‐based WPDS via VSC‐HVDC, irrespective of whether the dynamics of the PMSGs are similar or different. Finally, the SSS criterion is demonstrated and evaluated through three examples of the PMSG‐based WPDS via VSC‐HVDC. [ABSTRACT FROM AUTHOR]

The global energy landscape is undergoing a profound transformation, marked by an unprecedented integration of renewable sources. This paradigm shift brings forth the challenge of low inertia in power systems, posing significant uncertainties to grid stability and reliability. This paper addresses these challenges and proposes innovative solutions to ensure the resilience of future transmission networks. The paper leverages advanced modeling techniques, including dynamic simulation models and control methods, to analyse real‐world case studies, mainly focusing on wind power plants operating as hybrid plants with integrated energy storage systems and participating in reserves markets to provide frequency response. The analysis includes adapting the Nordic equivalent power system model, allowing a deeper understanding of the dynamics of low‐inertia environments and the impact of renewable energy integration. The aim is to provide valuable insights into the complex interactions within low‐inertia power systems and highlight the importance of adapting power systems to ensure resilience in evolving energy scenarios. Compared to existing strategies, the proposed method enhances the grid's ability to maintain frequency stability, even with significant renewable energy penetration. The paper contributes to the ongoing discourse on building sustainable and reliable future transmission networks through empirical analysis and theoretical modeling. It emphasizes technical strategies, operational advancements, and policy considerations essential for navigating the challenges posed by the transition to renewable energy sources. [ABSTRACT FROM AUTHOR]

To keep pace with the construction of the new‐type power system, virtual synchronous generator control, as a classical method of virtual inertia control, has been widely adopted due to its electromechanical characteristics similar to synchronous generator. However, the introduction of rotor motion equations leads to low‐frequency oscillation issues in virtual synchronous generator units similar to synchronous machines. To address this challenge, this paper constructs the Phillips‐Heffron model of the virtual synchronous generator grid‐connected system and analyses the mechanism of low‐frequency oscillation in virtual synchronous generator through the damping torque method. Subsequently, a virtual dual‐input power system stabilizer is proposed by drawing inspiration from the design principles of the traditional dual‐input power system stabilizer to suppress low‐frequency oscillations in the power system. The structure of the virtual dual‐input power system stabilizer is provided, and the phase compensation method is used to optimize the parameters of the virtual dual‐input power system stabilizer. Finally, the effectiveness of the proposed virtual dual‐input power system stabilizer is verified by simulation comparison. [ABSTRACT FROM AUTHOR]

The increasing penetration of power‐electronics interfaced resources brings new challenges regarding the small‐signal stability of power systems. To address this issue, grid‐forming (GFM) controlled converters have emerged as an alternative to their conventional grid‐following counterparts. This paper investigates the mechanisms behind converters driven stability and quantifies the stabilizing effect of GFM controls. The linearized state‐space model of different combinations of control strategies is analysed in a multi‐infeed system considering various operating points. Through a parametric sensitivity study and an examination of the participation factors of key eigenvalues of the linearized models, it is confirmed that GFM controls contribute to system stabilization. Moreover, this paper demonstrates that this stabilizing effect varies significantly depending on the specific GFM control implemented: whether a current control loop is used or not notably impacts stability. The article studies the stabilizing impact of grid‐forming (GFM) in power systems. All things being equal (inertia, damping, and tuning), the presence of a current control in the GFM scheme significantly reduces the stabilizing impact. This phenomenon is highlighted under different setups and is explained using the analysis of participation factors. [ABSTRACT FROM AUTHOR]

The future multi‐terminal direct‐current (MTDC) grid will require the interconnection of point‐to‐point high‐voltage (HV) DC links with different specifications such as DC voltage level, system grounding configuration and HVDC technology. To adapt these differences, it is obligatory for DC/DC converters to interconnect HVDC links. Additionally, they are capable of providing supplementary functionalities as they are highly controllable devices. In this article, a primary virtual resistance DC voltage controller associated with DC/DC converter is proposed for managing DC grid voltages of the interconnected HVDC grids, increasing the reliability of the system. The commonly known topology, Front‐to‐Front Modular Multilevel Converter (F2F‐MMC) is adopted for DC/DC converter. Time‐domain simulations are performed using EMTP software for validating the controller behaviour under power disturbances and large events of loss of one converter in a MMC‐based MTDC system. The converters are modelled using reduced order modelling (ROM) methodology. Apart from this, dynamic studies have been carried out using a linear state space model for small‐signal stability analysis of a HVDC system integrating DC/DC converter with a virtual resistance DC voltage controller. The results are examined through parametric sensitivity analysis. [ABSTRACT FROM AUTHOR]

Running power systems near their maximum operating limits can cause instability if a disturbance occurs. Conventional procedures can dampen the system's oscillations, such as speed and voltage regulation of generator units, or by using a flexible AC transmission system (FACTS) device. These devices are extensively utilized in power systems. In this paper, the transient stability of the power system has been enhanced by the proposed static var compensator (SVC) based on a fuzzy logic controller (FLC) compared with the conventional SVC based on a Proportional Integral (PI) controller. Six distinct categories of failures were examined. The proposed FLC responses were compared with the conventional PI controller regarding the difference in rotor angle between machines, the transmission line active power, synchronous machines speeds and terminal voltages variation, thyristor switched capacitor (TSC) response, reactive power, and thyristor controller reactor (TCR) response. MATLAB-Simulink evaluated the proposed SVC based on an FLC on a two-machine, 3-bus power system. The simulation result showed the superiority of the SVC based on an FLC over the conventional SVC based on a PI controller. The maximum overshoot improved by 11.94%, and the settling time improved by 9.47%. In addition, it is noted that the proposed FLC contributes to compensating about 16.2% of the system's equivalent kinetic energy. [ABSTRACT FROM AUTHOR]

This paper provides a review of power system stability, focusing on the rotor angle case. To gain a preliminary understanding of the stability studies, the discussion begins with an overview of generators in power system generation. The distinguishing parameters of synchronous generators as compared to their counterparts such as induction generators, inductor alternators, and direct current generators are also emphasized. The discussion that is not bounded within their stability issues and control strategies is briefly assessed. The shortcomings and advantages of various modeling approaches are also discussed therein. To extend the thoughts, this review includes a thorough discussion and classification of power system stability, which includes rotor angle stability, frequency stability, and voltage stability. The stability of the rotor angle is important as it ensures frequency stability and voltage stability. This paper also presents the power system modeling approach that is able to facilitate the rotor angle stability studies. This paper also aims to review the established rotor angle stabilizers and algorithms developed by previous researchers. [ABSTRACT FROM AUTHOR]

The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction. [ABSTRACT FROM AUTHOR]

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies. [ABSTRACT FROM AUTHOR]

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neurocontroller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability. [ABSTRACT FROM AUTHOR]

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The installation of ultra-fast dc charging infrastructures is rapidly increasing worldwide in response to the exponential growing trend of electric vehicle (EV) market. Due to their discontinuous and unpredictable high power absorption, ultra-fast dc chargers pose a challenge for the power system stability. However, their negative impact on the grid operation can be mitigated by making them bidirectional, leveraging the energy stored in EV batteries or in the installed separate storage. Therefore, the power system can exploit this amount of energy to deal with unexpected grid large power imbalances. Moreover, ultra-fast dc chargers can contribute to power system stability by embedding virtual synchronous machine (VSM) algorithms into their ac/dc stage, i.e., the active front-end (AFE) converter unit. The charging station is thus enabled to provide grid services normally in charge of traditional synchronous generators, such as inertial behavior and short circuit current injection during faults to trigger line protections. However, the provision of inertial active power involves a non-negligible reactive power contribution due to the active-reactive power coupling, thus increasing the output current of the converter. Nevertheless, the power coupling also affects the grid support during faults. Indeed, when the AFE injects a short circuit current into the grid, a fluctuating active power can propagate from the grid to the EVs, resulting in a potential cause of degradation for the EV batteries. Therefore, this article proposes a feedforward-based decoupling solution to guarantee the complete active–reactive power dynamic decoupling while the AFE of an ultra-fast dc charger is providing grid support. Moreover, the proposed method ensures a full-decoupled dynamic response also in case of power references variation during the normal EV charging operation. The proposed decoupling algorithm is experimentally validated on a down-scaled 15 kVA two-level three-phase inverter, emulating the AFE of the ultra-fast dc charger.

In the current era, power system stability faces typical problems due to the Renewable Energy Sources (RES) integration trend. This trend makes the coordination between power system controllers crucial to maintain stability across a wide-range of operating behaviors. To address this problem, this paper proposes the coordinated design of Power System Stabilizer and Virtual Inertia Control (PSS-VIC) to improve the stability of the power system integrated with RES. The proposed method uses the modified version of Harris Hawk Optimization with Memory Saving Strategy (HHO-MSS) to find the equilibrium point of global parameters of PSS-VIC through various simulations to ensure scalability. In this proposed method, PSS is focused on increasing the power system stability from the traditional generator sides with diesel engines, thermal, and hydro turbines. Meanwhile, the modified VIC design is proposed to increase the power system stability from the RES sides using virtual inertia emulation with the integration of wind generators, solar photovoltaic units, and energy storage systems. The global parameters of PSS-VIC are determined by calculating the optimal damping ratio which is permitted by grid codes alongside various stability criteria validation. Based on the obtained results, HHO-MSS is 1.44% to 9.28% more accurate and 34.63% to 53.94% more consistent than Electric Eel Foraging Optimization (EEFO), and Puma Optimizer (PO), Evolutionary Mating Algorithm (EMA). With the optimal damping ratios of 9.94% to 9.96% achieved by HHO-MSS, the overall power system stability improvements, including both local and interarea responses across 38 simulations involving sudden load changes, varying inertia, and different RES levels, are as follows: 41.17% to 70.89% frequency nadir improvement, 25.9% to 67.38% power angle deviation improvement, 84.83% to 85.26% settling time reduction, and 51.57% to 89.73% average error reduction calculated with performance indices. The proposed coordinated PSS-VIC design offers excellent scalability and can effectively improve power system stability across a wide-range of operating conditions.

Abstract Enhancing the stability of the Virtual Synchronous Generator (VSG) under transient conditions has become a new challenge for VSG operation. This paper presents the design of a Virtual Power System Stabiliser (VPSS) with virtual inertia calculations for the stability enhancement of the VSG system under transient conditions. The virtual inertia is calculated by considering the transient conditions resulting from a three‐phase ground fault and the allowable phase margin in the VSG. This aims to prevent the coupling effect, which can cause the active power loop control and reactive power loop control to operate non‐independently. Subsequently, the VPSS is specifically designed based on the determined virtual inertia characteristics. The VPSS design is developed by taking into account the phase angle shift of the VSG. The proposed combination of virtual inertia and VPSS is capable of providing accurate compensation for phase angle changes under transient conditions. To evaluate the performance of the proposed virtual inertia and VPSS, a system‐level VSG model is used to thoroughly analyse the system's performance. Based on the results and analysis, it is shown that the control strategy utilising the combination of virtual inertia and the proposed VPSS design can improve VSG stability under transient conditions.

Abstract To keep pace with the construction of the new‐type power system, virtual synchronous generator control, as a classical method of virtual inertia control, has been widely adopted due to its electromechanical characteristics similar to synchronous generator. However, the introduction of rotor motion equations leads to low‐frequency oscillation issues in virtual synchronous generator units similar to synchronous machines. To address this challenge, this paper constructs the Phillips‐Heffron model of the virtual synchronous generator grid‐connected system and analyses the mechanism of low‐frequency oscillation in virtual synchronous generator through the damping torque method. Subsequently, a virtual dual‐input power system stabilizer is proposed by drawing inspiration from the design principles of the traditional dual‐input power system stabilizer to suppress low‐frequency oscillations in the power system. The structure of the virtual dual‐input power system stabilizer is provided, and the phase compensation method is used to optimize the parameters of the virtual dual‐input power system stabilizer. Finally, the effectiveness of the proposed virtual dual‐input power system stabilizer is verified by simulation comparison.

Abstract Network operators with significant solar photovoltaic (PV) penetration are having difficulty maintaining grid frequency and voltage within acceptable bounds due to the progressive displacement of synchronous machines. Utility‐scale solar PV plants have a huge potential for participation in frequency and voltage regulation since they are linked to the grid through power electronic interfaces with flexible, decoupled control of active and reactive power. A comprehensive control strategy for a utility‐scale solar PV plant is proposed to simultaneously participate in frequency and voltage control without the aid of any energy storage. The frequency response is accomplished by maintaining some active power reserves that enable the PV plant to participate in both over‐ and under‐frequency events. The active power of the PV plant is modulated by operating the PV as a virtual synchronous generator (VSG). Unlike the classic notion of VSG, an intelligent fuzzy‐based technique is employed to adapt the gains of the VSG controller for improved control performance. Additionally, an adaptive droop‐based voltage control mechanism is proposed to control the reactive power reference for the PV plant. The gain of the droop controller is adapted to the varying maximum reactive power capacity of the PV plant. This ensures that the PV system's unused reactive power capability is fully utilised. Through simulation studies, the efficiency of the proposed frequency and voltage control mechanisms is validated under a range of realistic circumstances. The findings show that the suggested control approach can efficiently leverage the PV plants' capacity to handle both frequency and voltage events.

Abstract Contemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (>80%) and dynamic load (>75%), as well as encountering a large disturbance under uncertain circumstances.

Large capacity traditional resources based on fossil fuels are used to meet the ever-increasing energy demand in the world. Over-reliance on fossil fuels, combined with the resulting greenhouse gas emissions, forces power system engineers to consider alternative solutions. Distributed power systems established with renewable energy sources such as wind and solar, along with developing technology, are the prominent solution. Although they are seen as a solution, the intermittent and variable nature of these sources can have a significant impact on system stability, which has an important place in power system operation. In this study, power system stability analysis was performed to include uncertainty in wind, solar and load units. Various scenarios were created over the IEEE 14-bar power system and the stability responses of the system were examined. [ABSTRACT FROM AUTHOR]

Abstract To improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG.

Abstract This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.

The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.

Abstract Obtaining inverter controller information may be a premise for seeking its dynamic behaviour. But accurate knowledge of such information would be unrealistic for real functioning inverter‐interfaced generators (IIGs), which hinders the stability analysis of the IIG. A new data‐driven impedance identification method is proposed for stability analysis, which involves an improved sparse identification algorithm as an ancillary function within the system identification framework. It contains mainly two design stages. First, the transform basis matrix (TBM) is devised systematically as a prior knowledge library to contain the possibly existing control structures. In the second stage, a sparse identification algorithm is reformulated in order to extract the relevant structures in TBM while obtaining controller parameters. The authors demonstrate that the sparse vector between the TBM and output signal is closely related to the controller structure. The effectiveness of the proposed method is verified on grid‐connected inverters based on droop control and virtual synchronous machine control.

Abstract Large‐scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time‐domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed‐integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China.

Abstract The ultra‐low frequency oscillation (ULFO) imposes an emerging stability challenge to the high proportion of hydropower grids. To suppress ULFO without reducing the primary frequency regulation of the hydro governor, a novel idea to exploit the voltage regulation effect of load is implemented. First, the additional damping controller is designed and configured in static var compensator (SVC), and the frequency analysis model considering the configuration of SVC as well as damping controller is established. Based on the model, the specific influence of SVC and controller on ULFO is analysed through eigenvalue calculation. In addition, the influence of various load models and SVC location on the damping level are further studied. Consequently, a parameter optimization design method for SVC additional damping control is proposed, it is modelled as the optimization problem of damping ratio in ULFO mode under multi‐operation conditions, which is solved by a particle swarm optimization algorithm. Finally, the effectiveness of the designed SVC additional damping controller is verified in the improved four‐machine two‐area power system and the actual power grid in China.

Abstract Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.