Power System Stability Enhancement Research Articles

Impact of grading capacitor on transient recovery voltage due to shunt reactor de-energization for different values of current chopping

This paper investigates the impact of grading capacitors on transient recovery voltage (TRV) during shunt reactor switching in high voltage systems, considering different levels of current chopping. Shunt reactor de-energization can result in voltage surges and instability due to current chopping effects. The study utilizes simulation models using ATP-Draw software to assess the effectiveness of grading capacitors in mitigating TRV under various operating conditions as well as of using a proposed method to mitigate the excessive TRV across the circuit breaker. The findings provide valuable insights into managing TRV during shunt reactor switching, enhancing power system stability and reliability. The results obtained showed that the TRV across the circuit breaker decreased by 61.5% by using circuit modification as well as adding a grading capacitor.

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.

OPTIMAL LOCATION OF AN IDEAL TRANSFORMER UPFC MODEL USING OPF FIRST-ORDER SENSITIVITIES

This paper presents a screening technique for greatly reducing the computation involved in determining the optimal location of a unified power flow controller (UPFC) in a power system. The first-order sensitivities of the generation cost with respect to UPFC control parameters are derived. This technique requires running only one optimal power flow (OPF) to obtain UPFC sensitivities for all possible transmission lines. To implement a sensitivity-based screening technique for guidance in optimally locating a single UPFC in a power system, we propose a new UPFC model, wrach consists of an ideal transformer with a complex turns ratio and a variable shunt admittance. In this model, the UPFC control variables do not depend explicitly on UPFC input and output currents and voltages. Accordingly, this model does not require adding extra buses for UPFC input and output terminals. IEEE five-, 14- and 30-bus systems were used to frustrate the technique. Index Terms-Flexible ac transmission systems (FACTS), FACTS location, first-order sensitivity, optimal power flow (OPF), screening technique, unified power flow controller (UPFC), UPFC ideal transformer model, UPFC placement, UPFC uncoupled model. This study focuses on the development of an Ideal Transformer Unified Power Flow Controller (UPFC) model and its integration with Optimal Power Flow (OPF) first-order sensitivities analysis for determining optimal UPFC locations in power systems. The abstract outlines the significance of UPFCs in enhancing power system stability and efficiency by controlling voltage and power flow. It highlights the objectives of the research, including the development of the UPFC model, analysis of OPF first-order sensitivities, and their combined application for screening optimal UPFC locations. The study aims to contribute to the improvement of power system operation and control through strategic UPFC placement. The abstract provides a concise overview of the study, summarizing the key aspects of the research. It outlines the development of an ideal transformer Unified Power Flow Controller (UPFC) model, the analysis of Optimal Power Flow (OPF) first-order sensitivities, and their application in determining optimal locations for UPFC installation in power systems

Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi‐machine systems

AbstractThis paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. Power System Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead‐lag PSSs. In response to this challenge, the paper introduces a Tilt‐Integral‐Derivative (TID)‐based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm Optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID‐PSSs to uphold the stability of multi‐machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID‐PSS within the Western System Coordinating Council (WSCC)‐3‐machines 9‐bus test system. In summary, the proposed TID‐PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability.

Optimisation Methods Based on Soft Computing for Improving Power System Stability

In order to keep electrical grids running reliably and efficiently, power system stability is essential. The optimisation of power systems is fraught with complexity and uncertainty, yet soft computing approaches have been shown to be an excellent tool for dealing with these issues. In order to improve the reliability of the power system, this article presents a summary of optimisation techniques that rely on soft computing techniques. , the methods of evolutionary computing, such as particle swarm optimisation (PSO) and genetic algorithms (GA), are discussed. By efficiently searching for optimum solutions in huge solution spaces, these algorithms are used to optimise power system parameters and control techniques. In general, optimisation techniques based on soft computing provide effective and flexible ways to enhance power system stability. These technologies improve grid operation by using swarm intelligence, evolutionary computing, fuzzy logic, and neural networks to tackle the issues of power systems, which are dynamic and unpredictable.

A new virtual consensus‐based wide area differential protection

AbstractThis paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates.

Cooperative control of the DC-link voltage in VSC-MTDC grid via virtual synchronous generators

Introduction: Virtual Synchronous Generators (VSGs) are used in Voltage Source Converter-based Multi-Terminal High-Voltage Direct Current (VSC-MTDC) systems to enhance power system stability. However, the MTDC framework can lead to instability due to reduced inertia in certain grid areas, especially during load switching at VSC stations. This instability is exacerbated by untimely adjustments of the VSG's power setpoint, leading to voltage and frequency oscillations.Methods: This study introduces a cooperative control approach for the DC voltage of the VSG, employing a consensus algorithm and Model Predictive Control (MPC). This method aims to achieve incremental power for the VSG and provide interactive power commands for both the grid side and the wind farm side. The consensus algorithm ensures coherent system adjustments, while the MPC algorithm tracks DC-side voltage changes in real time.Results: The application of this cooperative control approach significantly enhances DC voltage regulation performance. It effectively reduces the extent of frequency drops and mitigates secondary frequency drop (SFD) issues, particularly those arising from the use of wind farms for frequency regulation and the associated speed recovery in wind turbine units.Discussion: The increase in supplemental power effectively utilizes the energy stored in DC-side capacitors for power balance regulation and introduces additional inertial power into the system. Electromagnetic transient simulations have confirmed the effectiveness of the Consensus MPC-VSG method, demonstrating its ability to optimize the dynamic performance of VSC-MTDC systems and promote stability in DC voltage and frequency.Conclusion: The findings suggest that employing the Consensus MPC-VSG method offers a promising solution for enhancing the stability and operational efficiency of VSC-MTDC systems, addressing the challenges posed by the inherent segmentation of the grid and the integration of renewable energy sources like wind farms.

Employing advanced control, energy storage, and renewable technologies to enhance power system stability

As the world witnesses a surge in the adoption of renewable energy sources to meet the surging global power demands, the dynamic and intermittent nature of these sources emerges as a significant hurdle. This article extensively explores the potential of advanced control systems, energy storage technologies, and renewable resources to fortify stability within power systems. Advanced control methodologies are strategically amalgamated with energy storage deployment and the utilization of renewable energy, to advance the reliability, predictability, and sustainability of power systems. The stability analysis, with a dedicated focus on Input-to-input-to-state stability (ISS), is conducted meticulously by applying the Lyapunov function and the Reciprocally Convex Approach, resulting in an impressive stability rate of 23.6%. Additionally, Positive Realness is substantiated by extracting Linear Matrix Inequalities (LMI) in the context of Enhancing Grid Stability with Wind Power. The study places particular emphasis on evaluating ISS and Passivity in both delayed and non-delayed systems, with a specific focus on neutral time-delay systems. This evaluation involves the selection of an appropriate Lyapunov-Krasovskii Functional (LKF) and its derivation, coupled with the integration of reciprocally convex methods, descriptive approach, and Jensen inequality. The outcomes of these analyses shed light on the causes of excess energy and its effective storage, along with highlighting the synergistic impact of integrating renewable sources and controlling grid frequency, voltage, and power in real time. The study also accentuates the robustness achieved through Enhanced Stability and the mitigation of Reduced Fluctuations, especially in the context of renewable energy sources.

Voltage stability improvement and power losses reduction through multiple grid contingency supports

A stable power system is a desirable state for the optimal performance and delivery of electricity to the end-users. This study evaluates the effectiveness of grid contingency support devices in power system stability enhancement. The new voltage stability pointer (NVSP) was employed to optimally infer the flexible alternating current transmission systems devices through N − 1 contingency ranking. In this research, the static synchronous compensator (STATCOM) was considered as a grid support device. The effectiveness of this technique was validated in R2018a MATLAB with the IEEE 30-bus system using different combinable contingency cases to ascertain the critical lines and vulnerable buses to outages. Based on the results obtained through the NVSP, buses 30, 26, and 29 were cited as STATCOM candidate buses for overall optimal grid performance. The results obtained from the simulation revealed that the total real power loss was reduced by 90.21%. Similarly, the voltage magnitude of the buses was significantly improved to a minimum of 1.00 p.u. after placement of STATCOMs. Further investigation on single STATCOM placement in the IEEE 30-bus system revealed that the highest level of voltage improvement, minimum level of voltage deviation and maximum active power loss reduction was achieved when the STATCOM was placed at bus 30. The overall assessment of the proposed technique yielded better results compared with other methods on the same test system.

Stability Study of New Power System Based On Multi-Intelligent Body Collaboration

Developing, implementing, and maintaining a multi-intelligent body collaboration system necessitates significant investments in finances, time, and expertise. While multi-intelligent body collaboration has the potential to enhance power system stability significantly, it also comes with challenges related to interoperability, security, system complexity, and resource allocation. Resource allocation and training costs can be substantial. Addressing these challenges is crucial to harnessing the full benefits of this approach and ensuring the reliable and efficient operation of power systems. Effective communication and coordination strategies among intelligent agents are integral to maintaining power system stability. Timely information exchange, load balancing, disturbance management, and the integration of AI contribute to a more resilient and adaptive energy grid. As technology advances, refining these strategies will be essential to meet the growing demands of an ever-evolving power landscape. As technology marches forward, it becomes increasingly evident that the refinement of these strategies is paramount. The dynamism of the power landscape, driven by technological advancements and evolving needs, necessitates an agile and adaptable power system. The fusion of multi-intelligent bodies and modern technology stands as a testament to our collective pursuit of a more reliable, efficient, and sustainable energy future. In this ever-evolving landscape, the innovation and enhancement of these strategies are our compass, guiding us toward a brighter and more efficient future.

Load frequency control for enhanced power system stability and reliability using hybrid RSA–HBA technique

Load frequency control for enhanced power system stability and reliability using hybrid RSA–HBA technique

Voltage-Adaptive Strategy for Transient Stability Enhancement of Power Systems With 100% Renewable Energy

Voltage-Adaptive Strategy for Transient Stability Enhancement of Power Systems With 100% Renewable Energy

Coordinated Robust Damping Control for Hybrid SVC/SSSC to Enhance Power System Stability in Large-Scale Systems

Coordinated Robust Damping Control for Hybrid SVC/SSSC to Enhance Power System Stability in Large-Scale Systems

Coordination of power system stabilizers using scenario based optimization for enhancement of small‐signal stability considering uncertainties

AbstractThis paper proposes a method based on scenario optimization and meta‐heuristic algorithm to optimize the power system stabilizers (PSSs) for enhancement of small‐signal stability (SSS) considering power system uncertainties such as photovoltaic generation, wind turbine generation, and load. The proposed method consists of two stages. In the first stage, the uncertainties are modeled using probability functions and scenarios are created based on them. These scenarios are then used to obtain a joint probability where the most likely probabilities are chosen for the second stage. The second stage utilizes these critical scenarios to formulate an optimization problem. The objective function of the optimization problem is to enhance system's small signal stability margin with PSS parameters as constraints. The proposed method is then solved using a new meta‐heuristic optimization algorithm known as Jelly Search Optimizer to obtain the optimized parameter of PSSs. The proposed method is tested on a two‐area and five‐area system, and the results show that the system SSS margin is improved for the majority of uncertainty situations. Moreover, the proposed method is around 1.6 times faster compared to the conventional probability method when tested in the modified two‐area system while 5.7 times faster when tested in the modified five‐area system.

“Enhancing power system stability: an innovative approach using coordination of FOPID controller for PSS and SVC FACTS device with MFO algorithm”

This paper investigates an optimal methodology for mitigating low-frequency oscillation concerns in power systems. The study explores the synergistic integration of a power system stabilizer (PSS) and a flexible alternating current transmission system (FACTS) to formulate an intelligent controller. A comprehensive analysis encompasses various PSS design strategies, including lead-lag (LL), proportional-derivative-integral (PID), and fractional-order proportional-integral-derivative (FOPID) controllers. The FACTS device selected for this investigation is a static VAR compensator (SVC), highlighting the exceptional efficacy of FOPID-based PSS over alternative strategies with a power oscillation damper. The study extends its scope to encompass a comparative assessment of two distinct optimization algorithms: the moth flame optimization (MFO) and the antlion optimization (ALO). The research is conducted using a single-machine infinite bus power system (SMIB) as the case study platform. A total of four diverse test scenarios are executed under varying operating conditions. The evaluation of the developed method employs six distinct performance indices to investigate the developed controller thoroughly. The outcomes reveal that the MFO-optimized FOPID-PSS and SVC controller outperforms other control schemes. This optimized configuration demonstrates substantial improvements across all performance indices. These findings underscore the superior capabilities of the proposed approach in enhancing power system stability and performance.

Simulation of Voltage Stability Enhancement at Primary Substation Using Static VAR Compensator

For the voltage stability enhancement of power system, the reactive power compensation is vital important. With the advancement in technology, the Flexible AC Transmission System Devices (FACTS) are widely used for stability improvement. In the primary substation, the stability is poor due to load conditions and system faults. In this research, the voltage stability enhancement for a Primary Substation using Static VAR (volt-amp reactive) compensator (SVC) is presented. SVC can be used to improve system voltage stability and addition of damping control. With the application of SVC, the voltage stability can be improved under normal condition as well as under contingency conditions. Shunt reactive power sources, including capacitive and inductive, are controlled by high power electronic switching devices in an SVC to enhance voltage stability. The goal of this attempt is to use SVC to improve the voltage stability of the 230/33 kV system. To evaluate the voltage stability improvement by SVC, a simulation model is implemented in Matlab/Simulink and performance analysis are carried out based on the simulation results.

Unified Power Flow Controller: A Brief Review on Tuning and Allocation for Power System Stability

The Power System can become unstable due to disturbances. To enhance system stability the Unified Power Flow Controller (UPFC) is tuned and allocated in the System. In this paper, a brief review of UPFC tuning and allocation studies for power systems stability is presented. The databases consulted for literature are the IEEE Xplore, ScienceDirect, Google Scholar and IOP Publications. The search terms used are Allocation, Tuning, UPFC, Power System and Stability to find the literature used in this review. A total of 26 Journal articles and conference papers were found and reviewed based on tuning and allocation studies. The Researchers applied Fuzzy coordination, Genetic Algorithm (GA), Particles Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Linear Quadratic Tracker (LQT) to tune the UPFC for enhancing power system stability. For studies on UPFC allocation in power systems, the Researchers applied frequency response of power system transfer function, power flow, Tabu Search (TS), PSO and GA. For allocation based on optimization, the Researchers minimized power losses, voltage index and investment costs considering equality and inequality constraints.

A non-PMU-based WAN protection scheme for swing detection and stability enhancement in power systems

This paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action.

Power System Stability Improvement Based on Virus Particle Swarm Optimization Algorithm

This article discusses the development of a Power System Stabilizer (PSS) utilizing the Virus Particle Swarm Optimization Algorithm (VEPSO) to enhance power system stability. PSS is commonly used in the industry to suppress power system oscillations. The proposed algorithm was implemented on a Single Machine Infinite Bus (SMIB) system, and the PSS controller coefficients were optimized using VEPSO. The system was simulated under a specific disturbance in the generator input power, and the generator's dynamic responses were presented. The VEPSO algorithm's results, such as faster convergence, were compared to the ICA algorithm. The simulation results demonstrate that the recommended PSS significantly improves the power system's oscillation damping performance.

Enhancing power system stability with AI-based relaying algorithms – A review

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies that are increasingly being used to improve various aspects of power systems. In particular, AI-based relaying algorithms have the potential to revolutionize the way power systems are protected from faults and failures. Relaying algorithms play a critical role in ensuring the stability and reliability of power systems. However, traditional relay protection algorithms face several challenges, including difficulty handling complex and dynamic systems, limited fault detection accuracy, and slow response times to changing conditions. AI-based relaying algorithms can address these challenges by leveraging the power of Artificial Intelligence and Machine Learning. This paper presents an overview of AI-based relaying algorithms and their potential applications in power systems. It explores the use of AI techniques such as Artificial Neural Networks (ANN), Decision Trees (DT), and expert systems for improving the accuracy and reliability of relay protection. It also discuss the steps involved in AI-based relaying algorithms, including feature extraction, classification, and result output. This paper highlights the importance of further research and development in this field to fully realize the benefits of AI-based relaying algorithms.