Power System Stability Control Research Articles

Optimizing fuzzy power system stabilizers for microgrid frequency stability using improved water cycle algorithm

Abstract This research assignment has incorporated electric vehicles and robust Fuzzy Power System Stabilizer (Fuzzy-PSS) controller for improving stability behavior of an AC microgrid. Load dynamics, wind fluctuation , variations in solar intensity, etc, have a major impact on the performance of an islanded AC microgrid. The anticipated Fuzzy-PSS controller acts as the secondary load frequency control loop of the microgrid and mitigates the frequency oscillation problems in the microgrid. The suggested Fuzzy Power System stabilizer (Fuzzy-PSS) technique is the best bet for addressing system frequency stability in different disturbances. Under various circumstances, the Improved Water Cycle Algorithm (I-WCA) is recommended for tuning the controller gains through a suitable objective function. Regarding microgrid’s frequency regulation, the effectiveness of the ideal Fuzzy PSS controller is contrasted with that of the conventional fuzzy controller and PID controller. The suggested Fuzzy-PSS approach strongly outperforms the standard Fuzzy PID controller in regard to stability improvement of the EV injected AC microgrid. The suggested Fuzzy-PSS technique has the ability to settle ∆F 2 (area 2 microgrid frequency) by 150.56% and 216.72%, respectively, as compared to the standard Fuzzy PID and PID approaches. To demonstrate the superiority of the suggested I-WCA over standard PSO approach, its dynamic performance is examined from an optimization point of view.

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

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 neuro-controller 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.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough set (NRS) is first utilized to simplify the redundant parameters of the controller to improve efficiency. Then, the methods of the Sobol sequence initialization population, nonlinear convergence factor, adaptive weight strategy, and random differential mutation strategy are introduced to improve the traditional whale optimization algorithm (WOA) algorithm. Finally, the improved whale optimization algorithm (IWOA) is utilized to optimize the remaining controller parameters. The simulation results show that the optimization parameters were reduced from 12 and 18 to 3 and 4 in the single-machine infinity bus system and dual-machine power system, and the optimization time was reduced by 74.5% and 42.8%, respectively. In addition, the proposed NRS-IWOA method exhibits more significant advantages in optimizing parameters and improving stability than other algorithms.

Coordination of Controllers to Development of Wide-Area Control System for Damping Low-Frequency Oscillations Incorporating Large Renewable and Communication Delay

The modern power systems incorporate high penetration of renewable is a large, composite, interconnected network with dynamic behavior. The small disturbances occurring in the system may induce low-frequency oscillations (LFOs) in the system. If the (LFOs) are not suppressed within a stipulated time, it may cause system islanding or even blackouts. Hence, it is essential to investigate the behavior of the system under various levels of disturbances and control action must be taken to damp these oscillations. The established approach to damping the LFOs is by installing power system stabilizers (PSS). PSS uses the local signals from generators to control the oscillations. The dominant source of inter-area oscillations in power systems is due to overloaded weak interconnected lines, converter-interfaced generation, and the action of the high gain exciter present in the system. Consequently, wide area control is needed to control the inter-area oscillations existent in the system. This paper developed a coordinated design of conventional PSS, static compensator, renewable converters, and wide area controller for damping the local and inter-area oscillations in renewable incorporated power systems. The performance of the developed controller is evaluated through the time domain analysis and eigenvalue analysis. A comparison of the introduced controller has been done with other standard conventional methods. The choice of input signals for the wide area controller from the wide-area measurement system is done based on the controllability index. Additionally, the location of the controller must be identified to dampen the inter-area oscillations in the system. In this paper, the controllability index is calculated to find out the highly affected wide area signals for considering it as the feedback signal to a developed controller. The location of the controller is recognized by computing the participation factor. The developed controller has experimented on renewable incorporated large study power systems when time delay and noise are present in wide area signals.

Impact and Integration of Electric vehicles on renewable energy based Microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach

The modelling of an electric vehicle along with its integration and impact over a renewable energy based microgrid topology is well addressed in this manuscript. The frequent charging and discharging of the electric vehicle makes an oscillation over grid frequency. The performance especially frequency of an islanded AC microgrid is also affected seriously under the actions of different uncertainties like load dynamics, wind fluctuation in wind plant, solar intensity variation of PV plant etc. In order to maintain standard frequency, this research work aims to regulate the net power generation of the system in response to total demand. To monitor net generation, this work has intended a fractional order fuzzy power system stabilizer (FO-Fuzzy PSS) control scheme in several dynamic situations. The proposed FO-Fuzzy PSS control scheme acts as most potential candidate to pertain stability in system frequency in above discussed disturbances. The controller gains are tuned optimally with suggesting an advanced- Sine Cosine Algorithm (a-SCA) under different conditions. The performance of the optimal FO- Fuzzy PSS controller is compared over standard fuzzy controller and PID controller in regard to frequency regulation of microgrid system. It is observed that proposed FO-Fuzzy PSS control scheme has the credential to reduce settling time of ΔF1 (area1 microgrid frequency) by 98.60% and 250.82% over fuzzy controller & PID controller correspondingly. Further, the dynamic optimal performance of the proposed a-SCA is compared over original SCA and PSO techniques to justify its superiority.

Carbon emission reduction capability assessment based on synergistic optimization control of electric vehicle V2G and multiple types power supply

With the increasing proliferation of electric vehicles (EVs) and the integration of renewable energy sources into the power system, significant challenges have arisen for power system load balancing and stability control. The carbon emission reduction capability of power systems through coordinated optimization control of electric vehicles and power sources has garnered considerable attention. This paper evaluates the impact of Vehicle-to-Grid (V2G) and power source coordinated optimization control on the carbon emission reduction capability of power systems, proposing a technique that takes into account the dynamic characteristics of electric vehicles, power sources, and carbon emissions. This approach integrates V2G technology, renewable energy generation, and load demand into a framework aimed at optimizing power system operations and minimizing carbon emissions. A case study using real load data from a city assesses the impact of EV V2G and power source coordinated optimization control on carbon reduction. The results indicate that by 2025, peak-valley regulation will reach 1211,000 MW, with a carbon reduction of 138,783.799 tons, corresponding to a total reduction ratio of 1.101%. This research can provide valuable insights for policymakers, power system operators, and researchers.

A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system

A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression

Focusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency stability control of power systems. First, a random forest model is constructed to analyze the key features and select the indexes significantly related to the generation capacity to form the input feature set. Second, by establishing an iterative construction model of the polynomial fitting surface, data are maximized by the upper envelope surface, and an effective sample set is constructed. Furthermore, a new energy maximum generation capacity assessment model adopts the support vector machine regression algorithm under the whale optimization algorithm to derive the correspondence between the input features and maximum generation capacity of new energy sources. Finally, we validate the applicability and effectiveness of the new maximum energy generation capacity evaluation model based on the results of an actual wind farm.

Improving Interarea Mode Oscillation Damping in Multi-Machine Energy Systems through a Coordinated PSS and FACTS Controller Framework

Power system stability is of paramount importance in the context of energy sustainability. The reliable and efficient operation of power systems is crucial for supporting modern societies, economies, and the growing demand for electricity while minimizing environmental impact and increasing sustainability. Due to the insufficient effect of power system stabilizers (PSSs) on damping the inter-area mode oscillations, Flexible AC Transmission System (FACTS) devices are utilized for damping this mode and stabilizing power systems. In the present study, a novel optimization framework considering different and variable weight coefficients based on eigenvalue locations is presented, and the parameters of PSS and variable impedance devices, including static Volt-Ampere Reactive (VAR) compensator (SVC) and Thyristor-Controlled Series Compensator (TCSC) (comprising amplifying gain factor and time constants of phase-compensating blocks), are optimized in a coordinated manner using the proposed optimization framework built based on genetic algorithm (GA). Moreover, in the suggested optimization framework, the locations of FACTS devices and control signals are considered optimization parameters. Numerical results for the IEEE 69-bus power system demonstrated an effective improvement in the damping of inter-area modes utilizing the offered approach.

Performance Evaluation of Optimally Placed SVC for Dynamic Stability of the Power System

Given the complexity of power systems, particularly in the deregulated power industry found in Nigeria, a consistent, safe, controllable, and high-quality power supply is required. The loss of the system's overall damping torque, which reduces the system's susceptibility to fluctuations and problems with dynamic stability, is one of the key downsides of network expansion. Flexible AC transmission systems (FACTS) controllers have been used to overcome problems with power system stability control. In order to improve dynamic stability, this study investigates how the generator's rotor angle, speed, voltage magnitude profile, and real power affect how well SVC operates under the effect of a three-phase fault. With a fault introduced on Bus 33 (Geregu Substation) and SVC placed optimally on Bus 21 (Jos Transmission Station) using voltage stability sensitivity factor (VSSF) after the simulation of continuation power flow (CPF), the 48-bus power system network in Nigeria was modeled using commercial PSAT software in a MATLAB environment. The power system's oscillation was significantly reduced, and the voltage profile was enhanced for power system dynamic stability, per simulation results with and without SVC.

“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.

Enhancement of Power Systems Transient Stability with TCSC: A Case study of The Nigerian 330 kV, 48-Bus Network

A steady, safe, controllable, and high-quality power supply is necessary given the complexity of power systems, especially in the deregulated power sector seen in Nigeria. One of the main drawbacks of network expansion is the loss of the system's overall damping torque, which mitigates the system resulting to fluctuations and transient instability. Power system stability control difficulties have been addressed using flexible AC transmission systems (FACTS) controllers. This study examines the responses of the generator rotor angle, speed, Q-axis and D-axis voltage components behind transient reactance as well as the voltage magnitude profile under the influence of a three-phase fault for the purpose of enhancing transient stability. The Nigerian 48-bus power system network was modeled using commercial PSAT software in a MATLAB environment with a fault induced on Bus 33 (Geregu SubStation) and TCSC optimally positioned on line 21-28 using continuation power flow (CPF). According to simulation results with and without TCSC, a significant amount of oscillation in the power system was dampened, and the voltage profile was improved for power system transient stability.

Fixed-/Preassigned-Time Stability Control of Chaotic Power Systems

A power system shows chaotic oscillation when it is subjected to periodic load disturbance, which makes it a challenging and interesting problem for stability control of chaotic power system. In this paper, a unified controller is designed to solve the fixed-time and preassigned-time stability problems of fourth-order chaotic power systems. In addition, based on Lyapunov stability theory, sufficient conditions are established for the fixed-time and preassigned-time stability of the underlying chaotic power systems, and a more accurate estimation of the settling time is also derived. Finally, according to numerical simulations, we validate the theoretical results.

Multi-task transient stability assessment of power system based on graph neural network with interpretable attribution analysis

Transient power angle stability analysis and voltage stability analysis are the key basis of power system security and stability control. The diversified operation modes of power system put forward higher requirements for real-time and topology generalization of transient stability assessment. Based on Convolution Neural Network (CNN) and Graph Attention Networks (GAT), this paper uses the wide-area transient response features to evaluate the transient angle stability and voltage stability of power system. Firstly, CNN is used to encode the temporal variation characteristics of transient features of electrical components in hidden layer space. Secondly, the encoded hidden layer features are mapped into graph data samples according to the spatial topological correlation of electrical components, so as to train and build a multi-task transient stability assessment model based on GAT. The model can adaptively capture the interaction relationship between encoded hidden layer features of topologically correlated components, and realizes real-time evaluation of transient angle stability and transient voltage stability by perceiving the temporal–spatial correlation of transient features. Finally, Shapley additive explanation (SHAP) method is used to explain the decision-making mechanism of the model and extract the key features that affect the transient stability, so as to enhance the ability of power grid operators to perceive the transient stability situation. The results of IEEE-39 system show that the transient stability assessment model has good accuracy and topology generalization, and the results of interpretable analysis are in line with the conclusions of traditional mechanism cognition, which helps to improve the credibility of the model results.

On the control interaction of synchronous machine and inverter-based resources during system-split situations

Motivated by the potential transient oscillation risks in inverter based resource (IBR) dominated power systems, the paper is to identify the unwanted control interactions between traditional synchronous machines (SMs) and IBRs during system-split situations. Modal impedance analysis method is applied to reveal oscillation risk and the underlying mechanism, while electromagnetic transient (EMT) simulation analysis is used to observe reliable oscillation phenomenon. Impedance models of IBRs considering both grid-following (GFL) and grid-forming (GFM) control schemes and SMs equipped with automatic voltage regulator (AVR) and power system stabilizer (PSS) are developed for grid conditions with large frequency and/or voltage deviations, and validated by EMT simulations. Potential adverse control interactions within an exemplary test system are analyzed taking into account the impacts of: 1) renewable penetration level; 2) converter control scheme (GFL or GFM); 3) converter control tuning; 4) SM control setting; 5) grid-support capabilities of converter-interfaced loads. It has been observed that oscillation instability at around several Hz can occur by high penetration of GFL controlled IBRs after system-splits, which can be mitigated by redesigning the AVR & PSS control, or retuning the power/voltage control loops and phase-locked loop (PLL) of GFL converter. However, such measures might inevitably induce other stability issues, e.g. synchronization instability. From the oscillation analysis, it has also been revealed, through replacing part of the GFL converters with GFM converter(s), the oscillation mode at several Hz can disappear, owning to the varied impedance including better damping of GFM than GFL for the frequencies between 1 and 10 Hz.

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.

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller

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.

A New Self-Tuning Deep Neuro-Sliding Mode Control for Multi-Machine Power System Stabilizer

In order to increase the accuracy and improve the performance of the power system stabilizer (PSS) controller compared to the methods presented in other studies, this paper presents a new method for tuning sliding mode control (SMC) parameters for a PSS using a deep neural network. This controller requires fast switching which can create unwanted signals. To solve this problem, a boundary layer is used. First, the equations of a multi-machine power system are converted into the standard form of sliding mode control, and then the sliding surfaces are determined with three unknown parameters. Calculating and determining the optimal values (at any moment) for these parameters are fundamental challenges. A deep neural network can overcome this challenge and adjust the control system regularly. In the simulation, a power system with 4 machines and 11 buses is implemented and both phase-to-ground and three-phase errors are applied. The simulation results clearly show the good performance of the proposed method and especially the importance of the deep neural network in the SMC structure compared to other methods.