Single Machine Infinite Bus System Research Articles

Research on Simulation and Power Regulation Control Strategy of Fengning Variable-Speed Pumped Storage Unit

Abstract Variable-speed pumped storage unit demonstrates rapid power response and flexible adjustment of unit speed, significantly enhancing unit operational efficiency and the power system’s stability. This paper focuses on simulating the control strategy of the Fengning variable-speed pumped storage unit. First, the water pump/turbine and motor models are established based on the relevant parameters of the Fengning variable-speed pumped storage unit. Second, according to the distinct responsibilities of the governor and the excitation system, the control strategies of the unit in the power priority mode and speed priority mode are designed. Finally, we compare and analyze the power response characteristics of a single-machine infinite bus system under various operating conditions and control strategies. We also summarize the applicable scope and existing problems under different operating conditions.

Stability analysis of single-machine infinite bus system with renewable energy sources using sine cosine chimp optimization

Stability analysis of single-machine infinite bus system with renewable energy sources using sine cosine chimp optimization

Innovative Approach to Enhance Stability: Neural Network Control and Aquila Optimization Integration in Single Machine Infinite Bus Systems

This paper highlights the need to improve the stability of single-machine infinite-bus (SMIB) systems, which is crucial for maintaining the dependability, efficiency, and safety of electrical power systems. The changing energy environment, characterized by a growing use of renewable sources and more intricate power networks, is challenging established stability measures. SMIB systems exhibit dynamic behavior, particularly during faults or unexpected load variations, requiring sophisticated real-time stabilization methods to avert power failures and provide a steady energy supply. This paper suggests a complex approach that combines power system stability analysis with a neural network controller enhanced by the Aquila optimization algorithm (AOA) to address the dynamic issues of SMIB systems. The study shows that the AOA-optimized neural network (AOA-NN) controller outperforms in avoiding disruptions and attaining speedy stabilization by exhaustively examining electrical, mechanical, and rotor dynamics. This method improves power system resilience and operational efficiency as demands and technology expand.

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

This study presents an optimized Proportional Integral Derivative Based Power System Stabilizer (PIDPSS) using Jaya Algorithm for angular stability enhancement. Jaya algorithm introduced by Rao, is an optimization technique with few control parameters which is used to minimize the objective function F(K). The modeling and simulation were done using Matlab /Simulink software version R2021b on IEEE 14-Bus system and Single Machine Infinite Bus (SMIB). A three-phase fault was introduced into the network at system runtime of 5s with a fault clearing time of 0.1s. The result of the simulation of the IEEE 14 Bus system showed a 74% and 24% reduction in overshoot time of speed deviation for generators 1 and 2, with settling times of 2.5s and 4s, respectively, in the presence of PIDPSS. The load angle experienced a 14% and 19% reduction in overshoot with settling times of 2s and 2.5s, respectively in the presence of PIDPSS for generators 1 and 2, respectively. The Electrical Power result showed 27% and 6% reduction in overshoot time as well as settling times of 2.5s and 4s, respectively, for generator 1 and 2 in the presence of PIDPSS. The result of the simulation of SMIB system also showed a 25% reduction in overshoot time in relation to deviation speed at a settling time of 4s in the presence of PIDPSS. The Load Angle showed- a 13% decrease in overshoot time at 2s settling time in the presence of PIDPSS. Also, the Electrical Power result highlighted a 15% drop-in overshoot time and settles within 2s. These results affirms that the PIDPSS introduced improved overall system stability.

Transient stability enhancement control strategy based on grid dispatching for multi-paralleled grid forming inverters during LVRT

In this article, the transient synchronization process of multi-paralleled grid forming (GFM) inverters is studied detailly. Firstly, based on the multi-paralleled inverters control structure and complementary cluster center of inertia relative motion (CCCOI-RM), the single machine infinite bus (SMIB) system is proposed. Subsequently, the power angle characteristic (PAC) equation of multi-paralleled inverters for different dispatching strategies with simplified damping coefficient is built. Meanwhile, a grid dispatching strategy considering communication delay is proposed, which avoiding the cascading failures of grid faults. Furthermore, according to the grid dispatching strategy, a cluster control strategy optimization method including critical cluster identification (CCI) method and transient stability prediction analysis method is designed, which is proposed to identify unstable cluster in multi-paralleled inverters. In addition, the synchronization stability criteria for dispatching strategies of multi-paralleled inverters are obtained based on the PAC equation, in which the transient instability phenomenon and mechanism for different fault stages is explained by extended equal area criterion (EEAC) method. Finally, the simulations results validate the effectiveness of the theoretical analysis and proposed method.

MFCDFT and impedance characteristic-based adaptive technique for fault and power swing discrimination

ABSTRACT In today’s fast-changing power systems, it is vital to have a protection system that can distinguish between power swing and fault conditions. With advanced protective devices (Digital Relays), an appropriate protection strategy can be established to identify power swing and fault conditions individually. The proposed algorithm uses the sliding window concept, which requires very little computational data unless any abnormal conditions exist in the power system. Modified Full Cycle Discrete Fourier Transform (MFCDFT) is more effective in the extraction of the fundamental phasors from the original complex signal by effectively removing the harmonics and DC components. An adaptive impedance characteristic-based MFCDFT is utilized for the discrimination of power swing and fault in this work. Several fault scenarios and power swings are generated on the test system concurrently and independently from the relay station at varied distances. PSCAD/EMTDC is utilized on a single-machine infinite bus system to analyze various test conditions. The simulation is utilized in this case to collect data from various test circumstances. After obtaining the data, the MFCDFT algorithm along with adaptive impedance estimation is performed using MATLAB code. After examining various test results, it is discovered that the suggested technique correctly recognizes power swings and various fault scenarios. It can also identify the difference between stable and unstable power swings. According to the results, the proposed technique is proficient at spotting fault and power swing conditions. In addition, the algorithm ensures dependable operation with quick detection and avoids maloperation under a typical swing condition.

Account of additional factors for damping torsional oscillations

Some parameters are influenced by the turbine unit's torsional oscillations. The fundamental comes from damping these oscillations, which are brought on by a departure in the turbine blades' speed from the device's prediction of the steam volume, and attenuation of fluctuations due to the distribution of energy in the turbine's productive components. The usual single-machine infinite bus system is used for the analysis. For various turbine-generator shafts and various generator operating situations, rotating mass mechanical system evaluations for small-signal stability and large disturbance are conducted. It is demonstrated that the shaft's “structural' damping (H) and “steam' damping (Kn) coefficients have a considerable impact on the damping of torsional modes. The goal of this work is to determine the effect of changing the damping factors in the mathematical model of the steam turbine shaft on the system's static stability, as well as the extent to which these variables' limits on damping rotational oscillations on the maximum torsional torques generated in the shaft masses. The mathematical model of the steam turbine shaft with a single machine and transmission line to an infinite bus system was simulated using Dymola software, and the static and dynamic effects of damping factors (H) and (Kn) on system stability were demonstrated. By evaluating the best case for parameters with the least influence on the system's stability, the results were obtained by changing the factors (Kn) from 0.005 to 0.5 and (H) from 0.005 to 0.2 and the extent of its effect on the maximum torque of the steam turbine masses and reducing it by 8.4 %, as well as by reducing the settling time of the system after disturbances occur and reaching to Steady state by about 90 %.

Application of Fractional-Order PID Controller to Improve Stability of a Single-Machine Infinite-Bus System

Application of Fractional-Order PID Controller to Improve Stability of a Single-Machine Infinite-Bus System

Alleviation and control of chaotic oscillations in SMIB power systems using a modified-Whale optimization-based battery-STATCOM

BackgroundChaotic oscillations within the power system give rise to instability. While these oscillations may not have an immediate impact on the synchrony of the machine, they stimulate one of the oscillation modes, ultimately leading to voltage collapse and a loss of synchronism. ObjectiveThis paper introduces a modified Whale Optimization Algorithm (WOA)-based Battery-STATCOM (Static Synchronous Compensator) as a solution to mitigate chaotic oscillations within a Single Machine Infinite Bus (SMIB) system. MethodologyAn adaptable controller is implemented to manage the gate signal within the Battery-STATCOM. The AC-DC currents of this controller are optimally governed by two distinct WOA-tuned Proportional-Integral (PI) controllers. The battery storage unit serves as a robust voltage source, with the intelligent controller maintaining the DC-link voltage at the desired level. Test CasesAdditional disturbances, such as gradual variations in reference voltage and electromagnetic torque, are introduced to exacerbate chaotic oscillations. This is done to assess the controller's real-world performance under adverse conditions. Results and ConclusionUnder zero damping conditions, rotor parameters, including rise time, settling time, peak time, and overshoot, initially remain undefined due to uncontrolled oscillations. However, once the Battery-STATCOM is applied, these parameters are defined and achieve values (in seconds) of 0.90, 6.21, 1.71, and 21.10, respectively. After further optimization through the proposed modified WOA optimizer, the parameters reach values of 0.25, 1.01, 0.89, and 1.78, respectively. These results underscore the effectiveness of the proposed metaheuristic controller in suppressing overall chaotic oscillations within the power system.

Using Particle Swarm Optimization for Power System Stabilizer and energy storage in the SMIB system under load shedding conditions

Generator instability, which manifests as oscillations in frequency and rotor angle, is brought on by sudden disruptions in the power supply. Power System Stabilizer (PSS) and Energy Storage are additional controllers that enhance generator stability. Energy storage types include superconducting magnetic (SMES) and capacitive (CES) storage. If the correct settings are employed, PSS, SMES, and CES coordination can boost system performance. It is necessary to use accurate and effective PSS, SMES, and CES tuning techniques. Artificial intelligence techniques can replace traditional trial-and-error tuning techniques and assist in adjusting controller parameters. According to this study, the PSS, SMES, and CES parameters can be optimized using a method based on particle swarm optimization (PSO). Based on the investigation's findings, PSO executes quick and accurate calculations in the fifth iteration with a fitness function value of 0.007813. The PSO aims to reduce the integral time absolute error (ITAE). With the addition of a load-shedding instance, the case study utilized the Single Machine Infinite Bus (SMIB) technology. The frequency response and rotor angle of the SMIB system are shown via time domain simulation. The analysis's findings demonstrate that the controller combination can offer stability, reducing overshoot oscillations and enabling quick settling times.

Adaptive finite-time anti-disturbance fuzzy control for uncertain time-delay nonlinear systems and its application to SVC

The adaptive finite-time anti-disturbance fuzzy control method for n-dimensional uncertain nonlinear systems with input delay is proposed in this paper. For unknown nonlinear terms of the controlled system, the fuzzy logic systems (FLSs) are utilized to approximate them. To further improve the robustness of the controlled system, the disturbance observer is established to estimate external disturbances, facilitating the designed controller to compensate for the influence of external disturbances. Moreover, the Pade approximation method is used to transform the time-delay system model, which is convenient for controller design. With the aid of the finite-time command filtered backstepping technique, the adaptive finite-time disturbance rejection controller with low computational complexity is designed, which can compensate for the errors caused by the filters and assure that system state variables are stable and bounded in a finite time. In the end, the simulation for the single-machine infinite bus (SMIB) system with static var compensator (SVC) is presented to illustrate the effectiveness of the proposed control approach.

Recurrent neural network based design of fractional order power system stabilizer for effective damping of power oscillations in multimachine system

The complexity of modern power systems has risen due to the growing demand for electricity. To mitigate this problem, the role of the electric utility in supplying safe and reliable electricity has become increasingly crucial. Low-frequency oscillations (LFOs) in a power system are an inevitable aspect. Therefore, the reliability of the system requires appropriate damping to overcome these LFOs. This paper proposes a novel design methodology for a fractional-order power system stabilizer (FO-PSS) to enhance the stability of the power system network. FO-PSS is designed to effectively dampen LFOs in both single-machine infinite bus systems (SMIB) and multi-machine power systems (MMPS). Recurrent neural network (RNN) is used to predict the parameters of proposed PSSs. To evaluate the efficacy of the proposed PSS, various test cases are considered. The results are compared to those obtained from traditional PSSs and optimization based PSSs. The effectiveness of the proposed RNN-based FO-PSS is demonstrated under diverse loading conditions.

Impact of superconducting fault current limiter with delayed recovery on transient rotor angle stability

A fault current limiter (FCL) is an economical option to limit the increased fault current levels, which may also improve the rotor angle stability. Previous studies on this topic mainly focused on the FCLs with quick recovery and concluded that the FCL improves the rotor angle stability. However, some commercially available FCLs have delayed recovery, which may pose different challenges and need to be studied. This paper studies the impact of a superconducting fault current limiter (SCFCL) with delayed recovery on transient rotor angle stability. This paper first develops an analytical understanding of the stability of a system with SCFCL using the equal area criterion. Later, time-domain simulations are employed to demonstrate the impacts of the SCFCL on the rotor angle stability of a single-machine infinite-bus system. The results show that the SCFCL with delayed recovery leads to rotor angle instability in some cases.

Gradient-enhanced physics-informed neural networks for power systems operational support

The application of deep learning methods to speed up the challenging power system problems has recently shown very encouraging results. However, power system dynamics are not snapshot, steady-state operations. These dynamics must be considered to ensure that the optimal solutions provided by these models adhere to practical constraints to avoid frequency fluctuations and grid instabilities. Unfortunately, dynamic system models based on ordinary or partial differential equations are frequently unsuitable for direct application in control or state estimates due to their high computational costs. To address these challenges, this paper introduces a machine learning method to approximate the behavior of power systems dynamics in near real-time. The proposed framework is based on gradient-enhanced physics-informed neural networks (gPINNs) and encodes the underlying physical laws governing power systems. A key characteristic of the proposed gPINN is its ability to train without the need of generating expensive training data. The paper illustrates the potential of the proposed approach in both forward and inverse problems in a single-machine infinite bus system and a three-bus power network for predicting rotor angles and frequency, and uncertain parameters such as inertia and damping to showcase its potential for a range of power systems applications. The model exhibited high accuracy in predicting the variables, achieving a range of 0.533–4.092 and an average L2 relative error improvement of up to 13.30× compared to the PINN model. The computational performance of the proposed gPINN model was compared to a conventional solver, revealing a remarkable speed-up of 31 to 171 times faster in solving differential–algebraic systems of equations in power systems.

Dynamic State Estimation of Synchronous Machines Using Iterated Square-Root Cubature Kalman Filter and Synchrophasor Measurements

Power system dynamic state estimation is the first prerequisite for control and stability prediction under transient conditions. For a stable and reliable power system, it is crucial and helpful to have accurate, precise, and up-to-date information on the states of the synchronous machines- rotor angle and rotor speed deviation. This paper proposes an application of the Iterated Square-root Cubature Kalman filter (ISCKF) to estimate these main states of synchronous generators. The ISCKF method consists of two step modification - one is the square-root step modification of CKF and the next step is the addition of iterative approach to the square-root CKF method. To demonstrate the performance of the proposed approach during a three-phase short circuit fault, the simulation results, are compared with that of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF). The test systems considered are a single machine infinite bus (SMIB) system, an IEEE 9-bus system and a 19-generator 42-bus test system. The estimation accuracy of the rotor angle using ISCKF method is increased by 6.8–36.54% when compared to that of the CKF method. Similarly, the improvement in accuracy is 4.4–28.57% for estimation of speed deviation.

TCSC Application for Transient Stabilization of Wind Turbine with Fixed Speed Induction Generator

Wind turbines (WTs) are a desirable alternative to traditional nonrenewable power resources as a result of recent environmental concerns. Some of them are fixed speed wind generator (FSWG) and have been integrated to power system by squirrel cage induction generator (SCIG). Induction machine absorbs reactive power during all operating conditions, especially at fault condition may result in severe voltage drop which can lead to generator outage. This outage disconnects a significant amount of active power and consequently leads to frequency instability. In order to prevent induction generator (IG) outages in short circuit failures, this paper investigates thyristor-controlled series capacitor (TCSC) device as a candidate solution. TCSC compensates the IG terminal voltage drop by adjusting transmission line impedance at fault condition. In the proposed method, a metaheuristic technique means that shuffled frog leaping algorithm (SFLA) has been utilized to optimize the TCSC controller gains. The proposed scheme can be applied for both SCIGs and wound rotor induction generators (WRIGs) which is another advantage of this method. Single-machine infinite bus system is considered as case study, and various operating conditions and disturbances have been considered to verify the effectiveness of the proposed method.

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.

A Grey Wolf Optimizer algorithm based fuzzy logic power system stabilizer for single machine infinite bus system

Low-frequency power oscillations in power systems have always been a serious risk for a stable power system. A fuzzy logic power system stabilizer (FLPSS) is proposed to mitigate system oscillation problems. Aimed at parameter configuration and rule setting of FLPSS, Grey Wolf Optimizer (GWO) based FLPSS is utilized to achieve quicker settling time and improve the damping oscillation effectiveness. Comparative studies between the proposed GWO-based FLPSS and conventional stabilizers are performed on a single-machine infinite bus system(SMIBS). The simulation results demonstrate the GWO-based FLPSS can better dampen out low-frequency oscillations and maintain the electricity network stability.

Model-Predictive Control Design for Power System Oscillation Damping via Excitation – A Data-Driven Approach

This paper presents a novel power system oscillation damping controller design based on a data-driven model-predictive control (MPC) approach. The system dynamics are extracted from the measurements and applied to the synthesis of control actions in an equation-free manner, without requiring explicit knowledge of the underlying system model. Dynamic mode decomposition with control (DMDc), originating from the Koopman operator theory, connects the collected data with the nonlinear dynamical system modeling. The discrepancy between the model identified by DMDc and the real plant is labeled as a total disturbance. It is estimated in real time by an extended state observer (ESO) and mitigated via a linear MPC, assuming the estimate to be constant over the prediction horizon. The overall control framework is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESO-Koopman-MPC (EKM)</i> . Two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKM</i> -based oscillation damping controllers are proposed - a standalone PSS and an integrated AVR+PSS. The controller operation is illustrated on the single machine infinite bus system (SMIB), Kundur two-area system and IEEE 39-bus system, and the performance is compared with conventional power system stabilizers (CPSSs) under various test scenarios.

Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Accurate design of the power system stabilizer (PSS) models is a crucial issue due to their significant impact on the stability of power system operation. However, identifying the parameters of a PSS model is a challenging task owing to its nonlinearity and multi-modality characteristics. Due to such characteristics, handling algorithms may be prone to stagnation in local optima. Therefore, this paper proposes a potent integrated optimization algorithm by comprising the weIghted meaN oF vectOrs (INFO) optimizer with chaotic-orthogonal based learning (COBL) and Gaussian bare-bones (GBB) strategies, named INFO-GBB, for achieving the optimal parameters of a PSS model used in a single-machine infinite-bus (SMIB) system. In the INFO-GBB, the COBL aims to enhance the searching capability to explore new regions using the orthogonal design aspect and thus improving the diversity of solutions. Also, the GBB is adopted to assist the algorithm to perform an immediate vicinity of the best solution and thus enhances the exploitation capabilities. The effectiveness and efficacy of the INFO-GBB algorithm is validated on CEC 2020 benchmark suits and the designing task of the PSS model. The achieved results by the INFO-GBB are compared with eighteen well-known algorithms. The statistical verifications along with the Friedman test have ascertained that the INFO-GBB is capable of achieving promising performances compared to the other counterparts. The results obtained based on the Friedman test illustrate that the INFO-GBB offers superior performance over the state-of-the-art algorithms as it outperforms fifteen out of eighteen algorithms by an average rank greater than 61% for benchmark problems while outperforming O-LSHADE, LSHADE, and TSA algorithms by 25%,33%, and 58%, respectively. Furthermore, the applicability of the INFO-GBB is realized through designing the PSS model used in a SMIB system. The obtained results indicate that the INFO-GBB algorithm exhibits accurate and superior performance compared to other peers as it provides the lowest value for the integral of time multiplied absolute error (ITAE) performance index which is used as an objective function. For example, the achieved results of the mean ITAE found by INFO-GBB is 1.36E−03 with improvement percentages of 24.93%, 19.78%, 13.04%, 26.64%, and 24.86%, over the LSHADE, GWO, EO, RSA, and original INFO algorithms, respectively. Therefore, the INFO-GBB can efficiently affirm its superiority and stability to deal with the function optimization task and parameters’ estimation of the PSS model.