Improve Power System Stability Research Articles

Techno-economic analysis of energy storage integration combined with SCUC and STATCOM to improve power system stability

Techno-economic analysis of energy storage integration combined with SCUC and STATCOM to improve power system stability

More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function

Gas turbines play a crucial role in absorbing the volatility of new energy sources such as wind and photovoltaic. Continuous degradation trend prediction for gas turbines is vital for rationalizing maintenance schedules and improving power system stability. Current prediction techniques do not consider the practicality of the prediction results. To address this issue, a prediction framework based on factor analysis and Multiple Penalty Mechanisms (MPM) loss function is proposed. Firstly, factor analysis is used to assess the health index of gas turbines. Secondly, an innovative loss function that incorporates penalties for prediction errors, lag prediction, and fluctuation prediction is proposed to improve forecast usability. A range-adjustable and asymmetric Hyperbolic Cosine with Exponential (CoshE) function is first proposed to address the prediction lag problem. Finally, Long Short Term Memory network is chosen as the predictive model, and dynamic weights are used to optimize the loss function. Experiments on the combustion chamber degradation dataset and C-MAPSS dataset show that the framework proposed performs optimally than the conventional loss functions and the CoshE function is more efficient in the MPM framework. Meanwhile, MPM significantly improves gate recurrent unit and convolutional neural network performance. The method proposed is noteworthy for its superiority and applicability.

Study on Pd-MoTe2 monolayer for adsorption and sensing of air decomposition products in air-insulated switchgear

Detecting characteristic decomposition components has become a popular method for assessing the operational stability of air-insulated switchgear. This paper investigates the adsorption and sensing properties of a Pd-modified MoTe2 (Pd-MoTe2) for two common defective gases, namely, NO2 and CO, in air-insulated switchgear and employs the first-principles density functional theory (DFT) to conduct the analysis. According to the findings, Pd has a preference for doping in the TMo site on the MoTe2 monolayer, with a binding energy (Eb) of −2.240 eV. The Pd-MoTe2 monolayer exhibits effective adsorption of NO2 and CO, with both molecules chemisorbed. The adsorption energies (Ead) for NO2 and CO are −1.307 eV and −1.550 eV, respectively. Furthermore, there is an evident reduction of the band gap for the Pd-MoTe2 upon adsorbing NO2, decreasing the resistance value. In short, the Pd-MoTe2 monolayer has shown great promise as a gas sensor, laying the groundwork for improving power system stability.

Stability Improvement of Power Systems Using Shunt Compensation

Stability Improvement of Power Systems Using Shunt Compensation

Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability. The control agents of Distributed Generations (DGs) are optimized using GWO algorithm based on the deviations in frequencies of DGs. Particle Swarm Optimization based Multi Agent System (PSO-MAS) and Conventional Multi Agent System performance are compared with the suggested technique's implementation on IEEE 30 bus system and testing in MATLAB/Simulink environment. The outcomes showed that the suggested approach is successfully improving the stability of an interconnected power system.

A Comparative Study on Wind Power Forecasting Models Based on the Use of LSTM

In the context of wind power generation's growing significance, this research tackles the critical problem of improving power system stability by reducing peak load and frequency control pressures using sophisticated wind power forecasting methods. Using the rapidly developing area of artificial intelligence and neural networks in particular, the study explores the efficacy of Long Short-Term Memory (LSTM), a recurrent neural network designed for event forecasting in time series data with long intervals and temporal delays. The research presents a novel forecasting model, LSTM that enhances prediction accuracy. This is corroborated by the calculated Root Mean Squared Error (RMSE) of 0.6782 and Mean Absolute Error (MAE) of 0.4614. The sustainability and dependability of wind energy integration into power systems may be enhanced by these findings, which highlight the possibility for more efficient and rapid convergence processes in wind power forecasting.

Performance Analysis of Various FACTS Controllers Used for Voltage Stability Improvement in Power System

Performance Analysis of Various FACTS Controllers Used for Voltage Stability Improvement in Power System

Robust design and best control channel selection of FACTs-based WADC for improving power system stability using Grey Wolf Optimizer

With recent advances in wide-area measurement systems (WAMS), wide-area damping controllers (WADCs) based on remote signals can effectively damp inter-area oscillations. However, these signals are inherently subjected to communication time-delay which may adversely affect the damping of inter area oscillations. The purpose of this paper is to present a optimization-based method for designing a WADC for a static synchronous series compensator (SSSC) to improve the damping of inter-area oscillations by considering the time delays of remote control signals. For this aim, the control gain value of the WADC, shifting of the critical modes to a desirable area, and the maximum time delay margins are simultaneously considered as an objective function. Moreover, to design a WADC as minimum-phase structure, the suitable constraints have been determined and added to the objective function as penalty factors. A grey wolf optimization (GWO) algorithm is utilized to solve the optimization problem. Robustness of the designed SSSC-based WADC in amplitude and phase control channels against the time delay has been compared and then the best control channels to damp inter-area oscillations has been selected. Based on simulations and statistical results conducted on 4 and 50 machine multi-machine power systems, it is evident that the proposed method based on the GWO outperforms other techniques in achieving the optimal design of a SSSC-based WADC for damping inter-area oscillations.

Analysis of the combined effects of SVC and SSSC controllers to improve power system stability

The primary challenge facing the electricity transmission system in the aftermath of the disruption is reestablishing stability. Through adjustment of the transmission parameters, the Flexible AC Transmission System (FACTS) devices have the potential to bring the system back into stable operation. It has been shown that using many FACTS devices together is more effective than using any one device alone. In this paper, the concept of coordination control between a Static Var Compensator (SVC) and a Static Synchronous Series Compensator (SSSC) is suggested in order to make the system more stable. The equivalent impedance of the line may be reduced by using the SSSC, which will also result in improved active power transfer capabilities for the line. A static var compensator, often known as an SVC, is used for shunt compensation in order to keep the bus voltage magnitude stable. When compared to the use of a single device, the use of SVC and SSSC coordinated control results in a much quicker arrival to the steady state response of the system. Both of these devices are controlled by using the same signal input tracking. It has also been seen that the system has resumed its previous synchronized state.

Coordinated control of multi band PSS (MBPSS) with type 2 fuzzy based SSSC controller design for power system stability improvement

Coordinated control of multi band PSS (MBPSS) with type 2 fuzzy based SSSC controller design for power system stability improvement

Dynamic Model of a Virtual Air Gap Reactor

Variable reactors have been a vital component of power networks for decades, where they have been used as fault-current limiting devices or for reactive power compensation. Traditionally, modifying the inductance of predominantly mechanically operated variable reactors requires seconds to minutes. In contrast, virtual air gap (VAG) reactors can change the inductance within milliseconds, potentially improving power system stability. Existing dynamic models of VAG reactors cannot capture the entire system dynamics, limiting their applicability for simulations in the time-domain. This research presents two dynamic VAG reactor models, one with and one without core losses. The models capture all significant system dynamics using electromagnetic principles and VAG reactor flux linkage behavior. The proposed models were experimentally validated using a small VAG reactor. Over a broad operating range, both models accurately reproduce the dynamic behavior, transient response, and dominant harmonics of the small VAG reactor. Consequently, the models may be used for a variety of applications, such as time-domain simulations, harmonic analysis, and the development of suitable controllers for VAG reactors. In addition, engineers may use the core loss omitting model as a VAG reactor design tool, as the actual reactor is not required for modeling.

HVDC 운영 전략에 따른 제주 전력 계통의 출력제어 및 운영비용 분석

The proportion of renewable power generation in power systems is rapidly increasing, resulting in frequent imbalances. This paper focuses on the impact of renewable energy penetration on power system stability in Jeju, South Korea, under the CFI 2030 policy. With the increase in renewable energy sources, power variability has also increased due to their intermittent nature, leading to supply and demand imbalances and system stability problems. Traditional methods of maintaining power balance, such as curtailment, result in cost and environmental losses for system operators. To address this issue, the paper proposes the use of HVDC power reversal transmission strategies to return excess power from Jeju to the mainland power system. The study analyzes the impact of three operational strategies of HVDC on unit commitment results, considering cost and environmental factors. The results suggest that using HVDC can mitigate curtailment and reduce oversupply, leading to improved power system stability in Jeju island.

Research on AC Excitation Regulation Control System for Synchronous Generators

Although there are many methods for classifying the excitation systems of synchronous motors, there are three types of excitation systems for synchronous motors based on the numerous methods of providing excitation power to synchronous generators. One is a DC excitation system, the second is an AC excitation system, and the other is a static excitation system. This paper mainly studies the AC excitation system. The rotor speed of AC excited generators is variable and has excellent characteristics such as improving power system stability, allowing for deep operation without losing step. AC excitation generators not only have variable speed and constant frequency power, but also have independent adjustment functions for speed, reactive power, and effective power. The use of high-performance variable frequency excitation power supplies and appropriate excitation control methods can maximize the reliability, operational flexibility, and exceptional adjustment performance of AC excitation generators. The excitation system is an important part of synchronous generator operation. A good excitation system not only ensures the stable and reliable operation of the generator, but also greatly improves the stability of the motor and power system.

Controllability analysis of renewable embedded AC microgrid with multi-band stabilizers

The depletion of conventional energy sources and the increasing demand for electricity due to industrialization and rising global living standards has led to the need for a transformation of the traditional power grid. The microgrid concept has emerged as a solution that integrates renewable energy sources into the main grid. This paper examines an AC microgrid consisting of solar, wind, hydro, and diesel power stations, and investigates the effect of various power system stabilisers (PSSs) on the silent-pole synchronous generator of the diesel and hydro power plant. The effectiveness of different PSSs, including - PSS, ΔPa-PSS, and Multi-band PSS, is compared using a grid-connected AC microgrid. The simulation results show that Multiband PSS performs better than conventional PSSs in eliminating electromechanical oscillation, even in the presence of Gaussian and colored noise. PSSs are useful in managing the power flow of various DERs and improving power system stability. The simulation is conducted using the MATLAB/SIMULINK software.

A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow. The purpose of this paper is to demonstrate how optimising LFC in a two-area interconnected energy system with hydro, thermal plants, and a particle swarm optimization (PSO) method may improve power system stability and save revenue on power generation. A standard (PID) controller is used to control the system. The PSO optimization approach is utilised to determine the optimal gain values of the controllers kp, ki, and kd.

Optimization of Power System Stabilizers Using Proportional-Integral-Derivative Controller-Based Antlion Algorithm: Experimental Validation via Electronics Environment

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively.

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.

Parameter coordination optimization of power system stabilizer based on similarity index of power system state-BP neural network

Power system stabilizer (PSS) is widely used to improve power system stability. The current parameter coordination optimization method is easy to fall into the local optimization, to solve the problem and find the optimal parameter combination of PSS, the sample screening method based on the similarity index of power system state (SIPSS) and BP neural network is proposed for global optimization parameters of PSS. The SIPSS screening method uses a similar metric index of the grid state variable as the criterion to screen out the required samples. The BP neural network fits the predicted and expected values of the minimum damping ratio of the system after random fluctuations of PSS parameters under various operating modes to minimize the mean square deviation by fitting the streamlined training samples. Firstly, the SIPSS-BP neural network model is obtained by analyzing the mapping relationship between generator power, node power, branch power, and minimum damping ratio. Then, the sensitivity of the PSS parameters damping ratio is calculated, and the PSS parameter optimization model is established. The optimal adjustment of the PSS parameter is obtained, and the minimum damping ratio is modified to improve the system’s stability. Finally, the minimum damping ratio after correction is obtained. The test results of the SIPSS-BP network with the IEEE 3-machines and 9-nodes show that the method can achieve good prediction accuracy, the parameter optimization effect of PSS can be remarkable, and the stability of the power system has been greatly improved.

Improvement of power system stability in wind connected generator by using PSAT

Voltage stability in power systems is a major problem that receives international attention. An open and free source piece of software, Power System Analysis Toolbox (PSAT) is used in this study to create a model of modified IEEE 14 bus system. Models of two recently created wind turbines—the Squirrel Cage Induction Generator (SCIG) and Doubly Fed Induction Generator (DFIG)—are connected to a modified IEEE 14 bus system. The stability of the power system is examined in this work in relation to the effects of Squirrel Cage Induction Generator (SCIG) and Doubly Fed Induction Generators (DFIG). Here, both DFIG and SCIG, which have variable speeds, are regarded as wind generators. In this work, a small signal stability investigation using SCIG and DFIG wind turbine systems was carried out on a modified IEEE 14 bus system. The results of their simulation were analysed.

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.