Power System Stabilizer Parameters Research Articles

Spiral-refraction mutation prairie dog algorithm: Optimization framework for engineering design of interconnected multimachine power system

Many real-world engineering problems, characterized by high-dimensionality, nonlinearity, nonconvexity, and multi-modality, demand advanced optimization methods. Traditional algorithms may struggle with these challenges. Prairie dog optimization (PDO) was proposed for function optimization but faces limitations in balancing exploration and exploitation due to two reasons. The first one is related to diversity features which may not be efficient, and the second one is related to having no leading mechanism for direct search feature towards the promising regions. With that in mind, this study introduces an enhanced PDO variant, improved PDO (IPDO), addressing PDO's drawbacks through two key strategies: refraction-based learning (RBL) and spiral search learning (SSL). RBL enhances exploration by generating refraction solutions, while SSL conducts deep local searches around the best solution, strengthening exploitation. IPDO's performance is evaluated on benchmarks, IEEE CEC2017/CEC2020 test suites, and real-world constraint engineering optimization problems. Comparative analyses, including statistical measures, convergence analysis, and boxplot analysis, demonstrate IPDO's superiority. Besides, the IPDO is applied to estimate more promising parameters of power system stabilizer utilized in an interconnected multimachine power system using Western System Coordinating Council three-machine, nine-bus power system. Results illustrate that the IPDO harvests the better estimation among the other methods, making it to be an efficient and powerful tool for dealing with the efficient operation of an interconnected multimachine power system which is a challenging real-world engineering problem.

Power systems stabilizers online tuning based upon parameters dynamic estimation

This paper deals with the tuning of power system stabilizers based upon an estimation procedure properly tailored for online applications. The growth of renewables in modern power systems imposes constant verification of the current operating condition and the effective tuning of power system stabilizer parameters to comply with a satisfactory damping factor of general system behavior. More specifically, an online procedure is proposed for the estimation of the current operating point and the consequent tuning of the parameters of the power system stabilizers. The estimation procedure combines the system’s dynamic response to a probing signal with the available measurements of power and voltage at the generator terminal side. The parameters of the current operating point are then derived through the least squares minimization between the measurement and power deviations. At the aim of the parameter tuning procedure, with respect to the estimated operating point, proper modeling that considers the presence of damper windings within the design methodology is proposed for better describing the dynamic performances. The proposed design procedure can combine both the specifications in the frequency domain of the phase compensation and those relating to the appropriate location of the eigenvalues of the dynamic matrix of the system. In the numerical application, the proposed approach is shown to be very effective in terms of current operating point estimation and consequent tuning of the power system stabilizer parameters in the range of interarea oscillations and local oscillations, thus counteracting the renewed requirements for stability issues in modern power systems.

STABILISATEUR DE SYSTÈME D'ALIMENTATION ROBUSTE BASÉ SUR UN CONTRÔLE H∞ STRUCTURÉ POUR LA STABILITÉ D'UN SYSTÈME MULTI-MACHINE

A robust design of power system stabilizers (PSSs) using H∞ output feedback control has been introduced in this work. To facilitate the implementation of the designed PSSs, the proposed technique employs the H∞ to tune the fixed-structure conventional lead-lag PSS parameters of the multi-machine system. These PSSs are used to improve the damping of the local and inter-area low-frequency oscillations in power systems under different operating conditions. The proposed control is tested on a multi-machine system, which is a three-machine nine-bus system. A comparative simulation study shows a significant enhancement and good performance of the proposed design compared to an IEEE conventional power system stabilizer.

Performance monitoring and redesign of power system stabilizers based on system identification techniques

To maintain stable power system operation, damping control systems are used in conventional power plants, known as Power System Stabilizers (PSS). However, to derive suitable controller parameters, the current methods require dynamic models that are difficult to maintain and update, and in some cases, might not be available. This makes it challenging for grid operators to maintain system stability when the system is under stringent operating conditions or undergoes the loss of major transmission corridors, which would require a new stabilizer design to provide adequate damping.Leveraging the availability of real-time measurements and “probing” technologies, this paper provides a complementary approach that does not require power system simulation models, but is based on system identification techniques that allow to derive simple and accurate models based on data collected on the system. The proposed method allows to monitor the performance of damping controllers and even to perform redesign based on the models derived with system identification. The resulting redesign could be used to update PSS parameters and improve damping without the need of removing existing damping control systems from service.

A Hybrid Controlling Parameters of Power System Stabilizer and Virtual Inertia Using Harris Hawk Optimizer in Interconnected Renewable Power Systems

A Hybrid Controlling Parameters of Power System Stabilizer and Virtual Inertia Using Harris Hawk Optimizer in Interconnected Renewable Power Systems

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

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.

Robust multi-machine power system stabilizer design using bio-inspired optimization techniques and their comparison

This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO.

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.

Power System Stability Enhancement Using Grasshopper Optimization Approach and PSSs

A new meta-heuristic algorithm namely Grasshopper Optimization Approach (GOA) for Power System Stabilizer (PSS) design problem is investigated in this paper. The parameters of PSSs are optimized by GOA to minimize the time domain objective function. The performance of the designed GOA based PSSs (GOAPSS) has been has been compared with Differential Evolution (DE) based PSSs (DEPSS) and the Particle Swarm Optimization (PSO) based PSSs (PSOPSS) under various loading events. The results of the proposed GOAPSS are confirmed via eigenvalues, damping ratio, time domain analysis, and performance indices. Moreover, the robustness of the GOA in getting good damping characteristics is verified.

Robust Optimization of Power System Stabilizer Parameters Based on Pole Placement Technique

To enhance the stability of power system, a power system stabilizer (PSS) is one of the control mechanisms installed in the power plants. A suitable PSS parameters set can help in reducing the power swing. In general, the design of PSS parameters is performed based on a nominal operating condition of the power system. In this paper, a multi‐objective optimization algorithm based on pole placement technique is proposed to find a robust PSS parameters set. The proposed strategy searches for the optimal PSS parameters under multiple operating conditions of the generator. Preliminary simulation results on a single machine infinite bus (SMIB) model show that the proposed PSS parameters design method can further improve the robustness of the overall power systems performance under different types of disturbance. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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.

Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flame optimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional–integral–differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional–integral–differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.

Identification and damping of low-frequency oscillations based on WAMS data and the revisited residue method – part I

The results of low-frequency oscillations identification in the Republic of Kazakhstan power grid by using a Wide Area Measurement System are presented and an algorithm for damping low-frequency oscillations is proposed in this paper. Analysis of weakly damped inter-area low-frequency oscillations revealed a constant mode with a frequency range of 0.3‒0.4 Hz. It was determined that at these low-frequency oscillations, the amplitude of active power fluctuations along the transmission line was 150 MW with a duration of 9 minutes. The modal analysis calculation of the Republic of Kazakhstan power system model in the «DigSilent Power Factory» software shows the dangerous low-frequency oscillation modes having a damping ratio is 2.2 % and an eigenfrequency 0.328 Hz. These oscillation modes identified by the real data and in the developed model indicate the incorrect tuning of power system stabilizer parameters at power plants. It is necessary to retune the power system stabilizer parameters whenever changing the system’s and mode’s configurations. An analysis of existing power system stabilizer tuning methods was performed, and revisited residue method was determined as sufficiently effective. Thus, the developed algorithm for identification and damping of low-frequency oscillation consists of three tasks. The first task is data collection from the Wide Area Measurement System and Supervisory Control and Data Acquisition system and updating the calculation model based on the current status of equipment (generators, transformers, transmission lines, etc.). The second task is the identification of dangerous electromechanical oscillations and modal analysis based on information obtained in real-time. The third task is tuning the power system stabilizer parameters for damping dangerous low-frequency oscillation modes based on the revisited residue method

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.

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds. In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions.

Revamped Sine Cosine Algorithm Centered Optimization of System Stabilizers and Oscillation Dampers for Wind Penetrated Power System

The mapping of damping ratios and damping factors by Sine-Cosine Algorithm (SCA) is significant in the analysis of low frequency oscillations generated in an integrated network having multiple conventional and renewable energy sources. Multiple random models with unknown search spaces and uncertain conditions and constraints, which incorporate the fluctuation of solutions towards or outwards at the initial stage for solving real-time problems can be analyzed by a revamped sine-cosine algorithm (RSCA) model. In this paper, the SCA has been revamped to explore the stability threshold for a stable system under dynamic nonlinear operating conditions. It is achieved with the infusion of suitable mathematical functions for optimizing the parameters of Power System Stabilizers (PSSs) and Doubly Fed Induction Generator (DFIG) system-based oscillation dampers to enhance the Small Signal Stability of power systems through effective damping of low-frequency oscillations (LFOs). These LFOs weaken the effective power generation and demand management cycle in conventional power systems when the system faces conditions like intermittent renewable energy sources, overloading conditions, impulsive faults etc. The small-signal and transient stability studies for various disturbances and different operating conditions are investigated, and the performance of the proposed RSCA for optimized parameter tuning of system stabilizers and oscillation dampers are analyzed on the modified benchmarking systems with wind generators using MATLAB Ⓡ simulations. The efficiency and robustness of the proposed algorithm has been verified under selective critical operating conditions, line outages, and load uncertainties to prove that the low frequency modes are damped with an elevated positive damping ratio and rapid settling time with reduced oscillations. The application of system stabilizers and oscillation dampers optimized through the proposed RSCA shows a reduced settling time in the LFOs created and improved damping ratio of 0.22 for an increase in 20% loading and 50% of wind power generation in the case study of Two Area Four Machine System.

Power System Stabilizers Design Using BAT Algorithm

A new metaheuristic method, the BAT algorithm based on the echolocation behaviour of bats is proposed in this paper for optimal design of Power System Stabilizers (PSSs) in a multimachine environment. The PSSs parameter tuning problem is converted to an optimization problem which is solved by the BAT search Algorithm. An eigenvalues based objective function involving the damping factor, and the damping ratio of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed BAT based PSSs (BATPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under various operating conditions and disturbances. The results of the proposed BATPSS are demonstrated through time domain analysis, eigenvalues and performance indices. Moreover, the results are presented to demonstrate the robustness of the proposed algorithm over the GA and conventional one.

Stability improvement of the PSS-connected power system network with ensemble machine learning tool

Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.

Application of Improved Quasi-Affine Transformation Evolutionary Algorithm in Power System Stabilizer Optimization

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method’s rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system’s stability.