Power System Stabilizer Parameters Research Articles

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.

Small signal stability analysis of isolated power system with different load types and power system stabilizer

With the development of isolated power system (IPS), stability analysis of IPS attracts wide attention. It is necessary to consider the load types in the small signal stability analysis of IPS because IPS is lack of the support from external power grid. In this paper, two different load types are considered: resistive and inductive load and constant power load. Based on the state-space model of IPS, the impact of droop parameters, connected line and load capacity is discussed. Then, it is essential to increase the droop parameters so that the power is well distributed among different inverters. But this will decrease the stability margin of IPS. To ensure IPS is still stable with good power distribution, a power system stabilizer (PSS) is introduced. Through the analysis of the small signal stability based on the state-space model, it is shown that the PSS can improve the stability of IPS. The advantage of proposed method is that there is no need to change the original control parameters, and the parameters of PSS can be calculated step by step. Finally, the PSCAD\EMTDC simulation verifies the effectiveness of the proposed method.

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.

Memetic SSA tuned fractional PSS for low frequency power system oscillations

Memetic salp swarm algorithm (MSSA) is proposed in the present work to optimally set the gains of fractional power system stabilizer (FPSS) for damping angular speed variation in power system. The optimization is based on the objective of minimizing variation in rotor angular frequency subject to disturbance which is change in reference voltage applied to generator. A single generator system connected with infinite bus has been experimented in this work for sudden and random variations in reference voltage applied to generator. The power system stabilizer (PSS) is based on fractional lead-lag control actions and MSSA is employed to tune the controller gains. Eigen values are analyzed with proposed control and MSSA has been compared with PSO (Particle swarm optimization) and salp swarm algorithm (SSA). It is observed from system response that MSSA can effectively tune the PSS parameters for oscillation damping subject to critical disturbances like random reference voltage variations.

Mitigation of Low-Frequency Oscillation in Power Systems through Optimal Design of Power System Stabilizer Employing ALO

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

SVC Parameters Optimization Using a Novel Integrated MCDM Approach

Nowadays, multi-criteria decision-making (MCDM) methods are used widely in many fields of research and applications. Many studies have shown that MCDM approaches are effective in determining the optimal solution to a variety of symmetrical and asymmetrical problems with numerous parameters. This article investigates a novel approach using multi criteria decision making (MCDM) to optimize the parameters of static var compensator (SVC) and power system stabilizers (PSS). The proposed technique integrates similarity membership function reduction algorithm (SMFRA), removal effects of criteria (REC) and combined compromise solution (CoCoSo). In the first stage, (SMFRA) is employed to select the most dominant controller parameters in the optimization process. Secondly, the weights of the reduced parameters are computed based on (REC). Finally, (CoCoSo) method searches for the optimal setting parameters. A detailed sensitivity analysis is presented to evaluate the obtained results. It is found that the suggested integrated technique is time saving, easily implemented and of low computation burden, which can successfully be implemented to solve a wide range of issues, both comparable and dissimilar.

Optimal adaptive control of electromechanical oscillations modes in power systems

The paper proposes methods and algorithms for optimal tuning of power system stabilizers (PSS). The methods are based on the representation of the power system in the form of a graph and the construction for the graph of the power system of an equivalent multi-connected system of automatic control of modes of electromechanical oscillations. The node dynamics is represented by a simplified model of an oscillator together with a proportional-differential regulator, which is fair for describing a real generator with a PSS only in the region of low frequencies of dangerous oscillations. The problem of optimal tuning PSS parameters is formulated and methods for its solution based on heuristic intelligence algorithms and the method of tuning with implicit reference model are proposed. The effectiveness of the proposed methods is demonstrated on the model of a modified classical two-area Kundur test system with tuning parameters of PSS of three generators.

Tuning the Parameters of Power System Stabilizer Using Runge Kutta Algorithm

Low-frequency oscillations due to unpredictable disturbances in an interconnected power grid are a serious threat to the stability of the power system. Reliable operation of a modern power system, when exposed to sudden disturbances, is crucial, and the safe operation of the system is directly related to success in damping oscillations. Power System Stabilizer (PSS) devices have been used to reduce fluctuations caused by short-time disturbances in power systems. These devices provide additional damping torque components to the generators as an auxiliary control device of the excitation system. Due to the non-linearity of electrical power systems, it is significant to design multi-machine power systems with optimum PSS parameters under critical conditions. In this paper, the PSS design problem was solved using the Runge Kutta Algorithm (RUN). The PSS design problem was considered an optimization problem in which an eigenvalue-based objective function has developed, and the proposed RUN method was tested in a WSCC 3-machine 9-bus test system using the linearized Heffron-Phillips model. In the linearized model, system stability has been enhanced by shifting the eigenvalues to the stability regions. When the results obtained from the test system are examined, it has seen that the proposed RUN is the most effective method in terms of system stability.

Damped Nyquist Plot for the Phase and Gain Optimization of Power System Stabilizers

This paper presents a method to optimize the tuning of a power system stabilizer (PSS) with the objective of simultaneously damping electromechanical poles of different nature. The damped Nyquist plot (DNP) is used to determine the PSS phase and gain compensation requirements to place each pole at a predefined location in the complex plane. A non–linear least–squares curve fitting problem is solved to find the set of PSS parameters that leads to the optimum trade–off for all dominant poles based on a single eigenvalue calculation per operation scenario. The PSS tuning is performed on a two–bus synthetic power system model whose results are validated against a three-machine IEEE benchmark model results. The advantages of using the DNP method together with the synthetic system to perform a PSS tuning in practice are emphasized.

Control of Low-Frequency Oscillation on Electrical Power System Under Large EV-Charging Station Installation Using PSO Technique for Turning PSS Parameters

This paper presents a low-frequency oscillation compensation in an electrical power system connecting large-scale electric vehicle loads (EVs). The Particle Swarm Optimization (PSO) method has been adapted to adjust the control parameters of the generator under Power System Stabilizer (PSS) parameters. The EVs charging stations have been directly installed to the power grid for the battery charging process of the EVs according to the increasing number of EVs. The DC fast charging of the large scale EVs has been significantly affected by the low-frequency oscillation of the grids that have been considered in terms of power system stability margin. This paper has aimed to solve the methodology for turning the PSS parameters by adapting the PSO via MATLAB® and DIgSILENT®. The PSS could control the low-frequency oscillation in the proposed power grids. The simulation system has selected the 6-bus testing system, which has increased the EVs loads by 50, 100, 150, 200, and 300 percentages at bus No. B3. The simulation results have indicated that the rotor low-frequency oscillation could control instability region. Therefore, the proposed PSS controller turning could handle the power system swing and adjust the power system stability. In this condition, the proposed PSO method should have been explicitly used for low-frequency swing control of the generator's rotor into a balanced state under the large-scale EVs penetration level.

Robust Design of Power System Stabilizers Using Improved Harris Hawk Optimizer for Interconnected Power System

In this present work, a new metaheuristic method called a Harris hawk optimizer (HHO) is applied to achieve the optimal design of a power system stabilizer (PSS) in a multimachine power system. Several well-known chaos maps are incorporated into the HHO to form a chaotic HHO (CHHO) with the aim of improving static operators and enhancing global searching. To assess the CHHO performance, exhaustive comparison studies are made between anticipated chaotic maps in handling unconstrained mathematical problems. At this moment, The PSS design problem over a wide permutation of loading conditions is formulated as a non-linear optimization problem. The adopted objective function defines the damping ratio of lightly damped electromechanical modes subject to a set of constraints. The best PSS parameters are generated by the proposed CHHO. The applicability of the proposed CHHO based on PSS is examined and demonstrated on a 10-generator and 39-bus multimachine power system model. The performance assessments of the CHHO results are realized by a comparative study with HHO through extensive simulations along with further eigenvalue analysis to prove its efficacy. The simulation results convincingly demonstrate the high performance of the proposed CHHO-PSS under various operating scenarios.

Optimal Design of PSS on SMIB Using Particle Swarm Optimization

Dynamic disturbances in the power system are caused by sudden changes in load. This condition causes the stability of the generator to be disturbed, such as the emergence of oscillations in the generator in the form of oscillations of frequency and rotor angle. Power System Stabilizer (PSS) is an additional control that can increase generator stability. To get optimal PSS performance, proper tuning of PSS parameters is needed. Optimal performance of PSS can cause the frequency response and angle of the SMIB rotor to be stable. In this study, PSO is used for optimization of PSS parameters. PSS is able to provide stability so that overshoot oscillations can be suppressed, as well as faster settling time performance for the system to reach steady state conditions. To test the reliability of the SMIB, a case study of adding and subtracting loads was used.

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

Generally, power systems experience a variety of disturbances that can result in low frequency electromechanical oscillations. These low frequency oscillations (LFOs) take place among the rotors of synchronous generators connected to the power system. These oscillations may sustain and grow to cause system separation if no adequate damping is provided. Power system stabilizers (PSSs) are one of the alternative devices used to solve this rotor oscillation problem. The major limitation of using PSSs at the excitation system of synchronous machine is that the conventional PSS is a permanent parameter type operating under a particular system operating condition, and its parameters are acquired through trial and error. An efficient way of operating the PSS has been by designing the PSS parameters using a powerful optimization procedure. However, designing PSS damping controller is a cumbersome task as it needs a comprehensive test system modeling and a heavy computational burden on the system. In this research, a novel, model-free neuro-fuzzy controller (NFC) is designed as the LFOs’ damping controller to substitute the traditional PSS system making the power system simple without complications in PSS design and parameter optimization. The proposed controller application implements the LFOs’ control without a linearized mathematical model of the system, as such it makes the system less complex and bulky. Single machine infinite bus (SMIB) test system was simulated in SIMULINK domain with the PSSs and with the proposed controller to compare the NFC effectiveness. The simulation outcome for the eigenvalue study with NFC stabilizer yields steady eigenvalues that enhanced the damping status of the system greater than 0.1 with decreased overshoots and time to rise via the proposed NFC process than with the conventional FFA-PSS. Similarly, the generator transient reaction also presents the ω and δ based on the time to settle was improved by 64.66% and 28.78%, respectively, via the proposed NFC process than with the conventional FFA-PSS. Finally, the conventional PSS was found to be complicated in its design, parameter optimization and less effective than the proposed controller for the LFOs’ control.

Robust Design of Dual-Input Power System Stabilizer Using Chaotic JAYA Algorithm

This work aims to improve the stability of a system that consists of one generator using a dual-input stabilizer of a power system. For this purpose, a new design method based on a chaotic JAYA algorithm is proposed. This algorithm hybridizes the original JAYA algorithm with chaos to improve its exploration and exploitation capabilities. Indeed, the disordered numbers generated by the chaotic map are exploited to control the standard JAYA algorithm’s search equations. The issue of design is stated as an optimization problem. The proposed technique is involved in the selection of optimal values of the dual-input power system stabilizer (PSS) parameters. The efficiency of the proposed controller is assessed under various loading conditions and compared with the original JAYA, cuckoo search and particle swarm optimization (PSO) algorithms. The target function consists of the integration of absolute error multiplied by the time, including the speed deviation is considered for the controller design method. The controller tuned by a chaotic JAYA algorithm is tested using a single machine infinite bus (SMIB) power system. The findings of the simulation results demonstrate the robustness and effectiveness of the suggested method in damping oscillations over an extended range of loading conditions.

A modified sine cosine algorithm with ensemble search agent updating schemes for small signal stability analysis

In this paper, a modified sine cosine algorithm (MSCA) is proposed and is applied to design the power system stabilizer (PSS) and lead lag compensator-based static synchronous series compensator (SSSC) controller to enhance the power system stability. Two test systems: single machine infinite bus (SMIB) and multi-machine power system (MMPS) working under various loading conditions are taken as case studies. The objective of the work is to enhance the stability of the system by minimizing the rotor speed deviation of the generator. For this, the optimal controller parameters of PSS and SSSC are determined by using the MSCA technique. In the proposed MSCA method, two newly proposed search agent updating schemes (ie, SCA/rand-target and SCA/best-target) with parallel use of sine, cosine operators have been ensemble to generate the new search agents. The proposed MSCA method combines the exploration property of SCA/rand-target search agent updating scheme with exploitation property of SCA/best-target search agent updating scheme, which enhances the chances of attaining the near optimal solutions in fewer generations. The simulation results obtained from the proposed MSCA method have been applied on the two test system configurations of power system, which shows significant reduction in oscillations as compared with some well-known heuristic algorithms. The mean rotor speed deviation achieved using the proposed method for SMIB system and MMPS is 0.0013858 and 0.00046857 pu, respectively. Extensive statistical analysis on obtained results using Wilcoxon signed rank test, and Friedman and Nemenyi hypothesis test ensure the reliability and robustness of the proposed method.

A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration

With increasing proportion of wind energy in power systems, the intermittence of such energy makes the system run a wide range of operating conditions. In this context, ordinary power system stabilizers (PSS) tuned based on the linearized model of the system at one operating condition may not be able to effectively damp low frequency oscillations (LFO), which brings great challenges to the stability of the system. To this end, this paper proposes a novel sparsity promoting adaptive control method for the online self-tuning of the PSS parameter settings. Different from the existing adaptive control methods, the proposed method combines deep deterministic policy gradient (DDPG) algorithm and sensitivity analysis theory to train an agent to learn the sparse coordinated control policy of multi-PSS. After training, the well-trained agent can be employed for online sparse coordinated adaptive control, and the control signal is only applied, when it is required and only to the key PSS parameters that have the maximum influence on the system stability. Simulation results verify that the proposed method can make the PSS achieve the better performance of damping oscillation and robustness against the change of wind energy in comparison with other methods.

Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.