Optimal Power System Stabilizer Research Articles

Power system stability enhancement through optimal PSS design

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics.

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.

Angular Stability Enhancement of Grid Integrated Wind Energy Using PSO to Optimize PSS

The majority of nations are making efforts to protect our planet by reducing emissions caused by the combustion of fossil fuels to create energy and increasing the production of green power from renewable energy sources. Wind energy is one of the renewable energy sources that is the least destructive to the environment and an efficient approach to reducing carbon emissions. Wind power is also one of the renewable energy sources that are the most cost-effective. However, even though wind energy has been employed for quite some time, there are still some challenges that this form of renewable energy must overcome, such as maintaining the stability of the power grid. The primary objective of this research is to enhance the transient stability of the power system by utilizing an optimized power system stabilizer (PSS) that has been improved. The purpose of this study is to investigate the effect that wind power has on the reliability of power systems that are outfitted with PSS and also to optimize the parameters of the PSS to increase the network’s reliability in two different domains. While PSS with the ST1A excitation system is utilized to reduce the transient period and peak value, oscillations of the generator’s speed deviation can be dampened. Through the management of the relative power angle stability of the network, the major objective of PSS is to enhance the performance of the system.

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.

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system.

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.

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.

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.

Designing optimal power system stabilizer for synchronous generator with and without damper windings

This paper aims at effective damping of low-frequency oscillations (LFO) associated with synchronous generators (SG). LFO are excited by system disturbances as sudden change on the load, switching events, and malfunction of the turbine controller. Poor damping of LFO affects the stability of the generation system, in severe cases may result in instability and damage to prime-movers. The negative effect of the automatic voltage regulator (AVR) is studied for SG without and with damper windings on the damping of oscillations. Minimizing the LFO is achieved by incorporating the power system stabilizer (PSS), which adds an additional control signal to AVR. Effective design of PSS to maximize the damping torque while maintaining the stability of SG variables. The problem is modelled as an optimization problem, the damping ratio of the mechanical modes is the objective function. The single-pole placement technique and the recently developed political optimizer are picked to determine the perfect PI parameters for PSS. The satisfactory performance of the proposed PSS is appraised by testing it against mechanical power disturbances. The obtained mathematical analytical expression makes it possible to determine easily the eigenvalues and step response for the speed and electromagnetic torque for SG in MATLAB environment.

Internal model control design based on approximation of linear discrete dynamical systems

In this paper, a new direct discrete approximation based internal model control design is proposed to the linear discrete dynamical systems. The approximation method is used to determine an accurate and stable reduced-order model for the considered original higher-order discrete-time system. The method involves an enhanced Differential Evolution algorithm to ascertain the stable denominator polynomial coefficients, and the preferable reduced numerator polynomial coefficients are evaluated by using the improved discrete multi-point Padé approximation approach. The method deploys on discrete step integral square error minimization between the original dynamical system and the approximated model, together with retaining their discrete impulse response energy values. The approximated model has been considered an internal (predictive) model and proceeds with an optimal internal model controller design to improve the discrete dynamical system behaviour according to the reference input/the set point. The controller's best performance is attained by tuning the single filter parameter ′λ′ by minimizing the integral square error between the reference input and the actual output of the dynamical system using the enhanced differential evolution algorithm. The acceptability and applicability of the proposed process reduction-based controller design have been validated on a single-input single-output supersonic jet engine inlet dynamical model. The controller robust study is conducted by inserting 10% disruption uncertainty in the system dynamical model poles and zeros. The method has also been extended to the discrete multi-input multi-output dynamical model of the single machine infinite bus power system to develop an optimal internal model control-based power system stabilizer. The simulation results showing better reference input tracking, comparison of performance indices, and also highlight the efficacy of the proposed controller design.

Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm

Optimal design of interconnected multimachine power system with power system stabilizers (PSSs) enduring critical conditions is a challenging assignment due to several nonlinear dynamic device and components associated to the system. In this research, the effective application and performance assessment of genetic algorithm (GA), particle swarm optimization (PSO) and a new metaheuristic-based farmland fertility algorithm (FFA) search algorithm for designing of coordinated multi PSSs in an interconnected multimachine power systems are presented and the limitations of the existing techniques (GA and PSO) are disclosed. The PSSs design problem is transformed into an optimization problem which an eigenvalue-based objective function is developed and both the GA, PSO and the proposed FFA optimization methods are applied to search for the optimal control parameters of the PSSs that are connected to the multimachine in the power system. The PSSs are tested on an interconnected two-area ten-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The optimization methods performance in terms of convergence characteristics, search computational cost, and the search quality solution are compared for justification. The eigenvalue and phasor simulation analysis results are compared so as to validate the efficiency of both optimization methods for the PSSs design thus, to control low frequency oscillations (LFOs) which improves the system dynamic stability. Also, the phasor simulation results exhibits that the transient responses of the system rise time, settling time, peak time and peak magnitude were all impressively improved by an acceptable amount for the test system with the proposed FFA-PSS. Moreover, at the end of the analysis, the FFA based PSSs design was found to converge faster with low computational cost and produces enhanced optimal PSSs parameters as compared to the other existing algorithms. Thus, the application and performance of the FFA has appeared as a promising method and can be considered as a remarkable method for optimal design of multi PSSs in a large interconnected power systems.

Robust multi-objective PSSs design via complex Kharitonov's theorem

This paper represents a step toward shooting an optimal robust three-parameter power system stabilizer (PSS) with the common form of (x1+x2s)/(1+x3s). The whole set of stabilizing PSSs is graphically characterized using d-decomposition with which the controller-parameter space is subdivided into root-invariant regions. Design is accomplished using the bench mark model of single machine-infinite bus system (SMIB). Rather than Hurwitz stability, d-decomposition is extended to consider d-stability where D refers to a pre-specified damping cone in the open left half of the complex s-plane to enhance time-domain specifications. Pole clustering in damping cone is inferred by enforcing Hurwitz stability of a complex polynomial accounting for the geometry of such cone. Parametric uncertainties of the model imposed by continuous variation in load patterns is captured by an interval polynomial. As a result, computing the set of all robust d-stabilizing PSSs calls for Hurwitz stability of a complex interval polynomial. The latter is tackled by a complex version of Kharitonov's theorem. A less-conservative and computationally effective approach based on only two extreme plants is concluded from the geometry of the stability region in the controller parameter plane. Simulation results affirm the robust stability and performance with the proposed PSSs over wide range of operating points.

Improvement of Small Signal Stability of SMIB System with Optimized Power System Stabilizer

As electrical power system is a complex system, there are more chances of stability issues may arise. One of the stability issues is Low Frequency Oscillations (LFOs) which makes the system unstable. As these oscillations are having low frequency i.e. large time constant with slowly increasing magnitude, they are referred to small signal stability. The main reason of these oscillations is due to lack of sufficient damping torque. Automatic Voltage Regulator (AVR) action in generator is providing sufficient synchronizing torque for system stability. This is possible with high gain and low time constant AVR which results in reduction of damping torque. Power System Stabilizer (PSS) is used together with AVR for providing necessary damping torque to minimize the LFOs. For effective damping, the PSS performance is improved by optimizing its parameters. In this paper, Single Machine Infinite Bus (SMIB) system is considered for studying the effect of LFOs. The SMIB system is simulated for a step disturbance in reference voltage and the results are carried out for different optimizing techniques Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching and Learning based Optimization (TLBO).

Improved DE Algorithm for Power System Stabilizer for Damping Low Frequency Osci llations

In the large interconnected power system, maintaining the Small signal stability of the system is of more concern, for the stable, secure and reliable operation. This paper proposes an Improved Differential Evolution (DE) Algorithm based Optimal Power system stabilizer (PSS) for damping the low frequency oscillations. Enhancing the damping of system is formulated as the optimization problem. DE/Best Mutation Operator is adopted for producing the mutation vector, to augment the convergence rate of DE algorithm. The effectiveness of the proposed approach has been tested in Single Machine Infinite Bus (SMIB) system under different operating conditions. The time response evaluations has justified the superiority of the proposed approach for damping the oscillations and thereby improving the Small signal stability of the system.

BAT ALGORITHM IMPLEMENTATION TO OPTIMALLY DESIGN THE STABILIZER POWER SYSTEM ON THE SUPPA GENERATOR

One of the control devices that can be used to strengthen the performance of PLTU Suppa is the installation of Power System Stabilizer. The problem of using Power System Stabilizer (PSS) in generator excitation is how to determine the optimal PSS parameter. To overcome these problems, the authors use a method of intelligent bats to design PSS. Bat's algorithm will work based on the specified destination function, which is an Integral Time Absolute Error (ITAE). In this research, we will see the deviation response of velocity and the rotor angle of the suppa generator in case of interference. The results of the analysis show that the uncontrolled system produces oscillation overshoot speed of -0.02437 pu to 0.006517 pu, conventional PSS about -0.02186 pu to 0.004623 pu and with PSS Bat overshoot of -0.01507 pu up to 0.0006223 pu. A loop for rotor angle response shows good results with reduced oscillation and rapidly leading to steady-state conditions. From the analysis results can be concluded, the performance of suppa generator is increased with the installation of Power System Stabilizer with optimal PSS parameters, with parameters respectively Kpss = 32.2077, T1 = 0.0173, T2 = 0.0401, T3 = 0.9174, T4 = 1.2575.

Designing of power system stabilizer based on neural-like P systems

Power system stabilizers play an important role in reducing the low-frequency oscillation. In this article, the problem of robustly selecting the parameters of the power system stabilizers is studied. A new neural-like P systems optimization algorithm is proposed in order to optimize the power system stabilizer parameters. First, the structure of the neural-like P systems is established. Then, the operation rules, including forgetting rule, spiking rule, evolving rule, and transferring rule, are designed. Furthermore, a new objective function is constructed on the eigenvalues and damping ratio. Finally, the proposed algorithm is tested on the 16-machine and 68-bus system. The simulation results show the effectiveness and robustness of the proposed methods to select the optimal power system stabilizer parameters for damping out the low f oscillation.

Multi-machine optimal power system stabilizers design based on system stability and nonlinearity indices using Hyper-Spherical Search method

Design of power system stabilizer (PSS) withstanding critical situations is a challenging task. In this paper, an effective method for the design of a coordinated multiple PSS in power systems are offered and weaknesses of the existing methods are disclosed. The setup includes the nonlinear model of the system, a specific multi-objective function and a newly developed meta-heuristic Hyper-Spherical Search (HSS) optimization algorithm. The objective function includes both eigenvalue stability index (SI) and nonlinearity index (NI). The employed new NI is the second order modal interaction of the lightly damped electromechanical modes. The performance of the proposed Stability-Nonlinearity index (SN) based PSS design by HSS (SNPSS-HSS) is compared with SNPSS-GA, SI based PSS (SPSS) and the conventional design method (CPSS) under various operating conditions and disturbances. The investigations are conducted over both a single lead PSS and PSS2B. The performance is detailed using time domain simulations, SI and NI analysis. A four-machine two-area system and the IEEE 39-bus system are utilized for test purpose. The results of extensive simulations indicate the superiority of SNPSS-HSS design with respect to the others. The obtained relative stability is higher and the nonlinearity index is lower than the product of the other designs. Besides, as it is expected, using a high degree of freedom PSS2B improves the performance of the aforementioned algorithms.

Cuckoo Search Optimization Algorithm for Designing of a Multimachine Power System Stabilizer

In this paper, new metaheuristic technique cuckoo search optimization (CSO) is explored for an optimal and robust power system stabilizer (PSS) designing for multimachine power systems and performance is evaluated by comparing with well-established techniques, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and new harmony search optimization (HSO). An eigenvalue-based multiobjective function is implemented for simultaneous control of damping factor and damping ratio to damp out low-frequency local and interarea modes of oscillations. The PSS parameters are so intended that unstable and/or lightly damped mode eigenvalues are shifted to a specified D-shape zone in the left half of the s-plane. The selected metaheuristic techniques GA, PSO, HSO, and CSO are used to obtain PSS parameters for 10-machines, 39-bus New England Power System and the controllers designed are named as GAPSS, PSOPSS, HSOPSS, and CSOPSS, respectively. The performance of all designed PSSs controllers is evaluated by eigenvalues analysis, nonlinear simulations, and performance indices for various operating conditions under different scenarios of severe disturbances and their comparative analysis is observed. The robustness of all designed PSSs controllers is observed by testing them on unseen operating conditions and results are compared for the same. The performance of CSOPSS is found to be better than other controllers designed.

Bat Intelligence For Tunning Power System Stabilizer At Barru Power Plant

Changes in load on the power system suddenly, can cause dynamic disruption. This disturbance can not be responded well by the generator, so it can affect the system dynamic stability, such as the occurrence of oscillation speed and rotor angle. Conventional control of excitation and governor, also unable to repair the oscillations, so that additional controllers such as Power System Stabilizer (PSS) are required. In the use of PSS, there are several problems that often arise, namely the correct tuning of PSS parameters. In this research, we proposed a method of smart computing based on bat algorithm, for tuning PSS parameters. From the analysis results can be concluded, the performance performance of generator barru increased with the installation of Power System Stabilizer with optimal PSS parameter, with parameters respectively Kpss = 44.0828, T1 = 0.0284, T2 = 0.0146, T3 = 0.7818, T4 = 1.2816.

Online tuning of power system stabilizer employing genetic programming for stability enhancement

This paper aims to design optimal power system stabilizer (PSS) employing genetic programming (GP) to enhance system stability through damping the low-frequency oscillations out. GP is used to develop model equations for PSS parameters where the inputs are generator terminal voltage, active and reactive powers. The dynamic performance of the proposed technique is validated through the values of statistical performance measures. The mean squared error (MSE) and mean absolute percentage error (MAPE) are quite low whereas the coefficient of determination (R2) is reasonably high for the developed model. In addition, the eigenvalue and minimum damping ratio analysis, as well as time domain simulation, were carried out, which also demonstrate the efficacy of the proposed model.