Tuning Of Power System Stabilizers Research Articles

GA and PSO Techniques Based Optimal Tuning of Power System Stabilizers in a PV Integrated Power Network

This paper involves the employment of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) methods for the optimal tuning of gain and time constant parameters of power system stabilizers in a power network with integrated Photovoltaic (PV) energy conversion systems. To examine the critical modes of the PV integrated system and the impact of the PV integration on various modes of oscillations, eigenvalue analysis is carried out under various PV penetration levels. The investigations indicate that the rotor angle oscillations following a disturbance have less damping as the PV penetration level increases. To enhance the small signal stability, the excitation systems are provided with the signal from power system stabilizers (PSS). The gain and time constant parameters of the PSS are tuned utilizing the GA and PSO methods and compared with the conventional proportional-integral (PI) PSS. The preliminary investigations on the PV integrated standard IEEE power system reveal that the PSO and GA-based PSS are significantly better than the PI-PSS, and further, PSO-PSS is marginally more effective than the GA-PSS in damping the rotor angle oscillations.

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.

Improved chaotic Bat algorithm for optimal coordinated tuning of power system stabilizers for multimachine power system

Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers’ gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location.

Power system stabilizers tuning for probabilistic small-signal stability enhancement using particle swarm optimization and unscented transformation

Power system stabilizers tuning for probabilistic small-signal stability enhancement using particle swarm optimization and unscented transformation

Wide area damping of electromechanical oscillations based on implicit reference model adaptive control

Assessing the stability of low-frequency inter-area oscillations remains an important issue in modern electrical power systems. The paper proposes a novel method for optimal tuning of power system stabilizers for wide area damping of electromechanical oscillations based on implicit reference model adaptive control. An implicit reference model of the system is formed on the basis of choosing the desired values of the roots of the characteristic polynomial of the system by computing the determinant of its characteristic matrix. An adaptive tuning procedure seeks to preserve the “reference” values of the coefficients in the characteristic polynomial of the swing equation model of the system identified at the current operating point. The proposed approach can be also considered as an adaptive pole placement method applied to the linearized swing equation model. Numerical experiments with the IEEE 30 bus test model confirm that the proposed method can be effective in maintaining dynamic and transient stability.

Using demand response to improve power system small-signal stability

With the increase of uncertain and intermittent renewable energy supply on the grid, the power system has become more vulnerable to instability. In this paper, we develop a demand response strategy to improve power system small-signal stability. We pose the problem as an optimization problem wherein the total demand-responsive load is held constant at each time instance but shifted between different buses to improve small-signal stability, which is measured by small-signal stability metrics that are functions of subsets of the system’s eigenvalues, such as the smallest damping ratio. To solve the problem, we use iterative linear programming and generalized eigenvalue sensitivities. We demonstrate the approach via a case study that uses the IEEE 14-bus system. Our results show that shifting the load between buses, can improve a small-signal stability margin. We explore the use of models of different fidelity and find that it is important to include models of the automatic voltage regulators and power system stabilizers. In addition, we show that load shifting can achieve similar improvements to generation shifting and better improvement than simply tuning power system stabilizers.

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.

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

Comparative study of three quantum-inspired optimization algorithms for robust tuning of power system stabilizers

Comparative study of three quantum-inspired optimization algorithms for robust tuning of power system stabilizers

Implementing Optimization Techniques in PSS Design for Multi-Machine Smart Power Systems: A Comparative Study

This study performed a comparative analysis of five new meta-heuristic algorithms specifically adopted based on two general classifications; namely, nature-inspired, which includes artificial eco-system optimization (AEO), African vulture optimization algorithm (AVOA), gorilla troop optimization (GTO), and non-nature-inspired or based on mathematical and physics concepts, which includes gradient-based optimization (GBO) and Runge Kutta optimization (RUN) for optimal tuning of multi-machine power system stabilizers (PSSs). To achieve this aim, the algorithms were applied in the PSS design for a multi-machine smart power system. The PSS design was formulated as an optimization problem, and the eigenvalue-based objective function was adopted to improve the damping of electromechanical modes. The expressed objective function helped to determine the stabilizer parameters and enhanced the dynamic performance of the multi-machine power system. The performance of the algorithms in the PSS’s design was evaluated using the Western System Coordinating Council (WSCC) multi-machine power test system. The results obtained were compared with each other. When compared to nature-inspired algorithms (AEO, AVOA, and GTO), non-nature-inspired algorithms (GBO and RUN) reduced low-frequency oscillations faster by improving the damping of electromechanical modes and providing a better convergence ratio and statistical performance.

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm’s considerable promise. This demonstrates the GTO-GBO algorithm’s improved balance of global and local search stages. The proposed GTO-GBO algorithm’s performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case’s findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

Efficient Multi-objective Optimizers by Meta-heuristics for Power System Control

This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective and mono objective functions proved the efficiency of the PSS adapted by multi-objective function based genetic algorithms in comparison with particle swarm optimization, it’s enhanced stability of power system works under different operating modes and different network configurations. The simulation results obtained under developed graphical user interface (GUI).

A Study on the Impact of DE Population Size on the Performance Power System Stabilizers

The population size of DE plays a significant role in the way the algorithm performs as it influences whether good solutions can be found. Generally, the population size of DE algorithm is a user-defined input that remains fixed during the optimization process. Therefore, inadequate selection of DE population size may seriously hinder the performance of the algorithm. This paper investigates the impact of DE population size on (i) the performance of DE when applied to the optimal tuning of power system stabilizers (PSSs); and (ii) the ability of the tuned PSSs to perform efficiently to damp low-frequency oscillations. The effectiveness of these controllers is evaluated based on frequency domain analysis and validated using time-domain simulations. Simulation results show that a small population size may lead the algorithm to converge prematurely, and thus resulting in a poor controller performance. On the other hand, a large population size requires more computational effort, whilst no noticeable improvement in the performance of the controller is observed.

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

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.

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. Validation of the modified optimizer is firstly investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time, and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS.

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.

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

This paper presents an improved Teaching-Learning-Based Optimization (TLBO) for optimal tuning of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. The original TLBO is characterized by easy implementation and is mainly free of control parameters. Unfortunately, TLBO may suffer from population diversity losses in some cases, leading to local optimum and premature convergence. In this study, three approaches are considered for improving the original TLBO (i) randomness improvement, (ii) three new mutation strategies (iii) hyperchaotic perturbation strategy. In the first approach, all random numbers in the original TLBO are substituted by the hyperchaotic map sequence to boost exploration capability. In the second approach, three mutations are carried out to explore a new promising search space. The obtained solution is further improved in the third strategy by implementing a new perturbation equation. The proposed HTLBO was evaluated with 26 test functions. The obtained results show that HTLBO outperforms the TBLO algorithm and some state-of-the-art algorithms in robustness and accuracy in almost all experiments. Moreover, the efficacy of the proposed HTLBO is justified by involving it in the power system stability problem. The results consist of the Integral of Absolute Error (ITAE) and eigenvalue analysis of electromechanical modes demonstrate the superiority and the potential of the proposed HTLBO based PSSs and SVC controllers over a wide range of operating conditions. Besides, the advantage of the proposed coordination design controllers was confirmed by comparing them to PSSs and SVC tuned individually.

Power System Stabilizer Tuning Algorithm in a Multimachine System Based on S-Domain and Time Domain System Performance Measures

One of the main characteristics of power systems is keeping voltages within given limits, done by implementing fast automatic voltage regulators (AVR), which can raise generator voltage (i.e., excitation voltage) in a short time to ceiling voltage limits while simultaneously affecting the damping component of the synchronous generator electromagnetic torque. The efficient way to increase damping in the power system is to implement a power system stabilizer (PSS) in the excitation circuit of the synchronous generator. This paper proposes an enhanced algorithm for PSS tuning in the multimachine system. The algorithm is based on the analysis of system participation factors and the pole placement method while respecting the time domain behavior of the system after being subdued with a small disturbance. The observed time-domain outputs, namely active power, speed, and rotor angle of the synchronous generator, have been classified and validated with proposed weight functions based on the minimal square deviation between the initial values in a steady-state and all sampled values during the transitional process. The system weight function proposed in this algorithm comprises s-domain and time-domain indices and represents a novel approach for PSS tuning. The proposed algorithm performance is validated on IEEE 14-bus system with a detailed presentation of the results in a graphical and table form.

Investigation and damping of electromechanical oscillations for grid integrated micro grid by a novel coordinated governor-fractional power system stabilizer

ABSTRACT Electro mechanical low frequency oscillation generated in a micro-grid (MG) when subjected to uncertain disturbances, and stochastic renewable penetration is more aggravated especially when connected to grid. This needs more investigation for the smooth operation of power system and employment of power electronics-based costly control may not be economic for a MG. Also in the current scenario of deregulated market, more stress is imposed on the optimal use of present resources like mechanical torque control provided by turbine governors. In this work, a synchronous generator(SG)-based grid connected MG with Solar Photo Voltaic (SPV) and wind integration has been investigated for low frequency oscillation pertaining to small signal stability enhancement. A new simple governor (Gov)-Power system stabilizer (PSS), which is GOV-PSS controller has been proposed here with proportional integral derivative (PID), lead-lag and fractional lead-lag PSS actions, which coordinates the governor and PSS for oscillation damping and can be easily implemented. The gains of proposed controller are tuned by a new hybrid differential evolution with improved gray wolf optimizer with a multi objective function. The SPV, wind generations, and reference voltages are randomly varied giving rise to critical oscillatory instability, and proposed optimal controller is implemented to damp these oscillations. The excitation gains are also tuned for all generators subject to each condition such that the tuning of PSS would not adversely influence terminal voltage control. Time and frequency response of the system have been analyzed with 9-bus and 33-bus MG structures employing three generators of 1.32 MVA, 0.91 MVA, and 0.67 MVA. Solar and wind penetrations are executed with the bus connected to 1.32 MVA for 9-bus system and with all generators for 33-bus system. The oscillations due to renewable penetrations are found to be enhanced with reduced synchronous generation contribution. It is observed that properly tuned governor and PSS coordinate each other to enhance stability in proposed controller much effectively in contrast to only governor and PSS actions considered individually. Also turbine governors need not be blocked to damp inter area oscillatory mode, rather their efficacy is much enhanced with proposed control action, and this can be implemented to any SG-based generation including gas, steam, and hydro power-based plants.