Lead-lag Power System Stabilizer Research Articles

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.

Design of Fuzzy Logic based Adaptive Active Power Controller to Enhance Power sharing among DGs in an Autonomous Microgrid

This paper presents the design of an adaptive active power controller to enhance the power-sharing capabilities of distributed generators (DGs) in an autonomous microgrid. Each DG in an autonomous microgrid consists of a droop-controlled inverter to control active and reactive power by regulating frequency and voltage correspondingly. The high droop gain can be used in the power controller to encourage faster power sharing among the DGs. However, high droop gain can cause undamped growing oscillations during load fluctuations or generation losses. During such events, attaining faster power-sharing between DGs using high droop gains is difficult. So, the problem with an autonomous microgrid is the conflict between faster power sharing and stability. Stability needs to be compromised to attain quicker power sharing and vice versa. Hence, to achieve power-sharing swiftly with high droop gain and to diminish the growing oscillations caused, this paper proposes a fuzzy logic-based adaptive active power controller (FLAPC). The proposed FLAPC is adaptive and easy to implement. It offers faster power sharing for different values of droop gains and step change in load. The FLAPC is developed in MATLAB 2018a/Simulink environment, and time domain simulations are performed to see the efficacy of the proposed controller. The results of time domain simulations are compared with a droop controller without any additional controller, conventional lead-lag power system stabilizer, and proposed controller for step change in load at different droop gains. The results show that the proposed controller enhances the power-sharing performance and also ameliorates the system’s stability by reducing the settling time and overshoot in active power responses of DGs.

An analytical approach for LQR design for improving damping performance of multi-machine power system

In a multi-machine environment, the inter-area low-frequency oscillations induced due to small perturbation(s) has a significant adverse effect on the maximum limit of power transfer capacity of power system. Conventionally, to address this issue, power systems were equipped with lead-lag power system stabilizers (CPSS) for damping oscillations of low-frequency. In recent years the research was directed towards optimal control theory to design an optimal linear-quadratic-regultor (LQR) for stabilizing power system against the small perturbation(s). The optimal control theory provides a systematic way to design an optimal LQR with sufficient stability margins. Hence, LQR provides an improved level of performance than CPSS over broad-range of operating conditions. The process of designing of optimal LQR involves optimization of associated state (Q) and control (R) weights. This paper presents an analytical approach (AA) to design an optimal LQR by deriving algebraic equations for evaluating optimal elements for weight matrix ‘Q’. The performance of the proposed LQR is studied on an IEEE test system comprising 4-generators and 10-busbars.

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.

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.

Damping Enhancement for SMIB Power System Equipped with LQR Tuned via Analytical Approach

Among several aspects of stability of synchronous machine operation, an important aspect is the mode of ‘small perturbation stability’ referred to as steady-state, dynamic or conditional stability. Attention has been increasing on the effect of excitation control in damping small-frequency rotor oscillations which characterize the phenomena of dynamic stability. In particular, it has been found useful and practical to incorporate stabilizing signals derived from speed and/or terminal frequency and/or power superposed on the normal voltage error signal of automatic voltage regulators to provide an additional damping to these oscillations. Conventionally, the lead–lag power system stabilizer (CPSS) is employed to supplement stabilizing signal to normal voltage error signal for damping these rotor oscillations of small frequency typically ranges from 0.2 to 3.5 Hz. In recent years, researchers have been proposing linear quadratic regulator (LQR) to replace CPSS for damping these small-frequency rotor oscillations. In this paper, an ‘algebraic approach’ derived from the relationship between algebraic Riccati equation and Lagrange multiplier optimization technique is proposed to tune LQR. The performance of single-machine infinite-bus power system equipped with LQR tuned via the proposed analytical method has been tested in MATLAB/Simulink® environment.

Analysis of the Effects of PSS and Renewable Integration to an Inter-Area Power Network to Improve Small Signal Stability

Power system often suffers from low frequency oscillations (LFOs) which might result in instability in the long run, if allowed to sustain in the system for a long time. In order to mitigate these oscillations, power system stabilizers (PSS) are used through excitation control. Three recently developed meta-heuristic algorithms namely: Collective Decision Optimization (CDO), Grasshopper Optimization Algorithm (GOA) and Salp Swarm Algorithm (SSA) have been applied for the optimal tuning of PSS parameters for small signal stability analysis of a renewable integrated power network. This was done by designing a conventional speed-based lead-lag PSS in a multi-machine interconnected power system, whose parameters have been tuned using CDO, GOA and SSA in a way to shift all the eigenvalues associated to electromechanical modes to the left half of S plane. Comparison of the results obtained by the algorithms demonstrates the superiority of SSA over GOA and CDO to boost the overall system stability over a wide range of operating conditions. The PSS controller designed using SSA is observed to be more robust and efficient in damping out oscillations under different operating conditions.

Modified Particle Swarm Optimization Based on Lead-Lag Power System Stabilizer for Improve Stability in Multi-Machine Power System

Modified Particle Swarm Optimization Based on Lead-Lag Power System Stabilizer for Improve Stability in Multi-Machine Power System

Adaptive Fuzzy Gain of Power System Stabilizer to Improve the Global Stability

The lead-lag power system stabilizer has several parameters to be optimized.In fact, the number of these latter increases with the number of generators constituting the multi-machine system.In this work, we propose anew approach of an adaptive and robust PSS; it achieves encouraging results by adjusting the gain using fuzzy logic and in the same time we use the same PSSs for each machine. In the first place, we could check that the gain is among the most critical parameters of the lead lag PSS. The parameters are globally optimized by the genetic algorithm, after that an expertise on the speed and the gain variations allow the value prediction according to the velocity deviation. To validate our results, a robustness test was made on a multimachine system IEEE (3 machines 9 bus), for different loads and the results showed good performance and robustness of the presented PSS.

A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem

This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices.

Modified Particle Swarm Optimization Based Proportional-Derivative Power System Stabilizer

During a change in operating condition, oscillations of small magnitude and low frequency often persist for long periods of time and in some cases even present limitations on power transfer capability. Generators in power systems are equipped with automatic voltage regulator (AVR) to control terminal voltage. It is known that AVR has a detrimental impact upon the dynamic stability of the power system. Power system stabilizers (PSS) are widely used to generate supplementary control signals for the excitation system in order to damp out low-frequency oscillations (LFOs). In this paper proportional- derivative power system stabilizer (PD-PSS) used to damping LFO after tuning the gains of the PSS by using PSO. The damping boundary condition of PSO technique is modified to improve its performance in the tuning and optimization process. Simulation studies performed on a typical single-machine infinite-bus (SMIB) system used in MATLAB Simulink program. Assessing the performance of the proposed modified PSO based PD-PSS with Speed deviation (∆ω) as an input signal using eigenvalue analysis. The proposed PSO based PD- PSS is evaluated and examined under different operating conditions and inertia constant each one of them applied with two test cases small disturbance and short circuit. A comparative study between the proposed PSO based PD-PSS, original PSO based PD-PSS, and lead-lag PSS is done in this work. The results ensure the superiority, the effectiveness, and the robustness of the proposed PSS over the other techniques. Index Terms—PSS, PSO, single machine infinite bus, and damping LFO.

Optimal Tuning of Multimachine Power System Stabilizer using Cuckoo Search Algorithm

In this paper, optimal multiobjective design of robust multimachine Power System Stabilizers (PSS) using Cuckoo Search (CS) optimization algorithm is presented for small-signal stability enhancement. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with Lévy flight behaviour of some birds and fruit flies. A conventional speed-based lead-lag PSS is used in this work. The parameters of the Cuckoo Search based Power System Stabilizer (CSPSS) are optimized simultaneously to shift the unstable or poorly damped electromechanical mode eigenvalues of all operating conditions to a D-shape zone in the left half of the s-plane. A multiobjective eigenvalue based objective function is designed to optimize a problem involving the damping factor and damping ratio of the electromechanical modes. To show the effectiveness and the robustness of CSPSS, time domain simulations, eigenvalue analysis and performance indices are presented and compared with Genetic Algorithm based PSSs (GAPSS) and Particle Swarm Optimization based PSO (PSOPSS) under wide range of operating conditions and severe disturbances. It is observed that CSPSS exhibits improved damping performance compare to GAPSS and PSOPSS.

Lead-lag PSS design satisfying H∞ control performance using particle swarm optimization

The aim of this paper is to describe how to obtain robustness of a power system stabilizer (PSS) which has existing controller structure in a smart manner. In recent years, robust PSS designs that adopt an H∞ controller have been investigated in order to guarantee the performance when the state of the system configuration and power flow change. However the H∞ controller has not been widely adopted into practical use because of the intricate nature of its theory and structure. We consider the H∞ control problem under the condition that PSS structure is fixed to be a lead-lag compensator. We optimize the parameters of the lead-lag PSS by a particle swarm optimization (PSO) that has an evaluation function which takes into account a closed loop H∞ norm and a desired response. In this way, we design a smart PSS which has a conventional controller structure and guarantees its H∞ control performance.

A PSO Procedure for a Coordinated Tuning of Power System Stabilizers for Multiple Operating Conditions

The problem of coordinated tuning stabilizers in multi-machine power systems is formulated here as a sequence ofoptimization problems. The design problem of stabilizers is converted to a nonlinear optimization problem with a multiobjectivefitness function. The proposed method employs particle swarm optimization (PSO), an algorithm to searchfor optimal parameter settings of a widely used conventional fixed-structure lead-lag power system stabilizers(CPSSs). One of the main advantages of the proposed approach is its robustness to the initial parameter settings. Inaddition, the quality of the optimal solution does not rely on the initial guess. The robustness and performance of thenewly designed controllers is evaluated in a large sixteen-machine power system subjected to different loadingconditions in comparison with the genetic algorithm (GA) based PSSs design. The superiority of the controllerdesigned is demonstrated through the nonlinear time-domain simulation and some performance indices studies. Theresults analysis reveals that the tuned PSSs with proposed objective function has an excellent capability in dampingpower system low-frequency oscillations.

Design of Pole Placement Power System Stabilizers for Multi-Machine Systems without the External System Information

Pole placement based power system stabilizer designs are well-known for their superior control capabilities compared with the conventional lead-lag power system stabilizers. They require feedback of all the state variables. As power system state variables are generally not measurable they have not seen much of practical realization. In multi machine systems pole placement designs require feedback of other machine states as well. This paper proposes a new approach for the pole placement based PSS. In this paper, recently proposed modified Heffron Phillip’s model is used for the PSS design. Modified Heffron Phillip’s model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of infinite bus. State variables of this model are locally measurable, which makes the pole placement design decentralized and independent of the external system information. The performance of the proposed controller is evaluated on 10 generator 39 bus multi - machine system over a wide range of operating conditions. The proposed PSS performance is found to be better than the conventional PSS. Keywords: Engineering, Power System Stabilizer, Small-Signal Stability, State Feedback, Linear Quadratic Regulator, Andhra Pradesh, India

Decentralized linear quadratic power system stabilizers for multi-machine power systems

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead–lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron–Phillip’s model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions.

Multi-machine power system stabilizer design using improved particle swarm optimization (PSO) with time-varying acceleration coefficients

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

Design of CPSS for Multimachine Power System Using Simulated Annealing Algorithm considering G-T Dynamics

In the present work, dynamic stability analysis of power system is investigated using conventional phase lead-lag power system stabilizer (CPSS) for multimachine power system with and without considering governor-turbine (G-T) dynamics. Parameters of CPSS are optimized by minimizing an objective function using simulated annealing algorithm (SA). Analysis reveals that there is a need to consider the G-T dynamics for dynamic stability analysis and CPSS gives better dynamic performances as compared to that of without PSS. Attempt is also made to examine whether gain and parameter settings of CPSS obtained at a particular operating condition works well or not to all other operating conditions. Analysis also reveals that the proposed CPSS works satisfactorily following a transitory three-phase fault.

Multi-machine power system stabilizer adjustment using genetic algorithms

In this paper, the problem of tuning lead-lag power system stabilizer parameters of a multi machine power system is considered. This problem is formulated as an optimization problem, which is solved using Genetic Algorithms (GA). Simulation results show the effectiveness of the proposed Genetic Algorithms-based lead-lag power system stabilizer to damp the oscillation of multimachine system and work effectively under variable loading and fault conditions. The proposed GA-PSS is evaluated on the IEEE 14 bus test system as a multi machine electric power system.

Multi-machine power system stabilizer adjustment using genetic algorithms

In this paper, the problem of tuning lead-lag power system stabilizer parameters of a multi machine power system is considered. This problem is formulated as an optimization problem, which is solved using Genetic Algorithms (GA). Simulation results show the effectiveness of the proposed Genetic Algorithms-based lead-lag power system stabilizer to damp the oscillation of multimachine system and work effectively under variable loading and fault conditions. The proposed GA-PSS is evaluated on the IEEE 14 bus test system as a multi machine electric power system.