Power System Stabilizer Design Research Articles

Optimal Design of Power System Stabilizer Using a Novel Evolutionary Algorithm

In this article, an Oppositional Differential search algorithm (ODSA) is comprehensively developed and successfully applied for the optimal design of power system stabilizer (PSS) parameters which are added to the excitation system to dampen low frequency oscillation as it pertains to large power system. The effectiveness of the proposed method is examined and validated on a single machine infinite bus (SMIB) using the Heffron-Phillips model. The most important advantage of the proposed method is as it reaches toward the optimal solution without the optimal tuning of input parameters of the ODSA algorithm. In order to verify the effectiveness, the simulation was made for a wide range of loading conditions. The simulation results of the proposed ODSA are compared with those obtained by other techniques available in the recent literature to demonstrate the feasibility of the proposed algorithm.

Parameter optimization of power system stabilizers via kidney-inspired algorithm

This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

Application of affine NARMA model to design of adaptive power system stabilizer

Application of affine NARMA model to design of adaptive power system stabilizer

Fractional‐order lead‐lag compensator‐based multi‐band power system stabiliser design using a hybrid dynamic GA‐PSO algorithm

Power system stabilisers (PSSs) are supplementary controllers connected to the excitation system of synchronous generators to damp electromechanical oscillations. Multi-band PSSs are reported as advanced PSSs with the ability to damp out all oscillation modes present in the power systems. This study presents the design of a robust fractional-order multi-band power system stabiliser (Fo-MBPSS) using a meta-heuristic hybrid algorithm for dynamic stability improvement of multi-machine power systems. The large bandwidth, memory effect and flat phase contribution in the frequency response of fractional-order controllers are exploited to make the Fo-MBPSS perform well against a wide range of system uncertainties. The parameter tuning problem of Fo-MBPSS is transformed to an optimisation problem that is solved using a hybrid algorithm by combining a dynamic genetic algorithm (DGA) with a standard particle swarm optimisation (PSO) algorithm. The performance of the proposed DGA-PSO-Fo-MBPSS is evaluated through eigenvalue analysis, non-linear time-domain simulations and some performance indices, in two different multi-machine systems under different loading conditions and disturbances. The results are compared with PSO-based conventional MBPSS and PSO based Fo-MBPSS (PSO-Fo-MBPSS) to establish the fractional parameter effect on the improvement of the system dynamic response and the relevance of the proposed hybrid optimisation technique in achieving robustness.

Desain Power System Stabilizer Berbasis Fuzzy Tipe-2 untuk Perbaikan Stabilitas Mesin Tunggal

The growth of type-2 fuzzy logic system is penetrating electric power system field, especially on electric power system control sub-field in order to simplify the design of power system stabilizer (PSS). Traditionally, the function of conventional PSS is to damp rotor oscillation when a disturbance occurred due to transmission configuration or/and loading changes. However, the response of conventional PSS is slow with long settling time and high peak overshoot. To cover this problem, PSS based on type-2 fuzzy logic system (PFT2) is proposed. Simulation results show that the PFT2 is able to improve the stability of a single machine with 3 scenarios on transmission configuration and loading variation. The PFT2 gives shorter settling time for all scenarios and loading variation than the settling time of conventional PSS. Also, the peak overshoot of the PFT2 is smaller than the peak overshoot of the other competing PSS.

Application of Information Gap Decision Theory to the Design of Robust Wide-Area Power System Stabilizers Considering Uncertainties of Wind Power

This paper proposes the application of information gap decision theory (IGDT) to the design of robust wide-area power system stabilizers (WPSSs) with consideration of wind farm (WF) power outputs variations and transmission line outages. According to IGDT, an optimization problem is constructed to tune WPSS parameters. Then, the derived optimal WPSSs can achieve explicit and favorable robustness to ensure the required damping control effects on the interarea oscillations over a maximum variation range of WF steady-state power outputs in normal and emergent operating conditions. Moreover, with the intent of using the excellent global searching capability of particle swarm optimization (PSO), a customized PSO algorithm is proposed to efficiently solve the resulting highly nonlinear programming problem. Finally, simulations are carried out on a modified New England (10-machine 39-bus) system to validate the efficiency of the IGDT-based design method. The derived WPSSs exhibit expected robustness with respect to the wind power variations and transmission line outages.

Model based PI power system stabilizer design for damping low frequency oscillations in power systems

This paper explores a two-level control strategy by blending local controller with centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favourably compared with some controllers prevalent in the literature.

LQR and LQG based optimal switching techniques for PSS and UPFC in power systems

Selection of better optimized unified power flow controller (UPFC) control inputs along with simultaneous coordinated design of power system stabilizer (PSS) is a challenge in the present scenario of power systems. Hence, in this paper, four sets of experiments performed are presented. First set of experiments are without disturbance scenario where switching is done using linear quadratic regulators (LQR’s). Second set is for power systems with disturbances using linear quadratic gaussian (LQG). Switching control algorithms presented here are tested on the single machine infinite bus (SMIB) linearised Phillips Heffron model of power system using MATLAB/SIMULINK® platform.

Damped Nyquist Plot for a pole placement design of power system stabilizers

This paper proposes a new frequency domain control design method through the use of a modified Nyquist diagram with an embedded partial pole-placement capability. The proposed Damped Nyquist Plot (DNP) method evaluates the open loop transfer function (OLTF) along a line of constant damping ratio to provide a graphical tool to design power system stabilizers (PSS). The graphical tool shows how the closed loop poles move around this damping ratio line for different choices of PSS parameters, all of them placing a selected pair of complex-conjugate poles at the same desired point in the complex plane. New formulas are developed to determine the exact PSS parameters that promote the several possibilities of partial pole placement considering the parameter ranges used in tuning practice. These formulas can also be used for phase compensation of conventional methods of PSS design, such as GEP or Residue Angle Compensation. Multiple PSSs can be tuned using the DNP method sequentially. The proposed method is applied to two stabilization examples, the first of tutorial nature and the second is the large actual Brazilian Interconnected Power System.

Optimum Design of Power System Stabilizer based on Improved Ant Colony Optimization Algorithm

This paper presents an improved technique on Ant Colony Optimization (ACO) algorithm. The procedure is applied on Single Machine with Infinite Bus (SMIB) system with power system stabilizer (PSS) at three different loading regimes. The simulations are made by using MATLAB software. The results show that by using Improved Ant Colony Optimization (IACO) the system will give better performance with less number of iterations as it compared with a previous modification on ACO. In addition, the probability of selecting the arc depends on the best ant performance and the evaporation rate.

Optimal Placement and Tuning Approach for Design of Power System Stabilizers and Wide Area Damping Controllers Considering Transport Delay

Abstract In this paper, a novel optimal placement and parameter tuning approach for Power System Stabilizer (PSS) is proposed to damp both local and inter-area modes in mesh like power system. The design target is two-level PSS consisting of ΔP type local PSS and Δθ type Wide Area Damping Controller (WADC). For the PSS placement, modeshape with eigenvector sensitivity and coherency analysis are used to determine the appropriate placement of both local PSS and WADC. For the PSS parameter tuning, metaheuristics based approach via Mean Variance Mapping Optimization (MVMO) is employed in order to consider the time domain analysis. The proposed objective function designs at first the local PSS only to improve damping coefficient and damping ratio of eigenvalue. After that, it designs the WADC overlapping with already designed local PSS by time domain analysis, taking into account the transport delay in the remote signal between Phasor Measurement Unit (PMU) and PSS. The proposed method is applied in IEEE New England 39-Bus (NE 39-bus) system model. Then, modeshape and coherent grouping analysis give us understandable system aspect, the local PSS and the WADC optimized by using MVMO considering the transport delay improve both local and inter-area modes whereas a method ignoring the transport delay make system poorly damped.

Optimal Design of Power System Stabilizer Using Remote Signal Considering the Transport Delay

Abstract This paper proposes a novel optimal Power System Stabilizer (PSS) design method to damp inter-area oscillations in power systems. As the input of PSS, remote signals which can be obtained by Phasor Measurement Units (PMUs) are used since remote signals represent global information of the power system. The method improves both small signal stability and transient stability, taking into consideration the fluctuation range of transport delay in the communication link between PMUs and PSSs, which may significantly affect the oscillation damping performance. In the design method, Mean Variance Mapping Optimization (MVMO) is employed to determine the PSS parameters. The proposed method is applied in IEEJ (The Institute of Electrical Engineers of Japan) WEST 10-machine system model. Then, network modeshape is used to select the most appropriate remote signal. As a result of optimal design, the PSSs with both local and remote signals can give better damping than the local PSS alone, even when maximum transport delay occurs, whereas PSS designed ignoring transport delay makes the system unstable in some cases of such delay.

A memetic algorithm for power system damping controllers design

This paper presents a hybrid algorithm for robust and coordinated design of power system stabilisers. Power system stabilisers are controllers installed on synchronous generators for excitation control in order to damp power system oscillations. The tuning procedure (gain and phase compensation stage) is cast as an optimisation problem which aims at maximising the damping coefficients in closed-loop operation. Robustness is dealt with by using multiple operating scenarios. For the optimisation problem solution, the bio-inspired Bat Algorithm is combined with the steepest descent method for local search capability enhancement. The proposed algorithm is applied to benchmark systems for validation.

Tuning of power system stabilizer for small signal stability improvement of interconnected power system

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

Design of Multi-Machine Power System Stabilizers with Forecast Uncertainties in Load/Generation

ABSTRACTOscillatory instability in modern power system has increased due to increasing complexity and integration of dynamic loads. Stability analysis of such interconnected power system is very prominent in the presence of uncertain load/generation. In this paper, a power system stabilizer (PSS) design approach, which aims at enhancing the oscillatory stability of the multi-machine power system over the specified uncertainty range in forecasted load/generation, is presented. With non-statistical uncertainty, problem of selecting design parameters of the PSS is formulated as an optimization problem with minimization of eigenvalues and damping ratios based multi-objective function. In order to account the non-statistical uncertainties, a boundary active power loss (BAPL) based objective function is proposed. This non-linear BAPL objective function is minimized for determining the optimal setting of the generators voltage and taps of the online tap changing transformers (OLTC) under various power system constraints. In this paper, both the objective functions are solved by a new metaheuristic technique known as gray wolf optimization (GWO). Boundary value-based approach is used to minimize the repeated load flows under uncertain load/generation scenarios. Improved small-signal stability (SSS) is achieved with optimal active power loss of uncertain power system. Eigenvalue and time domain analysis for New England system are carried out under wide range of disturbances to demonstrate the potential of the proposed approach.

LPV-based power system stabilizer: Identification, control and field tests

This paper shows the design and tests of an LPV power system stabilizer aimed at improving the damping of electromechanical oscillations in power systems. In order to capture the dynamic model for control design, LPV models were estimated from experimental data. The generator active and reactive powers were used as scheduling parameters. The control problem is formulated as a parameterized linear matrix inequality, which the positivity condition is relaxed through a sum-of-squares decomposition. The controller ensures stability and H∞ performance for a set of operating conditions. Field tests were carried out on a 10-kVA machine and on a 350-MVA hydroelectric generator.

An improved artificial bee colony algorithm for robust design of power system stabilizers

PurposeLow-frequency oscillations of 0.1 to 3 Hz are prejudicial to the power system stability. Within this context, this study aims to present an improved artificial bee colony (ABC)-based algorithm for optimal setting of multimachine power system stabilizers (PSSs) under several loading conditions simultaneously.Design/methodology/approachThe proposed approach symbolized by GCABC incorporates the grenade explosion technique and the Cauchy operator in the employed bee and onlooker bee phases to avoid random search. The parameters of the grenade explosion method and Cauchy operator based ABC(GCABC)-based PSSs (GCABC-PSSs) are tuned to place all undamped and lightly damped electromechanical modes in a prespecified zone in the s-plan.FindingsSimulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and the dominance of the proposed controllers GCABC-PSSs in the improvement of the system stability under several disturbances and large set of operating points compared with the classical ABC method and genetic algorithm-based PSSs.Originality/valueThe novelty of the study is to efficiently implement a new optimization method called GCABC for an optimum design of PSSs. The design problem is formulated as a multi-objective optimization problem. In addition, all PSS parameters have been included in the space research.

Robust Bi-level Model for Optimal Allocation and Design of Power System Stabilizer in MultiMachine Power Systems

Robust Bi-level Model for Optimal Allocation and Design of Power System Stabilizer in MultiMachine Power Systems

Gradient based hybrid metaheuristics for robust tuning of power system stabilizers

Power System Stabilizers are controllers installed on synchronous generators to damp power system oscillations through the excitation control. These controllers can have either a conventional fixed structure composed by stages of gain and phase compensation or a flexible modern structure composed by three bands that correspond to a specific frequency range (low, intermediate and high frequency) in which each band is composed by two branches that are based on differential filters (with a gain, lead-lag blocks and a hybrid block). Power system stabilizers design is a hard and time consuming task and an alternative for tuning controllers is by using optimization methods. This paper presents three hybrid metaheuristics for the robust and coordinated design of power system stabilizers. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients in closed-loop operation. Robustness requirement is met by using multiple operating scenarios in the design stage. For solving the optimization problem, three metaheuristics (Gravitational Search Algorithm, Bat Algorithm and Particle Swarm Optimization) are combined with the Steepest Descent Method for local search capability enhancement. The proposed hybrid algorithms are applied to benchmark systems for validation.

Robust pole placement for power systems using two‐dimensional membership fuzzy constrained controllers

This study presents a design method of power system stabilisers (PSSs) to damp system oscillations. The design is based on fuzzy logic and linear matrix inequality (LMI) techniques. The design guarantees a robust pole placement in a desired region in the complex plane for different loading conditions. Due to system non-linearity and load dependency, the whole wide range of operating condition is divided into three subregions. For each subregion, a norm-bounded uncertainty linear model is developed and a robust LMI-based pole placer is designed considering controller constraint. Takagi–Sugeno fuzzy model is used to guarantee smoothly switching between the LMI-based controllers of the three subregions. The proposed technique avoids solving many LMIs as in the polytypic approach and avoids the infeasibility problem that might arise from solving the LMIs for the complete region of operation. In addition, the proposed fuzzy logic switching controller has reduced drastically the fuzzy rules due to the use of two-dimensional (2D) membership functions. Particle swarm optimisation is used to tune the proposed 2D membership functions to guarantee smooth switching of the controllers while maintaining the desired constraints. The simulation results of both single-machine and multi-machine power systems confirm the effectiveness of the proposed PSS design.