Oscillations In Multimachine Power Systems Research Articles

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

A Novel Energy Flow Analysis and Its Connection With Modal Analysis for Investigating Electromechanical Oscillations in Multi-Machine Power Systems

In this paper, a novel energy flow analysis (EFA) is proposed based on the signal reconstruction and decomposition to investigate the electromechanical oscillations. In contrast to the conventional EFA, the connection between the proposed EFA and modal analysis (MA) can be quantitatively revealed for arbitrary models of synchronous generators in multi-machine power systems. Firstly, the time-domain implementation (TDI) of the proposed EFA is designed. Specifically, the measurements at the terminal of a local generator are reconstructed through an exponential operator and then decomposed with respect to an angular frequency. Then, the mode-screened damping torque coefficient is defined to extract the damping feature with respect to an electromechanical oscillation mode. After that, the frequency-domain implementation (FDI) is derived. Specifically, the Parseval's Theorem is applied to transform the proposed EFA from the time domain to frequency domain. On this basis, the consistency between the proposed EFA and MA is strictly proved, which is applicable for arbitrary models of synchronous generators in multi-machine power systems. Additionally, the application procedure of the proposed EFA in investigating electromechanical oscillations is given. Finally, the proposed EFA are substantially demonstrated in multiple case studies.

Performance Analysis of Data-Driven Techniques for Detection and Identification of Low Frequency Oscillations in Multimachine Power System

In power systems, identification and damping of low-frequency oscillations(LFO) is very crucial to maintain the small signal stability. Hence the computation of eigenvalues, eigenmode shapes, participation factors, and coherency of generators are essential parameters of critical LFO modes. The existing data-driven approaches explore either one or two aspects of modal parameters from the dynamic pattern of the measurement data. In the present work, two approaches i) Iterative Approach(IA) ii) Non-Iterative Subspace(SS) method of data-driven techniques are used to estimate the state-space model of the system under study from the measurement data in a holistic framework. Based on the estimated system model, the eigenvalues of LFO, eigenmode shapes, participation factor, and coherency of associated generators participating in electromechanical oscillations are computed. Finally, from the estimated participation factors for the Inter-area oscillation mode (IAM), the Static Synchronous Compensator (STATCOM) damping controller is designed and placed at the generator with the highest participation factor for damping of inter-area oscillation. The enhancement of damping ratio of inter area mode with STATCOM damping controller is estimated and verified using IA & SS data driven approaches for the first time. In this work, IA uses prediction-error minimization algorithm (PEM) & Parallel computing techniques and SS method uses Multivariable Output Error State Space (MOESP) algorithm for the estimation of Hankel matrix from the measured data. The effectiveness of data-driven approaches are demonstrated by the simulation of a IEEE 4-machine,10-bus power system using MATLAB / Simulink. IA & SS methods incorporating wavelet based denoising techniques are very effective in identifying the LFO modes even with noisy measurement. The efficacy of the denoising to suppress the effect of noise is demonstrated by comparing with noiseless environment. The results of data-driven approaches indicate their high degree of accuracy and efficiency in being consistent with Eigenvalue analysis (EA) performed on the system.

Application of partial eigenvalue assignment techniques to dampen electromechanical oscillations in multi-machine power systems

Abstract In this study, a methodology for partial eigenstructure assignment (PEVA) is applied to dampen electromechanical oscillations in electrical multi-machine power systems. The approach is anchored in allocating a small number of undesirable eigenvalues, for example, which are poorly damped, preserving the other eigenvalues in the system - the so-called no-spillover spectrum. The new position of the selected eigenvalues is carried out based on the partial controllability analysis of the system, in order to minimize the control effort. Simulation examples using a system with 68 buses, 16 generators and five areas showed that the presented methodology is efficient in dampening the local and inter-area oscillation modes when compared to the classic power system stabilizers (PSS). The quality of the solution is illustrated through computer simulations, eigenvalues tables and mode-shapes.

Comparing strategies to damp electromechanical oscillations through STATCOM with multi-band controller

This paper aims to compare two strategies for damping low-frequency electromechanical oscillations in multi-machine power systems through Static Synchronous Compensators (STATCOM) with a multi-band controller. STATCOM is represented by a controllable voltage source behind an impedance and the multi-band controller acts as a power oscillation damper that modulates parameters of the voltage source in the transient period. In the first strategy, the multi-band controller acts on the voltage-control loop through the voltage modulation. In the second one, the controller acts on the real power-control loop to modulate the phase angle of the voltage source. The coordinated design of multi-band controllers and power systems stabilizers is performed through an optimization approach taking into account several operation conditions.

Mitigation of power oscillations using hybrid DE-PSO optimization-based SSSC damping controller

This paper presents an optimal design of a static synchronous series compensator (SSSC)-based controller for damping of low-frequency oscillations in multi-machine power systems. The proposed controller design problem is formulated to the optimization problem. The tuning of controller parameters can be obtained by employing a new hybrid differential evolution and particle swarm optimization (hDE-PSO) algorithm. To justify the effectiveness of the proposed SSSC-based damping controller, three-machine and four-machine power systems have been considered. The hDE-PSO algorithm outperforms in the damping of oscillations over DE and PSO algorithms. Various simulation results are presented and compared for different load disturbances like three-phase fault, load rejection and tripping of one parallel transmission line. The simulation results ensure the robustness of the proposed controller.

Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

This article presents a novel comprehensive learning bat algorithm (CLBAT) for the optimal coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC) for damping electromechanical oscillations in multi-machine power systems considering a wide range of operating conditions. The CLBAT incorporates a new comprehensive learning strategy (CLS) to improve microbat cooperation; location updating is also improved to maintain the bats’ diversity and to prevent premature convergence through a novel adaptive search strategy based on relative travelled distance. In addition, the proposed elitist learning strategy speeds up convergence during the optimization process and drives the global best solution towards promising regions. The superiority of the CLBAT over other algorithms is demonstrated via several experiments and comparisons through benchmark functions. The developed algorithm ensures convergence speed, credibility, computational resources and optimal tuning of PSSs and SVCs of multi-machine systems under different operating conditions through eigenanalysis, nonlinear simulation and performance indices.

Multi-band power oscillation damping controller for power system supported by static VAR compensator

Low-frequency electromechanical oscillations damping is powerfully crucial in power system operation. In order to fulfill this requirement, power oscillation damping (POD) controllers are installed in synchronous generators and flexible alternating current transmission system devices. These controllers can have either a conventional fixed structure composed by stages of gain and phase compensation or a multi-band modern structure (PSS4B) composed by three bands that correspond to a specific frequency range (low, intermediate and high frequency). In the PSS4B structure, each band consists of two branches based on differential filters (with a gain, lead–lag blocks and a hybrid block). This paper investigates the application of PSS4B as a POD controller for the static VAR compensator (PSS4B-SVC-POD) to damp oscillations in multi-machine power systems. The proposed PSS4B-SVC-POD and power system stabilizers (fitted on generators) are simultaneously designed through an optimization approach aiming at maximizing the closed-loop damping ratio. Modal analysis, frequency responses and time simulations for the well-known two-area four-generator power system show the good performance of the proposed controller fitted on static VAR compensator.

Fast Frequency Response From Smart Induction Motor Variable Speed Drives

Using their fast power reduction capabilities, the induction motor variable speed drives can successfully participate in the power system primary frequency control. A new control strategy for fast frequency support from the diode front-end induction motor variable speed drive is presented in this paper. By this, practical restrictions are considered. In this context, an existing conventional scheme presented in literature is firstly modified by deploying an estimated motor's power losses for a more exactly control design. Next, a novel control scheme is proposed. It attains the fastest frequency support from the drive and simultaneously restricts its minimum power consumption to a given positive value to prevent regeneration mode during the frequency support process. The frequency of applied voltage to the motor is adjusted through a proportional-integrator compensator with deviation of the drive's consumption power from its reference power as input. The reference power is synthesized by permanent and temporary components, which are determined based on deviation and time derivative of the power system's frequency, respectively. The effectiveness of the proposed smart drive for the primary frequency control is investigated using the New England 39 bus and a more realistic Great Britain 36 zone test networks. It improves system frequency response in terms of the rate of change of frequency and the frequency nadir. Furthermore, the new control scheme can contribute to quickly damping inter-area oscillations in multi-machine power systems.

Coordinated design of power system stabilizers and TCSC employing improved harmony search algorithm

Power System Stabilizers (PSS) are generally employed to damp electromechanical oscillations by providing auxiliary stabilizing signals to the excitation system of the generators. But it has been found that these Conventional PSS (CPSS) do not provide sufficient damping for inter-area oscillations in multi-machine power systems. Thyristor Controlled Series Capacitor (TCSC) has immense potential in damping of inter-area power swings and in mitigating the sub-synchronous resonance. In this paper Improved Harmony Search Algorithm (IHSA) has been proposed for coordinated design of multiple PSS and TCSC in order to effectively damp the oscillations. The results obtained by using IHSA on WSCC 3-machine, 9-bus system are found to be superior compared to the results obtained using Bacterial Swarm Optimization (BSO) algorithm. The damping performance of conventional PSS and TCSC controllers is also compared with coordinated design of IHSA based PSS and TCSC on New England 10-machine, 39-bus system over wide range of operating conditions and contingencies. To demonstrate the effectiveness of the proposed technique the results obtained on this test system are also compared with the results obtained with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA) and Bacterial Swarm Optimization (BSO).

Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part

This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT) and employing a back-propagation-based approach. The stabilizer initialization is performed using an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to use any identification process. Kundur's four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS.

Comparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System

This paper explores the application of artificial neural networksfor online identification of a multimachine power system. A recurrent neural networkhas been proposedas the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions . The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.

Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System

An online auxiliary control was designed for Static Var Compensator (SVC) to improve the poorly damped oscillations in multi-machine power system subjected to small and large disturbances. This paper presents auxiliary control based on Adaptive NeuroFuzzy (ANF) control using triangular membership function. Such a model free based control does not require any prior information about the system and is robust to system changes quickly. To minimize the cost function and to tune the parameters of the antecedent and consequent part of the proposed control, a Gradient Descent (GD) learning algorithm is used. The time domain simulation results were carried out for two machine test system for four different cases. In order to exploit the performance and robustness of ANF control, the results were compared with conventional PI and no control. Simulation results and performance indices reveal that the proposed control outperforms during various fault conditions and hence improves the transient stability to a great extend.

Multi-machine power system stabilizer adjustment using Simulated Annealing

In this paper, the authors are willing to find the optimal and robust design for power system stabilizers (PSSs) in a multi-machine power system. In this regard a new Meta heuristic method as Simulated Annealing (SA) is used. The PSS parameters are computed to assure maximum damping performance under different scenarios. A multi machine electric power system is used to illustrative the performance of proposed method. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results. Simulation results are carried out by numerical simulations on MATLAB software. Keywords: Multi Machine, Power System Stabilizer, Low Frequency Oscillations, Simulated Annealing

Mixed integer non-linear programming via the cross-entropy approach for power system stabilisers location and tuning

Mixed integer non-linear programming (MINLP) via the cross-entropy approach for the optimal location and tuning of power system stabiliser (PSS) is presented. The considered problem is to maximise the damping ratio of the global system under a minimum number of PSS and simultaneously to find out the best candidate machines to be equipped with PSSs. The damping ratio objective is achieved by tuning the controllers to shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. This problem of tuning and location of PSS over a wide range of system configurations is formulated as a MINLP problem where the objective is the aggregation of the two objectives on the damping ratio and on the PSS's number. The mixed optimisation problem, with a great combinatory aspect, is solved by an extension of the cross-entropy approach from the rare event framework. The performance of this technique for damping the oscillations in multimachine power systems is confirmed through eigenvalues analysis over many scenarios. For comparative purpose the Monte Carlo simulation and the classical approach have been used to show the effectiveness of the proposed approach.

Optimal locations and tuning of robust power system stabilizer using genetic algorithms

Optimal locations and design of robust multimachine power system stabilizers (PSSs) using genetic algorithms (GA) is presented in this paper. The PSS parameters and locations are computed to assure maximum damping performance under different operating conditions. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results and eigenvalues analysis.

Optimal multiobjective design of robust power system stabilizers using genetic algorithms

Optimal multiobjective design of robust multimachine power system stabilizers (PSSs) using genetic algorithms is presented in this paper. A conventional speed-based lead-lag PSS is used in this work. The multimachine power system operating at various loading conditions and system configurations is treated as a finite set of plants. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The problem of robustly selecting the parameters of the power system stabilizers is converted to an optimization problem which is solved by a genetic algorithm with the eigenvalue-based multiobjective function. The effectiveness of the suggested technique in damping local and interarea modes of oscillations in multimachine power systems, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear simulation results.

Simultaneous stabilization of multimachine power systems via genetic algorithms

This paper demonstrates the use of genetic algorithms for the simultaneous stabilization of multimachine power systems over a wide range of operating conditions via single-setting power system stabilizers. The power system operating at various conditions is treated as a finite set of plants. The problem of selecting the parameters of power system stabilizers which simultaneously stabilize this set of plants is converted to a simple optimization problem which is solved by a genetic algorithm with an eigenvalue-based objective function. Two objective functions are presented, allowing the selection of the stabilizer parameters to shift some of the closed-loop eigenvalues to the left-hand side of a vertical line in the complex s-plane, or to a wedge-shape sector in the complex s-plane. The effectiveness of the suggested technique in damping local and inter-area modes of oscillations in multimachine power systems is verified through eigenvalue analysis and simulation results.

Robust power system controller design based on measured models

This paper presents combined power system identification and controller design methods to dampen low-frequency oscillations in multimachine power systems. An iterative closed-loop identification method is used to find a linear model for the power system. Linear quadratic Gaussian controller design with loop transfer recovery (LQG/LTR), based on a generalized technique for the nonminimum phase (NMP) power system model, is used to design the controllers. Simulation results are presented to demonstrate the robustness of these controllers based on closed-loop identified plant models and the amount of loop transfer recovery that is possible for NMP plant models.

Controlled series compensation for improving the stability of multi-machine power systems

The application of controlled series compensators (CSCs) to increase the damping of dynamic oscillations in multimachine power systems is described. A CSC controller design is presented based on a linearised model of the power system around a given nominal operating point. The pole placement technique is used for calculating the controller feedback gains. A procedure for determining an optimum location for installing a CSC in a power system to provide the most effective damping is investigated. A technique for coordinating CSCs when it is necessary to use more than one CSC in a power system is also discussed. Computer simulations are used to demonstrate the effectiveness of CSCs in increasing the damping of power system dynamic oscillations. A simple power system with nine buses and three synchronous machines is used to demonstrate the design procedure.