Power System Low Frequency Oscillation Research Articles

Identification the Low-Frequency Oscillation Stakeout of Power System by Wide-Area Measurement System

With the rapid development of interconnected power grid, the phenomenon of power system low-frequency oscillation appears unavoidably. Then, the appearance of wide-area measurement system provided a supporting technology for better scout low-frequency oscillation system and better recognize the oscillation mode . In this paper, the existing oscillation mode identifying methods are analyzed from the viewpoint of calculation speed, criterion, calculation accuracy, etc. Mainly compared the applicability of two methods, Prony and ESPRIT . Based on the above research, a new idea of monitoring power system low frequency oscillation based on WAMS is put forward in this paper.

Wavelet-Based Analysis of Power System Low-Frequency Electromechanical Oscillations

This paper addresses the use of the continuous wavelet transform (CWT) in the study of power system low-frequency electromechanical oscillations (LFEOs). Based on a modified complex Morlet mother-wavelet function, an approach is proposed to exploit the relationship between system low-frequency oscillation features and the Morlet CWT of a system ringdown signal for detection of modal parameter changes as well as for modal frequency and damping computation. Also, several guidelines for selecting the center frequency and bandwidth parameters, the scaling factor and the translation factor, are proposed in order to provide reliable modal identification estimates. The efficiency of the proposed approach is confirmed by applying it to synthetic, simulated and measured signals.

Damping Controller Based on Output Prediction Observer and LMI Method

To damp the inter-area low-frequency oscillation in power systems, a wide-area damping supplementary inter-area controller, based on output prediction observer and LMI method, was designed in this paper. Simulation results of a test system show that the wide-area damping controller can well damp inter-area low-frequency oscillation, and is insensitive to time-delay to some degree.

Pareto-Optimal Design of Damping Controllers Using Modified Artificial Immune Algorithm

This paper presents two approaches for multiobjective simultaneous coordinated tuning of damping controllers, a modified artificial immune network (MAINet) algorithm and a multiobjective immune algorithm (MOIA). The weighted-sum approach is used to handle the multiobjective optimization problem in the MAINet, while the Pareto-optimization approach is used in the MOIA. To investigate the ability of the proposed algorithms in designing the damping controllers, one small and one large power systems are considered. Two power-system stabilizers (PSSs) are designed for the small power system, while one PSS for a generator and one supplementary controller for a static var compensator (SVC) are designed for the large power system. The simulation studies show that the controllers designed by MOIA perform better than those by MAINet in damping the power-system low-frequency oscillations.

Power system low frequency oscillation monitoring and analysis based on multi-signal online identification

The advance in the wide-area measurement system (WAMS) is driving the power system to the trend of wide-area monitoring and control. The Prony method is usually used for low frequency oscillation online identification. However, the identified amplitude and phase information is not sufficiently used. In this paper, the amplitude is adopted to detect the occurrence of the oscillation and to obtain the mode observability of the sites. The phase is adopted to identify the oscillation generator grouping and to obtain the mode shapes. The time varying characteristics of low frequency oscillations are studied. The behaviors and the characters of low frequency oscillations are displayed by dynamic visual techniques. Demonstrations on the “11.9” low frequency oscillation of the Guizhou Power Grid substantiate the feasibility and the validation of the proposed methods.

COA based robust output feedback UPFC controller design

In this paper, a novel method for the design of output feedback controller for unified power flow controller (UPFC) using chaotic optimization algorithm (COA) is developed. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The selection of the output feedback gains for the UPFC controllers is converted to an optimization problem with the time domain-based objective function which is solved by a COA based on Lozi map. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization problem introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. To ensure the robustness of the proposed stabilizers, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller for damping low frequency oscillations is tested and demonstrated through non-linear time-domain simulation and some performance indices studies. The results analysis reveals that the designed COA based output feedback UPFC damping controller has an excellent capability in damping power system low frequency oscillations and enhance greatly the dynamic stability of the power systems.

Oscillation Damping Enhancement via Coordinated Design of PSS and FACTS-Based Stabilizers in a Multi-Machine Power System Using PSO

This paper investigates the enhancement of power system stability via coordinated design of Power System Stabilizers (PSSs), Thyristor Controlled Series Capacitor (TCSC)-based stabilizer, and Static Var Compensator (SVC)-based stabilizer in a multi-machine power system. The design problem of the proposed stabilizers is formulated as an optimization problem. Using the developed linearized power system model, the particle swarm optimization (PSO) algorithm is employed to search for optimal stabilizer settings that maximize the minimum damping ratio of all system oscillating modes. The proposed stabilizers are evaluated on a two-area weakly-connected multi-machine power system with unstable interarea oscillation mode. The nonlinear simulation results and eigenvalue analysis show the effectiveness of the proposed coordinated stabilizers in damping low frequency power system oscillations and enhancing the system stability.

Understanding Low-Frequency Oscillation in Power Systems

This paper presents a complete overview of low-frequency oscillation phenomena in power systems. The definition of low-frequency oscillation and its classification are clearly explained. Some power system blackout incidents due to low-frequency oscillation and the lesson learned from those incidents are also reported in the paper. Widely used methodology for studying low-frequency oscillation among power utilities is presented. Methods for oscillation damping, at both the operational and the planning stages of a power system, are briefly discussed. A comprehensive case study of low-frequency oscillation in a simple system is presented using eigenvalue analysis. Time domain results are presented to support the eigenvalue analysis. Conclusions are duly drawn.

Monitoring and Reporting of Security of Power System Low-frequency Oscillations

This article deals with the monitoring and visualization of security of power system oscillation modes. The monitoring is performed by means of a performance index based on the traditional stability measure, but it reports on relative changes in the system low-frequency characteristics in a more indicative manner. In addition to introducing a dramatic progression at marginal changes in the mode's characteristics, the index also communicates the “relative distance” to the dangerous system stages by reporting the available security margin. Using the index, system states can be meaningfully visualized. In this article, the matrix visualization technique is applied for that purpose. The technique sufficiently facilitates human interaction for identification of the system states under different network and operating conditions.

Tuning of damping controller for UPFC using quantum particle swarm optimizer

On the basis of the linearized Phillips–Herffron model of a single machine power system, we design optimally the unified power flow controller (UPFC) based damping controller in order to enhance power system low frequency oscillations. The problem of robustly UPFC based damping controller is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO) technique that has fewer parameters and stronger search capability than the particle swarm optimization (PSO), as well as is easy to implement. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller is demonstrated through non-linear time-domain simulation and some performance indices studies under various disturbance conditions of over a wide range of loading conditions. The results analysis reveals that the designed QPSO based UPFC controller has an excellent capability in damping power system low frequency oscillations in comparison with the designed classical PSO (CPSO) based UPFC controller and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the δ E based damping controller is superior to the m B based damping controller.

A Multi-step Q(Λ) learning Approach to Power System Stabilizer

Power system stabilizers (PSS) are used to generate supplementary control signals for excitation system in order to damp the low frequency power system oscillation. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. This paper proposes an optimal control methodology based on reinforcement learning (RL) for damping the low frequency power system oscillations. Q(Λ) learning is a relative new model-free RL algorithm that extends the one-step Q-learning by combining it with TD(Λ) returns for general Λ in a incremental way for the delayed RL problem. The Q(Λ) learning makes use of TD(Λ) returns as the value function estimator through a trace mechanism, and additional traces appear to help bridge the gap between frequency and recency information for heuristic events. In this paper, rotor speed deviation is adopted as the state feedback input, and the voltage deviation, power deviation and rotor speed deviation are formulated as reward function. The case study shows that both of them are very helpful to enhance the small-disturbance dynamics of power system. The multi-step Q(Λ) learning can enhance convergence rate of reinforcement learning algorithm.

Robust state feedback controller design of STATCOM using chaotic optimization algorithm

In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM) using Chaotic Optimization Algorithm (COA) is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.

A PSO based unified power flow controller for damping of power system oscillations

On the basis of the linearized Phillips–Herffron model of a single-machine power system, we approach the problem of select the best input control signal of the unified power flow controller (UPFC) and design optimal UPFC based damping controller in order to enhance the damping of the power system low frequency oscillations. The potential of the UPFC supplementary controllers to enhance the dynamic stability is evaluated. This controller is tuned to simultaneously shift the undamped electromechanical modes to a prescribed zone in the s-plane. The problem of robustly UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multiobjective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using particle swarm optimization technique (PSO) that has a strong ability to find the most optimistic results. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller is demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices studies. The results analysis reveals that the tuned PSO based UPFC controller using the proposed multiobjective function has an excellent capability in damping power system low frequency oscillations and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the δ E based controller is superior to the m B based controller.

Design of output feedback UPFC controller for damping of electromechanical oscillations using PSO

In this paper, a novel method for the design of output feedback controller for unified power flow controller (UPFC) is developed. The selection of the output feedback gains for the UPFC controllers is converted to an optimization problem with the time domain-based objective function which is solved by a particle swarm optimization technique (PSO) that has a strong ability to find the most optimistic results. Only local and available state variables are adopted as the input signals of each controller for the decentralized design. Thus, structure of the designed UPFC controller is simple and easy to implement. To ensure the robustness of the proposed stabilizers, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller for damping low frequency oscillations is tested and demonstrated through nonlinear time-domain simulation and some performance indices studies. The results analysis reveals that the designed PSO-based output feedback UPFC damping controller has an excellent capability in damping power system low frequency oscillations and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the δ E based controller is superior to both the m B based controller and conventional power system stablizer.

Optimal Design of UPFC Based Damping Controller Using Iteration PSO

This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the m B based controller.

On-line measurement of low-frequency oscillations in power systems

Low-frequency oscillations in the interconnected power systems are observed all around the electrical grids. This paper presents a novel technique for analyzing the low-frequency oscillations in power system networks. The proposed technique is a dynamic estimator based on stochastic estimation theory which is suitable for estimating parameters on-line. The method uses digital set of measurements for power system swings to perform the analysis process digitally. The goal is to estimate the amount of damping in the swing curve as well as the oscillation frequency. The problem is formulated and presented as a stochastic dynamic estimation problem. The proposed technique is used to perform the estimation process. The algorithm tested using different study cases including practical data. Results are evaluated and compared to those obtained using other conventional methods to show the capabilities of the proposed method.

Power System Stabilizer Design Using an Online Adaptive Neurofuzzy Controller With Adaptive Input Link Weights

A neurofuzzy controller (NFC) with adaptive input link weights (ILWs) and working as an adaptive power system stabilizer is presented. The control structure of the proposed adaptive neurofuzzy power system stabilizers (ANFPSSs) consists of a neuroidentifier to track the dynamic behavior of the plant and an NFC to damp the low-frequency power system oscillations. Usually, the input membership functions (IMFs) and consequent parameters (CPs) are adapted in order to enhance the performance of the NFC. However, the adjustment of IMFs can be realized indirectly by the tuning of ILWs introduced here, which is simpler due to the small number of parameters involved. Therefore, in this paper, ILWs and CPs are updated online by the gradient descent method. Simulation studies over a range of operating conditions and disturbances in a single machine-infinite bus system and a multimachine power system demonstrate the improvement in the dynamic performance of the system with the proposed ANFPSS.

A Heuristic-Dynamic-Programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique.

Analyzing the Influence of Induction Motor Inertia on Power System Low Frequency Oscillation

The influence of inertia constant (H D ) of induction motor on low frequency oscillation (LFO) is studied by integral manifolds theory. The oscillation frequency and the damping ratio are formulated in single-machine infinite bus (SMIB) system. The theoretical analysis shows that H D influences mainly the damping of LFO, the extent of which is related to the system structure and initial operating condition. The eigenvalue analysis of SMIB system and a simple two-area system shows the influence of H D on local and inter-area oscillation modes of LFO. The results of full-order system and reduced-order system by integral manifolds theory are compared.

Design of an adaptive neural network based power system stabilizer

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness.