Electromechanical Oscillations Research Articles

Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples.

Real-Time Damping Estimation on Nonlinear Electromechanical Oscillation

Real-time damping estimation for a dominant inter-area mode is important for situational awareness of potential angular instability in power systems. Electromechanical oscillations energized by large disturbances often manifest obvious nonlinearities in measurements on first several swings. Traditional methods based on linear system theory often discard first several swings intentionally to avoid nonlinearity; if not, the estimated damping ratios often vary with the length and starting point of the measuring window. By identifying a nonlinear oscillator to fit a dominant mode, this paper proposes a new measurement-based approach utilizing complete post-disturbance data for robust damping estimation independent of the measuring window. Case studies on the IEEE 9-bus system and a 48-machine Northeast Power Coordinating Council system validate the proposed approach for providing accurate and robust damping estimation compared with existing methods including the Prony's method. Meanwhile, three factors influencing damping estimation in practical applications are also addressed, including measurement noises, limited coverage of PMU measurements, and existence of multiple dominant modes.

Impact of the high share of converter‐interfaced generation on electromechanical oscillations in Continental Europe power system

This study investigates the effects of converter-interfaced generation integration on the dynamic response of the power system of Continental Europe. The system is analysed with a large-scale dynamic model of the entire synchronous area, considering different instantaneous integration percentages of converter-interfaced generation across the system. The study focuses on the reduction of the overall available kinetic energy and the impact on frequency dynamics and system oscillations. The dynamic model of the system originally provided by ENTSO-E is further developed according to a specific methodology, replacing a determined amount of synchronous generation and introducing a corresponding amount of converter-controlled current sources. The reference incident of a generation loss in Western Europe specified by ENTSO-E is considered in the analysis. The results of time-domain simulations and modal analysis show how the integration of non-synchronous generation affects the frequency dynamics of Western, Central and Eastern Europe, bringing to attention some relevant effects of the spatial distribution of different generation sources within an extensive system as the Continental Europe synchronous area.

WITHDRAWN: Analysis, design and implementation of hardware neural network based rotor oscillations compensator

In this paper, an Artificial Neural Network (ANN) based Rotor Oscillations Compensator (ROC) is proposed and its equivalent Hardware Realizable Neural Network (HARNN) has been developed on the Field Programmable Gate Array (FPGA) board. The basic function of a ROC is to minimize the low frequency electro mechanical oscillations created in the Electric power network. Whenever the fault is occurred outside the operating range of a fixed gain ROC, the system damping is insufficient and the machine is still under unstable mode. Hence a hardware having sufficient “intelligence” that is capable of producing necessary and sufficient damping to the system is proposed in this paper. A detailed study has been done with the plant response without any controller, with a fixed gain controller and a FPGA based ANN controller. The studies revealed that when an intelligent controller has been included in the Exciter loop, the oscillations have been eliminated more effectively. But it is also observed that the realization of an ANN absolutely on an FPGA is having its own design limitations. The ARTIX 7 FPGA development board has been used for the complete analysis and the results and outcomes discussed at the end.

A Method to Design Power System Stabilizers in a Multi-Machine Power System Based on Single-Machine Infinite-Bus System Model

This paper proposes a method to design power system stabilizers (PSSs) based on single-machine infinite-bus system models to mitigate the risk of low-frequency electro- mechanical power oscillations in an N-machine power system. First, models of N fabricated identical-machine power systems are established for the N-machine power system. Analysis in the paper indicates that the electromechanical oscillation mode of fabricated identical-machine power systems with the least damping is of less damping than the electromechanical oscillation modes of N-machine power system. Second, it is proved that models of fabricated identical-machine power systems are dynamically equivalent to the single-machine infinite-bus system models. Finally, it is suggested that the PSSs are designed using single-machine infinite-bus system models to enhance the least damped electromechanical oscillation mode of fabricated identical-machine power systems. This eventually improves the damping of electromechanical oscillation modes of N-machine power system, thus mitigating the risk of low-frequency electromechanical power oscillations. In the paper, the proposed method is demonstrated and evaluated by using three well-known example multi-machine power systems. Effectiveness of PSSs is confirmed by the results of modal computation and simulation.

Transition from Electromechanical Dynamics to Quasi-Electromechanical Dynamics Caused by Participation of Full Converter-Based Wind Power Generation

Previous studies generally consider that the full converter-based wind power generation (FCWG) is a “decoupled” power source from the grid, which hardly participates in electromechanical oscillations. However, it was found recently that strong interaction could be induced which might incur severe resonance incidents in the electromechanical dynamic timescale. In this paper, the participation of FCWG in electromechanical dynamics is extensively investigated, and particularly, an unusual transition of the electromechanical oscillation mode (EOM) is uncovered for the first time. The detailed mathematical models of the open-loop and closed-loop power systems are firstly established, and modal analysis is employed to quantify the FCWG participation in electromechanical dynamics, with two new mode identification criteria, i.e., FCWG dynamics correlation ratio (FDCR) and quasi-electromechanical loop correlation ratio (QELCR). On this basis, the impact of different wind penetration levels and controller parameter settings on the participation of FCWG is investigated. It is revealed that if an FCWG oscillation mode (FOM) has a similar oscillation frequency to the system EOMs, there is a high possibility to induce strong interactions between FCWG dynamics and system electromechanical dynamics of the external power systems. In this circumstance, an interesting phenomenon may occur that an EOM may be dominated by FCWG dynamics, and hence is transformed into a quasi-EOM, which actively involves the participation of FCWG quasi-electromechanical state variables.

A Novel Nature-Inspired Improved Grasshopper Optimization-Tuned Dual-Input Controller for Enhancing Stability of Interconnected Systems

Power system often experiences the problem of low-frequency electromechanical oscillations which leads the system to unstable condition. The problem can be corrected by implementing power system stabilizers (PSSs) in the excitation control system of alternator. This paper provides a novel and efficient approach to design an Improved Grasshopper Optimization Algorithm (IGOA)-based dual-input controller to damp the inter-area-mode power system oscillations. A three-fold optimization criterion has been formulated to calculate the optimum values of the controllers required for power system stability. The damping performance of the proposed controller is compared with conventional PSS and genetic algorithm-based controllers to validate the better performance of the proposed IGOA-based controller under various system loading conditions and disturbances.

Robust Linear Parameter Varying Scalar Control Applied in High Performance Induction Motor Drives

This article presents a robust stabilizer designed to damp low-frequency electromechanical oscillations which occurs in induction machines controlled by scalar control when operating at low speed and light load. The robust stabilizer design is based on the linear parameter varying (LPV) approach. A discrete LPV model is identified to allow the design of the robust LPV controller. Experimental tests are performed on a 7.5-kW induction motor dynamometer using an industrial inverter driver. The results show satisfactory improvement of the motor performance with the damping controller activated.

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.

Holistic data-driven framework for estimating electromechanical dynamic patterns from synchrophasor measurements in bulk power grids

The electromechanical dynamic patterns of the power system, which normally refer to electromechanical oscillation dominant modes, mode shapes, participation factors and coherent groups, are important for the study of power system dynamic behaviours. Different from the existing measurement-based methods which mainly estimate one or two facts of dominant modes, mode shapes or coherent groups, a holistic data-driven estimation approach is developed to estimate all the four electromechanical dynamic patterns from the measurements systematically and estimate the participation factors from measurements. In the developed approach, the multichannel continuous wavelet transform is firstly employed to dominant modes and mode shapes estimation. Besides, the estimated mode shapes are used to calculate the left eigenvalue vectors of the dominant modes. With the estimated mode shapes and left eigenvalue vectors, the participation factors are solved and coherent groups are separated. Finally, the proposed data-driven approach is evaluated by the 16-machine, 68-bus test system and China Southern Power Grid. The results validate that the proposed data-driven approach can accurately estimate all the four electromechanical dynamic patterns from synchrophasor measurements in a single way.

Amended GWO approach based multi-machine power system stability enhancement

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms.

Comparison of Different Methods for Identification of Dominant Oscillation Mode

Abstract This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and presented using the Fourier transform, Prony’s method, Matrix Pencil Analysis Method, S-transform, Global Wavelet Spectrum and Hilbert Huang transform (Hilbert Marginal Spectrum) in time-frequency domain representation. The analyses were performed on real frequency signals obtained from FNET/GridEye system during the earthquake that triggered the shutdown of the North Anna Nuclear Generating Station in the east coast of the United States. In addition, according to the obtained results the proposed methods have proven to be reliable for identification of the model parameters of low-frequency oscillation in power systems. The relevant analyses are carried out in MATLAB coding environment.

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.

Coordinated tuning of power system controllers using parallel genetic algorithms

This work proposes a computational methodology for coordinated tuning of the power system controllers based on Genetic Algorithm which acts maximizing simultaneously two objective functions, representing each of them the damping of electromechanical oscillations and the improvement of the automatic voltage regulator responses, considering at the same time several critical operating conditions. The coordinated tuning procedure was posted as a multi-objective optimization problem, through the weighted sum technique. The controllers considered into the adjusting scheme correspond to the automatic voltage regulators, power system stabilizers and static var compensators to enhance the electromechanical and voltage response for a several critical operating conditions considered. The Genetic Algorithm was adapted for parallel computing in order to achieve both dynamic responses encompassing several critical operating conditions to reduce high computational efforts. Simulation results of the parallel implementation were more significant than the sequential version, and the proposed approach becomes an interesting alternative tool for operation planning and stability studies.

Synchronised ambient data‐driven electromechanical oscillation modes extraction for interconnected power systems using the output‐only observer/Kalman filter identification method

Ambient-based oscillation modes extraction is an effective means of monitoring the small signal stability of a power system on-line. In this study, a data-driven approach based on output-only observer/Kalman filter identification (O3KID) combined with the eigensystem realisation algorithm (ERA) was proposed to extract electromechanical modes (frequency, damping ratio and mode shape) from synchronised ambient data. As a key for extracting a reduced-order state matrix using the ERA, the Markov parameters (impulse responses) are first estimated by employing O3KID on multi-channel ambient data. O3KID makes it possible to identify the modes with the ERA using only output ambient data, while ensuring the reliability of the extractions of frequencies, damping ratios and mode shapes. The performance of the proposed method was evaluated by employing the IEEE 16-generator 5-area system and measured data from a real power system. The estimation results in all cases as well as comparison results with the RDT-ERA method and NExT-ERA method indicate that the O3KID-ERA method is a promising method for ambient data-driven electromechanical oscillation modes extraction.

An analytic methodology to determine generators redispatch for proactive damping of critical electromechanical oscillations

This paper presents a model-based method for applying online proactive generators redispatch to improve damping of the critical electromechanical oscillations of power system. The proposed method comprises two stages: 1) monitoring modal characteristics of oscillatory modes in ambient condition, and 2) applying generators redispatch based on sensitivities of the critical mode to the generators active power changes using a new analytic method. An online identification method such as error feedback lattice recursive least square adaptive filter is applied for online estimation of the oscillatory modes. Then, whenever the damping ratio of an identified mode is less than a preset threshold, its sensitivities to the generators active power changes are calculated in terms of power system model parameters and power flow variables, which can be obtained via state estimation or measured directly by phasor measurement units. The formulae for calculating these sensitivity factors are developed considering 3rd order dynamic model of synchronous generators along with excitation system and power system stabilizer, and ZIP model of loads. The resultant equations are organized as a quadratic eigenvalue problem and the sensitivity factors are calculated in a systematic manner. Nextly, the candidate generators are ranked online by a new index based on sensitivity factors. Finally, using the proposed index, the most effective generators are proactively redispatched to improve the critical mode damping before occurrence of any fault that jeopardizes the system stability. Applicability and accuracy of the proposed formulae are investigated in the IEEE 39-bus New England power system. Effectiveness of the proposed method is also confirmed with time-domain simulation and state space analysis.

Real‐time identification of electromechanical oscillations through dynamic mode decomposition

The increasing penetration of inverter-based resources is affecting the overall power system security, so that effective tools are needed to provide system awareness and identify the most suited countermeasures. To tackle this problem, real-time monitoring and assessment of transmission grids based on synchrophasor measurements have drawn the attention of many researchers over the last decade, thanks to the high temporal resolution of data provided by phasor measurement units. In this study, a new version of the dynamic mode decomposition (DMD) is proposed as a tool for the real-time identification of electromechanical oscillations. This approach is able to provide information on the dynamic characteristics (frequency and damping) and the spatial correlation (mode shape) of the modes identified. Besides, an improved criterion is used to track and discern the dominant modes. The effectiveness of the DMD method has been tested on the two-area Kundur system and validated using data from a real event on the European synchronous grid, giving promising results. Thanks to its reliability and robustness, the DMD is now implemented in the Terna (the Italian transmission system operator) Wide Area Measurement System in use in the control room.

Synchronized Ambient Output-Only Based Online Inter-Area Transfer Capability Assessment Considering Small Signal Stability

Inter-area transfer power is a key factor affecting the damping of electromechanical oscillation. This paper presents an output-only based inter-area transfer capability (ITC) assessment method considering small signal stability constraint. Based on the construction of an equivalent two-machine system (ETmS) of an interconnected power grid, the measurements-based approach for estimating the parameters of the ETmS was studied. Then, we developed an optimization model with a maximum ITC target and damping as constraint. Under the premise of using recursive adaptive stochastic subspace identification (RASSI) to extract the oscillation modes (frequency and damping) from the ambient data, the optimization model was solved by employing the particle swarm optimization (PSO) algorithm. The effectiveness of the proposed approach was demonstrated using numerical simulations on the IEEE 16-machine 68-bus test system and measured data from a real system.

Detection and Analysis of Electromechanical Oscillation in Power Systems with Low-Sampled Data Using Modal Analysis Methods

Electromechanical oscillations between interconnected generators are considered a major threat to the secure operation of power systems. Therefore, oscillation monitoring systems in real-time are of critical importance to detect the danger of poorly damped oscillations. For the detection and analysis of the oscillations, high-temporal-resolution measurements are required according to the Nyquist theorem. This paper proposes a novel algorithm for the identification of electromechanical oscillations using low-sampled data such as supervisory control and data acquisition (SCADA) measurements. The lack of temporal resolution of the data is compensated by using low-sampled data sets at multiple different locations. At a target location, a high-sampled data-signal can be reconstructed using mode shape information obtained from model-based modal analysis. The variable projection method is then used to detect oscillations and estimate oscillation components including frequency and damping ratio. Case studies based on practical Korean power systems are presented to evaluate the performance of the proposed method. Simulation results show that the proposed method can detect and identify electromechanical oscillations with low-sampled data.

New Chaotic Sunflower Optimization Algorithm for Optimal Tuning of Power System Stabilizers

The problem of stability is generally caused by insufficient damping of electromechanical oscillations (EMOs). Power system stabilizers (PSSs) are the most advised and efficient devices to increase the system damping and enhance the dynamic characteristics of power networks during abnormal conditions. Unfortunately, the performance of the PSS controller is mostly dependent on the parameters of the lead-lag compensator. Within this context, this study presents a new chaotic-based sunflower optimization algorithm with local search (CSFO-LS) for optimum design of PSS controllers. In the proposed algorithm symbolized by CSFO-LS, the random parameters of the original SFO are substituted by chaotic sequences to avoid premature convergence at local optima and improve the accuracy of the optimum solution. Firstly, the CSFO-LS is tested and evaluated on various benchmark functions with different characteristics such as multimodality, separability and regularity. Then, it is applied for selecting the optimum parameters of the PSS controllers. These parameters are tuned in order to shift all electromechanical modes in a pre-specified zone in the left side of the s-plan. Simulation results based on eight benchmark functions show that CSFO-LS outperforms all the algorithms used for comparison. Moreover, to demonstrate the applicability and performance of the proposed method for providing good damping of low frequency oscillations, a standard power system test under various operating conditions and severe fault is used. Obtained results are compared with those obtained using the original SFO and other recent optimization techniques. In this study, an improved version of the SFO is proposed for providing optimum EMOs damping. All EMOs have to be shifted as much as possible to the left side of the s-plan instead of shifting them to a fixed zone. To our best knowledge, this technique is not suggested or used for any power system problem.