Autoregressive Moving Average Exogenous Model Research Articles

Clustered R3LS: A novel approach for online estimation of power system dominant dynamics

Estimating electromechanical oscillations of power systems plays a crucial role to infer about their stability. In this paper it is proposed a new method in which dominant dynamics of power systems are inferred by analyzing pole estimates produced by several regularized robust recursive least squares (R3LS) implementations operating individually — each one with a different set of autoregressive moving average exogenous (ARMAX) model orders. The proposed method is named “Clustered R3LS”, since it properly adapts the so-called K-means clustering algorithm to automatically model regions of pole agglomeration produced by these individual R3LS implementations. An additional strategy for ignoring (discarding) poles identified as spurious is also part of the proposed Clustered R3LS algorithm. From a practical point of view, Clustered R3LS is here shown to more effectively track dominant dynamics of power systems when compared to Classical R3LS, which is based on analyzing only pole estimates produced by a single R3LS implementation operating with a fixed set of ARMAX model orders that is usually hard to tune. Three case studies are here used to validate the proposed Clustered R3LS method for both ambient and ringdown data. The first and second case studies are based on two well known benchmark models proposed for the analysis of oscillatory dynamics in power systems, whereas the third case study considers actual power system data extracted during a large-scale event from the Brazilian Interconnected Power (BIP) system.

Identification of automatic guided vehicle (agv) based on magnetic guided sensor for industrial material transfer

Automatic Guided Vehicle (AGV) is a material transfer tool that is now widely used in the industry. One of the advantages is that it can transfer material automatically without the need for an operator who has to drive it. AGV using a magnetic guided sensor is one of the most widely used types. So that the tool can run according to the planned trajectory, it is necessary to design a controller that matches its characteristics. The design of the controller is needed identification of the model of the plant to know the characteristics of the plant. Identification with the Extended Least Square method is a form of parameter estimation recursively through a quadratic error gradient formulation with a criterion function involving all errors. The structural approach model used linearly uses Auto-Regressive eXogenous (ARX) and Auto-Regressive Moving Average eXogenous (ARMAX). The validation process of the model uses the Means Square Error (MSE) criteria and the FIT percentage. Based on the results obtained by the best plant with ARMAX model structure with MSE values 82.14 and percentage of FIT value 89.98%.

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. This paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. The results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.

PERAMALAN KUNJUNGAN WISATAWAN MENGGUNAKAN MODEL ARMAX DENGAN NILAI KURS DAN EKSPOR-IMPOR SEBAGAI FAKTOR EKSOGEN

Forecasting is science to estimate occurrence of the future. This matter can be conducted by entangling intake of past data and place to the next period with a mathematical form. This research aims to estimate the number of foreign tourists visiting Bali models using autoregressive moving average exogenous (ARMAX). The data used in this study is the number of tourists in Australia and the number of tourists in the RRC as a variable Y, and foreign currency exchange rate AUD, Chinese Yuan, and Export Import as the X factor from the period July 2009 to July 2014. In the analysis can be obtained in the best ARMAX models of the number of tourists in Australia is ARMAX(1,2,2) and the best model of the number of tourists in the RRC does not exist because the data for the ARMAX model parameters tourists no significant RRC.

Estimating electromechanical modes and mode shapes using the multichannel ARMAX model

A method of estimating the electromechanical modes and mode shapes from multiple synchrophasors is presented in this paper. The approach is based upon identifying the transfer function representation of the state space model of the linearized power system through the estimation of a multichannel AutoRegressive Moving Average eXogenous (ARMAX) model. The relationship between the transfer function and the ARMAX model, which is fully derived here, is shown to be general enough that virtually any multichannel ARMAX estimation algorithm may be applied to this approach. The two-stage least squares (2SLS) algorithm is used here for illustrative purposes. The proposed method is validated with the Monte Carlo procedure using simulated data from a reduced-order model of the Western Electricity Coordinating Council (WECC) system. The performance of the new method is compared with existing multichannel mode and mode shape estimation techniques and is seen to provide increased estimation accuracy with a decrease in computation time. The proposed method is further validated with measured data from the WECC system.

FEM Calibrated ARMAX Model Updating Method for Time Domain Damage Identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

Convergence of Recursive Identification for ARMAX Process with Increasing Variances

The autoregressive moving average exogenous (ARMAX) model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average (MA) part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.

Vibration control of an arc type shell using active constrained layer damping

Active constrained layer damping (ACLD) combines the simplicity and reliabilityof passive damping with the low weight and high efficiency of active control toattain high damping characteristics. A cantilevered shell type arc that is made ofcarbon/epoxy composite material is chosen to investigate the potential of ACLDeffectiveness. Using a system identification method, the arc/ACLD system ismodelled as an ARMAX (autoregressive moving average exogenous) model andconverted into a state space form to design optimal control laws. A linear quadraticregulator (LQR) controller is used to find optimal control gains. The performance ofthe ACLD system is determined and compared with passive constrained layerdamping (PCLD) in order to demonstrate the effectiveness of the ACLD treatment.

Active control of a flexible smart beam using a system identification technique based onARMAX

A study on active vibration suppression for a flexible structural beam via a systemidentification approach was experimentally performed. The beam, incorporating a pair ofpiezoceramic transducers as actuators and a set of surface-bonded strain gauges as sensors,was evaluated. The relationship between the input control voltage applied on theactuators and the consequently induced strain, measured by the sensor, was thenderived. An active control system, considering both the actuators and sensor, wasconfigured using the ARMAX (auto-regressive moving average exogenous) model. Acontinuous signal with step waveform was selected as the input excitation. Adigital-signal-processor-based real-time adaptive vibration control algorithm, supported byAgilent® E1415A, SCPs 1511 and SCPs 1532, was developed, and an algorithm was establishedusing a pole placement control method, so as to achieve the desired closed-loop control.The effectiveness of the ARMAX model was examined by comparing it with the ARX(auto-regression with extra inputs) model. The results show good performance of theARMAX model for system identification purposes and excellent effectiveness for activestructural vibration control.