System Identification Techniques for Power Systems Analysis Using Distorted Data

Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four‐tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First‐Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude‐modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A novel nonlinear modelling approach has been developed and implemented on Alstom gasifier using Wiener model. The linear element of the Wiener model was identified by a combined subspace state space method, which integrated MOESP (Multivariable Output-Error State Space) and N4SID (Numerical algorithms for subspace state space system identification) method in the estimation of system matrices. Then a single layer neural network was chosen as the nonlinearity of the model. The proposed model identification method was used to model Alstom gasifier with strong nonlinearity and multivariable couples. The results compared to a combined linear subspace identification method demonstrate that the nonlinear method proposed in this paper behave better approximation.

This paper addresses identification of Hammerstein systems using the sampled input-output data in frequency domain. When the static memoryless nonlinear part of Hammerstein model can be considered as a polynomial with a known order, the following linear part can be estimated as a multi-input single-output (MISO) systems with the numerical algorithm for subspace state space system identification method. Simulation results illustrate this approach is effective.

Summary form only given. In this paper, the authors use the numerical algorithm for subspace state space system identification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective even when the probing signal is small.

System identification based structural damage aggravation detection in a large masonry building

This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static nonlinearities follow from a low-rank approximation of a matrix obtained from this regression problem.

The performance of machines and equipment degrades as a result of aging and wear. This decreases performance reliability and increases the potential for faults and failures. To ensure proper functionality of complex systems, advanced technologies for performance diagnosis and control are being incorporated into engineering designs, which requires an ever-increasing number of sensors and measurement devices. Nevertheless, a sensor, just as any other dynamic system, degrades and fails. A faulty sensor may cause process performance degradation, process shut down, or even a fatal accident because it is no longer able to deliver accurate information about the monitored system. Therefore, it is essential to assess sensor performance to ensure system reliability. In this paper, a method is proposed to detect, isolate, and compensate sensor degradation. The numerical algorithm for subspace state space system identification is used to track the changes of the time constants and gains of the sensor and the monitored system. Without imposing requirements for redundant sensors and measurement devices, this method utilizes the fact that sensor readings depict dynamic characteristics of the sensors as well as those of the monitored system. The newly proposed method is verified in angular sensor degradation detection using high-fidelity simulations of an automotive electronic throttle system.

In this paper, we propose a subspace state space system identification method using prior knowledge. In this method, we give an algorithm to identify the partial state which corresponds to unknown modes of the system. This scheme is effective when some system poles cannot be identified because of the measurement noise or a high gain feedback controller. The effectiveness of the proposed method is evaluated by numerical examples including the comparison with the conventional 4SID method

In this paper, the inverted pendulum is controlled by Quanser universal benchtop equipment-direct current (QUBE DC) motor, and the motor inverted pendulum system is identified when the sinusoidal signal is input. However, there is a large error when the angle of the pendulum is pre-identified by numerical algorithms for subspace state space system identification algorithm. Therefore, this paper designs a multi strategy optimization simulated annealing particle swarm optimization algorithm, which can accurately identify the complex system with sinusoidal input signal. In the identification experiments of sinusoidal signals with multiple frequencies and amplitudes, this paper found that the algorithm performs relatively best at a frequency of 8 rad/s. Moreover, at a frequency of 8 rad/s, the algorithm can quickly reduce the error to 1.69% within 100 generations. Finally, based on the hardware model identified by particle swarm optimization, this paper designs an explicit model predictive control method to control the inverted pendulum, and tests the constraint processing and anti-disturbance performance of the system under the voltage pulse interference with different duty cycles, which realizes the inverted pendulum balance and has robustness under the interference.

Data based modelling is an important tool for the efficient planning and management of river systems. In this paper models of the water level in the upper part of Murray River in Australia are derived from observed operational river data by system identification methods. We consider Prediction Error Method (PEM) and Subspace Identification Method (SIM) and present the system identification procedure from collecting prior physical knowledge to model validation. It is shown that first order models capture the main trends in the data and perform well on validation datasets from different years.

This study presents the design of wide‐area fractional order damping controller for low frequency inter‐area oscillations in Western Electricity Coordinating Council (WECC) system. The developed wide‐area damping control scheme is implemented in a decentralised manner by examining the entire system through decentralised identification at the level of each generator bus and FACTS devices in large‐scale system. The decentralised identification is done by applying n4sid (Subspace State Space System Identification) algorithm to the time‐domain simulated response of WECC system. The main features of the proposed approach are: (a) measurement‐based control which does not require complete model of WECC; (b) minimise the communicating signals required for damping controller and incorporate practical method of time‐delay compensator (TDC) design to reduce the effect of time‐varying delay. The performance of wide‐area control (WAC) scheme is tested on the practical WECC system and verified by non‐linear time‐domain simulation. The proposed decentralised WAC scheme is compared with local and centralised control scheme, which enhances the damping of inter‐area oscillation effectively compared to local control scheme and more reliable compared to centralised control scheme and its robustness is verified over a wide‐range of operating conditions.

Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a full analytical model and a simple system identification (SI) method of a piezoelectric actuator, which includes piezostack elements and a three-stage amplification mechanism. The model was derived separately for each unit of the system. Next, the units were combined, while taking into account their coupling. The hysteresis phenomenon, which is significant in piezoelectric materials, is described extensively. The theoretical model was verified in a laboratory setup. This setup includes a piezoelectric actuator, measuring devices and an acquisition system. The measured results were compared to the theoretical results. Some of the most well-known forms of system identification are shown briefly, while a new and simple algorithm is described systematically and verified by the model. The main advantage of this work is to provide a solid background and domain knowledge of modelling and system identification methods for further investigations in the field of piezoelectric actuators. Due to their simplicity, both the model and the system identification method can be easily modified in order to be applied to other PEMs or other amplification mechanism methods. The main novelty of this work lies in applying a simple system identification algorithm while using the system-level approach for piezoelectric actuators. Lastly, this review work is concluded and some recommendations for researchers working in this area are presented.

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

This dissertation presents a new method for the statistical performance analysis of subspace system identification algorithms. A perturbation expansion of the singular value decomposition is used to approximate the effects of process and measurement noise on the identified system poles. Eigenvalue variance results are obtained that do not depend on asymptotic statistics and are therefore applicable for short data records. These performance measures are conditional upon the specific input and the parameters of the actual system being identified. This method is demonstrated on two variations of the Numerical Subspace State-Space System Identification (N4SID) algorithm. The accuracy of these theoretical performance measures is validated using simulation. Finally, this new method is applied to the first step of three well known subspace identification algorithms to determine the effects of row and column weighting on the variance of identified poles.

Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC.