Modeling and Identification of Electrical Parameters of Positive DC Point-to-Plane Corona Discharge in Dry Air Using RLS Method

Raouti, D.; Flazi, S.; Benyoucef, Djilali

doi:10.1109/tps.2016.2577634

Cited by 15 publications

(20 citation statements)

References 29 publications

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“…where U (z) is the z-th output of the system, U (z-i) is the (zi)-th output of the system, I (z) is the z-th input of the system, I (z-i) is the (z-i)-th input ϕ T (z) andθ z are obtained by discretizing the experimental input and output observation matrix ϕ T (k) in Eq. (19) and the parameter matrix θ of the equivalent circuit to be identified in the supercapacitor cell module, which are expressed as…”

Section: ) Charge and Discharge Branchmentioning

confidence: 99%

“…Furthermore, in [13], the electrical model based on parameter optimization of passive electrical circuit is proposed. The existing parameter identification methods of the supercapacitor cell model include circuit analysis method [14], binary quadratic equation fitting method [15], least squares method [16], particle swarm algorithm [17], [18], recursive least squares method [19], etc. It is relatively simple to identify model parameters by the circuit analysis method, which is of clear physical meaning, and meets the actual engineering application, and requires less experimental equipment, but the identification accuracy is not high.…”

Section: Introductionmentioning

confidence: 99%

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A parameters identification method of the equivalent circuit model of the supercapacitor cell module based on segmentation optimization

et al. 2020

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In practical settings, the supercapacitor is often used as the storage battery, which is composed of several supercapacitor cells in series. In order to accurately estimate the State of Charge (SoC) in the supercapacitor cell module, an equivalent model of supercapacitor cell module is invoked, which is expected to reflect the characteristics of supercapacitor cell module, especially the self-discharge characteristics during standing. The results of parameter identification directly affect the model accuracy. Hitherto, most supercapacitor equivalent models have been proposed for supercapacitor cells, but if the module equivalent model is characterized by connecting many equivalent models of supercapacitor cells in series, it would lead to the cumulative errors and the additional errors, which would incur errors in the parameter identification, and directly affect the model accuracy. The paper aims to obtain the accurate equivalent model parameters, the supercapacitor cell module is regarded as the object, the three-branch equivalent circuit model is established for the supercapacitor cell module, a discussion is given on the parameter identification methods about Circuit Analysis Method (CA) and Recursive Least Squares Method (RLS). This paper establishes the Simulink simulation model for the multi-method parameter identification of supercapacitor cell module, the simulation and analysis are performed to illustrate the advantages and disadvantages of CA and Circuit Analysis-Recursive Least Squares Method (CA-RLS). Then, it proposes a parameters identification method of the equivalent circuit model of supercapacitor cell module based on Segmentation Optimization (SO). The effectiveness of SO is verified by simulation and error analysis, the results indicate that SO can more effectively reflect the charging characteristics and self-discharge characteristics of the supercapacitor cell module. In particular, the comprehensive error in the static self-discharge phase is 0.28%, which is 6.83% and 0.64% lower than CA and CA-RLS, respectively.

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Section: ) Charge and Discharge Branchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A parameters identification method of the equivalent circuit model of the supercapacitor cell module based on segmentation optimization

et al. 2020

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“…Online parameter identification is currently used in many applications, and it mainly includes recursive least square [18,19], model reference adaptive system [20,21], extended Kalman filter [22,23]. The authors of [18] and [19] use the least square method (RLS) to identify the parameters of the motor, the identification process of RLS is simple and the amount of calculation is moderate, but it has the problem of data saturation and poor robustness.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

Xiao

Liao

Shi

et al. 2022

IET Renewable Power Gen

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Aiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct‐drive permanent magnet synchronous generator (D‐PMSG), a parameter identification method of D‐PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)‐EKF is proposed. Firstly, by analysing the principle of EKF, a dual‐thread identification model is established, then the particle swarm optimization (PSO) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved PSO algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.

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“…Online parameter identification can meet the needs of real-time monitoring of motor parameter changes, and it is one of the hotspots of current research. Traditional online parameter methods mainly include: Extended Kalman filter [9,10], model reference adaptive system [11,12], recursive least squares [13,14], etc. The authors of [9] and [10] propose an extended Kalman filter (EKF) for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

“…When multiple parameters are required to be identified at the same time, it is difficult to determine an appropriate adaptive law, and the selection of initial parameters has a great influence on the identification accuracy. The application of recursive least squares (RLS) method in [13,14] has the advantages of simplicity and ease of realization for motor parameter identification, but RLS needs to process a large amount of data and is prone to data saturation problems. In addition, the linear parameter model requires derivation during simplification and approximation, and noise will have a greater impact on the derivation result, which leads to poor identification accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

Wen¹,

Shi²,

Liao³

et al. 2021

PIER M

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When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor's stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance, and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.

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Modeling and Identification of Electrical Parameters of Positive DC Point-to-Plane Corona Discharge in Dry Air Using RLS Method

Cited by 15 publications

References 29 publications

A parameters identification method of the equivalent circuit model of the supercapacitor cell module based on segmentation optimization

A parameters identification method of the equivalent circuit model of the supercapacitor cell module based on segmentation optimization

Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

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