Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

ALQahtani, Ayedh H.; Alsaffar, Mohammad A.; El-Sayed, Mohamed A.; Alajmi, Bader N.

doi:10.1155/2016/2923731

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…In general, there are two major challenges in developing a data-driven model of a PV microinverter. First of all, including the effect of grid voltage variations in the model requires the development of a multi-input model; this significantly increases the complexity of the model, if compared to SISO models developed in literature, for example, [17]. Second, the model needs to be able to capture burst mode operation; at the actual stage only another approach has been proposed to model this type of operation [22].…”

Section: Resultsmentioning

confidence: 99%

“…However, these models are valid only in a small range of operating conditions, and abnormal conditions as well as burst mode operation are not considered. In [17] another data-driven model of a PV inverter for system level analysis is reported. In this case, the model uses the DC side current as an input and the generated power as an output; neither the grid voltage or burst mode operation is considered in developing the model.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Modeling of a Commercial Photovoltaic Microinverter

Abbood

Benigni

2018

Modelling and Simulation in Engineering

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We present a data-driven modeling (DDM) approach for static modeling of commercial photovoltaic (PV) microinverters. The proposed modeling approach handles all possible microinverter operating modes, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. The approach is based on Artificial Neural Network (ANN) and Fast Fourier Transform (FFT). To generate the data used to train the model, a Power Hardware in the Loop (PHIL) approach is applied. Instantaneous inputs-outputs data are collected from the terminals of a commercial PV microinverter at time domain. Then, the collected data are converted to the frequency domain using Fast Fourier Transform (FFT). The ANNs that are the core of the DDM are developed in frequency domain. The outputs of the ANNs are then converted back to time domain for validation and use in system level simulation. The comparison between measured and simulated data validates the performance of the presented approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

Abbood

Benigni

2018

Modelling and Simulation in Engineering

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“…Data-driven modeling can determine the structure, parameters, and temporal behaviors of a system or component of the system such as a PEC. Generally, the modeling of a dynamic system is classified into two approaches: first principle modeling and data-driven modeling [156]. First principle modeling utilizes the system's physics to derive the mathematical representation using established equations of the system or component.…”

Section: E Data-driven Modelsmentioning

confidence: 99%

“…The resulting dataset is then fed into a system identification algorithm, which typically minimizes a defined cost-function to estimate the reduced-order system FIGURE 10: Comparison of different types of data-driven models. Adapted from [156]. modelĜ(s).…”

Section: E Data-driven Modelsmentioning

confidence: 99%

Review of Dynamic and Transient Modeling of Power Electronic Converters for Converter Dominated Power Systems

et al. 2021

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In response to national and international carbon reduction goals, renewable energy resources like photovoltaics (PV) and wind, and energy storage technologies like fuel-cells are being extensively integrated in electric grids. All these energy resources require power electronic converters (PECs) to interconnect to the electric grid. These PECs have different response characteristics to dynamic stability issues compared to conventional synchronous generators. As a result, the demand for validated models to study and control these stability issues of PECs has increased drastically. This paper provides a review of the existing PEC model types and their applicable uses. The paper provides a description of the suitable model types based on the relevant dynamic stability issues. Challenges and benefits of using the appropriate PEC model type for studying each type of stability issue are also presented.

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“…In this study, the selection of nonlinearity estimators in the modelling process is determined by trial and error method. The estimators that being tested includes Linear Function, Sigmoid Network Function, Wavelet Network Function and Polynomial [17]. The combination of nonlinearity estimators which produced models with the highest Best Fit (BF) percentages is selected and considered to be the best.…”

Section: Fig 5 -Structure Of H-w Modelmentioning

confidence: 99%

The Correlation Model of Head Roll and Lateral Acceleration during Curve Driving via Hammerstein-Wiener

Saruchi¹,

Ariff²,

Zamzuri³

et al. 2019

IJITEE

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Generally, passengers are more prone to Motion Sickness (MS) than the drivers. The difference of their severity level of MS is due to their different head movement towards the direction of the lateral acceleration. During cornering, the passengers tend to tilt their heads according to the direction, while the drivers tends to tilt their head opposite to the direction. Based on this fact, the passengers are able to reduce their MS level if they can imitate the driver’s head movement or lessen their head tilt angle towards the direction of the lateral acceleration. However, it is easier to design MS mitigation method based on the head tilt movement strategy if the mathematical expression of their head behaviour is known beforehand. On way to derive the mathematical expression is by modelling the relationship between the occupant’s head tilt movements and the vehicle’s lateral acceleration during curve driving. Therefore, this study proposed the usage of Hammerstein-Wiener (H-W) method for the modelling purpose. Experiment is set up to obtain the naturalistic data for the modelling process. The modelling process is carried out by varying the input output nonlinearities estimators. The results show that the estimated output responses from the H-W models are similar with the real responses taken from the experiment.The derived models for both passenger and driver have 68.88% and 66.32% of Best Fit (BF) percentages. With further study, the passenger’s and driver’s models which are developed by the proposed H-W modelling strategy are expected to contribute in MS minimisation studies

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Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Cited by 12 publications

References 28 publications

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

Review of Dynamic and Transient Modeling of Power Electronic Converters for Converter Dominated Power Systems

The Correlation Model of Head Roll and Lateral Acceleration during Curve Driving via Hammerstein-Wiener

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