Modeling of Photovoltaic Grid Connected Inverters Based on Nonlinear System Identification for Power Quality Analysis

Patcharaprakiti, Nopporn; Kirtikara, K.; Tunlasakun, Khanchai; Thongpron, Juttrit; Chenvidhya, Dheerayut; Sangswang, Anawach; Monyakul, Veerapol; Muenpinij, B.

doi:10.5772/16914

Cited by 11 publications

(5 citation statements)

References 49 publications

(37 reference statements)

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“…Other nonlinearity estimators include wavelet network. 27 For model identification, mass flow rate readings at the pipeline's outlet are utilized as an output, and measurements of mass flow rate and pressure are used as inputs at the pipeline's inlet. The system identification (SID) toolkit is used to identify models using data from…”

Section: Model Identificationmentioning

confidence: 99%

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Adaptive thresholds‐based leak detection using real‐time transient modeling of two‐phase flows

Vandrangi,

Lemma,

Muhammad Mujtaba

2024

Process Safety Progress

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Pipelines are widely regarded as the most cost‐effective mode of transportation for oil and gas, particularly when it comes to covering long distances. Regrettably, pipelines are not impervious to accidents, which can result in colossal material damage and fatalities. As the demand for oil and gas rises, so does the need for pipelines. Therefore, issues in pipeline malfunctions cannot be overlooked. Leakage and blockage are the two main faults in pipelines that lead to pipeline incidents if not addressed timely. Detecting pipeline leaks is a major hurdle for industry, and various techniques have been developed to tackle this. This article focuses on the modeling of leakage detection in pipelines in two‐phase transient flows. Initially, OLGA, a multiphase software, is used for simulating the transient flows in a black oil case study. A leak was induced after 30 min of the simulation and leak cases were designed with varying parameters. Using the simulated data of mass flow rate, and pressure at the inlets and outlets, the model will be identified. The adaptive thresholds‐based model was able to predict the leaks accurately. The performance of the leak detection model was analyzed. The model was able to detect leaks of 2%–10% sizes successfully.

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Section: Model Identificationmentioning

confidence: 99%

“…During the identification process, nonlinearity estimators such as dead zone, piecewise linear, saturation, sigmoid network, and wavelet network can be utilized to estimate the parameters of the input and output blocks. Other nonlinearity estimators include wavelet network 27 …”

Section: Olga Simulations and Model Identificationmentioning

confidence: 99%

Adaptive thresholds‐based leak detection using real‐time transient modeling of two‐phase flows

Vandrangi,

Lemma,

Muhammad Mujtaba

2024

Process Safety Progress

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“…If only the output nonlinearity is present, the model is called a Wiener model. The available nonlinearity estimators to be used in the identification process, by estimating the parameters of the input and output blocks, are dead zone, piecewise linear, saturation, sigmoid network, and wavelet network [40].…”

Section: Hammerstein-wiener Modelmentioning

confidence: 99%

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

ALQahtani

Alsaffar

El-Sayed

et al. 2016

International Journal of Photoenergy

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Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

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“…In [15,16], models of a PV inverter are developed at system level relying on system identification techniques. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Modeling of Photovoltaic Grid Connected Inverters Based on Nonlinear System Identification for Power Quality Analysis

Cited by 11 publications

References 49 publications

Adaptive thresholds‐based leak detection using real‐time transient modeling of two‐phase flows

Adaptive thresholds‐based leak detection using real‐time transient modeling of two‐phase flows

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

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