Robustness of unified power quality conditioner by neural network based on admittance estimation

Aryanezhad, Majid; Ostadaghaee, Elahe

doi:10.1016/j.asoc.2021.107420

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…A LSTM unit, generally comprises of a memory cell and of 3 gates (forget gate, input gate, and output gate). These gates control the selective flow of non-linear information [22]. The state of an LSTM cell is preserved over time and is updated or modified whenever the cell receives an input.…”

Section: Lstm and Bi-lstmmentioning

confidence: 99%

“…For power systems with significant integration of photovoltaic (PV) sources, an effective approach has been proposed for coordinating various devices, including energy storages (ESs), step voltage regulators (SVRs), load tap changers (LTCs), and others, to enhance the voltage profile and reduce energy loss [21]. To address PQDs, a neural etwork based on admittance estimation (NN‐ADES) strategy has been proposed, leveraging Kohonen learning to estimate the admittance components associated with PQDs or voltage distortions [22]. By utilizing these estimated admittance values, a unified power quality conditioner (UPQC) can be modelled and implemented to effectively mitigate PQDs in the power system.…”

Section: Introductionmentioning

confidence: 99%

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Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

Khetarpal,

Nagpal,

Siano

et al. 2023

IET Generation Trans & Dist

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This paper proposes a recurrent neural network based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi‐directional long short‐term memory (BI‐LSTM) model with two different types of attention mechanisms is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, an attention layer is added to the BI‐LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI‐LSTM model. This attention gate applied in the LSTM cells improves effective and decisive key information extraction from future and past states of the PQD signal and the addition of attention layer improves the overall decision capability of the model, saving computation time, and increasing PQD classification accuracy. Finally, a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The proposed BI‐LSTM model with attention gate and attention layer mechanism is compared to different baseline models based on recurrent neural network and convolution neural network (CNN). From the simulation study, it is inferred that with the proposed method, the multiple combined PQD signals are easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time as compared to popular signal processing based approaches for which classification of multiple PQDs is computationally difficult.

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Section: Lstm and Bi-lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

Khetarpal,

Nagpal,

Siano

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) techniques, especially NNs, have a notable impact on power-electronics applications. To boost the dynamic performance of UPQC, an ANN based admittance estimation strategy (ANN-ADES) has been presented in [16]. The model has been implemented using MATLAB and an FPGA board for compensating the voltage sag/swell, unbalanced load conditions, and harmonic eliminations.…”

Section: Introductionmentioning

confidence: 99%

Implementation of ANN Controller Based UPQC Integrated with Microgrid

et al. 2022

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This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown.

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