Real-Time Implementation of Optimized Power Quality Events Classifier

Markovska, Marija; Taskovski, Dimitar; Kokolanski, Živko; Dimchev, Vladimir; Velkovski, Bodan

doi:10.1109/tia.2020.2991950

Cited by 22 publications

(20 citation statements)

References 39 publications

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“…Recently, some commercial HIL platforms such as Typhoon HIL [35], Opal-RT [36], and National Instruments (NI) RIO family [37] have appeared to implement HIL models. The latter, programmed through LabVIEW software, is used for several applications, not only HIL.…”

Section: Hil Modelmentioning

confidence: 99%

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

et al. 2021

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This paper aims to compare different design alternatives of hardware-in-the-loop (HIL) for emulating power converters in Field Programmable Gate Arrays (FPGAs). It proposes various numerical formats (fixed and floating-point) and different approaches (pure VHSIC Hardware Description Language (VHDL), Intellectual Properties (IPs), automated MATLAB HDL code, and High-Level Synthesis (HLS)) to design power converters. Although the proposed models are simple power electronics HIL systems, the idea can be extended to any HIL system. This study compares the design effort of different coding methods and numerical formats considering possible synthesis tools (Precision and Vivado), and it comprises an analytical discussion in terms of area and speed. The different models are synthesized as ad-hoc modules in general-purpose FPGAs, but also using the NI myRIO device as an example of a commercial tool capable of implementing HIL models. The comparison confirms that the optimum design alternative must be chosen based on the application (complexity, frequency, etc.) and designers’ constraints, such as available area, coding expertise, and design effort.

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Section: Hil Modelmentioning

confidence: 99%

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

et al. 2021

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“…In order to test the effectiveness of the proposed method, we consider the normal signals, and 14 types of single and compound disturbances, as given in Table 2. According to the mathematical models given in [23] and [24], we generate a total of 12,000 samples through MATLAB R2017b, and the corresponding variations in the parameters are compliant with the IEEE 1159 standards. In addition, in order to make the test effect more reliable, all parameters of the numerical model are allowed to change randomly when we simulate the signals.…”

Section: Methodsmentioning

confidence: 99%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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This paper presents a new concise deep learning-based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre-processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non-linearity and long-term memory, while enabling the simple gated recurrent network model to be superior to Long Short-Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource-constrained internet of things (IoT) micro-controllers.

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“…There are several feature extraction methods from the detail and approximation coefficients resulting from the application of the DWT to the signals. These features are kwon as wavelet-based features [16]. Table 2 shows some of these methods, as well as their mathematical definition, where 𝐶 𝑖,𝑗 are the 𝑁 coefficients of the 𝑖-th resolution level.…”

Section: Wavelet-based Feature Extractionmentioning

confidence: 99%

Evaluation of wavelet-based feature extraction methods for detection and classification of power quality disturbances

Rico-Medina¹,

Reyes-Archundia²,

Gutiérrez-Gnecchi³

et al. 2022

World J. Adv. Eng. Technol. Sci.

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The appearance of Power Quality Disturbances can cause serious damage to the utility grid. Their detection and identification are two of the major problems related to the improvement of Power Quality. This paper presents an evaluation of different combinations of wavelet-based features for the detection and classification of eight types of Single Power Quality Disturbances. A set of disturbances was generated in MATLAB through their mathematical models. The detection stage was performed using Multiresolution Analysis. The extracted features were normalized by Z-score to serve as input to four different classifiers: Multilayer Perceptron, K-Nearest Neighbors, Probabilistic Neural Network, and Decision Tree. The combination of Shannon Entropy and Log-Energy Entropy was found the best with the highest accuracy in all cases. Furthermore, the normalization stage has an impact on classification as it improves accuracy regardless of the classifier used. This fact makes it possible to reduce the computational expense by using only two types of features without compromising the accuracy.

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Real-Time Implementation of Optimized Power Quality Events Classifier

Cited by 22 publications

References 39 publications

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

A simple gated recurrent network for detection of power quality disturbances

Evaluation of wavelet-based feature extraction methods for detection and classification of power quality disturbances

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