Development of hardware and software for three-phase power quality disturbances detection, classification and diagnosis using Kalman Filter theory

The paper explores power quality disturbances on a specified section of the distribution network of a Textile Industry in Kaduna State of Nigeria. The 33kV PHCN incoming to the industry is stepped down to 11kV by a 7.5MVA, 33/11kV three-phase transformer. This transformer supplies various 11/.415kV transformers present in the distribution network. Another 11kV PHCN incoming is used in event of any failure from the 33/11kV transformer. The paper focuses on Transformer No. 1, a 150kVA, 11/.415kV three-phase transformer operating at 0.9 power factor, located at printing and dying (P/D) building 1. Majority of the loads on it are inductive. Measurements were taken at the secondary terminal of this transformer by the use of the Harmonitor 3000 power analyzer, which generates the voltage and current waveforms, power factor, voltage and current total harmonic distortion and the apparent power of the red, yellow and blue phases of the transformer. Analyses of these data reveal the disturbances due to harmonics in the phases and neutral of the transformer. The effect of the harmonic current is seen as poor power factor of the transformer. Considering the observations and analyses of the power quality of the transformer 1 (P/D), the paper proposes some recommendations for improving the power quality of the distribution network under study.

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A Generalized Approach for Power Quality Disturbances Recognition Based on Kalman Filter

IEEE Access ◽

10.1109/access.2021.3093367 ◽

2021 ◽

pp. 1-1

Author(s):

Abdelazeem A. Abdelsalam ◽

Almoataz Y. Abdelaziz ◽

Mohamed Z. Kamh

Keyword(s):

Kalman Filter ◽

Power Quality ◽

Power Quality Disturbances

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Detection of power quality disturbances using an adaptive process noise covariance Kalman filter

Digital Signal Processing ◽

10.1016/j.dsp.2018.01.013 ◽

2018 ◽

Vol 76 ◽

pp. 34-49 ◽

Cited By ~ 10

Author(s):

Yanhui Xi ◽

Zewen Li ◽

Xiangjun Zeng ◽

Xin Tang ◽

Qiao Liu ◽

...

Keyword(s):

Kalman Filter ◽

Power Quality ◽

Process Noise ◽

Adaptive Process ◽

Noise Covariance ◽

Power Quality Disturbances

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Cause Based Analysis of Power Quality Disturbances in a Three Phase System

2018 IEEMA Engineer Infinite Conference (eTechNxT) ◽

10.1109/etechnxt.2018.8385378 ◽

2018 ◽

Cited By ~ 1

Author(s):

Rajat Kumar ◽

Sanjay Marwaha ◽

Raj Kumar

Keyword(s):

Power Quality ◽

Phase System ◽

Three Phase ◽

Power Quality Disturbances

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Levenberg-Marquardt based MLP for Detection and Classiﬁcation of Power Quality Disturbances

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2227.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 155-161

Keyword(s):

Neural Networks ◽

Power Quality ◽

Multilayer Perceptron ◽

Hardware Implementation ◽

Mean Square ◽

Three Phase ◽

Proposal A ◽

Levenberg Marquardt ◽

Power Quality Disturbances

In recent years, power quality (PQ) has become an important issue for utilities and users. In order to improve PQ, a method for detecting and classifying power quality disturbances (PQDs) is proposed. Hence in addition to identifying the disturbance signals, the proposed method is able to determine its type when occurring. This approach is based on Multilayer perceptron and Levenberg-Marquardt training rule. It is inspired by the desire to take advantage of the parallelism inherent to neural networks in view of hardware implementation using reconfigurable chips. The inputs of these networks are the samples obtained on the power grid in various conditions. The proposed method is tested for sags and swells. To classify the disturbances, the neural architectures have been generalized and configured according to the number and type of disturbances to be treated. To validate and test the proposal, a grid model was built with a three-phase fault generator under Matlab / Simulink R2017a. After comparing the results with those obtained by certain methods in the literature, the proposal proves to be an efficient and reliable tool for monitoring PQ. In fact it has the smallest mean square error and a highperformance with precision of 96%.

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