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Improving Power Quality through Effective Condition Monitoring Techniques

Improving Power Quality through Effective Condition Monitoring Techniques


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Deep Learning based Condition Monitoring approach applied to Power Quality

Power quality is a term that refers to the quality of the voltage and current delivered by the electric power system to the end-users. Poor power quality can cause various problems, such as equipment malfunction, increased energy losses, reduced reliability, and increased maintenance costs. Therefore, it is important to monitor and analyze the power quality parameters, such as voltage magnitude, frequency, harmonics, flicker, sag, swell, interruption, etc.


One of the challenges of power quality monitoring is the large amount of data that needs to be processed and analyzed. Traditional methods, such as Fourier transform, wavelet transform, or statistical analysis, may not be able to capture the complex and dynamic features of power quality disturbances. Moreover, these methods may require prior knowledge of the types and characteristics of the disturbances, which may not be available in real-time applications.


Deep learning is a branch of machine learning that uses artificial neural networks to learn from large amounts of data and extract high-level features. Machine learning is a broader term that encompasses any method that can learn from data and make predictions or decisions. Deep learning is a specific type of machine learning that uses multiple layers of nonlinear processing units to learn hierarchical representations of the data. Deep learning has shown remarkable performance in various domains, such as computer vision, natural language processing, speech recognition, etc. Recently, deep learning has also been applied to power quality monitoring, as it can handle the high-dimensional and nonlinear data without requiring explicit feature extraction or prior knowledge.


In this blog post, we will introduce a deep learning-based condition monitoring approach applied to power quality. The main idea is to use a deep neural network to learn a representation of the normal power quality condition from historical data, and then use this representation to detect and classify any deviations from the normal condition in real-time.


The advantages of this approach are:

- It can handle different types of power quality disturbances without requiring specific models or rules.

- It can adapt to changing power system conditions and learn new patterns from online data.

- It can provide a comprehensive and interpretable diagnosis of the power quality condition by identifying the type, location, severity, and duration of the disturbances.


The proposed approach consists of three main steps: data preprocessing, feature learning, and condition diagnosis.


To learn more here

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