Description:FIELD OF THE INVENTION
The present invention relates to the field of signal processing, particularly to the detection of zero crossing points in distorted sinusoidal signals for applications such as grid synchronization, power conversion, and switch-gear protection. It leverages advanced machine learning techniques to improve detection accuracy in the presence of noise and harmonic distortion.
BACKGROUND OF THE INVENTION
Accurate detection of zero crossing point in a distorted sinusoidal signal for grid synchronization, power conversion and switch-gear protection
• The approach described in U.S. Pat. No. 6,285,140B1 involves employing a microcontroller equipped with a zero-crossing-point detector to create an AC-synchronized time-delay pulse to regulate a triac in a on or cut-off state in order to send varied electric power to a light-emitting diode load.
• In the China patent with no. CN101871965B, unary linear regression theory is used to detect the time of zero crossing point in sinusoidal power signal
• Currently electronics-based comparator circuits are using to identify the zero-crossing point in sinusoidal signal
Electronics based comparator circuits able to detect the zero-crossing point effectively in a pure sinusoidal signal, however it may fail in detection of zero crossing point in a highly distorted sinusoidal signal having noisy and harmonic contents.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to A process of building a deep neural network model detects zero crossing point in a distorted sinusoidal signal.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention provides a system for accurate detection of the zero-crossing point in a distorted sinusoidal signal using machine learning techniques, specifically deep neural networks (DNN).
The proposed solution captures and processes real-time sinusoidal signals containing noise and harmonic distortion. A dataset consisting of signals with varying levels of noise and total harmonic distortion (THD) is used to train the DNN. The system includes multiple hardware and software components to ensure robust and accurate zero crossing point detection in real-world applications.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Deep neural network8 (DNN) based approach which is able to develop highly complex and non-linear relation between input and output features is used to detect the zero-crossing point in a distorted sinusoidal signal
Highly distorted sinusoidal signals are generated based on noise level and total harmonic distortion (THD) as shown below, the data extracted from these signals are used to train the DNN model so that it is able to detect zero crossing point in real time distorted sinusoidal signals effectively
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: DNN TOPOLOGY FOR THE PROPOSED APPROACH
FIGURE 2: PROCESS FOR DATA PREPARATION
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Deep neural network8 (DNN) based approach which is able to develop highly complex and non-linear relation between input and output features is used to detect the zero-crossing point in a distorted sinusoidal signal
Highly distorted sinusoidal signals are generated based on noise level and total harmonic distortion (THD) as shown below, the data extracted from these signals are used to train the DNN model so that it is able to detect zero crossing point in real time distorted sinusoidal signals effectively
Dataset Noise THD Samples
1 10% - 50% - 4936
2 - 10% - 50% 4436
3 10% - 40% 50% 3949
4 5% - 20% - 3949
Hardware Components:
1. Signal Acquisition Unit:
o Sensors: Capture real-time sinusoidal signals, including voltage and current waveforms.
o Analog-to-Digital Converter (ADC): Converts analog signals to digital form for processing.
2. Data Processing Unit:
o Microcontroller or Digital Signal Processor (DSP): Controls the operation of signal acquisition and handles pre-processing of the signal, such as filtering and noise reduction.
3. Zero Crossing Detection Module:
o Deep Neural Network Processor: This processor runs the trained DNN model that is responsible for detecting zero crossing points from distorted signals.
o Memory Unit: Stores the trained DNN model and parameters like weights, biases, and hyperparameters.
4. Power Supply Unit: Provides power to the entire system, including the sensors, processors, and other components.
5. User Interface:
o A display or communication interface to show real-time information on the zero crossing points and any signal distortion statistics.
Software Components:
1. Data Preprocessing Module: Normalizes and segments the captured signals to extract meaningful features. It prepares the data for DNN input by removing high-frequency noise, low-frequency drift, and decomposing the signal into relevant sub-components like amplitude and frequency.
2. Feature Selection Module: Identifies critical features from the signal that are useful for the detection of zero crossing points. This includes features like intercept, slope, correlation, and RMSE (Root Mean Square Error), which are computed for specific window sizes.
3. Model Training Module: Utilizes machine learning algorithms, particularly deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). The DNN is trained on a dataset comprising signals with various levels of noise and harmonic distortion.
4. Zero Crossing Detection Module: Implements the trained DNN to detect the zero crossing points in real-time. This module is optimized through hyperparameter tuning, which includes adjusting the number of hidden layers, neurons, and activation functions to enhance accuracy.
Dataset:
A dataset is prepared using sinusoidal signals with varying levels of noise (10% - 50%) and harmonic distortion (THD from 10% to 50%). The dataset includes features such as intercept, slope, correlation, and RMSE extracted from the signals, and is used to train the DNN.
Dataset Noise THD Samples
1 10%-50% - 4936
2 - 10%-50% 4436
3 10%-40% 50% 3949
4 5%-20% - 3949
Working of the System:
1. Signal Acquisition: The signal acquisition unit captures the real-time distorted sinusoidal signal and passes it to the data processing unit.
2. Preprocessing: The signal is filtered and segmented by the data preprocessing module. Features like slope, intercept, and correlation are extracted.
3. Zero Crossing Detection: The processed signal is input into the DNN-based zero crossing detection module, where the trained model identifies the zero crossing points.
4. Output: The detected zero crossing points are relayed to the user interface and any connected devices (such as grid synchronization or protection systems). The system can also log the data for future analysis.
, Claims:1. A zero crossing point detection system for distorted sinusoidal signals, comprising:
A signal acquisition unit for capturing real-time signals,
A data preprocessing module for filtering and segmenting signals,
A deep neural network-based zero crossing detection module, and
A user interface for displaying detected zero crossing points.
2. The system as claimed in claim 1, wherein the deep neural network-based detection module is trained using signals with varying levels of noise and harmonic distortion.
3. The system as claimed in claim 1, wherein the data preprocessing module extracts features such as intercept, slope, correlation, and RMSE from the signal.
4. The system as claimed in claim 1, wherein the deep neural network-based detection module uses hyperparameter tuning to improve detection accuracy.