Modeling and event-driven treatment based on illumination of power quality disturbances in smart grids. Dissertation title: Modeling and event-driven treatment based on illumination of power quality disturbances in smart grids. It indicates a great performance advantage and reliability compared to peers in terms of power consumption and data transmission of the proposed solution.
The thesis summary consists of 490 words and is written in 1.5 spaced between lines. I declare that this thesis titled "Model-Based and Event-Based Processing of Power Quality Disturbances in Smart Grids" is based on my original work, except for properly acknowledged citations and citations.
- Overview
- PROBLEM STATEMENT
- Importance and Motivation
- Objective
The quality of the power supply is degraded due to PQ disturbances such as voltage drops, voltage fluctuations with and without harmonics, momentary interruption, harmonic distortion, flicker, notch, spike and transients. This leads to increased operational and planning complexity of electricity supply networks, which requires increased attention to the quality of the power supply [17]. The term power quality (PQ) is usually applied to a broad category of electromagnetic phenomena occurring within a power system network [14] The ability of power systems to deliver undisturbed voltage, current and frequency signals is referred to as the quality of the power supply [20].
Such variations occur in high frequency electrical networks due to competitive environment and constant change of power supply. To improve power quality performance, the PQ disturbances in a practical distribution system must be recognized in real time before appropriate mitigation action can be taken.
- Background
- Types of PQ disturbances
- Reasons of generation of these PQ disturbances
- Power quality disturbances monitoring techniques
- Facilities effected by power quality disturbances
- PQ Disturbances model
- Feature extraction
- Wavelet Packet Decomposition(WPD)
- Wavelet Transform(WT)
- S-Transform
- Short-time Fourier transform
- classification Techniques
Part of the energy is lost due to the heat generated in the transmission lines. Power quality (PQ) refers to maintaining the measured magnitude and frequency of the sinusoidal regulated voltage and current of the power grid [23]. Many parameters can characterize power quality, such as reliability of service, voltage variability, transient voltages and currents, harmonic content [24].
Proper monitoring is important to guarantee a stable level of power quality, and analytical devices are commonly used for energy efficiency [29]. One of the main factors for power quality analysis is the development of power quality standards. Flicker: It is the voltage fluctuation from the power source, which is between 90 and 110 percent of the nominal voltage.
Power quality disturbances occur due to the use of non-linear loads, such as variable speed drives, transformers, power supplies or photovoltaic inverters, among many others [11], [34]. Essential aspects in this context are an accurate understanding and real-time treatment of the PQ disturbances. The S transform is used as a time-frequency filter and helps determine the sign of the Power arrival.
The next consecutive part of the signal is selected to add some delay to the window function. Application of classification technique for automatic identification of power quality (PQ) disturbances such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) [43]. By adjusting the weights, the network predicts the correct class label for the input tuples.
They can work when the data is a small knowledge of the relationships between properties and classes. To get the best classification design to recognize among members of the two classes in the education data.
- Power Quality (PQ) disturbances Model
- Signal Reconstruction
- Event-Driven Sensing
- The Event-Driven Segmentation
- Features Extraction
- Time domain feature extraction
- Frequency domain feature extraction
- Classification
- Method 1: k-NN classifier
- Method 2: Naïve Bias
- Method 3: Support vector machine
- Method 4: Voting base classifier
- Evaluation Measures
- Compression gain
- Classification accuracy
The harmonics are sinusoidal voltages or currents of different integer frequencies from the base power frequency (50 Hz or 60 Hz). It provides a quasi-analog version of the incoming signal, which is used as an input to the event-driven detection module. A sample is taken just after the input band-limited analog signal x(t) exceeds one of the predefined thresholds.
EDADC only acquires the associated information and ignores the rest of the signal. So the use of classical techniques cannot be used for data processing or analysis [33], [34]. Windowing is an essential operation and is required for time-limited data acquisition to meet the specifications of a practical system implementation [33], [34]. ]. Compared to conventional examples, the computational gain of the proposed approach is therefore significantly increased.
The selection of the resampling frequency depends on the reference sampling frequency and the sampling frequency of the selected window. A simplified linear interpolation (SLI) technique is used for online resampling of the segmented signal. The mathematical equation used to calculate is represented in equation (26) where Q is the length.
So the time resolution (∆ti) and frequency resolution (∆fi) of the proposed STFT that. The goal of the support vector machine algorithm is to locate a hyperplane in a partition N (N - the number of features) that classifies the data points distinctly [52]. In the case of PQ entry, the candidate who receives the most votes is determined.
For event-driven detection, the sampling rate is not unique and adapts as a function of the temporal variations of the input signal [6]. Considering 59 time duration LT, the number of samples obtained may be different and is a function of the EDADC resolution, the quantization scheme used and the signal characteristics.
- First Study
- Second Study
- Third Study
- Extended for 6-Classes of PQ disturbances
The obtained number of samples N can then be determined as q combination of and to consider. He describes that the suggested solution achieves an overall compression gain of 10.7 times, 12.9 times, and 09.4 times for the case of pure signal, swell, and harmonic, respectively. 70% of these instances are used to prepare reference templates and the remaining 30% are used for testing purposes.
It shows that the classification accuracy of the obtained PQ signals is 98.3% for pure signal, 97.4% for swell and 98.5% for harmonics. 67 times, 15.3 times and 28.3 times compression gains in case of pure signal, case and interruption respectively. It shows the obtained PQ signals classification accuracy is 99.1% for the pure signal, 98.3% for the decomposition and 99.6% for the interruption.
72 Figure 23 Instances of PQ signals digitized with a 4-bit EDADC resolution for clean signal, low, high and low. It describes that the suggested solution achieves an overall compression gain of 10.7 times, 15.3 times, 12.9 times, 28.3 times, 13.5 times, and 12.7 times for the case of pure signal, decay, swell, cutoff, drop harmonic, and swell harmonic, respectively. . The percentage recognition accuracy obtained using the K-Nearest Neighbor classifier are summarized in Table 8. It describes the classification accuracy of the obtained PQ signals is 99.7%.
The obtained percentage recognition accuracies using Naive Bias classifier are summarized in Table 9. It depicts the obtained PQ signals classification accuracy is 88.5% for the pure signal, 87.2% for the bag, 86.3% for the swell, 90.8% for the break, 85.4 % for Harmonics with Sag, and 83.6% for Harmonics with Swell. 75 The obtained percentage of recognition accuracies using Support Vector Machine Classifier is summarized in Table 10. It depicts the obtained PQ signals classification accuracy is 96.2% for the pure signal, 94.4% for the sag, 93.5% for the swell, 97.1% for the interruption, 91.7% for Harmonics with Sag, and 92.6% for Harmonics with Swell. The obtained percentage recognition accuracies using Support Vector Machine Classifier are summarized in Table 11. It depicts the obtained PQ signals classification accuracy is 98.6% for the pure signal, 96.2% for the bag, 94.9% for the swell, 98.9% for the interruption , 92.6% for Harmonics with Soft, and 93.8% for Harmonics with Swell.
78 control efficiency such as: Fast Fourier Transform (FFT)[73], Singular Value Decomposition (SVD) [43], Artificial Neural Network (ANN) [43] etc. This strategy removes the need to set a threshold value for power quality detection Another technique for detecting and changing the harmonic issue is the Kalman Filtering (KF) solution is very useful for detecting dips, swells, short time breaks etc. . As stated above, power quality refers to maintaining the measured magnitude and frequency of the regulated near-sinusoidal voltage and current of a power system.
More precisely, some parameters such as service life, variance in voltage magnitude, transient voltages and currents, and harmonic content can characterize power quality. To explain the importance of problems related to energy efficiency, we can assume that low energy quality contributes to excess resources and economic losses[23]. This thesis demonstrates an original real-time solution focused on event-driven computing with extraction and classification functions for time-domain PQ signals.
The concept is based on a clever combination of event-driven signal acquisition and segmentation along with local feature extraction and different types of classification for an effective and high-accuracy solution. It shows a great performance advantage and result when compared to the classical approach in terms of energy consumption of the indicated system. This shows the advantages of using the suggested approach to build computationally efficient automated clarifiers of PQ perturbations.
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Lamba, "Identifying Power Quality Disturbances in a Renewable Energy-Based Smart Grid Using the Stockwell Transform," Vol. Efficiency (ICECE 2019) Conference entitled "Time - Domain Event-Based Power Quality Disturbance Identification - Guided Processing". The collaboration took place with a poster under the title "Time domain method with adaptive speed for effective identification of power quality disturbances".
