This paper presents a new automatic detection and classification approach of power quality (PQ) problems using Kalman filter. Kalman filter is used as an estimator to calculate the fundamental frequency and harmonic components amplitudes of the voltage or current signals. Then the instantaneous total harmonic distortion (iTHD) and the energy are calculated. For each half cycle of the processed signal, five decision quantities are calculated based on iTHD and energy and these quantities are the three consecutive maximum values of iTHD, standard deviation and energy difference between distorted signal and its fundamental frequency component. Decision rules based on these decision quantities are applied to identify and classify the PQ events in this captured signal. The proposed approach is tested on single and combined PQ events that are generated using the MATLAB with the help of mathematical models that are conformity with standard IEEE-1159. The performance is assessed using more than 100 dataset of every PQ event and the results show that the accuracy is 100 and 98.8 for noiseless and high-level of noise, respectively. In addition, the proposed approach performance is validated through comparisons with other classification. Several practical PQ events are generated by lab experiments to validate the proposed approach. The simulation and experimental results show that the proposed approach is efficient and robust and can be implemented to design PQ monitoring device.

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

: In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several ﬁelds related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network.

Current Status and Future Trends of Power Quality Analysis

A protection scheme for distribution utility grid with wind energy penetration

Quality power is a very important factor for the proper functioning of appliances and can be enhanced using shunt active power filters (SAPF). This work demonstrates the hardware implementation of synchronous reference frame (SRF) theory based SAPF. This paper presents the technique of particle swarm optimization (PSO) to tune the gain values of the SAPF PI controller to control the voltage of the DC-link and enhance its dynamic efficiency. The estimation of gain values of PI controllers using conventional methods does not yield the expected outcomes under varying load conditions. The PSO tuned controller provides better results as compared to traditional tuning methods. The switching scheme is implemented using hysteresis controller to control the SAPF. The main objectives of this approach are to extract the compensating currents and cancel out the harmonics produced by balanced, unbalanced and nonlinear loads. The planned scheme is designed and implemented in MATLAB/Simulink and then its performance on a developed laboratory prototype is validated experimentally. 

Experimental Investigations on Particle Swarm Optimization Based Control Algorithm for Shunt Active Power Filter to Enhance Electric Power Quality

Abstract Power quality is one of the most important research eras for the energy sector. Suddenly dropped voltages or suddenly rising voltages and harmonics in energy should be identified. All of these distortions are called power quality disturbances (PQDs). Deep learning based convolutional artificial neural networks with an attention model approach has been carried out. The main idea is to develop a new approach to convolutional neural network (CNN) based which classifies a particular power signal into its respective power quality condition. The attention model approach is based on the idea that the best solution will be taken from the newly produced data pool obtained by rescaling the available data according to the total number of pixels before the average data pool is created and then deep CNN processes will continue. In the attention model approach, all data is multiplied by the number of elements by the number of epoch time sixty-six tensors. The dataset used here contains signals which belong to one of the 9 classes. This means that each signal is characterized by 622 data points and 5600 data parameters. All signals provided are in time domain. Power quality (PQ) is directly depending on power disturbances’ absence or scarcity. The accuracy and error values of the developed model were obtained according to both the number of epochs and the number of iterations. 

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Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.

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A Hybrid Algorithm for Recognition of Power Quality Disturbances

Integration of RE sources to the utility grid offers technical and operational challenges causing problems of PQ, stability, identification of operational events, etc. This article presents an algorithm to identify events including islanding, grid integration, and outage of the solar PV and WG plants in grid using a ST. Islanding event may occur in the presence of any kind of plant. Processing of negative sequence component of voltage is performed by utilizing ST based multiresolution analysis at the test node and the output matrix is evaluated. The features (F1–F4), VI and STD indexes are obtained from this matrix. These features are utilized for identifying the events and transient phenomenon. The VI and STD indexes are used to recognize the type of RE source present during the islanding and outage events. Moreover, for recognizing the type of RE source at the time of synchronization event, an SI is proposed. This is computed by the ST depended processing of voltage signals. Performance of the algorithm is found satisfactory for all incidence angles and complete voltage cycle under the noisy conditions of 10 dB SNR. As compared to the time–frequency transform based coefficients of the voltage signal, the proposed technique is found to be superior in terms of small NDZ and low computation time and least affected by noise. Further, the developed technique is also efficient to detect various events stated above and the type of RE source. Study is performed using MATLAB/Simulink software and validated in real time using RTDS.

Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

A method using features computed from voltage by applying Hilbert transform (HT) and Stockwell transform (ST) for event recognition and power quality (PQ) estimation in distribution network (DN) interfaced to the solar photovoltaic (PV) generation is presented here. A PQ estimation factor (PEF) is proposed to recognize a PQ disturbances associated to an event in the DN with solar generation. Further, a time estimation factor (TEF) is also proposed which is effective for estimation of events and location of PQ issues. Features computed from the PEF and TEF are considered as input for rule based decision tree (RBDT) to classify the operational events. IEEE-13 buses test DN having an interfaced solar PV system is utilized for the proposed study. Performance of this technique is superior compared to approach using application of Discrete Wavelet transform (DWT).

Events Recognition and Power Quality Estimation in Distribution Network in the Presence of Solar PV Generation

Photovoltaic power is the most rising renewable energy source. Photovoltaic modules have changing properties regarding irradiance and temperature. So as to use the maximum power from the PV module, a maximum power point tracking algorithm is utilized. These algorithms are prepared by the dynamic qualities of the PV modules. Photovoltaic module clusters have unexpected conduct in comparison to individual Photovoltaic modules under halfway shading conditions. Under halfway shading conditions, the Photovoltaic cluster qualities have various pinnacles and the maximum power point algorithm should be prepared to discover the crest with the best power. Checking Photovoltaic cluster qualities under typical irradiance and halfway shading is fundamental to remove maximum power from the PV exhibit. In this paper, the different qualities of photovoltaic exhibits under halfway protecting conditions are examined, and a model for choosing the trademark locales of the cluster is proposed to follow the worldwide maximum power point.

Analyzing the Effect of Partial Shading on Performance of Grid Connected Solar PV System

The New beginning of computing technology is arriving such as the Internet of Things (IoT). It is a kind of Global Neural Network the cloud that interfaces various gadgets. Human life and the way work have been revolutionized by the Internet in the past decade. Currently, IoT is changing the trends of human life as the use of emerging technologies which consist of heterogeneous communication devices is increasing. IoT are relevant in different strategies of agriculture. India has agriculture as its essential occupation. As per IBEF (India Brand Equity Foundation), 58% individuals living in rural areas in India are reliant upon agriculture. The agricultural advancement is sped up with the increment in the profitability and up gradation of the plantation frameworks. The IoT has the capacity to change the world. In any case, the use of innovation like IoT in agriculture could have the best effect. Smart Agriculture is an idea wherein data and correspondence innovation is carried out to deal with every one of the exercises and cycles identified with the agriculture space. With the quick improvement of the world population, huge space of land is used to foster lodging and the capacity of creating food is decreased. Farming has gotten essential in present pattern and keeps food on the tables. Farming with IoT helps in moderating the lack of food by requesting the current land for more grounded usage at least expense. Smart agriculture is an idea that rapidly snaps on the agricultural field. In this paper present a novel design that are developing an automated system which is able to control the crop monitoring of the agriculture lands, which is quite difficult for human beings.

Rajkumar Kaushik

Papers

A Hybrid Algorithm for Recognition of Power Quality Disturbances

Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

Events Recognition and Power Quality Estimation in Distribution Network in the Presence of Solar PV Generation

Analyzing the Effect of Partial Shading on Performance of Grid Connected Solar PV System

Smart Agriculture Monitoring System Using Internet of Things (IoT)