This paper presents a methodology aimed at extracting features to obtain information that will highlight disturbances related to the field of power quality. Due to the concept of smart grids, it is ...clear that the classification of the disturbances should be undertaken using smart meters, so that a large amount of data corresponding to the voltage and current waveforms are not exchanged using the communication channels, i.e., between smart meter and Utility's database server. Thus, it is necessary to ensure a balance between computational effort (arising from the implementation of algorithms on hardware) and the satisfactory performance of the algorithm for the classification of disturbances. Based on the assumption that the classification task is onerous, this paper proposes a step of feature extraction that may be calculated and analyzed offline using synthetic waveforms/signals, which are subsequently validated using field measurements. It should be noted that this offline analysis is required to determine the most relevant features for the process of classifying each disturbance. However, in order to establish the effectiveness of the feature extraction step, the response of decision trees of the C4.5 type and of artificial neural networks of the multilayer perceptron type were verified during the phase of disturbance classification. In short, good results were obtained that corroborate the hypothesis that the feature extraction step is necessary to classify disturbances effectively and with low computational effort.
•A new method for power quality disturbance detection and classification based on deep learning at the edge.•Deep learning is used for automatic feature extraction and selection.•The proposed ...methodology is embedded in a low-cost smart meter.•Performance evaluation using accuracy, precision, recall and F1-Score.
The large amounts of data collected by smart meters (SM), such as electric energy, water gas consumption and power quality (PQ) metrics, can create a massive flow of data transmitted between consumers and utilities. In this context, an edge-fog-cloud architecture based on a low-cost SM is proposed. The employed SM acquires voltage and current signals to obtain their frequency and amplitude, allowing PQ to be monitored through methods of detection and classification of disturbances in order to send only information about the detected disturbances to the utility, thus reducing network traffic associated with PQ disturbances in Smart Grids. The proposed methodology was embedded at a low-cost SM to enable data exchange with the utility, offering an enormous potential for real scenarios.
Summary
Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to ...manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.
This paper proposes a methodology for monitoring power quality using a real‐time operation system embedded in low‐cost hardware. An architecture for demand‐side management focusing in power quality is described. The developed smart meter is a low‐cost one in order to encourage consumer participation in demand‐side management programs. Fast Fourier transform and statistics metrics are used for features extraction in order to decrease the computational cost. The proposal can be extended with demand response algorithms, Internet of Things, and loads control.
With the wide use of non-linear loads and the integration of multiple power systems, there is an increased risk of damaging power quality. Automatic detection of disturbance is the first step in ...dealing with power quality problems. Most of the related works founded in the literature, to detect power quality disturbances, use high computational cost techniques, making difficult to board in hardware. Thus, this work proposes a methodology with a low computational cost for disturbances detection in electrical power quality aiming embedded in hardware. In this way, the pre-processing stage employed a sliding window, with a one-point step, and for each window, two features are calculated: the root mean square and the harmonic distortion, to be used in the disturbance detection. From the results obtained through synthetic data, it was possible to observe that the proposed methodology can efficiently and rapidly detect the presence of disturbances with an accuracy rate greater than 90% for signals with more than 25 points of disturbance. The observed results can also be considered relevant for power quality analysis in Smart Grids because it can be shipped in a low-cost smart meter.
COVID-19 is an infectious disease caused by a type of coronavirus recently discovered, called SARS-CoV-2. It has infected more than 20 million people worldwide and it is responsible for more than ...737,000 deaths. This work presents a study that explores linear regression mechanisms combined with a sliding and cumulative time window approach to provide inputs to assist in decision making for public policies, within the scope of the COVID-19 pandemic evolution, whether they are hardening or easing the isolation. Data from five states of Brazil were collected and applied a Ridge regression to predict the curve behavior of cases and deaths of COVID-19. As a result, an Explained Variance Status (EVS) up to 0.998 and 0.999 is presented, considering cases and deaths, respectively. It was concluded that sliding time window bring more information about the infection than cumulative, since public policy changes in a few time-lapse.
The Smart Meter technology has become a trend in the future of power distribution systems and some applications should be provided with its installation in residential consumers. Thus, this paper ...presents a method of residential loads identification using data provided by a smart meter. In this sense, the ZigBee communication technology is proposed to exchange data between smart meter and consumer. Hence, a consumer-side software could be installed in a consumer device, such as: tablet, smartphone, microcomputer, etc. This software receive some measurements in order to show the loads identified. Moreover, this software is able to furnish the actual residential consumption. The identification method was developed using intelligent systems (neural networks, neural-fuzzy and neural-genetic) and its results were compared in order to determine its applicability.
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