Ivan Nunes da Silva

Load Profile Identification Interface for Consumer Online Monitoring Purposes in Smart Grids

This paper presents a method for the identification of consumer load profiles in a totally automatic way. The main objective was to develop an interface between utilities and consumers in order to obtain data from smart meters and identify the load profile. To this end, client/server software interfaces capable of transmitting and receiving data through TCP/IP sockets were developed. In this case, the consumer and utility were represented by client and server software, respectively. However, in order to be able to consider all the stages of this methodology, a database was created to store data related to consumers (meter readings, electricity bills and personal data). From this database, the utility software was able to furnish each consumers load profiles and to use this information to make decisions. Moreover, the software sends the electricity bill to the consumer by e-mail each month. Experimental and simulation results were obtained to validate the methodology and to show some benefits that can be achieved with the introduction of smart meters into power distribution systems in the context of load profile identification.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach.

Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

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 Utilitys 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.

Robust estimation of parametric membership regions

This paper is concerned with the robust identification of linear models when modelling error is bounded. A modified Hopfields neural network is used to calculate a membership set for the model parameters, with the internal parameters of the network obtained using the valid-subspace technique. These parameters can be explicitly computed to guarantee the network convergence. A solution for the robust estimation problem with an unknown-but-bounded error corresponds to an equilibrium point of the network. A comparative analysis with alternative robust estimation methods is provided to illustrate the proposed approach.

Identification of residential load profile in the Smart Grid context

This work presents an automatic method for identification of residential load profile in the Smart Grid context. Hence, in this research were used client/server software interfaces to transmit and receive data through the Internet. In this case, the residential consumers and utility were represented by client and server software, respectively. However, to consider all the stages of this method, a database was created to store fictitious data related to consumer measurements. From these data, the utility software was able to furnish information about consumers load profile and use this information to make decisions. The results were obtained using an experimental workbench that contains residential loads, a configurable power supply and an energy analyzer. It is show in an experimental way some benefits that can be achieved with the introduction of Smart Grid concept on distribution systems.

Evaluation of stator winding faults severity in inverter-fed induction motors

Graphical abstractDisplay Omitted HighlightsPresent a comprehensive evaluation of intelligent classifiers to identify stator faults in inverter-fed induction motors are presented.Proposed methodology uses the current signal in time domain as the inputs of the pattern classifiers for fault diagnosis.Experimental results with different inverters, operating frequencies and mechanical loads are presented.Three different intelligent methods are presented and compared for multiple faults under dynamic sampling rate. Three-phase induction motor are one of the most important elements of electromechanical energy conversion in the production process. However, they are subject to inherent faults or failures under operating conditions. The purpose of this paper is to present a comparative study among intelligent tools to classify short-circuit faults in stator windings of induction motors operating with three different models of frequency inverters. This is performed by analyzing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate according to machine frequency supply. To assess the classification accuracy across the various levels of faults severity, the performance of three different learning machine techniques were compared: (i) fuzzy ARTMAP network; (ii) multilayer perceptron network; and (iii) support vector machine. Results obtained from 2.268 experimental tests are presented to validate the study, which considered a wide range of operating frequencies and load conditions.

Very Short-Term Load Forecasting Using a Hybrid Neuro-fuzzy Approach

The purpose of this work is to employ the Adaptive Neuro Fuzzy Inference System for performing very short-term load forecasting in power distribution substations, which can enable the development of more efficient automatic load control of electrical power load systems. The system inputs are two load demand time series, composed of data measured in five minutes intervals up to seven days from substations located in the cities of Cordeirópolis and Ubatuba – SP, Brazil. The Adaptive Neuro Fuzzy Inference System is a universal approximator that can be used in function approximation and forecasting. The results of the Adaptive Neuro Fuzzy Inference System in this paper are promising, where the average MAPE of Cordeirópolis was 0.7264% and of Ubatuba was 0.5163%.

A novel multi-agent approach to identify faults in line connected three-phase induction motors

Graphical abstractDisplay Omitted HighlightsPresent a novel multi-agent approach to identify stator, rotor and bearing faults in three-phase induction motors.Proposed methodology uses the current amplitudes signal in time domain as the inputs of the multi-agent system for fault diagnosis.The multi-agent system incorporates pattern recognition techniques with better results for each type of fault.Experimental results gathered from three-phase induction motors operating with different load conditions and fed under unbalance voltage are provided. Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.

Speed Neuro-fuzzy Estimator Applied To Sensorless Induction Motor Control

This work proposes the development of an Adaptive Neuro-fuzzy Inference System (ANFIS) estimator applied to speed control in a three-phase induction motor sensorless drive. Usually, ANFIS is used to replace the traditional PI controller in induction motor drives. The evaluation of the estimation capability of the ANFIS in a sensorless drive is one of the contributions of this work. The ANFIS speed estimator is validated in a magnetizing flux oriented control scheme, consisting in one more contribution. As an open-loop estimator, it is applied to moderate performance drives and it is not the proposal of this work to solve the low and zero speed estimation problems. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink software. To determine the benefits of the proposed model, a practical system was implemented using a voltage source inverter (VSI) to drive the motor and the vector control including the ANFIS estimator, which is carried out by the Real Time Toolbox from Matlab/Simulink software and a data acquisition card from National Instruments.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.