Методи пошуку аномалій в даних вимірювань режимних параметрів електричної мережі

В статті проведено аналіз проблем при зборі та обробці даних моніторингу режимних параметрів електричної мережі та розглянуто класифікацію аномалій, проблеми, особливості та методи їх пошуку в даних синхронізованих векторних вимірювань електроенергетичних систем.The materials of the article are an overview of the problems of development of electric power systems in the context of data collection and processing of mode parameters and analytical review of methods of search and detection of anomalies in data of synchronized vector measurements of mode parameters of electric network. The classification of anomalies, problems that arise during their search, classification of methods of search and detection of anomalies, as well as modern methods of finding anomalies in the data of synchronized vector measurements of power systems are considered

Manutenção preditiva em centros de conformação na Vista Alegre Atlantis SA

A crescente competitividade do mercado derivada, em grande parte, pela inovação tecnológica, impõe às empresas a necessidade de reformar o modus operandi tradicional e adotar um modelo enquadrado no paradigma da indústria 4.0. A otimização do controlo do processo produtivo através da informatização em tempo real integrada ao longo de todo o chão de fábrica traz benefícios ao nível do custo, da qualidade e da segurança. A análise de dados provenientes da sensorização dos equipamentos em paralelo com algoritmos de Machine Learning constitui o cerne da manutenção preditiva, tema desta dissertação. A manutenção periódica e reativa têm vindo a ser substituídas por intervenções proativas que asseguram a continuidade da produção e a redução de defeitos de fabrico. O trabalho pode ser dividido em três fases: instalação de um equipamento para monitorizar variáveis de funcionamento de máquinas de um centro de conformação plástica da Vista Alegre Atlantis SA; desenvolvimento de um modelo de manutenção preditiva que consiga detetar, antecipadamente, um comportamento irregular de funcionamento; elaboração de uma interface gráfica de visualização. O modelo foi obtido com recurso a algoritmos de aprendizagem supervisionada (árvores de decisão) e algoritmos de clustering (K-Means e DBSCAN), tendo servido como prova de conceito.The increasing market competivity that derives, mostly, from the continuous tecnhological inovation forces companies to retire their traditional modus operandi and to addopt a model of business integrated in the Industry 4.0 paradigm. Production process control optimization by means of real-time informatization of cyber-physical systems scattered along the shop floor brings benefits at the cost, quality and safety levels. Data analysis from equipment sensors in paralel with Machine Learning algorithms constitutes the core of Predictive Maintenance, main subject of this thesis. Periodic and reactive maintenance have lately been replaced with proactive interventions that assure production continuity and reduction of manufacturing defects. The work can be divided in three phases: installation of equipment to monitor operating variables of machines in a plastic forming center at Vista Alegre Atlantis SA; development of a predictive maintenance model that can detect, in advance, an irregular operating behavior; elaboration of a graphical visualization interface. The model was obtained using supervised learning algorithms (decision trees) and clustering algorithms (K-Means and DBSCAN), serving as proof of concept.Mestrado em Engenharia Mecânic

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Abnormal event detection with high resolution micro-PMU data

With the unprecedented growth of renewable resources, electric vehicles, and controllable loads, power system has been incorporating increasing amount of unconventional generations and loads. As a consequence, significant dynamic and stochastic power flow are introduced into distribution network, requiring high resolution monitoring technology and agile decision support techniques for system diagnosis and control. In this paper, we discuss the application of micro-synchrophasor measurement unit (µPMU) for power distribution network monitoring, and we propose a novel data-driven method, namely Ensembles of Bundle Classifier (EBC), for event detection. The main idea is: multiple classifiers are learned each with a short slot of µPMU measurement generated by a single event. Then their decisions are combined with a “winner-takes-all” scheme. This framework naturally resolves the challenging issue of heterogeneity in the high resolution µPMU data, and significantly outperforms classic data-driven event detection methods. In this paper, the proposed framework is tested on an actual distribution network with µPMUs, and is compared to other state-of-the-art methods. The result justifies the effectiveness of EBC as a promising tool to improve the security and reliability of distribution network

Abnormal event detection with high resolution micro-PMU data

With the unprecedented growth of renewable resources, electric vehicles, and controllable loads, power system has been incorporating increasing amount of unconventional generations and loads. As a consequence, significant dynamic and stochastic power flow are introduced into distribution network, requiring high resolution monitoring technology and agile decision support techniques for system diagnosis and control. In this paper, we discuss the application of micro-synchrophasor measurement unit (µPMU) for power distribution network monitoring, and we propose a novel data-driven method, namely Ensembles of Bundle Classifier (EBC), for event detection. The main idea is: multiple classifiers are learned each with a short slot of µPMU measurement generated by a single event. Then their decisions are combined with a “winner-takes-all” scheme. This framework naturally resolves the challenging issue of heterogeneity in the high resolution µPMU data, and significantly outperforms classic data-driven event detection methods. In this paper, the proposed framework is tested on an actual distribution network with µPMUs, and is compared to other state-of-the-art methods. The result justifies the effectiveness of EBC as a promising tool to improve the security and reliability of distribution network