Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD), runs test (RT), and relevance vector machine (RVM). First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF) components and residual (RES) component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels) by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy

Tolérance aux Défaillances par Capteurs Virtuels : application aux Systèmes de Régulation d'un Turboréacteur

Over the years, market pressure has ensured that engine manufacturers invest in technology to provide clean, quiet, affordable, reliable, and efficient power. One of the last improvements is the introduction of virtual sensors that make use of non-like signals (analytical redundancy). This, is expected to improve weight, flight safety and availability. However, this new approach has not been widely investigated yet and needs further attention to remove its limitations for certificated applications.The concept of virtual sensors goes along with fault tolerance control strategies that help in limiting disruptions and maintenance costs. Indeed, a fault-tolerant control (FTC) scheme, allows for a leaner hardware structure without decreasing the safety of the system.We propose in this thesis work, to monitor through a passive FTC architecture, the Variables Geometries subsystems' of the engine: the VSV (Variable Stator Vane) and FMV (Fuel Metering Valve). A strong constrains, is not to change the parameters of the existing controllers. The approach named AVG-FTC (Variable Geometries Aircraft-Fault-Tolerant Control) is based on several cascaded sub-systems that allow to deal with the Linear Parameter Varying (LPV) model of the systems and modelling errors. The proposed FTC scheme uses a neural model of the sensor associated with a Takagi-Sugeno observer and a Neuronal Extended Kalman Filter Neural (NEKF) to account for those dynamics that cannot be explained with the LPV model to produce a real-time estimate of the monitored outputs. In case of sensor abnormality, the proposed virtual sensors can then be used as an arbitrator for sensor monitoring or as a healthy sensor used by the controller. To evaluate the approach, serval closed-loop simulations, on SNECMA jet-engine simulator have been performed. The results for distinct flight scenarios with different sensors faults have shown the capabilities of the approach in terms of stability and robustness.L'industrie aéronautique évolue dans un contexte concurrentiel qui encourage les motoristes et avionneurs à réduire les coûts de production et à améliorer leurs services aux compagnies aériennes tels que la réduction des coûts d'exploitation et de maintenances des avions. Afin de relever ce défi économique, nous proposons dans cette thèse de remplacer l'architecture de régulation actuelle de certains équipements du turboréacteur, par une architecture simplifiée plus économe en capteurs et harnais en remplaçant la redondance matérielle des capteurs par une redondance analytique. Ainsi, en cas de fonctionnement anormal, les capteurs virtuels proposés pourront être utilisés pour consolider la prise de décision sur l'état du capteur par des tests de cohérence et de validation croisée et le cas échéant se substituer aux mesures.Dans ce travail de thèse, on s'est intéressé à la surveillance des systèmes de régulation de géométries variables (régulation du flux d'air en entrée et la quantité de carburant) avec comme contrainte forte la non-modification des paramètres des lois de commande existantes et le maintien de l'opérabilité du turboréacteur avec une dégradation des performances acceptables selon les spécifications du cahier des charges.Pour répondre à ces contraintes opérationnelles, une approche FTC (Fault Tolerant Control) passive est proposée. Cette approche nommée, AVG-FTC (Aircraft Variables Geometries-Fault-Tolerant Control) s'articule autour de plusieurs sous-systèmes mis en cascades. Elle tient compte du caractère instationnaire des systèmes étudiés, des différents couplages entre géométries variables et des incertitudes de modélisation. Ainsi, l'approche utilise un modèle neuronal du capteur couplé à un observateur de type Takagi-Sugeno-LPV (Linéaire à Paramètres Variant) et à un estimateur non linéaire robuste de type NEKF (Filtre de Kalman Étendu Neuronal) qui permet de produire une estimation temps réel des grandeurs surveillées. En utilisant la plateforme de prototypage et de tests du motoriste, nous avons pu évaluer l'approche AVG-FTC en simulant plusieurs scénarios de vol en présence de défaillances. Ceci a permis de montrer les performances de l'approche en termes de robustesse, de garantie de stabilité des boucles de régulations et d'opérabilité du turboréacteur. To improve the availability, a solution that aircraft manufacturers and suppliers adopt was the fault tolerance

Optimization of electrical discharge machining of advanced engineering materials

Predmet istraživanja ove disertacije predstavlja unapređenje, modelovanje i optimizacija procesa elektroerozivne obrade (EDM) savremenih inženjerskih materijala. Prvo su predstavljene dve inovativne metode: EDM u dielektrikumu sa pomešanim prahom i EDM sa pomoćnom elektrodom, a zatim i njihova kombinacija. Za generisanje matematičkih modela primenjene su metodologija odzivne površine i alati veštačke inteligencije. U nastavku su postavljeni optimizacioni procesi određivanja ulaznih parametara sa jednom i više funkcija cilja koji su rešeni primenom klasičnih metoda optimizacije. U završnom osvrtu sprovedena je verifikacija dobijenih modela i optimalnih ulaznih parametara elektroerozivne obrade.The subject of the research of this dissertation is the improvement, modeling and optimization of the electrical discharge machining (EDM) of advanced engineering materials. First, two innovation methods are presented: EDM in powder mixed dielectric fluid and EDM with assisted electrode and that their combination. The method of response surface and artificial intelligence tools were applied to generate mathematical models. The optimization problems of determining the input parameters with single and multiple target functions are solved by the application of classical optimization methods. Finally, verification of the obtained models and optimal input parameters of electrical discharge machining was carried out