Improving the prediction accuracy of recurrent neural network by a PID controller.

International audienceIn maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions

Universal Activation Function For Machine Learning

This article proposes a Universal Activation Function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the optimization algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF's parameters. For the CIFAR-10 classification and VGG-8, the UAF converges to the Mish like activation function, which has near optimal performance $F_{1} = 0.9017\pm0.0040$ when compared to other activation functions. For the quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR) environments, the UAF converges to the identity function, which has near optimal root mean square error of $0.4888 \pm 0.0032$ $\mu M$. In the BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in $961 \pm 193$ epochs, which proves that the UAF converges in the lowest number of epochs. Furthermore, the UAF converges to a new activation function in the BipedalWalker-v2 RL dataset

Multi-Objective Optimisation of A Centrifugal Compressor for a Micro Gas Turbine Operated by Concentrated Solar Power

Solar powered micro-gas turbines (MGTs) are required to work over a wide range of operating conditions due to the fluctuations in the solar insulation. This means that the compressor has to perform efficiently over a wider range than in conventional MGTs. To be able to extend the efficient operating range of a compressor at the design stage, both impeller blades and diffuser passage need to be optimised. Vaneless diffusers could offer more flexibility to extend the operating range than typical diffuser vanes. This paper presents a methodology for the design and optimisation of a centrifugal compressor for a 6 kW micro-gas turbine intended for operation using a Concentrated Solar Power (CSP) system using a parabolic dish concentrator. Preliminary design parameters were obtained from the overall system specifications and detailed cycle analysis combined with practical constraints. The compressor’s geometry optimisation has been performed using a fast and computationally efficient method, which involves the Latin hypercube Design of Experiment (DoE) technique coupled with the response surface method (RSM) in order to build a regression model through CFD simulations. Three different RSM techniques were compared with the aim to choose the most suitable technique for this specific application and then a genetic algorithm was applied. The CFD analysis for the optimised compressor showed that the high efficiency operating range has increased compared to the baseline design. Cycle analysis for the plant has been performed in order to evaluate the effect of the new compressor design on the system performance. The simulations demonstrated that the operating range of the plant was increased by over 30%

Dynamics of thin liquid films over a spinning disk

Thin film dynamics over spinning disks is of central importance to a number of scientific research and industrial applications, such as heat/mass transfer, chemical reactions and chip devices. Although they have received a lot of attention in different applications, the key un- derlying dynamics governing the flow are not thoroughly understood, especially in terms of highly non-linear behaviour in free surface flows, in the presence of other physical forces or chemical reactions. The elucidation of the underlying mechanisms driving the flow is of utmost importance to both scientific research and industrial applications. In this research the dynamics of a thin film flowing over a rapidly spinning, horizontal disk, in presence of first-order chemical reactions is considered. A set of non-axisymmetric evolution equations for the film thickness, radial and azimuthal flow rates is derived using a boundary- layer (IBL) approximation in conjunction with the Karman-Polhausen approximation for the velocity distribution in the film. Numerical solutions of these highly nonlinear partial dif- ferential equations are obtained from finite difference scheme which reveals the formation of large-amplitude waves that travel from the disk inlet to its periphery. The equations with non- axisymmetric condition were investigated where elimination of azimuthal dependence presents different wave regimes across the disk radius, and three dimensional wave structures over the entire disk. Apart from hydrodynamics, the influence of these waves on the concentration and temperature profiles is analysed for a wide range of system parameters. It is shown that these waves lead to significant enhancement of the rates of heat and mass transfer, as well as chemical reaction due to the mixing associated with the flow. Additionally, due to the time-consuming implementation of the IBL model, the Neural Network (NN) technique is applied based on existing Finite Difference (FD) results, in order to predict the wave dynamics after initial times.The NN is trained on a dataset from various data points in space and time from IBL model, and then used to simulate the evolution of any wave characteristics of interest. Overall, the resulting NN model predicts the evolution of waves reasonably well when compared with the time-consuming finite difference scheme, and reduces the computation time significantly.Open Acces

The use of artificial intelligence techniques for power analysis

This thesis reports the research carried out into the use of Artificial Intelligence techniques for Power System Analysis. A number of aspects of Power System analysis and its management are investigated and the application of Artificial Intelligence techniques is researched. The use of software tools for checking the application of power system protection systems particularly for complex circuit arrangements was investigated. It is shown that the software provides a more accurate and efficient way of carrying out these investigations. The National Grid Company's (plc, UK) use of software tools for checking the application of protection systems is described, particularly for complex circuit arrangements such as multi-terminal circuits and composite overhead line and cable circuits. Also described, is how investigations have been made into an actual system fault that resulted in a failure of protection to operate. Techniques using digital fault records to replay a fault into a static model of protection are used in the example. The need for dynamic modelling of protection is also discussed. Work done on automating the analysis of digital fault records using computational techniques is described. An explanation is given on how a rule-based system has been developed to classify fault types and analyse the response of protection during a power system fault or disturbance in order to determine correct or incorrect operation. The development of expert systems for on-line application in Energy Control Centres (ECC), is reported. The development of expert systems is a continuous process as new knowledge is gained in the field of artificial intelligence and new expert system development tools are built. Efforts are being made for on-line application of expert systems in ECC as preventive control under normal/alert conditions and as a corrective control during a disturbance. This will enable a more secure power system operation. Considerable scope exists in the development of expert systems and their application to power system operation and control. An overview of the many different types of Neural Network has been carried out explaining terminology and methodology along with a number of techniques used for their implementation. Although the mathematical concepts are not new, many of them were recorded more than fifty years ago, the introduction of fast computers has enabled many of these concepts to be used for today's complex problems. The use of Genetic Algorithm based Artificial Neural Networks is demonstrated for Electrical Load Forecasting and the use of Self Organising Maps is explored for classifying Power System digital fault records. The background of the optimisation process carried out in this thesis is given and an introduction to the method applied, in particular Evolutionary Programming and Genetic Algorithms. Possible solutions to optimisation problems were introduced to be either local or global minimum solutions with the latter being the desirable result. The evolutionary computation that has potential to produce a global solution to a problem due to the searching mechanisms that are inherent to the procedures is discussed. Various mechanisms may be introduced to the genetic algorithm routine which may eliminate the problems of premature convergence, thus enhancing the methods' chances of producing the best solution. The other, more traditional methods of optimisation described include Lagrange multipliers, Dynamic Programming, Local Search and Simulated annealing. Only the Dynamic Programming method guarantees a global optimum solution to an optimisation problem, however for complex problems, the method could take a vast amount of time to locate a solution due to the potential for combinatorial explosion since every possible solution is considered. The Lagrange multiplier method and the local search method are useful for quick location of a global minimum and are therefore useful when the topography of the optimisation problem is uni-modal. However in a complex multi-modal problem, a global solution is less likely. The simulated annealing method has been more popular for solving complex multi-modal problems since it includes techniques for the search to avoid being trapped in local minimum solutions. Artificial Neural Network and Genetic Algorithm have been used to design a neural network for short-term load forecasting. The forecasting model has been used to produce a forecast of the load in the 24 hours of the forecast day concerned, using data provided by an Italian power company. The results obtained are promising. In this particular case, the comparison between the results from the Genetic Algorithm - Artificial Neural Network and Back Propagation - Neural Network shows that the Genetic Algorithm - Artificial Neural Network does not provide a faster solution than the Back Propagation - Neural Network. The application of Evolutionary Programming to fault section estimation is investigated and a comparison made with a Genetic Algorithm approach. To enhance service reliability and to reduce power outage, rapid restoration of power system is required. As a first step of restoration, the fault section should be accurately estimated quickly. The Fault Section Estimation (FSE) identifies fault components in a power system by using information on the operation of protection relays and circuit breakers. However this task is difficult especially for cases where the relay or circuit breaker fails to operate and for multiple faults. An Evolutionary Programming (EP) approach has been developed for solving the FSE problem including malfunctions of protection relays and/or circuit breakers and multiple fault cases. A comparison is made with the Genetic Algorithm (GA) approach at the same time. Two different population sizes are tested for each case. In general, EP showed faster computational speed than GA with an average factor of 13 times more. The final results were almost the same. The convergence speed (the required number of generations to get an optimum result) is a very important factor in real time applications. Test results show that EP is better than GA. However, as both EP and GA are evolutionary algorithms, their efficiencies are largely dependent on the complexity of the problem that might differ from case to case. The use of Artificial Neural Networks to classify digital fault records is investigated showing theat Self Organising Maps could be useful for classifying records if integrated into other systems. Digital fault records are a very useful source of information to the protection engineer to assist with the investigation of a suspected unwanted operation or failure to operate of a protection scheme. After a widespread power system disturbance, due to a storm for example, a large number of fault records can be produced. A method of automatically classifying fault records would be very helpful in reducing the amount of time spent in manual analysis, thus assisting the engineer to focus on records that need in depth analysis. Fault classification using rule base methods have already been developed. The completed work is preliminary in nature and an overview of an extension to this work, involving the extraction of frequency components from the digital fault record data and using these as input to a SOM network, is described

Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016Com uma crescente preocupação relativamente ao consumo energético global, a energia fotovoltaica surge como uma fonte energia renovável promissora. Esta dissertação é constru ída sob a premissa de que a capacidade de previsão de potência fotovoltaica produzida possibilita o aumento de performance da rede elétrica local através de um controlo eficiente da mesma. O trabalho desenvolvido propõe uma estrutura com a capacidade de previsão de potência produzida por um sistema fotovoltaico ligado a rede elétrica presente na Universidade do Algarve. A estrutura de previsão proposta é composta por dois modelos dinâmicos, não lineares, de previsão e um modelo estático não linear. Redes Neuronais Artificiais foram usadas como modelos. Os modelos de previsão têm como objectivo fazer previsões da temperatura do ar e irradiação solar em passos incrementais de 5 minutos para um horizonte de previsão de 4 horas. O modelo estático é construído para estimar a potência gerada pelo sistema fotovoltaico e é otimizado através de comparação entre vários tipos de redes neuronais como o perceptrão multicamadas e funções de base radial, e modelos com escalas temporais diferentes, aplicados a diferentes estações do ano, bem como um modelo anual.In a growing concern for the world energy consumption, photovoltaic energy sources are a reliable renewable energy alternative. This thesis is built upon the premise that the forecast of photovoltaic power production can increase performance of local electric network through an efficient network management. The work developed proposes a power production forecast structure based on a grid-connected photovoltaic system in the University of Algarve. The proposed forecast structure is composed of two non-linear dynamic forecasting models and one non-linear static model. Artificial Neural Networks were used in the development of these models which are intended to forecast solar irradiance and air temperature using Radial Basis Functions with 5 minutes time steps within a prediction horizon of 4 hours. The static model on the structure was created to estimate the power generated by the photovoltaic system and it was optimized through comparison between several network architectures (MLP and RBF) and several seasonal models, as well as a annual model

Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

Estimation problems are frequent in several fields such as engineering, economics, and physics, etc. Linear and non-linear regression are powerful techniques based on optimizing an error defined over a dataset. Although they have a strong theoretical background, the need of supposing an analytical expression sometimes makes them impractical. Consequently, a group of other approaches and methodologies are available, from neural networks to random forest, etc. This work presents a new methodology to increase the number of available numerical techniques and corresponds to a natural evolution of the previous algorithms for regression based on finite elements developed by the authors improving the computational behavior and allowing the study of problems with a greater number of points. It possesses an interesting characteristic: Its direct and clear geometrical meaning. The modelling problem is presented from the point of view of the statistical analysis of the data noise considered as a random field. The goodness of fit of the generated models has been tested and compared with some other methodologies validating the results with some experimental campaigns obtained from bibliography in the engineering field, showing good approximation. In addition, a small variation on the data estimation algorithm allows studying overfitting in a model, that it is a problematic fact when numerical methods are used to model experimental values.This research has been partially funded by the Spanish Ministry of Science, Innovation and Universities, grant number RTI2018-101148-B-I00

Advances on the morphological classification of radio galaxiesreview: A review

Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.<br/

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309