A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Experimental set-up for investigation of fault diagnosis of a centrifugal pump

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identificationPeer ReviewedPostprint (published version

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values

Flexible wavelet-neuro-fuzzy neuron in dynamic data mining tasks

Запропоновано нову гнучку модифікацію нео-фаззі нейрону та алгоритм навчання усіх параметрів. Запропонований алгоритм навчання дає змогу налаштувати не тільки синаптичні ваги, але й параметри функцій активації-приналежності та її форми, що дає змогу уникнути виникнення «дірок» у вхідному просторі. Запропонований алгоритм навчання має як фільтруючі, так і властивості слідкування, таким чином гнучкий нео-фаззі нейрон може використовуватися для вирішення задач прогнозування, фільтрації та згладжування нестаціонарних стохастичних и хаотичних послідовностей. Перевагами запропонованого підходу є простота обчислення у порівняні з відомими алгоритмами навчання гібридних вейвлет-нейро-фаззі-систем обчислювального інтелекту.Предлагается новая гибкая модификация нео-фаззи нейрона и алгоритм обучения всех его параметров. Предложенный алгоритм обучения позволяет настраивать не только синаптические веса, но и параметры функций активации-принадлежности и ее формы, что позволяет избежать возникновения «дырок» во входном пространстве. Предложенный алгоритм обучения обладает как фильтрующими, так и следящими свойствами, таким образом гибкий нео-фаззи нейрон может использоваться для решения задач прогнозирования, фильтрации и сглаживания нестационарных и хаотических последовательностей. Преимуществом предложенного подхода являются вычислительная простота в сравнении с известными алгоритмами обучения гибридных вэйвлет-нейро-фззи систем вычислительного интеллекта.A new flexible modification of neo-fuzzy neuron (FNFN) and adaptive learning algorithms for the tuning of its all parameters are proposed in the paper. The algorithms are interesting in that they provide on-line tuning of not only the synaptic weights and membership functions parameters, but also forms of these functions, that provide improving approximation properties and allow to avoid the occurrence of ”gaps” in space of inputs. The proposed algorithms have both the tracking and filtering properties, so the FNFN can be effectively used for prediction, filtering and smoothing of non-stationary stochastic and chaotic sequences. A special feature of the proposed approach is its computational simplicity in comparison with known learning procedures for hybrid wavelet-neuro-fuzzy systems of computational intelligence

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market