Search results for: industrial machine

Authors: J. G. Batista, K. N. Sousa, J. L. Nunes, R. L. S. Sousa, G. A. P. Thé

Abstract:

This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values.

Keywords: Modeling, AC servomotor, Permanent Magnet Synchronous Motor-PMSM, Genetic Algorithm, Vector Control, Robotic Manipulator, Control.

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

Abstract:

The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: Green Home, Resident Aware, Resident Profile, Activity Learning, Machine Learning.

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Authors: Banashree N. P., R. Vasanta

Abstract:

Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.

Keywords: OCR, Global Feature, End-Points, Neuro-Memetic model.

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Authors: Magdi M. E. Zumrawi, Mohamed Awad

Abstract:

Determination of Resilient Modulus (M_R) is quite important for characterizing materials in pavement design and evaluation. The main focus of this study is to develop a correlation that predict the resilient modulus of subgrade soils from simple and easy measured soil index properties. To achieve this objective, three subgrade soils representing typical Khartoum soils were selected and tested in the laboratory for measuring resilient modulus. Other basic laboratory tests were conducted on the soils to determine their physical properties. Several soil samples were prepared and compacted at different moisture contents and dry densities and then tested using resilient modulus testing machine. Based on experimental results, linear relationship of M_R with the consistency factor ‘F_c’ which is a combination of dry density, void ratio and consistency index had been developed. The results revealed that very good linear relationship found between the M_R and the consistency factor with a coefficient of linearity (R²) more than 0.9. The consistency factor could be used for the prediction of the M_R of compacted subgrade soils with precise and reliable results.

Keywords: Consistency factor, resilient modulus, subgrade soil, properties.

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Authors: Badr M. Alshammari, T. Guesmi

Abstract:

In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.

Keywords: Multi-objective optimization, particle swarm optimization, power system stabilizer, low frequency oscillations.

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

Abstract:

With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: Document processing, framework, formal definition, machine learning.

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Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

Abstract:

This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines

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Authors: Z. Abdollahnejad, M. Mastali, F. Pacheco-Torgal

Abstract:

Waste reuse is crucial in a context of circular economy and zero waste sustainable needs. Some wastes deserve further studies by the scientific community not only because they are generated in high amount but also because they have a low reuse rate. This paper reports results of 32 hybrid cement mortars based on fly ash and waste glass. They allow to explore the influence of mix design on the cost and on the embodied carbon of the hybrid cement mortars. The embodied carbon data for all constituents were taken from the database Ecoinvent. This study led to the development of a mixture with just 70 kg CO2e.

Keywords: Waste reuse, fly ash, waste glass, hybrid cements, cost, embodied carbon.

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Authors: J. Barati, A. Saeedian, S. S. Mortazavi

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The main objective of this paper is a comparative investigate in enhancement of damping power system oscillation via coordinated design of the power system stabilizer (PSS) and static synchronous series compensator (SSSC) and static synchronous compensator (STATCOM). The design problem of FACTS-based stabilizers is formulated as a GA based optimization problem. In this paper eigenvalue analysis method is used on small signal stability of single machine infinite bus (SMIB) system installed with SSSC and STATCOM. The generator is equipped with a PSS. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. This aim is to enhance both rotor angle and power system stability. The eigenvalue analysis and non-linear simulation results are presented to show the effects of these FACTS-based stabilizers and reveal that SSSC exhibits the best effectiveness on damping power system oscillation.

Keywords: Power system stability, PSS, SSSC, STATCOM, Coordination, Optimization, Damping Oscillations.

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Authors: Chihab BenMoussa

Abstract:

Firms have invested heavily in knowledge management (KM) with the aim to build a knowledge capability and use it to achieve a competitive advantage. Research has shown, however, that not all knowledge management projects succeed. Some studies report that about 84% of knowledge management projects fail. This paper has integrated studies on the impediments to knowledge management into a theoretical framework. Based on this framework, five cases documenting failed KM initiatives were analysed. The analysis gave us a clear picture about why certain KM projects fail. The high failure rate of KM can be explained by the gaps that exist between users and management in terms of KM perceptions and objectives

Keywords: Knowledge management, barriers to knowledge management, Knowledge-gaps, supply-driven approach to knowledge management.

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Authors: G. Zazzaro, F.M. Pisano, G. Romano

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During last decades, worldwide researchers dedicated efforts to develop machine-based seismic Early Warning systems, aiming at reducing the huge human losses and economic damages. The elaboration time of seismic waveforms is to be reduced in order to increase the time interval available for the activation of safety measures. This paper suggests a Data Mining model able to correctly and quickly estimate dangerousness of the running seismic event. Several thousand seismic recordings of Japanese and Italian earthquakes were analyzed and a model was obtained by means of a Bayesian Network (BN), which was tested just over the first recordings of seismic events in order to reduce the decision time and the test results were very satisfactory. The model was integrated within an Early Warning System prototype able to collect and elaborate data from a seismic sensor network, estimate the dangerousness of the running earthquake and take the decision of activating the warning promptly.

Keywords: Bayesian Networks, Decision Support System, Magnitude Classification, Seismic Early Warning System

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched direction to the digital world. The domain of politics, as one of the hottest topics of opinion mining research, merged together with the behavior analysis for affiliation determination in texts, which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 were constituted by Linguistic Inquiry and Word Count (LIWC) features were tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that the “Decision Tree”, “Rule Induction” and “M5 Rule” classifiers when used with “SVM” and “IGR” feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “Function”, as an aggregate feature of the linguistic category, was found as the most differentiating feature among the 68 features with the accuracy of 81% in classifying articles either as Republican or Democrat.

Keywords: Politics, machine learning, feature selection, LIWC.

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Authors: Vivian Guo, Yan Qu

Abstract:

Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: Cannibalization, machine learning, online marketplace, revenue optimization, yield optimization.

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Authors: S. Kızıltas Demir, A. S. Kipcak, E. Moroydor Derun, N. Tugrul, S. Piskin

Abstract:

Flue gas desulfurization gypsum (FGD) is a waste material arouse from coal power plants. Hydroxyapatite (HAP) is a biomaterial with porous structure. In this study, FGD gypsum which retrieved from coal power plant in Turkey was characterized and HAP particles which can be used as an adsorbent in wastewater treatment application were synthesized from the FGD gypsum. The raw materials are characterized by using X Ray Diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) techniques and produced HAP are characterized by using XRD. As a result, HAP particles were synthesized at the molar ratio of 5:10, 5:15, 5:20, 5:24, at room temperature, in alkaline medium (pH=11) and in 1 hour-reaction time. Among these conditions, 5:20 had the best result.

Keywords: FGD wastes, HAP, gypsum, wastewater.

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Authors: Andrey R. Khazbulatov, Moldir Nurpeiis

Abstract:

Contemporary science and technologies largely widen the gap between the spiritual and rational of the society. Industrial and technological breakthroughs might radically affect most processes in the society, thus losing the cultural heritage. The thinkers recognized the dangers of the decadence in the first place. In the present article the ways of preserving cultural heritage have been investigated. Memory has always been a necessary condition for selfidentification, - continuity is based on this. The authors have supported the hypothesis that continuity and ethnic memory are the very mechanisms that preserve cultural heritage. Such problemformulating will facilitate another, new look at the material, spiritual and arts spheres of the cultural heritage of numerous ethnic groups. The fundamental works by major European and Kazakh scientists have been taken as a basis for the research done.

Keywords: Continuity, cultural heritage, ethnic memory

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Authors: M. Azadi, N. Tahouni, M. H. Panjeshahi

Abstract:

A techno-economic evaluation for efficient use of energy in a large scale industrial plant of methanol is carried out. This assessment is based on integration of a gas turbine with an existing plant of methanol in which the outlet gas products of exothermic reactor is expanded to power generation. Also, it is decided that methanol production rate is constant through addition of power generation system to the existing methanol plant. Having incorporated a gas turbine with the existing plant, the economic results showed total investment of MUSD 16.9, energy saving of 3.6 MUSD/yr with payback period of approximately 4.7 years.

Keywords: Energy saving, Gas turbine, Methanol, Power generation.

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Authors: Sandra Muizniece-Brasava, Lija Dukalska, Solvita Kampuse, Irisa Murniece, Martins Sabovics, IlonaDabina-Bicka, Emils Kozlinskis, Svetlana Sarvi

Abstract:

The research object was apple-black currant marmalade candies. Experiments were carried out at the Faculty of Food Technology of the Latvia University of Agriculture. An active packaging in combination with modified atmosphere (MAP, CO2 100%) was examined and compared with traditional packaging in air ambiance. Polymer Multibarrier 60 and paper bags were used. Influence of iron based oxygen absorber in sachets of 500 cc obtained from Mitsubishi Gas Chemical Europe Ageless® was tested on the quality during the shelf of marmalade. Samples of 80±5 g were packaged in polymer pouches (110 mm x 110 mm), hermetically sealed by MULTIVAC C300 vacuum chamber machine, and stored in room temperature +20.0±1.0 °C. The physiochemical properties – weight losses, moisture content, hardness, aw, pH, colour, changes of atmosphere content (CO2 and O2) in headspace of packs, and microbial conditions were analysed before packaging and in the 1st, 3rd , 5th, 8th, 11th and 15th weeks of storage.

Keywords: Active packaging, marmalade candies, shelf life

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Authors: Ying-Hueih Chen, Jyh-Jeng Wu, Shu-Hua Chien, Yui-Chuin Shiah

Abstract:

Interorganizational knowledge sharing is the major driving force to maximize the operational benefits across supply chain. Trust is considered as the key to facilitate knowledge sharing. This research proposes shared values and relational embeddedness as antecedents of interorganizational trust. Survey based on managers in major industrial parks in Taiwan confirm that trust is enforced when organizations develop shared values and formed social relational embeddedness. Trust leads to interorganizational knowledge sharing. This research has theoretical and practical implications.

Keywords: Shared Goal, Relational Embeddedness, Trust, Knowledge Sharing.

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Authors: Wafa Halfaoui Mbarek, Khir Tahar, Ben Brahim Ammar

Abstract:

This study reports a comparison from an exergetic point of view between three configurations of vapor compression industrial refrigeration systems operating with R134a as working fluid. The performances of the different cycles are analyzed as function of several operating parameters such as condensing temperature and inter stage pressure. In addition, the contributions of component exergy destruction to the total exergy destruction are obtained for each system. The results are estimated to be used in the selection of the most advantageous configuration from an exergetic view point.

Keywords: Vapor compression, exergy, destruction, efficiency, R134a.

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Authors: Walid M. Adel, Liang Guo-Zhu

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To investigate the characterization of the mechanical properties of composite solid propellant (CSP) based on hydroxyl-terminated polybutadiene (HTPB) at different temperatures and strain rates, uniaxial tensile tests were conducted over a range of temperatures -60 °C to +76 °C and strain rates 0.000164 to 0.328084 s^-1 using a conventional universal testing machine. From the experimental data, it can be noted that the mechanical properties of AP/HTPB propellant are mainly dependent on the applied strain rate and the temperature condition. The stress-strain responses exhibited an initial yielding followed by the viscoelastic phase, which was strongly affected by the strain rate and temperature. It was found that the mechanical properties increased with both increasing strain rate and decreasing temperature. Based on the experimental tests, the master curves of the tensile properties are drawn using predetermined shift factor and the results were discussed. This work is a first step in preliminary investigation the nonlinear viscoelasticity behavior of CSP.

Keywords: AP/HTPB composite solid propellant, mechanical behavior, nonlinear viscoelastic, tensile test, master curves.

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Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: Rohit Gupta, Ruchika

Abstract:

In this paper, a PSO based fractional order PID (FOPID) controller is proposed for concentration control of an isothermal Continuous Stirred Tank Reactor (CSTR) problem. CSTR is used to carry out chemical reactions in industries, which possesses complex nonlinear dynamic characteristics. Particle Swarm Optimization algorithm technique, which is an evolutionary optimization technique based on the movement and intelligence of swarm is proposed for tuning of the controller for this system. Comparisons of proposed controller with conventional and fuzzy based controller illustrate the superiority of proposed PSO-FOPID controller.

Keywords: CSTR, Fractional Order PID Controller, Partical Swarm Optimization.

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

Abstract:

Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: Dimitar Karastoyanov, Vladimir Monov

Abstract:

The paper is concerned with the technological process of renovation of shafts used in industrial manufacturing for extruding of sheet material. In the classical renovation technologies, a chrome based coating is applied to the working surface of the shaft in galvanic baths. The process, however, is known to be exclusively harmful due to the waste cyanide products. In this work, we present an advanced nanotechnology based on nonelectric chemical laying of a nickel coating with included nanoparticles. The technology is environmentally harmless and the new coating features an increased hardness and wear resistance. Results from experimental tests of the nanostructured nickel coating are presented and discussed.

Keywords: Materials processing, nanoparticles, nickel coating, shafts renovation.

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Authors: K. D. Ro, J. T. Park, J. H. Kim

Abstract:

This experiment was conducted in attempt of improving hydrodynamic efficiency of the propulsion mechanism by installing a spring to the wing so that the opening angle of the wing in one stroke can be changed automatically, compared to the existing method of fixed maximum opening angle in Weis-Fogh type ship propulsion mechanism. Average thrust coefficient was almost fixed with all velocity ratio with the prototype, but with the spring type, thrust coefficient increased sharply as velocity ratio increased. Average propulsive efficiency was larger with bigger opening angle in the prototype, but in the spring type, the one with smaller spring coefficient had larger value. In the range over 1.0 in velocity ratio where big thrust can be generated, spring type had more than twice of propulsive efficiency increase compared to the prototype.

Keywords: Hydraulic Machine, Propulsion Mechanism, FluidForce, Elastic Wing.

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Authors: Seongykyoon Jeong, Sungki Lee, Jaeyun Kim, Seunghun Oh, Kiho Kwak

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The purpose of study is to demonstrate how the characteristics of technology and the process required for development of technology affect technology transfer from public organisations to industry on the technology level. In addition, using the advantage of the analytic level and the novel means of measuring technology convergence, we examine the characteristics of converging technologies as compared to non-converging technologies in technology transfer process. In sum, our study finds that a technology from the public sector is likely to be transferred when its readiness level is closer to generation of profit, when its stage of life cycle is early and when its economic values is high. Our findings also show that converging technologies are less likely to be transferred.

Keywords: Interdisciplinary, Technology transfer, Technology convergence.

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Authors: Neuza Ribeiro, Arménio Rego

Abstract:

The paper shows how the perceptions of five organizational virtuousness dimensions (optimism, trust, compassion, integrity, and forgiveness) explain organizational citizenship behaviors (altruism, sportsmanship, courtesy, conscientiousness, and civic virtue). A sample comprising 216 individuals from 14 industrial organizations was collected. Individuals reported their perceptions of organizational virtuousness, their organizational citizenship behaviors (OCB) being reported by their supervisors. The main findings are the following: (a) the perceptions of trust predict altruism; (b) the perceptions of integrity predict civic virtue.

Keywords: OCB, organizational virtuousness, psychological climate

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Authors: Dim En Nyaung, Thin Lai Lai Thein

Abstract:

Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Sheela Tiwari, R. Naresh, R. Jha

Abstract:

The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: Identification, Neural networks, Predictive control, Transient stability, UPFC.

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