Dynaamisten mallien puoliautomaattinen parametrisointi käyttäen laitosdataa

The aim of this thesis was to develop a new methodology for estimating parameters of NAPCON ProsDS dynamic simulator models to better represent data containing several operating points. Before this thesis, no known methodology had existed for combining operating point identification with parameter estimation of NAPCON ProsDS simulator models. The methodology was designed by assessing and selecting suitable methods for operating space partitioning, parameter estimation and parameter scheduling. Previously implemented clustering algorithms were utilized for the operating space partition. Parameter estimation was implemented as a new tool in the NAPCON ProsDS dynamic simulator and iterative parameter estimation methods were applied. Finally, lookup tables were applied for tuning the model parameters according to the state. The methodology was tested by tuning a heat exchanger model to several operating points based on plant process data. The results indicated that the developed methodology was able to tune the simulator model to better represent several operating states. However, more testing with different models is required to verify general applicability of the methodology.Tämän diplomityön tarkoitus oli kehittää uusi parametrien estimointimenetelmä NAPCON ProsDS -simulaattorin dynaamisille malleille, jotta ne vastaisivat paremmin dataa useista prosessitiloista. Ennen tätä diplomityötä NAPCON ProsDS -simulaattorin malleille ei ollut olemassa olevaa viritysmenetelmää, joka yhdistäisi operointitilojen tunnistuksen parametrien estimointiin. Menetelmän kehitystä varten tutkittiin ja valittiin sopivat menetelmät operointiavaruuden jakamiselle, parametrien estimoinnille ja parametrien virittämiseen prosessitilan mukaisesti. Aikaisemmin ohjelmoituja klusterointialgoritmeja hyödynnettiin operointiavaruuden jakamisessa. Parametrien estimointi toteutettiin uutena työkaluna NAPCON ProsDS -simulaattoriin ja estimoinnissa käytettiin iteratiivisia optimointimenetelmiä. Lopulta hakutaulukoita sovellettiin mallin parametrien hienosäätöön prosessitilojen mukaisesti. Menetelmää testattiin virittämällä lämmönvaihtimen malli kahteen eri prosessitilaan käyttäen laitokselta kerättyä prosessidataa. Tulokset osoittavat että kehitetty menetelmä pystyi virittämään simulaattorin mallin vastaamaan paremmin dataa useista prosessitiloista. Kuitenkin tarvitaan lisää testausta erityyppisten mallien kanssa, jotta voidaan varmistaa menetelmän yleinen soveltuvuus

Design of a Fractional Order CRONE and PID Controllers for Nonlinear Systems using Multimodel Approach

This paper deals with the output regulation of nonlinear control systems in order to guarantee desired performances in the presence of plant parameters variations. The proposed control law structures are based on the fractional order PI (FOPI) and the CRONE control schemes. By introducing the multimodel approach in the closed-loop system, the presented design methodology of fractional PID control and the CRONE control guarantees desired transients. Then, the multimodel approach is used to analyze the closed-loop system properties and to get explicit expressions for evaluation of the controller parameters. The tuning of the controller parameters is based on a constrained optimization algorithm. Simulation examples are presented to show the effectiveness of the proposed method

Self-Organising and Self-Learning Model for Soybean Yield Prediction

Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods

Adaptivno neizraziti pristup sustavima prediktivne zaštite od preopterećenja transformatora snage

The improving of the utilization factors of mineral-oil-filled power transformers is of critical importance in the competitive market of electricity. Utilities need to change dynamically the loadability rating of transformers without penalizing their serviceability. As a key issue of loadability all aspects of the thermal performance, and in particular those related to the determination of tolerable windings hot-spot temperature (HST), overload practice and its impact on remanent life expectation should be investigated. So, this paper deals with a methodology for the identification of a Takagi-Sugeno-Kang (TSK) fuzzy model able to reproduce the thermal behaviour of large mineral-oil-filled power transformers for implementing a protective overload system. The TSK fuzzy model, working on the load current waveform and on the top oil temperature (TOT), gives an accurate global prediction of the HST pattern. In order to validate the usefulness of the approach suggested herein, some data cases, derived from various laboratory applications, are presented to measure the accuracy and robustness of the proposed fuzzy model.Poboljšanje faktora iskoristivosti transformatora snage punjenih mineralnim uljem od kritične je važnosti na kompetetivnom tržištu električne energije. Zahtijeva se da dinamičke promjene opterećenja transformatora ne utječu na njegovu raspoloživost i pouzdanost. Kako je opteretivost ključna problematika, moraju se istražiti svi aspekti toplinskih svojstava, posebice oni koji se odnose na određivanje dopuštene vršne temperature namota (HST), te učestalost pojave preopterećenja na očekivani životni vijek transformatora. Ovaj se članak bavi metodologijom identifikacije Takagi-Sugeno-Kang (TSK) neizrazitog modela koji može reproducirati temperaturno ponašanje velikih transformatora snage punjenih mineralnim uljem za implementaciju zaštitnog sustava protiv preopterećenja. TSK neizraziti model s praćenjem valnog oblika struje opterećenja i vršne temperature ulja (TOT) daje točnu globalnu predikciju vršne temperature namota. Točnost i robusnost predloženog neizrazitog modela provjereni su na skupovima laboratorijskih podataka kako bi se verificirala korisnost predloženog postupka

Network anomaly detection research: a survey

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies