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Open access

M. Abdullah Eissa

Abstract

This paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main problem of the classical controller is lacking the required robustness against disturbers, measurement noise in industrial applications. The new formula of the proposed controller helps in fixing this problem based on the fuzzy logic technique. In addition, the genetic algorithm (GA) is used to optimize parameters of the SNPID controller. Because of the high demands on the availability and efficiency of electrical power production, the design of robust load-frequency controller is becoming increasingly important due to its potential in increasing the reliability, maintainability and safety of power systems. So, the proposed controller has been applied for load-frequency control (LFC) of a single-area power system. The effectiveness of the proposed SNPID controller has been compared with the conventional controllers. The simulation results show that the proposed controller approach provides better damping of oscillations with a smaller settling time. This confirms its superiority against its counterparts. In addition, the results show the robustness of the proposed controller against the parametric variation of the system.

Open access

Leszek Jarzebowicz

Abstract

Pulse width modulation (PWM) of inverter output voltage causes the waveforms of motor phase currents to consist of distinctive ripples. In order to provide suitable feedback for the motor current controllers, the mean value must be extracted from the currents’ waveforms in every PWM cycle. A common solution to derive the mean phase currents is to sample their value at the midpoint of a symmetrical PWM cycle. Using an assumption of linear current changes in steady PWM subintervals, this midpoint sample corresponds to the mean current in the PWM cycle. This way no hardware filtering or high-rate current sampling is required. Nevertheless, the assumption of linear current changes has been recently reported as over simplistic in permanent magnet synchronous motor (PMSM) drives operating with low switching-to-fundamental frequency ratio (SFFR). This, in turn, causes substantial errors in the representation of the mean phase currents by the midpoint sample. This paper proposes a solution for deriving mean phase currents in low SFFR PMSM drives, which does not rely on the linear current change assumption. The method is based on sampling the currents at the start point of a PWM cycle and correcting the sampled value using a model-based formula that reproduces the current waveforms. Effectiveness of the method is verified by simulation for an exemplary setup of high-speed PMSM drive. The results show that the proposed method decreases the error of determining the mean phase currents approximately 10 times when compared to the classical midpoint sampling technique.

Open access

Paweł Ewert

Abstract

The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB-Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.

Open access

Abimbola A. Akanni, Idowu Omisile and Choja A. Oduaran

Abstract

Workplace deviant behavior has been linked to a number of organizational losses such as decreased employee morale, increased turnover and loss of legitimacy among important external stakeholders. Therefore, this paper investigated the relationships between religiosity, job status and workplace deviant behavior. Participants consisted of 351 (F=178; Mean age=39.2) employees of the Local Government Service Commission in Nigeria. Data which were sourced through the Workplace Deviant Behavior Scale and Centrality of Religiosity Scale were analyzed using multiple regression. Results revealed that religiosity negatively related to workplace deviant behavior, but no significant difference was found between junior and senior staff in their display of workplace deviant behavior. In addition, both religiosity and job status jointly influenced respondents’ workplace deviant behavior. The findings imply that high religiosity among employees might reduce the risks of deviance and in turn create a better work environment.

Open access

Vasile Gherheş

Abstract

The study presents the results regarding the attitudes of students from humanities and technical specializations in Timișoara towards the emergence and development of artificial intelligence (AI). The emphasis was on the most likely consequences of the development of artificial intelligence in the future, especially the negative consequences that its development would entail. The method used for data collection was the sociological survey and the information gathering tool was the questionnaire. It was applied to a total of 929 people, ensuring a sample representativity margin of ± 3%. The analysis reveals that the participants in the study predict that due to the emergence and development of AI, in the future, interpersonal relationships will be negatively affected, there will be fewer jobs, economic crises will emerge, it will be used to make intelligent weapons, to increase military conflicts, to take control of humanity and, last but not least, to destroy mankind. The results revealed differences in responses depending on the type of specialization (humanities or technical) and the gender of the respondents.

Open access

Youssef Agrebi Zorgani, Mabrouk Jouili, Yassine Koubaa and Mohamed Boussak

Abstract

A sensorless indirect stator-flux-oriented control (ISFOC) induction motor drive at very low frequencies is presented herein. The model reference adaptive system (MRAS) scheme is used to estimate the speed and the rotor resistance simultaneously. However, the error between the reference and the adjustable models, which are developed in the stationary stator reference frame, is used to drive a suitable adaptation mechanism that generates the estimates of speed and the rotor resistance from the stator voltage and the machine current measurements. The stator flux components in the stationary reference frame are estimated through a pure integration of the back electro-motive force (EMF) of the machine. When the machine is operated at low speed, the pure integration of the back EMF introduces an error in flux estimation which affects the performance torque and speed control. To overcome this problem, pure integration is replaced with a programmable cascaded low-pass filter (PCLPF). The stability analysis method of the MRAS estimator is verified in order to show the robustness of the rotor resistance variations. Experimental results are presented to prove the effectiveness and validity of the proposed scheme of sensorless ISFOC induction motor drive.

Open access

R. Anand, B. Gayathridevi and B. K. Keshavan

Abstract

Permanent magnet motor drive is a widely used technology, offering many advantages, such as exceptional speed, torque control and greater flexibility. Improvement of reliability and efficiency has become a great research interest. Towards this direction and taking into account the major developments in permanent machine technology over the recent years, the use of energy recovery converters has been introduced in various industrial applications. In this paper, the effects of harmonics on a three-phase motor controlled by a drive are analysed, and the behaviours of the filter topology after adopting regenerative drives are studied. The main contribution of this study is a methodology to foresee the standards that can be achieved with the use of an active front end system topology with filters. Moreover, the use of an optimum filter that eases the power system distortion is presented. The analysis presented in this paper is validated experimentally.

Open access

Honoriu Valean, Cola Cristian, Andrei Wegroszta and Cristinel Costea

Abstract

This paper discusses mobile notifications in the context of health monitoring system that measure and store vital signs of the patient that are included in this program. The values measured are temperature and cardiac rhythm. This has two Android application, one is used by the patient to monitor his vital signs and the other is used by the physician to be able to see and receive push notifications of each individual patient. The sensors are connected to a Raspberry Pi and these devices send information to the Android smartphone via Bluetooth. The physician can monitor patient data in real time. All the information that is gathered by the smartphone from the sensors are sent to the cloud, can project a history and can detect some anomalies, for example, if the cardiac pulse is not within the limits of an accepted interval.

Open access

Mateusz Dybkowski

Abstract

In the paper, the concept of universal speed and flux estimator with additional parameters estimators is presented. Proposed solution is based on the Model Reference Adaptive System (MRAS) type flux and speed estimator and can be used in different industrial systems (especially in the automotive applications). Induction Motor (IM) parameters are estimated using the systems based only on simple simulators and adaptive systems (voltage model and current model). Proposed system was tested in the sensorless induction motor drive with the Direct Field Oriented Control (DFOC) algorithm. Simulation and experimental results are presented in the paper.

Open access

Bogumiła Hnatkowska and Paweł Woroniecki

Abstract

Domain ontologies are valuable knowledge assets with many potential applications, e.g. in software engineering. Their content is often a subject of bi-directional transformations. Unfortunately, a centralized transformation service which can be easily extended with new mappers is not available for ontology users. In consequence, they have to deal with many different translation programs, which have to be installed and learned separately. The paper presents a framework for universal ontology processing, dedicated to ontologies expressed in OWL2. The framework usefulness was verified by a proof-of-concept implementation, for which an existing OWL2 to Groovy translator was adapted. During the integration process, the translator functionality was enhanced with ontology individuals mapping. The exemplary implementation confirmed that the framework with plug-in architecture is flexible and easy for customization. The ontology stakeholders should benefit from the reduced cognitive load and more satisfying transformation process.