Bilal Gumus

Position control of induction motor a new‐bounded fuzzy sliding mode controller

Purpose – The aims of the paper are to improve the dynamic response of an induction motor based position servo system and to remove the chattering problem in the sliding mode control theory by using fuzzy logic principles. The obtained results are also compared with conventional sliding mode controller to show its performance.Design/methodology/approach – The main method used for the research is to form a thin boundary layer neighboring the switching surface by using fuzzy logic. The sliding mode control law is inherently discontinuous naturally. Therefore, there are some difficulties such as so many switches occurring between the control bounds, which cannot be carried out by real controllers. Therefore, fuzzy logic is used in the thin boundary layer to determine the control signal current. Thus, the chattering is eliminated.Findings – The results show that the designed controller has superior performance. But, there are also some difficulties. It is difficult to obtain fuzzy rules. The rules can be obta...

The transient and steady state performance of single‐phase induction motor with two capacitors fed by matrix converter

In this study, a single‐phase induction motor with constant v/f, fed by matrix converter, is considered under various frequencies. The modeling of the motor, mechanical load for single phase induction motor and matrix converter have been obtained by using Simulink package program. The performance of the motor under various frequencies is determined. A single‐phase induction motor considered in this work has a running capacitor. The analysis and simulation results are presented.

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

A new approach to detect stator fault in permanent magnet synchronous motors

In this paper, detection of the stator winding inter-turn short circuit fault (SWISCF) in surface-mounted permanent magnet synchronous motors (SPMSMs) and classification of the fault severity via pattern recognition system (PRS) are presented. In order to automatically detect stator winding short circuit fault and to estimate severity of this fault, artificial neural network (ANN) based PRS has been used. It has been observed that the amplitude of the 3rd harmonics of the current is the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To increase the fault clasification accuracy of PRS both fundamental (1st) and 3rd harmonics are used. In order to validate proposed method experimental results are presented.

Investigation of the effect of short term environmental contamination on energy production in photovoltaic panels: Dicle University solar power plant example

Solar power is an unlimited source of power in theory when compared to the needs of the planet, however, in practice it is still a source of power convertible to electrical energy on a limited basis. In this context, efficiency of this energy conversion by photovoltaic panels has a great importance. Panel surface taking a sufficient amount of light at the right angle has a direct effect on efficiency. This is related to keeping the panel surface as clear as possible of dust and similar objects which can be reflective and absorptive. The contamination of photovoltaic panels has a negative effect on the energy production of panels. In this study, the effect of panel surface contamination, one of the most important factors affecting panel efficiency, was investigated. The investigation was conducted by comparing data from two panel strings standing very close to each other in a plant, one of which was cleaned regularly and the other left contaminated.

TIME DEPENDENT PREDICTION OF MONTHLY GLOBAL SOLAR RADIATION AND SUNSHINE DURATION USING EXPONENTIALLY WEIGHTED MOVING AVERAGE IN SOUTHEASTERN OF TURKEY

This paper proposes a new approach for prediction of Global Solar Radiation and Sunshine Duration based on earlier years of data for the eastern region of Turkey which has a high potential of solar energy. The proposed method predicts the basic parameters using time series and an analysis method. This method is Exponentially Weighted Moving Average (EWMA). This model estimates next years’ Global Solar Radiation and Sunshine Duration and is evaluated by statistical parameters s, “Mean Absolute Percentage Error” (MAPE) and “Coefficient of Determination,” (R2) to examine the success of the proposed technique. In our study, the result shows that this method is effective in predicting Global Solar Radiation and Sunshine Duration as regards of MAPE and R2. The calculated MAPEs which are between 0 – 10 kWh/m2 per day were assumed excellent and R2s were found significant per every year.

Permanent magnet synchronous motor’s sensorless vector control with artificial neural network observer

In this paper, a new method for sensorless vector control of permanent magnet synchronous motor (PMSM) using artificial neural network (ANN) observer is developed. This method based on determination of rotor position and thereby speeds by ANN observers. The rotor position angle and rotating speed are estimated by evaluating the instantaneous values of stator voltages and currents. The proposed method was implemented in MATLAB/Simulink software package program. The obtained results are in acceptable error limits for a wide speed range.

Chaotic analysis of the gloabal solar irradiance

The use of solar energy is increasing for power generation and other uses. In order to meet these demands and make better predictions, it is necessary to understand and explain the dynamics of the solar parameters. Nonlinear dynamics and associated tools can provide better results in the analysis. The meteorological events are based time series and has a dynamic chacteristic therefore this paper proposes a different approach to analysis of solar parameter that is called chaotic analysis of the solar parameters such as global solar irradiance(GSI) based on times series approach. The chaotic behavior of global solar irradiation and sunshine duration are tested by phase spaces and Lyapunov Exponents. It is crucial to measure and analysis with a high accuracy solar parameters to benefit maximally form a specific region solar energy potential. In the application, four solar irradiation sites are considered from different solar energy potential locations in Turkey, namely, at Diyarbakir, Gaziantep, Batman and Mardin cities.

Analysis of stator inter-turn short-circuit fault signatures for inverter-fed permanent magnet synchronous motors

It is quite important to detect stator short-circuit fault, which is the most common fault type, at incipient stage. It is possible to carry out fault detection using Motor Current Signature Analysis (MCSA) method. In this study, stator current and voltage space vectors of PMSMs were analyzed with MCSA under various load torque, speed and fault percentages conditions. The Negative & positive harmonics were obtained by applying Fast Fourier Transform (FFT) to space vectors of stator current and voltage. It is suggested that by using the obtained fault signatures, stator inter-turn fault estimation can be achieved accurately. The results of comprehensive analysis carried out under various load torque and speed conditions show that characteristics fault signatures are both present in the current and the voltage space vectors spectra.

Speed Control of Permanent Magnet Synchronous Motor Using ClasSic Controller (PI) and Fuzzy Logic Controller and Comparing With Each Other

In this study, a Fuzzy Inference System (F.I.S.) controller using error and derivative of error inputs is proposed for the speed control of a Permanent Magnet Synchronous Motors (P.M.S.M.). However, the performances of the P.M.S.M. are very sensitive to parameter and load variations. To overcome with these problems several control strategies such as Fuzzy Logic Control (F.L.C.), Neural Network (N.N.) and Genetic Control (G.C.) have been proposed for speed control. In this study, Fuzzy and PI controls are compared with simulation and experimentally. It is showed that which control method can be chosen at which status by using their advantages and disadvantages.