Dataset on the performance of a three phase induction motor under balanced and unbalanced supply voltage conditions

Abstract

Three phase induction motors (TPIM) are extensively used for various applications in the industry for driving cranes, hoists, lifts, rolling mills, cooling fans, textile operations, and so forth. TPIM are designed to operate on balanced three phase power supply, but sometimes three phase supply line voltages to which the TPIM is connected may be unbalanced. In this data article, the operational data of a TPIM operating under changing voltage scenarios is profiled to determine the variations in the magnitude of the operational parameters of the motor. The magnitude of each of the line voltages was separately varied from the balanced state (0% unbalance) until 5% voltage unbalance condition was achieved, in line with the recommendations and guidelines of the National Electrical Manufactures Association. The motor parameters; both mechanical and electrical, at various slip values were collected in six sets for the 0%, 1%, 2%, 3%, 4%, and 5% unbalance voltage conditions. Frequency distributions and statistical analysis were carried out to identify the data pattern and data variation trends among the parameters in the dataset.

Classification of the Supply Voltage conditions of a Three-Phase Induction Motor with Machine Learning techniques.

“…The data presented here, in terms of slip refers to the interval where, −1 ≤ slip ≤ 2, thus ensuring all conditions for slip acordding NEMA . With this the specific data base the obtains electrical and mechanical data of the motor how, rotor current, stator current, winding copper losses, real input power, reactive input power the apparent power, and air gap power, torque and electromechanical power (Adekitan et al, 2019).…”

“…In this way, it is evident that any failures in machines of this type will reflect in energy losses, due to the drop in performance; economic, by spending wasted energy; and environmental, due to the natural resources used in the generation of unused energy. As a consequence of unbalanced voltages, a TPIM may exhibit sequence current and pulsating torque sequence, which causes an increase in the winding temperature and motor losses (Adekitan and Abdulkareem, 2019). In addition, the efficiency of the motor is also impaired due to increased losses and harmonics of current, also reducing the life of the machine (Adekitan and Abdulkareem, 2019).…”

“…As a consequence of unbalanced voltages, a TPIM may exhibit sequence current and pulsating torque sequence, which causes an increase in the winding temperature and motor losses (Adekitan and Abdulkareem, 2019). In addition, the efficiency of the motor is also impaired due to increased losses and harmonics of current, also reducing the life of the machine (Adekitan and Abdulkareem, 2019).…”

“…In Adekitan and Abdulkareem (2019), a comparison was made between the performance of the Tree Emsemble (TE), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM) techniques applied in the classification of under voltage (2-10%), rated voltage and over voltage (2-10%)…”

“…The differential of this work is the application of the techniques mentioned in a database different from the one used by previous works, obtained in Adekitan et al (2019). In addition, the use of Machine Learning techniques does not require the use of traditional features for the calculation of unbalance of supply voltages, being therefore, an alternative for cases in which the features of traditional methods are unavailable.…”