In this paper, experimental verification of the numerical optimization method two-stage PSO algorithm (TSPSO) for parameter identification of a cage induction motor using nominal motor data related to direct grid supply was performed. The TSPSO algorithm consists of two stages: the first stage estimates parameters for the rated operating mode, while the second stage estimates rotor parameters during motor startup. The TSPSO algorithm indirectly applies an approximation of the rotor parameter change as a function of speed to consider the influence of the skin effect at motor start-up and achieves the connection between the two stages. For the proper operation of control algorithms in frequency converters, knowledge of the parameters of the induction motor is necessary. Regulated electric drives use induction motors and frequency converters from various manufacturers. For this reason, different procedures for motor parameter identification are applied, which are integrated into the frequency converters. In this study, an experimental method was used for parameter identification of the cage induction motor based on the application of an offline procedure in a frequency converter for the identification of parameters of the cage induction motor in a standstill state. In this paper, independent parameter identification of the cage induction motor with the same nominal data was performed using two different methods. The results obtained show a certain level of agreement in the parameter values of the equivalent circuit, thereby experimentally verifying the numerical TSPSO algorithm.

This paper presents an experimental investigation of dynamic line rating (DLR) applied to aluminium conductor steel-reinforced (ACSR) overhead line conductors, with a specific focus on wind speed conditions up to 5 m/s. An experimental system was designed and implemented to provide controlled and repeatable cross-flow air conditions along a tested ACSR conductor, enabling direct measurement of wind speed in the immediate vicinity of the conductor surface. Conductor temperature, electrical current, voltage drop per unit length, the phase angle between them, and relevant meteorological parameters were continuously measured under controlled experimental conditions. Based on the measured data, the conductor heat balance was evaluated and the allowable current-carrying capacity was determined. The experimentally obtained conductor temperatures and ampacity values were compared with results calculated using thermal models and correlations recommended by IEEE, CIGRÉ, and IEC standards. The comparison demonstrates that, under low and moderate wind speed conditions, differences between standard-based predictions and experimental results can be significant, leading to deviations in the estimation of allowable current-carrying capacity. The results confirm the high sensitivity of DLR calculations to wind-related assumptions and provide an experimentally validated basis for assessing the applicability and limitations of existing standard thermal models for ACSR conductors under realistic operating conditions.

Motor parameters are crucial for the efficient implementation of modern control algorithms, particularly for vector-controlled drives. Consequently, nearly all manufacturers of frequency converters provide the option for automatic determination of motor parameters. In the case of scalar control methods, this algorithm is relatively simple, focusing on the determination of the resistance and stator inductance of the induction motor. In contrast, vector control methods require the identification of all parameters of the equivalent circuit. Typically, the self-commissioning function is activated, allowing the converter to autonomously determine the motor parameters according to the manufacturer’s defined algorithm. This paper analyzes the identification methods used by three renowned manufacturers of frequency converters. The results obtained were compared, revealing significant differences in parameter values. Based on laboratory measurements conducted under identical conditions for all three converters, parameters were identified through no-load and short-circuit experiments at various frequencies. Numerous experiments yielded frequency-dependent parameter relationships, which were then compared with results from the self-commissioning tests. The findings elucidate the reasons for the observed discrepancies and indicate that the accuracy of the self-commissioning algorithm may be questionable.

This paper analyzes errors in the estimation of induction-motor-equivalent circuit parameters using an improved combined two-stage Particle Swarm Optimization (PSO) method. The proposed method accounts for variations in rotor parameters based on both linear and square root speed approximations, as well as two different approaches for the stator and rotor leakage reactance ratios. The first approach assumes that the starting rotor leakage reactance is equal to the stator leakage reactance, while the second considers them as distinct. Improvement of the algorithm consists of increasing the accuracy of the approximations of parameter changes on the rotor. Thanks to more accurate determination of the initial rotor parameters, both approximations provide better results in parameter estimation. The analysis involved sixteen induction motors with four different power ratings and four different pole numbers. The analysis aimed to assess the impact of these approximations and assumptions on equivalent circuit parameter estimation errors. The estimated torque-speed characteristics closely matched the manufacturer’s reference data, including starting, maximum, and full-load torques. The deviation of the estimated torque-speed characteristics from the reference characteristics, within a defined speed range, is defined as the mean absolute percentage error. Based on the obtained results, the mean absolute percentage error is complex and depends on rotor parameter speed approximations, stator and rotor leakage reactance ratios, and the full power of the induction motor.

This paper provides an objective comparison of harmonic power flow (HPF) results, focusing on the methods and software tools used for harmonic analysis of distribution networks. Particular emphasis is placed on the influence of system element modeling on the accuracy of HPF results, including the modeling of linear and nonlinear loads, as well as the impact of the skin effect, harmonic current phase angles, and Thevenin equivalent impedance of the substation bus. Harmonic analysis was performed using the software tools ETAP, DIgSILENT, SINCAL, and PCFLO. The results obtained were compared with those derived using the decoupled HPF method. The testing was conducted on two widely used distribution networks: the IEEE 18and 33-bus. The results from various software tools demonstrated a high degree of agreement. The models of the tested IEEE systems, applied across different software tools, are publicly available at https://github.com/MilosFTN/Models-of-the-IEEE-18-and-33-bus-test-systems.