In-situ efficiency estimation of induction motors under unbalanced voltages using heuristic optimization algorithms

Voltage Unbalance (VU) is one of the most common power quality problems in industrial electrical systems and it is a subject of systematic study. This problem affects the operation and the efficiency of Induction Motors (ims), which are the loads that demand more energy in the industrial sector with around 68%. The Voltage Unbalance Factor (VUF), defined by the international IEC standard, is the main factor used to characterize this problem. This article aims to present a theoretical analysis of VUF focused on its limitations for characterizing the effects of VU on ims. As a result of the analysis, it is shown that the use of the VUF indicator only is insufficient since it does not consider other aspects that affect the operation of the ims such as voltage variation. As an alternative, the use of the Complex Voltage Unbalance Factor and the Equivalent Voltage Magnitude Factor are suggested as parameters that, together with the VUF, allow deepening the characterization of the effects of the UV on the ims.

Voltage unbalance is one of the power quality problems causing a lot of ill effects on induction motors. These are namely: Overheating, line-current unbalance, derating, torque pulsation, and inefficiency. The objective of the present work is to assess and quantify the effects of voltage unbalance on induction motors using both theory and experimentation. The analytical study is performed using the complex voltage unbalance factor model of this problem. The work deals specifically with the influence of the variations in the complex angle of the voltage unbalance factor on the stator and rotor currents, stator and rotor copper losses and total losses. The experimental investigation is carried out using an apparatus that measures currents, torque and power losses.

Effects of various unbalanced voltages on the operation performance of an induction motor under the same voltage unbalance factor condition

This paper aims to provide a smart design to improve the efficiency of surface permanent magnet synchronous motor. An efficient design strategy involving penalty approaches are considered for extracting all the possible parameter combinations and the solutions in the infeasible region. We compare the performance of tree heuristic optimization algorithms and six penalty methods. The heuristic optimization algorithms are: particle swarm optimization, differential search algorithm, and tree seed algorithm. The penalty methods are: three of which are static penalty approaches, two of dynamic penalty approaches, and Deb’s rule. Besides, the optimized motor design is tested with finite element analysis. Two conclusions were drawn from the experiments. First, heuristic algorithms using penalty approaches had significantly better performance compared to standard and popular heuristic algorithms. This emphasizes the importance of using heuristic algorithms with penalty approaches in SPMSM design optimization. Second, the compatibility of design optimization and numerical analysis results are acceptable and highly satisfactory for surface permanent magnet synchronous motor design. According to the analytical design, a 4% improvement in efficiency was achieved with the proposed approach.

Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms

This article proposes an unbalanced voltage compensation method for a dispersed wind farm (DWF) by cooperating permanent magnetic synchronous generators (PMSGs) with a star-connected cascaded H-bridges (CHB) static synchronous compensator (STATCOM). The topology of a typical DWF is given and the mathematical models of CHB-STATCOM and PMSG inverter are established to show that the unbalanced grid voltage can be suppressed by controlling their virtual negative-sequence output admittance (NSOA). The relationship between maximum phase voltage and dc voltage corresponding to zero-sequence voltage injection is obtained to quantitatively analyze the negative-sequence current injection ability of the CHB-STATCOM. Moreover, the phase current limit is introduced to address the over-current problem, and a virtual NSOA method for sharing unbalanced voltage is proposed. An allocation coefficient calculation method is also proposed in consideration of both remaining capacity and voltage unbalanced factor (VUF) for different PMSGs. Furthermore, a coordinated control strategy of PMSGs and CHB-STATCOM for unbalanced voltage suppression is proposed. The effectiveness of the proposed coordinated control strategy is verified by simulation on a 50 MW DWF with a ±5 MVar star-connected CHB-STATCOM.

Owing to the development of highly efficient electric power converters, the number of studies on the construction of low-voltage dc (LVDC) systems is gradually increasing. To use LVDC distribution systems, voltage regulation methods to compensate for the voltage drop and to limit the voltage unbalance are essential. In a bipolar LVDC distribution system, a voltage drop and voltage unbalance could occur because of the load current and the variation in the amount of power supplied to the poles. However, not enough research has been conducted on voltage regulation methods for LVDC distribution systems. To reduce the voltage drop and the voltage unbalance, this paper proposes a voltage regulation method based on the neutral to line drop compensation (NLDC) method, which employs a modified LDC to calculate the sending-end reference voltage. In the NLDC method, the neutral current and neutral line impedance are taken into consideration to compensate for the neutral line potential fluctuation and voltage drop on the pole. The next sending-end voltage is determined by checking the voltage unbalance factor, and it is adjusted to maintain it within the allowable voltage unbalance factor. A voltage regulation algorithm and a bipolar LVDC system model are implemented using the Electromagnetic Transients Program.

This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Specially connected transformers can reduce the voltage unbalance in a High Speed Railway (HSR) power supply system. Differently connected transformers reduce unbalance differently. Traditionally, a two-dimensional figure is used to represent the degree of unbalance associated with specially connected transformers. However, such a figure cannot completely express the conditions under which the voltage unbalance factor (VUF) is maximized. This work proposes a new approach to creating figures that represent the VUF of specially connected transformers under different dispatch schedules. Besides helping engineers to estimate voltage unbalance associated with various connected transformers, the analytical results can also provide a valuable reference for train dispatchers of HSR to plan dispatching.

Modern motor control drives start increasing the use of various optimization techniques to tune the controller because of its accuracy and simplicity. Usually, the classical controllers used in motor control drives are not adaptable to the changes such as parameter variation, voltage unbalances, changes in load etc. So, online control should be encouraged in order to make the system robust. In this connection, this paper presents online vector control of induction motor using Particle Swarm Optimization (PSO) techniques. The PI controller used in speed control loop is tuned using PSO. The results obtained in offline and online modes are compared and validated using MATLAB. The computational steps to implement PSO in Online Vector Controlled Induction Motor Drives are lucidly presented.

ABSTRACTOne of the most familiar stochastic heuristic search algorithm is Particle swarm optimization (PSO), which is motivated by social behavior of animals like birds, fishes, and so forth. The significant advantages of PSO algorithm are simple structure and limited parameters to be used. Among the parameters, inertia weight is considered as the most crucial one in PSO which brings trade-off between the characteristics of exploitation and exploration. A novel Interactive Self-Improvement based Adaptive PSO (ISI-APSO) method that traits better searching efficiency and accuracy than the traditional particle swarm optimization is proposed. More precisely, it can achieve faster convergence speed while on global search over the entire search space. The simulation results show that the performance of our proposed ISI-APSO is substantially improved than other heuristic algorithms in terms of the search efficiency and convergence speed.

• Seasonally adaptive PV–BESS dispatch framework for high EV penetration grids. • CNN–BiLSTM model achieves >92 % accuracy in short-term PV forecasting. • GA optimizes multi-objective PV-EV-BESS scheduling. • Reduces feeder losses by 63.55 % and Voltage Unbalance Factor by 55 %. • Enhances PV utilization, power quality, and grid resilience under stress. The rapid proliferation of photovoltaic (PV) systems and electric vehicles (EVs) poses significant challenges for distribution networks, including voltage unbalance, high feeder losses, and deteriorating power quality, further complicated by seasonal variability in solar output and demand. This paper proposes a seasonally adaptive PV–battery energy storage system (BESS) dispatch strategy that integrates short-term PV forecasting with multi-objective optimization. The primary novelty of this work lies in the tight integration of a seasonally adaptive, forecasting-driven dispatch strategy that synergistically combines a hybrid CNN-BiLSTM model for high-accuracy PV prediction with a Genetic Algorithm for multi-objective optimization in unbalanced distribution grids. A hybrid CNN–BiLSTM model, trained on distinct seasonal datasets (summer and winter) from NASA irradiance data, enables accurate real-time PV prediction, achieving an RMSE of 13.99 kWh, MAE of 12.94 kWh, and MAPE of 7.45 %, corresponding to a forecasting accuracy of 92.55 %. Meanwhile, a genetic algorithm (GA) optimizes coordinated PV–BESS scheduling under diverse seasonal scenarios. The approach is validated on a modified IEEE 13-bus system, demonstrating robustness across a wide range of EV penetration levels (40–70 %) and both summer and winter conditions, and showing reductions of up to 55 % in Voltage Unbalance Factor (VUF) and 63.55 % in feeder losses, alongside enhanced PV utilization. Compared to uncoordinated dispatch, the proposed framework delivers superior power quality, higher grid resilience, and practical scalability. The framework is designed for real-world application, with the forecasting model providing high-frequency predictions and the GA-based optimization operating on a computationally feasible hourly rolling horizon, making it suitable for deployment in larger distribution networks. These findings highlight the potential of forecast-driven, seasonally aware coordinated dispatch as a practical pathway toward reliable and sustainable smart grid operation.

3상 4선식 전원공급에서 부하의 불평형 운전에 의한 전압 불평형은 전류 불평형을 일으켜 기기의 손실증가에 따른 출력 감소로 전원용량을 증가시키는 것은 물론이고 전기 품질에도 나쁜 영향을 미치고 있다.BR 전력용 커패시터는 유도성 부하의 역률 보상과 전원을 안정시키기 위해 사용되며, 유도전동기의 여자에 필요한 자화성분을 전원을 대신하여 제공하기도 한다. 전압 불평형이 지속될 경우 커패시터에 전압 스트레스로 작용하여 절연 고장을 일으킬 수 있다.BR 본 논문에서는 역률 개선용 커패시터가 전압 불평형 증가에 의해 받는 영향을 해석한 결과 전압 및 전류의 크기가 함께 증가하여 스트레스는 점차적으로 높아진다는 것을 확인할 수 있었다.

Coupling induction motors to improve the energy conversion process during balanced and unbalanced operation

A sensor-less, low cost technique for parameter estimation under reduced flux condition is presented here for better evaluation of losses and performance of inverter fed induction motors in field weakening mode. For this purpose, transient voltage and current data have been recorded with a digital storage oscilloscope. From these measured inverter output voltage and current data, equivalent circuit parameters, losses and inertia of the machine have been estimated using particle swarm optimization. This estimation has been carried out considering dynamic model of a three-phase induction motor. Machine parameters have also been determined from classical dc resistance test, no load test and blocked rotor test as per IEEE guideline. The estimated parameters have been compared with the parameters obtained from the abovementioned tests. Performance characteristics have also been predicted for both estimated and experimentally obtained parameters and compared with the measured values. The above-mentioned procedure has also been carried out for sinusoidal supply voltage fed to the same induction motor. The comparative study has been presented in this paper.