محمود قنبری

In this study, an approach based on mixed integer quadratic conic programming is proposed for energy management of active distribution networks with renewable energy-based prosumers (photovoltaics and energy storage). In this approach, a multi-objective model is formulated to reduce the electricity cost of processors, the cost of network energy losses, and the cost of renewable power outages. The goal of this model is to optimize the performance of the distribution network under conditions of resource uncertainty. The simulation results in different distribution networks (33 buses and 69 buses) showed that the proposed method is significantly more effective in reducing energy losses, reducing voltage deviations, and improving load distribution than conventional methods such as Gray Wolf and Genetic algorithms. Specifically, the energy losses in the proposed method in the 69-bus network have been reduced by 0.2547 MW, which is a significant reduction compared to the Gray Wolf (0.3258 MW) and Genetic (0.3547 MW) algorithms. Also, the lowest voltage in the proposed method is equal to 0.9821 per unit, which is higher than the other algorithms (0.9721 and 0.9701 per unit). Compared to other methods, the peak load in the proposed method is reduced to 0.470 MW, which is an improvement over the Gray Wolf (0.521 MW) and Genetic (0.587 MW) algorithms. Also, the computation time in the proposed method is only 4 seconds, which is significantly faster than the Gray Wolf (69 seconds) and Genetic (196 seconds) algorithms. These results indicate the high capability of the proposed method in reducing energy losses, improving voltage quality, and increasing the efficiency of prosumers in active distribution networks.

The purpose of this research is the role of different emotional states of customers on the preference of choosing (type) of food. The current research is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of execution. The statistical population of the research in the qualitative part includes 26 people from the food consumers of restaurants and food courts in shopping centers of Tehran (Arg, Kurosh, Palladium, Sana, Iran Mall, etc.), and sampling was done in the targeted manner; and the quantitative part includes 448 persons who, like the qualitative part, were selected randomly available from the consumers of food. The research collection tool is a semi-structured interview and a questionnaire taken from the qualitative section. Data-based theory and MAXQDA software were used for data analysis, and multivariate analysis and structural equations were used in the quantity part. The results showed that there are two components in the experienced excitement: positive emotion, and negative emotion; and for background factors, three components of culture, health knowledge, and previous experience; and five components for food selection conditions in positive emotional conditions; and five components for negative emotional conditions have been identified. The results have shown that people go to different foods in different experienced emotions (positive and negative), and different characteristics of food (texture, color, temperature, aroma, taste) played an important role in their choice in both positive and negative emotions. On the other hand, background factors also affect the conditions of food selection and people have chosen food based on texture, color, temperature, aroma and taste in negative or positive emotional conditions.

In this paper, an adaptive concept of virtual generator based controller for dynamic stability of microgrids consisting of low inertia resources. For this purpose, considering a set of energy storage systems dispatched in the network, a controlled islanding scheme  is provided to dynamically control the microgrid frequency. In this context, based on the concept of center of inertia, the proposed virtual generator scheme is developed through mathematical formulation. Also, from the Inter-area torques evaluated by microgrid control areas in the  center of inertia frame are used as input signals of the developed virtual generator based controller. For the energy storage systems, an equivalent dispatch model is provided and developed through the microgrid dynamic model for improving frequency stability during inter-area oscillations. The proposed virtual generator-based controller is an online and non-model-based scheme which controls several microgrid systems together as an integrated network connected to the upstream network. In order to evaluate the ability of the proposed scheme, realtime simulations are carried out on one  test system consisting of several microgrids. Numerical results demonstrate the effectiveness of the proposed scheme in increasing system inertia constant resulting in a proper dynamic performance with a high damping ratio in facing severe inter-area oscillation.

In this paper, a novel combined Direct Torque Control (DTC) method and Stator-Flux Oriented Control (SFOC) system to increase general performances of Three-Phase Induction Motor (TPIM) drives is proposed. The introduced control scheme includes merits of DTC for instance simple structure, less dependent on PI controller coefficients, fast dynamics, and merits of SFOC such as high precision and constant switching frequency. Specifically, the proposed control scheme includes a table-based variable structure developed on DTC strategy and a PI controller in connection with a Pulse Width Modulation (PWM) algorithm based on SFOC strategy. To confirm the usefulness of the introduced controller, simulation studies are accomplished for a 2.5kW TPIM in different situations. Results under the presented control system approve the good performances of this technique in comparison with classic DTC and classic SFOC. Investigation in TPIM performances under the introduced control system indicates relatively quick dynamic responses with low torque and stator flux ripples.

Current sensors are required in Field-Oriented Control (FOC) strategies of Three-Phase Induction Motor (TPIM) drives. Nevertheless, the current sensors are subject to different electrical/mechanical faults which reduce the safety and dependability of the drive system. Single phase current sensor Fault-Tolerant Control (FTC) for sensorless TPIM drives using flux observer and Extended Kalman Filter (EKF) is proposed in this research. In the suggested FTC scheme, current sensor fault detection is based on axes transformation, a logic circuit is served as the fault isolation and reconstruction of faulted currents are achieved through flux observer and EKF. The presented FTC system is capable of detecting and localizing the current sensor fault and switching the drive system to tolerant FOC mode without the rotor speed measurement. The effectiveness of the suggested FTC system is confirmed by experiments on a 0.75kW TPIM drive platform.

Improving the insulating and thermal properties of transformer oil is one of the factors in the use of nanoparticles (NPs) in oil. However, the use of NPs may only have a positive effect on some properties of the oil or even have a negative effect on the other properties of the oil. For this reason, hybrids nanofluid(HNF) were used to improve the properties of the transformer oil. By performing the Breakdown Voltage (BDV) test on different weight percentages (wt%) of TiO2 and CNT, it was proved that the best wt% for TiO2 is 0.0075 and for CNT is 0.001 to maximize the BDV. In this case, the HNF was able to improve the BDV and heat transfer by 9% and 8%, respectively. Another surprise that the HNF has been able to reduce the amount of C2H4 and C2H6 dissolved in oil by more than 70%. This reduction in the number of gases has another very desirable result and has reduced the PD by 63%. HNF proved that by using the right combination of different nanomaterials in transformer oil, more properties of the transformer oil can be improved.

Reliability for electric motor drive systems is very vital in some industries. Selecting an appropriate control strategy for driving an electric motor during fault conditions is one of the most important issues mainly for safety-critical applications. Recently, vector control (VC) strategies have been extensively developed for star-connected three-phase induction motor drives during single-phase cut-off fault (COF) based on two different transformation matrices (TMs). Despite the effectiveness of these methods during the fault, these control systems are very complex due to their extensive on-line computation. This paper presents two simple methods based on indirect VC (IVC) and direct VC (DVC) methods for controlling a star-connected three-phase induction motor during the fault condition. The fault in this paper is limited to single-phase COF which can occur in motor stator coils. In this paper, it is shown that using a suitable TM and some changes in the control parameters, it is possible to control the faulted drive system. Performance of the proposed control methods is verified using MATLAB software and DSP/TMS320F28335 controller board for a 0.75kW star-connected three-phase induction motor drive system. The achieved results show the good performance of the introduced control systems in different operating conditions. In addition, the results demonstrate the performance of the proposed VC strategies and that of the previous works are almost the same. However, the proposed VC methods in this paper need less modification in the structure of the standard VC strategy than the previous works.

The Vector Control (VC) of Y-Connected Induction Motor (YCIM) drives is entirely demanding task. Furthermore, YCIM under an Open-Circuit Fault in the Stator Coils (OCFSC) leads to deterioration of the VC. Consequently, the VC of YCIMs under an OCFSC requires a suitable design. This research focuses on an accurate and modified Field-Oriented Control (FOC) strategy for 3-phase YCIM drives under an OCFSC. Most of the recent papers studying VC of YCIMs under an OCFSC ignore the leakage inductance in the VC equations. This paper presents an alternative VC technique, considering the leakage inductance in the VC equations of YCIMs under an OCFSC. In the presented VC system, two asymmetrical Rotating Transformations (RTs) for the stator current and voltage quantities are proposed and employed. In the proposed scheme, the genetic algorithm is used to regulate the parameters of the Proportional-Integral (PI) controllers. The developed VC system provides an accurate control against an OCFSC and can be employed for different industries that need Fault-Tolerant Control (FTC) systems. The effectiveness of the proposed approach is validated through experimentation in the laboratory. The proposed control scheme gives good responses during both steady state and transient sate. In addition, the proposed VC system gives better performances during the post-fault operation compared to previous works in terms of speed and torque ripples.

Permanent magnet synchronous motors due to its high efficiency and power density, reliable performance and simple construction, industrially used. One of the problems with these Motor need accurate information to control its speed and position. Recently, because of the difficulties of the speed sensors, speed estimation is used instead of measuring it. In this paper, the state dependent model reference adaptive controller based on pseudo linearization is used to control the permanent magnet synchronous motor that its parameters are determined based on Lyapunov theory. This controller show good results by production control law, despite changing circumstances and maintain system stability despite external disturbances. Also due to uncertainty in the estimation of the position and speed of motor parameters, high impact, online identification of these parameters is necessary. In this paper, adaptive augmented observer is used to estimate speed and identify motor parameters online. The main advantage of this estimator is that speed and load torque estimation and identification of parameters are simultaneously thus volume and time calculations are reduced. The simulation results show convenient tracking of desired speed with the load torque, changing the motor parameters and the speed reference.

Mitigating switching overvoltages (SOVs) and conducting well-suited insulation coordination for handling stresses are very important in UHV transmission Lines. The best strategy in the absence of arresters is controlled switching (CS). Although elaborate works on electromagnetic transients are considered in the process of designing transmission systems, such works are not prevalent in day-to-day operations. The power utility and/or operator have to carefully monitor the peak values of SOVs so this values not to exceed the safe limits. In this paper, we present a novel CS approach in dealing with EMTP/ATP environment, where trapped charge (TC) is intended to train a radial basis function network (RBFN) meta-model that is implemented to calculate SOVs. A new weighted maximum overvoltage factor proposed to find locations of critical failure risk due to SOVs occurred along transmission lines. Power utilities or design engineers can benefit from the presented meta-model in designing a well-suited insulation level without spending time for taking into account the feasible risk value. Besides, the operators can energize the lines sequentially upon their choice; i.e., a safe and proper energization.