Journal of Operation and Automation in Power Engineering
Volume:11 Issue: 2, Summer 2023

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.

This paper proposes a robust state feedback controller for Electric Vehicle aggregators to solve the challenging problem caused by the participation of Electric Vehicles in the load frequency control of the power system.  The Lyapunov-Krasovskii functional method is used to achieve two objectives of the robust performance and stability.  Then, by using teaching learning based optimization algorithm, both primary and secondary participation gains of EV aggregators in LFC are optimally determined. The Generation Rate Constraint and time delay, as nonlinear elements, are also taken into account.  Simulations are carried out on two nonlinear power systems by using the power system simulation software.  The results show that the designed controller gives a desirable robust performance for frequency regulation at the presence of uncertainties.

Dispersed energy resources and storage devices may be grouped as a Virtual Power Plant (VPP). In a competitive electricity market, VPP can exchange energy through a pool market or bilateral contracts. in order to maximize the profit, VPP needs to determine the optimal operating schedule. This paper provides a new decision-making framework based on information gap decision theory (IGDT) for robust self-scheduling of VPPs in power markets. In the proposed approach, the energy price is the uncertain parameter while the decision variables are the energy that needs to be exchanged in the pool market and through bilateral contracts, the reserve which should be provided, dispatch of distributed energy resources, the load which is needed to be curtailed, and the state of charging/discharging of energy storage devices. The proposed method specifies the self-scheduling considering the risk-taking level of the decision maker. A case study has been used to validate the proposed framework.

DC Microgrid is turning out to be more popular due to its appealing features such as high efficiency, excellent power quality, low cost and controllability. As the control strategies plays a key role in achieving the desired objectives such as power quality, power sharing, voltage regulation and efficiency. It is necessary to understand the classification and operation of control strategies in DC microgrids. From the control point of view, the traditional droop control methods are commonly employed for regulating proportional load sharing. However, depending on the primary control makes it challenging to maintain stable and coordinated operation in terms of maintaining both the voltage regulation and load sharing accuracy simultaneously in DC microgrids. So to avoid the trade-off in voltage regulation and power sharing accuracy, secondary control layers need to be introduced in the control structure. In this paper a review of primary control and secondary control methods (centralized, decentralized and distributed control) were discussed in detail with the classification along with the advantages and shortcomings of the control methods.

The conventional space vector pulse-width modulation (SVPWM) for cascaded H-bridge inverters (CHBIs) has problems of computational complexity and memory requirements. Operation in overmodulation mode is the other reason for the complexity in SVPWM. This paper proposes a novel modulation method, named as level vector pulse-width modulation (LVPWM), for voltage control of CHBIs. The concept of the proposed method is similar to the SVPWM but with different vector diagram and dwell times calculations. Unlike the SVPWM, the α and β axes and also their variables are considered separately without gathering in complex variables. The vector diagram has two separated α and β axes each of which contains individual switching vectors and reference vectors. The selection of the vectors to synthesize the reference vectors depends only on the amplitudes of the reference vectors. The computational overhead and memory requirement are independent of the number of cascaded H-bridges. Lower computational overhead and easy and continuous extension to overmodulation region are the advantages of the proposed method compared with the SVPWM-based methods. Moreover, the switching algorithm achieves improved efficiency for the inverter. Simulation and experimental results verify the effectiveness of the proposed algorithm.

An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.

In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness

Tremendous growth of wind power worldwide in the past decade requires serious research in various fields. Because wind power is weather dependent, it is stochastic and varies over various time-scales. Therefore, accuracy in wind power modeling is recognized as a major contribution for reliable large-scale wind power integration. In this paper, a method for generating synthetic wind power is proposed. The proposed method combines the random nature of wind with the operational information of the wind turbines (i.e., failure and repair rates). It uses chronological or sequential Monte Carlo Simulation (MCS) instead of non-sequential one owing to its usefulness and flexibility in preserving statistical characteristics of the chronological processes. The validity of the synthetic values generated by the proposed method and the Auto Regressive Moving Average (ARMA) time series is compared with the measured data in terms of reliability indices. Finally, the effect of some network parameters, such as network dimensions, the average coefficient of wind speed on the reliability of the power system has been evaluated. In this regard, historical wind speed data of Manjil area located in the north of Iran is used.