Abstract- The space vector pulse-width modulation (SVPWM) is a simple and suitable method for voltage control of three-phase two-level voltage source inverters (VSI)s. However, there are plenty of methods to improve the two-level VSIs performance by adding virtual vectors or sub-sectors to the SVPWM diagram which cause complexity in implementation of SVPWM for VSIs similar to multilevel inverters. Operation in overmodulation mode is the other reason for complexity in conventional SVPWM. This paper proposes a novel modulation method, named as level vector pulse-width modulation (LVPWM), for voltage control of VSIs. The concept of the proposed method is similar to 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 on only the amplitudes of the reference vectors. With lower computational overhead and easy and continuous extension to overmodulation region, the proposed method is a simple solution to the mentioned problems. Simulation and experimental results and harmonics analysis verify the effectiveness of the proposed algorithm.
The Internet of Things (IoT) is a multidisciplinary concept that includes a wide range of application domains and device capabilities. The IoT technology provides a platform that allows apparatus to be monitored by sensors, connected using a communication channel, and controlled remotely across a network infrastructure. This paper represents the IoT-based remote monitoring, control, and protection of irrigation water pumping systems. The sensors measure parameters; namely supply voltage and motor current to estimate operating conditions and provide protection. The measured parameters are sent to the user through the SIM900 GSM module for remote monitoring and control purposes. The android application has been developed to provide a better user interface, timer-based operation, status of pump and feedback notifications, etc. Protection to the pump (3-Փ Induction Motor) is provided through an ATmega 328P microcontroller embedded in the Arduino Uno board. Appropriate relay characteristics are programmed in the microcontroller for protection against various types of faults like thermal overload, single phasing, under/over voltage, voltage imbalance, reverse rotation. The system is tested in the lab and it is observed that all the protections are working satisfactorily. Single phasing is detected and the supply is tripped within 0.15 sec, undervoltage protection operated in 0.3 sec, and 5% unbalance in supply voltage is detected successfully and protection is operated within time.
The Frequency Response Analysis (FRA) technique has advantages in identifying faults related to power transformers, but it suffers from the interpretation of frequency responses. This paper presents an approach based on statistical indices and Artificial Neural Network (ANN) methods to interpret frequency responses. The proposed procedure divides frequency responses into four frequency regions based on frequency resonances and anti-resonances. Then, Lin’s Concordance Coefficient (LCC) index is used as one of the most appropriate numerical indices to extract features of the four frequency regions. Finally, the Multilayer Perceptron (MLP) neural network is trained by the extracted features to identify and differentiate the types of winding faults. Besides, other intelligent algorithms such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), and Radial Basis Function (RBF) neural network have been employed to compare the classification results. The proposed techniques have been practically implemented. The Axial Displacement (AD) and Disk Space Variation (DSV) faults are applied as two common mechanical faults in different locations and intensities on the 20kV windings of a 1.6MVA distribution power transformer and their corresponding frequency responses are calculated. Frequency responses calculated from the AD and DSV faults constitute the MLP input data set. The network is trained with part of the input data, and the rest of the data is allocated to validate and test the network. The results show that the suggested method has more proper performance than others using the phase component of the frequency responses in interpreting frequency responses and separation and identifying various mechanical fault types of transformer windings.
Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through due to fault current limiting and voltage restoring ability during the fault, respectively. This paper presents a method for optimal allocation and control of superconducting magnetic energy storage and superconducting fault current limiters in meshed microgrids. For this purpose, the doubly-fed induction generator voltage deviation, the point of common coupling power deviation, the fault current of transmission lines, and superconducting fault current limiter and superconducting magnetic energy storage characteristics were considered as objective functions. In this paper, the optimization is performed in single-step and two-step by particle swarm optimization algorithm, and the system with the optimal superconducting magnetic energy storage and superconducting fault current limiters are analyzed and compared. The results of simulations show superconducting fault current limiter and superconducting magnetic energy storage reduce 85% of voltage drop, decreases 63% of doubly fed induction generator power deviation, and limits the maximum fault current of transmission lines by 9.8 pu. Finally, the status of the studied system variables has been investigated, in two scenarios related to the different fault locations with equipment that the optimal allocated.
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.
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.
The power system in upcoming years will face issues of power frequency instability due to an increase in the share of Renewable Energy Sources (RES). The RESs are integrated into the power system through the power electronic converters. The operation and control of RES are drastically different than the conventional energy sources. This paper is focused on the effect of a rise in the share of RES on power system frequency stability and its possible solutions. The RESs are not taking part in the frequency regulation process in case of disturbance. Despite this, they generate disturbances in the power system caused by the intermittent nature of input energy. The RES doesn’t have extra active power for the frequency regulation as they already operate at their maximum power point. These power electronic-based generators don’t contain inertia like conventional generators. The inertia-less systems adversely affect the Rate of Change of Frequency (RoCoF) and frequency nadir. This is demonstrated on IEEE 9-bus system with different scenarios. According to that analysis, the RES should provide an inertial response during disturbances. In this paper, the proposed Modified Virtual Inertia Control (M-VIC) technique emulates inertia like conventional generators by using external Energy Storage Systems (ESS). In M-VIC the inertial response is replicated by controlling the rate and duration of power provided by ESS. The proposed technique is more effective to reduce the frequency nadir and RoCoF with better utilization of ESS. To demonstrate this, the PV integrated single-area power system model is simulated in MATLAB R2019a.
This paper presents the mitigation of subsynchronous resonance (SSR) based on wide-area wide-area fuzzy controller in power systems including a double-fed induction generator (DFIG)-based wind farms linked to series capacitive compensated transmission networks. SSR damping is achieved by adding the fuzzy controller as a supplementary signal at the stator voltage loop of the grid-side converter (GSC) of doubly-fed induction generator (DFIG)-based wind farms. In addition, delays due to communication signals are important in using WAMS. If these delays are ignored, it causes system instability. In this paper, the delays are modeled with a separate fuzzy input to the controller. The effectiveness and efficiency of the WAMS-based fuzzy controller has been demonstrated by comparison with the particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) optimization methods. The effectiveness and validity of the proposed Auxiliary damping control are verified on a modified version of the IEEE second benchmark model including DFIG-based wind farms via time simulation analysis by using MATLAB/Simulink.
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.
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.
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.
In recent years, as a result of remarkable increase in energy industry, discrimination between lower and higher loads as well as economic crisis which pestered a majority of countries; hence the usage of power plants became a significant issue. In addition, growing consumption of power and inexistence of valid source in satisfying the requirements has brought different problems such as diminish of fossil fuel resources, adversarial environmental influences, universal growth of Greenhouse Gases (GHGs). The associated issues have created technologies compatible with situations including Electric Vehicles (EVs). Regarding the efficiency of two-side exchange of energy within these vehicles, if there was a connection among the number of them and net under management and intelligent monitor of organization stability, so they can treat like a virtual tiny energy plant with start- up speed and free of cost. This paper presented the modeling and optimizing of the charge of electric vehicles with genetic algorithm in the presence of renewable energy sources. According to the results of this study, the cost of the HEV charge connected to the net is 75.88% less than the EV compared to the payment costs of the car (dis)charge in optimal patterns.
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.
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.
This study proposes a novel hybrid multi-objective evolutionary algorithm (MOEA) based on decomposition and invasive weed optimization (IWO) algorithm for the optimal power flow (OPF) problem in transmission networks. The conventional OPF is modified as a stochastic OPF with the integration of WE, PV, and PEV systems uncertainty. This paper proposes a new constraint-handling method (CHM) that adds the penalty adaptively and avoids parameter reliance on penalty computation. The selection features in the IWO method are deployed to improve the diversity of the proposed method. The OPF problem is represented as a multi-objective optimization (MOO) problem using four objectives: generation cost, emission, power loss, and voltage variation. The generation costs of WE, PV, and PEV sources are evaluated using Monte Carlo simulations to mitigate the whole cost and the influence of intermittency of these systems is investigated in terms of affordability and feasibility. Weibull, lognormal, and normal probability distribution functions (PDFs) are employed to define the uncertainty of WE, PV, and PEV sources respectively. The suggested method's viability is evaluated on IEEE 57 and IEEE 118- bus systems under all conceivable scenarios. In addition, one-way ANOVA test, a statistical approach, is used to evaluate the superiority of the suggested algorithm.
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.
Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.
Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia. This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.
کنترل برداری درایوهای موتور القایی اتصال ستاره از کارهای مهم مورد تقاضاست. علاوه بر این، موتور القایی اتصال ستاره تحت خطای مدار باز در سیم پیچ های استاتور منجر به خرابی کنترل برداری می شود. در نتیجه، موتورهای القایی اتصال ستاره تحت خطای مدار باز نیاز به طراحی مناسب دارد. این تحقیق بر روی یک استراتژی کنترل جهتدار میدان اصلاح شده و دقیق برای درایوهای موتور القایی اتصال ستاره سه فاز تحت خطای مدار باز در سیم پیچ های استاتور متمرکز شده است. بسیاری از مقالات اخیر مطالعه شده بر روی کنترل برداری موتورهای القایی اتصال ستاره تحت خطای مدار باز در سیم پیچ های استاتور، اندوکتانس نشتی در معادلات کنترل برداری را نادیده می گیرند. در این مقاله، با در نظر گرفتن اندوکتانس نشتی در معادلات کنترل برداری موتورهای القایی اتصال ستاره تحت خطای مدار باز در سیم پیچ های استاتور، یک روش کنترل برداری جایگزین ارائه می شود. در سیستم کنترل برداری ارائه شده، دو تبدیل چرخشی نامتقارن برای کمیت های جریان و ولتاژ استاتور پیشنهاد و استفاده می شود. در طرح پیشنهادی، از الگوریتم ژنتیک برای تنظیم پارامترهای کنترل کننده های تناسبی-انتگرالی استفاده می شود. سیستم کنترل برداری توسعه یافته کنترل دقیق در برابر خطای مدار باز در سیم پیچ های استاتور را فراهم می کند و می تواند برای صنایع مختلفی که به سیستم های کنترل تحمل پذیر خطا نیاز دارند استفاده شود. اثربخشی روش پیشنهادی از طریق آزمایش در آزمایشگاه تأیید می شود. طرح کنترلی پیشنهادی در حالت پایدار و حالت گذرا پاسخ های خوبی می دهد. علاوه بر این، سیستم کنترل برداری پیشنهادی در مقایسه با کارهای قبلی از نظر نوسانات سرعت و گشتاور عملکرد بهتری پس از خطا ارائه می دهد.
