مجله مهندسی برق و الکترونیک ایران
سال بیستم شماره 2 (تابستان 1402)

Due to the growing penetration of residential air conditioners (RACs) in Iran power grid, fault-induced delayed voltage recovery (FIDVR) has been recognized as a serious threat to the voltage stability. In order to confine FIDVR adverse impacts, it is vital to identify the most “at-risk" buses in a power grid. This paper by analyzing the real-world FIDVR events in Iran bulk network shows that in addition to the power system topology and reactive power support, the RAC penetration rate and the ambient temperature are also effective in order to identify the critical FIDVR-prone buses. By introducing the load incremental factor, and the effective voltage depression coefficient, this paper proposes an enhanced MVA-Volt index to distinguish the most at-risk buses. The proposed screening tool can be evaluated only by conducting steady-state simulations, though the FIDVR phenomenon is inherently a dynamic phenomenon. To examine the effectiveness of the proposed index, firstly, it has been verified over a restricted region of Iran power grid (Kerman region) by carrying out precise dynamic simulations. Then, it is validated considering the actual FIDVR events. The identified at-risk buses are in good agreement with the real-world reported FIDVR events, and therefore, the proposed index is found reliable and effective.

Electricity losses as an integral part of power delivery systems have always been recognized by electricity industry actors as a measure of how the network managed. In a way, the high losses of the network show the weak managers and the inefficiency of the programs implemented in this network. The exact amount of losses is measured by load flow; But distribution networks, especially low voltage networks, have many information deficiencies due to the expansion and lack of development of information systems; This has left no measure of losses available in this section. Therefore, the electricity network regulator and distribution companies do not have a good view to assessing the management status of the network, the efficiency of the implemented projects, future planning, and budget allocation. In this paper, by using statistical analysis and high responsiveness of deep neural networks to nonlinear problems, a new method for estimating losses in the weak space of information systems is presented.

Renewable energy sources can provide a suitable solution to meet the increasing load demand. Therefore, based on the objective functions and according to the structure of the distribution network, and the variety of solar radiation intensity, ambient temperature, and wind speed in different network nodes, the location, and capacity of renewable sources should be determined optimally. As in the distribution networks, the voltage stability of the lines and the voltage profile of the buses are usually not in the desired condition, so improving the voltage stability can be considered as one of the main goals in the allocation and capacity determination of renewable sources. For this purpose, in this paper, the optimal location and capacity of hybrid distributed generation sources including photovoltaic power plants, wind farms, diesel generators, and energy storage systems are done. The objective functions include reducing the probability of loss of power supply, minimizing the total net present costs, and improving the network voltage stability. For this purpose, based on annual information of radiation intensity, wind speed, temperature, and profile of residential, commercial, and industrial loads, optimal allocation and capacity determination of hybrid distributed generation are done in IEEE 33-bus radial distribution system using multi-objective genetic algorithm.

Nowadays, power quality issues and problems related to voltage variations have been emphasized by consumers of electrical energy and distribution utilities. Thus, compensating issues such as voltage sag and voltage deviations in the distribution network level are of paramount importance. This paper proposes a hybrid planning algorithm to locate distributed generations (DGs) and battery energy storage systems (BESSs) in an optimal way for solving economic and technical problems related to the occurrence of voltage sag in a distribution network. This algorithm is a nested problem, which is considered to represent the uncertainty of DGs along with load demand for simulating the occurrence of the voltage sag. In the next step, the DGs and BESSs obtained from the planning algorithm are used in a real-time post-fault network reconfiguration algorithm for economical operation and improvement of voltage deviations of the network. In the final part, total power loss is calculated with the presence of DGs and battery storage systems. The two proposed algorithms have been tested on a modified IEEE 33-bus distribution system. The obtained results demonstrate the 53 percent reduction of the cost function of the planning algorithm without the need for adding the cost of the occurrence of voltage sag and the 50 percent reduction of the total power loss due to the suitable compensation.

Today, with the increasing consumption of electricity and environmental problems caused by fossil fuels, the tendency to use renewable sources is increased. Also, due to the restructuring of power networks from the traditional structure to the market-based structure, different energy markets and ancillary services is designed. On the other hand, due to the uncertainty of the renewable sources generation, energy storage units are considered as a participant in energy and ancillary service markets alongside renewable sources or as an independent unit. Due to the high speed of battery energy storages, using these resources in frequency ancillary service markets such as the automatic frequency restoration reserve market is an appropriate choice. One of the issues in scheduling these units is how to participate in energy or ancillary services markets simultaneously, to maximize the net profit. In this paper, an optimal strategy for the simultaneous participation of the battery energy storage unit in the energy and the automatic frequency restoration reserve markets is proposed. Simulation results show the good performance of the proposed strategy for the simultaneous participation in the energy and the automatic frequency restoration reserve markets to maximize the battery energy storages profit.

Non-linear loads are a major contributor to harmonics in the power system. Harmonics harm electrical network components by destroying the sinusoidal waveform of current and voltage. The goal of this article is to provide and control active and reactive power, as well as to reduce network harmonic currents caused by non-linear loads. As a result, the proposed method decreases grid current harmonics by employing a battery energy storage system and providing a control mechanism for it, in addition to managing and supplying the amount of active and reactive power of the nonlinear load individually. Power control and harmonic reduction are the two parts of the proposed control system. Power control is done with classic PI controllers, whose coefficients are obtained with Ziegler-Nichols method, while harmonic reduction is done with PI-Fuzzy controllers. The current harmonic signal is sent into the PI-Fuzzy controller, while the main component (50 Hz) is isolated from it. The simulation results in three distinct scenarios demonstrate that the proposed approach can regulate the active and reactive powers of the nonlinear load under different charging and discharging situations, as well as reduce current harmonics up to the 31st harmonic.

In this paper, the effect high penetration of renewable energy resources on the transmission network stability is investigated. By increasing the number of inverter-based resources, the overall inertia of the network decreases and the stability of the network is affected. In order to analyze the behavior of low-inertia grids, initially according to the type of traditional power plants and wind turbines, appropriate dynamic modeling has been done. Then, by creating a short circuit, the effect of increasing the penetration of wind turbines on the voltage and frequency stability is evaluated. The effect of duration and location of the fault on network stability has also been investigated. The study network is a modified Kundur’s four-machine system modeled in PowerFactory.

In this paper, a stochastic multi-objective optimization model for planning distribution networks has been proposed. After running this model, the optimal configuration will be obtained. The proposed model has two main objectives (1) Minimizing the Net Present Value and (2) Minimizing the Expected Energy Not Supplied. In this model, based on the optimal solution, the network’s buses and feeders can be either AC or DC. Monte Carlo Simulation (MCS) has been used in order to model stochastic variations of load demands and distributed generations’ output. For obtaining the best solution, the planning problem will be solved with NSGA, and then a tradeoff between NPV and EENS will be calculated with Pareto Front. The proposed model will be tested on a 14-Bus network, which includes AC and DC load points, and DGs with AC or DC output. The test network will be planned as a pure AC network, and also as a hybrid AC/DC network for (1) an urban zone and (2) a case in which some of the load points are industrial loads and have a very large interruption cost. Finally, at the end of the paper, some results such as NPV, ENS, network’s loss, and a reliability index such as SAIDI will be compared.

With the expansion of renewable energy resources and the reduction of the power system total inertia, the problem of providing the primary frequency response (PFR) has been raised. Other methods were welcomed. The issue of generation/transmission expansion planning must also adapt to these methods. Therefore, in this paper, a model of generation/transmission expansion planning is presented in which each of the renewable energy sources such as wind and solar power plants has virtual inertia. So, they can participate in the primary frequency response. Moreover, Frequency security margin constraints are also included in the issue. Frequency security margin is the maximum allowable and tolerable unbalance between the generation and consumption power of the network which can lead to more accurate expansion planning than other PFR-based method, if considered. Compared to the traditional method, the network's tolerance to sudden events has increased, which has certainly can be achieved at a higher cost than the traditional one. The IEEE-RTS96 network was used to verify the proposed model. The results show a percentage increase in cost compared to the traditional method, and an increase in network frequency security margin. The combination of YALMIP and MOSEK software has been used to solve the mixed integer linear programming model, which have shown its superiority in solving these models.

This study is aimed to apply three machine learning techniques including Random Forest (RF), Support Vector Machine (SVM), and Multivariate Adaptively Regression Spline (MARS) in both short-term and long-term electric load forecasting problems and compare their performance. Last hour load, temperature, and solar altitude angle of the present hour with holidays were considered as inputs. Three different criteria including root mean square error, mean absolute error, and coefficient of determination were used to evaluate the performance of prediction methods. These methods are all applied to practical electric load demand data obtained from a sub-transmission substation in Hamedan using R programming language. The temperature data are collected from the nearest meteorological weather station and the hourly solar altitude angle for the whole year is accurately calculated with astronomical equations for the studied location. The results show that the implemented methods provide acceptable forecasts and RF and SVM models exhibit superb results and provide more accurate forecasts in short-term load and long-term load forecasting respectively.

The change in network topology and operation modes of microgrids is one of the most crucial challenges for protection systems, which might lead to some coordination constraint violations.  Although communication-based and adaptive protection schemes have various advantages, because of their required hardware and software infrastructures, the communication-free schemes have received a great deal of attention. The cyber-attacks and failures in communication-based protection schemes highlight the advantages of communication-free systems. In this paper, the main contribution is to develop a new communication-free method using double setting directional overcurrent relays considering different operation modes and network topologies. There is a research gap in developing a method incorporating the optimal selection of relay characteristics and curve breakpoints for dual setting directional overcurrent relays. The proposed method tries to fill such a research gap by proposing a new method using the dual setting relays, while the time settings, current settings, types of relay curves, and curve breakpoints are simultaneously optimized. The microgrids are simulated in DIgSILENT environment, and power flow and short circuit analyses are performed to determine the required data and information for the proposed optimization problem. Thus, the proposed optimization problem for protection coordination of microgrids is solved using the Genetic Algorithm (GA) in MATLAB. Test results of applying the proposed method on the IEEE 8-bus and the distribution portion of the IEEE 30-bus test systems result in 12.42% and 9.56% improvement in the operating time of the protection system, respectively, that illustrate the advantages of the proposed method. Obtained results show that optimizing the curve types for dual setting directional overcurrent relays besides the optimal selection of curve breakpoints significantly improves the performance of the protection schemes using the dual setting characteristics in various operation modes and network topologies.

In the last decades, microgrids have been developed for the techno-economic management of distributed generation (DG) penetration in distribution networks. Providing a primary secure and dependable protection scheme for AC microgrids is a challenging task due to the use of a variety of DG technologies. In this paper, a new independent protection scheme using current synchrophasor measurements obtained from microgrid buses is presented to detect the different kind of internal and external faults in different location. The performance of the proposed protection scheme is evaluated under different operating conditions of the microgrid (in the grid connected and islanded mode) by entering and disconnecting the DGs and changing their penetration level.  In this regards, a novel index based on differential phase angle of fault component current obtained from both ends measurements of microgrid buses is proposed to distinguish the fault condition from no-fault situations. The proposed protection index is tested on standard 15-bus microgrid considering high resistance faults and variation in DG penetration in the grid connected and islanded mode. The obtained results indicate the capability and superiority of the proposed approach in reliable microgrid protection

High voltage circuit breakers (CBs) play an important role in power grid stability and fast clearing faults on this grid. Therefore, real-time monitoring of these critical components is necessary to prevent possible failures and uundesirable interruptions on power grid. In this paper, the possibility of monitoring of HVCBs as well as fault diagnosis and classification are investigated using artificial intelligence. Accordingly, failure prevention is possible, therefore maintenance costs will be reduced. Here, monitoring of CBs has been implemented through condition-based maintenance strategy (CBM) using close/trip coil current signal (CC) of a real 72.5 kV CB, which helps to detect and predict faults in different parts of CB such as supply voltage, coil winding, latch and auxiliary contacts. Through simulation of the coil in COMSOL Multiphysics software and linking it with MATLAB software, a wide range of faults is obtained for training of machine algorithms. These algorithms are being used to detect faults, such as: logistic regression, support vector machine (SVM), decision tree and nearest neighbor (KNN). Results indicate that among these algorithms, SVM algorithm does not perform well because of overlapping, and the most accuracy is related to KNN algorithm, so this algorithm is selected for the fault diagnosis system.

Nowadays, to having greater protection of the environment and due to the pollution caused by using fossil fuels and the limitation of these resources, the global orientation towards is renewable energy sources. As a sustainable form of energy, solar energy is really important, because that is almost the cheapest source of energy production in areas with high solar radiation potential; so global demanding of electricity generation can be supplied with using this technology. There is possibility of direct or indirect lightning strike on photovoltaic system equipment due to the installation of this equipment outdoors or on rooftops. In this paper, the transient overvoltage crated by direct and indirect lightning strike to the photovoltaic frame in different parts of the photovoltaic system is investigated by an accurate modeling of the frame, pillars and ground of a photovoltaic system in EMTP-RV.

Use of Gas Insulated Substations (GISs) at high voltage power systems is expanding due to their advantages such as: less required space, resistance to environmental pollutions, high reliability and no need for continuous maintenance. One of the phenomena that threatens the insulation distances inside the closed enclosure in these substations is the occurrence of Very Fast Transient Overvoltages2 (VFTOs). These overvoltages that are resulted from switching3 operation in the GIS are dangerous for the equipment connected to or inside the GIS and can damage the internal insulation of the equipment. Moreover, these transient overvoltages can cause serious damage to the expensive substation equipment in special cases such as extremely high voltages. In this paper, while reviewing the existing methods to limit VFTO in different sensitive points of the GIS, a new hybrid method is proposed based on the simultaneous use of Nanocrystalline rings in busbars and parallel XLPE4 cable path to connect power transformer to the GIS. The simulation results for Shiraz University 230 kV GIS show that in the case of switching operation, the proposed method has the lowest amplitude of overvoltages compared to other methods at different places in this substation. Using the proposed hybrid method, the amplitude of VFTO in the terminal of power transformer of the under-study network is reduced by 59.7% and this reduction can be seen in other points of the substation, as well.

Due to its simplicity of implementation, the sliding mode control (SMC) method has been increasingly used in converters in the field of power electronics in recent years. The equivalent control approach can be leveraged to make the switching frequency stable in the sliding mode control of converters. In the SMC approach, most of the controller parameters are tuned by trial and error, and as a result, it is not possible to guarantee the stability of the controller in a wide range of operation. To cope with this challenge, a novel approach for designing the sliding mode controller and adjusting its parameters in a Z-source converter is proposed in this paper. To evaluate the proposed design, various important criteria including steady-state error and controller robustness against variations in the reference voltage, load, and input voltage have been investigated. The efficiency of the proposed method has been proved with the experiments through extensive MATLAB/Simulink simulations.

this paper presents finite control set-model predictive control (FCS-MPC) in the low and high speed range for the control of brushless DC (BLDC) motor. In the proposed control method, by removing the mechanical sensor and using the sliding mode observer (SMO), the electromotive force (BACK-EMF) as well as the speed and position of the rotor are accurately estimated. Then through fcs-mpc to improve speed control performance and reduce chattering, by minimizes a cost function, predicts the future behavior of the  system variables. The method is simulated with MATLAB software and the output speed and back-emf obtained from the control model control method with a limited control set are compared with the slip model observer method. The simulation results show that the control method used improves the speed convergence accuracy and reduces the steady-state error and the steady-state fluctuations.

Nowadays, electric vehicles have gained more attention due to environmental issues. In this regard, charging the batteries of electric vehicles is one of the main challenges. Because of recent advancements in power electronics, inductive power transfer (IPT) chargers are rapidly developing due to some advantages compared to conventional plug-in chargers, such as no cable required and higher reliability. This new solution allows the building of low-cost and intelligent energy transfer systems. IPT chargers have a transmitter coil on the ground and a receiver coil on the vehicle. Due to the air gap, the magnetic coupling between the primary and secondary is weak. In this paper, the structure of a typical bidirectional charger based on a resonant converter is developed by a weak magnetic coupling. Finally, the obtained model and the performed computational analyzes are confirmed by simulation on a 6.6 kW battery charging converter in the Simulink Matlab. It is shown that by changing the electromagnetic coupling in the range of 0.1 to 0.9, if the resonance frequency is kept constant with a variable resonance capacitor, the battery charging voltage remains almost constant in the range of 440 V to 450 V.

This paper presents a dynamic uninterruptible power supply (DUPS) consisting of both battery and flywheel energy storage systems with the aim of overcoming power quality problems such as temporary voltage drop and power outages. To supply a critical load at the moment of fault occurrence and a few seconds later, the flywheel energy storage system(FESS) and for a few minutes after that or in the case of power outage, the battery storage system and after that, a diesel generator are used respectively. The FESS system consists of a bidirectional inverter, a flywheel and an interior permanent magnet synchronous motor (IPMSM). In the IPMSM control scheme, vector control, torque maximization per ampere(MTPA) and field weakening technics are used. The battery energy storage system consists of a bidirectional DC / DC buck-boost converter and a charge-discharge control. The simulation results show that the FESS changes from standstill to charged and standby condition in less than10  seconds and is ready to supply critical load for short-term phenomena. In UPS mode, it also maintains the critical load voltage at the nominal value in less than one cycle. In the event of a long-term power outage, after discharging the flywheel energy for about 2 seconds, the battery energy storage system maintains the DC link voltage at its reference value and provides the necessary time to start the diesel generator.