JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS
Year:2022 | Volume:19 | Issue:1|Papers Count:32

Customer satisfaction is one of the important criteria in performance evaluation of electricity distribution companies. From the majority of customers’ point of view, interruption duration is the most important criterion. Electricity distribution companies control and reduce their interruption duration times using set of methods, including preventive maintenance. This paper develops an interruption duration model, considering preventive maintenance action. First, the system interruption data is extracted and organized from the operational dashboard in the Greater Tehran Electricity Distribution Company (GTEDC), and integrated with the meteorological data during the period under review. Then after cleaning the data, linear and nonlinear regression methods were used to model interruption duration. Innovations and contributions of this research can be considered as: 1) data-driven modeling of interruption duration using linear and nonlinear methods, 2) considering the effect of the repair crew locations and urban traffic, in terms of the repair crew travel time to the accident site, 3) considering the preventive maintenance operations history in modeling the interruption duration and sensitivity analysis of interruption duration to preventive maintenance, and 4) analyzing the influence of input features on interruption duration prediction.

Electricity utility have long sought to identify and reduce energy theft, which represents significant part of non-technical losses. On the other hand, once a fraudulent customer is detected, on-site inspection is necessary for final verification. Since inspecting all customers is expensive, utilities seek to reduce the range of inspection to cases with a higher probability of theft. One way to reduce the scope of inspection is to use artificial intelligence-based methods. An essential challenge here is data imbalance in terms of the ratio of normal to fraudulent customers, which leads to the poor performance of algorithms. In This paper in order to overcome this challenge, assuming that suspicious behavior can be expressed as a mathematical function of normal behavior, in the first stage, the consumption pattern of normal and suspicious customers is categorized using clustering algorithms. Then a deep neural network is trained to model suspicious customers. Next, using trained network, possible theft scenarios for normal costumers are predicted. Finally, a secondary deep neural network is trained to separate the normal and suspicious customers. Assessment of the proposed algorithm for different scenarios on a real data-set with more than 6000 customers and comparison with previous research shows its high performance.

Recent changes in electricity distribution networks, such as extensive penetration of renewable energy resources (RERs), have brought about new challenges for operators. Transition from centralized energy management schemes toward decentralized ones is one of the major transformations that has been made to improve the performance, sustainability, and resilience of distribution networks and to adapt them for their ongoing changes. Internet has made its way to electricity distribution networks through the Internet of things (IoT). This information technology has emerged in the distribution networks to better accommodate the operational and management transformations. As a result, the intrinsically decentralized technology of blockchain has gained momentum as a secure platform for information exchange through IoT in distribution networks. This paper initially introduces the blockchain technology and elucidates its operation. Further, it analyzes the transition of the distribution networks to what is called active distribution networks and their functionality. Next, blockchain is represented as an effective technology for maintaining the information security and also eliminating trusted third parties in managing distribution networks. Finally, peer-to-peer energy markets are explained as an application of blockchain technology in distribution systems.

Nowadays, computer network attacks are considered as the most serious issue in everybody’s life. Network hardening is a necessity which requires prioritizing vulnerabilities and finding the most dangerous ones. So, risk assessment should be done as much accurate as possible. The most widely used system for risk assessment is CVSS (Common Vulnerability Scoring System) that has some difficulties which make risk analysis inaccurate. Limited number of risk scores is the most critical issue which causes inefficient vulnerability discrimination. Here one novel approach is proposed which amends the Impact formula of CVSS by the aim of increasing risk estimation accuracy and scores diversity.

Fault management optimization in electrical distribution networks especially in critical situations of multiple faults due to adverse weather conditions as an attempt to reduce the destructive social and economic consequences of extensive and long network blackouts in recent years has attracted the attention of researchers in this field. In this paper, an optimal load restoration with the focus on planning the dispatch of repair and operation crews and coordinating the automation system and mobile power generation resources with the operations of these crews is presented. The proposed model designs remedial actions to reduce customer outages while maintaining network operational constraints, including radial structure, allowable voltage range, and the thermal limits of network conductors. The mathematical formulations of the proposed model are stated in the framework of the mixed-integer linear problem (MILP). In the numerical results of this paper, in order to evaluate performance of the model, IEEE 69-bus network is employed. The obtained numerical results confirm the efficiency of the model in optimal decision making in fault situations.

Industrial electrical panels require a regular inspection as maintenance. The project applied a single board computer to the IoT system, making the electrical panel smart, which makes the maintenance procedure much easier and faster. The electronic design used a Raspberry Pi single board computer as the central system controller, and an electronic circuit for data communication was designed. The Python programming language was used for software design. Dataplicity Cloud Commander on phones and computers was used as an interface for accessing the electrical panel online via the Raspberry Pi. The design allows technicians to continuously monitor devices such as protective relays, panel temperatures, and DC power supply voltages online via mobile phones or computers. The system was tested for various operating conditions and possible errors of the power panel. The result is a low-cost smart system for electrical panels that provides essential information quickly and easily, greatly reducing panel troubleshooting time.

In this article, the control of interfaced converters of distributed generations (DGs) in order to improve microgrid power quality with emphasis on voltage unbalance is considered. The control scheme of these converters is coordinated so that the DGs contribute to voltage unbalance compensation proportional to their rated power and by considering the residual capacity of each DG. The control structures presented in this article are classified into two categories: voltage_controlled mode (VCM) control scheme and current_controlled mode (CCM) control scheme. VCM units using capacitive virtual impedance contribute to voltage unbalance compensation, while in the control scheme of CCM units, a virtual admittance is implemented in order to compensate voltage unbalance with considering the residual capacity of the inverter. Also, VCM and CCM units contribute to reactive power sharing by droop and reverse droop control methods, respectively. Droop coefficients are adjusted considering the limited capacity of these inverters and the unbalance power. Finally, the results were then presented to show the effectiveness of the proposed control structure at different stages.

In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.

Energy and water are two important factors that are crucial for human survival. In an energy hub, integrated management of water and multi-type of energies such as electrical power, gas, and thermal power increases the economic efficiency of using vital resources. One of the major challenges that deter the effectiveness of this integrated management is the electricity price uncertainties of the upstream market. To address this problem, this paper provides a novel mathematical model along with a robust optimization approach for uncertainty management in smart water and energy hubs. In the proposed robust uncertainty approach, the objective function is optimized subject to the uncertainty set of the problem. Therefore, the optimal solution of the proposed optimization problem is an effective point referring to the amount of specified uncertainty budget by the energy hub system operator. Moreover, a new water infrastructure including a water desalination system and water storage is considered in the proposed smart water and energy hubs. The proposed power and water robust optimization (PWRO) is a Mixed Integer Linear Programing (MILP) model and is solved by CPLEX solver in GAMS environment. The effectiveness of the proposed approach is examined in a case study. Results show that the operation cost of the proposed model increases 1.6 % percent approximately in the worst case. However, the robustness of the system is significantly increased.

In recent years, the tendency to use renewable energy resources in remote areas has been raised due largely to increase in the energy demand and fossil fuel costs. In this paper, the aim is to find the optimal number of components of the off-grid hybrid PV/wind/battery/diesel system, in the presence of the load and renewable power generation uncertainty. Optimization is based on minimizing the value of the annualized cost of system, which meets the requirements of the desired loss of power supply probability (LPSP).Three various optimization algorithms including PSO, CPSO, and Modified PSO are proposed to solve the optimization problem. To exam the efficiency of the proposed model and optimization strategies, the proposed approach has been applied to a stand-alone hybrid energy system in Sistan and Bluchestan, Iran. Furthermore, the effect of different desired LPSP and optimization algorithms on the economic operation have been discussed. The results demonstrate that the MPSO has a faster solution and better performance than the PSO and CPSO. In this paper, while previous research has mainly focused on theoretical results only, for practical implementation, the maximum allowed constraint for diesel fuel consumption is added to the optimization algorithm and practical implementation has been done based on the simulation results and actual data.

In this paper, the economic load dispatch is investigated in order to minimize the operation cost in DC Microgrids. The operation cost includes the DG production cost and the grid electricity, while other parameters like component’s efficiency and demand response are considered in this study. In order to model the DC Microgrids, an electrical grid with wind and photovoltaic units, fuel cell and energy storage systems are considered connected to the main grid. This study is done in MATLAB and GAMS software, and the results are analyzed and compared in both cases. The results prove the suitability of both algorithms especially at peak hours. The voltage profile is within the limits for all cases. The economic load dispatch shows that energy storage systems are more applied in the optimization in MATLAB software; however, DGs are more applied in the optimization in GAMS software, while Microgrid is transferring more electricity with main grid.

Load-frequency control in an isolated microgrid is very important.By turbulence the load, the frequency of the microgrid begins to oscillate which must be damped using the load-frequency control system (secondary control). In this paper, we have reduced the number of utilized controllers for the power supplies in the microgrid (less complexity) and we have incorporated the Model Predictive Controller (MPC), whose weight parameters are optimized using the novel Hybrid Craziness based Particle Swarm Optimization-Pattern Search (HCRPSO-PS) algorithm, for controlling the load-frequency in a two-area microgrid. The results of the proposed controller, in a number of different scenarios, are compared with other methods such as the PID controller optimized with the Social Spider Optimization (SSO) algorithm and the model predictive controller whose weight parameters are obtained using the Craziness based Particle Swarm Optimization (CRPSO) algorithm (to demonstrate the efficiency of the novel hybrid algorithm), considering load variations and the distribution of the sources in the microgrid. We have demonstrated the efficiency of the proposed method in terms of response speed, overshoot/undershoot reduction and control complexity(less complex). Simulations are performed using MATLAB software

Recently, environmental, economic and reliability oriented motivations facilitate cellular operation of smart power systems in the light of microgrid paradigm. Obviously, due to low inertia stack, high variety of generation units with various technical characteristics and different islanded and grid-connected operational modes, make the protection and control structures become new challenges. On the other hand, by integration of advanced metering and communication infrastructures in the microgrids, differential protection as the most reliable relaying logic, play a vital role in the protection structure of the microgrids. Owing to nature of occurring faults in the microgrids, high resistance faults have created a major problem in applying differential protection and directional fault. In this paper is used a new method for quick and accurate detection of fault in DC micro-grid, so that it detected internal fault from external fault, the performance of this new algorithm is also shown for increasing the fault resistance and compared with the conventional differential protection. The basis of new method is to use the Fault Component Current of the two sides of the study line and plot them on the  plan to identify the type of fault. In this paper simulation of internal and external fault in EMTP-ATP for different values of fault resistance is done and the implementation of the new algorithm and conventional differential protection is done in MATLAB. The simulation results indicate the accuracy and high speed of the operation of the new expressed method.

In this paper, the problem of optimal stochastic operation of thermal units, combined heat and power (CHP) units, wind turbines (WTs) and photovoltaic (PV) units with the objective of reducing total operating costs is defined as an optimization problem. In this problem, the uncertainties in load, photovoltaic and wind units are considered in terms of solving the two-point estimation method. The amount of power and heat output in different units, the position of the transformers tap and the reactive power injection by the parallel compensators are the control variables of the optimization problem. A particle swarm optimization algorithm with comprehensive learning is used to solve this optimization problem. In order to evaluate the performance of the proposed method, the IEEE 118-bus standard test system consisting of thermal, CHP, WT and PV units is selected. The simulation results in MATLAB software environment show the superiority of the comprehensive learning particle swarm optimization method compared to other optimization methods.

Traditional systems are faced with problems such as energy loss, growing in energy demand, reliability and security, so they are turning into smart grids (SGs) to solve these problems. SGs provide a bi-directional energy connection between service providers and consumer. On the other hand, SGs have various devices for monitoring, analysis and controlling in different parts of the network. Therefore, the SGs needs a communication infrastructure between these different devices. As a result, this connectivity will be achieved by a new infrastructure such as the internet of things (IoT). IoT helps SGs systems to perform various network functions throughout the generation, transmission, distribution, and consumption of energy by using IoT equipment (such as sensors, actuator, and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, the day-ahead generation scheduling is optimized in the deregulated power market with IoT infrastructure where the goal is to maximize the profit of generation companies (GENCOs). Moreover, this paper has used the demand response (DR) programs to better explore the challenges ahead in the future of SGs with IoT infrastructure. The results help to assess the quality and efficiency of such a system.

In variable speed pumped storage hydropower plant (VS-PSHP), unlike fixed speed power plants where synchronous machines are used, they are powered by a doubly fed induction motor-generator (DFIM) coupled with a back-to-back converter. In this paper, first, the mathematical model of different components of VS-PSHP is presented. The control sections, the machine side converter (MSC) and the grid side converter (GSC), each contain two control paths for the dq axes, and each path containing an external loop and an internal loop. Therefore, the inner and outer loops is extracted and transfer functions are presented. To adjust the control coefficients, the open-loop and closed-loop transfer function of different control sections, MSC, GSC, and pump-turbine, are extracted and its stability is guaranteed by drawing the Lucas root diagram and tuning of the zero position and system poles. Finally, in order to validate the design, a VS-PSHP is simulated in Matlab/Simulink/SimPowerSystem environment and its results are presented to test the load change in two modes of generator motor operation. The simulation results verify the operation of the designed control system.

Phase, frequency, and amplitude of grid voltage are important information in grid-connected photovoltaic systems. For proper and stable operation of a system, a fast and correct estimation of grid information under grid variations and disturbances is very crucial. In this paper, an adaptive phase-locked loop (PLL) structure is proposed which tracks the phase jumps of grid voltage fast and smoothly. If a jump occurs in grid voltage phase transient state at the frequency estimation of frequency lock loop (FLL) will be almost zero. The settling times of the estimated phase, frequency, and amplitude of the proposed PLL are also slightly low. The proposed PLL consists of the second-order generalized integrator (SOGI) and frequency locked loop (FLL). The whole system has been simulated in MATLAB/Simulink. The settling time of estimated frequency and amplitude for proposed PLL are 0.023 and 0.024 s, respectively.

Nowadays, due to increasing usage of sensitive loads, power quality in power grids is becoming more and more important. On the other hand, distribution networks have high losses due to high R/X ratio. Network reconfiguration is proposed as a tool that is changing power quality of the network in addition to power losses. In this paper, the effectiveness of network reconfiguration is evaluated on improvement of voltage sag. 1ph-to-ground fault is applied for modelling of voltage sag. IEEE Standard 69-bus distribution network is utilized as case study and particle swarm optimization algorithm for finding the optimum configuration. Results show that network reconfiguration is capable of improving voltage sag in addition to the improvement of power losses. This improvement leads to reduction of outages in sensitive loads during the occurrence of network faults. In addition, sensitivity analysis on the optimum configuration is performed considering the switching and losses cost

This paper expresses the decentralized strategy based on multi-agent system (MAS) for distribution network restoration with considering coupling neighboring microgrids (CNMGs) which is modelled renewable energy sources (RESs), electric vehicles (EVs), battery storage system (BSS) and load. The MAS approach can be obtained suitable restoration path with the different zone agents distributed along the network and it can be managed the overloading microgrids (MGs) due to unbalancing between of RES, BSS, EV and load using interconnecting these MGs to neighboring non-overloading MGs. This strategy is new method that is implemented in this paper, and it is based on expert system rules, and it also considers the system operational limits such as voltage and line flow limits. Finally, this technique applied to 12.66 kV and 33-bus radial distribution network, where agents and power flow calculation are simulated in MATLAB environment for decisions making and validate the correctness of the agents’ decisions, respectively.

One of the most effective ways to reduce power loss and improve voltage profiles in distribution networks is to reconfiguration or redirect network lines. Reconfiguration is actually the opening of some closed lines and the closing of some open lines in order to achieve predetermined goals such as reducing power loss. Accordingly, this paper presents a new mathematical model for reconfiguration of distribution network in order to improve the voltage profile and reduce power loss in the presence of distributed generations. The innovation of this article is the simultaneous optimization to reduce the voltage deviation of different parts of the network and the energy dissipation of the network by considering the network load distribution constraints, limiting the bus voltage range, limiting power transmission lines, relationships governing distributed generations and limiting power transmission from the upstream network. Also, one of the other innovations of the present article is the normalization of different parts of the multi-objective function, which makes all the cases as a percentage and the mentioned function is converted from a multi-objective state to a single goal, and therefore there is no need to define weighting coefficients. Also, since the issue of reconfiguration considered in this paper is nonlinearly intertwined with integers, it is recommended to use an evolutionary algorithm of new improved adaptive colonial competition to reduce the time to solve the problem. The new proposed method is implemented in this article on the 69 IEEE distribution network. The results show the success of this method in achieving the predicted goals. Accordingly, the network losses have been reduced by 57% and the network voltage profile is within the allowable framework, so that the minimum amount of voltage after the proposed method was 0.95 (pu). Comparison with previous studies shows that the results of the proposed method have been much more successful in improving the intended goals.

After occurrence of a fault and outage of loads in a distribution network, it is tried to reconnect as much as possible loads in the minimum time by a process which is generally known as restoration. Computational and decision-making tools can play an important role to have a successful restoration. One of these tools is multi-agent system, which is a software-based system that includes components or agents. These agents have the ability to exchange information and make local decisions. In this paper, a multi-agent system is proposed for restoration of radial distribution networks. The multi-agent system brings about decentralized management in the restoration. Also, the proposed system considers priority for zones in distribution feeders. The priority is in accordance with type of loads in the zones. Implementation of the proposed method using Java Agent Development Framework (JADE) and its evaluation on a test distribution network, demonstrates an effective restoration with consideration of load priorities.

The electricity smart grid market is complex and dynamic. Brokers, which mediate the sale of electrical power between retailers and wholesalers, are widely used in new markets for smart grids. Due to the complexity and distribution properties of the market in smart grid networks, multi-agent systems are appropriate to solve its problems. In these approaches, we have autonomous agents exchanging information with other agents all 24 hours of a day. These agents encounter major challenges including diverse consumption patterns of consumers, price changing according to consumption patterns, and the amount of electricity consumed during the day. In this paper our goal is to increase profit in the electricity grid market while modeling the components of the electricity market with multi-agent systems. In the proposed method, we first process the customer diversity using a sequential clustering method suitable for time series data. Then, for each cluster, we apply an active policy reinforcement learning algorithm named Actor-Critic reinforcement learning. Finally, we evaluate the impact of the reward shaping on the profit earnings and we offer an hourly tariff for each cluster according to their respective consumption time

Electricity demand has increased drastically during recent years. Expansion of power generation and transmission systems to address the growing demand for electricity is a lengthy process and requires extensive capital investment. Moreover, conventional methods of power generation can cause severe environmental issues. Discussed concerns have attracted considerable attention to demand-side management (DSM) policies by which system operators can monitor and control consumers in order to better match the electricity demand with power supply conditions. Meanwhile, power systems are going through a transition period due to the high penetration of renewable energy resources (RERs) and widespread use of smart devices. To remain efficient, DSM policies need to adapt to these transformations. Transactive energy (TE) is a newly-introduced DSM policy which is intended for the changing environment of power systems and is able to maintain the dynamic power balance in smart grids. This paper studies the reasons that led to the initiation of DSM programs. Further, the concept of demand response (DR) is elaborated and various types of DR programs and the existing challenges toward these programs are evaluated. Last but not least, TE is introduced as a general form of DR and the benefits and challenges that implementation of TE frameworks may bring about are analyzed.

Performance appraisal in electricity industry by the meaning of assessment of management decisions’ effectiveness and all workers’ efforts in regional companies in using resources and facilities of the industry as an infrastructural and nurturing industry is quite vital that in turn helps recognize the performance  in the past and make plans for the future activities. On this basis, weakness and strength of the companies could be identified and appropriate planning be made according to the facilities, budget and existing human resources in order to maintain the strength points and make improvements on the weaknesses. This research work is practical and its data has been gathered from the reports issued by the statistical society of the independent auditors’ and the state audit firm as well as the board’s reports to the General Assembly of the Shareholders, which are 16 Regional Electricity Companies in charge of supplying reliable and sustainable electricity in the country. Along with analyzing various methods for performance appraisal for a two decade period using methods such as KPI, BSC, EFQM and so forth, regional companies’ performance have been assessed at each stage and totally to finally rank them (Cloud-efficiency Model) using Data Evaluation Analysis (DEA) model in 1395 by designing a 3-stage model with 5 input indices, 2 intermediate indices and 7 output indices with constant returns to scale in the form of multiplicative inputs utilizing GAMS software package. The conducted analysis demonstrate that two models yield different results such that only ranking of three regional companies are close and the rest show substantial discrepancy. In terms of the regional companies’ performance assessment using DEA model, one company is performing efficiently, ten companies are close to the borderline and five companies perform far from being efficient. Finally, it could be stated that small and medium size companies perform more effective than large size companies.

In this paper, scientific papers related to the participation of electricity retailers in the electricity market will be reviewed. In general, the planning of the participation of retailers in the electricity market can be divided into long-term, mid-term, and short-term planning. Various sources of energy purchasing for retailers include the electricity market, bilateral contracts, generating units and new energy sources, including wind turbines and photovoltaic systems. Also, electric vehicles, battery storage systems and hydrogen storage systems can be more flexible in energy management issues as energy storage systems. Also, the issue of determining the price of electricity sales to consumers is considered by electricity retailers. In addition, retailers' consumer participation in demand response programs as a virtual production unit has been explored to reduce consumer spending and increase retail profits. Also, in view of the fact that the relevant papers, the uncertainties of the retailers that typically model the prices of the electricity market and the consumption of their consumers, will be reviewed in a review of the sources. Finally, the risk modeling due to alternative uncertainties will be discussed. We will look into the details of the retailers' shareholding in the electricity market.

HVAC systems, as the largest commercial and household power consumers, play an important role in demand response programs. In this research, using weather forecast data and using electricity price forecast, optimal scheduling for HVAC systems set points is performed in such a way that the user’s electricity consumption cost is minimized. Electricity price forecasting is achieved using an ARIMA model. A Markov’s chain has been employed for temperature forecasting. As these forecasts are not deterministic, the risk must be considered. To accomplish this, several scenarios were considered and CVaR was utilized to handle the risk aspect. In this research, the above-mentioned data was employed, and GWO algorithm was invoked to schedule the optimal set points of the HVAC systems and symmetrical dead band width. The results show that this planning method can have an important effect on the participation of the HVAC systems in demand response programs. Also, it has been shown that the dead-band width management plays an important role in energy consumption management. Different users with different attitudes toward the risk were analyzed; the results show that risk-averse users are more involved in demand response programs; and hence, they bear lower energy costs.

To forecast the electricity load of a city or country and facilitate the strategic decision-making, it is common to collect the historical data from different zones of the city or different cities of the country. However, normally all the zones or different sectors’ load (residential, industrial, and commercial) are not important equally. In other words, a certain zone or a sector may have the most effect on decision making. Therefore, the simple algebraic sum of the different zones’ forecasting may not be meaningful for the ultimate objective. There are different methods for aggregation of the different zones’ forecasts. The most convenient method is the simple algebraic sum of the different zones’ forecasts, which is not only inefficient but also needs more details about the effective factors on the electricity demand in each zone. In this paper, different aggregation approaches such as bottom-up, top-down, optimal combination methods are presented. It should be mentioned that any research paper in the field of the electrical power system and load forecasting have not studied the hierarchical forecasting; therefore, presenting the hierarchical method for load forecasting is a strict innovation of this paper. The Auto-Regressive Integrated Moving Average (ARIMA) and Exponential Smoothing methods are embedded in proposed aggregation approaches. The proposed methods are applied to forecast Australian electric load in short-term and long-term horizons.

In this paper, a new method for locating fast electric charging stations with a regional perspective and taking into account the coefficient of interchange between regions is proposed. First, the city must be divided into specific areas, then, using the parameters of electric vehicle density, the required earth and social visibility of the region, the initial candidate points in each area are determined. Given land price and distance from the network substation as the main parameters, the location of the construction of the fast charging station is defined as an optimization problem and then solved by the genetic algorithm. In the proposed method, in addition to the location of the fast charging stations, the number of fast charging units (capacities) at each station is also determined. In the proposed algorithm, by defining the coefficient of exchange between adjacent regions, it is possible to relocate charging units so that the number of charge units can be flexible in proportion to the price of land in each area. The proposed method has been implemented for Mashhad metropolitan area with consideration of nine areas in accordance with the Mashhad power distribution company and the results of simulation for different scenarios are presented.

So far, state estimation in distribution networks has been less attentioned than transmission networks. Measurement devices are not exist in different parts of the network in a distribution system unlike a transmission network. Therefore, proposed state estimation methods which require large number of measurements in the network can not be used in distribution systems. Also, due to the presence of dynamic loads and sudden changes in the industrial network, major challenges take place in the state estimation calculation process. In this paper, a method is proposed for estimating the industrial distribution network state with the help of limited measurements. For this purpose, different performance modes of the downstream loads on which the measurement device is placed are made available to the estimator and the network state is determined by the received measurement values. The proposed method has been implemented in the Power Systems Analysis Laboratory of Amirkabir University of Technology as a hardware device by connecting two computers and has been tested on a distribution network of a chemical plant in the form of software in POUYA real-time simulator. One computer plays the role of the SCADA system of the real power system, and the other computer acts as the simulator model (dynamic estimator).

In this paper, while dynamically evaluating the capabilities of Static Inter phase power controller (SIPC), its effect on the transient stability of a sample power system has been investigated, as well as a technique based on its discontinuous control has been presented to improve the stability of the system. The research method is based on the computational solution of the dynamic equations of the system, the repetition of the simulation stages, the calculation of the critical clearing time (CCT) and the determination of the  peak overshoot generated in the rotor angles of the machines due to a symmetrical three-phase short-circuit fault. For this purpose, the system and the related device were simulated in the DIgSILENT software envierment and the research issues were followed by considering multiple scenarios. In this regard, first all the capabilities of the device were confirmed in the time domain, and then the transient stability limit of the system with and without it was investigated and compared, resulting in a relative decrease in the transient stability limit of the system. Finally, in order to apply the technique, the main control of the device was transferred to another control called transient control so that the transmitted power of accelerated machines can be maximized within the time interval after the fault clearing, which ultimately results in a relative improvement in the transient stability limit of the system.

The compound-structure permanent-magnet synchronous machine (CS-PMSM) system is a novel drive system used for hybrid electric vehicles (HEVs). In this paper, firstly, the mathematical model of the CS-PMSM motor is examined in detail, and then the control strategy of the CS-PMSM is investigated and established. The CS-PMSM is composed of two permanent magnet synchronous motors: a Double Rotor Machine (DRM) and a Stator Machine (SM). Different control strategies are applied for the two machines. According to their special structures and applications, the DRM performs the torque control, and the SM carries out the speed control. The DRM implements the id=0 control, while the SM carries out the maximum torque per ampere control during the constant torque region, and flux weakening control during the constant power region. Secondly, the direct torque control method is investigated and applied for both machines. The feasibilities of the vector control and direct torque control methods are both validated by the simulation results in the MATLAB/SIMULINK, and these two methods are further compared. The optimum control methods are finally established for the DRM and SM and also the selection of the optimal number of turns for the stator is considered.

Earth horizon sensor is one of the fundamental instruments in satellite navigation system. This sensor is required to simulate satellite control system and to determine the satellite direction; hence, Pyroelectric detectors are employed to achieve this target. In this paper, the physical model of Pyroelectric detector and the mathematical model of an earth horizon sensor were presented to simulate a satellite. Pyroelectric detectors are applied to calculate the step response and field of view (FOV), which are significant parameters to recognize the earth horizon. To improve the detection of objects in far distances of detector, FOV should be as small as possible. Furthermore, the effect of solar interference has been reduced by the use of narrow FOV for sensors. The evaluation of the device has been performed through the use of test system that allows sensor performance to be determined. The observed results show the optimal value of FOV has decreased to 50° using plastic pipes. The modeling results verified that the Pyroelectric detectors are feasible to achieve functional requirements.