نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال بیستم شماره 1 (پیاپی 68، بهار 1401)

Integrating renewable resources to provide local load has created a concept called microgrid. With the widespread introduction of microgrids, energy management and system utilization and resources in the electricity market are important tasks of microgrid management. In this paper, the problem of microgrid utilization is modeled taking into account economic, technical and uncertainties related to power consumption, wind speed and solar radiation in electricity market conditions. One of the most important issues in the electricity market is the discussion of the participation of units in real price conditions. In this paper, a framework for the exploitation of electricity and the consumption of controllable loads through integrated utilization of distributed energy sources of uncertainty is presented from a consumer perspective. The optimization problem is a two-step stochastic linear programming that minimized the cost of microgrid operation and expected cost of consumers considering the consumer’s requirement for controllable loads in the desire time interval and distribution company constraints that solved by using Salp swarm optimization algorithm. RBT and IBR tariffs are employed for modeling retail power market for better reflection of wholesale price volatility and avoid of the concurrent use of consumers. In this method price announced to the consumers by retailers only is limited specific later hours instead of the entire operation period. In this condition any timing of controllable loads need to price forecasting, while this forecasting have some uncertainties. These uncertainties are modeled using Monte Carlo method for stochastic price variable scenario generation. MATLAB software is employed for simulation and verification of the proposed method.

This paper presents an efficient method based on the adaptive virtual impedance and distributed communication link with a hierarchical control system in the resistive AC islanding micrigrids for accurate power sharing and circulating current reduction. In existing methods, the adaptive virtual resistance can take negative values and violate the assumption of feeders’ resistive dominance based on which the droop controller is designed, and as a result, deteriorate its performance. Besides, the negative virtual resistance, with a reduction in the system overall damping, can reduce the stability margin and lead to side effects on the closed-loop system performance, especially during transients. In the proposed method, the problems associated with the negative virtual resistance are removed via the intelligent implementation of a new distributed communication link among microgrid inverters. The advantages of the proposed method include: circulating current elimination, accurate power sharing among distributed generators proportional to their rated capacities, prevention of voltage and frequency deviations from their reference values in point of power coupling (PCC) bus, guarantee of the resistive or inductive dominance of the feeder impedance in various operating points, decoupling between active and reactive powers, and as a result, guarantee of a desirable performance for droop controller in different operating points, performance and stability improvement, and finally using a simple, one-sided and a low bandwidth communication link instead of the complex, two-sided, and centralized communication system. Simulation results in MATLAB/SIMULINK environment demonstrate that the proposed control strategy has obviated effectively the shortcomings of the conventional droop and adaptive virtual impedance controllers.

Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantage of novel approaches of intelligent metaheuristic optimization for designing integrated navigation systems. For this purpose, the inclined planes system optimization algorithm with several modified and new versions have been used along with two well-known methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS inertia measurement modules. Process and measurement noise covariance matrices are considered as design variables and the sum of mean-squares-error as an objective function in the form of a single-objective minimization problem. Outputs are presented in terms of statistical and performance indicators such as runtime, fitness, convergences, angular-velocity accuracy, latitude, longitude, altitude, roll, pitch, yaw, and routing along with the ranking of algorithms. The overall assessment indicated the correctness of the performance and the relative superiority of the IPO and IIPO over the competitors and competitive performance of the assumed algorithms in comparison with the volume of considerations and calculations of the base problem.

In this paper, two schemes of model predictive control (MPC) method, named finite control set model predictive control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set model predictive control (CCS-MPC) are applied and compared to control the current of a permanent magnet synchronous machine in energy recovery mode for the use of electric vehicles. The FCS-MPC strategy selects the optimal voltage vector and applies the control pulses directly to the inverter without using any modulators. In other side, DB-MPC is implemented through space vector pulse width modulation (SVPWM). The performance and results of both types of control strategies are extracted and compared using MATLAB Simulink software. The comparisons are made mainly in steady state and transient modes. Both control strategies are applied to a permanent magnet synchronous machine with the same parameters and with the same operating mode. The results show that the current steady state fluctuation is further reduced in the DB-MPC strategy and the transient state response is faster in the FCS-MPC strategy.

The problem of pushing objects by a group of cooperative robots has many applications on land and sea level and due to its importance, it has become a standard problem for evaluating various theories of robot cooperation. In this case, each robot produces distributed control force to push the object in the desired direction. The proposed methods for distributed control of an object on a time-varying path require information about the position of the robots relative to the object. The problem of the lack of sufficient knowledge of each robot of how the robots are positioned relative to the body can be solved by proposing a consensus issue on positional moments. In this case, the robots must reach a consensus on these moments by exchanging information through the communication network between them. The effect of communication network between robots on the process of reaching consensus and the effect of delay in consensus on the results of control of object on the desired path is the subject of this article. In this paper, the appropriate control law for achieving consensus in the absence of full connection between all bots, delay and the probability of data loss in the communication network is presented. The maximum allowable network delay is also specified to prevent the instability of object motion control. The simulation results show the capability of the proposed method for controlling the velocity of the object on the desired variable path and show the effect of network constraints on the performance of the controller.

The reaction wheel is one of the most sensitive devices used in spacecraft, which is easily disturbed. Nowadays, maintaining the status of the satellite and the ability to control it, is one of the most important issues due to the costly design and construction of such projects. To improve this process, identifying and modeling perturbations and analyzing their effects on system parameters to spot the defects, are very important. As a result, accurate identification and estimation of perturbations on reaction wheels through studying the effect of input uncertainty on the system state variables is necessary to reveal the internal condition of the spacecraft and identify its defects. For this reason, in this paper, a new observer is designed to estimate the uncertain perturbation and the system state vector. In this regard, by considering the dynamics of variable micro-turbulence with wheel imbalance time, we obtain the proposed observer’s design matrices at any time by performing a series of linear matrix inequality (LMI) calculations that converge and stabilize the estimation error based on Lyapuanv theorem. Then, the results are presented in a series of simulations in MATLAB software included the characteristic of estimated uncertain inputs and state vector of micro-turbulence model, in section four.

Biosensors have various applications especially in medical diagnosis. In this paper, nanotube junctionless transistor is employed for label-free detection of biomolecules. The proposed device works based on dielectric modulated principle. In this transistor, the gate voltage is responsible for controlling the drain current and in case of gate capacitance variation, the drain current can be modulated. A nanogap is embedded in the gate insulator region for immobilization of biomolecules. Since each individual biomolecule has its specific dielectric constant, the accumulation of different biomolecule in the nanogap changes the dielectric constant of the nanogap, which eventually leads to the variation of gate capacitance and the drain current. Threshold voltage variation and drain current modulation are considered as two measures for detecting biomolecules and determining the biosensor’s sensitivity. The proposed device has two internal and external gates with low static power consumption as well as simpler low temperature fabrication process. One of the main advantages of the proposed device is its high selectivity and sensitivity, especially for biomolecules with low dielectric constant. Impact of critical physical and structural design parameters on the operation of the biosensor are thoroughly investigated. Gate workfunction and channel doping density are two critical parameters that affect the sensitivity of the biosensor and as a consequence, optimum values should be determined for them. Due to the low power consumption and high sensitivity, this sensor can be considered as a potential candidate for applications in nanoscale regime.