International Journal of Industrial Electronics, Control and Optimization
Volume:2 Issue: 4, Autumn 2019

A new non-isolated, coupled-inductor, single-switch boost DC-DC converter for photovoltaic (PV) power application is introduced in this paper. A coupled inductor and voltage multiplier cells is used in the presented converter to obtain a high voltage conversion ratio. Also, a passive clamp circuit is applied in the converter structure to reduce voltage stress of the power switch. This leads to using a power switch with lower on-state resistance in the converter which decreases the conduction loss. In addition, zero current switching (ZCS) condition for the power switch is achieved due to the use of the clamp circuit. Several advantages such as low operating duty cycle, high voltage conversion ratio, reduced voltage stress of semiconductors, low turn ratio for the coupled inductor, leakage inductance reverse recovery and high efficiency operation make the presented converter suitable for renewable energy applications. The steady state operation of the suggested structure in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is expressed and analyzed. Then, the presented topology is compared with several similar high gain topologies to prove its advantages. Finally, experimental measurement results of a laboratory prototype of the proposed DC-DC converter with about 213W output power and 435V output voltage at 50 kHz switching frequency are presented to corroborate its feasibility and performance.

This paper deals with the optimal scheduling of a microgrid (MG) equipped with dispatchable distributed generators (DGs), renewable generators and electrical storages (batteries). A chance-constrained model is developed to handle normal operation and emergency conditions of MG including DG outage and unwanted islanding. Purchasing reserve from the upstream grid is also considered. Moreover, the uncertainties of loads and renewable resources are incorporated into the model. Furthermore, a novel probabilistic formulation is presented to determine the amount of required reserve in different conditions of MG by introducing separate probability distribution functions (PDFs) for each condition. Accordingly, an index named as the probability of reserve sufficiency (PRS) is introduced. The presented model keeps a given value of PRS in normal and emergency conditions of MG operation. In addition, some controllable variables are added to the chance constraints as an innovative technique to reduce the complexity of the model. Finally, a test microgrid is studied in different case studies and the results are evaluated.

This paper presents a wide range gain buck-boost type PFC rectifier based on the conventional buck-boost DC-DC converter. This novel rectifier is fed by a buck-type Z-source network at the DC side with the help of an inductor smoothing the AC side input current. The proposed PFC rectifier offers better input and output waveform qualities compared to the conventional buck-boost PFC rectifier. Maintaining near to unity power factor (PF) and low total harmonic distortion (THD) in a wide range of load variations and also the simple single-loop control are the main features of the proposed PFC rectifier. Furthermore, the switching frequency of the proposed rectifier can be chosen very higher than the competitors, since it successfully lets incredibly increase the duty cycle for a same voltage gain compared to the traditional solutions. This leads to reduced size of passive components and volume of the rectifier. A 250 W prototype circuit is designed and simulated considering most practical issues to evaluate the performance of the proposed PFC in both buck and boost modes. The results are compared with some of the well-known conventional PFC rectifiers that confirm the superior performance of the proposed topology.

Abstract—In this article, a new hybrid feedback system is introduced, which integrates the behavior- based planning by reactive agent-based control scheme through subsumption architecture. At first, subsumption protocol studies the interactions of robot with its environment which cover problems including translating of agent action into an outcome, interactions with the environment, and cooperative actions. Second considers deliberative behavior given the prevailing protocol. It determines the best and quickest response for each agent and tunes the actions based on an objective function obtained by a leader agent. More specifically, tasks are arranged as a hierarchy, where the high-level task is obstacle avoidance. Conflicting lower level tasks are removed by the leader agent decisions. Indeed, the leader agent can adjust the priority of all action to provide an optimal behavior. In other words, our new agent-based method optimizes the subsumption architecture by producing an approximating objective function that made not only behaviors but also optimization done in incremental procedure. We also define an emergency avoidance factor that made higher speed still stable and better interaction of robot in the presence of obstacles. For obstacles avoidance, the leader agent projects a plane to investigate the space ahead and continues. Finally, the leader agent makes a basic stand by task sharing behaviors in decentralized manner using subsumption architecture to draw an optimal path. Simulation results show that although the proposed apporach has little knowledge about the unexpected and adhoc situation in the robot’s environment, it is able to provide suitable performance.

A hybrid unknown input estimation based on a new two-sample backward model and data fusion for high maneuvering target tracking is proposed. This new approach is based on the consideration of more than one state and input components from the current single observation. These extracted state and input components would be augmented in a single vector, and the final estimation for unknown target acceleration will be determined. Using a combination of the new backward modeling and traditional modified input estimation (MIE) technique, more information will be extracted. This new hybrid scheme which using more input information can better estimate the target maneuvering structure. Despite the traditional methods, the proposed algorithm introduces two different strategies to state the input estimation including online and delayed estimation scenarios. Also, this paper suggests several different data fusion methods through these strategies. The results are compared with a typical MIE method to evaluate the performance of the proposed hybrid scheme especially for problems in high maneuvering target tracking. The results show that the backward algorithm makes advantages such as reduction of the transient state error and more stability for the estimation by an appropriate combination of the MIE estimator.

Nowadays, the power distribution companies are working in a competitive atmosphere. Therefore, a major goal for electricity distribution managers is to provide the electrical energy with high reliability level with considering the economic issues.The Reliability- centered maintenance (RCM) is an efficient way of realizing this aim and improving the network maintenance processes. This approach is an operative step to improve the reliability of critical equipment and overall system performance which may reduce the costs of utilities.This paper, by using RCM provides a practical model for maintenance scheduling by considering the economic risk function and the budget restriction based on the cost of preventive maintenance (PM) and value of lost load (VOLL). In this method, PM schedules are proposed based on failure causes of different network elements. Studied elements include overhead lines, underground cables and power switches (circuit breaker (CB), manual and remote control switches (RCSs)). Failure cause and average repair time of each element is determined through data mining in geographical information system (GIS) and ENOX. For more realistic modeling and the consideration of the network loads’ uncertainties, the fuzzy triangular method is used. Due to the inconsistency between the problem’s goals, the non-dominated sorting genetic algorithm (NSGA II) is employed. Results show the effectiveness of the proposed method.

The Local Model Network (LMN) is one of the common structures to model systems and fault detection and identification. This structure covers the disadvantages of training in fuzzy systems and interpretations in neural networks at the same time. But the algorithms that have been introduced to create LMN, such as LOLIMOT, are very sensitive to the dimension of input space. In other words, the search space and the number of network parameters are increased exponentially by increasing the input dimension, which is called the curse of dimensionality. Therefore in this paper, the LMN structure has been developed, and a new incremental algorithm has been proposed which is based on Genetic algorithm and LOLIMOT algorithm that is called GLOLIMOT. The proposed idea reduces the search space dimension and also optimizes it. The proposed idea and the traditional structure are tested on single-shaft industrial gas turbine prototype model, which has high complexity and high dimension. The results indicate improvement in performance of the proposed structure and algorithm.

The advent of DG and SEGs has led to fundamental changes in various fields of power system operation. The current paper is aimed to investigate the reliability of SEGs considering DGRs based on the self-healing concept. Due to the emergence of new uncertainties in the power system resulted from the presence of DGRs, this paper is dedicated to comparing network reliability indices before and after the entry of DGRs and analyzing their effect on improving network reliability. To do so, improving the indices based on customer satisfaction, such as reducing the SAIFI, and SAIDI, is evaluated. More specifically, the improvement of the most important index based on load and energy, namely energy not supplied (ENS), is investigated. To do this, the MCS method is used given the pdf of the samples due to the presence of uncertainty created by the presence of DGRs, demanded load change and network restoration time after the presence of DG. Also, after providing an appropriate model for problem analysis, results of applying this model to the case study system are investigated using reliability indices. Subsequently, in order to improve performance of the system, impacts of the changes of various parameters on the given indices are reported. One of the most important points in this regard is to investigate the impacts of the changes in the system configuration on the results. It is observed that self-healing positively affects the reduction of the electrical energy restoration time as well as the system reliability.

Multilevel inverters as a main component of electrical systems have been discussed over the past decade. Todays, improving their performance is one of the important challenges that has led to many studies in their topology and control system. In this paper, a new topology of symmetrical multi-level inverters is provided that is able to feed inductive loads. The proposed topology has fewer power elements such as IGBTs, driver circuits, diodes and dc voltages. To increase the number of output voltage levels, several basic topology can be in cascaded structure. Comparison of proposed inverter topology with some conventional symmetrical topologies shows that the proposed topology has better efficiency in aspects of the number of IGBTs, losses and blocked voltage blocked in the switches. The performance and efficiency of the proposed inverter are verified by simulation and experimental results of a 9-level symmetrical inverter.The quality of a multi-level inverter specifies by its number of output levels.

In this paper, a new robust hybrid controller (RHC) is proposed to regulate current, voltage and, as a result, power output of a three-phase grid connected PV system. The creative process of the proposed controller consists of two steps. First, an input-output linearization method is used to eliminate system nonlinearities. Then, a robust PI controller is designed to reach the desired control objective. The robustness of PI is guaranteed by Lyapunov stability theory. Also, a maximum power point tracking (MPPT) algorithm is provided to adjust the PV output voltage for extracting MPP under various atmospheric conditions. To evaluate the performance of the proposed controller, different system conditions such as standard, considering uncertainties and three-phase short-circuit fault, are simulated and the results are compared with a feedback linearization controller (FLC). The results show superiority of the performance and robustness of the designed RHC versus various uncertainties such as solar irradiation and ambient temperature.