International Journal of Industrial Electronics, Control and Optimization
Volume:3 Issue: 3, Summer 2020

Harmonic reduction is an essential issue in a grid-connected variable-speed wind energy conversion system to decrease the electrical losses of the system and improve the power quality. In this paper, a new Shunt Active Filter (SAF) based on a three-phase four-switch inverter is used to improve the output current harmonics of the wind energy conversion system. The SAF contains three parts: identification, modulation, and inverter. In the proposed filter, an identification algorithm, which is not sensitive to the input source harmonic, is used to cancel out most harmonics impact. Moreover, a modified modulation technique based on the identification of the output signal is utilized. In the previous active filters, a three-phase six-switch inverter has often been used; however, in the proposed SAF, the number of switches is decreased to four in order to decrease the inverter losses. Simulation results confirm the superior performances of the proposed active filter versus the prior one for a wind energy conversion system.

Distance relays calculate the path impedance between the fault point and the relay location by sampling the voltage and current at the relay location. By using the ratio of the impedance estimated by the relay to the impedance of the line where the relay is installed, the location of the fault can be estimated by a distance relay. However, several factors influence the estimated impedance and proper operation of distance relays. The most important of these factors is the resistive fault occurrence, which results in an increase in the impedance and deviation of the impedance estimated by the relay as well as causes relay under-reach. Therefore, in the present study, an adaptive method is proposed to modify the protection zones of distance relays settings under different operating conditions and resistive fault occurrence. Furthermore, the adaptive distance protection of transmission lines, wind farm collector lines and the protection coordination of the relays in these lines are investigated. In this method, an adaptive coefficient is added to the conventional characteristics of distance relays to improve the accuracy and coordination. The proposed adaptive method can also maintain the coordination of different protection zones of primary and backup relay pairs. In addition to analytical verification, the numerical results obtained from simulation show the efficiency of the proposed method. The proposed method is implemented on a power system with transmission lines and wind farms and simulated in MATLAB/Simulink environment.

A hybrid AC-DC microgrid consists of an AC and a DC subgrid that are connected to each other through an interlinking converter (IC). The main function of an IC under islanded conditions is to transfer power between the two subgrids. In this paper, a scheme is presented to reduce the voltage unbalance factor in a hybrid AC-DC microgrid by using the free capacity of the IC. The free capacity of this converter is determined based on the current passing through each leg, and the amount of voltage unbalance compensation on the AC side of the microgrid is then obtained. The reference current of voltage unbalance compensation is calculated by using the positive, negative, and zero sequence components of the voltage of IC terminals. The total reference current is obtained by adding the reference current of voltage unbalance compensation and the current calculated for power transfer. Furthermore, a proportional-resonant (PR) controller is used in the control system of the four-leg inverter. Therefore, the reference current is properly tracked by the power stage of the inverter. Simulation results verify the accuracy of the proposed scheme under different conditions.

In this paper, the DC-DC boost converter in chaos mode is controlled using the adaptive Back-stepping method. One of the main features of power electronic converters is their nonlinear behavior due to existence of nonlinear elements such as diodes, switches, comparator and limiters in control circuits. Different types of non-linear behaviors such as bifurcation and chaos may occur in power electronic converters. These undesirable behaviors reduce efficiency of converters greatly and also cause dielectric losses, copper losses, undesirable sound noises and system failure. Back-stepping method is one of the common nonlinear techniques for controller design. This method is capable of generating an asymptotic and stabilizing control law in order to suppress chaos and synchronize chaotic systems. Also, optimal controller coefficients are determined using Chaotic Particle Swarm Optimization algorithm. By selecting an objective function, parameters of the back-stepping controller are determined using CHPSO algorithm. The system with adaptive Back-stepping controller is compared with the optimized adaptive back-stepping controller.

In this paper, a comprehensive centralized structure is proposed for Microgrids (MGs) operation incorporating active and reactive power resources. In this approach, the Distributed Generation (DGs), Energy Storage Systems (ESSs), Demand Response (DR) program, load shifting scheme, switchable capacitor banks and Plug-in Hybrid Electric Vehicles (PHEV) are considered simultaneously. The operation modes of PHEVs is modeled to schedule their charging/discharging and calculate the pollution produced in fossil fuel mode. Fifteen types of costs are integrated into the objective function, and several operational constraints are considered. They include power generation costs from the main grid and DG units, the cost of pollution emitted by DG units and PHEVs, and the degradation of plug-in hybrid electric vehicles batteries. The proposed method is programmed using GAMS software as a Mixed-Integer Second-Order Cone Programming (MISOCP) problem, and it is implemented on a test MG. simultaneous management of active and reactive power sources can result in less cost compared to the separated scheduling.

The idea of this paper is behind the development of sizing optimization model based on a new optimization algorithm to optimize the size of different stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery system components to electrify a remote location including ten residential buildings located in Rafsanjan, Kerman, Iran. Then, the optimal system is estimated on the basis of various inconstant parameters related to the renewable energy system units: the number of batteries, occupied region by the turbine blades rotation, and occupied space by the group of solar panels. The solar radiation, ambient temperature, and wind velocity data are achieved from the website of renewable energy and energy efficiency organization of Iran. The ant lion optimizer is suggested to find the optimal values of the parameters for satisfying the electrical load demand in the most cost-effective way. The results obtained from the simulation illustrate that the off-grid PV/WT/battery hybrid power system is the more promising method to provide the electricity consumption of an urban location. To evaluate the performance of the proposed method, the simulation results are compared with other hybrid energy systems, which optimized by particle swarm optimization (PSO), harmony search (HS), firefly algorithm (FA), and differential evolutionary (DE) algorithm. The results obtained by the investigated algorithms show that the PV/WT/battery system that is optimized by ALO method is more economical in compared with PV/battery and WT/battery hybrid systems.

Nowadays, using the photovoltaic powered LED street lights are spreading due to their higher efficiency and longer lifetime. In this paper, the Zeta-Sepic converter is used to manage the LED light, the PV panel and a battery storage in these systems, since it offers compact and single-stage power conversion. The PV panel and LED light connect to the converter input port using simple relay, while the battery source puts at the converter output port. During the day, the PV panel energy saves in the battery through the converter forward direction power flow. Besides, in the reverse direction power flow of the converter the battery supplies the LED light required power during night. For these operation cases, voltage regulation is realizable at the both converter input and output ports. This performance provides MPPT to the PV panel, SOC for the battery and light control with the LED light. For LED lights, there exist a relationship between light, electricity and thermal that is optimized to achieve the luminal flux to the input power highest ratio. As a result, this paper uses the photo-electro-thermal theory, heatsink characteristics and bidirectional Zeta-Sepic converter to drive the LED lights at the operating voltage, in which the LED light optimal luminous flux occurs. Finally, the proposed system is analyzed and validated in different operation conditions using MATLAB/SIMULINK. Also, photo collector sphere was built in the laboratory that is able to achieve the optimal working point of the light by measuring the photo through sensors installed on it.

The interruptible load program (ILP) is one of the most common demand response programs, which is often used by industrial customers. The purpose of implementing this program is to reduce the customer's peak demand in response to the incentives set by a contract. If no attention is paid to characteristics of the industrial customer's production process, the participation in ILP will not be possible. Therefore, complying with customer's technical constraints and optimal design of the program is of great importance. This study aims to present a new model for the optimal design of ILP from the perspective of a cement manufacturing company as an industrial customer. In this regard, the manufacturing process and the constraints of operation of a cement plant are fully modeled and all relevant constraints and requirements are considered. The objective function is the maximum profit, so the ILP is designed such that the cement plant's profit can be maximized without disrupting the normal performance of the production process. The proposed optimization problem is a mixed-integer non-linear one for which a suitable genetic algorithm is designed and used. In the proposed approach, the optimal incentive level is determined by assuming a specific budget level (related to the power company). The implementation of the proposed optimal design will satisfy the power system operator for reducing peak demand and the customer for earning more profit.

In recent years, concerns about environmental pollutions have risen and in this respect, the power system and transportation section have been introduced as the main sources of their emission. Therefore, renewable energy sources (RESs), predominantly wind generation, can be effective for reducing emissions caused by the power system, and electric vehicles (EVs) can be very useful for decreasing emissions in the transportation section. However, RESs are intermittent and uncertain, and on the other hand, high penetration of EVs into the system can be challenging for power system operation. Consequently, the stochastic behavior of RESs and charging demand of EVs should be considered in the daily operation scheduling of generating units that is known as the unit commitment (UC) problem. In this regard, this paper presents a two-stage stochastic programming model for the security-constrained unit commitment (SCUC) taking into account the effect of EVs penetration and wind power integration into the power system. The effect of EV travels on the demand of busses is modeled in the proposed framework. Moreover, the impact of demand response (DR) programs on the operation cost of the system is considered. The results of simulations in a six-bus test system illustrate that high EVs penetration reduces power system security and increases the system operation cost, but DR programs can compensate for these negative effects. Moreover, the increase in cost in a controlled charging mode can be insignificant.

This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive time-varying perturbation function which presents the interconnections and is generally uncertain and nonlinear, but it satisfies a quadratic bound. Using the dual-mode MPC stability theory and Lyapunov theory for discrete-time systems, a sufficient condition is constructed for synthesizing the decentralized MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. To guarantee robust asymptotic stability, sufficient conditions for designing MPC stabilizing components are characterized in the form of an LMI optimization problem. The proposed control approach is applied to a system composed of five coupled inverted pendulums, which is a typical interconnected system, in a decentralized fashion. Simulation results show that the proposed robust MPC scheme is quite effective and has a remarkable performance.

In this paper, two semi-analytical techniques are introduced to compute the solutions of differential-algebraic equations (DAEs), called the Least Squares Repetitive Homotopy Perturbation Method (LSRHPM) and the Least Squares Span Repetitive Homotopy Perturbation Method (LSSRHPM). The truncated series solution by the homotopy perturbation method only is suitable for small-time intervals. Therefore, to extend it for long time intervals, we consider the Repetitive Homotopy Perturbation Method (RHPM). To improve the accuracy of the solutions obtained by RHPM and to reduce the residual errors, least squares methods and span set are combined with RHPM. The proposed methods are applied to solve nonlinear differential-algebraic equations and optimal control problems. The results of the proposed methods are compared using some illustrative examples. The obtained results demonstrate the effectiveness and high accuracy of the new modifications. The effect of the parameters on the accuracy and performance of the methods are studied through some illustrative examples

In this paper, the main focus is on blood glucose level control and the possible sensor and actuator faults which can be observed in a given system. To this aim, the eligibility traces algorithm (a Reinforcement Learning method) and its combination with sliding mode controllers is used to determine the injection dosage. Through this method, the optimal dosage will be determined to be injected to the patient in order to decrease the side effects of the drug. To detect the fault in the system, residual calculation techniques are utilized. To calculate the residual, it is required to predict states of the normal system at each time step, for which, the Radial Basis Function neural network is used. The proposed method is compared with another reinforcement learning method (Actor-Critic method) with its combination with the sliding mode controller. Finally, both RL-based methods are compared with a combinatory method, Neural network and sliding mode control. Simulation results have revealed that the eligibility traces algorithm and actor-critic method can control the blood glucose concentration and the desired value can be reached, in the presence of the fault. However, in addition to the reduced injected dosage, the eligibility traces algorithm can provide lower variations about the desired value. The reduced injected dosage will result in the mitigated side effects, which will have considerable advantages for diabetic patients.

In the grid-tied PV inverter systems, the design of a proper power conditioning system is an important issue to ensure high-quality power injection to the grid. Among various control methods for grid-interfacing inverter with LCL filter, converter-side current feedback (CCF) method has been widely used due to its inherent resonance damping feature. Applying the CCF method requires extra attention to the delay effect of the control system. In such systems, the delay narrows stability region of resonance frequency of the CCF control method, especially, in the presence of wide variations of the grid inductance. Adequate tuning of the LCL filter and consequently the proper choice of the resonance frequency can remarkably affect the performance of the CCF control. In this paper, a tuning procedure for LCL filter has been proposed. Mathematical tuning of the Proportional Resonant (PR) controller parameters has also been included to avoid typical trial and error procedures of tuning. Simulation of the overall system also includes solar panels, maximum power point tracking algorithm, Modified-Y-Source inverter, and LCL filter to model the grid-tied PV system with the most possible details. Simulations are carried out in MATLAB/Simulink and it has been proved that the proposed control system maintains its stability against grid parameters variations.

Ocean thermal energy conversion (OTEC) systems utilize from the difference between the temperatures of surface and deep water and drive a thermodynamic Rankine cycle for electric power generation. The generated power depend on the temperature of the surface water as warm source and due to the variation in the temperature of surface, the output power of the OTEC system frequently changes. This uncertainty nature results in the variation in the generated power and so, integration of large-scale OTEC generation units to the power system is a challenging problem and new techniques must be developed for studying the effects of resources on the power system. Therefore, the balance between generation and consumption is important and from reliability point of view, spinning reserve must be scheduled to prevent load curtailment in the events such as forced outages of generation units, transmission lines and so on. In a power system containing large-scale OTEC power plants, the uncertainty nature of these plants must be considered in the reserve scheduling and for this purpose, a reliability model considering both failure of composed components and variation in the output power, is developed and for determining a suitable multi-state model, fuzzy c-means clustering technique and XB index is utilized. Then, the proposed multi-state model is used for spinning reserve determination of a power system containing OTEC plants using of the modified PJM method. Numerical results associated to RBTS and IEEE-RTS present the effectiveness of the proposed technique for operation studies of power systems containing OTEC systems.