• Energy Research

Abstract This study presents a control system for a single-phase DC/AC inverter operating in a microgrid with the capability of operating in both grid-connected and islanded mode with no need to reconfigure the control system which offers seamless transition between different modes. In this control scheme, in the grid-connected mode, the adopted control system emerges as an active-reactive power controller, while in the islanded mode; it inherently alters in order to form a microgrid with controlled voltage and frequency. As the control system is the same in both modes, the seamless transition is provided as a result. Moreover, the proposed control system is not reliant on synchronization of DC/AC inverter with the main grid which provides an effective solution for the case in which no communication system is intended or the DC/AC inverter and microgrid point of common coupling (PCC) are far away from each other. The small signal modeling of the proposed controller is used to corroborate the stable operation of the proposed system. Simulation studies in MATLAB and experimental evaluations confirm the effectiveness of the proposed solution.

This paper introduces a robust decentralized Load-Frequency Controller (LFC) for a power system, which is configured based on the Structured Singular Values (SSVs). A new Linear Quadratic Regulator (LQR) solution is applied for a two-area power system, using an Artificial Neural Network (ANN). Subject to a restriction which is based on SSVs, each LFC can be designed independently. The proposed Neuro-Optimal controller combines the advantages of ANN and the good performance of the SSV-LQR controller. To overcome the problem of SSV-LQR, which needs the measurement of all system states; the suggested controller has only one input from system states in each area and regenerates the other states just using lags of the frequency variations. The results show that the proposed controller has improved dynamic behavior of the system, compared to the both SSV-LOR controller and a Genetic Algorithm (GA) optimization applied to LFC.

Abstract The fuzzy control of the super-conducting magnetic energy storage (SMES) is proposed to improve the transient stability and damping requirements of multi-machine power systems. The effects of the SMES control on the line power transfer are considered to design a fuzzy controller. The theoretical realization of the proposed control scheme is presented. The simulation study of the New-England test system is carried out. The practical implementation based on the local measurements is explained. The improvement of the system's critical clearing time and also the system damping requirements are clearly demonstrated Finally a conclusion is provided.

AbstractThis paper describes the study to analyze the effect of the resistive-type superconducting fault current limiters (RSFCLs) on the test feeder with the wind-turbine generation, which is a representative renewable energy source. The presented test case is the IEEE 34-bus test feeder augmented with two induction machines that would be typical of wind generation. It is worthy to mention that wind-turbines which are only squirrel-cage induction generator have been considered. The connection of wind-turbines to existing distribution networks may lead to the increasing fault levels beyond the capacity of existing switchgear. This can aggravate the reliability of the overall power system. The short-circuit currents are expected to be controlled by RSFCL. The analysis investigates the effectiveness of using RSFCL as remedial measures for fault level management in a wind power system. As a result, the highly efficient operation of the wind power system becomes more possible by introducing the RSFCL. All mod...

New interconnection standards for Photovoltaic systems are going to be mandatory in some countries. Such that the next generation of PV should support a full range of operation mode like in a power plant and also support Low-Voltage Ride-Through (LVRT) capability during voltage sag fault. Since the voltage sag period is short, a fast dynamic performance along with a soft behavior of the controller is the most important issue in the LVRT duration. Recently, some methods like Proportional Resonant (PR) controllers, have been presented to control the single phase PV systems in LVRT mode. However, these methods have had uncertainties in respect their contribution in LVRT mode. In PR controllers, a fast dynamic response can be obtained by tuning the gains of PR controllers for a high bandwidth, but typically the phase margin is decreased. Therefore, the design of PR controllers needs a tradeoff between dynamic response and stability. To fill in this gap, this paper presents a fast and robust current controller based on a Model-Predictive Control (MPC) for single-phase PV inverters in other to deal with the LVRT operation. In order to confirm the effectiveness of the proposed controller, results of the proposed controller are compared with the classical PR controller. They are also implemented in a 1 kW single-phase transformerless HERIC (Highly Efficient and Reliable Inverter Concept) inverter.

This paper presents a new detection method of fault and partial shading condition (PSC) in a photovoltaic (PV) domestic network, considering maximum power point tracking (MPPT). The MPPT has been executed by employing a boost converter using particle swarm optimization (PSO) technique. The system is composed of two photovoltaic arrays. Each PV array contains three panels connected in series, including distinct MPPT. The PSC detection exploits the neighboring PV system data. This suggested innovative algorithm is proficient in detecting these subjects: (a) fault, (b) partial shading condition, (c) solar panel (d) panel’s relevant bypass diode failure, (d) converter failure alongside specifying the failed semiconductor, and (e) PV disconnection failure. The simulation process has been implemented using MATLAB/Simulink software. To this end, the proposed method was investigated experimentally using two 250 W PV solar set under various PSCs and faults. A data exchange link is used to implement an integrated management system. The Zigbee protocol was also chosen to data exchange of converters. The results validated the applicability and practicality of this algorithm in domestic PV systems.

New and efficient configurations of adaptive Static Synchronous Series Compensator (SSSC) and Super-conducting Magnetic Energy Storage (SMES) controllers based on the Artificial Neural Network (ANN) and the fuzzy control is presented in this paper. The proposed Neuro-Fuzzy controllers combine the advantages of fuzzy controller and quick response and adaptability nature of ANN. The on-line Neuro-Fuzzy control of the SSSC and the SMES has been applied to this study. The ANN structures were trained using the generated database by fuzzy controllers of SSSC and SMES. The results prove that each of proposed SSSC and SMES Neuro-Fuzzy controllers can improve the tie line load ability and transient stability of the test system more efficient than fuzzy controller of the Flexible Alternating Current Transmission System (FACTS) devices.

Abstract The solar cell characteristics are non-linear and largely influenced by solar radiation, temperature and load condition. The power output of a PV array changes with varying temperature and irradiation. A maximum power point algorithm is investigated to obtain maximum power from a PV array on varying operating conditions. So far various methods have been proposed to achieve the maximum power from PV module. The incremental conductance (INC) and perturb and observe (P&O) algorithm are more noteworthy. In this paper two high performance and simple maximum power point tracker (MPPT) are proposed. These algorithms are modified P&O and INC algorithms. These modified algorithms are capable to track maximum power under rapidly changing atmospheric conditions with higher accuracy than their conventional methods. They increase the harvested power from PV array and thus improve the efficiency of MPPT algorithm. The algorithms using a high step-up-DC/DC converter are implemented on MATLAB/SIMULINK tool. The results demonstrate a good performance and accurate tracking under rapidly changing atmospheric conditions.

The objective of this paper is to investigate the application of FACTS devices to increase the maximum loadability of the transmission lines which may be constrained by a transient stability limit. Hence, the on-line fuzzy control of the superconducting magnetic energy storage (SMES) and the static synchronous series compensator (SSSC) are suggested. The fuzzy rule-bases are defined and explained. The validity of the suggested control strategies are confirmed by simulation tests. The simulation results show that by the use of the proposed method, the line power transfer can be increased via the improvement of the transient stability limit. Finally, the effect of the control loop time delay on the performance of the controller is presented.