Optimal Power Flow Using Adaptive Fuzzy Logic Controllers

This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs) devices, using adaptive fuzzy logic controller (AFLC) driven by adaptive fuzzy sets (AFSs). The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC)) and the setting of their control parameters (QSVC) are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC), driven by a fixed fuzzy set (FFS). Simulation studies were carried out and validated on the standard IEEE 30-bus test system

Real time parameter estimation for power quality control and intelligent protection of grid-connected power electronic converters

This paper presents a method to identify power system impedance in real-time using signals obtained from grid- connected power electronic converters. The proposed impedance estimation has potential applications in renewable/distributed energy systems, STATCOM, and solid state substations. The method uses wavelets to analyze transients associated with small disturbances imposed by power converters and determine the net impedance back to the source. A data capture period of 5ms is applied to an accurate impedance estimation which provides the possibility of ultra fast fault detection (i.e. within a half cycle). The paper describes how the proposed method would enhance the distributed generation operation during faults

Enhanced Intelligent Energy Management System for a Renewable Energy-based AC Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%