Novel Application of Selfish Herd Optimisation Based Fractional Order Cascaded Controllers for AGC Study

This article deals with Automatic Generation Control (AGC) of a three-area power system having five diversified sources of generation like thermal unit, hydro unit, wind unit, diesel unit and a gas unit are interconnected together. Area-1 of the power system consists of a thermal, a hydro and a wind unit, area-2 has a thermal, a hydro and a diesel unit and area-3 consists of a thermal, a hydro and a gas unit. To make system more realistic different nonlinear components like governor dead band (GDB), generation rate constraint (GRC), Boiler dynamics and communication delay are taken into account. A novel two degree of freedom fractional order PID with derivative filter and fractional order PD with derivative filter (2-DOF-FOPIDN-FOPDN) cascaded control strategy is adopted to improve the dynamic performance of the power system. Results obtained with the proposed cascaded controller are compared with that of PID, FOPID and 2-DOF-PIDN-PDN cascaded controller to prove its superiority. To enumerate the gains of different controllers optimally, a recently developed bio-inspired optimisation algorithm named Selfish Herd Optimisation (SHO) is capitalised. Further, the work is extended by taking a two area hydro thermal system to compare the result of the SHO tuned PID controller with that of modern hybrid firefly algorithm-pattern search (hFA-PS) technique. Transient analysis is carried out by applying a sudden load disturbance of 0.01 p.u in area-1 and the robustness of the controller is examined by varying both system parameters and applying a randomly varying load in area-1. From the investigation it is concluded that the 2-DOF-FOPIDN-FOPDN controller gives a flawless and a distinct performance.

Frequency stability analysis with fuzzy adaptive selfish herd optimization based optimal sliding mode controller for microgrids

This article articulates the frequency control in an isolated microgrid (MG) under a centralized secondary controller. The penetration of distributed generators (DGs) which are weather dependant, and some of them are inertia less cause the instability in the MG. Besides this, unusual/abrupt load change, communication delay, and parameter change uncertainties make the MG more unstable. So, to restore the stability of the MG a sliding mode controller (SMC) is employed. The design of the SMC is carried by selfish herd optimization (SHO) algorithm. To validate the performance of SHO-SMC controller, it is compared with the results obtained by GOA-FOPID-(1+PI), SHO-PID, SHO- FOPID, and SHO-FOPID-(1+PI) controllers. Further, to establish an ameliorated dynamic response of the MG, SHO is modified by applying fuzzy logic named as fuzzy adaptive SHO (FA-SHO). In addition to this, in a two area MG, the potential of SHO/FA-SHO SMC controllers over SHO-SMC, and SHO/FA-SHO FOPID-(1+PI) controllers has been examined. Finally, with some crucial intermittent uncertainties like abrupt load change, time delay, and parameter variation, the robustness of the proposed controller is established.

Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.