Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System

Abstract

The small signal stability of power systems integrated with fallback large‐scale distributed energy resources is of utmost importance during the perturbation conditions. Among the various renewable resources in power systems: photovoltaic, fuel cell, wind turbine, diesel engine, aqua electrolyser, battery, ultra‐capacitor, and flywheel is becoming more favored in last decades. AGC, a main control system of frequency modulation, must correctly alleviate the low frequency oscillations caused by the perturbation and variable output power of renewable resources. In this regard, FOBELBIC is here introduced to enhance the damping capability of AGC. Since both the frequency and tie‐line power oscillations must be concomitantly alleviated, the small signal stability problem has been multi‐objectively formulated. Due to the exploration capability of MOALO, it has been chosen to extract the optimal parameters of FOBELBIC. Furthermore, MOPSO, MOBA, and NSGA‐II have been studied to demonstrate the capability of MOALO. To verify and validate the damping performance of suggested controller, it has been evaluated in three‐area reconstructed power system under the load perturbations as well as wind speed and sun radiation variations, and at the same time compared with BELBIC and PID controllers. Finally, the simulation results have evidently confirmed the damping the performance of MOALO‐based FOBELBIC to enhance the small signal stability of hybrid power system. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

“…In [17], the authors presented an up-to-date and state-of-the-art report on the challenges and future directions of automatic generation control (AGC) and LFC in both conventional and renewable energy-based PS. In the works of Darvish et al [18][19][20], various optimization-based and intelligent techniques employing fractional-order-PID controllers were developed for AGC in PSs. These methods include type-2 fuzzy logic, multi-objective-based optimizations utilizing algorithms such as ant lion optimizer, particle swarm optimization (PSO), and bees algorithm, as well as non-dominated sorting genetic algorithm II.…”

“…Due to those reasons, we must estimate SSVs; thus, the state observer is implemented based on the previous study [59], and explained in this section. The state-space model of state observer based on the l th area model as shown in Equation ( 14) is illustrated as Equation (18).…”

“…In the works of Darvish et al. [18–20], various optimization‐based and intelligent techniques employing fractional‐order‐PID controllers were developed for AGC in PSs. These methods include type‐2 fuzzy logic, multi‐objective‐based optimizations utilizing algorithms such as ant lion optimizer, particle swarm optimization (PSO), and bees algorithm, as well as non‐dominated sorting genetic algorithm II.…”

A New Hybrid Ant Colony Optimization Based PID of the Direct Torque Control for a Doubly Fed Induction Motor

“…Using this strategy, one can assure that there is no coupling between the fluxes and the torque control. Using a voltage inverter, you can enable seven different positions in the phase plane, which corresponds to eight different voltage vector sequences at the inverters' outputs [1,39].…”

Discrete Centralized AGC Using LQR-Based Cost Functional Minimization for Multi-Area Interconnected Power Systems

“…For example, control strategies such as classical linear control methods problems [4,5], A fractional 𝑃𝐼 𝜆 𝐷 control [6], Adaptive neuro-fuzzy interference system (ANFIS) [7,8], Robust control methods such as Active disturbance rejection control (ADRC) [9], Variable structure control [10], and H-infinity robust control [11]. For a three-area hydro-thermal power system integrated with distributed energy resources, a Fractional order brain emotional learning-based intelligent controller (FOBELBIC) was proposed to suppress frequency and tie-line deviations [12]. Hybrid type-2 fuzzy logic controllers with fractional order proportional integral derivative (FOPID) have recently been developed to enhance the frequency and tie-line deviations of two control areas under different perturbations [13].…”