This chapter presents the comparative analysis between perturb & observe (P&O), incremental conductance (Inc Cond), and fractional open-circuit voltage (FOCV) algorithms using fractional order control & a new meta-heuristic called Grey Wolf optimizer (GWO) for extracting the maximum power from photovoltaic (PV) array. PV array systems are equipped with maximum power point tracking controllers (MPPTCs) to maximize the output power even in the case of rapid changes of the panel's temperature and irradiance. In this chapter, three cost effective MPPTCs are introduced: FOCV, P&O and Inc. Cond. The output voltage of the array is boosted up to a higher value so it can be interfaced to the local medium voltage distribution network.
The inherited uncertainties in the Photovoltaic array (PV) system make it one of the most difficult nonlinear problems in the control theory. In this research work, a practical case study for 20 MW Egyptian PV solar power plant with battery backup system connected to the medium voltage distribution network is presented. This power plant installed in Komombo, Aswan, Egypt. The battery is used to provide the necessary power for the station's monitoring and control devices during the absence of the sun. Also, the battery system improves the steady state and dynamic behavior of PV array system. Proportional-Integral (PI) and Fractional Order Proportional-Integral (FOPI) with Incremental Conductance MPPT algorithm (IC)are used for extracting maximum power from PV power plant. The optimal gains of PI and FOPI based MPPT controllers are attained using Grey Wolf Optimizer (GWO). Various types of disturbances are applied to the system to test the robustness of the systems based on the tuned controllers. The performance is promising.
A new optimal control strategy for the grid side converter (GSC) and rotor side converter (RSC) of a doubly-fed induction generator (DFIG) is developed in this paper using the Marine Predators algorithm (MPA). To accomplish this study, a comprehensive comparison between the suggested MPAbased control strategy and a well matured Particle Swarm Optimizer (PSO) to enhance transient stability of large-scale wind systems has been presented. MPA is used to determine the optimal gains of proportionalintegral (PI) controllers for GSC and RSC to ensure a maximum power point tracking (MPPT) of a largescale wind farm. The proposed optimal control strategy is analyzed and verified via a benchmark 9-MW DFIG wind farm using MATLAB/SIMULINK simulation. The attained results of the suggested MPA-PIbased controllers are compared to the conventional PI-based MPPT controllers to validate the efficacy of the developed optimal control strategy. The superiority of the proposed MPA-PI and PSO-PI-based optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance has been proved. The presented MPA-PI-based control scheme has been succeeded in extracting the maximum power of the DFIG wind farm with a reduced settling time of about 1.8% and overshooting range 97% lower than the conventional controller.
Nowadays Maximum Power Point Tracking (MPPT) controller is considered the central part of any photovoltaic system to achieve the maximum power at all time under the change in weather conditions. MPPT techniques like, Perturb and Observe (P&O), Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) are the most commonly algorithm used due to low cost, easy implementation and simplicity. These algorithms are differing from each other according to sensors number, easy or complexity implementation and cost. The best algorithm is select according to accurate and fast tracking performance and minimum error due to changing conditions of weather. In this paper the optimal design for Incremental Conductance (IC) MPPT technique based on Fuzzy Logic controllers tuned by new optimization technique called Grey Wolf Optimizer (GWO) is applied for the largest PV project planned in Egypt. This PV project installed in Komombo, Aswan, Egypt and will have a total capacity of 20 MW. This study provides a comprehensive comparative study based on Average Power (A.P), transient behavior, Energy Availability (E.A) and Array Fill Factor (A.F.F) to choose the optimum control technique which is more suitable for controlling MPPT in Komombo PV power plant. MATLAB/SIMULINK is used to provide technical study and comprehensive analysis for the proposed PV power plant.
Atom search optimization algorithm (ASOA) has recently been explored to develop a novel algorithm for distributed optimization and control. This chapter proposes the ASOA-based design of maximum power point tracking controllers (MPPTCs) for controlling the boost converter voltage to harvest the maximum power and enhance the damping of oscillations in the output power of the photovoltaic power plants. The proposed ASOA-based MPPTCs are PI and fractional-order PI controllers. ASOA is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed ASOA-based MPPTCs has been compared to the MPPTCs optimized by grey wolf optimizer (GWO) to demonstrate the superior efficiency of the ASOA-based MPPTCs. Simulation results emphasis on the better performance of the proposed MPPTCs compared to MPPTCs and GWO-based PI- FOPI controllers over a wide range of operating conditions.
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