Adaptive Power Point Tracking Control of PV System for Primary Frequency Regulation of AC Microgrid with High PV Integration
ABSTARCT :
With the increasing integration of PV generation in AC microgrids, it is challenging to keep the stability of system frequency due to the intermittent and stochastic nature of PVs. Thus, in order to reduce the investment and maintenance costs of storage systems, the electric utility has shown increasing interest in calling on PVs to provide frequency regulation services. In this paper, a novel sliding mode control (SMC) based adaptive power point tracking (APPT) control strategy is proposed to provide bi-directional primary frequency regulation (BPFR) of an AC microgrid. In this strategy, in order to restrain the variation of frequency, the sliding mode surface is adaptively regulated to provide an adaptive power reserve. Thus, the PVs will have a power headroom to regulate the frequency both up and down through releasing the reserved power or increasing the reserves of the PV systems. Furthermore, the sliding mode surface is adaptively regulated only based on the locally measured frequency of the microgrid without the requirements of communication, detailed PV model, irradiance sensors as well as MPP estimators. Thus, the proposed control strategy shows the advantages of easy to implement and reduces the investment and maintenance costs of installing storage systems or irradiance sensors.
EXISTING SYSTEM :
? The flexibility of the proposed adaptive FPPT algorithm has been demonstrated experimentally on a 3-kVA laboratory setup under different conditions.
? The tracking error of the proposed algorithm has been reduced significantly in all experimental tests, while the settling has also been decreased.
? The results demonstrated the applicability and effectiveness of the proposed FPPT algorithm as an additional function for existing MPPT algorithms in GCPVPPs.
? Therefore, the existing maximum power point tracking algorithms in GCPVPPs, should be replaced by flexible power point tracking (FPPT) algorithms in GCPVPPs in order to comply with these demands.
DISADVANTAGE :
? In this method, first, the problems are expressed in an optimisation process, then a controller is applied to solve the problems .
? The controller suffers from chattering problems. Therefore, the SMC parameters are optimised based on output ripple waves to overcome this issue; and an extra integral term of the grid current is added to the sliding surface to eliminate tracking errors.
? To overcome these problems, different control approaches are proposed in the literature with their relative advantages and disadvantages.
? Moreover, the problem of reactive power sharing is solved in this method as frequency is a global parameter, which is used in controlling reactive power.
PROPOSED SYSTEM :
• In this paper, an adaptive FPPT algorithm is thus proposed, which features fast dynamics under rapidly changing environmental conditions (e.g., due to passing clouds), while maintaining low power oscillations in steady-state.
• The proposed algorithm is an adaptation of the P&O method considering the P-V characteristics of PV panels.
• In the proposed algorithm, the operation mode of the converter and the current operation point of the PV panel are considered in the calculation of the voltage-step in each calculation step.
• The proposed FPPT algorithm in this paper can also be used to extract the maximum power from the PV strings, while it is able to limit the PV power to a required value upon demands.
ADVANTAGE :
? The primary control in microgrids has several roles, including: enhancing system performance and stability, maintaining the voltage and frequency stability, offering plug and play features of DG units, ensuring accurate power sharing in the presence of linear and non-linear loads, and eliminating circular current.
? A transformation of the controlled parameters is, sometimes, essential to improving a controller performance.
? The effective dynamic performance of the deadbeat (DB) predictive controller facilitates the current control of an inverter.
? The self-learning feature of the NN algorithm gives feasibility and easy design for different operating conditions and grid disturbances, and augmenting a robust control performance
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