Grid-integrated Solar Photovoltaic System Research Articles

Power quality enhancement of dual stage three phase grid integrated SPV system using sequential neural network-based algorithm

In this paper, a single-layer sequential neural network (SNN) control algorithm has been proposed for managing the power quality (PQ) in the double-stage three-phase grid-integrated solar photovoltaic (SPV) system. The proposed algorithm efficiently extracts the active and reactive fundamental components of the load current, with a higher convergence rate. The extracted reference currents are matched with the sensed source currents to generate gating pulses for the voltage source converter (VSC). To handle power quality issues in distribution networks such as harmonic imbalances, voltage fluctuations (sags/swells), and the need for active and reactive power compensation, the VSC is controlled very precisely. The control algorithm consists of two-stage processes. In the initial stage, the incremental conductance (INC) based DC-DC converter has been incorporated to extract the peak power from the SPV system. In the later stage SNN-based algorithm is used to control the VSC. A 100 KW solar PV system connected to the utility grid has been designed by using a three-leg VSC in the MATLAB/Simulink environment. The system performance has been evaluated by comparing it to existing techniques. The simulation results comfortably meet the requirements of the IEEE-519–2014 and IEEE 1547–2003 standards for utility grid connection.

Alleviation of Power Quality Issues in MVF-DEANF-PLL Based Solar PV Systems under Polluted Grid Conditions

Solar energy is a sustainable and environmentally safe power source due to its widespread availability and cleanliness. Nowadays, the grid-integrated solar photovoltaic system (SPVS) has to work with a polluted grid, an imbalanced load, and changing solar irradiation. When the grid is polluted, it is also crucial to enhance power quality (PQ) at a common coupling point (CCP) while supplying significantly distorted and unreliable loads. For effective synchronization and the production of unit templates, it is necessary to retrieve positive sequence components (PSCs) from distorted/imbalanced grid voltages. In this study, a control algorithm for a grid-integrated SPVS is developed using a multi-variable filter dual-enhanced adaptive notch filter phase-locked loop (MVF-DEANF-PLL) which offers seamless grid synchronization and PQ issue alleviation. In a polluted grid environment, the proposed control approach aids in the reduction in current/voltage harmonics, DC offset, unity power factor (UPF) operation, and rapid estimation of sequence components. Even in unbalanced grid conditions, the proposed control approach efficiently extracts PSCs of both unbalanced load current and polluted CCP grid voltages. These PSCs are utilized to generate unit templates and reference source currents. By using a flexible step-size incremental conductance (FSSINC) maximum power point tracking (MPPT) technique, the highest available power of SPVS is gathered. MATLAB/Simulink is utilized for modelling a 7.22 kW SPV system, and results from simulations which depict that the proposed algorithm efficiently resolves PQ concerns in distribution networks with a polluted grid. Test observations of a 1 kW laboratory-developed SPVS prototype were recorded in compliance with the IEEE-519 standard. The suggested control technique complies with the aforementioned standards by providing a sinusoidal balanced source current that has a THD of 2.5%. Comparisons between the proposed control’s performance and that of a conventional SRF-PLL-based control technique were also performed.

Active power coefficient control for grid-tied photovoltaic system under voltage distortions

ABSTRACT Grid-integrated solar photovoltaic (GSPV) system requires a simpler and computationally less intensive control algorithm for continuous maneuver of real power inoculation and power quality enhancement underneath diverse operating conditions. In order to reduce the computational burden, this article presents an active power coefficient (APC)-based control strategy for extracting the fundamental weight factor. The proposed structure utilizes only three mathematical operators for weight extraction, and thereby reduces the computational burden. Therefore, only 35 µs sampling time is required to implement the proposed control through low-cost ARM cortex M4-based microcontroller. As a result, the proposed control provides following advantages: 1) faster response 2) lesser current distortions, 3) lesser DC-link oscillations, and 4) lower implementation cost. Moreover, the improved linear time invariant-enhanced phase look loop (LTI-EPLL) structure is also proposed for synchronization of GSPV system. This structure accurately trails the phase angle and frequency of voltage signals despite the presence of DC component and harmonics. Besides, the amplitude estimation loop removes the dependency of supply magnitude from unit template. Further, the proposed APC algorithm and synchronizing control is designed in waijung environment of MATLAB/Simulink for testing various operating conditions. Finally, the total harmonic distortion of source current is remarkably decreased to 1.67%, which is well within the limits of IEEE 519 standard.

Integration of Solar PV With Low-Voltage Weak Grid System: Using Normalized Laplacian Kernel Adaptive Kalman Filter and Learning Based InC Algorithm

This paper proposes a novel normalized Laplacian kernel adaptive Kalman filter (NLKAKF) based control technique and learning based incremental conductance (LIC) maximum power point tracking (MPPT) algorithm, for low-voltage weak grid-integrated solar photovoltaic (PV) system. Here, a two-stage topology of three-phase grid integrated solar PV system is implemented, where the loads are connected at the point of common coupling. Proposed LIC is the improved form of incremental conductance (InC) algorithm, where inherent problems of traditional InC technique, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed NLKAKF control is to meet the active power requirement of the loads from generated solar PV power, and after feeding load, excess power is fed to the grid. However, when generated PV power is less than the required load power, then NLKAKF control meets the load by taking extra required power from the grid. During this process, power quality is improved at the grid. The controller action provides reactive power compensation, power factor correction, and harmonics filtering and mitigation of other power quality issues. Moreover, when the solar irradiation is zero than voltage source converter acts as a distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed prototype, in solar insolation variation conditions, unbalanced loading, as well as in different grid disturbances such as overvoltage, undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc.

Load harmonics extraction based decoupled control of grid connected solar photovoltaic system

The global energy shortage and the requirement for energy systems that are sustainable has led to the widespread penetration of Renewable Energy Sources (RES). The intermittent characteristics of these Renewable Energy Sources make it a daunting task to integrate renewable energy, especially solar energy resources into the electric grid. The increasing number of Distributed Generation (DG) systems along with the existing non-linear loads create several issues due to variable output power and further worsens power quality problems like overvoltage, voltage fluctuations and harmonics. This paper describes an attempt on the development of a control strategy based on decoupled control with load harmonics extraction for the mitigation of power quality issues in grid integrated Solar Photovoltaic (SPV) systems. The performance of the grid integrated SPV system with linear and non-linear loads is investigated by simulation using MATLAB/Simulink. The effectiveness of the developed control strategy is validated by comparing the performance with an uncompensated system consisting of grid with linear and non-linear loads.

Optimal sizing of a grid integrated solar photovoltaic system

This study proposes an optimal sizing methodology for a solar photovoltaic (SPV) system considering lifetime cost requirements. The aim of the design is optimal sizing of SPV system, which is obtained by calculating SPV system output power at certain location, taking into account the calculated optimal number of SPV modules, optimal number of inverters, optimal tilt angle, for a given dimension of land. This design is aimed for minimising the annual cost of grid-integrated SPV system over its life or years of operation. The cost function takes into account the capital cost of installation, operation and maintenance, for each component of the system and the cost of selling energy to the grid. The sizing optimisation has been formulated as a non-linear, multi-variable problem and the particle swarm optimisation algorithm has been tested using MATLAB platform for a particular location to swot up the feasibility of integrated system. The monthly averaged daily and hourly solar radiation data for a given location is calculated using empirical relations on MATLAB platform. Other inputs are specifications of commercially available devices and meteorological details of location.