Comparison of PV inverter controller configurations for CERTS microgrid applications

Photovoltaic (PV) sources are inherently nonlinear power devices and maximum power derivation depends on Maximum Power Point Tracking (MPPT) algorithms. The algorithms require testing in real time situation using PV source which will be troublesome due to higher dependency on environmental factors and larger space requirements for higher power rating. The increased and faster testing lead to develop a PV emulator with desired performance and lower price compare to the commercially available PV emulators. In this paper, design of a digitally controlled PV emulator with varying resistive load is proposed to replicate the behaviour of real PV sources. The power stage of the PV emulator is realized using dc-dc buck converter topology and the controller stage uses an algorithm-in-loop Newton-Raphson (NR) method. Finally, the PV emulator is tested with MPPT controlled dc-dc boost converter for battery charging. The design of the power and control stages of the PV emulator has been compared with the real PV source and it is observed that the obtained V-I characteristic is in good agreement. Moreover, an analysis is presented for different switching frequencies of the boost converter connected as load to the fixed switching frequency buck converter, i.e., PV emulator. Further, a schematic hardware circuit diagram has been given to realize the proposed digitally controlled PV emulator.

Off-grid systems based on Solar Photovoltaic (PV) source have emerged as a promising solution for electrification in grid in-extensible areas. Major design challenges faced are requirement of: (a) operating PV at Maximum Power Point (MPP) to extract utmost possible power from the PV source, (b) protecting the battery damage due to over-charge and over-discharge, (c) converting the dc voltage obtained from PV side to requisite ac voltage with good efficiency, and (d) achieving the requisite voltage boosting operation in the system as using a low voltage battery and PV source is desirable for domestic application. As multiple requirements are to be met, majority of the previously worked-upon schemes for off-grid system required a minimum of three power converters involving several components leading to considerable system reliability and efficiency fall. A very few two-stage schemes are also proposed but they need higher component count clubbed to complex control. In an attempt to address this issue, a reduced component count off-gird system with only two stages, one bidirectional converter (BDC) and one dc-ac inverter followed by a step-up transformer is proffered. A suitable control strategy is also proposed. Matlab/Simulink platform is employed to validate the overall scheme, and relevant results are presented.

This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.

Nowadays, Photovoltaic (PV) energy sources are responsible for more than 720TWh of the global energy consumed. Therefore, among all the arising research topics regarding PV sources, Maximum Power Point tracking (MPPT) algorithms are widely studied, since they have critical importance in order to properly track and acquire the Maximum Power Point (MPP) of energy that can be harvested from the PV source; where, metaheuristic optimization algorithms have been adapted as MPPT solutions, showing an improved settling time with steady-state oscillations reduction. Yet, many of the Metaheuristic-based MPPTs have issues against dynamically variable MPP through time, since they cannot work properly without a reset signal upon temperature and irradiance parametric changes, due the fact that those algorithms converge on a global solution after some iterations and need to know when to start looking for the MPP again in order to avoid getting stuck in old MPP points. Hence, this work shows the implementation of Artificial Neural Networks (ANN) for pattern recognition through the power data acquisition, which enables an efficient solution that allows detecting changes in solar irradiation, which leads to a reliable reinitialization signal through the detection of solar irradiation through the ANN; where, results show over 99% of accuracy from the change in solar irradiation detection. Moreover, the ANN was trained using the metaheuristic Earthquake Algorithm (EA), which was again validated as a training method for ANN. In addition, this work enables other users to suite the EA algorithm to other applications and the implementation of ANN into the Simulink environments; both achievements, through the presentation and exploration of a complete MATLAB code example for the implementation of the EA as ANN training method, and the complete Simulink testbed design with a full description of the ANN implementation for the Simulink environment.KeywordsArtificial neural networksMetaheuristicsPhotovoltaic systems

Renewable energy sources particularly solar energy have a great potential in fulfilling the energy demand in far-off rural areas where providing electricity is difficult from the existing transmission line network of the main grid. Photovoltaic (PV) sources possess stochastic nature either operating standalone or connected to the main power grid. The standalone systems have various issues like voltage fluctuations and power quality which also have an adverse effect on equipment utilizing the power generated from PV sources. Standalone PV systems consist of PV panels, a storage system, and a charge control unit (CCU). To fully utilize the energy generated from PV panels all these components as well as loads must be optimized to provide an efficient operating scenario in microgrids. Required operating condition is achieved by a distributed optimization method with the lower and upper bound constraints in terms of population dynamics of microgrid components. To implement this, a microgrid is considered with 10 Standalone PV systems with residential loads. The analysis shows how power converges to a reference value and provides a stable voltage profile for the standalone microgrid systems in rural areas.

A line-frequency zig-zag transformer is beneficial to integrate ac grid and photovoltaic (PV) source in a unified ac-dc (UACDC) system. In this paper, the zig-zag transformer efficiency is characterized during UACDC operation. For reliable estimation of efficiency, winding resistances and hence copper loss are accurately determined with inclusion of eddy current effects. Furthermore, an optimal power sharing ratio between the ac grid and PV source, is deduced to maximize the transformer efficiency at a given output power. A 1.0 kW, 415/130 V, 50 Hz, delta/zig-zag transformer is used to experimentally validate these attributes of UACDC operation. At rated output of 1.0 kW, a transformer efficiency of 93.55% is achieved, an improvement of 1.34% with respect to conventional ac power transfer. For the same secondary current capacity, UACDC operation allows an additional 17.64% of output power transfer without any thermal overloading of the secondary winding. Following benefits are therefore derived from the UACDC operation e.g. integration of PV with the ac grid, improved efficiency and effective utilization of installed thermal capacity of the zig-zag transformer.

This study deals with the comprehensive modelling, analysis, and control of a DC microgrid (MG) in islanded mode. The proposed DC MG comprises a wind turbine, a photovoltaic (PV) source, battery storage, DC/DC source, and load side converters with DC loads. To this aim, a circuit‐oriented modelling of the whole system is developed. The PV source is modelled with a single‐diode electrical circuit. Afterward, a mathematical model of the system with state‐space representation was derived. A detailed analysis of PV system design is performed because the parameters of PV are appearing in the dynamic model of DC MG. For the purpose of controller development, the dynamic model of the DC MG, which is modelled by a non‐linear‐non‐affine eight‐order system, is linearized around an equilibrium point using the Jacobian matrix framework, while stability using eigenvalue is carried out showing that the stability is guaranteed under operating condition. Finally, for the first time, the state‐dependent Riccati equation (SDRE) technique is proposed to find the optimal regulation problem for the DC MG with non‐linear‐non‐affine dynamics. The numerical simulation studies first confirm the validity of the performed mathematical, then the effectiveness of the proposed non‐linear controller is evaluated under illumination and load change.

Overcurrent faults and excessive dc bus ripple are key challenges for three-phase photovoltaic (PV) sources during unbalanced grid voltage sags. Compared to widely-used grid-following PV sources, droop-controlled sources with voltage and frequency regulation capabilities can deliver much improved responses. The low-voltage ride-through (LVRT) capability of grid-forming PV sources is investigated. A fast-responding active power control for grid-forming PV sources is proposed that includes fast current-limiting to avoid protection circuit trip events. In addition, negative sequence current compensation is added to the basic droop control to minimize PV dc-bus power oscillations. MATLAB/Simulink-based simulations and controller hardware-in-the-loop (CHIL) results demonstrate the enhanced LVRT capability of the proposed control scheme for grid-forming PV sources under both balanced and unbalanced grid voltage sags.

Power curtailment of grid-forming photovoltaic (PV) sources to provide power reserves is a promising solution to deal with significant survivability challenges in PV microgrids. A control scheme is presented that is capable of providing both maximum power point (MPP) estimation and active power reserve regulation for grid-forming PV sources. Model-based MPP estimation is proposed that avoids the need for irradiance and temperature sensors. This power reserve control algorithm can provide adjustable levels of power reserves under different operating conditions. MATLAB/Simulink-based simulations and controller hardware-in-the-loop (CHiL) emulation results demonstrate the accurate and fast response of the control scheme for grid-forming PV sources in islanded microgrids. Experimental verification of the estimation and control algorithms using an installed outdoor PV array during grid- connected operation is also included to confirm its effectiveness in real-time environments.

This paper proposes a fast and accurate time domain (TD) methodology for the assessment of the dynamic and periodic steady state operation of microgrids with photovoltaic (PV) energy sources. The proposed methodology uses the trapezoidal rule (TR) technique to integrate the set of first-order differential algebraic equations (DAE), generated by the entire electrical system. The Numerical Differentiation (ND) method is used to significantly speed-up the process of convergence of the state variables to the limit cycle with the fewest number of possible time steps per cycle. After that, the cubic spline interpolation (CSI) algorithm is used to reconstruct the steady state waveform obtained from the ND method and to increase the efficiency of the conventional TR method. This curve-fitting algorithm is used only once at the end part of the algorithm. The ND-CSI can be used to assess stability, power quality, dynamic and periodic steady state operation, fault and transient conditions, among other issues, of microgrids with PV sources. The results are successfully validated through direct comparison against those obtained with the PSCAD/EMTDC simulator, widely accepted by the power industry.

The microgrid with wind and photovoltaic (PV) power sources unavoidably encounters the power fluctuation problem. To solve this problem, the superconducting magnetic energy storage (SMES) can be used. Nevertheless, large power fluctuation from wind and PV sources, and severe system faults may cause the overcharge or deep-discharge state of SMES. These abnormal states highly degrade the dynamic performance of the SMES. To handle these situations, this paper concentrates on the new SMES power controller design considering state-of-charge (SOC), robustness, and optimal inductance of the superconducting coil for microgrid stabilization. The active and reactive power controllers of SMES are represented by the proportional-integral (PI) control. The SOC deviation control and the mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control are proposed to optimize the SMES coil inductance and PI parameters. Simulation study is performed to signify the control effect of the proposed SMES.

The trend of growing penetration of renewable energy resources in power systems has become pertinent in wake of fast depleting fuel and rising carbon emission. It has, therefore, become relevant to study the dynamics of their integration with the available technologies. Of all renewable resources, Photovoltaic generation is the most prevalent resource in India. Keeping this in mind, a multistage cascaded converter system is developed in this paper. The proposed technique comprises of a PV (Photo voltaic) fed MPPT (Maximum Power Point Tracking) controlled boost converter in cascade with a sliding mode controlled inverter. The paper aims at extracting maximum power from a PV source and converting it to a good quality ac power. The effect of variable output of PV generation due to varying solar irradiation has also been taken into consideration to establish the effectiveness of MPPT controlled boost converter. The performance of the sliding mode controlled voltage source inverter, when compared with a PI controlled voltage source inverter, is found to be superior in terms of the quality of the ac power.

This paper explores the area of double-input single-output DC-DC converters to develop a photovoltaic (PV) source based DC microgrid which is incorporated with maximum power point tracking control technique. The converter used is a fifth-order converter utilizing boost plus single ended primary inductor converter topology which draws power from the two PV sources simultaneously and supplies upstream with reduced ripple current. A control strategy is developed for load voltage regulation by operating the PV in derating mode. The PV sources are controlled to remove any power mismatch, thus maintaining constant voltage at the load end. The voltage is measured at the load end and the error signal is sent to the controller through power line communication (PLC). Results obtained in MATLAB/ Simulink show that the proposed scheme helps in regulating the load voltage by derating the PV sources. This also demonstrates the successful application of the PLC technique in a DC system.

Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review

This work proposes an adaptative neuro-fuzzy inference system (ANFIS) method to model the behavior of solar photovoltaic (PV) module. The performance of the solar PV module is greatly influenced by various environmental factors and it is therefore necessary to operate the PV module at its optimal point ensuring that maximum power is extracted from the PV source. Several fixed step and variable step maximum power point tracking (MPPT) algorithms have been proposed in the literature. This paper presents a simple and fast MPPT method based on a structure that combines the agility of neuro-fuzzy system which a self-tuning and the precision of the perturb and observe (online method) (P&O), providing reduced oscillation, this way improving the power control efficiency.