This paper proposes a deterministic particle swarm optimization to improve the maximum power point tracking (MPPT) capability for photovoltaic system under partial shading condition. The main idea is to remove the random number in the accelerations factor of the conventional PSO velocity equation. Additionally, the maximum change in velocity is restricted to a particular value, which is determined based on the critical study of P-V characteristics during partial shading. Advantages of the method include: 1) consistent solution is achieved despite a small number of particles, 2) only one parameter, i.e., the inertia weight, needs to be tuned, and 3) the MPPT structure is much simpler compared to the conventional PSO. To evaluate the idea, the algorithm is implemented on a buck-boost converter and compared to the conventional hill climbing (HC) MPPT method. Simulation results indicate that the proposed method outperforms the HC method in terms of global peak tracking speed and accuracy under various partial shading conditions. Furthermore, it is tested using the measured data of a tropical cloudy day, which includes rapid movement of the passing clouds and partial shading. Despite the wide fluctuations in array power, the average efficiency for the 10-h test profile reaches 99.5%.

A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors’ point of view, are also provided in the final concluding part.

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Review on Control of DC Microgrids and Multiple Microgrid Clusters

Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. This paper focuses on line–line faults in PV arrays that may be caused by short-circuit faults or double ground faults. The effect on fault current from a maximum-power-point tracking of a PV inverter is discussed and shown to, at times, prevent overcurrent protection devices (OCPDs) to operate properly. Furthermore, fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. Particularly, this paper examines the challenges to OCPD in a PV array brought by unique faults: One is a fault that occurs under low-irradiance conditions, and the other is a fault that occurs at night and evolves during “night-to-day” transition. In both circumstances, the faults might remain hidden in the PV system, no matter how irradiance changes afterward. These unique faults may subsequently lead to unexpected safety hazards, reduced system efficiency, and reduced reliability. A small-scale experimental PV system has been developed to further validate the conclusions.

Line–Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays

Principal curves are defined as self-consistent smooth curves passing through the middle of the data, and they have been used in many applications of machine learning as a generalization, dimensionality reduction and a feature extraction tool. We redefine principal curves and surfaces in terms of the gradient and the Hessian of the probability density estimate. This provides a geometric understanding of the principal curves and surfaces, as well as a unifying view for clustering, principal curve fitting and manifold learning by regarding those as principal manifolds of different intrinsic dimensionalities. The theory does not impose any particular density estimation method can be used with any density estimator that gives continuous first and second derivatives. Therefore, we first present our principal curve/surface definition without assuming any particular density estimation method. Afterwards, we develop practical algorithms for the commonly used kernel density estimation (KDE) and Gaussian mixture models (GMM). Results of these algorithms are presented in notional data sets as well as real applications with comparisons to other approaches in the principal curve literature. All in all, we present a novel theoretical understanding of principal curves and surfaces, practical algorithms as general purpose machine learning tools, and applications of these algorithms to several practical problems.

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Locally Defined Principal Curves and Surfaces

Harmonic current filtering and resonance damping have become important concerns in the operation and control of the islanded microgrids To address these challenges, this paper proposes a control method for the inverter-interfaced distributed generation (DG) units to autonomously share the harmonic currents and resonance damping burdens The approach employs a load compensator based on the decomposition of output current, in addition to the outer droop-based power controller and the inner voltage and current controllers The load compensator consists of a virtual-fundamental-impedance loop for the enhanced reactive power sharing and a variable-harmonic-impedance loop which allows to counteract the harmonic voltage drops across the grid-side inductance of the DG inverter and also to dampen out harmonic resonance propagation in the microgrid Finally, the laboratory tests on a three-phase islanded microgrid setup are carried out to validate the performance of the proposed control scheme

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Autonomous Control of Inverter-Interfaced Distributed Generation Units for Harmonic Current Filtering and Resonance Damping in an Islanded Microgrid

Grid-connected photovoltaic (PV) inverters employ an islanding-detection functionality in order to determine the status of the electrical grid. In fact, the inverter must be stopped once the islanding operating mode is detected according to standards and grid-code limits. Diverse islanding-detection algorithms have been proposed in literature to cope with this safety requirement. Among them, active methods based on the deliberate perturbation of the inverter behavior can minimize the so-called nondetection zone, which is a range of conditions in which the inverter does not recognize that it is operating in an undesired island. In most cases, the performances of these methods have been analyzed considering a highly dispersed generation scheme, where only one distributed-generation power system is connected to the local electrical power system (EPS). However, in some studies, it has been highlighted that if two or more PV inverters are connected to the same local EPS, their anti-islanding algorithms do not behave ideally and can fail in detecting the islanding condition. However, there is no systematic study that has investigated the overall capability of different anti-islanding methods employed on several inverters connected to the same EPS to detect islanding condition. This paper is a first attempt to carry out a systematic study of the performances of the most common active detection methods in a case of two inverters connected to the same EPS. In order to evaluate the global capability of the two systems to detect islanding condition, a new performance index is introduced and applied also to the case when the two inverters employ different anti-islanding algorithms.

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Performance Evaluation of Active Islanding-Detection Algorithms in Distributed-Generation Photovoltaic Systems: Two Inverters Case

Load harmonic currents and load unbalances reduce power quality (PQ) supplied by electrical networks. Shunt active power filters (SAPFs) are a well-known solution that can be employed to enhance electrical PQ by injecting a compensation current at the point of common coupling (PCC) of the SAPF, the load, and the electrical grid. Hence, SAPF controllers must determine the instantaneous values of the compensation reference current, including nondesirable components of the load current. A family of SAPF controllers, which evaluates the compensation reference current using synchronous rotating frames (SRFs), employs a structure based on Park transformations: direct transform, low- pass filtering (LPF), and inverse transform. The cutoff frequency and the filter order of the LPF stage must be designed properly in order to obtain an accurate reference current and a fast dynamic response of these SAPF controllers. This paper proposes a recursive implementation of the direct Park transformation that avoids the filtering stage and allows accurate SRF controllers to be designed. Moreover, the proposed implementation is not dependent on PCC conditions. The proposed implementation is evaluated using a three-phase, three-wire SAPF and compared with LPF-based controllers by simulation and experiment.

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A Recursive Park Transformation to Improve the Performance of Synchronous Reference Frame Controllers in Shunt Active Power Filters

Grid-connected Distributed Generation Power Systems (DGPS) based on inverters require the employed controller to include an islanding detection algorithm in order to determine the grid status and operate properly. In certain cases, such as low-power low-cost current-controlled inverters used in residential photovoltaic (PV) systems, the inverter must be stopped once the islanding condition is detected according to the standard and grid-code limits. Passive, active and monitoring-based algorithms for islanding detection have been analyzed in literature considering one DGPS connected to the local Electrical Power System (EPS). Only in case of active methods, such as Active Frequency Drift (AFD), the effect of two DGPS in the same local EPS on the islanding capabilities has been analyzed. This paper analyzes the performance of diverse passive and active islanding detection methods (IDM) in multiple inverters connected at the same local EPS interact.

An overview of anti-islanding detection algorithms in photovoltaic systems in case of multiple current-controlled inverters

Clustering techniques for equalization have been proposed by a number of authors in the last decade. However, most of these approaches focus only on time-invariant single-input single-output (SISO) channels. In this paper we consider the case of fast time-varying multiple-input multiple-output (MIMO) channels. The varying nature of the mixing matrix poses new problems that cannot be solved by conventional clustering techniques. By introducing the time scale into the clustering process we are able to untangle the clusters, which in this way behave like intertwined threads. Then, a spectral clustering algorithm is applied. Finally, the identified clusters are assigned to the transmitted symbols using only a few pilots. The geometry of the transmitted constellation is exploited within the spectral clustering algorithm in order to reduce the number of clusters. As shown in the paper, the proposed procedure saves a considerable amount of pilot symbols in comparison to other recently proposed techniques.

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A spectral clustering algorithm for decoding fast time-varying BPSK mimo channels

This paper presents the design and implementation of a distance e-learning course on design and control of photovoltaic plants. Simulation software and remote lab services are used in the development of the course. Three basic educational aspects related to the structure of this e-learning course are described: technological platform, contents and services. The obtained academic results are analyzed establishing the benefits and future developments.

Emilio J. Estebanez

Papers

Performance Evaluation of Active Islanding-Detection Algorithms in Distributed-Generation Photovoltaic Systems: Two Inverters Case

A Recursive Park Transformation to Improve the Performance of Synchronous Reference Frame Controllers in Shunt Active Power Filters

An overview of anti-islanding detection algorithms in photovoltaic systems in case of multiple current-controlled inverters

A spectral clustering algorithm for decoding fast time-varying BPSK mimo channels

An experience on e-learning in renewable energy: design and control of photovoltaic plants