The sudden outage of a transformer due to a fault can cause irreparable damage to the electricity industry. Hence, by conducting momentarily inspections of the transformer's condition, faults can be promptly detected, and the transformer can be disconnected from the power grid to prevent subsequent failures in this equipment. Detecting faults at an early stage can also result in reduced repair costs. One recent promising technique for fault detection is Frequency Response Analysis (FRA), which compares the transformer's response in healthy and faulty conditions for understanding the occurrence of transformer faults. This paper presents a comprehensive and accurate modeling approach for the behavior of the transformer at different frequencies, followed by an exposition of the requirements for implementing this method in order to find the fault type, severity, and location. Additionally, various methods for analyzing the results of frequency response are introduced and discussed. In this regard, attempts have been made to introduce advanced complementary methods to address the weaknesses and limitations of the frequency response method. Finally, the concepts are summarized, and suggestions for further research with applications in this field are presented and compared.

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The connection of renewable energy sources (RESs) to the distribution network has been rising at a steady pace over the past decades. The great penetration of RESs such as grid-connected photovoltaic system brings new technical challenges to the distribution networks such as unintentional islanding. Conceptually, this situation occurs when a portion of the network that has been isolated from the main grid remains energised by the embedded RESs. This unexpected scenario should be thereby identified effectively to avoid frequency and voltage deviations and their hazardous effects. The aim of this paper is to provide a comprehensive review on the recently developed islanding detection methods for grid-following/grid-connected photovoltaic system, analyse their existing limitations, and suggest possible future research implementations. In this context, an in-depth comparison is provided considering the main features used in islanding detection methods such as non-detection zone, detection time, implementation cost and complexity, and power quality degradation. Finally, the main technical requirements established by the current grid codes are recalled identifying potential multi-functional approaches to expand the current islanding detection capabilities.

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Islanding is a condition when distributed generators (DGs) are disconnected electrically from the upstream network. This unwanted situation should be detected effectively to ensure the safety of the maintenance staffs and power quality (PQ) requirements. This paper presents a new high PQ maximum power point tracking (MPPT)-based methodology for detecting the islanding operating mode of grid-connected photovoltaic systems (GCPVSs). In the recommended two-level scheme, a disturbance is injected into the MPPT algorithm under suspicious conditions, recognized by a passive criterion. This disturbance declines the DG active power output remarkably, drifting the output voltage beyond the minimum standard set in islanding state while its impact is negligible at the network presence. The effectiveness of the proposed technique has been evaluated through several hardware-in-the-loop simulations for a case study system, containing two power plant GCPVSs equipped with a pair of multi-functional relays. The results highlight precise islanding classification within 137 ms with the small non-detection zone. Moreover, the results of PQ analyses indicate acceptable total demand distortion and harmonic spectra of the output current in compliance with the existing standards under various DG power penetrations. Since the presented scheme diminishes the active power output in case of suspicious islanding events, its influence on GCPVS efficiency has been studied as well. The outputs underline that the efficiency drops by 0.52% whilst the disturbance is stimulated every minute of the time. It is finally concluded that the proposed technique provides a reliable islanding classification as well as insignificant degradation of PQ and efficiency.@en

This paper proposes a novel islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) through a disturbance injection in the maximum power point tracking (MPPT) algorithm. When an absolute deviation of the output voltage exceeds a threshold, the applied disturbance shifts system operating point from its maximum power point (MPP) condition. This leads to a sharp active power output reduction and consequently, a significant voltage drop in islanded mode beyond the standard voltage limit. The proposed algorithm is defined in a way that the distributed generator (DG) can be restored to MPP after islanding classification. It is thereby effective in microgrid in where the power injection at maximum level to cater the critical loads and maintain the stability of the isolated area are pursued. An intentional time delay has also been considered to avoid nuisance tripping in short-circuit faults which do not require tripping. The assessment of the proposed technique has been conducted for a sample network containing two GCPVSs in a real-time platform including actual relays in hardware-in-the-loop (HiL). The provided results under extensive islanding scenarios defined in islanding standards endorse timely and accurately detection with negligible non-detection zone (NDZ) as well as no false tripping in non-islanding disturbances. The comparative analysis of the presented scheme with a few recent IDMs for GCPVS highlights its overall superiorities, including very small NDZ, fast detection, thresholds self-standing determination, no adverse effect on power quality, and simple and inexpensive integration.

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This article proposes a fast and reliable two-level islanding detection method (IDM) for grid-connected photovoltaic system (GCPVS)-based microgrid. In the first level of the proposed IDM, the magnitude of the rate of change of output voltage (ROCOV) is computed. If this variable exceeds a predefined threshold, a disturbance is injected into the duty cycle of DC/DC converter after a given time delay to deviate the system operating point away of its maximum power point (MPP) condition. This leads to a substantial active power output and voltage reduction in an islanded mode. Therefore, the ROCOV and the rate of change of active power output (ROCOP) indices, measured in the second stage, pose great negative sets at the same time in islanding states. However, the variation of at least one of these variables is near-zero in non-islanding switching events. The assessment of the presented algorithm has been conducted under extensive islanding and non-islanding scenarios for a case study system with two PV power plants using hardware-in-the-loop (HiL) simulation tests. The provided results remark precise islanding classification with an eminently small non-detection zone (NDZ) within 510 ms. The presented IDM has the advantages of self-standing thresholds determination, no improper effect on the output power quality, and simple and inexpensive structure. Moreover, the fast MPP restoration of the proposed scheme after islanding identification boosts the chance of seamless reconnection and DG autonomous operation in microgrid.

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This paper investigates the economic viability of a commercial grid-connected photovoltaic system (GCPVS) in the Middle East region. In this regard, an economic assessment of a 120 kW_p GCPVS connected in December 2017 under a feed-in tariff (FiT) scheme in Iran—the leading country in the region establishing a supportive policy—is carried out. In this plan, private enterprises can install GCPVS and sell whole generated energy at a high guaranteed price for twenty years. Several economic indices, including net present value (NPV), internal rate of return (IRR), benefit-cost ratio (BCR), payback period time (PBT), and levelized cost of energy (LCOE) are determined to unveil the effectiveness of the enacted program. This paper exploits one-year recorded energy data of this commercial system to boost the reliability of the results. Moreover, PV module degradation factor is taken into account to make the analysis as realistic as possible. The computed outputs imply that this commercial system, with 3.36 BCR, 31.88% IRR, 5.24 years PBT, and 0.0477 $/kWh LCOE, is highly appealing. The sensitivity analysis also highlight that the profitability of the GCPVS investment is secure under a wide range of unpredictable parameters. It is shown for instance that the PBT and IRR are deteriorated by 5.48% and 1.50 years, while the generated energy lowers by 20% compared with the predicted value for the upcoming years. Having said that, it is still far away from the infeasible condition. A comparative analysis between the current findings and similar researches endorse the Middle East region as the highest potential site for PV installation. It is finally deduced that a properly modified FiT scheme can be set in the region's countries concerning the local meteorological and economic conditions to stimulate the investment of this technology.

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This study proposes a fast and precise voltage feedback-based islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) based microgrid. In this algorithm, a disturbance containing the absolute deviation of the output voltage is injected into the inverter's d-axis reference current which tunes the active power output. In islanded mode, the applied disturbance reduces the active power output and consequently point of common coupling voltage beyond the standard setting while its effect at the presence of the grid is negligible. The assessment of the proposed IDM has been conducted in the MATLAB/Simulink platform under extensive scenarios defined by IEEE 1547-2008 and UL 1741 standards for a case study system with two large-scale GCPVSs. The provided outputs remark accurate islanding classification in all cases within 810ms, much lower than the maximum permissible time postulated in islanding standards. This time is short enough to restore GCPVS for autonomous operation of microgrid as well. The comparative analysis of the proposed strategy with a few existing IDMs confirmed its overall superiorities in the terms of non-detection zone, detection time, being applicable into the microgrid, simple threshold determination, and straightforward and cost-effective implementation.

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Islanding refers to a condition where distributed generators (DGs) inject power solely to the local load after electrical separation from power grid. Several islanding detection methods (IDMs) categorized into remote, active, and passive groups have been reported to detect this undesirable state. In active techniques, a disturbance is injected into the DG's controller to drift a local yardstick out of the permissible range. Although this disturbance leads to more effective detections even in well-balanced island, it raises the total harmonic distortion (THD) of the output current under the normal operation conditions. This paper analyzes the power quality aspect of the modified sliding mode controller as a new active IDM for grid-connected photovoltaic system (GCPVS) with a string inverter. Its performance is compared with the voltage positive feedback (VPF) method, a well-known active IDM. This evaluation is carried out for a 1 kWp GCPVS in MATLAB/Simulink platform by measuring the output current harmonics and THD as well as the efficiency under various penetration and disturbance levels. The output results demonstrate that since the proposed disturbance changes the amplitude of the output current, it does not generate harmonics/subharmonics. Thereby, it has a negligible adverse effect on power quality. It is finally concluded that the performance of the sliding mode-based IDM is reliable from the standpoints of islanding detection and power quality. @en

Nowadays, grid–connected photovoltaic (GCPV) system is known as a top leading technology among all resources. However, it still suffers from drastic investment costs. Detailed economic studies should be conducted in this regard to make this technology as gainful as possible. A practical approach is the “optimum economic design”, trying to find an electrically possible layout, i.e. number of series modules and parallel strings as well as the inverter number with the highest profit. This problem is inherently an quadratic integer programming owing to the multiplication of two integer variables in its objective function and some technical constraints. This nonlinear problem should be solved by exhaustive search methods, including comparative and evolutionary algorithms (EAs) such as particle swarm optimization (PSO) and genetic algorithm (GA). In this paper, a new formulation based on the definition of new binary variables has been proposed to convert this problem to the binary linear programming (BLP). The provided method finds the global optimum solution in a scale of seconds while EAs have to be run numerous times in a scale of hours to reach a sufficiently good answer. Moreover, although current methodologies are rarely covered GCPV systems with multiple inverter types, this formulation can be easily developed for systems with several inverter types. The simulations of a 1.1 MW power plant system endorse that the output design provided by the proposed method assures 95,000 $ (1.94%) higher profit compared with those presented by GA. The sensitivity analysis, provided for the prototype system by the efficient new algorithm, also unveils it is economically viable for even 52% of the current feed–in tariff, 40% energy generation lower than the estimated value and 1.1 $/W price rise for the initial investment.@en

Grid–connected photovoltaic (GCPV) systems are currently known as a top leading source of energy among all distributed generators. Despite numerous benefits, this technology suffers from the high initial burden cost. Hence, governments have been decided to encourage the investors to install GCPV systems through different support mechanisms such as feed–in tariff (FIT). This paper investigates the viability of GCPV technology under a new dynamic FIT strategy. The new scheme introduces the annual update of the FIT regarding the goods retail prices and Euro exchange rate rather than the retail electricity cost. These objects are two parameters mostly affect the justification of a project in developing countries with predominantly imported equipment and unstable economic conditions. The new policy is described, and the economic perspective is cleared by computing the economic indices, including net present value (NPV), internal rate of return (IRR), and payback period time (PBT) as well as the levelized cost of energy (LCOE) for Iran as a case study. The computed outputs and significant rise in the installed capacity rate after running the proposed scheme, remark the technology viability for short and mid–term courses in the country. The outputs demonstrate proper attractiveness of the power stations for foreign investment probably does not benefit from excess FIT regarding the utilization of foreign PV module. Finally, it has been concluded that this program can be implemented in developing countries, especially in the Middle East region with almost similar climate patterns.@en

Nowadays installed power capacity of grid-connected photovoltaic (GCPV) systems has an exponential increase around the world. Since these systems are more expensive than other conventional electricity resources, optimal economic designing is much necessary. In this paper, a new intelligent-based approach is proposed to design GCPV systems using Genetic Algorithm (GA). By defining the net present value (NPV) of system as the objective function and considering electrical constraints, the optimal value for sizing factor and also system configuration are determined. In order to calculate the annual produced energy of system with high accuracy, the accurate efficiency model and power equations are used for inverter and PV module, respectively. Also, five-parameter, i.e. linear and five-point, i.e. nonlinear, models of PV module are used to evaluate the behavior of PV array in different temperature and solar radiation conditions. This approach is presented for GCPV systems in all sizes including two or more inverters even with different topologies. The proposed method is applied for designing of a power plant system with multiple inverters.@en