Non-homogeneous irradiation conditions due to environmental changes introduce multiple peaks in non-linear P-V characteristics. Hence, to operate PV at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the MPPT methods presented in literature fail to guarantee Global Maximum Power Point (GMPP) operation. In this paper, a new detection technology to identify global MPP zones using hybrid Enhanced Leader Particle Swarm Optimization (ELPSO) assisted by a conventional Perturb and Observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader; whilst, P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with conventional ELPSO and the hybrid PSO-P&O methods. Experimental results along with energy evaluations confirmed the superiority of the ELPSO-P&O method in obtaining the maximum available power under all shaded conditions.

Optimizing the operational performance of Maximum Power Point trackers (MPPTs) during multiple peak partial shading Conditions (PSCs) prevails as a major challenge in PV power generation. Though many of the newly evolved soft computing and heuristic methods are compatible during PSCs, the need for such techniques in uniform irradiation levels is uncertain because most of these algorithms produce extensive oscillations before converging to the global maximum power point (GMPP). On the other hand, adopting conventional Perturb and Observe (P&O) algorithm is meritorious to reduce the transient power and voltage oscillations. Therefore, this works presents a proficient hybrid tracking technology that provides an adequate trade-off between conventional P&O and advanced soft computing techniques by accurately detecting shade occurrences. For which, the uniqueness in the operating point conductance of P&O at the left most power peak (LPP) in the P-V curve is utilized. Subsequently, the proposed convention utilizes P&O for two major purposes: 1) to track MPP in uniform irradiance and 2) to detect PSCs. More importantly, even during PSCs, the detection algorithm is well designed to operate at GMPP with conventional P&O method itself, and, only for extreme shade cases, Flower Pollination Algorithm (FPA) is introduced to track GMPP.

Detection of abnormal photovoltaic (PV) system operation is essential to ensure safe and uninterrupted performance. In this study, the authors present a data-driven two-stage method for PV fault detection and diagnosis (FDD). We exploit an inherent characteristic of PV systems, i.e., voltage and current changes at maximum power point (MPP) caused by faults. In the first stage, fault occurrences are detected using predefined criteria based on the MPP values. The second stage employs ${I}$ – ${V}$ characteristic curve data and a densely connected convolutional network (DenseNet) model to diagnose the fault type. The DenseNet model is rigorously trained using a very large dataset of ${I}$ – ${V}$ curves; this ensures precise and efficient fault diagnosis. We validate our approach via simulations and hardware analyses employing a $5\times3$ PV array that initially operates normally, but then develops line-to-line faults (LLFs), open-circuit faults (OCFs), degradation faults (DFs), and partial shading faults (PSFs). We compare our DenseNet-based PV FDD model to the latest PV FDD models. The results confirmed that the new method accurately detect and diagnose PV faults.

Superabundant utilization of electricity in the residential sector is one of the major reasons for frequent peak demand. Hence, power sector necessitates an appropriate solution to control and monitor the peak demand. In this regard, implementation of an appropriate home energy management system becomes mandatory at customer premises to have an effective control over peak demand. Thus, in this research a simple home energy management using Two-Phase Simplex Method (TPSM) is proposed with an objective to (i) reduce peak demand, (ii) reduce consumer consumption cost, and (iii) conserve consumer comfort level. Further, the research proposes detailed investigations on the smart energy-home management model monitored by IoT. For simulations, different load scenarios are considered and the results are compared with the existing benchmarks available in the literature. On validations, the proposed TPSM method is found simple, reliable and efficient. More importantly, the multipurpose objectives has certainly given better results in consumer consumption cost that can give better control to peak demand. Furthermore, the usage of lucid simplex method has almost reduced the computational complexity to fasten the response time. In this regard, consumer comfort is served here is considered as a major accomplishment with the proposed work.

Soiling poses a significant challenge to the performance of photovoltaic (PV) systems in desert climates. This phenomenon can be attributed to three primary factors: 1) the accumulation of non-dispersed shades, 2) the emergence of multiple power peaks, and 3) the inability of conventional maximum power point trackers (MPPTs) to locate the global MPP (GMPP). To address these issues, this study proposes a new array design that disperses shades caused by soiling through the relocation of PV modules. Distinct to existing works, this method not only enhances power delivery during soiling conditions but also ensures inverter-friendly MPP tracking by aligning the GMPP with the array's open circuit voltage. This alignment is crucial for eliminating voltage fluctuations, allowing even conventional MPPTs to effortlessly track the GMPP without the need for complex tracking algorithms. Furthermore, an advanced tracking strategy has also been proposed to improve the tracking speed. For validation purposes, both shade dispersion and GMPP tracking capabilities of the proposed method are fully tested using various soiling shade profiles. The results clearly demonstrate that this developed methodology offers the flexibility to operate the PV array in both MPP and constant power generation modes, making it highly advantageous for grid-connected applications.

Modeling a reliable electrical equivalent circuit to simulate the output I–V characteristics of organic solar PV (OPV) cells still prevails as a challenging task. This is because, estimating unknown parameters from the transcendental current equation determining the nonlinear OPV characteristics is extremely difficult. More importantly, predicting the parameters for dynamic changes in irradiation profile demands reliable optimization-based models. Therefore, this article proposes the application of adaptive wind-driven optimization (WDO) algorithm for a three-diode electrical equivalent model to estimate the OPV circuit parameters. Application of WDO algorithm has produced precise PV parameters to reproduce the exact I–V characteristics. In particular, the ability to replicate the kink effect in OPV characteristics is a noticeable improvement. Compared to its counterparts, various factors that enable WDO to enhance its compatibility towards nonlinear OPV modeling are: i) unique velocity update strategy via Coriolis and Gravitational forces, ii) excellent tradeoff between exploration and exploitation of control variables, iii) adaptive capability to maintain solutions within the search space even if the limits are breached, and iv) easiness in parameter tuning. For the validation, extensive testing has been conducted to reproduce the widely used characteristics of nav100 OPV cell at various operating conditions. Quantitatively, WDO method showcases excellent accuracy with an individual absolute error value in the order of 10 ⁻⁶ and convergence within the first 50 iterations itself, demonstrating its supremacy to solve OPV parameter identification problem.