Search Results (60)

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

Solar-powered pumping systems using series pumps are commonly applied in the delivery of water to remote agricultural regions, particularly in hilly tropical terrain. The synchronization of these pumps typically depends on reliable communication; however, dense vegetation, elevation changes, and weather conditions often disrupt signals. To address these limitations, a fully decentralized, communication-free control system is proposed. Each pumping station operates independently while maintaining synchronized operation through emulated neighbor sensing. The system applies a discrete-time control algorithm with virtual sensing that estimates neighboring pump statuses. Each station consists of a solar photovoltaic (PV) array, variable-speed drive, variable inlet valve, reserve tank, and local control unit. The controller adjusts the valve positions and pump power based on real-time water level measurements and virtual neighbor sensing. The simulation results across four scenarios, including clear sky, cloudy conditions, temporary outage, and varied irradiance, demonstrated steady-state operation with no water overflow or shortage and a steady-state error less than 4% for 3 m³ transfer. The error decreased as the average power increased. The proposed method maintained system functionality under simulated power outage and variable irradiance, confirming its suitability for remote agricultural areas where communication infrastructure is limited. Full article

Photovoltaic (PV) energy is gaining traction due to its direct conversion of sunlight to electricity without harming the environment. It is simple to install, adaptable in size, and has low operational costs. The power output of PV modules varies with solar radiation and cell temperature. To optimize system efficiency, it is crucial to track the PV array’s maximum power point. This paper presents a novel fixed-point FPGA design of a nonlinear maximum power point tracking (MPPT) controller based on synergetic control theory for driving autonomously standalone photovoltaic systems. The proposed solution addresses the chattering issue associated with the sliding mode controller by introducing a new strategy that generates a continuous control law rather than a switching term. Because it requires a lower sample rate when switching to the invariant manifold, its controlled switching frequency makes it better suited for digital applications. The suggested algorithm is first emulated to evaluate its performance, robustness, and efficacy under a standard benchmarked MPPT efficiency (${\eta }_{MPPT}$) calculation regime. FPGA has been used for its capability to handle high-speed control tasks more efficiently than traditional micro-controller-based systems. The high-speed response is critical for applications where rapid adaptation to changing conditions, such as fluctuating solar irradiance and temperature levels, is necessary. To validate the effectiveness of the implemented synergetic controller, the system responses under variant meteorological conditions have been analyzed. The results reveal that the synergetic control algorithm provides smooth and precise MPPT. Full article

The limits of direct current (DC) injection and output current distortion of grid-connected photovoltaic (PV) inverters are specified in the IEEE 1547-2018 standard. The standard prescribes limits of output current harmonics, but the input voltage and power at which output current distortion is measured are not specified. This manuscript presents the results of DC injection and output current distortion measurements for three commercial single-phase PV inverters, with 3 kVA, 3.3 kVA, and 6 kVA rated power. During the measurements, the inverters are powered by a programmable DC source that emulates the power voltage characteristic of a PV array, providing different input conditions. In addition to steady-state measurements at constant input voltage and power, the change in the output current spectrum over time during the maximum power point tracking (MPPT) is also measured. The results show that the output current distortion depends on the input voltage and power. Moreover, the current distortion of some of the tested inverters exceeds the limits specified by the standard in some cases. The presented results suggest that further research on the dependence of the output current distortion from PV inverters on their input power and voltage is needed. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform’s potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization. Full article

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization. Full article

The deeper penetration of renewables in the energy mix is an intense requirement in order to reduce global carbon dioxide emissions. In addition, new technologies are being developed, such as electric mobility and Distributed Generation (DG) in urban areas. However, the unpredictable fluctuations in energy generation from roof-installed PVs and the switching operation of their inverters greatly aggravate the already-present grid quality problems. In this paper, the Smart Boiler (SB) concept for grid quality improvement is presented. Furthermore, its experimental verification is implemented on a flexible testbed that accurately emulates several realistic scenarios for the low voltage distribution grid, under complex operating conditions. The proposed low-cost electronic kit, which contains a converter of fairly simple topology and requires connection to the internet, is used to upgrade conventional domestic boilers to smart devices. The SB automatically regulate the local reactive power flow, helping to stabilize the voltage level and suppress the grid current harmonic content, with both services provided in a matter of seconds. The higher the active power consumed and the denser the SB cluster, the wider the beneficial impact on the affected network area. While this service is provided, excess energy generated by PVs is temporarily stored as heat in the boiler tanks, given the users’ hot water consumption habits. The whole application, as a powerful demand-side management tool, proves beneficial for both the network operator and the end-user, especially when self-consumption is desirable in order to achieve a Nearly Zero Energy Building. Full article

A photovoltaic emulator (PVE) is a specialized device designed to mimic the static and dynamic properties of a solar panel. It serves as a crucial tool for testing and validating PV systems. Despite the unpredictable variations in real environmental conditions, the PVE offers a controlled environment, facilitating the smooth implementation and testing of PV subsystems. The PVE consists of two essential components: the reference PV model and the PVE power electronics controller. For simplicity, both these elements must be inherently straightforward. Numerous advanced methods have been proposed in the literature to efficiently integrate the PV model into the PVE system. However, these approaches often involve complex iterative computations of intricate equations related to solar panels, making them less practical. To address these limitations, this paper introduces a novel PVE that aims to simplify the implementation and integration of the PV model. The proposed system employs a simple and non-iterative approach to provide the reference to the PVE, using a straightforward explicit model of the solar panel. In comparison to existing works, the proposed PVE stands out for its high flexibility and simplicity as it does not require the complex iterative computations of implicit equations related to the solar panel model. The overall PVE system is implemented with a basic proportional-integral (PI) controller and a DC-DC buck power electronic converter. It is then validated using a 200 W solar panel through a series of experiments, including the EN 50530 Test. The results of these experiments demonstrate that the proposed PVE efficiently reproduces the static and dynamic characteristics of a solar panel. Full article

Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications. Full article

Incorporating bypass diodes within photovoltaic arrays serves to mitigate the negative effects of partial shading scenarios. These situations can lead to the appearance of multiple peaks in the performance of solar panels. Nevertheless, there are cases where conventional maximum power point tracking (MPPT) techniques could encounter inaccuracies, causing them to identify the highest power point within a specific area (the local maximum power point; LMPP) instead of the overall highest power point across the entire array (the global maximum power point; GMPP). Numerous methods based on artificial intelligence (AI) were proposed to address this issue; however, they frequently used cumbersome and unreliable methodologies. This research presents the energy-valley-optimizer-based optimization (EVO) technique, which is designed to efficiently and dependably tackle the issue of partial shading (PS) in detecting the maximum power point (MPP) for photovoltaic (PV) systems. The EVO algorithm enhances the speed of tracking and minimizes power output fluctuations during the tracking phase. Through the utilization of the Typhoon hardware-in-the-loop (HIL) 402 emulator, extensive validation of the proposed technique is conducted. The effectiveness of the suggested method is compared with the established cuckoo search algorithm for achieving maximum power point tracking (MPPT) within a photovoltaic (PV) system. This comparison takes place under equivalent conditions to ensure a fair performance evaluation. Full article

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. Full article

The use of photovoltaic (PV) panels has increased rapidly in the last few years and as a result has become one of the main sources of renewable energy. In this context, it is important to understand in detail how a PV panel reacts to different environmental conditions and how these affect total performance. An experiment has been designed to investigate the performance of a PV panel under various highly non-uniform temperature and irradiance profiles, generated by artificial lighting. Measurements of irradiance and temperature distribution are related to measured I–V curves and used as input to the five-parameter model. The results show the limitations of the model to emulate the PV response under such extreme conditions and provide useful insights about the effect of the temperature profile on the PV performance. Full article

Rapid growth in the integration of new consumers into the electricity sector, particularly in the industrial sector, has necessitated better control of the electricity supply and of the users’ op-erating conditions to guarantee an adequate quality of service as well as the unregulated dis-turbances that have been generated in the electrical network that can cause significant failures, breakdowns and interruptions, causing considerable expenses and economic losses. This research examines the characteristics of electrical variations in equipment within a company in the industrial sector, analyzes the impact generated within the electrical system according to the need for operation in manufacturing systems, and proposes a new solution through automation of the regulation elements to maintain an optimal system quality and prevent damage and equipment failures while offering a cost-effective model. The proposed solution is evaluated through a reliable simulation in ETAP (Energy Systems Modeling, Analysis and Optimization) software, which emulates the interaction of control elements and simulates the design of electric flow equipment operation. The results demonstrate an improvement in system performance in the presence of disturbances when two automation schemes are applied as well as the exclusive operation of the capacitor bank, which improves the total system current fluctuations and improves the power factor from 85.83% to 93.42%. Such a scheme also improves the waveform in the main power system; another improvement result is when simultaneously operating the voltage and current filter together with the PV system, further improving the current fluctuations, improving the power factor from 85.83% to 94.81%, achieving better stability and improving the quality of the waveform in the main power grid. Full article

Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a wide spectrum of irradiance intensity to emulate a PV cell’s working conditions while meeting cost targets. In this paper, we propose a method for calculating the irradiance distribution for a given number and position of LED sources to meet irradiance and uniformity requirements in LED-based light simulators. In addition, we establish design guidelines for minimizing non-uniformity under specific constraints and utilize a function to evaluate the degree of non-uniformity and determine the optimal distance from the illuminated surface. We demonstrate that even with a small number of low-cost LED sources, high levels of irradiance can be achieved with bounded non-uniformities. The presented guidelines serve as a resource for designing tailored, low-cost light simulators that meet users’ area/intensity/uniformity specifications. Full article