• Energy Research

The integration of clean energy sources (CESs) into modern power systems has been studied using various power converter topologies. The challenges of integrating various CESs are facilitated by the proper design of multi-port power converter (MPPC) architecture. In this study, a brand-new two-stage MPPC is suggested as a solution to the intermittent nature and slow response (SR) of CESs. The suggested system combines a DC\DC and a DC\AC converter and storage unit, and the suggested circuit additionally incorporates a number of CESs (PV\wind\fuel cell (FC)). This article discusses the power management and control technique for an integrated four-port MPPC that links three input ports (PV, wind, and FC), a bidirectional battery port, and an isolated output port. One of the recent optimization techniques (Harris Hawk’s algorithm) is applied to optimize the system’s controller gains. By intelligently combining CESs with complementary characteristics, the adverse effects of intermittency are significantly mitigated, leading to an overall enhancement in system resilience and efficiency. Furthermore, integrating CESs with storage units not only addresses SR challenges but also effectively combats intermittent energy supply. The proposed system exhibits improved dynamic capabilities, allowing it to efficiently distribute excess energy to the load or absorb surplus energy from external sources. This dual functionality not only optimizes system operation but also contributes to a reduction in system size and cost, concurrently enhancing reliability. A comprehensive investigation into operational principles and meticulous design considerations are provided, elucidating the intricate mechanics of the suggested MPPC system. Employing MATLAB/Simulink, the proposed architecture and its control mechanisms undergo rigorous evaluation, affirming the feasibility and efficacy of this innovative system.

The integration of clean energy sources (CESs) into modern power systems has been studied using various power converter topologies. The challenges of integrating various CESs are facilitated by the proper design of multi-port power converter (MPPC) architecture. In this study, a brand-new two-stage MPPC is suggested as a solution to the intermittent nature and slow response (SR) of CESs. The suggested system combines a DC\DC and a DC\AC converter and storage unit, and the suggested circuit additionally incorporates a number of CESs (PV\wind\fuel cell (FC)). This article discusses the power management and control technique for an integrated four-port MPPC that links three input ports (PV, wind, and FC), a bidirectional battery port, and an isolated output port. One of the recent optimization techniques (Harris Hawk’s algorithm) is applied to optimize the system’s controller gains. By intelligently combining CESs with complementary characteristics, the adverse effects of intermittency are significantly mitigated, leading to an overall enhancement in system resilience and efficiency. Furthermore, integrating CESs with storage units not only addresses SR challenges but also effectively combats intermittent energy supply. The proposed system exhibits improved dynamic capabilities, allowing it to efficiently distribute excess energy to the load or absorb surplus energy from external sources. This dual functionality not only optimizes system operation but also contributes to a reduction in system size and cost, concurrently enhancing reliability. A comprehensive investigation into operational principles and meticulous design considerations are provided, elucidating the intricate mechanics of the suggested MPPC system. Employing MATLAB/Simulink, the proposed architecture and its control mechanisms undergo rigorous evaluation, affirming the feasibility and efficacy of this innovative system.

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

In Egypt, the climate has a direct impact on the dried date fruit production. The traditional drying method or open sun drying (OSD) leads to pollution of the final product caused by sand-laden winds, rain, or animals and harmful insects, etc. This study is aimed at designing, implementing, and experimentally validating a solar dryer based on IoT technology and integrated with a PV system in Aswan, Egypt. The purpose of the dryer is to monitor and control the quality of the three most popular date fruit varieties and determine the most effective drying method, with an algorithm that operates the system automatically for optimal performance and high-quality products. The automatic solar dryer (ASD) significantly affects the final moisture content and color characteristics and reaches the equilibrium moisture content (EMC) at a faster rate for dried date samples compared to the OSD. The drying rate of ASD was 29.03% (Sakkoti), 31.04% (Malkabi), and 25.49% (Gondaila) higher than OSD. Also, the dried date fruit samples reached EMC on the ASD after 8 days for both Malkabi and Gondaila and 9 days for Sakkoti, while it took 14 to 15 days on OSD. The maximum open circuit voltage V oc , short circuit current I sc , and output power P output were 41.70 V, 8.84 A, and 365.09 W, respectively. All values of total color change ( Δ E ∗ ) after open-air drying of dry date varieties were higher than solar drying for both drying systems. This study can be then more helpful for producers of dried foods.

In Egypt, the climate has a direct impact on the dried date fruit production. The traditional drying method or open sun drying (OSD) leads to pollution of the final product caused by sand-laden winds, rain, or animals and harmful insects, etc. This study is aimed at designing, implementing, and experimentally validating a solar dryer based on IoT technology and integrated with a PV system in Aswan, Egypt. The purpose of the dryer is to monitor and control the quality of the three most popular date fruit varieties and determine the most effective drying method, with an algorithm that operates the system automatically for optimal performance and high-quality products. The automatic solar dryer (ASD) significantly affects the final moisture content and color characteristics and reaches the equilibrium moisture content (EMC) at a faster rate for dried date samples compared to the OSD. The drying rate of ASD was 29.03% (Sakkoti), 31.04% (Malkabi), and 25.49% (Gondaila) higher than OSD. Also, the dried date fruit samples reached EMC on the ASD after 8 days for both Malkabi and Gondaila and 9 days for Sakkoti, while it took 14 to 15 days on OSD. The maximum open circuit voltage V oc , short circuit current I sc , and output power P output were 41.70 V, 8.84 A, and 365.09 W, respectively. All values of total color change ( Δ E ∗ ) after open-air drying of dry date varieties were higher than solar drying for both drying systems. This study can be then more helpful for producers of dried foods.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS’s DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS’s DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.