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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. [ABSTRACT FROM AUTHOR]

The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)‐based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training‐based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in maximum power point (MPP) detection for PV systems. DTBO improves tracking speed and reduces fluctuations in the power output during the tracking period. The proposed method is extensively verified using the Typhoon hardware‐in‐the‐loop (HIL) 402 emulator and compared to conventional methods such as particle swarm optimization (PSO), and JAYA, as well as the recently proposed adaptive JAYA (AJAYA) method for MPPT in a PV system under similar conditions. [ABSTRACT FROM AUTHOR]

Transmission congestion is one of major drawbacks of deregulated power system. Congestion gradually violates the physical, operational and policy constraints of the present electricity market. Congestion management in huge power system remains a challenging and tough task, which can be achieved by introducing one or more Distributed Energy Resources (DERs) such as Distributed Generation (DG), electric vehicles, modern micro grid, Energy Storage Systems (ESSs) etc. over congested lines. Consequently, this work proposes the hourly congestion management by optimal allocation of DG in '24 hours' time frame. Further, Transmission Congestion Rent is used to determine the optimal and sub optimal locations for DG, whereas Differential Evolution and Particle Swarm Optimization based hybrid optimization technique is exploited to identify optimal DG size. In this work, solar based DG along with battery based ESS is considered to act as Distributed Energy Storage System 'DESS. Subsequently, Interval computation method is introduced in this work to consider the intermittency of solar DG. Battery Energy Storage System 'BESS' is integrated with DGs due to its excess energy storage capability during off‐load period and make it available at peak load conditions, thereby enhancing the overall efficiency of the network. The '24 hours' solar irradiance and temperature data of Delhi, India is taken into account to mathematically model the power generated from Solar DG. The proposed methodology is tested on IEEE 30‐bus system and '24 hours' demand data is generated from the original IEEE load data. From the obtained results, it has been established that the hybrid optimization technique results into a better solution as compared to their individual enforcement in managing available generation resources and congestion problem. [ABSTRACT FROM AUTHOR]

Impedance mismatching between different phases of a multiphase transformer is generally observed e.g., in a three-phase to seven-phase transformer, due to an unequal number of turns in different coils. This mismatching introduces error in the study of per phase equivalent circuit diagrams as well as induces an imbalance in output voltages and currents. Therefore, it is a challenging task to develop a per-phase equivalent circuit for the secondary and primary sides (In some cases) too. This paper proposes an artificial intelligence optimization technique like PSO based modeling of the per-phase equivalent circuit of the secondary side. This paper deals with the modeling and simulation of a three-phase to seven-phase power transformer using Artificial Intelligence technique like particle swarm optimization (PSO) and Genetic Algorithm (GA). The proposed model is optimized through PSO and GA algorithms and tested for minimum voltage error in each phase. The proposed model is designed and the objective function is optimized by PSO & GA in MATLAB environment. It is found that the optimized model can be effectively implemented as a per-phase equivalent circuit for the secondary side. [ABSTRACT FROM AUTHOR]

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations. • An Archimedes Optimization Algorithm (AOA) based novel MPPT approach is presented. • The proposed MPPT approach has been tested to capture the Maximum Power Point (MPP) under various partial shading (PS) conditions. • The performance of proposed MPPT approach has been evaluated against the other state-of-the-art approaches such as particle swarm optimization (PSO), Jaya, and the latest proposed variant of Jaya called the adaptive Jaya (A-Jaya). • The proposed MPPT method is analysed in MATLAB/Simulink software and then validated using real-time Hardware-in-the-loop (HIL) emulator. • The proposed MPPT approach outperforms the previous approaches in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations. [ABSTRACT FROM AUTHOR]