In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.

Renewable energy or alternative energy is extracted through renewable resources. These are considered as an alternative from conventional fossil fuel-based sources because conventional energy sources are depleting rapidly and raised concerns over increasing environmental impacts. Among many renewable sources, solar energy has a substantial part to meet the increased energy demand with reduced environmental effects. Solar irradiance and temperature are key factors upon which photovoltaic (PV) power generation depends but its optimum operating point gets affected by variation in the above-mentioned environmental factors. Finding the optimum operating point is a challenge due to the nonlinear solar behavior and varying nature of environmental conditions. To overcome these challenges, maximum power point (MPP) searching algorithms are exploited to get optimum power from the PV energy system. Maximum power point tracking (MPPT) behavior is different for various weather conditions, for instance, partial shading (PS), and uniform irradiance (UI) conditions. Numerous MPPT methods came to be used to find the optimum power. This work deals with the development of a novel technique for MPP finding of a PV system on the basis of the Water Cycle Algorithm (WCA) under PS conditions. It turns out to be good in terms of exploration and exploitation. Thus, it has the capability to avoid getting stuck in local minima (LM) and to find the global maxima (GM). The performance of the WCA technique is examined on four different types of P-V patterns for UI, PS and fast changing environmental conditions through MATLAB simulation and experimental setup. The findings of WCA are compared with the previous well-known soft computing methods such as PSO, ACS, DFO, and conventional method P&O to evaluate performance. The outcomes reveal that the WCA algorithm overtakes P&O from the perspective of robustness, accuracy, efficiency, and stability, as well as PSO in respect of converging speed and efficiency.

Due to the dominance of renewable energy sources and DC loads, modern power distribution systems are undergoing a transformative shift toward DC microgrids. Therefore, this article is structured to present information on the design, optimization, control, and management of DC microgrids, demonstrating that DC systems have superseded AC systems across power production, transmission, and distribution. The core cause of this superiority is the DC microgrid’s scalability, flexibility, and ease of control. This review is focused on the structural analysis, intelligent and management schemes, market employability, and reliability analysis of a DC microgrid. After this work, some methods are presented that ensure the engineered DC microgrid remains robust to various environmental and operational conditions throughout its service life. The article is enriched with methodological flowcharts and block diagrams, from which design insights can be gained to design a reliable, resilient, robust DC microgrid. The article ends with an indication of how the future energy landscape will look, with the realization of modern technologies through DC microgrids.

Hybridizing a photovoltaic (PV) system with a thermoelectric generator (TEG) enhances overall energy density and input utilization. PV efficiency decreases, whereas TEG efficiency improves with increasing temperatures, making them complementary for boosting system efficiency. However, controlling the hybrid PV-TEG system is challenging due to the difficulty of maintaining operation at the maximum power point (MPP) under partial shading conditions (PSC), non-uniform temperature distribution (NUTD) and varying environmental conditions. PSC in PV arrays arises from uneven irradiance, while NUTD occurs within TEG arrays due to uneven temperature. Both issues cause multiple local maxima on the power-voltage curve, often trapping conventional maximum power point tracking (MPPT) methods in local maxima and suboptimal energy harvesting. To address this, an Improved Water Cycle Algorithm (IWCA) has been implemented for MPPT in series connected hybrid PV-TEG systems. An improvement to the conventional WCA algorithm’s mathematical model enhances exploitation and accelerates particle convergence. This is achieved by dynamically adjusting the stream movement equations, boosting performance without increasing computational complexity. Its performance is compared with the conventional WCA (CWCA), Particle Swarm Optimization (PSO), Dragon Fly Optimization (DFO), Flying Squirrel Search Optimization (FSSO), Grey Wolf Optimization (GWO), and the classical Perturb and Observe (P&O) techniques. Results indicate that IWCA successfully achieved the global maxima under all operating scenarios, with an average efficiency of 99.41%, tracked and settled at the global maxima within 80 to 120 ms. The algorithm exhibits high MPPT stability, maintaining consistent performance across varying conditions while avoiding power oscillations once achieve steady state. Furthermore, the results demonstrate that IWCA outperforms other techniques in speed, accuracy, and efficiency, demonstrating its capability to reliably track and operate at the global maxima of the hybrid system's power-voltage curve.

The dynamics of power converters are highly influenced by uncertainties, nonlinearities, and external disturbances. Thus, high-performance, extremely resilient, and robust control strategies are necessary for their control. For the robust operation of power converters, this article presents an adaptive and finite-time convergent active disturbance rejection control (ADRC) framework inspired by Professor Han’s seminal paper. Based on ADRC’s philosophy, this article proposes a control scheme that integrates adaptiveness and finite-time convergence in both the extended state observer and the control law. The proposed framework ensures quick disturbance estimation and its rejection, thus ensuring that the required response is tracked successfully. The controllers for different power converters, such as buck converters, boost converters, and single-phase inverters, are designed to ensure the desired dynamics, including low settling times and zero-percent overshoots. The controllers are implemented in the discrete-time domain using forward differences. Simscape simulation experiments on buck converters, boost converters, and single-phase inverters demonstrate that the responses are achieved with finite settling time with no overshoots. Thus, such control strategies are highly crucial for mission-critical power applications.