In this paper, a new switched capacitor based multilevel inverter topology is proposed. The proposed topology generates seven level (7L) output voltage using a single dc voltage source along with two floating capacitors. The output voltage of the proposed topology is three-times higher than the input voltage (i.e., gain factor of Vin:Vout = 1:3) along with self-voltage balancing of capacitors. The proposed seven level triple voltage gain boost inverter (7L-3GBI) inherently generates a bipolar waveform without backend H-bridge that reduces the total standing voltage of the topology. The practicability of the proposed topology has been demonstrated by having a quantitative and cost comparison with similar topologies. Furthermore, the workability of the proposed 7L topology has been validated through different results taken from a prototype laboratory setup.

This paper elaborates a comprehensive overview of a photovoltaic (PV) system model, and compares the attributes of various conventional and improved incremental conductance algorithms, perturbation and observation techniques, and other maximum power point tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system. Following a discussion of the MPPT algorithms in each category, a table is drawn to summarize their key specifications. In the performance evaluation section, the appropriate PV module technologies, atmospheric effects on PV panels, design complexity, and number of sensors and internal parameters of the PV system are outlined. In the last phase, a comparative table presents performance-evaluating parameters of MPPT design criterion. This paper is organized in such a way that future researchers and engineers can select an appropriate MPPT scheme without complication.

Based on the concept of switched-capacitor based multilevel inverter topology, a new structure for a boost multilevel inverter topology has been recommended in this paper. The proposed topology uses 11 unidirectional switches with a single switched capacitor unit to synthesize nine-level output voltage waveform. Apart from the twice voltage gain, self-voltage balancing of capacitor voltage without any auxiliary method along with reduced voltage stress has been the main advantages of this topology. The merits of proposed topology have been analyzed through various comparison parameters including component counts, voltage stresses, cost and efficiency with a maximum value of 98.3%, together with the integration of switched capacitors into the topology following recent development. Phase disposition pulse width modulation (PD-PWM) technique and nearest level control PWM (NLC-PWM) have been used for the control of switches. Different simulation and hardware results with different operating conditions are included in the paper to demonstrate the performance of the proposed topology.

This paper presents a new boost inverter topology with nine level output voltage waveform using a single dc source and two switched capacitors. The capacitor voltages are self-balancing and thus is devoid of any sensors and auxiliary circuitry. The output voltage is twice higher than the input voltage, which eliminates the need for an input dc boost converter especially when the inverter is powered from a renewable source. The merits of the proposed topology in terms of the number of devices and cost are highlighted by comparing the recent and conventional inverter topologies. In addition to this, the total voltage stress of the proposed topology is lower and have a maximum efficiency of 98.25%. The operation and dynamic performance of the proposed topology have been simulated using PLECS software and are validated using an experimental setup considering a different dynamic operation.

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.

UAVs have been contributing substantially to multi-disciplinary research and around 70% of the articles have been published in just about the last five years, with an exponential increase. Primarily, while exploring the literature from the scientific databases for various aspects within the autonomous UAV path planning, such as type and configuration of UAVs, the complexity of their environments or workspaces, choices of path generating algorithms, nature of solutions and efficacy of the generated paths, necessitates an increased number of search keywords as a prerequisite. However, the addition of more and more keywords might as well curtail some conducive and worthwhile search results in the same pursuit. This article presents a Systematic Literature Review (SLR) for 20 useful parameters, organized into six distinct categories that researchers and industry practitioners usually consider. In this work, Web of Science (WOS) was selected to search the primary studies based on three keywords: “Autonomous” + “Path Planning” + “UAV” and following the exclusion and inclusion criteria defined within the SLR methodology, 90 primary studies were considered. Through literature synthesis, a unique perspective to see through the literature is established in terms of characteristic research sectors for UAVs. Moreover, open research challenges from recent studies and state-of-the-art contributions to address them were highlighted. It was also discovered that the autonomy of UAVs and the extent of their mission complexities go hand-in-hand, and the benchmark to define a fully autonomous UAV is an arbitral goal yet to be achieved. To further this quest, the study cites two key models to measure a drone’s autonomy and offers a novel complexity matrix to measure the extent of a drone’s autonomy. Additionally, since preliminary-level researchers often look for technical means to assess their ideas, the technologies used in academic research are also tabulated with references.