• Energy Research
• 2021-2025close

Abstract An intractable but common problem in photovoltaic systems is that the power generated by photovoltaic will reduce seriously due to partial shading. In order to solve this problem, the photovoltaic array reconfiguration methods are developed to mitigate the impact of partial shading and increase output power. This work aims to undertake a comprehensive review on state-of-the-art photovoltaic array reconfiguration methods through a thoroughly investigation of 125 recently published papers. Compared with prior reviews, this work makes a more exhaustive classification, in which sixty-four methods are thoroughly categorized into nine groups. In addition, nine evaluation criteria are summarized for researchers to choose according to their specific requirements. Furthermore, a comprehensive comparison is provided based on ten specific indicators, such as monitor variables, complexity, response speed, rate of shadow dispersion, merits-demerits and application range, etc. Among these methods, the dynamic methods represented by meta-heuristic algorithms show more desirable performance than the static methods due to their faster response speed and prominent adaptability (e.g. the water cycle algorithm has the best performance with a power enhancement of 28%–37%, the TomTom algorithm has inferior performance with power enhancement of only 5%–25%). Finally, this review proposes six constructive suggestions and perspectives to offer technical inspirations for future research in the related fields.

Abstract Global interests in solid oxide fuel cells (SOFCs) has been dramatically increased over the last few decades thanks to their high energy conversion efficiency, prominent stability, and powerful sustainability. In particular, fault diagnosis of SOFC systems plays a crucial role in their overall optimal control and reliable operation. Thus far, a large number of fault diagnosis methods have been developed to improve the durability and promote the commercialization of SOFC systems. Until now, several reviews have been proposed to summarize these methods but all exist distinct drawbacks, such as incomplete classification and discussion, lack of practical recommendations, etc. Hence, this paper attempts to offer a systematic summarization and classification of various SOFC systems fault diagnosis approaches. Note that eighteen specific faults are addressed detailedly, along with fifteen fault detection and isolation (FDI) methods which are categorized into four groups, i.e., electrochemical impedance spectroscopy (EIS)-based, model-based, data-driven, and hybrid FDI. Besides, practical applications, diagnosis accuracy, diagnostic diversity, complexity, advantages, and disadvantages of all methods are thoroughly compared and evaluated. Lastly, eleven perspectives are proposed for future in-depth researches.

Abstract Wind energy has the inherent nature of intermittence and randomness, such that its accurate prediction is extremely critical to ensure safe and stable operation of power system with increased wind power integration. This paper aims to present a thorough state-of-the-art one-stop handbook on various approaches applied in wind forecasting based on three perspectives: wind speed and power forecasting, uncertainty forecasting, and ramp events forecasting. Firstly, four classifications of wind data according to data source, along with twenty-seven data pre-processing technologies which can efficiently improve prediction precision are carefully addressed. Then, a systematic literature review of model principle based wind speed and power forecasting strategies is investigated, which is categorized into three groups: physical approaches, statistical approaches, and combined approaches. Peculiarly, it has reported that combined approaches can realize more than 70% accuracy improvement compared with single model. Besides, three novel technologies developed in recent years are also discussed, e.g., spatial correlation forecasting, regional forecasting, and offshore forecasting. Moreover, thirty-seven evaluation criteria of wind speed and power forecasting are thoroughly summarized for performance verification. Additionally, uncertainty forecasting and ramp events forecasting which can provide more risk information for operators to handle decision-making issues in power system are also elaborated. Lastly, eight recommendations for further development in wind forecasting are also proposed. The prominent merit of this work is that a total of one hundred and seven wind forecasting methods from three perspectives are comprehensively summarized and compared based on inputs, time-scale, space-scale, forecasting variables, metrics, and features, which aims to help readers more effectively utilize these approaches for future in-depth researches.

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

Abstract A novel interacted collective intelligence (ICI) algorithm was developed in this work for maximum power point tracking (MPPT) of centralized thermoelectric generation (TEG) systems at non-uniform temperature gradients (NTG), upon which waste heat recovery ability can be considerably enhanced to enhance resources utilization efficiency. As centralized TEG system often exists numerous local maximum power points (LMPPs) under NTG, ICI is adopted to effectively seek global maximum power point (GMPP), upon which energy exploitation and utilization can be efficiently improved. To achieve a higher searching efficiency, a sub-optimizer with the best solution from all sub-optimizers is dynamically selected during each iteration to guide others. Although multiple sub-optimizers based ICI might lead to a higher computation complexity, a wider global search and more stable convergence can be achieved compared with single meta-heuristic algorithm. Four case studies, e.g., start-up test, step variation of temperature, stochastic temperature variation, and sensitivity analysis, validate the effectiveness and superiority of ICI. Simulation results indicate that ICI based MPPT can produce the largest energy with minimum power fluctuation under NTG compared against other five sub-optimizers, e.g., 109.24%, 112.54%, 108.61%, 107.10% and 116.75% to that of dragonfly algorithm (DA), firefly algorithm (FA), salp swarm algorithm (SSA), moth flame optimization (MFO) and multi-verse optimization (MVO) respectively in the step variation of temperature.

Abstract With soaring growth in the utilization of proton exchange membrane fuel cell (PEMFC) systems on various applications, researches on fault diagnosis techniques of PEMFC systems have gained widespread attention in the last few decades. Inspired by the ever-increasing demand for a reliable fault diagnosis, numerous diagnosis techniques have been proposed which aim to enhance system reliability and durability. Hence, this paper attempts to carry out an in-depth and comprehensive overview of various typical faults and state-of-the-art diagnosis methods of PEMFC systems, which are classified into five main categories, i.e., analytical model based, black-box model based, data driven based, statistical, and experiments testing. Furthermore, based on a thorough investigation of over 140 literatures, this paper not only systematically reviews various existing fault diagnosis approaches but further summarizes their performances with focus on their basic characteristics, such as main merits and drawbacks, practical applications, future developments, etc. Besides, an overall summary together with thirteen other advanced techniques is provided for a more comprehensive analysis with enriched strategies diversity. At last, six recommendations and perspectives for future researches are presented. In general, this paper is envisioned to offer insightful guidance to prompt related researchers/engineers to broaden the width of their researches.

Abstract Parameter estimation of photovoltaic cells is essential to establish reliable photovoltaic models, upon which studies on photovoltaic systems can be more effectively undertaken, such as performance evaluation, maximum output power harvest, optimal design, and so on. However, inherent high nonlinearity characteristics and insufficient current–voltage data provided by manufacturers make such problem extremely thorny for conventional optimization techniques. In particular, inadequate measured data might save computational resources, while numerous data is also lost which might significantly decrease simulation accuracy. To solve this problem, this paper aims to employ powerful data-processing tools, for instance, neural networks to enrich datasets of photovoltaic cells based on measured current–voltage data. Hence, a novel improved equilibrium optimizer is proposed in this paper to solve the parameters identification problems of three different photovoltaic cell models, namely, single diode model, double diode model, and three diode model. Compared with original equilibrium optimizer, improved equilibrium optimizer employs a back propagation neural network to predict more output data of photovoltaic cell, thus it can implement a more efficient optimization with a more reasonable fitness function. Besides, different equilibrium candidates of improved equilibrium optimizer are allocated by different selection probabilities according to their fitness values instead of a random selection by equilibrium optimizer, which can achieve a deeper exploitation. Comprehensive case studies and analysis indicate that improved equilibrium optimizer can achieve more desirable optimization performance, for example, it can achieve the minimum root mean square error under all three different diode models compare to equilibrium optimizer and several other advanced algorithms. In general, the proposed improved equilibrium optimizer can obtain a highly competitive performance compared with other state-of-the-state algorithms, which can efficiently improve both optimization precision and reliability for estimating photovoltaic cell parameters.

Photovoltaic (PV) cell parameter identification is of great significance to accurate PV cell modelling, which can further critically influence overall optimal control and output characteristics simulation design of PV systems. Nevertheless, this high non-linearity obstacle often simultaneously exists multiple local optimums, thus conventional optimization approaches can hardly maintain a consistently satisfactory performance to obtain global optimum. Hence, an adaptive compass search (ACS) algorithm is employed in this paper to identify several critical unknown parameters of the most common utilized PV cell model, i.e., double diode model (DDM). Compared with fixed sequence based original compass search (CS) algorithm, ACS algorithm can dramatically improve global exploration ability via adaptive sequence of exploration directions via historical searching results. Particularly, case studies verify the feasibility and merits of ACS algorithm, which validates that it can achieve more desirable performance compared against whale optimization algorithm (WOA) in terms of optimization precision and convergence rate.

Abstract This study proposes a robust fractional-order PID (RFOPID) control approach for supercapacitor energy storage (SCES) system applied on distribution network. At first, nonlinearities, unmodelled dynamics, parameter uncertainties, and external disturbances of SCES systems are effectively estimated by the high-gain perturbation observer (HGPO). Afterward, a fractional-order PID (FOPID) controller is employed for online complete compensation for the perturbation estimation to enhance the robustness. Moreover, the performance of dynamical responses are significantly improved due to the fractional differentiator and integrator orders. In addition, accurate SCES system model is not needed while merely dq-axis currents require to be determined. Meanwhile, controller gains and observer gains can achieve optimal adjustment by interactive teaching-learning optimizer (ITLO). Four case studies assess the practical performance of RFOPID control compared with that of other typical linear/nonlinear control strategies. At last, a dSpace based hardware-in-the-loop (HIL) test is performed to verify its effectiveness for practical applications.