This paper delivers an in-depth review of the state-of-the-art technologies relevant to rail flaw detection giving emphasis to their use in detection of rail flaw defects at practical inspection vehicle speeds. The review not only looks at the research being carried out but also investigates the commercial products available for rail flaw detection. It continues further to identify the methods suitable to be adopted in a moving vehicle rail flaw detection system. Even though rail flaw detection has been a well-researched area for decades, an in-depth review summarizing all available technologies together with an assessment of their capabilities has not been published in the recent past according to the knowledge of the authors. As such, it is believed that this review paper will be a good source of information for future researchers in this area.

The integration of battery energy storage (BES) with photovoltaic (PV) systems is becoming economically attractive for residential customers. The conventional approach for the interconnection of PV and battery systems requires at least two separate power converters that results in multistage power conversion for some power flows. The dc-dc three port converters (TPCs) are an alternative solution. These converters have many topological variants and possess different operating principles, topological benefits and limitations, and complexities. This paper concentrates on the topological study of TPCs for integrated PV and BESS applications in the power range from a few hundred watts to 350 kW. These are classified into three different categories based on their isolation features between the ports to establish a topological mapping of the reported TPCs. This provides a framework that systematically explores the full range of technical benefits and limitations of each TPC topology. This paper also examines the possible extension of the TPC topologies for grid-interactive PV-BES systems where bidirectional power flow capability is required between grid and BES systems. This extensive review will provide a useful framework and a strong point of reference for researchers for the selection of TPC topologies to meet the system requirements for PV and energy storage applications.

This paper presents a dual active bridge (DAB) converter for a battery energy storage system (BESS) or an electric vehicle (EV) to deliver battery-to- home (B2H) and battery-to-grid (B2G) or vehicle-to-home (V2H) and vehicle-to-grid (V2G) services. The DAB topology is preferable for EV charging systems due to galvanic isolation and bidirectional power flow capabilities, however this topology increases reactive current peak and loses zero voltage switching (ZVS) operation at light loads if it is not controlled with improved modulation strategies and proper reactive power optimization (RPO) algorithm. This paper proposes a mixed modulation method and a RPO algorithm based on power system concepts that reduce the reactive power and provide ZVS operation over a wide voltage range. The mixed modulation allows one of the bridges of DAB converter to run with 2-level while and other to run with 3-level square wave modulation. Due to the complexities associated with the analytical solution using time domain analysis, this paper develops detailed universal expressions of a DAB converter using harmonic analysis method. Analytical identification of ZVS boundaries and DSP implementation of RPO algorithm become easier with these universal expressions. Multiple modes analysis for different operational states are no longer required. Simulation models of the DAB converters have been developed to show the results with different modulation strategies. The theoretical analysis of modulation strategies, RPO algorithm and ZVS operation are validated by experimental results on a laboratory scale prototype. Results show that the proposed mixed modulation with RPO algorithm improve the DAB converter performance.

Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of BESS’ participation in frequency control ancillary service (FCAS) markets. This review synthesises the current state of knowledge on the evolution of the energy market and the role of battery energy storage systems in providing grid stability, particularly frequency control services, with a focus on their integration into evolving high-renewable-energy-source (RES) market structures. Specifically, solar PV and wind energy are emerging as the main drivers of RES expansion, accounting for approximately 61% of the global market share. A BESS offers greater flexibility in storage capacity, scalability and rapid response capabilities, making it an effective solution to address emerging security risks of the system. Moreover, a BESS is able to provide active power support through power smoothing when coupled with solar photovoltaic (PV) and wind generation. In this paper, we provide an overview of the current status of energy markets, the contribution of battery storage systems to grid stability and flexibility, as well as the challenges that BESS face in evolving electricity markets.

Load forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply. In addition to that, accurate load forecasting can result in saving large amounts of money. This article provides a systematic review based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework. This article presents a complete framework for short-term load forecasting using metaheuristic algorithms. This framework consists of three sub-layers: the data-decomposition layer, the forecasting layer, and the optimization layer. The data-decomposition layer decomposes the input data series to extract important features. The forecasting layer is used to predict the result, which involves different statistical and machine-learning models. The optimization layer optimizes the parameters of forecasting methods to improve the accuracy and stability of the forecasting model using different metaheuristic algorithms. Single models from the forecasting layer can predict the results. However, they come with their limitations, such as low accuracy, high computational burden, stuck to local minima, etc. To improve the prediction accuracy, the hyperparameters of these models need to be tuned properly. Metaheuristic algorithms cab be used to tune these hyperparameters considering their interdependencies. Hybrid models combining the three-layer methods can perform better by overcoming the issues of premature convergence and trapping into a local minimum solution. A quantitative analysis of different metaheuristic algorithms and deep-learning forecasting methods is presented. Some of the most common evaluation indices that are used to evaluate the performance of the forecasting models are discussed. Furthermore, a taxonomy of different state-of-the-art articles is provided, discussing their advantages, limitations, contributions, and evaluation indices. A future direction is provided for researchers to deal with hyperparameter tuning.