New Trends of Power Electronics Technology and Application in Energy Field

In the process of geo-energy drilling, the real-time vibration measurement of drill pipes is of significance for an understanding of the downhole conditions and the properties of rock. However, the vibration sensors used in downhole areas at present require additional power sources, such as batteries, and replacing the batteries would significantly reduce production efficiency and increase costs. In our work, a new vibration measurement method using a triboelectric nanogenerator is proposed which will synchronously achieve axial and lateral vibration, and also simultaneously be self-powered. The triboelectric nanogenerator is mainly made of nanomaterials, such as polyimide film and polytetrafluoroethylene (PTFE), and depends on the pulse signal generated by the contact of the two friction layers to measure the vibration frequency. Axial vibration tests show that the output voltage signal amplitude is approximately 3 V, the measurement range is from 0 to 9 Hz, the measurement error is less than 4%, and the maximum output power is 5.63 uW. Additionally, the lateral vibration tests show that the output voltage signal amplitude is approximately 2.5 V, the measurement range is from 0 to 6.8 Hz, the measurement error is less than 6%, and the maximum output power is 4.01 uW. The nanogenerator can typically work in an environment where the temperature is less than 145 °C and the relative humidity is less than 90%. Full article

Because of their low flexibility, traditional vibration sensors cannot perform arbitrary bending adjustments when facing curved surfaces and other complex working conditions during the drilling process; therefore, this research proposes a ring-shaped vibration sensor (RSV−TENG) that can deform freely in the bending direction, and which can be used in working conditions where the inner bending angle of the drill pipe changes greatly. Test results show that the vibration frequency measurement range is from 4 Hz to 16 Hz, with a measurement error less than 4%, the vibration amplitude measurement range is less than 20 mm, with a measurement error less than 5%, the output voltage and current signal are 120 V and 60 nA, respectively, when three RSV−TENGs are connected in parallel, and the maximum output power is 6 × 10⁻⁷ W when the external resistance is 10⁶ Ω. Compared with traditional downhole sensors, this sensor has self-powered and self-sensing functions, eliminating the shortcomings of battery and cable power supply; in addition, this sensor can be installed in the drill pipe space with different curvature radii, so it is more suited to complex and changeable downhole working conditions. Full article

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. Full article

Riserless Mud Recovery (RMR) technology, as an emerging and efficient drilling method, is advantageous to reduce the shallow flow hazards and the number of casings. The wave current effect is one of the reasons limiting the application of RMR technology in deep and ultra-deep water, and fewer quantitative and qualitative analyses of the effect of the current are made on the stability of conductors. This paper investigates the influence of the overturning moment generated by the continuous subsea internal wave flow and the soil resistance to the conductor. The numerical simulation software ABAQUS is used to study the effects of sea state recurrence period, seabed soil properties, conductor material, driving depth in the mud, and conductor wellhead height on the stability of the conductor, and the influence weights of the factors affecting the stability of the conductor are analyzed using the weight analysis algorithm of extreme learning machine-mean impact value (ELM-MIV). Finally, the qualitative and quantitative analyses affecting the stability of the conductor are carried out, which provide reference values for the application of the RMR technology. Full article

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10⁻⁸ and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods. Full article

The function of vehicle powertrains (including hybrid powertrains) is to transmit power from the power source (engine or electric machine) to driving wheels. The planetary gear train (PGT) is a core structure of mechanisms of powertrains. The detection of topological symmetry is helpful for improving the efficiency of mechanism design. In this paper, we present a fully automatic and reliable method for detecting symmetry of plane kinematic chains and extend this method to symmetry detection and the topological design of mechanisms of powertrains. First, the topological model and adjacency matrix are introduced to represent various kinds of plane kinematic chains. Then, the moment matrix of the kinematic chain is established to obtain link groups, based on which we propose an algorithm to generate the unique numerical code of each link and precisely detect the symmetry. Our method is applied to synthesize different kinds of plane kinematic chains and mechanisms, which can improve the design efficiency of mechanisms of powertrains and other mechanical devices. Full article

Helical gears are widely used in powertrain systems. The computerized design of a new type of non-generated external helical gears based on critical control points at the transverse tooth profile is presented. The entire tooth profile is divided into different parts including the active tooth profile and fillet by control points. Involutes, circular arcs and Hermite curves are defined between two critical control points and smoothly connected with each other at those control points. The parametric equations for the tooth surfaces are derived considering the position of the mentioned control points. The basic design parameters and equations of the geometric sizing are proposed. The contact patterns, variation of the maximum stresses and peak-to-peak level of loaded transmission errors for six cases of design of the proposed new geometry of helical gear drives are studied with two cases of traditional helical gear drives as a reference, including gears with and without micro-geometry modifications. One case of an external helical gear drive designed with a combination of a circular arc and an involute to form the active tooth profile for both the pinion and the gear shows a much lower maximum bending stress and similar lower peak-to-peak level of loaded transmission errors with respect to the other cases of design. The proposed design method of tooth profiles based on critical control points lays the foundation for the topological optimization of helical gear drives. Full article

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits. Full article

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries. Full article