A two-stage algorithm is proposed to compute switching angles for selective harmonic elimination or selective harmonic control. In the first off-line computing stage, the multivariate nonlinear equations are equivalently converted to a group of univariate linear equations by using the theories of symmetric polynomials and Groebner bases, which are then stored into the target microcontrollers (MCUs) and solved on-line; In the second on-line computing stage, the linear equations are solved and their solutions are used to construct a polynomial whose real roots are actually the final solutions, then, an algebraic-numerical hybrid method is developed to solve the constructed polynomial, which uses the Sturm's theorem to bracket each real root into an individual interval and then the bisection method is used to find all the exact real roots. Compared to the traditional numerical methods, the executing efficiency is increased siginificantly. This method is implemented on several commonly-used MCUs and shows very good real-time performance. Experiments verify the correctness of the solved switching angles and demonstrate the feasibility of applying this method in the real control system.

To meet the needs of energy storage system configuration with distributed power supply and its operation in the active distribution network (ADN), establish the dynamics of the all‐vanadium redox flow battery energy storage system (BESS). On this basis, an energy storage operation of ADN strategy is proposed to stabilise the power fluctuation of the system. The energy storage configuration model with optimising objectives such as the fixed cost, operating cost, direct economic benefit and environmental benefit of the BESS in the life cycle of the energy is constructed, and the energy storage installation capacity, power and installation position are used as decision variables, which are solved by the dynamic programming algorithm. On the basis of case 33 and case 69 node examples and typical daily load and distributed generations output curve, the simulation analysis is carried out to obtain the optimal configuration result. At the same time, the system tie‐line power and the dynamic characteristics of the energy storage system before and after the installation of the energy storage device are obtained. The simulation results show the validity of the model.

This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.

In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper proposes an online operation risk assessment of the wind power system of the convolution neural network (CNN) considering multiple random factors. This paper analyzes multiple random factors of the wind power system, including uncertain wind power output, load fluctuations, frequent changes in operation patterns, and the electrical equipment failure rate, and generates the sample data based on multi-random factors. It uses the CNN algorithm network, offline training to obtain the risk assessment model, and online application to obtain the real-time online operation risk state of the wind power system. Finally, the online operation risk assessment model is verified by simulation using the standard network of 39 nodes of 10 machines New England system. The results prove that the risk assessment model presented in this paper is more rapid and suitable for online application.

Accelerometers are some of the most familiar micro electronic mechanical system (MEMS) sensor. Accelerometers are used in many fields, such as vehicle braking, mobile phone, gauge check and so on. In this paper, we summarize how accelerometers sensors can be operated, and their working principle is presented, and then the fabrication process of the accelerometers sensors is presented. Then we conducted a preliminary test on the performance of the sensor and discuss the optimization scheme of manufacturing process.