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Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we first develop an active set identification technique, and then we suggest a modified nonmonotone line search rule, in which a new parameter formula is introduced to control the degree of the nonmonotonicity of line search. By using the modified line search and the active set identification technique, we propose a global convergent method to solve the NMF based on the alternating nonnegative least squares framework. In addition, the larger step size technique is exploited to accelerate convergence. Finally, a large number of numerical experiments are carried out on synthetic and image datasets, and the results show that our presented method is effective in calculating speed and solution quality. [ABSTRACT FROM AUTHOR]

Pulse width modulation (PWM) technology is widely used in traction converters for high-speed railways. The harmonic distribution caused by PWM is quite extensive, and increases the possibility of grid-train coupling resonance in the traction power supply system (TPSS). This paper first analyzes the mechanism of resonance, when the characteristic harmonic frequency of a four-quadrant converter (4QC) current that injects into the traction grid matches the resonant frequency of the traction grid, which may result in resonance in the system. To suppress resonance, this paper adopts specific harmonic elimination-pulse width modulation (SHE-PWM) technology combined with a transient direct current control strategy to eliminate the harmonics in the resonant frequency, which may suppress the grid-train coupling resonance. Due to the fact that the SHE-PWM process with multiple switching angles contains complex transcendental equations, the initial value is difficult to provide, and is difficult to solve using ordinary iterative algorithms. In this paper, an active-set secondary optimization method is used to solve the equation. The algorithm has the benefits of low dependence on initial values, fast convergence and high solution accuracy. Finally, the feasibility of the resonant suppression algorithm is verified by means of Matlab simulation. [ABSTRACT FROM AUTHOR]