An efficient way to raise the proton exchange membrane fuel cell’s (PEMFC’s) power generation efficiency and power supply quality is to use maximum power point tracking (MPPT). Conventional MPPT approaches often have difficulty producing an effective control effect due to the PEMFC’s inherent nonlinear characteristics. Another challenge for systems that track maximum power points is dealing with fast changes in operational conditions that affect FC’s maximum power point (MPP). The main contribution of this study is the introduction of two artificial intelligence-based MPP control approaches for fuel cells operating under rapidly changing operating conditions. These methods are based on imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems (ANFIS) (ICA NN). The proposed approaches determine the fuel cell voltage that corresponds to the maximum power point. Following that, a fuzzy logic controller is used to modify the duty cycle of a DC/DC boost converter for FC MPP tracking. The MATLAB environment is used to run simulations. The results of the proposed method are compared with those of the conventional fuzzy method. The results demonstrate that the suggested solutions function excellently in both normal operating conditions and quickly varying operating conditions. On the other hand, the suggested approaches can quickly locate and monitor the MPP of FC. Additionally, the suggested techniques increase the FC system’s efficiency by absorbing more power.

Real‐time estimating harmonic distortion levels near the Point of Interconnections of grid‐connected inverter‐based resources (IBRs) is critical for maintaining the operational reliability and security of the power grid, particularly during transients when IBRs generate excessive harmonics. Traditional methods like Total Harmonic Distortion (THD) are unsuitable for time‐varying waveforms as they suffer from aliasing, spectral leakage, and picket‐fence effects. This paper proposes a novel approach that utilizes convolution‐based measures for estimating harmonic distortion, which offers a more suitable way for ongoing real‐time applications. The first measure uses a 60 Hz sinusoidal function as the convolution kernel, while an enhanced measure employs the Gaussian function as the kernel. Two convolution‐based measures are proposed and compared with THD. The comparison criteria are based on the two significant features describing the variation of harmonics: min/max values and variation curvature of harmonics showing the accurate performance of proposed measures. Moreover, short‐term estimations of harmonic distortion are used to validate the effectiveness of the proposed measures, while long‐term estimations are used to determine the ability of the proposed measures to determine harmonic distortion contributions among power network components. The proposed technique's intrinsic characteristics provide a sampling window with low sensitivity to deviations in the fundamental frequency and signal's stationary condition to prevent aliasing and spectral leakage and rule out the impact of the picket‐fence effect on the harmonic distortion level estimation.

The increasing level of harmonics in the power grid, driven by a substantial presence of coupled inverter-based energy resources (IBRs), poses a new challenge to power grid transient stability. This paper presents the findings from experiments and analytical studies on the impact of the topological configuration of coupled IBRs on the level of power flow harmonics in a distribution grid: (i) our findings report that the impact of grid topology on harmonics is nonlinear, which is in contrast to the common perception that the power grid operates as a large linear low-pass filter for harmonics; (ii) importantly, this study highlights that the influence of the topological configuration of inverters on the reduction of system-level harmonics is more substantial than the effect of line impedance, emphasizing the significance of grid topological configuration; (iii) furthermore, the observed reduction in harmonics is attributed to a harmonic cancellation effect achieved through self-compensation by all the coupled inverters without affecting the active power flow in the power grid. These findings propose a new approach to limit the penetration of complex IBR harmonics in the power grid from a system-wide perspective. This approach significantly differs from the component-level or localized solutions used today, such as inverter control, power filtering, and transformer tap changes.

In a railroad feeding system, detecting a location of pole to earth faults is important for safe operation of the system. The goal of this paper is to use a combination of the evolutionary algorithm and neural networks to increase the accuracy of single pole-to-earth fault detection and location on Tehran railroad power supply system. Accordingly, Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network for enhancing learning process accuracy and the convergence. Owing to the nonlinearity of system, the fault detection is an ideal application for the proposed method where 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer and the silicon rectifier has been developed by typical Tehran metro parameters. Required data for the network learning the process have been gathered from simulation results. 600Hz components value will change with the change of the location of single pole to earth fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the fault location can be accurately predicted in proposed methods.