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In response to the shortcomings of existing image noise detection algorithms that rely on the flawed intuitionistic fuzzy entropy (IFE) theory, a method of image noise detection and removal based on intuitionistic fuzzy amount of knowledge (IFAK) is proposed by introducing the latest knowledge measure (KM) theory and model. In the noise detection stage, the optimal average intensity of the noisy image foreground and background is determined based on the maximum IFAK, and the parametric model of noise detection is constructed accordingly to mark the probability of noise pixels and suspected noise pixels, showing excellent performance of noise detection. In the noise removal stage, a denoising model based on IFAK and probability of noise pixels is proposed by using the noise probability matrix, which can not only effectively denoise, but also better protect the characteristics of image edges and non-noise extreme pixels. Comparative experiments are carried out on standard datasets and classical test images, respectively. Experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising. The overall performance outperforms other similar algorithms. The key metrics PSNR and SSIM are increased by 14.81% and 11.35%, respectively. In this paper, the latest KM theory is applied to image denoising, and excellent evaluation metrics and visual effects are obtained, while innovative applications of this theory in other related fields are also achieved.

The worldwide occurrence of wind droughts challenges the balance of power systems between energy production and consumption. Expanding inter‐day energy storage serves as a strategic solution, yet optimizing its capacity depends on accurately modeling future renewable energy uncertainties to avoid over‐ or under‐investment. Existing approaches that use the historical extreme scenario set (HESS) to represent future conditions are contentious due to potential inadequacies in forecasting future extreme scenarios (ESs), including those on a decadal or centennial scale. This study addresses the issue by proposing an advanced energy storage expansion framework that leverages Extreme Value Theory (EVT) and a novel Deep Generative Model, namely the Diffusion Model. To model the extremes in a principled way, this work leverages EVT to establish a severity‐probability mapping for wind droughts, guiding the training process of the Diffusion Model. This model excels in generating ESs that accurately reflect the distribution of real‐world extremes, thereby significantly enhancing the predictive capacity of HESS. Case studies on a real‐world power system confirm the method's capacity to generate high‐quality ESs, encompassing the most severe historical wind droughts not included in the training dataset, thereby facilitating resilient energy storage expansion against unforeseen extremes. [ABSTRACT FROM AUTHOR]

With the explosive growth of photovoltaic (PV) penetration, there is an increasingly urgent expectation for PV sources to provide a full range of ancillary services to the utility. The most critical issue for a PV source to support the power system is the flexible power regulating capability, whereas the PV sources generally operates in the maximum power point tracking (MPPT) mode to maximize the solar harvest. This paper proposes an active power control strategy for PV sources based on a characteristic curve fitting method. With the proposed control strategy, PV sources can operate in the MPPT mode or adaptively switch to the power dispatch mode to flexibly regulate its output power to track the power reference. The characteristic curve fitting method does not require an irradiation sensor, but it has a more stable performance under rapid and continuous change in irradiation with simple calculation and error control since its extremely high similarity with the P–U characteristics of the PV source. Simulation is performed based on a detailed PV dynamic model to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]