The accurate diagnosis of power quality disturbances (PQDs) is crucial for improving energy efficiency and advancing the development of the smart grid. However, the widespread adoption of solar and wind power introduces numerous power electronic converters, complicating PQDs and heightening identification challenges. This article introduces a novel automatic detection framework based on the adaptive Kaiser S-transform (AKST) and JetLeaf Synth network (JSTN), enabling the automatic analysis and detection of intricate PQD signals. To begin, AKST is employed to analyze the time-frequency characteristics of PQD signals. By adaptively refining the parameters of the Kaiser window based on maximum energy concentration, the time-frequency resolution is effectively improved, providing more detailed information. Subsequently, JSTN is developed to automatically extract and recognize crucial distinctive features of PQDs from the time-frequency matrix generated by AKST. Within JSTN, it inherits the local detail capability of the twin leaf mixer (TLM) and the global context ability of the jet-stream transformer (JST), significantly enhancing diagnostic accuracy. The integration of AKST and JSTN results in a detection framework known as hybrid adaptive time-frequency JetLeaf SynthNet (HAJSTN), which is proposed to achieve accurate diagnosis of various PQDs. Multiple simulations and an extensive experimental activity validate that HAJSTN outperforms some advanced PQD identification methods, demonstrating its commendable performance.

中文翻译：

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使用自适应 KS-Transform 和 JetLeaf Synth 网络的复杂电能质量扰动的高级诊断框架


电能质量扰动（PQD）的准确诊断对于提高能源效率和推进智能电网的发展至关重要。然而，太阳能和风能的广泛采用引入了众多电力电子转换器，使 PQD 变得复杂并增加了识别挑战。本文介绍了一种基于自适应 Kaiser S 变换 (AKST) 和 JetLeaf Synth 网络 (JSTN) 的新型自动检测框架，能够自动分析和检测复杂的 PQD 信号。首先，采用 AKST 来分析 PQD 信号的时频特性。通过基于最大能量集中自适应细化Kaiser窗参数，有效提高时频分辨率，提供更详细的信息。随后，JSTN 被开发出来，可以从 AKST 生成的时频矩阵中自动提取和识别 PQD 的关键特征。在JSTN中，它继承了双叶混合器（TLM）的局部细节能力和喷射流变压器（JST）的全局上下文能力，显着提高了诊断准确性。 AKST 和 JSTN 的集成产生了一种称为混合自适应时频 JetLeaf SynthNet (HAJSTN) 的检测框架，旨在实现各种 PQD 的准确诊断。多次模拟和广泛的实验活动验证了 HAJSTN 优于一些先进的 PQD 识别方法，展示了其值得称赞的性能。