Under the guidance of the “ dual-carbon” goals, wind energy, as a clean and renewable energy source, has been widely harnessed. Wind turbines (WTs), which are crucial for converting wind energy into electrical energy, have seen a growing cumulative installed capacity. However, WTs operate in harsh environments with highly variable conditions, leading to frequent failures. To ensure their safe and efficient operation, fault diagnosis and intelligent maintenance technologies are urgently needed. Aiming to address the complex and variable operating conditions of WTs and the issues of gradient disappearance and explosion in recurrent neural networks during time-series learning, this paper proposes a condition monitoring method for WTs that integrates operating condition recognition with echo state networks (ESNs). First, the maximum mutual information coefficient is employed to select features from the supervisory control and data acquisition (SCADA) system data, prioritizing those with high relevance to the operational status of WTs. Second, the K-means clustering algorithm is utilized to construct a model for effective classification of different operating conditions. Subsequently, ESN models are optimized under various conditions using the differential evolution algorithm to enhance their adaptability to complex operating conditions, enabling active power prediction of WTs under different conditions. Then, by analyzing the residuals of power prediction, corresponding health thresholds are determined to assess the operating conditions of WTs. Finally, case studies of two actual WTs demonstrate that the proposed method can effectively monitor the operating status of WTs. It can detect abnormal operating conditions earlier than the SCADA system when faults occur, thus realizing early fault warning.