Recent developments in data-driven and hybrid intelligent techniques for hydropower fault analysis

A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.

Toward Intelligent Fault Diagnosis in Renewable Energy Systems: Integrating Data-Driven and Model-Based Approaches Under Variable Environmental Conditions

Renewable energy systems, including solar photovoltaic arrays and wind turbines, operate under highly variable environmental and operating conditions. Factors such as changing irradiance, temperature fluctuations, wind variability, and component aging make Fault Detection and Diagnosis (FDD) particularly challenging. Therefore, developing reliable, accurate, and interpretable diagnostic methods is essential to ensure system efficiency, safety, and long-term operation. Traditional model-based approaches, which rely on physical system models, offer clear interpretability and solid theoretical foundations. However, their effectiveness can be limited by modeling inaccuracies and difficulties in capturing complex nonlinear behaviors. On the other hand, data-driven and Artificial Intelligence (AI) techniques have demonstrated strong capabilities in pattern recognition and fault classification, but often face challenges related to data dependence, limited transparency, and reduced robustness under unseen conditions. This paper provides a comprehensive and structured review of FDD techniques for renewable energy systems, covering model-based, signal-based, data-driven, and hybrid approaches. A unified perspective is presented to clarify the strengths, limitations, and application domains of each category. Particular attention is given to recent advances in hybrid methods that combine physical modeling and AI, including feature fusion, ensemble learning, attention-based models, and transfer learning. Moreover, advanced signal processing techniques are discussed for their role in extracting meaningful features from noisy and non-stationary data. Rather than ranking methods by headline accuracy, which has become saturated and is only weakly comparable across heterogeneous datasets, the review adopts a critical, deployment-oriented perspective that emphasizes cross-condition robustness, standardized benchmarking, and the constraints of real-world deployment. The review also highlights the growing importance of digital twin technology as a promising framework for next-generation FDD systems, enabling real-time monitoring, adaptive learning, and predictive maintenance. Furthermore, Explainable AI is explored as a key direction for improving the transparency and trustworthiness of AI-based diagnostic models. Finally, the paper identifies major challenges and open research issues, such as data scarcity, generalization among different operating conditions, computational efficiency, and system reliability. Future research directions are outlined toward developing more robust, adaptive, and interpretable FDD solutions that can operate effectively in dynamic and uncertain environments.

Intelligent fault diagnosis and localization of hydropower station equipment using multi-source data fusion and deep residual networks

A Review of Physics-Informed and Data-Driven Surrogate Models for Power Transformer Fault Diagnosis

Power transformers are critical components of modern power grids, and their operational reliability directly affects power system security, stability, and continuity. With the increasing intelligence and complexity of power systems, condition monitoring and fault diagnosis of transformers have received growing attention. However, conventional diagnostic methods often face limitations such as complex modeling procedures, high computational costs, weak adaptability, and insufficient generalization under nonlinear and coupled operating conditions. In recent years, surrogate models have emerged as effective tools for transformer fault diagnosis because of their advantages in high-dimensional nonlinear mapping, rapid prediction, and data-driven approximation. This paper systematically reviews the research progress of surrogate models in transformer fault diagnosis and establishes a classification framework from the perspectives of model types, modeling strategies, data sources, and application scenarios. The principles, applicable conditions, and performance characteristics of representative surrogate models are comparatively analyzed. Furthermore, the advantages and limitations of different surrogate modeling approaches are discussed in terms of diagnostic accuracy, stability, generalization ability, interpretability, and computational efficiency. Although surrogate models show strong potential for intelligent transformer fault diagnosis, challenges remain in small-sample learning, data quality dependence, model interpretability, and cross-condition generalization. Future research should focus on multi-source information fusion, integration of physical mechanisms with data-driven learning, lightweight intelligent modeling, and standardized evaluation systems. This review aims to provide methodological guidance and technical references for the development of reliable, efficient, and interpretable transformer fault diagnosis methods.

Data-driven fault detection in renewable energy systems using hybrid machine learning techniques

Abstract. The explosive growth of grid-connected renewable energy systems (RES) has increased the complexity of the operation of the modern power infrastructure, making the detection of the faults reliably an inevitable condition of the stable functioning and safety. Traditional single-algorithm and rule-based monitoring systems are not sufficiently flexible or discriminatory to distinguish between the many varieties of faults that occur in photovoltaic (PV) arrays, wind turbines, battery management systems, and grid-tie inverters. The paper suggests a new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner. Publicly available SCADA and lab bench data were used to create a curated multi-source dataset of 8,400 labelled samples to represent five operational states. The proposed framework achieved an accuracy of 97.8, a macro-averaged F1-score of 97.1, and a Matthews Correlation Coefficient (MCC) of 0.972, outperforming all the compared baseline methods at least by 3.3 percentage points. The findings verify the effectiveness of the hybrid stacking paradigm in identifying faults in real-time and multiple classes in heterogeneous renewable energy settings.

Application and Comparative Study of Time Series Analysis Algorithms in New Energy Forecasting

The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.