In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

https://www.mdpi.com/1424-8220/22/8/2822/pdf?version=1649311634

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

Data-Driven Fault Diagnosis for Electric Drives: A Review.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/pel2.12098

Open-switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach

In this article, a fast and efficient open-circuit switch and clamping diode fault diagnosis for neutral-point-clamped inverters is proposed. Multilevel converters employed in safety-critical applications should include fault-tolerant capability, where fault diagnosis plays a vital role. The proposed fault diagnosis algorithm is based on failure mode analysis, which is a logic-based approach. The required signals and measurements are available in the healthy control system. Furthermore, component modeling, load parameters, and complicated calculations are not required. The fault detection and identification of the faulty switch or clamping diode are carried out based on the available data such as a switching pattern and terminal voltage. Although switches are more fragile than clamping diodes, clamping diodes are also subjected to breakdown. Hence, it is also crucial to achieve a clamping diode fault diagnosis strategy. Moreover, for the fault detection procedure, a voltage quantifier is proposed to avoid any misdiagnosis arising from measurement errors and voltage drop in the circuit. The simulation and experimental results are presented to validate the effectiveness of the proposed approach.

Open-Switch and Open-Clamping Diode Fault Diagnosis for Single-Phase Five-Level Neutral-Point-Clamped Inverters

A simultaneous diagnosis method for power switch open-circuit faults and current sensor faults of grid-connected three-level neutral point clamped (NPC) inverters is proposed herein. First, by designing the adaptive reaching law and reducing the steady-state resonance, a novel interval sliding model observer is developed to track and estimate the three-phase currents accurately and rapidly. Next, a faulty phase detection scheme using an adaptive threshold is proposed to achieve sensitive and robust detection results. Also, the measured three-phase currents sum and estimated three-phase currents sum are used to distinguish power switch open-circuit faults and current sensor faults. Subsequently, a faulty switch location method and the current sensor fault type identification method are proposed, using some of the same identification variables. Finally, combining the faulty phase detection variables with the identification methods ensures the feasibility of the detection and identification of 12 open-circuit faults and 9 current sensor faults. The hardware-in-the-loop test results are performed to validate the effectiveness and robustness of the proposed method.

A Simultaneous Diagnosis Method for Power Switch and Current Sensor Faults in Grid-Connected Three-Level NPC Inverters

In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-pel.2019.0835

Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches

A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-pel.2020.0226

Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

/pdf/multi-source-data-processing-and-fusion-method-for-power-3asjmgk5.pdf

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Applying the fault diagnosis techniques to power electronic equipments is beneficial to improve the stability and reliability of renewable energy system, because power electronic equipments are a key component of renewable energy system that largely defines their performance. This paper presents a novel intelligent fault diagnosis method for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods. Firstly, the three-phase AC currents of the power-electronics energy conversion system are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to preprocess the fault data. After feature transformation, the slopes of current trajectories (transformed features) are not affected by different loads. And then random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier with the processed fault data. Finally, the proposed method is implemented online on an actual three-phase PWM rectifier platform. The results show that the proposed fault diagnosis method can successfully detect and locate the open-circuit faults in IGBTs of the three-phase PWM rectifier. In addition, the proposed method is suitable for most of three-phase power-electronics energy conversion systems.

/pdf/an-intelligent-fault-diagnosis-method-for-open-circuit-33cb2pem7c.pdf

Quan-de Yuan

Papers

Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

An Intelligent Fault Diagnosis Method for Open-Circuit Faults in Power-Electronics Energy Conversion System