Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

Abstract

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

Wind Turbine Anomaly Detection Based on SCADA Data Mining

“…The classification results 0 and 1 are used to determine the status of the WT: 0 is anomaly, and 1 is normal. As shown in Equation (11). The classification accuracy of the prediction will be divided into three parts: the abnormal accuracy, the normal accuracy and total accuracy.…”

“…Represented by Q1, Q2 and Q, shown in Equations ( 12), (13), and ( 14), respectively. y = 0, y < 0.5 1, y ≥ 0.5 (11) where y indicates the states of the output.…”

“…In recent research, many excellent methods were proposed for anomaly detection. The existing methods can be divided into three categories: model-based [9], signal-based [10], and data-driven [11,12]. In model-based approaches, the nonlinear relationship among the sub-component of a wind turbine makes it difficult to build numerical models [13].…”

“…Till now, various data-driven methods using SCADA data, such as fuzzy inference system (FIS) [23], support vector machine (SVM) [24] and deep neural network (DNN) [25] have been widely used. In [11], based on fuzzy theory, a generalized wind turbine anomaly detection model is proposed. In [12], a SVM-based method for fault detection in wind turbines was proposed, and the operating states of the wind turbine is classified.…”