Jagath Sri Lal Senanayaka

Sliding-mode observer based sensor-less control of a small wind energy conversion system

Small wind turbines are becoming an attractive solution for household applications. These micro generation units can be used as standalone applications or grid connected applications. However to get the full potential benefits of these wind turbines, systems should be low cost and reliable. Introducing the wind speed and rotor speed sensors at the generator shaft may reduce the reliability of small wind turbines. In this study, a grid connected sensor-less 5 kW small wind energy conversion system has been studied. The maximum power point tracking method of the wind turbine is totally independent from wind speed and rotor speed measurements. Optimum rotor speed and actual rotor speed are estimated using output current and voltage of the generator. To estimate the optimum rotor speed of the wind turbine, power signal feedback method has been used. Moreover, a sliding-mode observer is designed to estimate the rotor speed. Performance of the sliding-mode observer system has been compared with the measured rotor speed based wind energy conversion system. The simulation results show the effectiveness of the proposed sensor-less control system for the system under consideration.

Integration of distributed energy systems in micro-grid architecture for making a virtual power plant

This paper presents a novel concept for increasing the penetration level of distributed renewable energy systems into the main electricity grid. When increasing the renewable energy penetration, it is important to implement the frequency based power delivery in distributed generators and work as traditional synchronous generators. This can be achieved by improving the power processing unit of each renewable generation units to work as active generators. But in existing grid architecture, the grid frequency is controlled as one common variable over the electricity grid. With such a method, it is difficult to use frequency based power sharing in small distributed generators and participate in grid frequency control activities. In this proposed grid connected micro-grid architecture, the micro-grid is connected to the main-grid via back-to-back converter, which gives the facility to use distributed local frequency droop settings within the micro-grids and isolate the two AC grids via intermediate DC link. Frequency Droop control has been implemented in the micro-grid side of back to back converter. With this method, the small renewable generations in micro-grid can adjust their power level in response to the local frequency variations and sharing loads effectively while reducing the large power variations from the main-grid.

Early detection and classification of bearing faults using support vector machine algorithm

Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine operators have time to take preventive action before a large-scale failure. The usefulness of the algorithm is validated by using a run-to-failure experimental test data.

Current signature based fault diagnosis of field-oriented and direct torque–controlled induction motor drives

In this article, the operation of three-phase squirrel-cage induction motors is analysed under faulty conditions in closed loop with state-of-the-art controllers, namely, the field-oriented control and the direct torque control. The motivation behind this study is to examine the effectiveness of current signature–based fault detection schemes under closed-loop operation, in the presence of inverter harmonics. Various commonly occurring induction motor fault conditions are modelled based on the modified winding function theory, and each fault case is further simulated in a closed-loop framework to verify the fault detectability. The effectiveness of current signature–based diagnostics in varying fault severity, loads and speeds is studied. Furthermore, the faults are artificially seeded in a laboratory test set-up of an induction motor, and the effectiveness of current signature analysis is verified with commercially available field-oriented and direct torque control drives in the closed-loop framework.

Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller

In this paper. the method of direct torque control in the presence of a sliding-mode speed controller is proposed for a small wind turbine being used in water heating applications. This concept and control system design can be expanded to grid connected or off-grid applications. Direct torque control of electrical machines has shown several advantages including very fast dynamics torque control over field-oriented control. Moreover. the torque and flux controllers in the direct torque control algorithms are based on hvsteretic controllers which are nonlinear. In the presence of a sliding-mode speed control. a nonlinear control system can be constructed which is matched for AC/DC conversion of the converter that gives fast responses with low overshoots. The main control objectives of the proposed small wind turbine can be maximum power point tracking and soft-stall power control. This small wind turbine consists of permanent magnet synchronous generator and external wind speed. and rotor speed measurements are not required for the system. However. a sensor is needed to detect the rated wind speed overpass events to activate proper speed references for the wind turbine. Based on the low-cost design requirement of small wind turbines. an available wind speed sensor can be modified. or a new sensor can be designed to get the required measurement. The simulation results will be provided to illustrate the excellent performance of the closed-loop control system in entire wind speed range (4-25 m/s).

Sensorless small wind turbine with a sliding-mode observer for water heating applications

Water heating applications consume a considerable portion of electricity demand in most of countries. Small wind turbines are one of attractive alternatives for grid electricity based water heating systems. Wind energy can be converted to heat energy in a high efficient manner. However it is essential that wind turbine based water heating systems should be economical and reliable. Maximum power point tracking algorithm of most of available wind turbines requires information from a wind speed sensor and a rotor speed sensor which reduces the reliability of the system. In this paper, the proposed 5 kW wind turbine does not require external wind speed sensors and rotor speed sensors. The system is consistent with sensorless maximum power point tracking algorithm, which eliminates the need for both wind speed and rotor speed sensors and gives a highly reliable solution for water heating applications. Internal voltage and current sensors are used to measure the output voltage, the output current and the power of the generator. Using those measurements, the sliding-mode observer can accurately estimate the rotor speed and position and which is used in the maximum power point tracking algorithm. To calculate the optimum rotor speed, the generator output power measurements are used with power signal feedback method. The effectiveness of the proposed system is verified using a simulation model by comparing the performances with a sensor based model.

A robust method for detection and classification of permanent magnet synchronous motor faults: Deep autoencoders and data fusion approach

Permanent magnet synchronous motors become popular in wind turbines and industrial applications. In critical machines, it is necessary to use robust condition monitoring and fault diagnosis algorithms to prevent faults or shutdowns. The data-driven approach with machine learning algorithms is widely used in industrial and research communities as this method does not require a mathematical model of the system, which is difficult to obtain in practical cases. Most of the successful machine learning methods are based on supervised learning approach, requiring labelled training data. The supervised learning approach cannot use the unlabelled data, while only a few labelled data is in place in the industry. This work uses a deep autoencoder based unsupervised learning method to identify the features of the fault classification algorithm in a self-supervised way, which overcome the shortage of labelled data. The proposed algorithm uses the benefits of available unlabelled data, but it needs only a few labelled data. The fault classification algorithm is based on artificial neural network SoftMax layer and Bayes classifier. The robustness of the algorithm is improved by fusing the current and vibration information. Experimental results are used to validate the robustness of proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.

Fault detection and classification of permanent magnet synchronous motor in variable load and speed conditions using order tracking and machine learning

Permanent magnet synchronous machines have gained popularity in wind turbines due to their merits of high efficiency, power density, and reliability. The wind turbines normally work in a wide range of operations, and harsh environments, so unexpected faults may occur and result in productivity losses. The common faults in the permanent magnet machines occur in the bearing and stator winding, being mainly detected in steady-state operating conditions under constant loads and speeds. However, variable loads and speeds are typical operations in wind turbines and powertrain applications. Therefore, it is important to detect bearing and stator winding faults in variable speed and load conditions. This paper proposes an algorithm to diagnose multiple faults in variable speed and load conditions. The algorithm is based on tracking the frequency orders associated with faults from the normalised order spectrum. The normalised order spectrum is generated by resampling the measured vibration signal via estimated motor speeds. The fault features are then generated from the tracking orders in addition to the estimated torque and speed features. Finally, support vector machine algorithm is used to classify the faults. The proposed method is validated using experimental data, and the validated results confirm its usefulness for practical applications.

A novel soft-stall power control for a small wind turbine

In this paper, the problem of Soft-stall power control design for a small wind turbine is considered. Passive stalling and furling methods are widely used to limit the output power of small wind turbines at above-rated wind speed conditions. However, these methods have substantial limitations, for instance, related to tracking the maximum power at some wind speed levels, limited variable speed operation and introducing unbalanced forces on wind turbine blades. Soft-stall power control is a promising technique to overcome above limitations and improve the performance of small wind turbines. Small wind turbines have a comparatively low moment of inertia value, and it is possible to make fast speed changes by generator torque control which is essential to the successful implementation of proposed control method. A sliding-mode controller is developed as the wind turbine speed controller. Furthermore, two sliding-mode current controllers are utilized in the field oriented control system of the generator. A simulation study is illustrated to validate the proposed soft-stall power control technique and results are compared with two other speed control strategies which confirm the applicability of the proposed control technique for small wind turbines.

A two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine

This article presents a two-stage fault detection and classification scheme, for induction motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms. The adopted strategy utilizes three phase motor current sensing at the pitch drives for fault detection and only when a fault is detected at this stage, features extracted from the current signals are transmitted to a central support vector machine classifier. The proposed method is validated in a laboratory setup of the pitch drive.