Methods of condition monitoring and fault detection for electrical machines

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An overview of measurement standards for power quality

Received: December 7th, 2020 ; Accepted: April 7th, 2021 ; Published: May 13th, 2021 ; Correspondence: [email protected] Quality (PQ) is a vital aspect of electrical power systems, which cannot be neglected anymore, as an ample PQ guarantees the essential compatibility between consumer equipment and the electricity network. The analysis of electrical parameters related to distributing electricity is recognized as a complex engineering problem. It remains a critical task to maintain and improve PQ in modern evolving networks as the overall system performance highly depends on it. Future smart grids will also require a further increase in PQ levels in terms of observability, affordability, data exchange, flexibility, and net metering, thus making the network much more complex as it will be featuring a large amount of variable renewable-based distributed generation. This will further require the need for the introduction of novel, efficient and intelligent monitoring, control, and communication systems with various demand manageable resources. In this paper, a review and comparisons have been made for different IEEE and IEC measurement standards that are used for PQ with a specific focus on harmonic distortion as it is one of the most important parameters in PQ and some guidelines have been suggested for future electricity networks

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical MachinespublishedVersio

Current challenges in operation, performance, and maintenance of photovoltaic panels

The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems

A Detailed Testing Procedure of Numerical Differential Protection Relay for EHV Auto Transformer

Abstract: In power systems, the programmable numerical differential relays are widely used for the protection of generators, bus bars, transformers, shunt reactors, and transmission lines. Retrofitting of relays is the need of the hour because lack of proper testing techniques and misunderstanding of vital procedures may result in under performance of the overall protection system. Lack of relay’s proper testing provokes an unpredictability in its behavior, that may prompt tripping of a healthy power system. Therefore, the main contribution of the paper is to prepare a step-by-step comprehensive procedural guideline for practical implementation of relay testing procedures and a detailed insight analysis of relay’s settings for the protection of an Extra High Voltage (EHV) auto transformer. The experimental results are scrutinized to document a detailed theoretical and technical analysis. Moreover, the paper also covers shortcomings of existing literature by documenting specialized literature that covers all aspects of protection relays, i.e., from basics of electromechanical domain to the technicalities of the numerical differential relay covering its detailed testing from different reputed manufacturers. A secondary injection relay test set is used for detailed testing of differential relay under test, and the S1 Agile software is used for protection relay settings, configuration modification, and detailed analysis

Experimental Setup to Explore the Drives of Battery Electric Vehicles

This paper describes an experimental setup for research and exploring the drives of battery-fed electric vehicles. Effective setup composition and its components are discussed. With experimental setup described in this paper, durability and functional tests can be procured to the customers. Multiple experiments are performed in the form of steady-state system exploring, acceleration programs, multi-step tests (speed control, torque control), load collectives or close-to-reality driving tests (driving simulation). Main focus of the functional testing is on the measurements of power and energy efficiency and investigations in driving simulation mode, which are used for application purposes. In order to enable the examination of the drive trains beyond standard modes of operation, different other parameters can be studied also

Development case study of first Estonian Self-driving car ISEAUTO

Rapid development of intelligent control technics has brought also changes in automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues self-driving cars use a number of sensors for vision and navigation system, actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution form the different fields. In our particular case four different university departments and two companies are directly involved into self-driving car project. The main aim of the paper is to discuss the challenges faced in development of first Estonian self-driving car. The project realization time was 20 months for four work packages: preliminary study, software development, body assembly and systems tuning/testing of the self-driving car. This paper describes a development process stages and tasks that were spread between sub-teams. Moreover, a paper present technical and software solution that were used to achieve the goal and present a self-driving last mile bus called ISEAUTO. Special attention is putted on discussion of safety challenges that self-driving electrical car project can meet. Main outcomes and future research possibilities are outlined

Development case study of the first estonian self-driving car, iseauto

The rapid development of intelligent control technology has also brought about changes in the automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues, self-driving cars use a number of sensors for vision as well as a navigation system and actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution from different fields. In our particular case, four different university departments and two companies are directly involved in the self-driving car project. The main aim of the paper is to discuss the challenges faced in the development of the first Estonian self-driving car. The project implementation time was 20 months and the project included four work packages: preliminary study, software development, body assembly and system tuning/testing of the self-driving car. This paper describes the development process stages and tasks that were distributed between the sub-teams. Moreover, the paper presents the technical and software solutions that were used to achieve the goal and presents a self-driving last mile bus called ISEAUTO. Special attention is paid to the discussion of safety challenges that a self-driving electrical car project can encounter. The main outcomes and future research possibilities are outline