Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenanceprogram. Partialdischarges(PDs)phenomenaaffecttheinsulationsystemofanelectrical machine and\u2014in the long term\u2014can lead to a breakdown, with a consequent, signi\ufb01cant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is speci\ufb01cally designed to run on low-cost embedded devices

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

Methods of condition monitoring and fault detection for electrical machines

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Design and Realization of Electronic Measurement Systems for Partial Discharge Monitoring on Electrical Equipment

The monitoring of insulations that composing high voltage apparatus and electrical machines is a crucial aspect for a predictive maintenance program. The insulation system of an electrical machine is affected by partial discharges (PDs), phenomena that can lead to the breakdown in a certain time, with a consequent and significant economic loss. Partial discharges are identified as both the symptom and the cause of a deterioration of solid-type electrical insulators. Thus, it is necessary to adopt solutions for monitoring the insulation status. To do this, different techniques and devices can be adopted. During this research activity, two different systems have been developed at the circuit and layout level, which base their operation respectively on the conducted and on the irradiated measurement, in compliance with the provisions of current standards, if foreseen. The first system is based on the use of a classic signal conditioning chain in which gain value can be set through PC control, allowing the conducted measurement of partial discharges in two frequency bands, Low Frequency (LF) and High Frequency (HF). Based on these bands, the application of the system is diversified. In this case, the information obtained from the measurement can be analysed by an expert operator or processed by an intelligent system, obtaining in both cases information on the status of the machine under test. The second makes use of a UHF antenna built on PCB, which takes care of detecting the irradiated signal generated in the presence of discharge activity, which is then appropriately conditioned and processed by analog electronics, to then be acquired through a programmable logic, which interprets it and returns information on the status of the machine, which can also be checked by an expert user. The application of this system is linked to the type of insulation and the type of power supply adopted, which differentiate its characteristics. In both systems, the analysis of the measurement of partial discharges is suitable for the prevention of failures and the planning of suitable maintenance interventions

Bibliography on Induction Motors Faults Detection and Diagnosis

International audienceThis paper provides a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults detection and diagnosis

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Nonintrusive Load Monitoring of Variable Speed Drive Cooling Systems

To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it's imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based 'mapping' techniques for tracking the performance of a building's central air conditioning from smart electrical meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner's power draw from that of the total building by exploiting the correlations between the building's line-current harmonics and the power consumption of the air conditioner's VSD motors. A second mapping then estimates the air conditioner's heat rejection performance using as inputs the estimated power draw of the first mapping, the building's zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building's air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented