Carlos Boya

Bio: Carlos Boya is an academic researcher from Interamerican University of Puerto Rico. The author has contributed to research in topics: Blind signal separation & Partial discharge. The author has an hindex of 5, co-authored 16 publications receiving 93 citations. Previous affiliations of Carlos Boya include Charles III University of Madrid & Latin University of Panama.

Identification of multiple partial discharge sources using acoustic emission technique and blind source separation

Abstract: The goal of an automatic monitoring system of partial discharges (PDs), based on acoustic emission (AE) detection, is the identification of the type of source of PD and its localization. In the event that multiple deterioration processes are present in the electrical equipment, more than one PD source may be active and their AE signals may overlap on the sensors. This overlapping effect modifies the temporal and frequency characteristics of the measured signals compared to the characteristics of the signals from a single PD source and thus, automatic classification becomes very difficult. In this paper we have proposed applying blind signal separation (BSS) techniques to recover the signals from each source, therefore separating each temporal and frequency characteristic. We have tested the proposed algorithm: firstly using synthetic mixed signals from two types of PD sources and secondly using real signals from a test bench specifically designed to control the position, time and amplitude of the AEs.

Location of partial discharges sources by means of blind source separation of UHF signals

Abstract: Partial discharges (PD) detection is a widely extended technique for the diagnosis of electrical equipment. Ultra-high frequency (UHF) detection techniques appear as the best choice if the goal is to detect PD online and to locate devices with insulation problems in substations and overhead lines. The location of PD is based on the determination of the difference of the time of arrival of electromagnetic pulses radiated by a source of PD to an array of antennas distributed around the monitored area. However, when measuring electromagnetic pulses radiated by PD activity many interfering signals, such as those coming from television (TV), global positioning system (GPS), wireless communication signals and others coming from electrical equipment distort the waveform detected by the sensors. Under these circumstances, the application of traditional techniques to estimate the time differences may fail. In this paper, the use Blind Source Separation (BSS) techniques applied to pairs of UHF sensors is proposed to extract the information of the difference of the time of arrival of the electromagnetic pulses radiated by a source of PD. The paper is focused on the application of the algorithm and the description of an experimental setup for controlled generation and detection of PD to verify the performance of the proposed technique.

Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation

Abstract: The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.

Supply chain and hybrid modeling: the Panama Canal operations and its salinity diffusion

Abstract: This paper deals with the simulation modeling of the service supply chain and the salinity and its diffusion in the Panama Canal. An operational supply chain model was created using discrete-event simulation. Once complete, a component based on differential equations was added to the model to investigate the intrusion of salt and the resulting salinity diffusion into the lakes of the canal. This component was implemented in the AnyLogic simulation modeling environment by taking advantage of the concept of hybrid modeling that is embedded in AnyLogic.

Analyzing the Relationship between Temperature and Load Demand in the Regions with the Highest Electricity Consumption in the Republic of Panama

Abstract: In this work, the relationship between temperature and the electrical consumption using load demand is analyzed. It is done using data from the two geographical areas with the highest percentage of consumption in the Republic of Panama: Metropolitan Region of Panama City and the Central Provinces. Data are analyzed with Regression Analysis and Wavelet Coherence. It is determined that in general, the power changes proportionally with the temperature, although not linearly, in all the years considered. However, evidence is presented that indicates that the temperature influences linearly over the load demand in certain years, which implies an oscillating relationship between temperature and electrical consumption.

Global security

Abstract: In any enterprise, there are many aspects to security, and they apply to different divisions of the enterprise: manufacturing, shipping, sales, administration, etc. Those of us who work with computers know that we have to think about security just as much as those working in other divisions have to. In each different area, there are threats or dangers which must be protected against. Some of these threats may be specific to a particular department while others are common to several different departments. For each particular threat there may several different measures that can be taken to protect against it. We can divide all measures into three categories: physical, operational and administrative. When we examine each of these categories, we find that they all apply to the security of every division and department of an enterprise, including that of the computer department.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

Applications of the Acoustic Method in Partial Discharge Measurement: A Review

Abstract: This paper reviews the main features of acoustic emission technique. First, the characteristics of acoustic signals and their physics are addressed. Then, the structure of acoustic sensors employed for capturing signals is studied. In the next step, the acoustic method of PD measurement is compared with the standard electrical method. Afterward, the applications of AET in PD measurement in different equipment are summarized. All acoustic method and combined acoustic-electrical method for PD localization in transformers are discussed. Moreover, the applications of AET in the monitoring of GIS systems are explained, along with the acoustic behaviors of moving particles in these systems. Finally, the general considerations in the signal processing of the acoustic signals are reviewed.

Overview and Partial Discharge Analysis of Power Transformers: A Literature Review

Abstract: The high voltage power transformer is the critical element of the power system, which requires continuous monitoring to prevent sudden catastrophic failures and to ensure an uninterrupted power supply. The most common failures in the transformer are due to partial discharge (PD) in electrical insulations which are the results of the insulation degradation over time. Different approaches have been proposed to monitor, detect, and locate the partial discharge in power transformers. This paper reviews and evaluates the current state-of-the-art methods for PD detection and localization techniques, and methodologies in power transformers. Detailed comparisons of PD detection techniques have been identified and discussed in this paper. The drawbacks and challenges of different partial discharge measurement techniques have been elaborated. Finally, brief reviews of PD denoising signals, feature extraction of PD signals, and classification of partial discharge sources have been addressed.