Data Quality Control of the Malta Seismic Network (2015-2024)

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded.

The following article published in IET Generation, Tranmission and Distribution, Nikoobakht, A., Aghaei, J., Mardenah, M., ‘Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures’, Gener. Transm. Distrib., 2017, 11, (10), pp. 2664 – 2676, doi:10.1049/iet-gtd.2016.1956, has been withdrawn by agreement between the authors, the Editors-in-Chief, Innocent Kamwa and Christian Rehtanz, and the Institution of Engineering and Technology. This is because the incorrect text was uploaded due to a technical error. The correct version has now been published as: Nikoobakht, A.,Aghaei, J., Mardenah, M. et al, 'Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties', Gener. Transm. Distrib., 2018, 12, (15), pp. 3780 – 3792, 10.1049/iet-gtd.2017.0617.

The photoelectric wireless sensor network is composed of multiple photoelectric sensor nodes in the area. In addition to the basic sensing functions, the multiple micro and small photoelectric sensor stages contained in the area can also self-organize to form a wireless sensor network. According to the measurement method of power equipment and photoelectric sensor technology, the study equations the intelligent photoelectric wireless sensor structure of power equipment and the corresponding hardware composition. Meantime, the augmented reality (AR) technology is introduced to inspect the power equipment. Among them, multiple photoelectric sensors are concentrated on the power poles of the long-distance transmission line of the power grid and within 100 m around them, and meanwhile, a wireless sensor network centered on a single power pole is built in this area; the combination of AR and deep neural network (DNN) is used for the fault identification of power equipment. In the experiment, power equipment monitoring interface is generated based on the .NET framework, and data can be obtained with the help of the query button to realize the parameter monitoring of the power equipment on the client-server side. By binding the data source, the figure of power monitoring can be read and written in the database without modifying the display settings of the interface. The power measurement value is helpful for the dispatch of operators. With the help of ZedGraph, power data collected by the photoelectric sensor can be displayed on the interface corresponding to the dynamic data. Comparing the photoelectric sensor network of power poles and towers and the photoelectric sensor network of power poles that have not been constructed, it is confirmed that the power poles and towers sensor network can reduce the energy consumption and failure of detection data. Compared with SVM algorithm and BP neural network, DNN algorithm based on AR technology can conduct inspections accurately on failures of power equipment.

The new method of diagnostics of current technical state for power equipment at substations of electrical networks and industrial plants is proposed. It is based on the analytic hierarchy process and fuzzy logic. It was shown that it is possible to make a plausible prediction on the failures and fault reasons for power equipment and to minimize the time for decision making and fault reason elimination using pairwise comparisons of expert evaluations. To perform such analysis, it is necessary to have high-qualified experts and symptoms of defective state of power equipment. The comparison of these symptoms is realized using nine intensities of importance of the fundamental scale of Saaty. Fuzzy evaluations are based on the attempts to digitize verbal information (i.e. to represent information as linguistic variables by words or phrases in the form of numbers). Symptom ranking for possible faults is performed according to expert preferences that allows deriving the most considerable symptoms for each suggested defect and making a decision on a further operation of power equipment. The reliability of the proposed method is validated by calculations that demonstrate the adequacy of the model applied to transformers with high temperature superconducting windings (HTS transformers). It was shown that a conclusion on the further operation of a superconducting transformer or its removal for repair could be made if some fuzzy symptoms are observed with their evaluation using the scale of intensities of importance in the analytic hierarchy process. The new mathematical model developed with the use of fuzzy relations of transformer faults includes the elements of prediction for possible failures of power equipment in power supply systems and can be implemented into the systems of short-term forecasting and on-line diagnostics.

The new method of expert diagnostics of technical state for transformer power equipment at substations of power systems and facilities is proposed. It is based on the analytic hierarchy process. It was proved that it is possible to make a plausible prediction on the reasons of failures and faults for power equipment using some symptoms and pairwise comparisons of expert evaluations. The paper focuses on diagnostics of the current technical state of transformers with high temperature superconducting windings (HTS transformers). Reliability of the results of the proposed method is validated by calculations that demonstrate the adequacy of the model applied to HTS transformer. The new mathematical model developed using fuzzy relations of fault symptoms includes elements for predicting possible failures of power equipment, that can be used as a subsystem of on-line diagnostics. As a result, it allows implementing the HTS transformers as an innovative solution for electric power industry.

Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

Power equipment is one kind of basic element in smart grid, and how to design an efficient detection and analysis scheme of electric signature (ES) for power equipment failure (PEF) monitoring is a key and challenging issue. This article proposes an ES detection and analysis method which can monitor multiple kinds of PEF in smart substation. The bottleneck of ES analysis is explored in the view of Heisenberg uncertainty, and an optimal time–frequency analysis method is designed to solve the problems. The proposed method (PM) is based on union of time and frequency bases whose decomposition is realized by Bayesian compressive sensing using Laplace prior. Simulated and field ESs are employed to test PM with comparisons of existing methods. Also, PM is applied in a smart substation of China. Several typical PEFs and measurement soft failures caused by electromagnetic interference are discussed. The results indicate that the PM can accurately monitor PEFs whose mechanism can be revealed by time–frequency features of ESs, if the required sampling rate and sampling time are satisfied because of its immunity of the uncertainty principle restriction. The robustness in noise environment and optimal time–frequency representation of ESs make the PM an efficient general-purpose PEF monitoring in smart grid by time–frequency analysis.

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed‐integer non‐linear programming model, this study transforms it into a mixed‐integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118‐bus and 662‐bus test systems in pre‐ and post‐OTS action. Obtained results demonstrate the effectiveness of the proposed model.

Partial discharge (PD) is one of the major causes of insulation degradation/ageing in power equipment, it is significant to detect PD accurately for guarantee equipment's safety and reliable operation. To achieve an accurate PD monitoring and PD location, a new type of PD on-line monitoring system was presented in this paper. The PD phenomena taking place of equipment's surface and/or interface was detected by 32∗32 ultrasonic array sensors, which is capable of collecting and processing multi-sensor synchronized ultrasonic signal came from PD taking place. Based on Acoustic Doppler theory, the measured sound intensity is decided by received the number of entire waves in unit time, which produced by PD. According to the measured sound intensity sequence, a mapping relationship in plane-coordinate between PD event and its location system could be established. Furthermore, 2D image of object is taken by HD camera in this system, the PD location visualization could be achieved by digital image fusion. The system can intuitively show the discharge location in the image and its status in the development and changes, timely find power equipment's failure and has extensive application value.

Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously-one to capture the sequence of PD pulses and another to acquire PD pulse waveforms-and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.

La contaminacion ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, estan contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicologicamente como fisiologicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparicion de nuevas estrategias para medir dicha contaminacion. Asi, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los telefonos inteligentes emergen como tecnologia habilitadora, y cuya adopcion generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A traves del crowdsensing, los telefonos inteligentes pueden convertirse en unidades de deteccion flexibles y multiuso que, a traves de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interes con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseno de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminacion ambiental, especificamente del ruido y de la contaminacion del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los ultimos anos. Los resultados de nuestro estudio demuestran que todavia hay mucha heterogeneidad en terminos de tecnologias utilizadas y metodos de implementacion, aunque los disenos modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminacion del aire, proponemos una arquitectura que permita medir la contaminacion del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor movil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientacion del sensor y el periodo de muestreo, dentro de ciertos limites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminacion acustica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya caracteristica principal es que no requiere intervencion del usuario. En esta tesis detallamos aspectos tales como la calibracion de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseno del servidor, y la interaccion cliente-servidor. Ademas, hemos validado nuestra solucion en escenarios reales para demostrar el potencial de la solucion alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es…

Ensuring the safe operation of power equipment is the basis for the stability of power system and the temperature is one of the most concerned parameters. Most power equipment failures are accompanied by the abnormal fever. In order to analyze current status and running trend of the power equipment under the power communication networks, a temperature pre-warning analysis technique based on association rules mining is proposed in this paper. By analyzing multiple influencing factors and equipment temperature data, we establish an equipment temperature characteristics analysis model. Then, an intelligent analysis and early warning algorithm based on association rules is proposed. By a top-down method, the connection relations of influencing factors and the power equipment temperature are found out. Based on these association rules, the trend monitoring of power equipment temperature status and the active warning based on the perception information can be realized, providing theoretical support for safe operation of the power grid. The simulation results show that the algorithm can accurately predict the temperature of the power device in the acceptable prediction error rate and realize accurate temperature warning in time.

In this paper, we introduced a new type of partial discharge on-line monitoring by ultrasonic visualization system, which is based on ultrasonic diffusing technology. The system uses ultrasonic positioning and image processing method to achieve the partial discharge ultrasonic position visualization. System uses the array type of ultrasonic sensor and a corresponding signal processing device of ultrasonic signal, processes the synchronization acquisition signals of 32 calculation ultrasonic sensor, gets the partial discharge power plane coordinates, establish a mapping relationship between image coordinate data of partial discharge and the actual equipment through image processing technique and finally achieves the visualization of partial discharge intensity and location. The system can intuitively show the discharge location in the image and its status in the development and changes, timely find power equipment's failure and has extensive application value.

Relative humidity, temperature measurement and monitoring are very useful for the purpose of analysis. The variation in relative humidity and temperature at a particular place may be caused by factors such as power failure or equipment failure. This paper uses basic knowledge in combining analog circuit and digital circuit theory together with programming techniques to handle control of the hardware. The temperature and relative humidity sensing circuit converts the relative humidity and temperature into analog signals, which are then applied to a micro controller based data logger for storage purposes. The data are then transferred to a computer through a RS232 standard serial port. The user interface program will handle the data transfer between the data logger and the computer as well as allow the user to input important parameters such as sampling interval and starting date and time for the logging operation. A backup power supply is also connected, included to sustain operation in case of power failure. The system can perform both real time and also stand-alone methods.

Currently, the large-scale grid connection of distributed generation, represented by wind electricity and photovoltaics, into distribution networks has become a trend. However, this massive integration can lead to a series of problems including grid frequency and voltage fluctuations, power system instability, equipment failures, power outages or interruptions, and a decline in power quality. This paper presents a way for assessing the carrying capacity of distributed generation under different load levels. Considering various factors influencing the carrying capacity, a comprehensive evaluation method is proposed by utilizing a multi-time scale factor matrix, an improved entropy weight method, and the Analytic Hierarchy Process (AHP) combined with a comprehensive weight way. Based on the thermal stability limit, the maximum DG injection is calculated using the reverse load ratio, and the carrying capacity of the distributed generation system is determined by gradually decreasing it at each voltage level to meet the specified criteria, considering the actual operating conditions and safety boundaries. Finally, through a case study analysis of a distribution network model in Zhejiang province, the carrying capacity of distributed generation in that region is obtained.