Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

Water pipeline leakage detection is still an important issue, particularly for the development of smart cities. Thus, this paper reviews water pipeline leakage detection techniques, which can be classified into three different categories, namely, software-based, hardware-based, and conventional methods. We compare the advantages and disadvantages for all the methods in the groups and thoroughly discuss the hardware-based method, which is our focus. Specifications on water pipeline testbeds used in the previous works are also highlighted. Since many recent techniques are based on accelerometer or vibration sensors, a comparative study that includes the cost and accuracy in identifying the pipeline leaks is presented. The theoretical computation of the vibration induced from our water pipeline testbed is also demonstrated and compared with the actual vibration data collected from the experimental works using three different sensors, namely, MPU6050, MMA7361, and ADXL335. INDEX TERMS Water pipeline, pipeline leakage, vibration leak detection, accelerometer, pipeline test bed.

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

Bangladesh's constant growth with an annual 6% plus Gross Domestic Product (GDP) for more than the last two decades and achievements in other socio-economic metrics in recent times is impressive and recognized by various global authoritative bodies. The extent of overwhelming economic ventures in the private sector coupled with the commitments of the government clearly demonstrates the transformation of the country from a primarily agro-based economy to one influenced by the manufacturing and service sectors. Bangladesh is fortunate to have fossil fuel reserves on a limited scale, though these are not enough to run the ongoing massive scale development activities, both in private and public sectors. Thus, the constant and uninterrupted supply of energy at an affordable price remains a serious concern for the successive governments. Therefore, this issue of supply of constant energy has turned to be an important part in the national development agenda. Besides, the country is one of the worst victim nations of the devastating effects of global warming and climate change. As Bangladesh is geographically located in a favorable place in the world map with the availability of plenty of renewable energy sources (RES), the policymakers started to take initiatives leading to exploiting these sources to meet the energy demand of the country. There are both prospects and administrative, legal, technological, socio-cultural and environmental challenges. To address these challenges, it requires comprehensive policy initiatives. A good number of technical and scientific research containing findings and recommendations are available. This paper, which is based on adopting a qualitative research methodology where the contents of secondary sources were analyzed, is an initial attempt to highlight the renewable energy developments in Bangladesh, and subsequently, to evaluate the relevant legal and policy initiatives in the light of international best practices. We advance several recommendations that the stakeholders can consider exploiting RES effectively to attain inclusive, equitable and sustainable development in Bangladesh. These include, inter alia: (1) Enhancing government participation to lead the development of renewable energy (RE); (2) ensuring localization of RE technology; (3) reducing the expenses of energy generation through RES and providing assistance in initial investments; (4) introducing comprehensive legal and regulatory policy for the development of RE industry in Bangladesh; and (5) conducting effective public awareness. Sustainability 2019, 11, 5774 2 of 30 every year [25], and starting from 2014, more than 164 nations have embraced the renewables targets 199 [26]. In 2012, the utilization of RES helped to provide up to 13.2% of the worldwide essential energy 200 supply. The same figure rose to 22% of worldwide power usage in 2013. It was estimated that this 201 number is expected to rise to 26% in 2020. To share this in a practical setting, this number is greater 202 than the current overall power demands...

This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (φ-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.