Power transformers play a critical role in electric power networks. Such transformers can suffer failures due to multiple stresses and aging. Thus, assessment of condition and diagnostic techniques are of great importance for improving power network reliability and service continuity. Several techniques are available to diagnose the faults within the power transformer. Frequency response analysis (FRA) method is a powerful technique for diagnosing transformer winding deformation and several other types of problems that are caused during manufacture, transportation, installation and/or service life. This paper provides a comprehensive review on FRA methods and their applications in diagnostics and fault identification for power transformers. The paper discusses theory and applications of FRA methods as well as various issues and challenges faced in the application of this method.

This paper presents look-ahead energy management system for a grid-connected residential photovoltaic (PV) system with battery under critical peak pricing for electricity, enabling effective and proactive participation of consumers in the Smart Grid’s demand response. In the proposed system, the PV is the primary energy source with the battery for storing (or retrieving) excessive (or stored) energy to pursue the lowest possible electricity bill but it is grid-tied to secure electric power delivery. Premise energy management scheme with an accurate yet practical load forecasting capability based on a Kalman filter is designed to increase the predictability in controlling the power flows among these power system components and the controllable electric appliances in the premise. The case studies with various operating scenarios demonstrate the validity of the proposed system and significant cost savings through operating the energy management scheme.

The defects caused by the presence of metallic particles are the most frequent threats in Gas-Insulated Substation (GIS). These particles can move freely in the GIS enclosure and thereby when they come in the vicinity of spacers they can adhere to the solid spacer surface due to electrostatic forces resulting in the initiation of partial discharges (PDs) affecting the GIS reliability. The magnitude and frequency of these PDs depend among others on the size and position of particles. Thus the knowledge of the size and position of contaminating particles and its PD characteristics is essential for the improvement of the reliability of such equipment. This paper is aimed at the use of the Back-Propagation Artificial Neural Network (BP-ANN) technique for recognizing the PD patterns to estimate the particle size (length) and position on the spacer surface in a simulated GIS. The PD patterns were characterized by a number of statistical operators describing the shapes of distributions of the PD signals acquired from the measurements carried out using IEC 60270 method. In developing the BP-ANN, some parameters were varied to find the most optimal network. The results show that the best-developed ANN in this study is able to recognize various PD patterns in the employed GIS model and is able to estimate particle length and position on the spacer surface at different SF6 pressures. The achieved accuracy in detecting the size and location of particle was about 92%. Thus, the proposed method constitutes a helpful tool in improving the reliability of GIS as well as for diagnosis.

Freshwater is only 2.5% of the total water on the Earth and rest is contaminated or brackish. Various physical and chemical techniques are being used to purify the contaminated water. This study deals with catalytic plasma treatment of contaminated water collected from different sites of Faisalabad-Pakistan. A non-thermal DC plasma jet technique was used to treat the water samples in the presence of TiO₂ catalyst. The plasma-assisted catalytic treatment introduced some oxidative species (O₃, H₂O₂, HO₂⁻, OH⁻) in the water. These species reacted with pollutants and cause the degradation of harmful contaminants, especially dyes. The degradation of dye sample during plasma treatment was more pronounced as compared to other samples. pH, conductivity and TDS of dye containing sample decreased after catalytic plasma treatment. The degradation of organic pollutants increased due to presence of several oxidants, such as TiO₂, ferrous ions and hydrogen peroxide. FT-IR analysis revealed the degradation of some functional groups during treatment process and confirmed the effectiveness of the process. The residue of the treated samples was consisted of amines, amides and N-H functional groups. XRD analysis showed the presence of Alite, Ferrite, aluminate, Si, S and some heavy metals in the residue. The effect of plasma treatment on activity of gram-negative Escherichia coli (E. coli) bacteria in water was also checked. The bacterial activity was reduced by almost 50% after 2 min of plasma treatment.