Mineral oil is the most popular insulating liquid for high voltage transformers due to its function as a cooling liquid and an electrical insulator. Kraft paper has been widely used as transformer solid insulation for a long time already. The degradation process of transformer paper due to thermal aging in mineral oil can change the physical and chemical structure of the cellulose paper. Fourier transform infrared (FTIR) spectroscopy analysis was used to identify changes in the chemical structure of transformer paper aged in mineral oil. FTIR results show that the intensity of the peak absorbance of the O–H functional group decreased with aging but the intensity of the peak absorbance of the C–H and C=O functional groups increased with aging. Changes in the chemical structure of the cellulose paper during thermal aging in mineral oil can be analyzed by an oxidation process of the cellulose paper and the reaction process between the carboxylic acids in the mineral oil and the hydroxyl groups on the cellulose. The correlation between the functional groups and the average number of chain scissions of transformer paper gives initial information that the transformer paper performance can be identified by using a spectroscopic technique as a non-destructive diagnostic technique.

Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some metaheuristic (MH) algorithm to track the multiple-peak P-V curve of partially shaded PV system. Cuckoo search (CS) is a new optimization algorithm based on the MH approach. It has been used to solve an optimization problem in many applications, including the maximum power point tracking (MPPT) problem. The CS algorithm performs well in tracking the global maximum power point (GMPP). However, just like any other MH algorithm, there is still a dilemmatic trading between their accuracy and the tracking time needed to find GMPP. This paper proposes a new MPPT algorithm by combining the CS algorithm with golden section search (GSS) to take beneficial features from both the algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental results show a noticeable performance improvement compared with the original CS algorithm and other MH algorithms.

Accurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used gases fall outside the specified limits. Incorrect interpretation of DGA measurements can lead to mismanagement and may lead to catastrophic consequences for operating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles to integrate existing interpretation methods into one comprehensive technique. The robustness of the proposed method is assessed using DGA data collected from several transformers under various health conditions. Results indicate that the proposed multi-method, based on the scoring index and random forest; offers greater accuracy and consistency than individual conventional interpretation methods alone. Furthermore, the multi-method based on random forest demonstrated higher accuracy than employing the scoring index only.

Oil-paper compound insulation has been widely used in power transformers for quite a long time because of its good performance. The paper insulation degrades under the combined stresses of thermal, electrical and chemical stresses during routine operations, resulting in the reduction of its degree of polymerization (DP) and hence its mechanical and electrical strength. Space charges in a dielectric material have a close relation to its electrical performance. Therefore, in this work, the effect of paper-aged states (with different DP values) on the space charge behavior in oil-impregnated paper insulation is investigated, and the pulsed electro-acoustic (PEA) method is used for the space charge distribution measurement. In order to achieve this purpose, new oil-impregnated paper samples are thermally-accelerated aged for different time interval to obtain different DP values, and then the residual oil in each sample is removed, and the Kraft paper samples are dried in a vacuum dry oven. After that, all paper samples are impregnated with new oil, and PEA measurements are performed at room temperature. The experimental results show that, when the external dc voltage is applied on the oil-paper sample, homo-charge injection occurs for all samples. The samples with different aging states exhibit quite different space charge characteristics for both voltage-on and voltage-off conditions. When the aged states are worse, for voltage-on condition, more charges are induced at cathode and anode, as well as more space charges are accumulated in the bulk. For voltage-off condition, the space charge decay rate shows no obvious change for different aged samples.

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.