Transformers are utilized in generation, transmission, and distribution power system network and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this work. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity that we achieve in this work is an advantage of the proposed measurement technique. In this regard, we employ techniques from machine learning and numerical analysis to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.

Interpreting results of transformer frequency response analysis (FRA) is quite challenging. One of the common methods to summarize FRA data is to employ Statistical Indicators (SIs) over FRA spectra. However, SI-specific boundary conditions for various operational modes of transformers are left unexplored. The lack of such boundary conditions renders interpretation of SIs difficult and subjective. Here, in an attempt to find data-driven boundary conditions, first the conventional measurement setup of FRA technique is modified to emulate inter-turn winding short circuit. Then the boundary conditions of various SIs for normal, suspicious, and critical operational modes of transformers under fault are obtained. Nevertheless, the price of moving subjective boundaries to their objective data-driven counterparts is paid in an intrinsic uncertainty introduced by the process of data collection per se. In order to capture and quantify this uncertainty, a novel solution inspired by bolstered error estimation used in pattern recognition is proposed. In particular, the proposed method allows reporting the level of confidence that an observed magnitude of SI belongs to a specific operational mode. Having this confidence level is also warranted from an operational perspective because it enables utility operators to enhance the decision-making process and estimate the severity of transformer faulty conditions.

In this study, the influence of using acid batteries as part of green energy sources, such as wind and solar electric power generators, is investigated. First, the power system is simulated in the presence of a lead–acid battery, with an independent solar system and wind power generator. In the next step, in order to estimate the output power of the solar and wind resources, a novel forecast model is proposed. Then, the forecasting task is carried out considering the conditions related to the state of charge (SOC) of the batteries. The optimization algorithm used in this model is honey bee mating optimization (HBMO), which operates based on selecting the best candidates and optimization of the prediction problem. Using this algorithm, the SOC of the batteries will be in an appropriate range, and the number of on-or-off switching’s of the wind turbines and photovoltaic (PV) modules will be reduced. In the proposed method, the appropriate capacity for the SOC of the batteries is chosen, and the number of battery on/off switches connected to the renewable energy sources is reduced. Finally, in order to validate the proposed method, the results are compared with several other methods.

The current interpretation procedure for the transformer frequency response analysis (FRA) is practically based on either subjective visual inspection or an observed value of a statistical indicator (SIn) programed in commercial software. However, both solutions are unable to provide very detailed information on transformer conditions in the industry. The former approach highly depends on the experience of the technical personnel, while the latter suffers from uncertainties of SIn-based decision boundaries for classification. This study has specifically focused and introduced a new comprehensive FRA data interpretation and classification method. Evaluation of the proposed technique is performed by emulation of winding short-circuit faults in transformers via a parallel connected rheostat. The method is able to categorize the operational mode of the transformer into the normal, suspicious, and critical classes based on the cumulative effect of 12 frequently used SIns and then provide a confidence level (CL) associated with classification to support operators for making a precise decision. Bootstrap sampling technique is introduced for the first time in FRA data evaluation to simulate the effect of many experiments in silico and overcome the difficulty of limited available measured data. Particularly, in a novel approach, a large set of SIn-specific decision boundaries is established by bootstrap sampling of the real experimentally obtained boundaries. In addition, a new transformer condition assessment method that employs the cumulative effect of individual SIns is developed and examined. Classification results and the CLs in FRA evaluation have a substantial practical significance and will support on-site utility managers or engineers for further diagnosis and maintenance.

Due to the inherent slow response time of diesel generators within an islanded microgrid (MG), their frequency and voltage control systems often struggle to effectively manage rapid load changes. To mitigate this challenge, energy storage systems (ESSs) emerge as pivotal solutions. Flywheel energy storage systems (FESSs) are well-suited for handling sudden power fluctuations because they can quickly deliver or absorb large amounts of electricity. On the other hand, battery energy storage systems (BESSs) excel at storing large amounts of energy for extended periods and can handle gradual changes in power demand. This research introduces a coordinated control mechanism for a mixed energy storage setup that combines BESS and FESS elements to manage the frequency of a standalone MG. A new frequency detection method is employed to assign high, medium, and low-frequency deviations in power distribution to FESS, BESS, and diesel generators, respectively. Simulations are carried out to determine the exact performance of the proposed control method. The MATLAB/Simulink software platform is used to run these simulations. Furthermore, dynamic load changes are precisely emulated using a programmable load emulator enhancing the reliability and relevance of our analysis.

Frequency response analysis (FRA) is almost certainly the most efficient and well-established method for evaluating the mechanical integrity of transformer active part. FRA has a solid background in both industrial practice and academic research. Besides conventional visual analysis, a statistical analysis has been applied to the FRA data interpretation. The existing standards and recent studies have considered different statistical indicators (SIs) on an individual basis. However, the utility of each SI in its “silo” may lead to different and possibly contradictory decisions. Inspired by the bolstered error estimation technique used in pattern recognition, this article presents a new method that can utilize multiple SIs obtained from the FRA data to classify transformer operating conditions and estimate the level of confidence in decisions made. The probabilistic method proposed herein is an attempt to bridge the gap between these decision-making silos. The practical implementation of the proposed technique on distribution and power transformers revealed a reliable interpretation and classification results. At the same time, having an estimate for the level of confidence in the decision made by the method further helps engineers and utility operators make informed decisions and have a better understanding of the level of the transformer fault severity.