Milad Soleimani

Bio: Milad Soleimani is an academic researcher from Texas A&M University. The author has contributed to research in topics: Distribution transformer & Transformer. The author has an hindex of 6, co-authored 15 publications receiving 199 citations. Previous affiliations of Milad Soleimani include University of Tehran.

Dissolved gas analysis evaluation in electric power transformers using conventional methods a review

Abstract: Transformers are the most important equipment in power systems, and their failure can cause serious problems. In order to avoid hazardous operating conditions and reduce outage rates, fault detection in the incipient stage is necessary. Incipient faults cause thermal or/and electrical stresses on the transformer with a major consequence on insulation decomposition. The insulation decomposition causes the evolution of gases which can be dissolved in oil. Dissolved gas analysis (DGA) interpretation is one of the main techniques used for fault diagnosis in oil-immersed transformers. In this paper, DGA interpretation is evaluated in detecting different faults and the techniques considered as conventional methods of DGA are investigated. The evaluation is based on DGA data obtained from oil samples of real transformers.

Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis

Abstract: Transformers are vital components of power systems as they are situated between energy generation and consumers and their failure disrupts the use of electrical energy. Therefore, diagnosing an incipient fault is essential in avoiding hazardous operating conditions and minimizes downtime cost. In transformers, faults take place due to electrical or thermal stresses that cause insulation decomposition in transformers. In oil-filled transformers, insulations are cellulose and oil, and the products of the insulation decomposition are gases which can be dissolved in the oil. Therefore, dissolved gas analysis (DGA) can be used for fault diagnosis in oil filled transformers. In this paper, DGA interpretation methods, conventional and intelligence, are investigated and compared. For evaluating consistency and accuracy of the methods, "No Result" cases are not considered. It can help the newcomers to this field to have access to a comprehensive comparison about the application of computational intelligence and conventional methods in transformer fault detection using DGA.

Mitigating Transformer Loss of Life and Reducing the Hazard of Failure by the Smart EV Charging

Abstract: The impact of uncoordinated charging of electrical vehicles (EVs) under high penetration on distribution transformers is studied. It was shown that EV chagrining may cause prolonged transformer overload condition, that may in turn result in transformer loss of life and increased hazard of failure. To mitigate the impact, a fuzzy logic-based system for determining EV charging schedule is devised. It uses four main inputs: 1) EV battery state of charge; 2) required state of charge for the next trip; 3) estimated time of EV departure; and 4) customer comfort level. The resulting output is a performance index that the distribution system operators can utilize in a decision-making tool to determine whether to delay the charging of given EV and pay the incentive to the EV owner. The data for the city of College Station, Texas, USA including temperature, price of electricity and load profile are collected from various sources to simulate different use cases. The example illustrates how the proposed EV management approach could mitigate the impact of EV charging on the transformer loss of life and hazard of failure. The main advantage of the proposed approach is the low cost due to simple design implementation. The information that needs to be sent from the consumer to the distribution system operator is minimized, which helps in maintaining costumers’ privacy.

Optimal Participation of PEV charging stations integrated with Smart Buildings in the Wholesale Energy and Reserve Markets

Abstract: The emerging nano-Grids (n-Grids), which we designated as commercial or residential buildings hosting electric vehicle charging stations, a fixed battery storage and photovoltaic panels integrated with the building's load, all being interfaced to the grid through a bidirectional energy exchange framework may provide substantial advantages to the power grid and n-Grid owner. The n-Grids, because of their substantial energy storage capacity and ramp rate, have an added capability, if aggregated can provide ancillary services, reserve in particular, for the wholesale market. In this paper, the optimal framework for their aggregated participation in the day-ahead energy and reserve markets is developed. In the developed bi-level optimization, at a centralized level the aggregator attempts to maximize its profitability by offering optimal energy and reserve prices to the n-Grids. At the decentralized level, the n-Grid optimizer, based on the prices offered by its aggregator, while assuring its own flexibility and dependability, runs the scheduling of its resources to maximize their profit from energy and reserve product procurement. The simulation results demonstrate that the n-Grids are able to provide significant flexibility and resilience to the system by leveraging their high ramp and storage capacity, and as a consequence, provide financial profit to their owners.

Temperature Measuring-Based Decision-Making Prognostic Approach in Electric Power Transformers Winding Failures

Abstract: The electric power transformer is a vital apparatus in power systems, and failure prognostics is significant for the protection of this asset. In addition to the asset damage, its unexpected failure would interrupt power delivery and jeopardize the stability of the system. There are several fault diagnosis methods introduced for the detection of this kind of fault; however, their functionality is for the postfault condition when the asset is already damaged, and the operation of the system is interrupted. Electric insulation deteriorations make the transformers susceptive to faults due to thermal and electrical stresses. In this article, the impact of early stages insulation deteriorations on the temperature inside the transformer is studied using a finite-element electromagnetic–thermofluid method and based on the observations an online sensor-based decision-making predictive fault diagnosis approach is proposed. Finally, the results are experimentally verified.

A critical review of plant-based insulating fluids for transformer: 30-year development

Abstract: As a non-renewable resource, mineral oil has been re-evaluated for its value and suitability as the coolant and insulator in transformers because of its environmental unfriendliness, fire hazards, and operating efficiency dissatisfaction. As a result, alternative dielectric liquids meeting the demands for more reliable, safer, and cleaner energy has been a critical need. Plant-based insulating fluids, non-toxic to the environment and ultimately biodegradable, have been flowing into the mainstream for achieving cost efficiencies, performance advantages, and enhancing safety. With more than 20 years of field experience, plant-based oils are now employed in more than 600,000 transformers worldwide, and have been established an enviable performance track record. Far from being just a mineral oil replacement, but more significantly, plant-based oils have filled a gap in some specific application scenarios where mineral oils fail to satisfy the fire-safety and environmental standards. This article reviews the research status of plant-based insulating fluids for transformer, including both challenges and outlook circa 2020. Plant-based oils have many inherent advantages over mineral oil, but also have numerous and unique challenges.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis

Abstract: Transformers are vital components of power systems as they are situated between energy generation and consumers and their failure disrupts the use of electrical energy. Therefore, diagnosing an incipient fault is essential in avoiding hazardous operating conditions and minimizes downtime cost. In transformers, faults take place due to electrical or thermal stresses that cause insulation decomposition in transformers. In oil-filled transformers, insulations are cellulose and oil, and the products of the insulation decomposition are gases which can be dissolved in the oil. Therefore, dissolved gas analysis (DGA) can be used for fault diagnosis in oil filled transformers. In this paper, DGA interpretation methods, conventional and intelligence, are investigated and compared. For evaluating consistency and accuracy of the methods, "No Result" cases are not considered. It can help the newcomers to this field to have access to a comprehensive comparison about the application of computational intelligence and conventional methods in transformer fault detection using DGA.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Abstract: Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.