Design of an Automated Dual IPCs 240 System for Asymmetric Power Flow Compensation in an AC Electric Network

In this paper, an automated dual IPCs 240 system for asymmetric power compensation is designed from an analytical analysis of the power flow modes through the transmission line. Then, the obtained ACL (Automatic Control Logic) consists of serial gates arrays of the standard NAND/AND and OR operators. It has been implemented within the Matlab/Simulink framework, and the obtained simulation results show the feasibility and great relevance of the dual IPCs 240 technology using, in power transmission networks under normal and contingency conditions

Design of an Automated Dual IPCs 240 System for Asymmetric Power Flow Compensation in an AC Electric Network

In this paper, an automated dual IPCs 240 system for asymmetric power compensation, is designed from an analytical analysis of the power flow modes through the transmission line. Then, the ACL (automatic control logic) obtained consists of serial gates arrays of standard NAND/AND and OR operators.  It has been  implemented within Matlab/Simulink framework, and the simulation results obtained show the feasibility and great relevance of using the dual IPCs 240 technology, in power transmission networks  under normal and contingency conditions

Hybrid DGA method for power transformer faults diagnosis based on evolutionary k-means clustering and dissolved gas subsets analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique

Duty cycle modulation - fuzzy logic technique to track the maximum power point of a solar-wind hybrid Source

In this article, we propose a strategy for finding the maximum power point (MPP) of a hybrid plant (solar and wind), in order to maximize the power extracted from this production plant. This strategy exploits the perturbation and observation method, based on fuzzy logic coupled with the Duty Cycle Modulator (DCM). The main objective of this study is to extract the maximum power from this hybrid power plant, while ensuring the precision and speed of convergence towards this point of maximum power. This method, tested under the Matlab / Simulink environment for a 160 kW hybrid power plant, gave results that we compared to those obtained with the Fuzzy - PWM (Pulse Width Modulator) strategy. It emerges that the Fuzzy-DCM strategy gives better precision (around 2.6 times) and a speed (around 2 times) of convergence compared to the Fuzzy-PWM strategy

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method

A novel brushless de‐excitation system for synchronous generators using a buck chopper with a freewheeling discharge resistor

Abstract A new de‐excitation system for advanced brushless excitation based on a synchronous generator is presented. The proposed system uses a discharge resistor in series with a freewheeling diode of a buck chopper. It improves the reliability of the entire power plant and can be used on several excitation systems. It can be used in case of programmed shutdown of the generator and also shows good results in voltage regulation without destroying the efficiency of the excitation system under nominal operation. An analysis of the system has allowed us to suggest a methodology to obtain the appropriate value of the discharge resistance to be used. The validation of the proposed system has been done through simulations in the Matlab/Simulink environment, supported by experimental tests

Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future

A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented