Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.publishedVersio

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.publishedVersio

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.publishedVersio

Cascaded Multilevel Inverter-Based Asymmetric Static Synchronous Compensator of Reactive Power

The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmetrical reactive loads and provides up to three times lower power losses in the compensator as compared with the situation when the compensator is based on the conventional three-level inverters implemented using IGBT transistors.publishedVersio

Parallel system for analysis of meander delay line

Meander microstrip delay lines (MMDL) are widely used in electronic systems. The basic difficulty designing MMDL is the solution of the dispersion equation, which defines the relation between phase coefficient of electromagnetic wave in the free space and in the investigated MMDL. To shorten the time of solving the dispersion equation the parallel algorithm is offered. The algorithm has been implemented on 8 computers cluster МРICH2. Examination of the operation of the cluster has shown that each doubling of the number of nodes increments the efficiency of the cluster approximately by 40%. Article in Lithuanian. Dispersinės lygties sprendimo būdai Santrauka. Straipsnyje nagrinėjami dispersinės lygties sprendimo būdai. Dispersinės lygties sprendiniai taikomi meandrinių lėtinimo sistemų elektriniams parametrams apskaičiuoti. Sprendžiant dispersinę lygtį būtina žinoti analizuojamos meandrinės lėtinimo sistemos konstrukcinius parametrus ir ją modeliuojančios daugialaidės linijos laidininkų elektrinius parametrus, esant lyginiam ir nelyginiam sužadinimui. Dispersinė lygtis bendruoju atveju sprendžiama skaitiniais metodais, taikant ekstremumo paieškos būdą. Skaičiuojant meandrinės lėtinimo sistemos dažnines charakteristikas, sprendinių paiešką tenka vykdyti daug kartų, taigi, šiuos skaičiavimus racionalu vykdyti pasitelkus lygiagrečiąją kompiuterių sistemą. Straipsnyje pasiūlytas ir įgyvendintas lygiagretusis dispersinės lygties sprendimo būdas. Parodyta, kad, taikant aštuonių kompiuterių telkinį, dispersinės lygties skaičiavimo sparta padidėja tris kartus lyginant su vieno kompiuterio sparta. Reikšminiai žodžiai: meandrinė lėtinimo sistema, fazės lėtinimo koeficientas, dispersinė lygtis, lygiagretieji skaičiavimai, kompiuterių telkinys

Investigation of the electrodynamic retard devices using parallel computer systems

Disertacijoje nagrinėjamos mikrobangų įtaisų analizės ir sintezės proble-mos, taikant lygiagrečiąsias kompiuterines sistemas. Pagrindiniai tyrimo objektai yra daugialaidės mikrojuostelinės linijos (DML) ir meandrinės mikrojuostelinės vėlinimo linijos (MMVL). Šie objektai leidžia perduoti, sinchronizuoti bei vėlinti siunčiamus signalus ir yra neatsiejama dalis daugelio mikrobangų prietaisų. Jų operatyvi ir tiksli analizė bei sintezė sąlygoja įtaisų kūrimo spartinimą. Pagrindinis disertacijos tikslas – sukurti lygiagrečiąsias metodikas ir algoritmus, skirtus sparčiai ir tiksliai atlikti minėtų linijų analizę ir sintezę. Sukurtų algoritmų ir metodikų taikymo sritis – mikrobangų įtaisų modeliavimo ir automatizuoto projektavimo progra-minė įranga. Disertaciją sudaro įvadas, penki skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tematika sąrašai bei du priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomi tyrimų objektai, formuluojamas darbo tikslas ir uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema, autoriaus paskelbtos publikacijos ir pranešimai konferencijose bei disertacijos sandara. Pirmasis skyrius skirtas literatūros apžvalgai. Jame pateiktos DML ir MMVL analizės ir sintezės problemos, be to apžvelgtos lygiagrečiosios skaičiavimo sistemos ir jų taikymas elektrodinamikos uždaviniams spręsti. Skyriaus pabaigoje formuluojamos išvados ir tikslinami disertacijos uždaviniai. Antrajame skyriuje siūlomi du DML modeliai, leidžiantys skaitiniais metodais apskaičiuoti DML elektrinius parametrus, pateikti siūlomų modelių patikros rezultatai, atitinkantys elektromagnetinius procesus baigtinio dydžio DML. Trečiajame skyriuje plačiau nagrinėjama DML analizė ir sintezė, pasiūlyti lygiagretieji DML analizės ir sintezės algoritmai, atlikta jų patikra. Įgyvendinti algoritmai leidžia sparčiau apskaičiuoti elektrinius ir surasti konstrukcinius DML parametrus. Ketvirtajame skyriuje pateikiama lygiagrečioji MMVL dispersinės charakte-ristikos apskaičiavimo metodika. MMVL dispersinė charakteristika gaunama skaitiniais metodais sprendžiant dispersinę lygtį. Lygties sprendiniui rasti buvo sudaryti ir įgyvendinti keturi algoritmai. Remiantis šiais algoritmais pasiūlyta ir išbandyta lygiagrečioji MMVL dispersinės charakteristikos apskaičiavimo metodika. Penktasis skyrius skirtas MMVL sintezei. Pasiūlyti ir įgyvendinti skiltinio artėjimo ir Monte Karlo metodais grįsti lygiagretieji MMVL sintezės algoritmai, atlikti pasiūlytų algoritmų sintezės tikslumo ir našumo tyrimai. Disertacijos tematika paskelbti 5 straipsniai, iš kurių 3 straipsniai paskelbti recenzuojamuose mokslo žurnaluose. Rezultatai viešinti 9 mokslinėse konferencijose Lietuvoje ir užsienyje

Parallel synthesis of multiconductor microstrip lines

The parallel technique for the synthesis of the multiconductor microstrip lines (MML) is presented in this article. Main parameter of the synthesised MML is characteristic impedance, variable parameters: width of the conductors, space between conductors, the thick of dielectric substrate and its permittivity. Authors’ created parallel algorithm is used in this technique, wherein analysis operations are distributed among cluster nodes. The MML model created using the finite difference method. To reduce analysis area it is assumed, what MML is infinite and periodic. Proposed technique was realized and proved in the sixteen nodes cluster. Cluster is based on Mpich2 software. Proved, that synthesis error is less than 1 %. Characteristics of the synthesised MML, compared with characteristics which calculated with other methods differ less than 6 %. It may be noted that, performance coefficient of the parallel synthesis algorithm is growing linear when cluster has no more than 16 nodes. Linear growth of performance coefficient demonstrates that adding more nodes to cluster reduce execution time of the synthesis. Article in Lithuanian. Lygiagrečioji daugialaidžių mikrojuostelinių linijų sintezė Santrauka. Pateikta metodika daugialaidėms mikrojuostelinėms linijoms (DML) sintezuoti. Tikslinis sintezuojamos DML parametras – charakteringasis impedansas. Varijuojami parametrai: laidininkų plotis, tarpas tarp laidininkų, pagrindo storis ir dielektrinė skvarba. Metodika remiasi autorių sukurtu lygiagrečiuoju sintezės algoritmu, kuriame analizės operacijos paskirstytos tarp kompiuterių telkinio mazgų. DML modelis sudarytas taikant baigtinių skirtumų metodą. Analizuojamai sričiai sumažinti padaryta prielaida, kad DML yra begalinė ir periodinė. Pasiūlytoji metodika buvo įgyvendinta ir išbandyta šešiolikos mazgų kompiuterių telkinyje. Kompiuterių telkinys pagrįstas Mpich2 programinėmis priemonėmis. Nustatyta, kad sintezės paklaida neviršija 1 %, o sintezuotos DML charakteristikos, lyginant su charakteristikomis, gautomis kitais metodais, skiriasi mažiau nei 6 %. Raktiniai žodžiai: daugialaidės mikrojuostelinės linijos sintezė; lygiagretusis algoritmas; mpich2 programinės priemonės

Parallel algorithms for the synthesis of the multi-tapped meander delay lines

An algorithm, based on the successive approximation technique for the parallel synthesis of the multi-tapped meander microstrip delay line (MTMDL), is presented. Multiconductor line model and finite difference method are used for analysis of the MTMDL. Parallel computations in the proposed algorithm are organized according to both – data parallelism and task parallelism concepts. Efficiency of two parallel synthesis algorithms one proposed, and another based on Monte Carlo method, is compared. It is shown that while number of computational nodes is small by comparison (e.g. not larger than 3) then efficiency of the proposed algorithm about 5 times exceeds Monte Carlo method based algorithm efficiency. It is revealed that efficiency of both algorithms becomes similar when computational nodes number increases several times

Acceleration techniques for analysis of microstrip structures

This paper discusses the acceleration techniques for analysis of microstrip structures. Accurate calculation of parameters of such structures with numerical techniques requires the solution of dense matrix equations involving thousands of unknowns. Solution of this large problem takes long time. In this paper we present three techniques for such computations acceleration: parallel algorithm implemented in computer cluster, sparse bound-matrix technique, and graphic processing unit in conjunction with CUDA technology. The execution time and speed-up of proposed techniques are evaluated through comparing of different numbers of processors and unknowns. The results indicate that all presented techniques can significantly reduce computation time