Transient overvoltages generated by lightning strikes or switching operations represent a significant risk to bushings and windings of power transformers. They cause stress on the insulation system and can, over time, cause dielectric failure and damage to power transformers. Many transformer failures are reported as dielectric failures and they are not necessarily linked to any particular event when they occur but may be the result of prior damage from transient overvoltage events. Lightning and switching overvoltage waveforms appearing at transformer terminals in real operating conditions may significantly differ from standard impulse voltage waveforms used during laboratory testing. The number and amplitudes of overvoltages which stress the insulation depend on various parameters such as the lightning strike density in the considered area, since it determines how often the transformer is stressed by lightning overvoltages. Since the overvoltage amplitudes at transformer terminals are usually unknown, an on-line overvoltage transient recorder can be used with the ability to sample, analyse and store transients in real-time. In this paper, an on-line transient overvoltage monitoring system (TOMS) for power transformers is presented that is capable to continuously record in real-time various kinds of transient overvoltages such as lightning or switching overvoltages. Special attention is paid to lightning caused transient overvoltages recorded at the terminals of 150 MVA power transformer. Recorded waveforms originating from lightning strikes to overhead lines are correlated with data from the lightning location system (LLS) and supervisory control and data acquisition (SCADA) system. Collected data about overvoltage stresses can be used as the basis for the assessment of the transformer insulation condition, estimation of health index and for analysis of various kinds of events such as faults or equipment failures.
APSTRAKTRad je usmjeren na istraživanje uticaja poreza na dobit, poreza na plate i drugih vrsta poreza na priliv stranih direktnih investicija (SDI) u Bosni i Hercegovini, posmatrajući period od 2006 do 2014 godine.U prvom dijelu obrazložiće se dosadašnja istraživanja na temu poreza na dobit i poreza na plate kao i njihovi efekti na SDI, u okviru čega će biti prikazane i informacije o prilivu SDI u Bosnu i Hercegovinu, kao i o kretanju poreskih stopa u državi. Nakon toga će biti objašnjena metodologija i metode prikupljanja podataka. O interpretaciji dobijenih rezultata će biti govora na kraju ovoga rada, gdje će se u tu svrhu koristiti SPSS programa za statističke analize.Ključne riječi: strane direktne investicije, porez na dobit, porez na plate, Bosna i Hercegovina ABSTRACTThe article is based on the research of income taxes impact, payroll taxes and other taxes on Foreign Direct Investment (FDI) in Bosnia and Herzegovina, considering the period from 2006 to 2014. The first part will explain the hitherto literature on the subject of income and payroll taxes, as well as their effects on FDI.This will include the information regarding FDI inflow to Bosnia and Herzegovina, as well as the movement of tax rate in the state. After that the methdologies and date collections methods are going to be discussed. The interpretation of the results will be discussed at the end of this paper where SPSS program for statistical analysis will be used. Keywords: foreign direct investment, corporate income tax, labor tax, Bosnia and Herzegovina UVODSkoro svaka država u svijetu zainteresovana je za privlačenje stranih direktnih investicija. (SDI). SDI mogu generisati nova radna mjesta, doprinijeti razvoju nove tehnologiju, a njihov poseban doprinos se ogleda u doprinosu ka razvoju i zaposlenosti u državi. Pored ovoga država ima direktne prihode od SDI kroz oporezivanje plata, profita kompanija u stranom vlasništvu i druge poslovne poreze. SDI također može imati uticaj na domaću privredu kroz efekte prilivanja, kao što su obogaćivanje ljudskog kapitala i uvođenje novih tehnologija i know-how. Kreatori politike bi trebali u kontinuitetu da provjeravaju svoje procedure oporezivanja kako bi mogli privući strane investitore i omogućiti priliv stranih direktnih investicija [OECD, 2008a].Akademska literatura nije uvijek usaglašena kada je u pitanju definisanje stranih direktnih investicija (SDI), tako da je najefikasnije konsultovati se sa oficijelnim institucijama kao što je OECD. OECD definiše stranu direktnu investiciju kao: "Strane direktne investicije su investicije koje uključuju dugoročne odnose i održavaju trajne interese i kontrolu firme-rezidenta jedne zemlje (investitora strane direktne investicije ili matičnog preduzeća) u preduzeće koje je rezident druge zemlje". Trajni interes podrazumijeva postojanje dugoročnih odnosa između direktnog investitora i preduzeća, te značajan stepen uticaja na upravljanje preduzeća" ". [OECD, 2008b]. OECD je dalje proširio ovu definiciju: "Postotak ili više od uobičajenih akcija ili moći glas...
With the increasing complexity of power system structures and the increasing penetration of renewable energy, driven primarily by the need for decarbonization, power system operation and control become challenging. Changes are resulting in an enormous increase in system complexity, wherein the number of active control points in the grid is too high to be managed manually and provide an opportunity for the application of artificial intelligence technology in the power system. For power flow control, many studies have focused on using generation redispatching, load shedding, or demand side management flexibilities. This paper presents a novel reinforcement learning (RL)-based approach for the secure operation of power system via autonomous topology changes considering various constraints. The proposed agent learns from scratch to master power flow control purely from data. It can make autonomous topology changes according to current system conditions to support grid operators in making effective preventive control actions. The state-of-the-art RL algorithm—namely, dueling double deep Q-network with prioritized replay—is adopted to train effective agent for achieving the desired performance. The IEEE 14-bus system is selected to demonstrate the effectiveness and promising performance of the proposed agent controlling power network for up to a month with only nine actions affecting substation configuration.
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In this paper an artificial neural network (ANN) based methodology is proposed for determining an external network equivalent. The modified Newton-Raphson method with constant interchange of total active power between internal and external system is used for solving the load flow problem. A multilayer perceptron (MLP) with backpropagation training algorithm is applied for external network determination. The proposed methodology was tested with the IEEE 24-bus test network and simulation results show a very good performance of the ANN for external network modeling.
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