دانلود ترجمه مقاله استراتژی بهینه برای راه اندازی ژنراتور جهت بازگردانی سیستم قدرت حجیم – مجله IEEE

فهرست مطالب:

چکیده
۱ مقدمه
۲ روش بازگردانی سیستم
۳ به حداکثر رساندن قابلیت تولید در طول بازگردانی سیستم
A شاخصه ها و محدودیت های ژنراتور
B استراتژی بهینه برای راه اندازی ژنراتور
۴ تبدیل مسئله بهینه سازی
A تابع هدف
B محدودیت ها
۵ روشهای دیگر
۶ نتایج عددی
۷ نتیجه گیری

بخشی از ترجمه:

مقدمه

یکی از مهمترین کارها برای برنامه ریزی و راه اندازی سیستم قدرت، بازگردانی سیستم بعد از خاموشی سراسری، می باشد. پروسه بازگردانی سیستم را به شرایط عملیاتی نرمال بازمی گرداند. طرح ها و برنامه های بازگردانی تهیه شده به صورت آفلاین، دیسپچرها را راهنمایی می کند در حالیکه آنها مسئولیت ارزیابی شرایط سیستم، راه اندازی واحدهای BS ، تعیین مسیرهای انتقال برای کرانک واحدهای تولیدی NBS ، انتخاب بارهای ضروری برای تثیبیت شرایط سیستم قدرت و سنکرون سازی جزایر الکتریکی را برعهده دارند. بازگردانی سیستم قدرت مسئله ای پیچیده مشتمل بر محدودیت های زیاد در زمینه های تولید، انتقال و توزیع و بار می باشد. در [۴]، پروسه بازگردانی به سه مرحله تقسیم شده است: آماده سازی، بازگردانی سیستم و بازگردانی بار. با این وجود، یک مسیر و رشته رابط بین این مراحل، دسترس پذیری تولید در هر مرحله می باشد.

بخشی از مقاله انگلیسی:

INTRODUCTION

SYSTEM restoration following a blackout is one of the mostimportant tasks for power system planning and operation.The restoration process returns the system back to a normal operatingcondition following an outage of the system. Dispatchersare guided by restoration plans prepared offline while they assesssystem conditions, start BS units, establish the transmissionpaths to crank NBS generating units, pick up the necessary loadsto stabilize the power system, and synchronize the electrical islands[1], [2]. Power system restoration is a complex probleminvolving a large number of generation, transmission and distribution,and load constraints [3]. In [4], the restoration processis divided into three stages: preparation, system restoration, andload restoration. Nevertheless, one common thread linking thesestages is the generation availability at each stage [5]. North American Electric Reliability Corporation (NERC)is in the process of revising the System Restoration andBlackstart standards to enhance reliability for the interconnectedNorth American power systems. The revised standardsEOP-005-2-System Restoration from Blackstart Resources [6]and EOP-006-2-System Restoration Coordination [7] proposeda new definition of blackstart resource and identified requirementsfor Transmission Operators (TOP), Generator Operators(GOP), and Reliability Coordinators (RC). These two standardsrequire each TOP to have a restoration plan approved by itsRC and each GOP has a blackstart procedure, together with atraining program for their blackstart unit operators and blackstarttesting requirements of its TOP [8]. Therefore, dispatchersmust be able to identify the available blackstart capabilitiesand use the blackstart power strategically so that the generationcapability can be maximized during the system restorationperiod. This requirement originates from the concept of generationdispatch scenario (GDS), which was first proposed in[9] and then further investigated in an EPRI project [3] using aknowledge-based system (KBS) approach.Power system dispatchers are likely to face extreme emergenciesthreatening the system stability [10]. They need to be awareof the situation and adapt to the changing system conditionsduring system restoration. Therefore, utilities in the NERC ReliabilityCouncil regions conduct system restoration drills to traindispatchers in restoring the system following a possible majordisturbance. There are simulation-based training tools; for example,EPRI-OTS and PowerSimulator offer training on system restoration for control center dispatchers. However, practicallyno system restoration decision support tool has been widelyadopted in an online operational environment of the bulk transmissionsystems. Decision support tools have been developedand implemented in the distribution system level [11]–[۱۳].The system restoration problem can be formulated asa multi-objective and multi-stage nonlinear constrained optimizationproblem [14]. The combinatorial nature of the problempresents challenges to dispatchers and makes it difficult to applyrestoration plan system-wide. To better support the dispatchersin the decision-making process, several approaches and analyticaltools have been proposed for system restoration strategies.Heuristic methods [15] and mathematical programming [14]are used to solve this optimization problem. However, eitherthe optimality of the solution cannot be guaranteed or the complexityaffects the effectiveness of the restoration proceduresfor large-scale systems. KBSs [16]–[۱۹] have been developedto integrate both dispatchers’ knowledge and computationalalgorithms for system analysis. However, KBSs require specialsoftware tools and, furthermore, the maintenance of large-scale knowledge bases is a difficult task. The technique of artificialneural networks [20] has been proposed for system restoration.Reference [21] reports a new method for blackstart serviceannual selection analysis; however, the heuristic nature of theapproach does not assure global optimality.In a related paper [5] that reports preliminary results ofthis project, a proposed method is used to solve the generatorstart-up sequencing problem that only achieves optimalityfor each time step. This paper proposes a new algorithm thatformulates this optimization problem as an MILP problem. Theresult is a linear formulation that leads to an optimal solution.Moreover, an optimal generator start-up strategy is proposedto provide an initial starting sequence of all generators andalso updates the generation capability as system restorationprogresses. The developed algorithm can adapt to changingsystem conditions and can be used to provide guidance todispatchers in the operational environment.