In recent years, important efforts to improve monitoring, protection and control of power systems have been explored. In this connection, several novel approaches for assessing vulnerability in real time have been developed. However, most of the work is commonly focused on tackling stability phenomena, while the possible overloads are often treated as negligible in real-time power system security. But sometimes, high electric post-contingency currents might provoke overloads which could increase the system vulnerability problem. This paper presents a novel method for assessing the possibility of fast post-contingency overloads using Statistical Distribution Factors (SDFs) that allow computing an Overload Index (OVI) in real time. First, Monte Carlo-based contingency analysis is performed to iteratively calculate ac Distribution Factors (ac-DFs). After, SDFs are defined by the mean and standard deviation of ac-DFs. These SDFs are then used together with principal component analysis (PCA) and support vector machine classifier (SVM-C) in order to structure a table-based real-time post-contingency overload estimation algorithm, which allow computing OVIs depending on the actual operating state and the type of contingency. The proposal is tested on the IEEE New England 39-bus test system. Results show the feasibility of the methodology in alerting about fast possible overloads. Index Terms--Monte Carlo, overload, principal component analysis, security, smart grid, support vector machine, vulnerability assessment.
In recent years, important efforts to improve monitoring, protection and control of power systems have been explored. In this connection, several novel approaches for assessing vulnerability and improving security in real time have been developed. However, most of the work is commonly focused on tackling stability phenomena, while the possible overloads are often treated as negligible in real-time power system security. But sometimes, high electric post-contingency currents might provoke overloads which could increase the system security problem. This paper tackles the possible overload issues by applying a novel adaptive load shedding (ALS) method. First, the possibility of fast post-contingency overloads is evaluated using Statistical Distribution Factors (SDFs) that allow computing an Overload Index (OVI) in real time. The results are then used for triggering an adequate ALS scheme that allows improving the system security level when the OVI reaches a critical value. For this purpose, Monte Carlo-based contingency analysis is firstly performed to iteratively calculate ac Distribution Factors (ac-DFs). After, SDFs are defined by the mean of ac-DFs. These SDFs are then used together with principal component analysis (PCA) and support vector machine classifier (SVM-C) in order to structure a table-based real-time post-contingency overload estimation algorithm, which allow computing OVIs depending on the actual operating state and the type of contingency. In the cases where OVI reaches inadmissible levels, an ALS scheme is triggered using a centralized strategy. The aim is to alleviate the current in overloaded transmission lines before the local overload protection acts, since it might initiate a cascading event that would eventually drive the system to a blackout. The proposal is structured to act in coordination with the traditional underfrequency and undervoltage load shedding. For this purpose, PMU measurements of voltage phasors and frequency are used for evaluating the event evolution in order to determine the adequate amount of load to be shed due to overload problems. The scheme also determines the location of the load to be shed by analyzing the participation of each load in the overloaded line via the computation of electrical distances between the overloaded-line receiving-end bus and the load bus. The proposal is tested on the IEEE New England 39-bus test system. Results show the feasibility of the methodology in alerting about possible overloads and defining an appropriate ALS scheme that allows alleviating the current in the possible overloaded lines.Index Terms-Monte Carlo, overload, principal component analysis, security, smart grid, support vector machine, vulnerability assessment, adaptive load shedding..
Atmospheric factors, such as clouds, wind, dust, or aerosols, play an important role in the power generation of photovoltaic (PV) plants. Among these factors, soiling has been revealed as one of the most relevant causes diminishing the PV yield, mainly in arid zones or deserts. The effect of soiling on the PV performance can be analyzed by means of I–V curves measured simultaneously on two PV panels: one soiled and the other clean. To this end, two I–V tracers, or one I–V tracer along with a multiplexer, are needed. Unfortunately, these options are usually expensive, and only one I–V tracer is typically available at the site of interest. In this work, the design of a low-cost multiplexer is described. The multiplexer is controlled by a low-cost single-board microcontroller manufactured by ArduinoTM, and is capable of managing several pairs of PV panels almost simultaneously. The multiplexer can be installed outdoors, in contrast to many commercial I–V tracers or multiplexers. This advantage allows the soiling effect to be monitored on two PV panels, by means of I–V indoor tracers. I–V curves measured by the low-cost multiplexer are also presented, and preliminary results are analyzed.
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