This article introduces the application of a data mining method with the purpose of contingency screening, by rapid recognition of hazardous, reoccurring power system operating conditions. The method, suitable for real-time applications, is demonstrated on the north-western part of the Slovenian power-system, for first-swing stability issues. The presented demonstration consists of two steps: First, a database containing a set of prefault operating states and the corresponding critical clearing times of several contingencies is constructed. Second, the prefaultmeasurements matrix is decomposed using the principal component analysis method and represented in a coordinate system, defined by the principal components. Since operating states form dense clusters of points in this coordinate system, the similarity between current and past conditions is established by identifying the shortest Euclidean distance metric. In this manner, the indication of each contingency impact is provided rapidly as long as a similar operating state exists within the database. Otherwise, the case is thoroughly investigated and included in the database. This approach is applicable to a wide spectrum of dynamic problems, providing that problem-relevant sets of input data are considered.
COBISS.SI-ID: 11728724
An unexpected triggering of 400/100 kV transformer protection was detected in the electrical vicinity of a nuclear power plant in the Slovenian power system, caused by a magnetizing process of the second parallel-connected transformer on its low-voltage side. The phenomenon lasts tens of seconds and originates from a non-negligible share of ohmic component in the system impedance of 110 kV network. In addition, a current-measurement transformer saturation was detected as well, which resulted in distorted PMU measurements. A successful reconstruction of a so-called sympathetic inrush current phenomenon is provided in the research paper, validated by PMU measurements originating from 400 kV voltage level within the same substation.
COBISS.SI-ID: 11352916
This paper presents the impact of the stochastic electric-drive vehicles' driving range on the charging reliability of charging infrastructure. For this purpose, it incorporates an additional uncertainty distance in addition to the initial driving range of the electric vehicle to address all probabilistic occurrences that can affect the range, such as the battery charge level, driving style and mobility behaviour, road configuration, air-conditioning, etc. The analysis is performed based on the proposed optimisation model on a test road network applied for different stochastic driving range scenarios, Quality of Service, electric vehicles' trajectories and the types of charging technologies. In general, a dependency is observed where a shorter uncertainty distance increases the number of candidate locations included in the charging reliability criterion resulting in higher overall charging infrastructure placement costs and vice-versa. The proposed model is applicable in long-term planning procedures of the charging infrastructure.
COBISS.SI-ID: 11820884
In this article, a new method for planning of electric vehicle charging infrastructure is presented. Charging reliability and quality of service are considered in the procedure that presents the novelty of the proposed solution. Adequate charging infrastructure is of crucial importance when transferring from vehicles with internal combustion engines to electric vehicles.
COBISS.SI-ID: 11608148
Smart metering and various consumption-feedback systems can be used as applicable technology to encourage end-use energy efficiency in the residential sector. This paper evaluates the effect of customized consumption feedback and other information interactions on energy-behaviour patterns and energy savings in low-income households.
COBISS.SI-ID: 30061351