Data play an essential role in asset management decisions. The amount of data is increasing through accumu-lating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middleaged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

Power systems are becoming more complex, which increases instability issues and outage risks. The development of smart grids could help manage such complex systems. One important pillar in smart grids is big data analytics. In this poster paper, we discuss where and how machine learning could contribute to more efficient asset management. We also identify challenges that stand in the way of the widespread use of big data analytics in smart grids. While the nature of data, as well as data and asset management systems themselves make the use of big data challenging, data analytics could improve the reliability of power supply by providing the functions of detection, prediction, and selection. 

Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.

Monitoring the condition of components helps taking preventive actions to avoid failures, and increases reliability. However, performing such monitoring for all components of the distribution grid is prohibitively expensive. Instead, distribution system operators could focus efforts only on the most critical components. In particular, importance indices enable to prioritise components according to a chosen criterion, and to adapt monitoring strategies. This study presents methods to rank grid components using outage data. The importance indices are based on: 1) de-energisation time; 2) frequency of failures; 3) disconnected power; 4) energy not supplied and 5) customer outage time. The results depend largely on the time period of the outage data considered for analysis. Some components' rank varies with the chosen criterion. This indicates that they are critical with respect to a specific criterion. Other components are ranked high with all the methods, which means that they are critical, and need focused monitoring.