Abstract
Electrical measurements of smart grids form the backbone of various data driven applications. Proper analysis of these measurements can result in improved system planning, operation, monitoring and protection. However, the efficacy of smart grid data mining is highly influenced by the quality of the data. Hence, quality assessment of smart grid data is essential prior to the usage of the data in various applications. A fuzzy assessment method is proposed in this paper for assessing quality of multivariate electrical measurements of smart grids. At first, relevant quality dimensions of smart grid data are identified. Then, based on certain desirable characteristics, novel membership functions are proposed for assessing the data quality with respect to each of the considered dimensions. The proposed membership functions are evaluated on the current and real power measurements obtained from the power flow analysis of the IEEE 14-bus system. In addition, the proposed method is also implemented on the voltage, current and the real power measurements obtained from the power flow analysis of an actual 34 node feeder located in Arizona. The impact of measurement noise is also investigated by polluting the original measurements with Gaussian noise. It is found that the quality of the noisy measurements worsens with the increase in variance of the added noise. The proposed method has also been validated on a database containing practical SCADA and PMU measurements of the Southern Regional Grid of India. It is found that the PMU datasets are relatively incomplete compared to the SCADA datasets. In addition, the obtained results indicate that PMU data are suitable for use in more number of applications compared to the SCADA datasets. Unlike the existing methods, the proposed method can be used for quantifying the quality of any smart grid dataset that contains electrical measurements of multiple power system variables including boolean variables such as circuit breaker status. Moreover, unlike the existing methods, the proposed method can measure the consistency among the measurements. In addition, the proposed method is found to be sensitive to the distribution of the bad measurements in a given database.
Highlights
S MART grid data are increasingly being used in various data driven power system applications ranging from power theft detection [1], harmonic state estimation [2], forced oscillation source location [3] to transmission line parameters’ estimation [4] and high impedance fault detection in distribution systems [5]
The proposed membership functions are evaluated on (i) the current and the real power measurements obtained from power flow analysis of the standard IEEE 14 bus system and (ii) on the voltage, current and the real power measurements obtained from power flow analysis of an actual 34 node feeder located in Arizona
RESULTS the performance of the proposed membership functions is demonstrated by evaluating them on (i) the line current and the real power measurements obtained from the power flow analysis of the standard IEEE 14 bus system and (ii) the voltage, line current and the real power measurements obtained from the power flow analysis of an actual 34 node feeder located in Arizona
Summary
S MART grid data are increasingly being used in various data driven power system applications ranging from power theft detection [1], harmonic state estimation [2], forced oscillation source location [3] to transmission line parameters’ estimation [4] and high impedance fault detection in distribution systems [5]. Quality assessment of smart grid data is essential prior to the usage of the data in various applications. The method proposed in [29] may not be applicable for assessing smart grid data quality. Methods for quality assessment of sensing data may not be directly applicable to smart grid data. In this paper, a fuzzy assessment method is proposed for assessing quality of multivariate electrical measurements of smart grids. The proposed membership functions are used for measuring the degree of membership of a smart grid dataset in various fuzzy sets that represent various quality dimensions of smart grid data. Novel membership functions are proposed (based on certain desirable characteristics) for fuzzy assessment of quality of multivariate electrical data of smart grids. The impact of measurement noise on the performance of the proposed membership functions is investigated using various case studies. The proposed method is compared with some of the existing data quality assessment methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
