Bad data analysis in power system measurement estimation using complex artificial neural network based on the extended complex Kalman filter

Abstract

SUMMARYThis paper proposes a method for bad data analysis in power system measurement estimation using complex artificial neural network (CANN) based on the extended complex Kalman filter (ECKF). The proposed algorithm is better in noise immunity since the link weighting in the CANN can be automatically adjusted with trained data through the ECKF. Moreover, the CANN is quite suitable for complex training data such as complex power in a power system since its input and output performs a nonlinear mapping. Four systems including a 6-bus system, the IEEE 30-bus system, IEEE 118-bus system, and a practical system are used as examples to verify the feasibility of the ECKF-CANN approach. Results show the proposed algorithm has increased sensitivity in identifying gross measurement errors with respect to the standard ANN.

RETRACTED: A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

“…As the availability of phasor measurements at substations will increase gradually, the authors of [19,20] studied how the state estimator can be enhanced to handle both the traditional state estimator and the linear state estimator simultaneously. A complex artificial neural network was used to adjust the link weighting in power system bad data analysis and estimation in [21]. Using fuzzy clustering and a pattern matching method, a fuzzy pattern vector for power state estimation was generated based on the analog measurement vector in [22].…”

Roots, achievements, and prospects of power system state estimation: A review on handling corrupted measurements

“…Robust algorithms capable of further mitigating the influence of corrupted measurements—eg, promising algorithms based on the moving horizon strategy (sliding window of past measurements)—have been recently proposed. In addition, approaches that lead to the creation of algorithms competent in processing uncorrelated measurement residuals deserve further research. Various techniques, borrowed from the computer science field and applied to SE—based for instance on artificial intelligence, information theory, data integration, and evolutionary computing techniques—can integrate promising research directions. Deregulation of energy markets requires power companies to supervise their networks over vast areas, which entails the development of distributed (multi‐area) SE, aiming at the enhancement of the computational performance and reliability of the estimation process. Distributed SE approach has challenges to be faced, for instance, in communication (eg, delays), time skewness among measurements, and robustness to BD .…”

“…In addition, approaches that lead to the creation of algorithms competent in processing uncorrelated measurement residuals deserve further research. • Various techniques, borrowed from the computer science field 111 and applied to SE 20 -based for instance on artificial intelligence, [112][113][114] information theory, 115 data integration, 116 and evolutionary computing techniques 117 -can integrate promising research directions. • Deregulation of energy markets requires power companies to supervise their networks over vast areas, which entails the development of distributed (multi-area) SE, 118 aiming at the enhancement of the computational performance and reliability of the estimation process.…”

Fast Super Decoupled State Estimator With Voltage Magnitude Measurements

“…A fuzzy clustering and a pattern matching method, for power system state estimation has been generated based on the analog measurement vector in [9]. A complex artificial neural network has been used to adjust the link weighting in power system bad data analysis and estimation in [10]. An adaptive Kalman filter has been introduced for realtime power system state estimation in [11], but Kalman filter achieve optimal performance only when the system noise characteristics have known statistical properties [12].…”