With the wide deployment of advancing metering infrastructure (AMI) in power distribution systems, the quantity of power consumers’ electricity data is increasing rapidly and the data also become more and more accurate. To make full use of these power consumers’ AMI data, a data-driven abnormity assessment algorithm for low-voltage power consumers is proposed based on the CRITIC (CRiteria Importance Though Intercrieria Correlation) method and the improved radar chart method. First, the indexes that characterize the consumer’s abnormal features of power consumption and supplies are extracted from the original AMI data. Then, the abnormity assessment algorithm is used to determine power consumers’ abnormal features of power consumption and supplies by using the extracted indexes, in which the weights of indexes are determined by the CRITIC method and the assessment value of abnormal features is determined by the improved radar chart method. Next, the abnormity assessment algorithm is used again to assess power consumers’ power consumption and supplies abnormities. Finally, the effectiveness of proposed algorithm is demonstrated in case studies by employing AMI data collected from power utilities in Zhejiang Province, China, and the results show that the algorithm can be used in actual applications.

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Data-Driven Abnormity Assessment for Low-Voltage Power Consumption and Supplies Based on CRITIC and Improved Radar Chart Algorithms

The main problem for the state estimation with Gaussian mixture model is the exponentially growing number of Gaussian components. To solve this problem, an efficient Gaussian sum filter (GSF) based on the prune-cluster-merge (PCM) scheme-based Gaussian mixture reduction method is proposed. First, an adaptive weight-censored pruning strategy named as the j-th order statistical technique is presented to delete components with little contributes to the posterior distribution. Then, the Gauss clustering method is proposed to partition the remainder components into clusters based on the newly defined distribution similarity criterion. Integrating the acquired clusters with the covariance intersection algorithm, the components in the same cluster are merged into a standard Gaussian component by keeping the shape of the original distribution. Meanwhile, an extended integral square error cost function is constructed to optimize the performance of the cluster-merge operation. Finally, an efficient Gaussian sum filter is derived by combining the PCM scheme with extended Kalman filters. Numerical results show that the proposed filter can not only keep a better approximation to the original distribution with fewer Gaussian components comparing with the number-limited GSF and Runnalls's GSF, but achieve a higher cost-effectiveness than the particle filter.

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An Efficient Gaussian Sum Filter Based on Prune-Cluster-Merge Scheme

A novel health assessment method for pumped storage units (PSUs) is presented in this article. First, multihead self-attentive mechanism (MSM) combined with quantile regression neural network (QRNN) are proposed to establish a health benchmark model for PSUs to reveal the intricate relationship between the vibration and its multiple influencing factors. Especially, MSM automatically learns the complex interaction features among multiple influencing factors, while QRNN explores the upper bounds of health vibration under specific operational parameters. Then, a fuzzy dimensionless function is constructed to map the deviation of the currently measured vibration from the predicted health vibration to the performance degradation indexes. Finally, an improved radar chart method is proposed to visually illustrate the health condition of multiple measurement locations and give comprehensive health assessment for PSUs. The proposed method is applied in a PSU in Zhejiang province of China. The results of comparative experiments illustrate its effectiveness and feasibility. 

Health Condition Assessment for Pumped Storage Units Using Multihead Self-Attentive Mechanism and Improved Radar Chart

Accurate geolocation and tracking of Radio-Frequency Interference (RFI) sources, which affect wireless and satellite systems such as Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom) systems, are considered to be a significant issue. Several studies connected to civil and military operations on this issue have been investigated recently. The literature review has surveyed many algorithm simulations for optimizing geolocation and target-tracking estimation. Although most of these algorithms have their own advantages, they have weaknesses, such as accuracy, mathematical complexity, difficulties in implementation, and validation in the real environment, etc. This study has been concerned with investigating the accuracy of geolocation and tracking under high speed and powerful rotation using extracted data from the Orolia Skydel simulator, which simulates the space environment involving Low Earth Orbit (LEO) satellites as sensors and Unmanned Aerial Vehicles (UAV) as RFI emitters. Various scenarios modeled using the Orolia Simulator for quasi-real dynamic trajectories of LEO satellites have been created. The assumed approaches have been verified by Cramer–Rao Lower Bound (CRLB) and Posterior CRLB (PCRLB) to determine the increase in Root Mean Square Error (RMSE) value. The simulation scenarios have been performed using the Monte Carlo iteration. Eventually, the overall achieved results of the considered approaches using data acquired from the Orolia Simulator were presented and compared with theoretical simulation.

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Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

A novel health assessment method for pumped storage units (PSUs) is presented in this article. First, multihead self-attentive mechanism (MSM) combined with quantile regression neural network (QRNN) are proposed to establish a health benchmark model for PSUs to reveal the intricate relationship between the vibration and its multiple influencing factors. Especially, MSM automatically learns the complex interaction features among multiple influencing factors, while QRNN explores the upper bounds of health vibration under specific operational parameters. Then, a fuzzy dimensionless function is constructed to map the deviation of the currently measured vibration from the predicted health vibration to the performance degradation indexes. Finally, an improved radar chart method is proposed to visually illustrate the health condition of multiple measurement locations and give comprehensive health assessment for PSUs. The proposed method is applied in a PSU in Zhejiang province of China. The results of comparative experiments illustrate its effectiveness and feasibility.

Comprehensive measures for the estimation performance evaluation (EPE) has become increasingly prominent. This paper proposed a new radar chart evaluation method to measure the estimation performance. Firstly, the new radar chart index, which is composed of several popular incomprehensive measures, are presented, and the method of the weight of the each index is calculated based on vector ranking method. Secondly, the new comprehensive measures for the EPE is designed according to the fan area and the fan arc length. Finally, two cases study are provided to verify the effectiveness of this method.

Yun Wu

Papers

Radar Chart for Estimation Performance Evaluation