Some Aspects of Static and Dynamic Distribution System State Estimation with Optimal Meter Placement Studies

In a power distribution system, due to the evolution of Active Distribution Networks (ADNs), there is a possibility of violation of the system operational constraints. A state estimator provides an approximate snapshot of the distribution system operation when the bus voltages and power measurements are available. Thus it plays a key role in monitoring the system, thereby ensuring a safe state of operation. According to the nature of the system, Distribution System State Estimation (DSSE) can be classified in to static DSSE and dynamic DSSE. Static DSSE is commonly designed as a Weighted Least Square (WLS) estimator using either bus voltages or branch currents as system states. For dynamic DSSE, the performance of static state estimators are limited. A Kalman filter based state estimator can be used in such time varying systems. A study of the algorithms used for these two DSSE methods is necessary in order to analyze the factors affecting the estimation accuracy. In a power distribution system, with limited availability of measurements, and additional measurements being expensive, careful selection of the location for the placement of meters becomes important. The measurement meters typically considered are Phasor Measurement Units (PMUs) and power (PQ) meters. The existing placement problems lay more emphasis on minimizing the cost of installing such meters, while the quality of estimation remains ignored. Thus there is a need to formulate methods for optimal allocation of meters in a cost effective way without altering the accuracy of DSSE. In this work, a detailed study is conducted on the two static DSSE algorithms, Node Voltage based State Estimation (NVSE) and Branch Current based State Estimation (BCSE) and the DSSE performance is compared based on Average Root Mean Square (ARMSE) Value of state estimates. The thesis also analyzes the impact of the number of PMU measurements available on DSSE performance. Several optimization based approaches are proposed to address the optimal meter placement problem considering different objectives such as minimization of cost, WLS residual estimate, a multi-objective function comprising cost and WLS, and the ARMSE of the estimated bus voltage. An Iterative Extended Kalman Filter (IEKF) is used for performing dynamic DSSE. The dependency of various parameters such as selection of time frame, apriori estimate information length and PMU measurement errors on the accuracy acquired by DSSE is also presented. The studies and proposed models are simulated in a 33-bus distribution feeder. The results illustrating the efficiency and speed of convergence of different static and dynamic DSSE methods are discussed. The various optimization models for meter allocation are formulated and compared based on meter placement cost and ARMSE of voltage estimates

電力系統の静的および動的セキュリティ評価増強のための同期位相計測装置の最適配置

九州工業大学博士学位論文 学位記番号：工博甲第490号 学位授与年月日：令和2年3月25日1 INTRODUCTION|2 PMU-BASED POWER SYSTEM MONITORING AND CONTROL|3 OPTIMAL PMU PLACEMENT PROBLEM AND STATE ESTIMATION|4 MULTI OBJECTIVE PMU PLACEMENT WITH CURRENT CHANNEL SELECTION|5 INFLUENCE OF MEASUREMENT UNCERTAINTY PROPAGATION IN PMU PSEUDO MEASUREMENT|6 PHASOR-ASSISTED VOLTAGE STABILITY ASSESSMENT BASED ON OPTIMALLY PLACED PMUS|7 PMU PLACEMENT FOR DYNAMIC VULNERABILITY ASSESSMENT|8 CONCLUSIONS九州工業大学令和元年

Sensor Selection Based on Generalized Information Gain for Target Tracking in Large Sensor Networks

In this paper, sensor selection problems for target tracking in large sensor networks with linear equality or inequality constraints are considered. First, we derive an equivalent Kalman filter for sensor selection, i.e., generalized information filter. Then, under a regularity condition, we prove that the multistage look-ahead policy that minimizes either the final or the average estimation error covariances of next multiple time steps is equivalent to a myopic sensor selection policy that maximizes the trace of the generalized information gain at each time step. Moreover, when the measurement noises are uncorrelated between sensors, the optimal solution can be obtained analytically for sensor selection when constraints are temporally separable. When constraints are temporally inseparable, sensor selections can be obtained by approximately solving a linear programming problem so that the sensor selection problem for a large sensor network can be dealt with quickly. Although there is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small, numerical examples suggest that the algorithm is near-optimal in many cases. Finally, when the measurement noises are correlated between sensors, the sensor selection problem with temporally inseparable constraints can be relaxed to a Boolean quadratic programming problem which can be efficiently solved by a Gaussian randomization procedure along with solving a semi-definite programming problem. Numerical examples show that the proposed method is much better than the method that ignores dependence of noises.Comment: 38 pages, 14 figures, submitted to Journa

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This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

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In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter