Power system Vertical Division State Estimation (VDSE) – A parallel processing technique

Abstract

Keywords:SE e state estimation WLS e weighted Least Square NR e NewtoneRaphson ISE e integrated state estimation JPSE e Jacobian Parallel-State Estimation VDSE e vertically divided state estimation NA e node area e a node along with its connected node is referred as node area SNSE e selected node state estimation a b s t r a c t Power system state estimation is very important analysis both for off-line and online purposes. Solving the non-linear large power system equations by iterative solution consumes huge computational time and memory. This paper presents a unique technique to divide the Gain matrix/Jacobian products of the large system into smaller independent Jacobian products at each node area on an imaginary plan. The Jacobian product can be represented in three dimensional form having 'm'-two dimensional layer arranged on the third axis, where 'm' is the total number of system measurements taken. The method of dividing the three dimensional view of Jacobian product from vertically downward is termed as "Vertical Division" technique. The fact is, in any electrical power system, the state of a bus/node depends on the power flow between the neighboring (connected) node/bus. This logic is applied to solve the state estimation which resulted in "Vertical Division" technique for Power system state estimation. In this technique the measurements are grouped and processed independently corresponding to respective Node. The two main focus of this paper is, 1) to evaluate its mathematical formulations and validate its results with standard, established Integrated State Estimation (ISE) technique, and 2) to demonstrate the computational time advantage of Vertical Division technique. The approach is tested on 13 and 30 bus test system for integrated state estimation along with Vertical Division technique and the results are encouraging.

Research on the Novel Non-Contact Power Supply System Tuning Methodology based on Fuzzy Neural Network Theory under the Random Load Condition

“…In a controlled system allows discretization, and make the controlled system based on the configuration method can also be indirect chaos is applied to simulate the controlled chaotic system control. Controlled system with the combination of the state feedback control mode has three, namely: a controlled linear system and nonlinear state feedback control, the controlled nonlinear system applying linear state feedback control, the controlled nonlinear system applying nonlinear state feedback control [26][27]. Applying nonlinear state feedback of the controlled system of chaos control is the most perfect in theory research, on the application of the most mature methods of applying piecewise linear state feedback of nonlinear control method.…”