Yutaka Kokai

Development of transient stability control system (TSC system) based on on-line stability calculation

This paper describes a new transient stability control system named the TSC system developed for application to the trunk power system of Chubu Electric Power Co. (CEPCO). The TSC system prevents wide-area power system blackout by shedding optimal generators when a serious fault occurs. This system has the following features: (1) the TSC system performs detailed stability calculations based on online information telemetered from the actual network, and it periodically evaluates the stability of the power system against contingencies with a high degree of accuracy. The system selects the optimum generators to be shed; (2) if a contingency actually occurs, the generators to be shed that were determined in advance are shed about 150 ms after fault occurrence to maintain the stability of the power system; and (3) the system can be applied to any power system configuration of CEPCO, such as a loop network, radial network, etc. The TSC system will be put into regular service in June 1995. It will be the worlds first real-time stability control system that performs detailed transient stability calculations on a large power system online.

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data

With the installation of many new multi-channel phasor measurement units (PMUs), utilities and power grid operators are collecting an unprecedented amount of high-sampling rate bus frequency, bus voltage phasor, and line current phasor data with accurate time stamps. The data owners are interested in efficient algorithms to process and extract as much information as possible from such data for real-time and off-line analysis. Traditional data analysis typically analyze one channel of PMU data at a time, and then combine the results from the individual analysis to arrive at some conclusions. In this paper, a spatial-temporal framework for efficient processing of blocks of PMU data is proposed. A key property of these PMU data matrices is that they are low rank. Using this property, various data management issues such as data compression, missing data recovery, data substitution detection, and disturbance triggering and location can be processing using singular-value based algorithms and convex programming. These functions are illustrated using some historical data from the Central New York power system.

Multiprocessor based generator module for a real-time power system simulator

A novel generator module for a real-time power system simulator was developed. In this simulation module, a multiprocessor and an analog three-phase sinusoidal oscillator are used to simulate the generator behavior. A multiprocessor consisting of four microprocessors solves the differential equations in parallel to lessen the simulation time interval, and uses floating-point arithmetic to obtain high accuracy of the simulations. Any type of generator can be simulated using the developed module, since the parameters of the generator are easily modified by simply changing the data of the multiprocessor program. The initial condition of the power system simulator is automatically set up by a host computer. The accuracy of the generator module is validated by comparisons with the offline simulation program EMTP (Electromagnetic Transients Program). >

Combining direct and inverse factors for solving sparse network equations in parallel

A new parallel algorithm for solving the forward and back substitution part of the solution of sparse network equations for power systems is proposed. The approach assumes that the network equations are solved by LDU factorization and sparse vector techniques. The parallelization approach is based on the factorization path tree and its main feature is the switching from direct factor to inverse factors to best exploit the parallelism during the solution. The algorithm can be easily mapped on a multiprocessor architecture that exhibits both global and local memories. An illustrative example and validation results showing the effectiveness of the algorithm are also presented. >

Big data analytic empowered grid applications — Is PMU a big data issue?

With the proliferation of digital measurement devices, such as smart meter on the distribution systems and phasor measurement units on the transmission systems, power companies find themselves inundated with increasingly growing data and long for efficient tools and analytical techniques to identify, digest and utilize critical information to improve the efficiency and reliability of grid operations. Many power researchers believe that the PMU related power system analytics falls under the category of Big Data Science and are keen to apply typical technologies for solution, including machine learning, data mining, cloud based computation and so on. This paper explores the reason behind such presumption, challenges to deal with PMU data, and trends of analytical techniques.

Feasibility study of a feed-forwarding voltage-reactive power control method based on linear programming for a central control facility

Voltage–reactive power control (VVC) on power systems becomes difficult when a load increases or decreases rapidly, especially in the morning and at noon. This is caused mainly by two problems. One is delayed operation and the other is noncooperative operation of facilities. To solve these problems, an advanced method and algorithms for a centralized feed-forwarding control system are presented. They are based on two main steps: forecasting the power system state for several minutes and dispatching reactive power sources optimally based on stepwise linear programming. The proposed method is evaluated and tested for data of a large-scale power system. The results show that the proposed method keeps the voltage constraints well and reduces redundant operation of facilities.