| Project/Area Number |
60550186
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
電力工学
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| Research Institution | Hokkaido University |
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Principal Investigator |
HASEGAWA Jun Fac. of Engineering, Hokkaido University, 工学部, 教授 (40001797)
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| Co-Investigator(Kenkyū-buntansha) |
TANAKA Eiichi Fac. of Engineering, Hokkaido University, 工学部, 助手 (10124538)
NISHIYA Ken-ichi Fac. of Engineering, Hokkaido University, 工学部, 講師 (30002033)
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| Project Period (FY) |
1985 – 1987
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| Project Status |
Completed (Fiscal Year 1987)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1987: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1986: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1985: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Security Monitoring / Security Control / Contingency Analysis / Preventive Control / Emergency Control / SCADA |
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| Research Abstract |
The purpose of this research is to complete the integrated security monitoring and control system, which will play an important role in the operation of power systems, by developing the algorithm of main functions. The results may be summarized as follows:
1. On-line contingency analysis function: A fast realistic method based on a P-Q decoupled linearized model was developed for the contingency analysis. Bus voltage magnitudes and phase angles could be calculated within one third of the computation time of the fast decoupled load flow calculation. Mean errors of voltage magnitudes were less than one per cent and those of voltage phase angles were less than one degree.
2. Determination of preventive control strategy: The global preventive control strategy was determined by the following procedures. At first, violations of state variables, i.e., real power flows in transmission elements and voltage magnitudes of buses, were calculated. Then these violations were minimized considering the operating cost in the real power part (P-part) and the transmission losses in the reactive power part (Q-part). The nonlinear optimization technique was used to solve the problem.
3. Determination of emergency control strategy: The problem was formulated considering the characteristics of control variables; i.e., the control period was minimized at P-part and the number of control variables to be adjusted were minimized at Q-part. The emergency control strategy was determined by optimizing these two subproblems successively.
4. On-line data acquisition function: It could be proved that the accuracy of the dynamic state estimation was three times that of the static state estimation by optimization and evaluation of staste estimation theories. The basis of optimal allocation of measurements for power system state estimation was established by introducting the idea of bad data detectability and identifiability as well as observability of state estimation and data acquisition system.
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