Research On Time Process Oriented Power System State Estimation

Accuracy and timeliness of Information provided by state estimation (SE) is very important to safe and economical operation of power grid. The existing period triggering mode state estimation (SE) has its limitations in the two purposes of power grid state monitoring and operating control. On one hand, there is big difference between SE triggering period and supervisory control and data acquisition (SCADA) scanning period. Therefore it is hard for dispatch control center to grasp the intensively operating state varying in power grid operating process. On the other hand, dispatch control center can make control strategies only by the single time section state provided by SE. However, the single time section state cannot reflect the power grid operating status in its entirety in the process. Therefore it is difficult for the control strategies to take account of security and economy.With the ceaseless change of power grid state, it ought to be triggered ceaselessly for state estimation. Therefore, research based on time process meets the essence of physical phenomenon. Time process oriented state estimation is presented to improve the utility of state estimation in power grid state monitoring and operating control.With the concept of process function, state estimation model based on time process is described. The mode is put forward in which state estimation is triggered by the variation of measured quantities. That is, state estimation is triggered when the state estimator receives certain proportion of telemetry measurements that changed comparatively obviously, or there is a remote signaling transposed. Changed measurement triggering mode (CMTM) is an intelligent triggering mode.Tracking SE of polling type is an incomplete-processing SE with polling type telemechanics, which needs algorithms that can handle measurements flexibly. It handles the renewed measurements only and solve state variable by tracking method. It is a method that exchanges a little estimation precision for greater calculation speed. Then two tracking SE algorithms of polling type are put forward, which are recursive least squares method sequential tracking SE algorithm and sensitivity analysis method sequential tracking state estimation algorithm. The former algorithm only handles renewed measurements one by one at every calculation moment based on recursive least squares estimate to renew the state vector. And the latter algorithm handles only the renewed measurements one by one using the relations of sensitivity among state variable, measurements and estimate of power.Based on the relations of sensitivity between state variable estimate and measurements, the concept of state affectois is presented representing the influence capacity of measurements to state variable estimate. With certain topology and line parameter, factors having effect on state affectois includes power grid load level, measurement redundancy, measurement type, and so on. Individual selective method of triggering threshold is put forward based on state affectois.Characteristic time section SE is presented, which is based on conventional SE and provides state vectors of characteristic time sections within a period of time. At least, characteristic time sections include extreme time sections and expected time section.Time process oriented state estimation mode is designed in this dissertation. Dynamic period of time process oriented SE is provided by SE link of CMTM, and it is also the tracking period of tracking SE link of polling type and operating control analysis period of characteristic time section SE link. Through adjustment of parameters, such as triggering threshold, the dynamic period can be a period of a few minutes. Three links are organically connected through the bond of dynamic period.Test results prove the method presented in this dissertation predominant and feasible. Firstly, SE of CMTM can select dynamic period, according to the changing speed of power grid state. And it can provide accurate initial value for tracking SE of polling type. Secondly, Tracking SE of polling type not only has a great calculation speed in handling a measurement, but also guarantees a good estimation precision, which is very close to that of WLS algorithm. Tracking SE of polling type can track the measurement updating closely. Lastly, Characteristic time section SE can provide the key state information within a time process for dispatch control center, and help to make overall control decision. The three links assume their respective roles and cooperate perfectly. Therefore time process oriented state estimation cannot only achieve intensive monitoring power grid state, but also be the forceful basis of stable-state operating status assessment and decision-making.