"Weighted Least Square state estimator is an optimal estimator in power systems"
This statement is true when only noise exists in the measurements and system model. However, it is observed that bad data are unavoidably present in the measurements and system model in power systems. Considering WLS SE is essentially an average estimator in statistics., therefore WLS SE is actually a biased state estimator in power systems.
Zero injection measurements are given a huge weight in power companies
In traditional state estimation theory, zero injection measurements are often given significant weight due to their perceived reliability. However, this practice can create leverage points in the estimation process. Leverage points are data points that strongly influence the solution, and when associated branches to a zero injection measurement have topology or parameter errors, they can lead to divergence or biased solutions in conventional state estimators.
SE+ offers a notable advantage in handling this issue. It effectively addresses erroneous zero injection measurements by appropriately rejecting them during the estimation process. By doing so, SE+ avoids the adverse effects of leverage points caused by faulty or erroneous measurements associated with zero injection measurements. This capability of SE+ contributes to more accurate and reliable state estimation outcomes, ensuring the integrity and quality of the estimated results even in the presence of problematic zero injection measurements or associated errors in power system topology or parameters.
It was said: "Our SE converges most of the time, so it works very well."
You are absolutely correct. In the power industry, it is widely recognized that a good state estimator should not only achieve convergence but also effectively reject bad data to ensure an accurate solution. Using erroneous data in the voltage estimation process can introduce bias, leading to a range of issues in the converged solution, such as negative loads, significant mismatches, or branches generating real power. These biased solutions can have adverse impacts on the performance of other critical functions like contingency analysis, Locational Marginal Price (LMP) calculation, security assessment, and more.
Consequently, the primary goal of a state estimator is indeed to provide an accurate solution. While convergence is a basic requirement, accuracy holds greater significance as it directly affects the reliability and quality of subsequent analyses and operational decisions in the power industry.
To summarize, a state estimator must not only converge but also effectively identify and reject bad data, ensuring accurate results. This emphasis on accuracy is crucial for enabling reliable power system operation and facilitating robust decision-making processes.
It was said: "SE at our company converges more than 99%, therefore we will not put efforts on 1% convergence improvement."
You raise an important point regarding the impact of divergence in state estimators, especially during critical or emergency conditions. While a convergence rate of 99.8% may seem high, it still implies the possibility of at least one divergence per day on average. In normal operating conditions, such occasional divergences may not have significant consequences. However, during alert states or extreme situations like the 2003 Northeast blackout, where the system is already under stress, even a small probability of divergence can have substantial implications, including economic losses and potential blackouts.
It is true that state estimators can face challenges in accurately estimating system states during unusual or emergency conditions, precisely when their performance is most crucial. The complexity of these situations, coupled with the limitations of current state estimators, makes it difficult to achieve reliable and accurate estimations.
Addressing the 1% divergence rate in state estimators becomes even more challenging during alert states. The risk of system-scale blackouts increases significantly in such cases, and rectifying this divergence becomes exceptionally difficult due to the limitations of current state estimation algorithms.
Recognizing these limitations and working towards the development of more robust and accurate state estimation techniques is vital to improve the resilience of power systems and enhance their ability to handle emergency conditions effectively.
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