This study presents the phasor measurement unit (PMU) placement strategy in the presence of false data injection attacks which is one of the most serious security threats against power grid. It is focused on applications related to supervisory control and data acquisition (SCADA) systems where measurement data can be easily corrupted by adversaries without getting caught by the system. To safeguard power grids against malicious attacks, procedures have been proposed to facilitate the placement of secure PMUs to defend against false data injection attacks in a highly cost-effective way. It has formulated a method of identifying measurements that are vulnerable to false data injection attacks. It was discovered that a weak power grid can be transformed into a robust power grid by adding a few PMUs at vulnerable locations. Simulations on the IEEE standard test systems demonstrate the benefits of the proposed procedure.

In permanent magnet synchronous motor (PMSM) sensorless drive systems, the motor inductance is a crucial parameter for rotor position estimation. Variations in the motor current induce changes in the inductance, leading to core magnetic saturation and degradation in the accuracy of rotor position estimation. In systems with constant load torque, the saturated inductance remains constant. This inductance error causes a consistent error in rotor position estimation and some performance degradation, but it does not result in speed estimation errors. However, in systems with periodic load torque, the error in the saturated inductance varies, consequently causing fluctuations in both the estimated position and speed errors. Periodic speed errors complicate speed control and degrade the torque compensation performance. In this paper, we propose a wavelet denoising-group method of data handling (GMDH) based method for accurate inductance estimation in PMSM sensorless control systems with periodic load torque compensation. We present a method to analyze and filter the collected three-phase current signals of the PMSM using wavelet transformation and utilize the filtered results as inputs to GMDH for training. Additionally, a method for magnetic saturation compensation using the inductance parameter estimator is proposed to minimize periodic speed fluctuations and improve control accuracy. To replicate the load conditions and parameter variations equivalent to the actual system, experiments were conducted to measure the speed ripples, inductance variations, and torque component of the current. Finally, software simulation was performed to confirm the inductance estimation results and verify the proposed method by simulating load conditions equivalent to the experimental results.

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with constant load torque, the magnetically saturated inductance value remains constant. This constant inductance error causes minor performance degradation, such as a constant rotor position estimation error and non-optimal torque current, but it does not introduce a speed estimation error. Conversely, in a sensorless PMSM motor system subjected to periodic load torque, the magnetically saturated inductance error fluctuates periodically. This fluctuation leads to periodic variations in both the estimated position error and the speed error, ultimately degrading the load torque compensation performance. This paper applies the maximum energy-to-Shannon entropy criterion for the optimal selection of the mother wavelet in the wavelet transform to remove the motor signal noise and achieve more accurate inductance estimation. Additionally, the coherence and correlation theory is proposed to address the time delay in the least-order observer and improve the time delay. A self-saturation compensation method is also proposed to minimize periodic speed fluctuations and improve control accuracy through inductance parameter estimation. Finally, experiments were conducted on a sensorless PMSM drive system to verify the inductance estimation performance and validate the effectiveness of vibration reduction.

The controlled synthesis of long hydrophobic blocks in amphiphilic block copolymers remains challenging in living anionic ring-opening polymerization (LAROP), particularly when competing effects such as back-biting and solubility limitations are involved. In this study, we investigated the temperature-dependent growth of poly(allyl glycidyl ether) (PAGE) blocks in PEO-b-PAGE block copolymers synthesized via LAROP using potassium naphthalenide as a co-initiator. Systematic variation in reaction parameters revealed that reaction temperature plays a significant role in governing effective PAGE block extension and dispersity control. Lower temperatures facilitated the formation of longer PAGE blocks with dispersities below 1.1 and DP values approaching targeted compositions, whereas elevated temperatures limited block growth. A group method of data handling (GMDH) polynomial neural network was employed as an auxiliary tool to prioritize influential variables within the experimental design matrix. The GMDH-guided analysis consistently identified temperature as the most influential variable, in agreement with experimental observations. These results provide quantitative insight into the temperature-controlled propagation behavior of PAGE in LAROP systems and offer a practical framework for improving block copolymer synthesis under kinetically and thermodynamically constrained conditions.