Iet Science Measurement & Technology最新文献

To address the high workload and low efficiency in on-site detection of operational errors for gateway electricity meters, and the difficulties in applying existing energy conservation methods due to the influence of transformers and their secondary circuits, a remote detection method for gateway electricity meter error based on a two-step ridge regression solution is proposed. First, a linear conservation equation is established using data from the bus, and the metering point error is obtained through ridge regression combined with the L-curve. Then, the conservation equations between the primary and secondary electricity meters and their error conservation equations with the transformer and secondary circuit are constructed. Finally, the operation errors of the primary and secondary meters are addressed using generalized ridge regression in conjunction with the variance inflation factor method to enable remote detection for gateway electricity meters. Compared to a set of benchmark models, the proposed error estimation method demonstrates higher accuracy, confirming its ability to promptly detect potentially misaligned energy meters and support efficient performance inspection.

To address the high workload and low efficiency in on-site detection of operational errors for gateway electricity meters, and the difficulties in applying existing energy conservation methods due to the influence of transformers and their secondary circuits, a remote detection method for gateway electricity meter error based on a two-step ridge regression solution is proposed. First, a linear conservation equation is established using data from the bus, and the metering point error is obtained through ridge regression combined with the L-curve. Then, the conservation equations between the primary and secondary electricity meters and their error conservation equations with the transformer and secondary circuit are constructed. Finally, the operation errors of the primary and secondary meters are addressed using generalized ridge regression in conjunction with the variance inflation factor method to enable remote detection for gateway electricity meters. Compared to a set of benchmark models, the proposed error estimation method demonstrates higher accuracy, confirming its ability to promptly detect potentially misaligned energy meters and support efficient performance inspection.

To address the high workload and low efficiency in on-site detection of operational errors for gateway electricity meters, and the difficulties in applying existing energy conservation methods due to the influence of transformers and their secondary circuits, a remote detection method for gateway electricity meter error based on a two-step ridge regression solution is proposed. First, a linear conservation equation is established using data from the bus, and the metering point error is obtained through ridge regression combined with the L-curve. Then, the conservation equations between the primary and secondary electricity meters and their error conservation equations with the transformer and secondary circuit are constructed. Finally, the operation errors of the primary and secondary meters are addressed using generalized ridge regression in conjunction with the variance inflation factor method to enable remote detection for gateway electricity meters. Compared to a set of benchmark models, the proposed error estimation method demonstrates higher accuracy, confirming its ability to promptly detect potentially misaligned energy meters and support efficient performance inspection.

The paper concerns the theoretical problem of the electromagnetic force surface integral representation for diagonally anisotropic regions. The next theorem formulates an extension of the previous theorems, first and second (19), paragraph III. In anisotropic regions for evaluating force by means of the Maxwell stress tensor, an additional term has to be applied. The third theorem describes this additional term in simpler form than in (21), the volume integral of Maxwell stress tensor component differences. Moreover, it is pointed out that the surface integral of the Maxwell stress tensor describes the total electromagnetic force only in case of its symmetry. As an example, the levitations of a magnetically anisotropic cylinder and ball are presented.

The paper concerns the theoretical problem of the electromagnetic force surface integral representation for diagonally anisotropic regions. The next theorem formulates an extension of the previous theorems, first and second (19), paragraph III. In anisotropic regions for evaluating force by means of the Maxwell stress tensor, an additional term has to be applied. The third theorem describes this additional term in simpler form than in (21), the volume integral of Maxwell stress tensor component differences. Moreover, it is pointed out that the surface integral of the Maxwell stress tensor describes the total electromagnetic force only in case of its symmetry. As an example, the levitations of a magnetically anisotropic cylinder and ball are presented.

The paper concerns the theoretical problem of the electromagnetic force surface integral representation for diagonally anisotropic regions. The next theorem formulates an extension of the previous theorems, first and second (19), paragraph III. In anisotropic regions for evaluating force by means of the Maxwell stress tensor, an additional term has to be applied. The third theorem describes this additional term in simpler form than in (21), the volume integral of Maxwell stress tensor component differences. Moreover, it is pointed out that the surface integral of the Maxwell stress tensor describes the total electromagnetic force only in case of its symmetry. As an example, the levitations of a magnetically anisotropic cylinder and ball are presented.

This research aimed to investigate charge-based position estimation/control of piezo-actuated nanopositioning systems using a data-driven approach. In the analysis of these systems, piezoelectric actuators are widely approximated as capacitors with a fixed capacitance from an electrical viewpoint. This assumption was examined and found to be highly inaccurate. It was evidently demonstrated that the capacitance of piezoelectric actuators varies significantly with operating conditions (i.e., the frequency and amplitude of the excitation voltage). This paper also offers an alternative: considering the piezoelectric actuator as a capacitor with varying capacitance based on its operating conditions for analysis and design purposes. A linear model and an artificial intelligence (AI) model were developed to estimate the actuator capacitance on the basis of its operating conditions. The results demonstrate that the AI model outperforms the linear model and accurately estimates the capacitance of the piezoelectric actuator within the experimented range. Findings of this research pave the way to uplift the precision of piezo-actuated nanopositioning systems.

This research aimed to investigate charge-based position estimation/control of piezo-actuated nanopositioning systems using a data-driven approach. In the analysis of these systems, piezoelectric actuators are widely approximated as capacitors with a fixed capacitance from an electrical viewpoint. This assumption was examined and found to be highly inaccurate. It was evidently demonstrated that the capacitance of piezoelectric actuators varies significantly with operating conditions (i.e., the frequency and amplitude of the excitation voltage). This paper also offers an alternative: considering the piezoelectric actuator as a capacitor with varying capacitance based on its operating conditions for analysis and design purposes. A linear model and an artificial intelligence (AI) model were developed to estimate the actuator capacitance on the basis of its operating conditions. The results demonstrate that the AI model outperforms the linear model and accurately estimates the capacitance of the piezoelectric actuator within the experimented range. Findings of this research pave the way to uplift the precision of piezo-actuated nanopositioning systems.

This research aimed to investigate charge-based position estimation/control of piezo-actuated nanopositioning systems using a data-driven approach. In the analysis of these systems, piezoelectric actuators are widely approximated as capacitors with a fixed capacitance from an electrical viewpoint. This assumption was examined and found to be highly inaccurate. It was evidently demonstrated that the capacitance of piezoelectric actuators varies significantly with operating conditions (i.e., the frequency and amplitude of the excitation voltage). This paper also offers an alternative: considering the piezoelectric actuator as a capacitor with varying capacitance based on its operating conditions for analysis and design purposes. A linear model and an artificial intelligence (AI) model were developed to estimate the actuator capacitance on the basis of its operating conditions. The results demonstrate that the AI model outperforms the linear model and accurately estimates the capacitance of the piezoelectric actuator within the experimented range. Findings of this research pave the way to uplift the precision of piezo-actuated nanopositioning systems.

Currently, the perception of temperature rise inside the gas insulated switchgear (GIS) is mainly achieved by fitting and training steady-state temperature values, whereas abnormal temperature rise is a long and slow transient process, so there is a lack of real-time monitoring of the temperature rise inside the GIS busbar for early warning. Therefore, this article proposes and derives the GIS busbar transient temperature rise mathematical model (TTRMM) considering ambient temperature. The GIS busbar TTRMM demonstrates that the ratio of the temperature rise rate variation to the temperature rise relationship is within a constant interval, thus defining the influence coefficient K. Subsequently, a conductor temperature pre-evaluation criterion is established, which links the ambient, enclosure, and conductor temperature evolution characteristics. A temperature rise experiment platform was built to validate the model. The experiment results proved the correctness of TTRMM, and the temperature evolution trend of the conductor conforms to an exponential function and is divided into three stages. This article not only reveals the transient evolution mechanism between the ambient environment, enclosure, and conductor but also achieves the purpose of sensing the rate of temperature rise of the GIS busbar conductors by real-time monitoring of the ambient environment and enclosure temperatures.