Development of lightwave frequency response analyzer for characterizing O/E conversion devices

This paper presents various types of windings in a power transformer, their FEM methods, frequency response analysis etc. Frequency response analysis (FRA) standards are used in all places. In frequency response, one reference spectrum is first done and after fault is done, spectrum is compared with reference one. A 3-D model of a transformer is considered to show the field distribution, inductance and capacitance matrices, force analysis etc. The forces developed in a transformer winding has also been explained and transient case has been studied.

Frequency Response Analysis (FRA) is a test method used for assessment of mechanical condition of transformer active parts. Its biggest problem is the interpretation of test results, namely the relationship between scale of differences between compared curves and the decision for further operation of the given transformer. Very often visible differences between two FRA curves do not mean that there is a deformation in the winding. The cause of the curve shift may come from other elements of the transformer that influence inductive or capacitive parameters. This paper takes under consideration the influence of both capacitance and inductance changes on transformer frequency response (FR). The analysis is performed with the computer model of a transformer and also some experimental results are presented, showing the influence of capacitance and inductance changes on the FR of real transformers. The results of research showed that this influence may lead to misleading effects on the shape of FR characteristics. The paper presents an analysis that can be used in the assessment of FRA measurement, especially in the case of uncertain data comparison results.

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources-such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems-has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system's transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources.

This paper examines a frequency response analysis (FRA) method which aims at obtaining an precise linear plant model for a controller design, for fast and precise positioning of mechatronic systems with nonlinear friction. As well known, friction generated at linear guideways and/or bearings shows nonlinear and complicated behaviors, which causes deterioration of the motion control performance. In addition, the frequency response of the plant system dynamically changes depending on the measurement condition such as displacement and/or excitation force in the frequency domain analysis. The dynamic change of the frequency response makes it difficult to identify the actual linear plant characteristic for design of a precise simulator and/or an effective compensator. In this study, therefore, effects of parameter identification errors due to the dynamic frequency property on the fine positioning performance are clarified by simulation analyses with a nonlinear friction model. Then, a fundamental friction model-based FRA method is introduced, and the effectiveness of the presented FRA and effects of the friction model errors are demonstrated by frequency-domain simulations.

For the purpose of considering the spatial-temporal characteristics (STC) of large-scale interconnected power system in the modeling, calculation, and analysis of dynamic frequency response (FR), this paper establishes a two-machine equivalent frequency response (TEFR) model and gets its closed-form solution (CFS), which can be used to analytically analysis and fast calculation. In the modeling, the network structure is idealized through simplified matrix of DC power flow and the system is reduced to a typical two-degree-of-freedom vibration system with viscous damping and excitation forces through breaking closed-loop and variable approximation. After that, modal analysis method is managed to obtain TEFR-CFS which represent the dynamic FR of TEFR model as the superposition of system FR and frequency oscillation. And the influence of parameters on frequency oscillation, such as electrical distance, inertial distribution, initial disturbance power distribution coefficient, etc., are analyzed. Furthermore, the analytical expressions of the key characteristics of dynamic FR, such as the initial decline slope, the nadir and the arrival time of nadir, etc., are derived. Numerical simulation proves that STC has significant influence on FR and can be described by TEFR model, TEFR-CFS and the expressions of its key characteristics with accuracy and efficiency.

A traveling-wave microwave fiber-optic link (TW MFL) is proposed and demonstrated for the first time. The structure is electrically equivalent to a traveling-wave microwave amplifier in which the transistors are replaced by individual optical fiber link elements using laser diode (LD)-photodiode (PD) pairs. This results in two transmission-line structures that are periodically loaded with LDs and PDs, respectively, and connected to one another by a fiber array. A three-element TW MFL has been constructed using commercially available components to demonstrate the concept. Over the design bandwidth of 200 MHz-2 GHz, the input and output return loss is better than approximately 10 dB each, while the link gain of -20 dB is in agreement with simulation results. The link gain is seen to increase as the number of link elements is increased. Measured S-parameter results also show the deleterious impact of terminating impedance mismatches and optical delay detuning on the TW MFL's frequency response.

An MBE grown InGaAs metal semiconductor metal (MSM) photodiode (PD) with an InAlAs barrier enhancement layer is reported that has very low dark current and high speed characteristics. The detector using Cr/Au Schottky metal fingers with 4m spacing on a large active area of 300×300m2 shows a low dark current of 38nA at 10V. This corresponds to a dark current density of 0.42pA/m2 and is, to our knowledge, the best dark current ever obtained from a large area InGaAs MSM PD. The device also shows a low capacitance of 0.8pF and a high 3dB bandwidth of 2.4GHz. By fitting the measured frequency response to a model consisting of both RC time and transit time limited responses, we show that the device has an RC time and a transit time limited 3dB bandwidth of 3.0 and 4.9GHz, respectively.

In this letter, we present an 850-nm Si octagonal photodiode (PD) with deep n-well implantation formed using the CMOS process without process modifications. Two different bias schemes (normal bias and extra bias) on the deep n-well were used to analyze the effects of deep n-well bias on the bandwidth and gain-bandwidth performances of Si PDs. The avalanche gain, frequency response, and optical pulse measurements in this letter demonstrate that the extra bias on deep n-well improves the PD performance. To the best of our knowledge, this design achieves the highest bandwidth (8.7 GHz) and a large gain-bandwidth product of 542 GHz with a reverse bias of 11.45 V and an extra voltage of 11.45 V in standard CMOS technology.

Frequency response analysis is widely used to diagnose transformer winding deformation faults due to its high sensitivity, strong anti-interference capability, and equipment portability, but the results of frequency response analysis can be affected by insulation aging and moisture in the transformer, leading to errors in the diagnosis of winding deformation faults. Currently, there is no effective method to prevent such errors. This paper focuses on optimizing the criterion for diagnosing winding deformations when insulation aging and moisture are present. First, the winding frequency response curves of oil-paper insulation were determined by combining insulation aging and moisture tests of the oil-paper insulation with frequency response simulations of the transformer winding. Next, the winding deformation criterion predicting the likelihood and extent of errors diagnosing transformer winding deformations due to the insulation aging and moisture content is discussed. Finally, the corresponding criterion optimization method is proposed. The corresponding results show that insulation aging and moisture can lead to errors when using the correlation coefficient R criterion to diagnose the transformer winding deformations. Moreover, the possibility of winding deformation errors caused by the change of insulation state can be reduced by introducing the corresponding auxiliary criterion through comparing the capacitance change rate based on the frequency response method and that based on the dielectric spectrum method.

The Frequency Response Analysis (FRA) is an efficient tool to diagnosis mechanical defects in transformer windings. After measurement of frequency responses, they must be compared to find any defects in the winding. The lack of clarity in the interpretation of measured frequency responses is the main problem of this method. This paper tries to solve this problem via a dimensional reduction of frequency response traces by Principal Component Analysis (PCA) to diagnose any short circuits in the winding. The precise location of the fault in the transformer winding is considered to be the main index, the effectiveness of which is presented by the results. Based on the approved transformer model, a dimensionality reduction method and feature selection are implemented to transformer's frequency response traces to map it from its main high-dimensional vector space to a lower dimensional one. It was shown that frequency response traces of Turn-to-Turn faults were separated from each other and the exact location of each fault was determined.

In this work, for the first-time dynamic behavior of an SU-8 polymer based single proof mass, 3-axis MEMS piezoresistive accelerometer is presented. The beams dimensions were optimized to induce the maximum stress with an applied acceleration of 100g. Polysilicon is used as a piezoresistive material to understand the sensitivity and deduce the gauge factor of the device. Finite Element Modeling analysis is used to simulate the dynamic behavior, frequency response and effects of squeeze-film damping. Frequency response and dynamic analysis are then validated. The Eigen frequencies for the first three modes were found to be 1.0847 kHz, 1.5067 kHz and 1.5073 kHz, respectively. The quality factor deduced from the frequency response is approximately 15. The maximum gauge factor is found to be 11.2.

Frequency response has received a lot of attention in recent years at the national level, which culminated in the development and approval of North American Electricity Reliability Corporation (NERC) BAL-003-1 Frequency Response and Frequency Bias Setting Reliability Standard. This report is prepared to describe the details of the work conducted by Pacific Northwest National Laboratory (PNNL) in collaboration with the Bonneville Power Administration and Western Electricity Coordinating Council (WECC) Joint Synchronized Information Subcommittee (JSIS) to develop a frequency response analysis tool (FRAT). The document provides the details on the methodology and main features of the FRAT. The tool manages the database of under-frequency events and calculates the frequency response baseline. Frequency response calculations are consistent with frequency response measure (FRM) in NERC BAL-003-1 for an interconnection and balancing authority. The FRAT can use both phasor measurement unit (PMU) data, where available, and supervisory control and data acquisition (SCADA) data. The tool is also capable of automatically generating NERC Frequency Response Survey (FRS) forms required by BAL-003-1 Standard.

Frequency response analysis (FRA) is considered as an accurate, fast, effective and sensitive method for detection of mechanical defects within power transformers, providing worthy information on active part structure. FRA measurement has been industrialized and employed since the last decade while FRA spectrum interpretation is still under development. The interaction of distributed resistances, series and shunt capacitances as well as self- and mutual-inductances in transformer winding leads to FRA spectrum oscillations. In fact, each and every parameter has its own impact on this spectrum. This study has concentrated on shunt capacitance influences on frequency response spectrum oscillations in Bode diagram. Shunt capacitance impacts on low-, mid- and high-frequency bands of FRA spectrum are discussed. A practical study on a single phase air-core glassy model transformer is carried out to explore the influence of shunt capacitances of HV and LV windings with respect to the transformer tank on FRA spectrum. In addition, a single phase 11/0.25 kV, 25 kVA core type distribution transformer is also tested to evaluate the shunt capacitance impacts on FRA fluctuations.

Frequency response analysis (FRA) method has been proven to detect the transformer winding damage. However, there is no practical study which investigates the damages on transformer tap changer even though it has a high failure rate. Hence, this paper investigates normal and faulty transformer tap changer using FRA. The FRA measurements conducted using four different test configurations. Also, normal tap changer, pitting and coking tap changer contact were investigated. In addition to FRA measurement, winding resistance measurement has been conducted. The transformer turn ratio measurement also conducted to identify the taps positions. This study results significant to understand the effect of pitting and coking tap changer on the transformer frequency response. The results showed that pitting has a slight influence on the frequency response. However, coking shows a huge variation in low-frequency response. This effect observed during the winding resistance measurement.

Frequency response analysis (FRA) has gained attention as a reliable diagnostic tool, to assess the mechanical integrity of power transformers, due to standardised FRA measurement methods. However, the assessment of FRA results is still a challenge as it demands skilful personnel for diagnosis. Since there is no definitive and reliable code available for FRA interpretation. In this study, the three‐dimensional (3D) finite integration technique is used to simulate the physical geometrical dimensions of a three‐phase transformer which emulate the behaviour of real transformer and frequency response measurements. This novel method eliminates the need for lumped parameter circuit models, as it provides the ability to extract the FRA signatures directly from the 3D winding model. Axial displacement of different levels is simulated in several connection schemes. It was found that variation of the inter‐winding capacitance due to the axial displacement possesses a linear relationship with the degree of axial displacement fault. This provides the possibility for the quantitative determination of winding axial displacement fault using FRA. A simple mathematical relation is derived for calculating the variation of inter‐winding capacitance and a geometrical factor is introduced to generalise the method. Finally, lookup charts are introduced to diagnose axial displacement fault in different transformers.