Health Index Calculation Research Articles

Calculation of power transformer health index using the combination of principal component analysis and mass-spring-damper model

Calculation of power transformer health index using the combination of principal component analysis and mass-spring-damper model

Research on early warning model of electric submersible pump wells failure based on the fusion of physical constraints and data-driven approach

At present, there are too few characteristic parameters for early identification of electric submersible pump (ESP) wells failures. In addition, existing predictive models rely only on data-driven techniques, which do not provide a comprehensive understanding of the intrinsic mechanisms behind the occurrence of failures. This article presents the development of a health index calculation approach for evaluating the operational status of the ESP wells. The wellbore energy conservation equation and the health index calculation formula were employed to develop a physical constraints (PC) for the loss function in the network model of Long Short-Term Memory (LSTM). This constraint was then utilized to construct the PC-LSTM ESP health warning model. The study examines the health index trends associated with three types of faults: ESP clogging, sand production in oil wells, and reservoir pressure rise. The results indicate that the PC-LSTM network structure exhibits faster convergence during training. In relation to the accuracy of prediction, PC-LSTM demonstrates a notable enhancement. Specifically, when the ESP clogging fault, the traditional LSTM achieves a mean relative error (MRE) of 211.15%, whereas the PC-LSTM achieves a reduced MRE of 12.74%. Similarly, for sand production in oil wells, the traditional LSTM yields an MRE of 19.07%, while the PC-LSTM achieves a reduced MRE of 9.62%. Lastly, in the scenario of reservoir pressure rise, the traditional LSTM exhibits an MRE of 41.07%, whereas the PC-LSTM achieves a reduced MRE of 12.74%. In the meantime, the model demonstrates a high level of accuracy in capturing the pre- and post-trends of the health status of the ESP wells. This improved prediction accuracy and interpretability of the underlying mechanisms serve as valuable tools for analyzing the lift performance of ESP wells and providing early warning signals for potential failures.

CORAL REEF HEALTH INDEX CALCULATION FROM REMOTE SENSING DATA: A REVIEW

The coral reef ecosystem plays an important role as a provider of ecosystem services and has various economic benefits to the coastal community. However, the coral reefs ecosystem continues to degrade by 19% globally. This degradation caused some coastal and archipelagic countries have developed methods of calculating coral reef health index, including Indonesia. However, some literature shows that there is no common standard method for coral reef health index as the analysis depends on the data availability and the purpose of the study. Remote sensing technology that currently provides much open-source data is a potential method /tool to calculate the health index of coral reefs if the required parameters are met. This article review aims to identify remote sensing data used in the existing coral health indexes and then analyze the integration of all spatial data for coral reef health index calculation. Reference searches are sourced from the SCOPUS database combined with search engines Harzing and Mendeley. There are five coral reef health index calculation models from 25 references consisting of 19 publications and six reports out of 209 filtered references using keywords of Coral Reef Health Index. As a result, coral reef cover and algae are commonly used data obtained from remote sensing imagery. However, remote sensing technology cannot estimate other important parameters such as fish biomass. In addition, physical information of the waters, such as sea surface temperature (SST) and water clarity indicators (turbidity and diffused attenuation coefficient), are parameters contained in the five indexes that can be obtained from remote sensing data. In general, the literature review shows that coral reef health indicators (e.g. index or individual benthic cover) are significantly related to the various parameters affecting coral reef degradation such as algae cover, rubble cover, SST and river plump either as an individual or multivariate factors.

Application of a novel index for understanding vascular health following pharmacological intervention in a pre-clinical model of metabolic disease.

While a thorough understanding of microvascular function in health and how it becomes compromised with progression of disease risk is critical for developing effective therapeutic interventions, our ability to accurately assess the beneficial impact of pharmacological interventions to improve outcomes is vital. Here we introduce a novel Vascular Health Index (VHI) that allows for simultaneous assessment of changes to vascular reactivity/endothelial function, vascular wall mechanics and microvessel density within cerebral and skeletal muscle vascular networks with progression of metabolic disease in obese Zucker rats (OZR); under control conditions and following pharmacological interventions of clinical relevance. Outcomes are compared to "healthy" conditions in lean Zucker rats. We detail the calculation of vascular health index, full assessments of validity, and describe progressive changes to vascular health index over the development of metabolic disease in obese Zucker rats. Further, we detail the improvement to cerebral and skeletal muscle vascular health index following chronic treatment of obese Zucker rats with anti-hypertensive (15%-52% for skeletal muscle vascular health index; 12%-48% for cerebral vascular health index; p < 0.05 for both), anti-dyslipidemic (13%-48% for skeletal muscle vascular health index; p < 0.05), anti-diabetic (12%-32% for cerebral vascular health index; p < 0.05) and anti-oxidant/inflammation (41%-64% for skeletal muscle vascular health index; 29%-42% for cerebral vascular health index; p < 0.05 for both) drugs. The results present the effectiveness of mechanistically diverse interventions to improve cerebral or skeletal muscle vascular health index in obese Zucker rats and provide insight into the superiority of some pharmacological agents despite similar effectiveness in terms of impact on intended targets. In addition, we demonstrate the utility of including a wider, more integrative approach to the study of microvasculopathy under settings of elevated disease risk and following pharmacological intervention. A major benefit of integrating vascular health index is an increased understanding of the development, timing and efficacy of interventions through greater insight into integrated microvascular function in combination with individual, higher resolution metrics.

Health Index Estimation of Wind Power Plant Using Neurofuzzy Modeling.

According to the Tamil Nadu Energy Development Agency (TEDA) in the 2019-20 academic year, the wind power plant produces 23% of the biomass power supply in the Indian electrical commodities. To maintain the power withstanding capability needed for future electrical commodities, a yearly power shutdown program is implemented. An additional wind power plant unit will be erected and create more electricity, thereby balancing India's commercial electricity needs. Even in a nonstationary working environment, continuous monitoring and analyzing the efficiency of wind turbines is a more difficult task. Consequently, in this paper, a health index calculation for wind power plants is proposed utilizing neurofuzzy (NF) modeling. Wind generator efficiency can be measured mathematically by recording three crucial primitivistic such as observed rotation speed, generation wound temperature, and gearbox heat. Fuzzy rules are used to design the parameters of the neural network (NN), and the accumulated signal is compared using the nonlinear extrapolation approach to determine the wind generator's behavior and evaluate the hazards. During the experimental study, two windows of 24 hours and 60 hours are used, where the deviation signal required for the hazard induction is investigated. The proposed approach can accurately calculate the wind generator's health state. As a result of an improved health operating and management (HOM) system, the amount of power generated by industrials and domestic appliances has increased dramatically.

Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

A Practical Health Index for Overhead Conductors: Experience From Australian Distribution Networks

Quantifying the health condition of bare overhead conductors is an enabler for an effective condition-based risk management of the power distribution network. This paper presents a health index calculation methodology derived for the bare overhead conductors in the Australian power distribution networks. Proposed methodology is based on a customizable weighted sum algorithm and uses a set of input parameters, which are readily available for the asset managers. Industry experts’ experience and conductor failure statistics are used for determining the most significant input parameters and corresponding numerical values. Finally, a methodology for correlating the conductor health index with the probability of failure is demonstrated on a set of copper and aluminum conductors. The methodologies developed in this paper could improve the accuracy and effectiveness of condition monitoring and asset management of bare overhead conductors in the power distribution networks.

Power transformer health index estimation using evidential reasoning

Market-oriented power distribution system requires a well-planned budget with scheduled preventive and corrective maintenance during a replacement of units that are in an unsatisfactory condition. In recent years, the concept of the transformer health index as an integral part of resource management was adopted for the condition assessment and ranking of ETs. However, because of the lack of regular measurement and inspections, the confidence in health index value is greatly reduced. The paper proposes a novel methodology for the ET condition assessment and the lifetime increase through the establishment of priorities for control and maintenance. The solution is based on the upgraded health index, where the confidence to the measurement results is calculated using Evidential reasoning algorithm based on Dempster - Shafer theory. A novel, two - level hierarchical model of ET health index is proposed, with real weighting factors values. This way, the methodology for ET ranking includes the value of available information to describe ET current state. The proposed methodology is tested on real data of an installed ET and compared with the traditional health index calculation.

Condition Assessment of Power Transformers Based on Health Index Value

In electric power systems, health index algorithms are mostly used for evaluation of the transformer population. In this method, some assessment criteria are insensitive when it comes to judging the technical state of the edges of the age spectrum. This paper presents a new health index calculation method that aims to improve the overall effectiveness of the assessment. The proposed algorithm is based on regularly conducted oil diagnostics and easily available maintenance data to enable estimation and updating of the device’s health status in short intervals from an operational point of view. This method is compared to another health index algorithm built from the same parameters, but with different weights and an alternative result assessment philosophy. The two health index calculation methods are tested on a population of 96 power transformers and then compared to results obtained with an expert system, which is based on much more advanced diagnostic tests to determine the technical condition of the unit. The results of the experiment show that proper selection of weighting factors of the transformer’s technical condition parameters during health index calculation may help in simplifying its assessment while maintaining satisfactory accuracy in comparison to a highly advanced expert method.

Determining Health Index of Transmission Line Asset using Condition-Based Method

Assessment of overhead transmission lines is a crucial task in the asset management of electric power infrastructures. Any assets have different life spans and require proper assessment and maintenance actions. Disruption of the power supply may cause national problems. Therefore, it is essential to ensure that the distribution and transmission of electric power from the power plant to end consumers is achieved without fail. This paper presents a proposed framework of health index of the transmission line using a condition-based method. This study refers to previous methods in determining the health index of electrical power assets, mainly transformer and transmission line. Three main indicators contributed and need to be considered in determining the health index. The indicators are structural, electrical and environmental aspects. The health index of these three indicators with 14 items was calculated, and the overall health index of the transmission line determined. From the case study conducted for this study, the specific location, tower and item can be acknowledged that cause the failure and the service interruption of energy supply to the consumer. It is found that the implementation of the health index calculation gives a more accurate description of the health status of a transmission line. The health index can be used for the prioritizing of maintenance, refurbishment or replacement to avoid disruption.

Application of AHP and EIE in Reliability Analysis of Complex Production Lines Systems

It is necessary to grasp the operation state of the production system for scientific scheduling, process improvement, fault analysis, equipment maintenance, or replacement. The matter-element information entropy is proposed to evaluate the health index of the product line, and the parameter self-optimization support vector machine is used to predict the future health index. A new type of three-dimensional cross compound element is established by synthesizing the operation state of equipment, energy consumption, production efficiency, and human factors. The subjective, objective, and joint weights are determined by the analytic hierarchy process (AHP) method, entropy, and the combination weighting method, respectively. The health index is calculated by complex element correlation entropy. The calculations of the beer filling production line show that the combined weighting method is an effective method on the health index calculation and can accurately reflect the actual operation state of the production. Support vector machine (SVM) optimized by multiparameters is established to predict the health index; the simulation shows that Least Squares Support Vector Machine (LSSVM) based on radial basis function (RBF) has prominent prediction effect. It can provide accurate data support for the production and management of enterprises.

Application of a general regression neural network for health index calculation of power transformers

A power transformer is one of the most important components in a transmission network. To assess the overall condition of this valuable asset, health index calculations are recently gaining more attention from the utility companies that operate networks. Only limited research has been conducted on health index calculations of transformers. Most of the past approaches are based on the linear combination of weighted scores of measurements following the industry standards such as IEEE, IEC and CIGRE. A few previous methods based on artificial intelligence and statistical approaches such as fuzzy logic, multivariate analysis and binary logistic regression have been published in recent years. In this paper, a General Regression Neural Network (GRNN) which has a nice nonlinear property and can work with measurements without quantization has been evaluated. The GRNN allows multi-dimensional measurements to be combined through an optimal weighting and scoring system to compute a quantitative health index of power transformers. The weighting of each test was assigned based on a smoothly interpolated continuous function. The efficacy of the model has been validated against expert classifications and data sets published in the literature. The comparative results demonstrate that, the proposed method is reliable and very effective for condition assessment of transformers through an automated health index calculation.

Selection criteria for oil transformer measurements to calculate the Health Index

In this paper, a study of the effect of a group of transformer measurements on Health Index (HI) calculation is presented. Different methodologies for selecting the most efficient group of diagnostic measurements used in classifying transformer HI are investigated. A Binary Cat Swarm Optimization (BCSO) technique is undertaken based on Support Vector Machines (SVM). The technique depends on selecting the optimal parameters for SVM. The effect of selecting HI classes as well as class's boundaries is also studied. The measurements of fourteen diagnostic transformer tests, including the furan analysis, dissolved gas analysis, and further oil analysis for 724 distribution transformers are studied, and the corresponding HI is calculated according to industrial standards. The model renders the best-selected group of measurements that assist in the formulation of the health index with minimum error and high confidence.

The intelligent health index calculation system

The problem of finding logical patterns based on precedents in order to calculate an individual's health index is considered. Local metrics and informative sets of diverse features are used to distinguish the patterns.

Power Transformers Health Index Calculation Method Based on Cloud Model and Fuzzy Evidential Reasoning

Transformers health index calculation method based on cloud model and fuzzy evidential reasoning is proposed. According to the multi-level and multifactor of evaluation index information of power transformers, a layered evaluation index model is established. In order to deal with the ambiguity and uncertainty information of evaluation index, a normal cloud model is introduced, inferred the fuzzy degree of belief in the health state of evaluation index. Then use the fuzzy evidential reasoning method merge information of evaluation Index, inferred the degree of belief in the health state-level of transformer, calculated the health index of transformer. The results of an example analysis test its rationality and effectiveness.

Health Assessment of Ecosystem in Manufacturing Enterprise Life Cycle Oriented

Based on the enterprise ecosystem theory and brittleness of complex systems theory, to propose the enterprise life-cycle manufacturing enterprise ecosystem health evaluation index system, from the growth, the stability, the resistance to brittle, the recoverability and the adaptability five dimensions, building enterprise ecosystem health index, applying Projection Pursuit Principal Component Analysis to achieve ecosystem health index calculation. By the example of Tianjin manufacturing enterprise ecosystem health evaluation, to verify an index system and algorithm reliability and operability constructed in the paper.

The Analysis of Circuit Breaker Health Index and the Characteristic Quantity

During circuit breaker risk condition assessment, the most important thing is to calculate the circuit breaker health index (HI). Aiming at the collected data for risk assessment is incomplete or it is not useful for the assessment, this paper analyzed the principle from of the health index calculation process, finding the various characteristics, which has an effect on the health, further more calculated the contribution to the health index, streamlining the data list for assessment. It has an important guiding significance for the future data collection.