An Improved Framework Of Region Segmentation For Diagnosing Thermal Condition Of Electrical Installation Based On Infrared Image Analysis

The abnormality of electrical equipment will occur when its internal temperature reached beyond its limits, which can lead to subsequent failure of the equipment. Therefore, early prevention is required in order to avoid this fault while maintaining the reliability of the equipment. This research proposes a new framework of region segmentation and thermal fault detection method for diagnosing the thermal condition of electrical installation by considering both qualitative and quantitative infrared image analysis. Since most of the electrical installations are normally fixed repetitively, a new region detection method is proposed that is able to detect all identical structure of electrical devices within an infrared image. The method employs the combination of the scale invariant feature transform (SIFT) and maximally stable extremal regions (MSER) keypoint detectors for improving the number of keypoint detection. A method for matching and translating clusters is presented by introducing a voting procedure for finding a group of matched clusters. The region detection is achieved by employing a grid approach to divide the repeated cluster before properly segmenting the target region. For evaluating the condition of electrical installation, the effectiveness of thirteen types of input features is investigated. A wrapper model approach is utilized for selecting feature where the multilayer perceptron (MLP) artificial neural network and the support vector machine (SVM) are used to evaluate each of the possible combinations of the feature set. Based on experimental results on the proposed segmentation method, about 94.27 % of the regions were correctly detected with the average area under curve (AUC) value of 0.79 was achieved. Meanwhile, for assessing the thermal condition, it was found that the integration of Tdelta, Tskew, Tkurt, Tσ and dB features yield the best result when classified by SVM using radial basis kernel function. The highest classification rates are achieved at 99.46% and 97.78% of the accuracy and f-score value, respectively

Evaluating the Thermal Condition of Electrical Equipment via IRT Image Analysis

The integrity of electrical power equipment is of paramount importance when itsupplies electricity throughout a facility. However, the reliability of the equipments will degraded after sometime, and appropriate maintenance has to be taken accordingly to avoid future faults. Infrared thermography (IRT) image analysis is a commonly used technique for diagnosing the reliability of electrical equipments. Conventionally, the analysis of infrared image is done manually and takes very long time for further analysis. This paper proposes an automatic thermal fault detection and classification system for evaluating thecondition of electrical equipment by analyzing its infrared image. First, the image is segmented to find the target region of interest (ROI). The detected regions which have the same region properties are grouped together in order to remove the unwanted regions. Finally, statistical features from each detected region are extracted and classified using the support vector machine (SVM) algorithm. The thermal condition of electrical equipments is evaluated based on qualitative measurement technique. The experimental result shows that the proposed system can detect and classify the thermal condition of electrical equipments

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Reliable performance of an equipment or structure depends on pre-service quality and in- service degradation of the equipment or structure under operating conditions. The role of non-destructive testing (NDT) is to ensure integrity, and in turn, reliability of equipment or structure. Besides, NDT can also monitor in-service degradation and to avoid premature failure of the equipments/structures and prevent accidents as well as save human life. Up to now, NDT has been used in various fields of applications such as the inspection o

Development of Smart System for Renewable Energy Hybrid Power System Base on SCADA

This paper presents the development of smart energy monitoring and control system for Renewable Energy Hybrid Power Generation System (REHPGS) using SCADA. REHPGS model consists of photovoltaic, wind and gen-set system. SCADA is used to monitor and control the operation of Hybrid Power System (HPS) in real time. This paper planned a concept of smart grid by VisconDua Remote Terminal Unit (RTU)-IO Module. The RTU is responsible to collect information from power HPS plant. SCADA Expert controller is introduced to automatically control renewable energy sources. The monitoring and control system of REHPGS operated in real time and can perform under various operating conditions. The HPS plant will be fully automated, which mean the power plant would rely on the SCADA system on all its operations. Results show that the proposed system has the capability to monitor and optimize the output power generation of REHPG

A simulation study of wireless power transfer for electric vehicle

This study proposes a wireless power transfer (WPT) simulation for electric vehicle (EV) charging. Using the latest WPT technology rather than traditional charging, this study analyses the best way for boosting power transfer in internal combustion engine (ICE) vehicles. In a WPT system for EV applications, compensating circuits are critical for increasing WPT capacity and power transfer efficiency. Compensation topologies such as series-series (SS), series–parallel (SP), parallel-series (PS), and parallel-parallel (PP) each have their own set of advantages and limitations. It is found that, due to their load resistance of 1 kΩ, high coupling coefficient of 0.5, and capacity to operate at 86 kHz, the SP topology is more viable to employ as a composition circuit for WPT system in EV charging

A simulation-metaheuristic approach for finding the optimal allocation of the battery energy storage system problem in distribution networks

This paper proposes a simulation study to solve the optimal allocation of the Battery Energy Storage System (BESS) problem in distribution networks. The effect of BESS's installation in the selected distribution networks is surveyed for a 24-hour period, where time-of-use electricity charges are divided into three periods: standard, peak, and off-peak hours. This study will use Teaching Learning-Based Optimization (TLBO) as the main optimizer for the problem simulation. The objective function is to minimize the combined cost of purchasing electricity and energy loss, where the optimal location of BESS and its operated power at each hour are treated as the control variables to be optimized. Two distribution systems are utilized, viz. 18-node and 33-node systems are considered to assess the performance of TLBO in solving the mentioned problem, where a comparison with other recent metaheuristic algorithms also have been conducted. The study's findings demonstrated the promising results of TLBO in terms of minimizing the energy cost and significantly reducing the peak loads during peak hours in the 24 h. The simulations also show that TLBO can be used as an effective tool for position and power of BESS optimization solution, where for the 18-node system, there is about 3.7 % cost reduction and for the 33-node system, about 12% cost saving for power purchased for the surveyed 24-h period

Forensic of Solar PV: A review of potential faults and maintenance strategies

Photovoltaics (PV) technology has grown strongly over the last decade, and along with this growth, there are issues and challenges related to electrical faults and fire hazards. Forensic is the investigation of any incidents or crimes in a legal context. Forensic electrical in a PV system is usually associated with incidents due to electrical equipment damage, design errors, improper installation, or product defects. Forensic work aims to identify the cause of the damage and ways to repair the damage. This paper aims to present electrical-related faults analysis and safety issues in PV systems, including fault maintenance involving corrective, predictive and preventive. A comparative study of these different maintenances strategies is discussed. The study is necessary for the improvement of PV system development safety, efficiency and reliability. Also, to speed up operating time and prevent financial losses consequences. Overall this study shall provide researchers and PV system/plant's operators with a valuable reference for better maintenance options to sustain PV systems optimally

Dynamic model and robust control for the PEM fuel cell systems

In response to the escalating challenges posed by climate change, the global energy sector has witnessed a paradigm shift towards sustainable alternatives. The promising fuel cell technology known as the proton exchange membrane fuel cell (PEMFC) has found widespread use in a variety of mobile and stationary applications. This paper presents a super-twisting sliding mode (STSM) control for maximum power point tracking (MPPT) on the proton exchange membrane fuel cell (PEMFC) system incorporated floating interleaved boost converter (FIBC). This work aims to extract the maximum power from the PEMFC by means of robust control in conjunction with FIBC to improve current ripple. The proposed controller is designed for the PEMFC system by combining the STSM and MPPT methods. The designed MPPT control tracks down the maximum power point of the system, and the corresponding current values act as the reference values for the STSM controller. The results show that incorporating the PEMFC with the FIBC can result in current ripple reduction when compared with a traditional interleaved boost converter (IBC). The obtained results demonstrate the capability of the PEMFC closed-loop control system to maintain the system's robustness under fuel cell parameter variations

Comparative Study on the Performances of PV System with Various Combinations of Converter Topologies and PWM Types

Increasing demand for electricity generated from renewable energy sources forced power utility provider to develop more and larger power plant. In Malaysia, among renewable energy sources, photovoltaic (PV) is the most reliable. Hence, large-scale PV power plants have been developed and many more to be developed in the future. In case of large GridConnected PV (GCPV) power plant, the converter which is nonlinear load will generate Total Harmonic Distortion (THD) into the electrical power system. Harmonic generated by the PV system may downgrade the quality of power grid and affect the reliability and safety. In addition, other main concerns in GCPV system are switching losses of the switching devices in the converter and the cost of the converter. These performances can be improved by the right choice of Pulse Width Modulation (PWM) type and converter topology. This paper will discuss which PWM type and converter topology should be chosen to produce the optimal performance based on THD, switching losses and cost. In general, the best combination is a two-level converter with continuous PWM (CPWM) where the cost is almost half compared to the three-level converter and having optimal value in the combined performance of THD and switching losses

A multi-scale smart fault diagnosis model based on waveform length and autoregressive analysis for PV system maintenance strategies

Nonlinear photovoltaic (PV) output is greatly affected by the nonuniform distribution of daily irradiance, preventing conventional protection devices from reliably detecting faults. Smart fault diagnosis and good maintenance systems are essential for optimizing the overall productivity of a PV system and improving its life cycle. Hence, a multiscale smart fault diagnosis model for improved PV system maintenance strategies is proposed. This study focuses on diagnosing permanent faults (open-circuit faults, ground faults, and line-line faults) and temporary faults (partial shading) in PV arrays, using the random forest algorithm to conduct time-series analysis of waveform length and autoregression (RF-WLAR) as the main features, with 10-fold cross-validation using Matlab/Simulink. The actual irradiance data at 5.86 °N and 102.03 °E were used as inputs to produce simulated data that closely matched the on-site PV output data. Fault data from the maintenance database of a 2 MW PV power plant in Pasir Mas Kelantan, Malaysia, were used for field testing to verify the developed model. The RF-WLAR model achieved an average fault-type classification accuracy of 98 %, with 100% accuracy in classifying partial shading and line-line faults