Provincial Electricity Authority Research Articles

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.

Characteristics of Various Single Wind-Power Distributed Generation Placements for Voltage Drop Improvement in a 22 kV Distribution System

A major challenge in distribution systems is the issue of voltage drop along the distribution line resulting from an increased load capacity connected to the utility. A significant voltage drop can affect the performance of a distribution system and cause quality issues for end users, impacting the system’s long-term sustainability and reliability. Therefore, regulations have been set stating that the voltage level should not be more that 5% higher or lower than the rated voltage. Thus, in this study, we aimed to evaluate the voltage level characteristics of a 22 kV distribution system that replicates the actual distribution system in the Provincial Electricity Authority. A voltage improvement technique based on distributed generation placement was proposed. In addition, the distribution system characteristics with and without distributed generation placement were evaluated under fault conditions. The results indicate that distributed generation placement in the distribution system can improve the voltage level along the distribution line. However, the level of increase in voltage depends on the size of the load, the capacity of the distributed generation, and the location of the distributed generation system on the distribution line. Furthermore, placing a distributed generation system with a minimum capacity at the proposed location can improve the voltage within the utility’s standard level. Thus, the installation of a distributed generation system in the distribution system is beneficial in terms of voltage improvement in the distribution system and provides the power system with a sustainable method to address the issue of voltage drop.

Feature Extraction of Risk Group and Electricity Theft by using Electrical Profiles and Physical Data for Classification in the Power Utilities

Non-technical loss (NTL) is one of the problems that has been a major issue in lost revenue for many years. Electricity distributors have attempted to reduce NTL by detecting electricity theft using various methods. Some events are difficult to detect that conventional meters inspection is inadequate. Moreover, many anomaly patterns found are very complex, confusing in identifying or distinguishing what types of electricity customers are at abnormal risk or energy theft that affects NTL. This paper proposes five key feature extraction methods and six classifying electricity customers using supervised learning. The main problem was studied and collected information, including kilowatt meters, electronic meters, TOU meters, and AMR meters, which cover four customer types that were recorded in the Provincial Electricity Authority (PEA) of Thailand. An electrical profile to be extracted for in-depth analysis of the behavior of each type of electricity customer, combined with the information of physical data to help enhance and increase efficiency. All features examined the relationships in each feature using Pearson correlation and handled unbalanced data using random oversampling (ROS). Then, the extracted data has been trained, validated, and tested to classify three classes: normal, risk, and theft, where we evaluate the results with performance metrics. The results show that random forest (RF) outperforms the rest of the classifiers by achieving a precision-recall area under the curve of 90% and a receiver operating characteristic curve of 78%. Significantly, the results were compared to previous studies and benchmark datasets, which revealed that the proposed method gave better results than other techniques.

Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand’s electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO’s performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors’ competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business.

An approach for voltage drop improvement in 22 kV PEA distribution system based on high voltage capacitor placement

The distribution system has been constantly expanded with rising load demand due to population and economic growth. The result is a distribution line facing a voltage drop issue voltage drop, which results from increasing interconnected load and the distance from the substation. To mitigate this issue, this paper proposed an approach to improve the voltage level on the distribution line using a high-voltage capacitor bank. The system under study is modeled after the Provincial Electricity Authority (PEA) 22-kV distribution line. The placement of the high voltage capacitor bank will be based on the 2/3 rule technique. The PSCAD/EMTDC has been used to simulate the characteristics of the case study distribution system and evaluate the performance of the proposed technique in comparison with the PEA voltage regulation standard. The result has demonstrated the ability to use the proposed high voltage capacitor bank placement technique to improve voltage levels in the distribution system.

Spatial Predictive Modeling of Power Outages Resulting from Distribution Equipment Failure: A Case of Thailand

This research develops a location-based predictive model for distribution equipment failure for use in preventative maintenance scheduling and planning. This study focuses on equipment-related failures because they are one of the main causes of outages in Thailand. Geographic Information Systems (GIS) data was integrated with asset data to predict the equipment failure of distribution equipment. Data on assets and outages from the Provincial Electricity Authority (PEA) was merged with GIS data from multiple sources, including elevation data, weather data, natural landmarks, and points of interest (POIs). Data was split into four regional datasets, and Random Forests (RF) feature selection and structural equation modeling was used to identify and confirm the most important features in each region. Logistic regression and RF regression were then used to estimate failures. RF regression was more effective than logistic regression at estimating equipment failure. The asset age and electrical load were significant predictors of outages. There were also geographic features that were significant predictors in each region, but which features affected outages varied by region. Thus, the study concluded that the approach developed could be used in preventative maintenance planning with some modification for regional characteristics, including geographic location and patterns of urbanization and industrialization.

Impact on Protective Device Sequence of Operation in Case Distributed Generation Integrated to Distribution System

This study aims to evaluate the impact of the distributed generator (DG) connection to the grid. The simulated results present the parameters of the system required to install DG on the end of the main distribution feeder. Various parameters, such as voltage, current, and protective relay coordination are modelled after the actual provincial electricity authority (PEA) distribution system. Various case studies compared the coordination without and with DG connections to the grid by finding the difference of protective devices. The results indicate that the malfunction can be fixed in order of priority protective devices, which operate according to the parameter setting. Additionally, the coordinate functions between the recloser and fuse devices in both phase and ground configurations in the operating zone prevented the drop-out fuse melting or burning out. Based on the result, this problem is fixed by providing a directional recloser device and increasing the fuse-link rated with 40k installation for replacing the conventional sizing, which can improve the performance in case of fault occurrence to investigate the reliability and stability of the distribution system.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Characteristic Evaluation of Wind Power Distributed Generation Sizing in Distribution System

Energy consumption and environmental issues have become major drivers of increasing renewable energy penetration levels. The electricity generated from renewable energy sources is decentralized throughout distributed generation (DG), which is located at the distribution level. However, the presence of DG can change distribution system characteristics and affect protection systems. Thus, this study aims to investigate the impact of DG in term of its sizing and placement on distribution systems under both normal and fault conditions. In addition, the effects on voltage improvement under normal conditions and current under fault conditions are also considered. The case study system in this study was modelled after an actual section of a 22 kV distribution line from the Provincial Electricity Authority of Thailand using PSCAD software. For DG, wind turbine generation was selected as a renewable energy source. The simulation results demonstrated that the presence of DG has a significant impact on both voltage and current characteristics under both normal and fault conditions. These impacts on the distribution system caused by DG can affect the operation of conventional distribution systems, which require further analysis and preventive measures in order to ensure good system reliability.

Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

Voltage Regulation in Excessive Penetration of Solar Rooftop Distribution System Using Battery Energy Storage System

This paper presents the methodology and solutions for mitigating voltage variation under steady conditions caused by differing levels of PV penetration in low-voltage (LV) rural networks. The LV rural area of the Provincial Electricity Authority (PEA) North-East area 3 District of Thailand is used as a case study with load profiles from Geographic Information System (GIS) software. Moreover, rooftop PV power generation is calculated using recorded data on irradiation in Nakhon Ratchasima Province. DIgSILENT Power Factory software is used to simulate and analyze the quality of the proposed method by focusing on the voltage level at the end of the line or dead-end (DE) of the LV network. The results show overvoltage at the DE under the high rooftop PV penetration. The battery energy storage system (BESS) is applied to mitigate the problem of low quality. Meanwhile, the system is operated by examining load demand and rooftop PV power generation to define the state of charging and discharging conditions. The proposed method can be integrated into the algorithm of a rooftop PV system and BESS local control in the home energy management system (HEMS). The results show that the proposed method can reduce the voltage variation problem existing in the PEA standard while improving the voltage level at all DEs.

The Impacts of Internal and External Factors on Developing Global Digital Innovation: A Case Study of the Provincial Electricity Authority in Thailand

The study aimed to investigate the impacts of internal and external factors on developing global digital innovation by means of a case study of the Provincial Electricity Authority (PEA) in Thailand. The research framework was based on the concepts of disruptive leadership, Thailand 4.0, Industry 4.0, PEA Digital Utility or Electric Utility of the Future, ICT/Digital Innovation, and Sufficiency Economy Principles (SEP). The research sample group comprised 419 PEA employees randomly selected from throughout the country. The research tools consisted of structured questionnaires on content and technical quality validated by five qualified experts. Assumptions of multiple regression analysis- normality, linearity, no multicollinearity, independence, and homoscedasticity were examined. The data verifying the assumptions were analyzed by multiple regression and PEA Digital Utility, Industry 4.0, ICT/Digital Innovation, Disruptive Leadership, and Thailand 4.0 were estimated for the development of global digital innovation. It was also found that most PEA employees, or 51.55 percent, refer to the innovation they know of as PEA Smart Plus. This is because PEA will focus on the use of communication technology to improve efficiency in the distribution system which is the foundation for further development of other parts of the system. Furthermore, most PEA employees identified innovation as being environmentally friendly.

Techno-Economic Assessment of a 100 kWp Solar Rooftop PV System for Five Hospitals in Central Southern Thailand

This paper presents a techno-economic assessment of a 100 kWp solar rooftop photovoltaic (PV) system at five hospitals in central southern Thailand. The system encompasses 100 kWp PV panels, 100 kW grid-tied inverters and balance of system (BOS) under the grid code of the Provincial Electricity Authority (PEA). The latest PV technology of bifacial mono-crystalline solar panels, inverters and BOS were simulated along with the Meteonorm 7.3 database using the PVsyst simulation toolkit with different tilt angles, orientations, solar radiations and ambient temperature. The technical aspects of solar rooftop PV power generation systems include the annual energy output and the performance ratio (PR) under IEC standard. Further, an economic analysis of the model was examined using a cost benefit analysis (CBA) and various assumptions. Four main financial criteria, i.e., benefit cost ratio (BCR), net present value (NPV), internal rate of return (IRR), and payback period (PBP) were evaluated under three different scenarios: (1) self-consumption scheme, (2) feed-in tariff (FiT) scheme, and (3) private power purchase agreement (PPA) scheme. Finally, the levelized cost of energy (LCOE) was also calculated. The results reveal that the Takua Thung hospital is characterized by the maximum average global horizontal irradiation (GHI) and the maximum annual produced energy of 199 kWh/m2 and 164.8 MWh/year, respectively. The PR calculated for all hospital sites is above 85%. The outcomes of the financial analysis show that the optimum scenarios are PPA and FiT schemes. The LCOE analysed in this study indicates that the Takua Thung hospital site has the lowest LCOE at 2.47 THB/kWh (0.07 USD/kWh). This research confirms the potential for hospitals and stakeholders in central southern Thailand for investments in solar rooftop PV systems

Unbalanced Data Handling Techniques for Classifying Energy Theft and Defective Meters in the Provincial Electricity Authority of Thailand

Unbalanced Data Handling Techniques for Classifying Energy Theft and Defective Meters in the Provincial Electricity Authority of Thailand

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Inside Out with Knowledge Management toward Internal Communication Facilitating Transformational Change Efficiency

This study is designed to focus on analyzing how to facilitate transformational change toward knowledge management and internal communication, in the form of internal marketing. It employed a case study approach (as a privatization) wherein a qualitative data collection was conducted. It used interviews to identify key issues from the perspective of those employed in the case study organization as well as the documentary methods. With special reference to Thailand, the case of the privatization of the Provincial Electricity Authority (PEA) was examined as a transformational change. The findings of the research will provide a comprehensive insight into the concept of privatization in a state-owned enterprise (SOE) in the context of internal marketing. As such the results cannot be generalized to other organizational environments because they have different complexity systems. On the other hand, the research should provide significant insights into how knowledge management in the direction of internal communication can facilitate a privatization process in a state-owned enterprise in Thailand. It is anticipated that it will present these notions in positive light, thus encouraging more widespread application to assure successful outcomes in similar projects.

The Affecting of Service Innovations Capability on the Efficiency of the Provincial Electricity Authority Employees in Phra Nakhon Si Ayutthaya, Thailand

The Affecting of Service Innovations Capability on the Efficiency of the Provincial Electricity Authority Employees in Phra Nakhon Si Ayutthaya, Thailand

Electricity Consumption Behavior Analysis: A case study Provincial Electricity Authority, Region 2, (Central Thailand) Chon Buri Province

Electricity Consumption Behavior Analysis: A case study Provincial Electricity Authority, Region 2, (Central Thailand) Chon Buri Province

Quantitative image analysis to identify the feasible areas of rice straw biomass VSPP power plant at provincial level, Thailand

The studying the feasible potential areas for rice straw power plants with a capacity of less than 10 MWe of a provincial level propose in this work. The potential area for producing electric energy was analyzed using the Image technique. An imaging technique assess based on the technical and economic feasibility of potential areas, i.e., transportation, water supply and the 22 kV grid connection system of Provincial Electricity Authority (PEA). The results showed the geographic distribution of the potential location of rice straw biomass at the feasible areas in the case of Nakhon Sawan province. It also found that Nakhon Sawan province has very high potential of rice straw biomass for power plant total capacity of 41.1 MWe, especially, the areas within the collection rice straw radius of 25 km, i.e., Banphot Phisai district and Tha Tako district in Nakhon Sawan province have the available surplus rice straw residues for power generation with capacity of 8.3 MWe and 5.4 MWe, respectively. Moreover, two districts are thus proposed to the suitable locations for rice straw power plant construction. The suitable areas of Banphot Phisai district and Tha Tako district were around 362.47 km2 and 206.71 km2, respectively.

Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.