Customer Load Research Articles

The Data Mining Based Model for Detection of Fraudulent Behavior in Water Consumption

Abstract: There are many significant problems facing by water supplying companies and agencies because of fraudulent water consumption. Which is resulting a higher loss of income to water supplying agencies. Finding efficient measurements for detecting fraudulent activities has beenan active research area in recent years. To detect this fraudulent behaviour faced by water companies’ intelligent datamining techniques can be used to reduce the loss. This research explores the use of two classification techniques SVM and KNN to detect suspicious fraud water customers. The SVM based approach uses customer load profile attributes to expose abnormal behaviour that is known to be correlated with non-technical loss activities. The data has been collected from the historical data of the company billing system. The accuracy of the generated model obtained 74% which is better than the current manual prediction procedures.The system will help the company to predict suspicious water customers.

Research on method of demand-side participating in grid interaction based on time-of-use pricing and coupon guidance

The electricity grid is facing the dual pressure of a high percentage of renewable energy generation and increasing load-side spike load. The participation of demand-side resources in grid interaction is an effective solution to this problem. To address this issue, a demand-side guidance method based on time-of-use pricing and coupon is proposed to increase the revenue of power retailers and reduce peak-valley difference. First, a guidance mechanism combining time-of-use pricing and coupon is proposed, and an adjustment load prediction model based on Attention-LSTM network is constructed to predict the adjustment load of electricity customers under different coupon coefficients. Based on this, a power purchase-sale optimization decision model in multi-level electricity market with the goal of maximizing the revenue of power retailers is established to set a suitable coupon strategy to guide electricity customers to participate in the interaction more precisely. Finally, the effectiveness of the proposed method is demonstrated by arithmetic examples.

Research on planning optimization of integrated energy system based on the differential features of hybrid energy storage system

At present, it is very important to build the new power system with new energy as the mainstay. Some Regional Integrated Energy Systems (RIES) have installed renewable energy generation equipments with wind turbines and photovoltaics as the main power supply facilities. Due to the volatility in the wind turbine, photovoltaic output power and the customer's electricity load, there is energy waste in the RIES. In order to enhance the economic and renewable energy consumption rate of the park where the RIES is applied, an integrated energy system(IES) planning optimization model is constructed，which considers the mixed storage differentiation characteristics. The model expands the hybrid energy storage system(HESS) on the basis of the original RIES. A two-stage decomposition model combining wavelet packet decomposition method and power decomposition method based on discrete Fourier transform is established, in which the difference between power generation of renewable energy and customer load is decomposed. Depending with the difference in response speed of energy storage devices, the power of different frequencies is moderated. With the comprehensive energy system economy as the goal, the genetic algorithm based on elitist preservation strategy is adopted to optimize the planning model and verify the correctness of the model through simulation. The results show an improvement of 8.23 % and 23.79 % in economy and renewable energy consumption rate compared to the RIES before the HESS expansion.

Poster 254: Mechanical Response of ACL to Submaximal Fatigue Loading

Poster 254: Mechanical Response of ACL to Submaximal Fatigue Loading

A Data Mining Based Model for Detection of Fraudulent Behaviour in Water Consumption

Abstract: Drinking water fraud is a major issue for water delivery businesses and authorities. This behaviour results in a significant loss of income and is responsible for the bulk of non-technical losses. Finding appropriate criteria for detecting fraudulent behaviour has been a prominent focus of research in recent years. Intelligent data mining methods can be used by water delivery companies to detect fraudulent activity and reduce losses. This study looks into the usage of two classification techniques (SVM and KNN) to discover suspicious water fraud clients. The main purpose of this research is to assist the Yarmouk Water Company (YWC) in Irbid, Jordan, in overcoming their earnings shortfall. The SVM-based technique uses customer load profile attributes to expose known abnormal behaviour.

Learning customer behaviour for effective load forecasting

Cloud computing is quickly expanding, and there are an increasing number of cloud providers. To stay competitive while earning a profit, cloud providers must focus on cost efficiency and resource cost maximization. The profit maximization challenge in federated cloud systems has been investigated in order to maximize the degree of multiplexing. Outline unique economics-inspired resource allocation strategies to address the profit maximization challenge from the standpoint of a purely active cloud provider. Admission control strategies have been presented that are adjusted within a profit management framework to maximize resource cost. Existing abstractions for in-memory storage on clusters, including as distributed shared memory, key value stores, databases, and Piccolo, use fine-grained modifications to mutable state as their interface. It has been fine-tuned to anticipate cluster load. Summing the loads of each cluster yields the total load for the entire grid. He suggested Smart Grid load forecasting approach has two primary benefits. Learning consumer habits enhances forecast accuracy while also being computationally efficient. SCCRF can simulate a single customer's load forecasting problem while concurrently selecting important features to determine its energy consumption trend. Experiments conducted from various angles show the benefits of the suggested load forecasting system.

Feasibility Analysis of a Mobile Microgrid Design to Support DoD Energy Resilience Goals

This research investigates the feasibility of using mobile hybrid microgrids to increase energy resilience in DoD Installations. The primary question examined is whether a standardized mobile microgrid, constrained within an International Standards Organization (ISO) Triple Container (TriCon) and not to exceed 10,000 lbs (approximately 4535 kg), can provide the necessary power for small critical sites with an average 10 kW load on DoD installations with similar resilience to a customized single load microgrid or emergency backup generator. Key assumptions for this research are that power outages may be accompanied by a fuel constrained environment (e.g., natural disaster that restricts fuel transport), an existing installation microgrid is in place, and the risk of outages does not warrant the development of redundant customized single load microgrids for each critical load. The feasibility of a mobile hybrid microgrid is investigated by constructing an architectural design that attempts to find a satisfactory combination of commercial off-the-shelf components for battery energy storage, photovoltaic power, and generator power within the constraints of an 8 ft × 6 ft 5 in × 8 ft (approximately 2.4 m × 2 m × 2.4 m) shipping container. The proposed design is modeled and simulated over a two-week period using Global Horizontal Index solar irradiance data, and a randomized average 10 kW load. Results of the model are used to analyze the feasibility of the system to meet the load while reducing dependency on fuel resources. Trade-offs between a customized single load microgrid and standardized mobile microgrid are discussed. The result of this research indicates that a standardized mobile microgrid holds significant promise for DoD and other potential users (public safety, private industry, etc.) in having a rapidly deployable solution to bring critical loads back online during an emergency situation that reduces generator usage.

Research on customer’s electricity consumption behavior pattern

The fine-grained customer load data provides a large amount of information for the analysis of customer’s electricity consumption behavior pattern. Therefore, this paper proposes an algorithm for identifying the electricity consumption patterns of electric power users based on k-means algorithm. Our method is verified on the electricity consumption data of Irish residents and Shanghai Southern Industrial and Commercial users. The experimental results show that our method can divide the curves with similar electricity consumption patterns efficiently.

Review Paper on Computer Network Security and Privacy

Abstract: As the sizable recognition of laptop community applications, its safety is likewise acquired a excessive diploma of attention. Factors affecting the protection of community are complex. Network safety is a scientific paintings, has the excessive challenge. For protection and reliability troubles of laptop community gadget, this paper mixed with realistic paintings experience, from the risk of community safety, safety technology, community a few Suggestions and measures for the gadget layout principle, with a purpose to make the loads of customers in laptop networks to decorate protection attention and grasp positive community safety technology. Keywords: Network, Security; Technology, Strategies, Principle.

Fuzzy Hysteresis Current Controller for Power Quality Enhancement in Renewable Energy Integrated Clusters

Steady increase in electricity consumption, fossil fuel depletion, higher erection times of conventional plants, etc., are encouraging the use of more and more onsite renewable energy. However, due to the dynamic changes in environmental factors as well as the customer load, renewable energy generation is facing issues with reliability and quality of the supply. As a solution to all these factors, renewable energy integrated cluster microgrids are being formed globally in urban communities. However, their effectiveness in generating quality power depends on the power electronic converters that are used as an integral part of the microgrids. Thus, this paper proposes the “Fuzzy Hysteresis Current Controller (FHCC)-based Inverter” for improving the power quality in renewable energy integrated cluster microgrids that are operated either in grid-connected or autonomous mode. Here, the inverter is controlled through a fuzzy logic-based hysteresis current control loop, thereby achieving superior performance. System modelling and simulations are done using MATLAB/Simulink®. The performance analysis of the proposed and conventional inverter configurations is done by computing various power quality indices, namely voltage characteristics (swell, sag, and imbalance), frequency characteristics (deviations), and total harmonic distortion. The results reveal that the proposed FHCC-based inverter achieves a better quality of power than the traditional ST-PWM-based multilevel inverter in terms of IEEE/IEC/EN global standards for renewable energy integrated cluster microgrids application.

Biomechanical Comparison of 2 Double Plating Methods in a Coronal Fracture Model of Bicondylar Tibial Plateau Fractures.

Because management of bicondylar tibial plateau fractures are complicated even for expert surgeons, with using a coronal fracture model, we aimed to compare 2 kinds of double locked plating techniques that consisted of the lateral locking plate and the medial locking plate inserted medial anteriorly (MA-ly) or medial posteriorly (MP-ly). Fourteen fresh-frozen tibias stabilized with the MA or MP methods were allocated into 2 groups with similar bone mineral density values. Implanted samples were tested under incremental fatigue loading conditions using a customized load applicator. An optical motion tracking system was used to assess relative displacements and rotations of fracture fragments during loading. Static and dynamic global stiffness, failure load, failure cycles, as well as movements of fracture fragments were measured. There were no significant differences between the 2 fixation methods regarding global stiffness, failure load, or failure cycles (P = 0.67-0.98, depending on the parameter). The kinematic evaluations, however, revealed that different positions of the medial locking plates altered the directions of movements for the medial-anterior or medial-posterior fracture segments. The mechanical stability of tibia-implant constructs fixed with the double plating methods was not remarkably affected by the location of the medial locking plate. Depending on clinical conditions and surgeons' preferences, bicondylar tibial plateau fractures can be managed with either MA or MP methods.

Multi objective demand side storage dispatch using hybrid extreme learning machine trained neural networks in a smart grid

The typical mode of operation of electric energy systems is unidirectional and they follow a top-down approach. A few large power plants feed the power grid and the aim is to ensure that the demand and supply are always balanced. The traditional energy management schemes in a grid are aimed towards energy system optimization. Although there is considerable experience with the optimization of energy production and distribution, researchers and industries are increasingly focused on the demand side. The reliable operation of a grid demands the electricity supply and load demand to be constantly and precisely balanced in real-time. However, it is difficult to achieve this balance since both supply and demand could change unexpectedly and significantly due to intermittent power surge/drop from Renewable Energy Resources (RES), sudden load changes, and forced outages in generation units or other components of the power grid. In such scenarios, Demand Side Management (DSM) in addition to generation control can help to manage and match the customer load with the supply system. Past research works have demonstrated how various heating and cooling loads in buildings/industries can be used as a Demand Side Storage (DSS). In this paper, a water storage plant has been used as a DSS. A novel Extreme Learning Machine (ELM) based Genetic algorithm has been utilized to optimize two different objectives. The first objective is to find the dispatch schedule of the DSS while improve the generation efficiency of the conventional generating plant. This has been achieved without affecting its overall intended operation. The second objective is to maximize the operational lifetime of the pumps in the water storage plant by reducing their switching operations. The results have been compared with those obtained from a deterministic and other AI methods in order to verify its effectiveness under various conditions. In addition, real time simulation results have also been provided for the same case study in an IEEE 24 bus test system.

Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks

In power distribution networks, there are many practical fault cases such as high impedance faults, faults and so on. Especially when the faults with electric arc persist, it is dangerous for human beings and circumstances. Nevertheless, it is difficult to classify faults due to various customer load conditions or the presence of distributed energy resources. In this paper, we propose a new mathematical arc model based on experimental event data. For implementing the arc phenomenon, wet gravel was used as a contact conductor after the fault. The experimental results validate the arc transient model. Then, the simulations were performed to verify the success of arc modeling using Kizilcay’s arc model as a comparison method. Finally, we developed a fault detector using the kurtosis detection method, and power system simulations were conducted to evaluate fault detection performance using Matlab/Simulink.

Design of Automatic e-Cart for Shopping-Malls in Smart Cities

Abstract: In this fast-moving world, time is no less than a luxury. People prefer technology and services which reduce their workload and are time efficient. Busy shopping malls with loads of customers and long queues during billing make it loads of inconvenient for people, to enjoy their shopping experience. So here comes our proposed idea into the highlight as “Smart Shopping E-cart using IoT”. Providing convenient, faster, and less stressful way, to enjoy our shopping. The project consists of an RFID reader module (ES18), RFID tags, a Liquid crystal display, LED lights, a buzzer, a motion detector, NodeMCU, and push-buttons. This paper is focused on the implementation of the Smart Shopping E-cart which not only provides the basic functionality of a cart but also provides a convenient way of shopping. Reducing the task of cashier and long billing calculations. Keywords: Smart E-cart, RFID, IoT, Shopping Malls, Anti-theft.

5kV급 MV-LVDC 독립형 마이크로그리드의 보호협조 운용방안에 관한 연구

Recently, demands on MVDC distribution system is being significantly increased due to interconnection of renewable energy sources, expansion of DC load and high quality and reliability of the power supply system etc., while commercialization of facilities for MVDC is yet in early stage. Accordingly, demonstration projects on applying existing AC devices and cables to MVDC distribution system are being carried out. However, the protection on high-cost facilities such as converters and cables may not be reliable in the event of fault, and then operation method of protection coordination for MV-LVDC micro grid(MG) system is being required. For the purpose of stable operation of the 5kV MV-LVDC micro-grid, this paper proposes operation methods of protection devices depending on scenarios, such as faults on the MVDC line, LVDC line, or customer load. And also, a modeling of 5kV MV-LVDC MG system, which is composed of PV system, ESS, MVDC cable, DC/DC converter, DC/AC inverter, protection devices etc., is performed using PSCAD/EMTDC S/W. From the simulation results based on the proposed modeling and operation method of protection devices, it is confirmed that this paper is useful tool to perform the proper protection coordination.

Optimal Sizing and Management of Distributed Energy Resources in Smart Buildings

This article investigates optimal sizing and realtime control of electrical and thermal distributed energy resources (DERs) in smart buildings. Initially, a comprehensive system architecture is presented considering both electrical and thermal DERs. Then, the DERs are optimally sized through a planning optimization problem with respect to minimization of the total initial investment cost, replacement cost, operations-and-management cost and environmental cost normalized by energy delivered from renewables and controllable non-renewables. Finally, the DERs are optimally controlled in a realtime through a one-slot-look-ahead (OSLA) optimization technique with respect to minimization of load scheduling delay cost, energy procurement cost, energy storage degradation cost, DERs operations-and-management cost, and environmental deterioration cost. The proposed OSLA based technique has the following distinct features and benefits: (i) it introduces a customer-oriented specific duration based modelling that makes it useful for practical customer energy needs and available budget limitation; (ii) it relies on the current states of the system inputs only making it applicable in general scenarios; (iii) it employs special techniques of problem modification, transformation, approximation and separation with a significantly lower computational complexity making it useful for practical implementation. Performance of the proposed approach is validated through simulations. Results show that the proposed algorithm can reduce the monthly energy consumption bill of BEMS customers by 20.53%. Also, it can execute 88.15% of customer load tasks accurately accounting for realtime changes in system inputs.

Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load

To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty. By modelling a probabilistic dependence among flow, load, and electricity tariffs, the expected cost function is obtained and used in the constrained optimization. The proposed control strategy explicitly incorporates the cycling nature of customer load. Furthermore, for daily cycling load, a fixed-end time and a fixed-end output problem are addressed. It is demonstrated that the proposed control strategy is a convex optimization problem. While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations. Also, the expected cost across the flow variations is considered. The density function of load probability improves load prediction over a progressive prediction horizon, and a nonlinear battery model is utilized.

Integrating Transactive Energy Into Reliability Evaluation for a Self-Healing Distribution System With Microgrid

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution system's reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. A transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

To mitigate the low frequency problem in a transmission system in an event of a power station failure or during low renewable generation production, UK National Grid (NG) Electricity System Operator has balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so call fast reserve services. This paper presents an alternative method to aggregately control the existing distribution network primary on load transformer tap changers as a voltage-led customer load active service. The main benefits of the proposed method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to transmission network. In this paper an optimal control strategy based on genetic algorithm is proposed and developed to achieve an optimized voltage-led customer load active service with the aim of finding the optimal dispatch of on load transformer tap changers by minimizing each transformer tap switching operation as well as network losses. Two practical 102 buses and 222 buses UK distribution networks have been modelled and used to demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than other two methods. These are important findings as the fast reserve service by transmission network requires the accuracy of the load demand reduction delivery within 2 minutes.

Algorithm Reliance Under Pressure: The Effect of Customer Load on Service Workers

Algorithm Reliance Under Pressure: The Effect of Customer Load on Service Workers