Power Transmission Cost Research Articles

A framework for optimal agent deployment and opportunistic routing in flying Ad-Hoc networks for precision weather forecasting

Drone based precision weather prediction and forecasting is a prevailing area of research in recent times. Modern flying ad-hoc network (FANET) leverages advanced low latency communication protocol between small flying drones and base stations. In this work, a FANET framework for efficient and precision weather prediction and forecasting that leverages ultra low latency opportunistic message transfer protocol (MTP) is proposed. An, edge empowered intelligent computing quasi-stationary sink nodes are employed to develop the FANET ecosystem further. Flying sensor nodes and edge computing quasi-stationary sink nodes are deployed in the target area in order to achieve optimal sensor coverage minimizing the cost of data transmission power thus ensuring long endurance operation of drones. An ensemble model is deployed in edge level for localized weather prediction. Opportunistic routing strategies are utilized for converge casting. The experimental results show the maximum of 0.98 message delivery ratio and a minimum of 820 ms latency is achieved through opportunistic MTP. An accuracy of 96.5% is achieved in predicting the temperature and humidity data metric.

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

Multi-Slot Over-the-Air Computation in Fading Channels

IoT systems typically involve separate data collection and processing, and face the scalability issue when the number of nodes increases. For some tasks, only the result of data fusion is needed. Then, the whole process can be realized in an efficient way, integrating the data collection and fusion in one step by over-the-air computation (AirComp). Its shortcoming, however, is signal distortion when channel gains of nodes are different, which cannot be well solved by transmission power control alone in times of deep fading. To address this issue, in this paper, we propose a multi-slot over-the-air computation (MS-AirComp) framework for the sum estimation in fading channels. Compared with conventional data collection (one slot for each node) and AirComp (one slot for all nodes), MS-AirComp is an alternative policy that lies between them, exploiting multiple slots to improve channel gains so as to facilitate power control. Avoiding to obtain instantaneous channel gains of all nodes at the sink is a key point. Specifically, the transmissions are distributed over multiple slots and a threshold of channel gain is set for distributed transmission scheduling. Each node transmits its signal only once, in the slot when its channel gain first gets above the threshold, or in the last slot when its channel gain remains below the threshold. Theoretical analysis gives the closed-form of the computation error in fading channels, based on which the optimal parameters are found. Noticing that computation error tends to be reduced at the cost of more transmission power, a method is suggested to control the increase of transmission power. Simulations confirm that the proposed method can effectively reduce computation error, compared with state-of-the-art methods.

IoT Supervised PV-HVDC Combined Wide Area Power Network Security Scheme Using Wavelet-Neuro Analysis

Power system networks are one of the most widely used methods in the real world for transferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission.Due to an increase in power demand, integration of renewable sources to minimise the voltage fluctuations and compensate for power loss is necessary. This is a mandatory requirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under various weather conditions as well as electrical parametric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters calibrated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at different frequencies, which provides important information related to the detailed analysis of faults in power networks. The major goals of this research are to analyse faults using detailed coefficients of current signals through the bior-1.5 mother wavelet for fault identification and artificial neural network analysis for fault localization. This proposed approach furnishes an IoT supervised Photovoltaic - High Voltage Direct Current (HVDC) combined wide area power network security scheme using wavelet detailed coefficients under various types of faults with Fault-Inception-Angles.

DEVELOPMENT OF ANN CONTROLLED UPFC BASED PROTECTION MODEL FOR NIGERIAN 330KV POWER LINES

This paper, proposed the use of artificial neural network (ANN) based UPFC for transmission network loss minimization. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. Power loss leads to high cost of power generation, transmission and distribution. Unlike exiting up change and regulating transformer techniques for loss reduction, FACTS devices have fast switching capability and can be subjected to very free control algorithms for more optimal performance in loss reduction application in power systems. The neural network was modeled to output the firing angle to enable the FACTS device effectively control the adsorption and injection of reactive power for transmission loss reduction. The Nigerian 330KV power grid was used as a case study for the evaluation of the proposed power loss reduction system A digital model of the case study power system with the proposed neural network controlled UPFC integrated was created in the MATLAB/SIMULINK programming environment. Genetic algorithm was used for the optimal placement of the FACTS device in the MATLAB/SIMULINK model of the Nigerian 330KV transmission system. Simulation carried out involved alteration of power flow in order to cause different levels and distribution of losses in the network. With each variation, load flows was carried out to evaluate the distribution of losses and the active and reactive loss reduction achieved by the proposed system. The simulation and evaluation were carried out under two scenarios: (i) with the UPFC installed and (ii) without the UPFC installed. With each variation of the load at the bus, load flow is run to determine total system loss either with the UPFC installed or without the UPFC installed. Results obtained showed that the proposed system achieved an average active power loss reduction of 12.8416% and an average reactive power loss reduction of 21.82%.

Analysis and Prediction of Differential Operation and Maintenance Cost of Power Transmission and Transformation

The operation and maintenance expenses of power transmission and transformation projects, as a significant power supply carrier of the nation, continue to rise as a result of the sustained and quick expansion of China's social economy and the quick growth of the country's power demand. Power grid businesses are under a lot of market pressure. To increase the level of lean management of the operation and maintenance costs of power transmission and transformation projects, power grid enterprises must significantly enhance their capacity to estimate the operation and maintenance costs of their organizations in advance. Machine learning algorithms are gradually applied to the operation and maintenance cost prediction of power transmission and transformation projects of power grid enterprises as a result of the ongoing development of big data technology, effectively increasing the accuracy of operation and maintenance cost prediction. In this paper, by analyzing the variables affecting the differential operation and maintenance cost of power transmission and transformation projects, a scientific and reasonable investment analysis model for the differential operation and maintenance cost of power transmission and transformation projects is constructed using the stochastic forest algorithm of particle swarm optimization, and the variables affecting the differential operation and maintenance cost of substations and transmission lines are obtained, which proves that the trend of the prediction model in this paper is more consistent with the actual situation.

Modified of Mixed Strategy Algorithm for Power Allocation on D2D Underlay Communication

As time passes by, the need for telecommunication services will increase along with increasement of human’s population. To anticipate the flooding requests of telecommunication services, one of the solutions offered is by implementing D2D communication, especially in 5G architecture. In D2D 5G, focusing on underlay communication D2D uses the same frequency spectrum, so that opportunity of interference may happen, but the value of spectral efficiency (SE) may increase as a result of the effectiveness of the spectral that occurs. In terms of financing and its development, underlay D2D communication is considered more profitable for service providers. Based on this point, the authors propose a novel algorithm of modified mixed strategy algorithm for power allocation to make the power transmission cost be more effective. After the experiment conducted, the result shows that modified mixed strategy outperform baseline model which uses no power allocation algorithm.

Construction of Power Transmission and Transformation Project Cost Information Platform Based on Big Data Analysis

With the reform of China’s power system, power transmission and transformation project (hereinafter referred to as PTATP) are gradually developing in the direction of integration, informatization, large-scale and systematization. Therefore, the traditional project cost can no longer meet the needs of the society, which requires the project cost based on BD (hereinafter referred to as BD) technology. Through the information platform (hereinafter referred to as IPF), we can collect a lot of information, including policies and regulations database, talent and machine price information database, project cost index database, industry information database, etc., which will provide important support for project cost. Project cost informatization will solve the problems of low information sharing rate, low information value and high information cost, which will more scientifically complete the cost of PTATP. Based on BD technology, we can collect, sort out and analyze the cost information data of PTATP, which will fully explore the data value. Firstly, this paper analyzes the main algorithms needed for project cost. Finally, this paper constructs a PTATP cost IPF based on BD analysis, which will provide accurate countermeasures.

Stochastic Game Based Cooperative Alternating Q-Learning Caching in Dynamic D2D Networks

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks.

Modern manufacturing technology for modular photovoltaic panels: State-of-the-art and future trends

The technological advancement of the recent years has triggered a significant increase in the global energy consumption. Within this frame, the fossil fuels still account for most of the energy consumption as the renewable energy systems often lack reliability and predictability in terms of total energy supply. These shortcomings may be overcome if various design and manufacturing process optimization methods are applied so as to gain more benefits from the current solar power systems. A modern modular photovoltaic (PV) panel is an assembly of photovoltaic cells mounted in an adaptable framework for quick installation. The photo-voltaic cells use sunlight as source of energy and generate current electricity, which can be harvested directly or through an existing grid. A collection of PV modules can be arranged in any desirable manner to form a PV panel. Also, the implementation of such modern modular systems brings numerous advantages, including the reduction of power transmission cost and the minimization of the global warming problems. This work presents the latest findings related to the manufacturing technology and the equipment train used in the production of modern photovoltaic systems.

Performance analysis of hybrid techniques for evaluation of power transmission cost and loss allocation based on transmission reliability margin

Performance analysis of hybrid techniques for evaluation of power transmission cost and loss allocation based on transmission reliability margin

Research on Cost Prediction of Power Transmission and Transformation Project Based on Combination Prediction Model

Based on the project division of power transmission and transformation projects, this paper analyzes a large number of practical engineering cases, and summarizes the influencing factors of substation and overhead line project cost combined with the practical engineering experience of many experts, A number of cost control indicators are extracted, and a combination prediction model of project cost based on neural network and support vector machine is constructed. The actual project data are taken as samples for empirical research. The results show that the combination prediction model is feasible and accurate for the prediction of power transmission and transformation project cost, which provides a new idea and implementation method for the prediction of power transmission and transformation project cost.

Research on the Forecast Model of Transmission and Distribution Project Cost Based on Support Vector Regression Algorithm

Based on historical power transmission and transformation project data, this paper constructs a support vector regression forecasting model by screening the key influencing factors of power transmission and transformation projects, and then predicts the cost of power transmission and transformation projects above 110kV in a certain province. The results show that the support vector regression forecasting model can quickly predict the project cost of power transmission and transformation projects by inputting key factors of the project. The establishment of this prediction model provides a more scientific basis for rationally controlling the cost of power transmission and transformation projects.

Optimal scheduling of a micro-grid with multi-period islanding constraints using hybrid CFCS technique

This paper proposes an efficient technique for optimum scheduling of micro-grids with multi-period islanding restrictions. The proposed method is joint implementation of Cuttle Fish Algorithm (CFA) and Crow Search Algorithm (CSA); is known CFCS method. Here, the CFA search behavior is modified by the CSA update position. The proposed CFCS system is utilized for optimal scheduling of micro-grid and considerably decreases the calculation load. The main purpose of proposed work is diminish the micro-grid operation cost together with the dispatch-able units operation cost, cost of power transmission as main grid with inconvenience cost recognized with consumers. The cost of power transmission from main phase can be positive or negative based on the flow direction of transmission line linking the microcircuit to main phase. Negative cost that represents an export of power to main grid emerges as economic advantage of micro-grid. Cost of inconvenience implies the penalty of modifiable loads programming outside the time intervals particular by the consumers. Constant penalty factor is utilized for prioritizing loads with respect to operation sensitivity inside the particular time intervals, in which a superior value of the penalty factor implies a least flexible load based on time interval operation settings. The value of the penalty factor is chosen reasonably greater to unit generation cost and market price. In proposed technique, the CFCS is used for establishing the correct program of the MG combinations according to load side power range. The CFCS method, objective function can be defined as system data subject to equality and inequality limitations. At that time, CFCS model is performed at MATLAB/Simulink working platform and performance is estimated to existing techniques.

A Two-Stage Multiple Criteria Decision Making for Site Selection of Solar Photovoltaic (PV) Power Plant: A Case Study in Taiwan

At the heart of Covid-19 responses, the transition from fossil sources to green energy is an urgent issue for nations to address the crisis and secure sustainable economies. As a country in a seismically active zone that relies heavily on imported fossil fuels, Taiwan is vigorously taking the next step in renewable energy development, which is pivotal to securing its position in global supply chains. Solar energy is today the most suitable renewable energy source for Taiwan. However, land prices and policies, and challenges of scale still hinder its development. In this context, identifying optimal sites for solar photovoltaic (PV) construction is a crucial task for major energy stakeholders. In this paper, a two-stage approach, combining the data envelopment analysis (DEA) models and the analytic hierarchy process (AHP), has been done for the first time to identify the most suitable locations among 20 potential cities and counties of Taiwan for constructing solar PV farms. DEA models were applied to filter out the areas with the most potential by measuring their efficiency indices with temperature, wind speed, humidity, precipitation, and air pressure, as inputs, and sunshine hours and insolation, as outputs. The locations with perfect efficiency scores were then ranked with the AHP method. Five selected evaluation criteria (site characteristics, technical, economic, social, and environmental) and sub-criteria of each were utilized to prioritize the locations with solar energy potential. AHP was used to determine the relative weights of the criteria and sub-criteria and the final weights of the areas. For criteria weighting results, “support mechanisms,” “electric power transmission cost,” and “electricity consumption demand” with weights of 0.332, 0.122, and 0.086, respectively, were found as the most significant sub-criteria. The final ranking suggests Tainan, Changhua, and Kaohsiung as the top three most suitable cities for constructing solar PV energy systems.

АНАЛІЗ ПРИСТРОЇВ РЕГУЛЮВАННЯ НАПРУГИ

The paper considers an electrical substation with a voltage of 330/110/35 kV. Means of voltage regulation on the substation autotransformer are investigated. The paper considers the means of voltage regulation on autotransformers of the 330/110/35 kV substation to ensure uninterrupted power supply and the desired voltage level on the busbars of consumers. The choice of the optimal version of the on-load tap-changer of the autotransformer is shown. The electric network with voltage of 110 and 35 kV is considered. The power supply center is substation 330 kV. The connection between voltages is provided by three three-phase autotransformers of the ATDCTN200000/330/110 type. Autotransformers are used instead of three-winding transformers to reduce the cost of conductive material and increase transmission power, but unlike three-winding transformers, they have only one voltage control device - load control regulation. Steady-state and emergency modes are investigated. The PowerFactory program has created a network model, which is used to calculate the mode and evaluate the methods of voltage regulation. On the basis of calculations the point of economic flow distribution in which it is expedient to open a network in a normal mode, and on the switch to provide the device of automatic input of a reserve is defined. The principles of control of modes of operation of electrical systems are investigated: minimum costs for generation, transmission and consumption of electricity (technological losses), reliability of operation of electrical systems, quality of electricity. The purpose of this work is to substantiate the optimal operational scheme of the electrical network, the study of the modes of operation of the 330/110/35 kV network. The simulation of steady and emergency modes is performed in the work. The optimal operational scheme of the electric network is substantiated in order to improve the reliability of power supply and ensure the desired level of voltage on the busbars of consumers.

Leak Detection on Air Reservoir via Acoustic Models with TensorFlow Based

Fluid, both gas and liquid, is a widely used substance which can be used in pneumatic and hydraulic system. However, the pneumatic system with compressed air system integrated has a flaw that leakage air during power transmission cost a lot of loss both resources and performance. Leak detection is one of the main solution to plug the flaw. In this research, we use acoustic signal to detect the leakage by using it as an input for model. Artificial Neural Network (ANN) is used in our model to achieve deep learning property via Tensorflow. Acoustic signal is recorded in different situation and is used as a model input. So, our model can be trained with leak data and predict the leakage in pneumatic system. We evaluate model using test data and shows the leakage prediction in probability distribution.

CAREC energy corridor: Opportunities, challenges, and IMPACT of regional energy trade integration on carbon emissions and energy access

The research paper examines Central Asia Regional Economic Cooperation (CAREC) integrated energy market potentials, challenges, and opportunities for 1) harnessing untapped renewable energy potential; 2) promoting the cross-border trade to cater the rising regional demand mismatch of electricity; 3) improving energy security; and 4) reducing the carbon footprint of the region. The research demonstrates that the CAREC Energy Corridor (CEC) will optimize regional trade cooperation by building cross-border energy trade infrastructure, systems, and governance mechanisms by employing a Regional Public Goods (RPG) framework. The research finds that a one percent increase in energy imports within the region is associated with the reduction in carbon dioxide emissions by 0.245 kg per $ of GDP; the increase in the Gross Domestic Product (GDP) per unit of energy use by 0.233 per kg of oil equivalent; and the growth in electricity production from renewable sources by 0.414 kW hour, thus improving regional electricity affordability, access, and energy security. The cost benefit analysis reveals that traditional (fossil fuel or hydro) power generation and transmission costs must either beat or match the price competition and transmission flexibility options offered by renewables to sustain competitiveness of the regional electricity market.

A review on development of offshore wind energy conversion system

Wind energy conversion system, aiming to convert mechanical energy of air flow into electrical energy has been widely concerned in recent decades. According to the installation sites, the wind energy conversion system can be divided into land-based wind conversion system and offshore wind energy conversion (OWEC) system. Compared to land-based wind energy technology, although OWEC started later, it has attracted more attentions due to its significant advantages in sufficient wind energy, low wind shear, high power output and low land occupancy rate. In this paper, the principle of wind energy conversion and the development status of offshore wind power in the world are briefly introduced at first. And then, the advantages and disadvantages of several offshore wind energy device (OWED), such as horizontal axis OWED, vertical axis OWED and cross axis OWED are compared. Subsequently, several major constraints, such as complex marine environment, deep-sea power transmission and expensive cost of equipment installation faced by offshore wind conversion technology are presented and comprehensively analysed. Finally, based on the summary and analysis of some emerging technologies and the current situation of offshore wind energy utilization, the development trend of offshore wind power is envisioned. In the future, it is expected to witness multi-energy complementary, key component optimization and intelligent control strategy for smooth energy generation of offshore wind power systems.

A Unidirectional Hybrid HVDC Transmission System Based on Diode Rectifier and Full-Bridge MMC

To reduce the cost of bulk power transmission using voltage source converter high-voltage DC (HVDC) technology, a unidirectional hybrid converter is proposed, where a diode rectifier (DR) and a modular multilevel converter (MMC) based on full-bridge (FB) submodules are connected in series on DC side. The FB-MMC controls its DC voltage to regulate the transmitted power. The majority of the power transmission is via the DR considering its cost and efficiency superiority and only low power rating FB-MMC is required. A thyristor valve is equipped at the DC side of the FB-MMC to prevent potential overcharge of the FB submodules during DC faults. Compared to conventional MMCs, losses can potentially be reduced by around 20%. An active power controller is proposed to regulate the DC voltage of the FB-MMC so as to control the transmitted power. With the inverter station controlling its DC terminal voltage constant, the FB-MMC increases the output DC voltage to increase the transmitted power and vice versa. To alleviate overvoltage of the HVDC link during AC grid faults of the inverter station, a dynamic DC voltage limiter is designed to actively reduce the DC output voltage of the FB-MMC. Simulation results confirm the proposed converter operation and control.