Real Power Generation Research Articles

Nonlinear Self-Synchronizing Current Control for Grid-Connected Photovoltaic Inverters

Three-phase inverters for photovoltaic grid-connected applications typically require some form of grid voltage phase-angle detection in order to properly synchronize to the grid and control real and reactive power generation. Typically, a phase-locked loop scheme is used to determine this real-time phase angle information. However, in the present work, a novel method is proposed whereby the phase angle of the grid can be accurately identified solely via the grid current feedback. This phase-angle observer is incorporated into a current controller which can manage the real and reactive power of the grid-connected PV inverter system. Moreover, the maximum power point of the photovoltaic arrays is achieved without using a DC–DC converter. The proposed method achieves the grid current and DC-link voltage control objectives without the knowledge of the grid information and without the need for a cascaded control scheme. The design of this combined observer/controller scheme is motivated and validated via a Lyapunov stability analysis. The experimental setup is prototyped utilizing a real-time Typhoon HIL 603 and National Instrument cRIO embedded controller in order to validate the proposed observer/controller scheme under different operation scenarios such as irradiation changes, frequency changes, reactive power injection, and operation with a distorted grid. The results show that the DC-link voltage and the active and reactive powers are well regulated from the proposed control scheme without the measurement of the grid phase and frequency.

Online Flow Estimation for Condition Monitoring of Pumps in Aircraft Hydraulics

Hydraulic systems in conventional civil aviation are currently monitored in a very rudimentary way. Normally, measured values are compared with a fixed threshold. If these measured values are outside the predefined limits, the entire hydraulic system is usually shut down. To overcome this deficit, a study regarding a novel prognostic health management method for aircraft hydraulic pumps, which allows a statement about the pump condition, is presented in this paper. The method is based on measuring differential pressure and temperature at a suitable resistance. In the first part of the study, the overall concept for monitoring the motor pump unit is analyzed. This is followed by a discussion of possible measurement methods and suitable resistors to determine the condition of the pump. In the second part of the study, the implementation for online monitoring of the pump is discussed. After a suitable approximation is found, the quality of the proposed method is evaluated with real hydraulic power generation and consumers.

Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation.

Scaled-up multi-anode shared cathode microbial fuel cell for simultaneous treatment of multiple real wastewaters and power generation

Microbial fuel cell (MFC) is lauded for its capacity to valorize organic substrates in wastes, providing a solution to environmental pollution and energy crisis. While different types of organic substrates affect removal efficiency and current output, most MFCs are designed to only be able to utilize one type of wastewater. However, many real wastewater treatment sites generate more than one type of wastewater which hinders the installation of most MFCs. This study aimed to investigate the performance of the novel-designed multi-anode shared cathode MFC (MASC-MFC) compared with a standard single anode/cathode MFC (SAC-MFC) and the simultaneous treatment of different types of real wastewaters (sewage, slaughterhouse, and hospital) in one MFC unit. The MASC-MFC (9025 mW/m2 at 23.332 mA/m2) produced 1.7 times and 1.6 times higher in power density and current density and 2.2 times lower in internal resistance than the standard single anode/cathode MFC (SAC-MFC). A maximum COD removal efficiency of 62.7% was achieved with synthetic wastewater. Feeding the MASC-MFC with multiple real wastewaters decreased maximum power density 3.5 (2599 mW/m2) times and increased internal resistance 2.7 times. Stable current generation 1.575 mA was achieved over 300 h despite the different and complex wastewater physio-chemical compositions. The MASC-MFC achieved over 40% and approximately 30% coulombic efficiency independently in all the anode chambers irrespective of the type of real wastewater used, demonstrating the MASC-MFC's capacity to treat different real wastewaters with the added benefit of electricity production.

Transient stability improvement of wave energy conversion systems connected to power grid using anti-windup-coot optimization strategy

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions.

Detection of False-Reading Attacks in Smart Grid Net-Metering System

In the smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. This causes hefty financial losses to the utility and may degrade the grid performance because the reported readings are used for energy management. This article is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign data set for the net-metering system by processing a real power consumption and generation data set. Then, we propose a new set of attacks tailored for the net-metering system to create a malicious data set. After that, we analyzed the data and found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources, such as solar irradiance and temperature. Based on the data analysis, we propose a general multidata-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with a high detection rate of 98.59% and a low false alarm of 2.92%.

Experimental investigation of solar organic Rankine cycle with parabolic trough concentrator using nitrate salt as heat transfer and storage fluid

Nitrate salt with low melting point as heat transfer and storage fluid simplifies the heat transfer, storage, and exchange processes, and organic Rankine cycle as heat and power conversion system can realize the distributed energy supply. This study experimentally investigated the solar organic Rankine cycle with parabolic trough concentrator using nitrate salt as heat transfer and storage fluid, and the main problems and phenomena during the operation process have been described and analyzed in detail. Experimental results showed that the temperature difference of collector tube changes with direct normal irradiation and lags behind the changes of direct normal irradiation. Molten salt heat storage system can effectively restrain the temperature fluctuation at the collector tube outlet. The abrupt temperature drop at the evaporator inlet was caused by the heat imbalance between R123 and nitrate salt and inappropriate design of the evaporator. The effects of expander inlet pressure and expansion ratio on the ORC electrical power output were obtained at different superheating degrees of the expander inlet. The researches on each component are instructive to the technology of the parabolic trough concentrated solar power generation system with molten salt as heat transfer and storage fluid and provide some guides for the subsequent research. Furthermore, the energy and exergy analyses on overall system were developed, and results indicated the system average efficiency can reach 1.5%. Novelty statement Experimental study on parabolic trough concentrated solar ORC system with nitrate salt as heat transfer and storage medium were firstly analyzed. Single screw expander was firstly applied in a real solar thermal power generation system and showed a stable performance. Dynamic changes on the temperatures of PTC, molten salt tanks, and ORC were shown, and energy and exergy analyses on overall system were carried on.

Multimachine stability enhancement with hybrid PSO-BFOA based PV-STATCOM

The article explores the mitigating capabilities of a large PV-farm as a PV-STATCOM (Static synchronous compensator) in quenching the power system oscillations in a multimachine power system. Power system oscillations include fluctuation in the rotor dynamics (generator speed and angle), coupling voltage, and shaft torsions. At dusk, the PV-farm starts operating as VSC (Voltage Source Converter)-STATCOM deploying its entire inverting capabilities for suppressing the oscillations. While at the full sun during the faults, the solar-farm instantly ceases the real power generation and, operating as a PV-STATCOM until the normal operating states are resumed. The paper demonstrates the optimizing capabilities of a Bacterial Swarm Optimization (BSO), a hybrid of particle swarm and bacterial foraging optimizations in controlling the power flow of PV-STATCOM deployed in the literature. Kundur’s modified two-area model integrated with a largescale PV-farm is designed in Matlab to probe the rotor swings with torsional deviations in the interconnected shaft segments. The results for several test cases including without-with and BSO-tuned PV-STATCOM reveal the optimizing capabilities of the proposed BSO-based controller in alleviating the power system oscillations in the multi-machine power system.

Backtracking search optimization applied to solve short-term electrical real power generation of hydrothermal plant

This article aims to determine the feasible optimal solution for the short-term hydrothermal scheduling (STHS) problem using the efficient backtracking search optimization technique. The problem formulation for hydrothermal systems is highly complex owing to its nonlinear nature. Hydrothermal scheduling is considered with two decision variables simultaneously: thermal states and water discharge. These two variables are instrumental in determining the optimal hourly schedule of power generation in hydrothermal power plants. The optimum solution is obtained using the backtracking search algorithm (BSA) with two primary operations, namely, mutation and crossover. The BSA is an optimization technique free from high-sensitivity control parameters, because it has only one control parameter for finding the optimal solution. It also has powerful exploration and exploitation capabilities. The simulation results corroborate that BSA is highly efficient compared to existing optimization techniques (e.g. oppositional real coded chemical reaction-based optimization, differential evolution) in terms of overall efficiency and the quality of the solutions.

An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy

In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimated using lognormal and Weibull probability distribution functions respectively, after simulation of 8000 Monte Carlo scenarios. The control variables include the forecast real power generation of SPV and WT, real power of thermal generators except slack-bus, and voltages of all voltage generation buses. The total generation cost of the system is considered the main objective function to be optimized, including the penalty and reserve cost for underestimation and overestimation of SPV and WT, respectively. The proposed solution approach for OPF problems is verified on the modified IEEE 30 bus test system. The performance and robustness of the proposed hybrid PSO-GWO algorithm in solving the OPF problem is assessed by comparing the results with five other metaheuristic optimization algorithms for the same test system, under the same control variables and system constraints. Simulation results confirm that the hybrid PSO-GWO algorithm performs well compared to other algorithms and shows that it can be an efficient choice for the solution of OPF problems.

Daily Power Generation Forecasting Method for a Group of Small Hydropower Stations Considering the Spatial and Temporal Distribution of Precipitation—South China Case Study

This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the “time difference” between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long–short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%.

Exergoeconomic perspective to evaluate and optimize supercritical carbon dioxide coal-fired power generation system

The application of supercritical carbon dioxide (SCO2) Brayton cycle to coal-fired power generation system has attracted increasing attention since 2018. In this study, the SCO2 coal-fired power generation system is evaluated and optimized from the exergoeconomic perspective. The purpose is to conduct comprehensive and in-depth analysis of this system. The simulated model of the studied system and corresponding exergoeconomic model are initially established. Subsequently, the cost formation process of system is revealed and the exergoeconomic performances of components and overall system are evaluated. The effects of five crucial operation parameters on exergoeconomic performance evaluation parameters are explored. Finally, the exergoeconomic performance of the studied system is optimized and compared with the preliminary design case. The results show that the unit exergy cost of coal, flue gas, SCO2, shaft work, and electricity are 4.705 $/GJ, 6.586 $/GJ, 9.329 $/GJ, 10.175 $/GJ, and 10.331 $/GJ respectively. Design changes should firstly be targeted to superheat cooling wall and high temperature recuperator due to its higher expense cost rate. Moreover, for the overall system, the exergy decrease cost rate is dominant and the total product unit cost is 15.988 $/GJ under preliminary design case. In parametric analysis, importantly, the method of increasing maximum operation temperature/pressure to reduce total product unit cost is limited. From exergoeconomic optimization results, for the system with high facility cost, a moderate decrease of maximum operation temperature/pressure can achieve the reduction of total product unit cost. The ultimate conclusion indicates that the selection criterion of operation parameters is to maintain the balance of exergy efficiency and total facility cost of the overall system. In conclusion, this study could provide reference to the construction of real SCO2 coal-fired power generation system.

Optimal Corrective Rescheduling of Real and Reactive Powers for Power System Security Enhancement.(Dept.E)

This paper presents an optimal technique for solving the corrective rescheduling problem in p ower system. A successive dual linear programming approach is used with linearized p ower flow model. The system real and reactive power generations and transformer tap ratios are optimized within their operating limits so that no limit voilations of the line flow and bus voltage magnitudes in either the base case and contengency cases. The rest results on IEEE-30 bus test system show that the proposed technique can be successfully employed in conjunction with any fast security assessment scheme for a fast on-line security assessment and control.

Bamboo grid versus polyvinyl chloride as packing material in cooling tower: Energy efficiency and environmental impact assessment

As an abundant and fast-growing biomass, bamboo can be used as construction materials owing to its desirable physical and mechanical properties, environmentally friendly features, and alternative to replace toxic and hazardous wastes in industrial processing. In this study, grid material made from bamboo (termed ‘bamboo grid’) was developed and compared to commercially used polyvinyl chloride (PVC) as packing material in cooling towers; PVC packing has drawbacks such as fouling, deposit buildup, low durability, and is harmful to environments. The cooling capacity, energy efficiency and environmental impact of bamboo grid packing were evaluated via life cycle assessment (LCA), particularly the cumulative energy demand (CED) and the Building for Environmental and Economic Sustainability (BEES). Although the thermal performance of the PVC packing was found higher than that of the bamboo grid packing, the bamboo grid packing showed improved resistance characteristic, recording a total saving of 529.2 tons of standard coal during a six-month field test in a real thermal power generation plant. LCA results revealed that the utilization of bamboo-grid packing to replace PVC packing in cooling towers reduced total CED from 3420 MJ to 561 MJ per functional unit, achieving 6 times reduction. A desirable reduction ranging from 1.5 to 10.5 times was also recorded for the BEES indices. This LCA comparison analysis confirmed the improvement of energy efficiency and reduction of environmental impact by using the bamboo grid to replace PVC as packing material in cooling towers. The major environmental impact (BEES) indices (e.g., the total Global warming potential, Acidification, Eutrophication and Smog) were reduced by 1.5–10.5 times via the use of bamboo grid. The results demonstrate that bamboo grid packing is a good alternative to replace existing grid packing materials such as concrete and PVC that are harmful to human health and environments.

Modelling and Experimental Validation of Aging Factors of Photovoltaic Solar Cells

Photovoltaic solar energy has evolved to be a viable and popular alternative for the generation of electricity. To analyze the profitability of these renewable energy systems, computer modelling of the solar devices has become a necessary and widespread practice in the academic and industrial world. The modelling not only allows the estimation of the electric productivity but also the estimation of the amortization of a solar installation. However, aging and deterioration of photovoltaic modules have been little studied yet and when these aging effects can be an important source of power degradation on solar cells and fault generation, and thus a cause of mismatching on amortization deadlines. In this work, based on a proposed long-term behavioral generator model, the most common aging mechanisms of solar panels have been modelled and simulated. The results have been validated against a real solar medium-high power generator designed for grid connection in Spain. Results allow to measure the efficiency of these photovoltaics energy systems, get better accuracy of their amortization and estimate the power degradation range of photovoltaic modules.

Power-Sensitivities-Based Indirect Voltage Control of Renewable Energy Generators With Power Constraints

Recently, renewable energy has received considerable attention worldwide due to environmental problems such as climate change. However, there are still many problems to be solved to increase the penetration level of renewable energy sources in a power system. Above all, a power system with a high renewable penetration level requires an entirely novel operational strategy and control approach to achieve reliable operation by considering the intermittency of variable renewable energy sources. This paper proposes power-sensitivities-based indirect voltage control of renewable energy generators with power constraints. The proposed method realizes seamless control of the voltage by compensating with the reactive power if the real power of the distributed generator (DG) is insufficient due to its intermittency. To achieve the proposed method, the real and reactive power references of DGs are initially operated based on the power sensitivities between generation and loads if all the DGs operate within the power constraints. However, if at least one of the DGs reaches its constraint limit, the references can be modified using other sensitivities between real and reactive power generation enabling indirect voltage control of DGs. Therefore, a high level of renewable penetration in power systems can be accomplished with the proposed method. The proposed method is verified with several case studies that are based on a microgrid with practical data from a real island power system. Verification is carried out using the power system computer aided design and electromagnetic transient including DC (PSCAD/EMTDC™) simulation software.

Wind integrated power system to reduce emission: An application of Bat algorithm

The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved.

Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm

Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm

Recovering waste energy of the combined gas turbine system using paraffin melting

This work covers waste energy utilization of the combined power cycle by using it in the candle raw material (paraffin) melting process and an economic study for this process. After a partial utilization of the burned fuel energy in a real bottoming steam power generation, the exhaust gas contains 0.033 of the initially burned energy. This tail energy with about 128 ºC is partly driven in the heat exchanger of the paraffin melting system. Ansys-Fluent Software was used to study the paraffin wax melting process by using a layered system that utilizes an increased interface area between the heat transfer fluid (HTF) and the phase change material (PCM) to improve the paraffin melting process. The results indicate that using 47.35 kg/s, which is 5% of the entire exhaust gas (881.33 kg/s) from the exit of the combined power cycle, would be enough for producing 1100 tons per month, which corresponds to the production quantity by real candle's factories. Also, 63% of the LPG cost will be saved, and the payback period of the melting system is 2.4 years. Moreover, as the exhaust gas temperature increases, the consumed power and the payback period will decrease.

Day‐ahead renewable scenario forecasts based on generative adversarial networks

With the increasing penetration of renewable resources such as wind and solar, especially in terms of large-scale integration, the operation and planning of power systems are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised distribution learning method for renewable scenario forecasts that considers spatiotemporal correlation based on generative adversarial network (GAN), which has been shown to generate realistic time series for stochastic processes. We first utilize an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generate a large number of forecasted scenarios using stochastic constrained optimization. For validation, we use power generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The simulation results show that the generated trajectories not only reflect the future power generation dynamics, but also correctly capture the temporal, spatial, and fluctuant characteristics of the real power generation processes. The experimental comparisons verify the superiority of the proposed method and indicate that it can reduce at least 50% of the training iterations of the generative model for scenario forecasts.