Decision support system for corrective maintenance in large-scale photovoltaic systems

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy storage and obtaining the best capacity of the energy storage battery and corresponding replacement cycle considered battery degradation. The case study of Qinghai Gonghe 100 MWp demonstration base PV power station showed that the optimal energy storage capacity was 5 MWh, and the optimal replacement period was 2 years. Therefore, the annual abandoned electricity was reduced by 3.051 × 10 4 MWh compared with no energy storage. The utilization rate of both the PV power station and quality of the delivered electricity were modelled to realize a long-distance transmission to the grid net. This will have an important guiding significance to develop and construct large-scale single PV power stations.

A review of generation dispatch with large-scale photovoltaic systems

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

Photovoltaic (PV) power generation is the fastest growing segment of distributed generation technologies. In addition, the need for reliable, stable and quality power is on the rise. Large-scale PV power connected to the grid contributes to the safe operation of the grid to bring new challenges in GCC. This study focuses on future large-scale PV energy system integration into the GCC power grid to address its energy security and environmental challenges. A new simulation model is developed to analyze and investigate the impact of integrating large-scale solar PV facilities into the distribution grid, and to carry out transient stability analysis.

A novel method for the site selection of large-scale PV farms by using AHP and GIS: A case study in İzmir, Türkiye

This paper presents a new grid-connected delta inverter system (DIS) for large scale photovoltaic (PV) power systems. The delta inverter achieves DC to three phase AC inversion by using only three power semiconductor devices instead of six devices as in a conventional full bridge three phase inverter. This reduction in the number of switching devices contributes to higher power density for PV energy conversion systems and potentially, an increase in reliability and lifetime. Two configurations of the proposed DIS are discussed in the paper: (a) A large scale PV field grouped into three DC voltage blocks, each block connected to a front end MPPT boost converter which is connected to a 3-switch DIS interfaced to the utility-grid; (b) PV field connected to a flyback converter with high frequency isolation, followed by a DIS interfaced to utility grid. Advanced pulse width modulation (PWM) techniques are investigated for DIS for operation under unbalanced condition. A design example and simulation results of a 30 kW DIS based PV plant are presented to explore the feasibility of such a system. Experimental results on a scaled down laboratory delta inverter system are shown to demonstrate the operation of this topology.

Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure includes in detail the semi-hourly average time meteorological data from the location to maximise the accuracy and detailed characteristics of different PV modules and inverters. The minimum levelised cost of energy (LCOE) and maximum annual energy are the objective functions in this proposed procedure, whereas the design variables are the number of series and parallel PV modules, the number of PV module lines per row, tilt angle and orientation, inter-row space, PV module type, and inverter structure. The design problem was solved using a recent hybrid algorithm, namely, the grey wolf optimiser-sine cosine algorithm. The high performance for LCOE-based design optimisation in economic terms with lower installation, maintenance and operation costs than that resulting from the use of maximum annual energy objective function by 12%. Moreover, sensitivity analysis showed that the PV plant performance can be improved by decreasing the PV module annual reduction coefficient.

Operation and Maintenance (O&M) becoming increasingly important in the asset management of photovoltaic (PV) systems. Compared to large PV power plants, distributed PV systems are widely distributed and often located in complex terrain, which seriously increases the pressure of O&M tasks. However, at present, there is still a lack of effective O&M scheduling method for large-scale distributed PV systems, resulting in the untimeliness of O&M and high costs. To this end, the O&M tasks, staff, and equipment are firstly analyzed. Second, considering both planned and random tasks, an O&M dynamic scheduling model for large-scale distributed PV systems is proposed, which comprehensively considers labor cost, depreciation cost, power loss, and transportation cost to optimize the O&M benefits. Afterward, aiming to solve the problem that traditional optimization algorithms are prone to fall into local optima, a hierarchical hybrid algorithm named HGA-BPSO is proposed, which combines the advantages of genetic algorithm (GA) and binary particle swarm optimization (BPSO). Finally, the validity and rationality of the proposed method are verified by the case study of distributed PV poverty alleviation project in Badong County, Hubei Province. The results show that in the three different cases, the proposed method can reasonably dispatch O&M staff and equipment. The total cost of case 1 is 879.8 yuan, the total cost of case 2 is 2962.6 yuan, and the total cost of case 3 is 1728.3 yuan. At the same time, compared with the traditional meta-heuristic algorithm algorithm, the proposed GA-BPSO algorithm can better search for the global optimal.

In this paper, a novel modular circuit configuration to integrate the large-scale photovoltaic (PV) systems to the grid using the modular multilevel converter (MMC) is proposed. The proposed circuit is modular, scalable, and has excellent performance during PV shading conditions. Unlike other PV-MMC circuits, this circuit configuration diminishes the power unbalance between the arms of the converter allowing the control to deal with the power unbalance occurred between the converter phases. It also eases the control process and assures a wide range of power balancing capability of the MMC. The control strategy can balance the power between the MMC phases by only injecting the dc circulating current component while the ac component is kept minimal which increases the stability and reduces the internal losses of the converter. The proposed circuit configuration and the control strategy are designed and simulated. The corresponding results are presented to demonstrate their effectiveness during different PV power generation conditions.

Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India)

This study developed a rational approach to the consideration of solar photovoltaic (PV) power plants applied to electric utility systems, then performed a requirements assessment and preliminary impact and penetration analyses by studying PV generation in three actual utility systems. Conventional utility loss-of-load probability and production simulation methods were used, along with a PV plant performance model developed for the study.Evaluations were based on comparison of total utility generation system costs with and without PV plants. The study is reported under the following sections: 1) geographic areas for photovoltaic power plants; 2) siting of photovoltaic power plants; 3) electric utility characteristics; 4) photovoltaic power plant characteristics; 5) methods of study; 6) study inputs; 7) results; 8) cost and performance goals for viability; 9) reference PV selection; 10) reference PV plant definitions; 11) comparison with conventional generation; 12) requirements for PV applications; 13) preliminary impact analysis; and 14) preliminary penetration analysis. (WHK)

This article presents a detailed analysis of the performance, rate of degradation, and power and energy loss of a 1 MWp scale solar photovoltaic (PV) plant in the academic institution GITAM (Deemed to be University), located in the coastal region of Andhra Pradesh, India. The PV plant consists of 3,078 polycrystalline PV modules of 325 Wp rating, installed on the rooftop of the institute buildings. The annual energy generated is 1684.881 MWh. In this study, performance analysis involves the calculation of efficiency, capacity factor, and performance ratio with data simulated using the PVsyst tool. Degradation analysis involves energy light-induced degradation (LID) and degradation rate (DR). The predicted result provides an estimate for optimal functioning of PV plant with an annual capacity factor, performance ratio, and energy loss of 11.3%, 87.9%, and −26%, respectively. Energy loss by light-induced degradation is predicted as −2.7%/year, and the degradation rate of module per year is −0.6% to −5%.

The Federal Highway Administration's Office of Transportation Operations Road Weather Management Program began a project in FY 1999 to develop a prototype winter road maintenance decision support system (MDSS). The MDSS capabilities are based on feedback received by the FHWA in 2001 from maintenance managers at a number of state departments of transportation (DOTs) as part of an initiative to capture surface transportation weather decision support requirements. The MDSS project goal is to seed the implementation of advanced decision support services provided by the private sector for state DOTs. This has been achieved by developing core software capabilities that serve as a basis for these tailored products. After the 2001 user needs assessment was completed, the MDSS program was extended with the objective of developing and demonstrating a functional prototype MDSS. Field demonstrations of the prototype MDSS were conducted in Iowa between February and April 2003, and during the winter of 2004. The performance of the prototype MDSS was much improved during the second winter. The weather and road condition predictions were more accurate, and the treatment recommendations generated by the system were reasonable given the predicted conditions. Iowa garage supervisors actively considered the treatment guidance, and on occasion they successfully used the recommended treatments without modification. This paper describes the status of the MDSS project, results and lessons learned from the field demonstrations, and future development efforts.

Space weather is the combination of conditions on the sun, in the solar wind, magnetosphere, ionosphere and thermosphere that can influence the performance and reliability of space-borne and groundbased technological systems and can endanger human life or health. Space weather can cause manifold problems to spacecraft (S/C) components, such as degradation of sensors and solar arrays and changes in on-board memories by Single Event Upsets (SEU). The final effect is the degradation of the S/C’s overall performance and in extreme cases complete unavailability of services. When certain alarm conditions are reached, risk avoidance procedures may be invoked, e.g. switching off high voltages/biases/filters etc. and transition to protected operating modes. In this paper a Decision Support System (DDS) is outlined that provides Flight Control Teams with useful space weather information (past, current and predicted) to increase the ability to protect spacecraft components from hazardous events and therefore prolong the lifetime of the satellites. The system design was inspired by traditional business oriented DDS, based on Data Warehousing storage techniques, which collect and integrate historical and real-time data from heterogeneous sources. The provided services include exploration and correlation analysis of data by established On Line Analytical Processing (OLAP) techniques and near real-time monitoring of spacecraft sensors susceptible to space weather conditions. The forecasting services are based on well-known and commonly used physical radiation models complemented by prediction techniques based on non-linear models such as Artificial Neural Networks. The system main goal is to support the spacecraft operators in taking decisions about how to react to space weather conditions possibly causing spacecraft degradations. In addition the system will facilitate the awareness and understanding of how space weather affects satellite performance, paving the way to possibly prolong mission lifetime and increase the quality of services and the safety of the payloads.

Renewable energy systems (RESs), such as photovoltaic (PV) systems, are providing increasingly larger shares of power generation. PV systems are the fastest growing generation technology today with almost ~30% increase since 2015 reaching 509.3 GWp worldwide capacity by the end of 2018 and predicted to reach 1000 GWp by 2022. Due to the fluctuating and intermittent nature of PV systems, their large-scale integration into the grid poses momentous challenges. This paper provides a review of the technical challenges, such as frequency disturbances and voltage limit violation, related to the stability issues due to the large-scale and intensive PV system penetration into the power network. Possible solutions that mitigate the effect of large-scale PV system integration on the grid are also reviewed. Finally, power system stability when faults occur are outlined as well as their respective achievable solutions.