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The purpose of this paper is to present a practical method for computing contingency‐based reliability and quality indices in power systems and to answer questions related to how much the system is reliable, how robust it is in surviving random contingencies, how much it is costing to maintain appropriate system security and reliability levels and, finally, to what extent the desired balance is maintained between generation facilities, transmission capabilities and consumer demand levels in various zones of the electric power system.

The methodology adopted in this paper is based on a combined contingency analysis/reliability evaluation scheme. A three‐component system model is utilized, which can be used effectively for evaluation and sensitivity analysis of reliability and quality in power systems. The model is a reduced (equivalent) system representation that comprises generation, transmission and load components with multi‐state values. The computational scheme presented in the paper integrates both the contingency effect and its probability of occurrence into one routine of analysis while reducing the power system around the region of interest.

The computational scheme presented in the paper can effectively assess both service reliability and system quality. The practical applications presented demonstrated that lower service reliability levels would jeopardize energy supply continuity and increase the likelihood of additional maintenance and restoration costs due to the resulting higher rate of system outages. Poor system quality levels, on the other hand, imply either deficiency or excess in the overall system capabilities as designed by its planners.

The work of this paper contributes to the solution of the reliability and quality assessment problem in practical power systems. As part of the present work, an advanced computerized scheme for fast composite system reliability and quality assessment was developed and then applied to an equivalent system model of the Saudi electricity system. The results obtained are claimed to have far‐reaching implications on various planning and operation aspects of the power system.

The paper aims to present a grid-connected multi-inverter for solar photovoltaic (PV) systems to enhance reliability indices after selected the placement and level of PV solar.

In this study, the associated probability is calculated based on the solar power generation capacity levels and outages conditions. Then, based on this probability, dependability indices like average energy not supplied (AENS), expected energy not supplied and loss of load expectations (LOLE) are computed, also, another indices have been computed such as (customer average interruption duration index (CAIDI), system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI)) addressing by affected customers with distribution networks reliability assessment, including PV. On the basis of their dependability indices and active power flow, several PV solar modules installed in several places are analyzed. A mechanism for assessing the performance of the grid's integration of renewable energy sources is also under investigation.

The findings of this study based on data extracted form a PV power plant connected to the power network system in Diyala, Iraq 132 kV, attempts to identify the system's weakest points in order to improve the system's overall dependability. In addition, enhanced reliability indices are given for measuring solar PV systems performance connected to the grid and reviewed for the benefit of the customers.

The main contributions of this study are two methods for determining the reliability of PV generators taking into consideration the system component failure rates and the power electronic component defect rates in a PV system which depend on the power input and the power loss using electrical transient analysis program (ETAP) program.

The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.

This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.

The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.

What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

Wind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.

Wind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.

The wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.

This paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.

The use of power sectionalizers in electric power distribution networks as disconnecting devices for optimum network configuration is indispensable. Major reasons to use sectionalizers, here manual sectionalizers, is their lower installation and operating prices compared to other types of disconnecting devices and that most of conventional realistic electric power distribution systems are still using manual sectionalizers due to their ease of procurement. However, in case of failure for these switches, power supply interruptions are unavoidable unless optimum solutions are used for configuration (and possibly reconfiguration) of sectionalizers. Thus, in this research, binary exchange market algorithm (BEMA) as a novel evolutionary metaheuristic is used to meet the maximized customer satisfaction by optimized configuration of sectionalizers within electric power distribution networks in the presence of distributed generations (DGs). To solve the problem, BEMA is used on sectionalizing switch placement problem, which has only two open and close (0/1) states. A novel multi-objective optimization problem has been formulated as a function of two aspects, namely, improved reliability index (for customer benefit) and minimized sectionalizing switch costs (for utility benefits). Simulations are carried out in three different case studies to validate the effectiveness of the BEMA both in theory and practice: Standard IEEE 33-bus test system, practical feeder-8 of MeshkinShahr Town’s electric power distribution network in northwest of Iran; and Roy Billinton test system Bus 4 (RBTS-Bus 4). The obtained results are compared with those of the previously validated ant colony optimization (ACO) technique in RBTS-Bus 4.

The optimum configuration of sectionalizers in the presence of DGs has been formulated as a multi-objective function consisting of two conflicting objectives. First objective is to improve the power distribution network reliability indices. Second objective is to fulfill the first objective with a minimized sectionalizing switch cost. The latter is probably obtained by reducing the number of installed sectionalizers. The obtained results by BEMA have been compared with those of ACO technique.

In this paper, optimal configuration of sectionalizers has been performed based on a multiobjective function by binary exchange market algorithm. By simulations carried out on two standards and one practical test systems, the proposed algorithm effectiveness was confirmed and the obtained results were compared to ACO algorithm. Changing weighting factors shows that better satisfaction can be obtained when difference between the weighting factors is relatively greater. In other words, the reliability membership function is more than switch cost membership, and thus, if the values of two weighting factors are close enough, the satisfaction level reduces. The number of installed sectionalizing switches by BEMA and ACO techniques in different scenarios were performed.

Proposal of a novel multi-objective function for finding optimal location of sectionalizers in the presence of DGs with binary exchange market algorithm whose merit over the other heuristics is to consider all the problem specifications only in one multi-objective function. Despite previously reported works that have used various high-priced protective devices for achieving the enhanced reliability this research only utilizes inexpensive manual sectionalizers with the least possible cost in the presence of DGs. Two standard test cases IEEE 33-bus test system and RBTS-Bus 4 and one realistic test case feeder-8 of MeshkinShahr Town power distribution network in northwest of Iran are used to demonstrate the effectiveness of the proposed technique in theory and real-world applications. Thus, utilities may take the advantage of the proposed method for configuration of sectionalizers in their own local power distribution systems throughout the country.

Renewable generation is a main component of most hybrid generation systems. However, randomness on its generation is a characteristic to be considered due to its direct impact on reliability and performance of these systems. For this reason, renewable generation usually is accompanied with other generation elements to improve their general performance. The purpose of this paper is to analyze the power generation system, composed of solar, wind and diesel generation and power outsourcing option from the grid as means of reserve source. A multi-objective optimization for the design of hybrid generation system is proposed, particularly using the cost of energy, two different reliability indexes and the percentage of renewable energy as objectives. Further, the uncertainty of renewable sources and demand is modeled with a new technique that permits to evaluate the reliability quickly.

The multi-state model of the generators and the load is modeled with the Universal Generating Function (UGF) to estimate the reliability indexes for the whole system. Then an evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm) is used to solve the multi-objective optimization model.

The use of UGF methodology reduces the computation time, providing effective results. The validation of reliability assessment of hybrid generation systems using the UGF is carried out taking as a benchmark the results obtained with the Monte Carlo simulation. The proposed multi-objective algorithm gives as a result different generators combinations that outline hybrid systems, where some of them could be preferred over others depending on its results for each independent objective. Also it allows us to observe the changes produced on the resulting solutions due to the impact of the power fluctuation of the renewable generators.

The main contributions of this paper are: an extended multi state model that includes different types of renewable energies, with emphasis on modeling of solar energy; demonstrate the performance improvement of UGF against SMC regarding the computational time required for this case; test the impact of different multi-states numbers for the representation of the elements; depict through multi-objective optimization, the impact of combining different energies on the cost and reliability of the resultant systems.

The purpose of this paper is to evaluate the reliability and signature reliability of solar panel k-out-of-n-multiplex system with the help of universal generating function.

Energy scarcity and global warming issues have become important concerns for humanity in recent decades. To solve these problems, various nations work for renewable energy sources (RESs), including sun, breeze, geothermal, wave, radioactive and biofuels. Solar energy is absorbed by solar panels, referred to as photovoltaic panels, which then transform it into electricity that can be used to power buildings or residences. Remote places can be supplied with electricity using these panels. Solar energy is often generated using a solar panel that is connected to an inverter for power supply. As a result, a converter reliability evaluation is frequently required. This paper presents a study on the reliability analysis of k-out-of-n systems with heterogeneous components. In this research, the universal generating function methodology is used to identify the reliability function and signature reliability of the solar array components. This method is commonly used to assess the tail signature and Barlow-Proschan index with independent and identically distributed components.

The Barlow-Proschan index, tail signature, signature, expected lifetime, expected cost and minimal signature of independent identically distributed are all computed.

This is the first study of solar panel k-out-of-n-multiplex systems to examine the signature reliability with the help of universal generating function techniques with various measures.

This paper proposes a method of probabilistic security analysis of power systems including protection system failures. Protection system failure is the main cause of cascading outages. A protection system reliability model including two major protection failure modes is adopted to demonstrate the effects of different protection failure modes on power system reliability. The mechanism and scheme of protection system have been analyzed for their contribution to cascading outages as well as system stability after a fault occurs. All contingencies and responses in the power system are depicted in their inherent stochastic manner. Therefore, all simulations in this paper contain the features of a real power system. Non‐sequential Monte Carlo simulation approach is used to implement the stochastic properties of component contingencies and protection system failures. The WSCC‐9 bus system is used as the security test system. The security index “probability of stability” is calculated to quantify the vulnerability of a power system under cascading outages.

The purpose of this paper is to analyze the electric propulsion use in civil aviation and propose a framework for certification of electric propulsion subsystems. Although electric propulsion architectures are discussed as key technology for the future of aviation, the industry standards as well as regulations fail to cover the application in full extent, specifically for commercial large airplanes. This paper proposes an approach for the analyses of reliability and certification of the new-generation propulsion system by pointing out the “common structure” among the possible architectures.

The research process used in this paper consists of following steps: the challenges of the hybrid-electric propulsion is listed, the architectures of the hybrid-electric applications in the literature are identified, the differences of the hybrid architectures from the present applications by means of application and standardization are discovered, the architectures are analyzed and the two main subsystems are defined – the present combustion system and the common unit, which is a similar structure used in all-electric aircraft. For this purpose, the standards used for design basis and certification of the present propulsion system and their relationship with the subsystems of the architectures have been analyzed. The procedure for the reliability assessment of the system is given, a framework for the safety assessment and the certification of the propulsion systems is proposed to make it easier and without sacrificing the already accumulated experience. This study shows that by using the common unit, the present certification framework can be used, by focusing on the reliability of the common unit and its integration with the rest of the architecture.

A specific definition of common unit is proposed, to point out the difference in certification efforts of hybrid-electric propulsion architectures. Yet, there is no data available for propulsion-level airborne battery and electrical systems to assess the reliability. Thus, dividing the propulsion system into two main systems and providing a model for certification of the common unit sub-system would be beneficial for easy deployment of the hybrid architectures both for design and for certification. In this paper, it is proposed that by using this common unit, the present certification framework can be used as it is, by focusing on the reliability of the common unit and its integration with the rest of the architecture.

The aircraft certification regulations act in two ways: they provide a starting point for new design projects, and they are a basis for certification of the final system. This study aims to draw focus on certification issues on the new-generation hybrid-electric propulsion systems. With the introduction of hybrid-electric propulsion for large aircraft, the present standards (CS-25, CS-E, CS-P, CS-Battery and CS-APU) create an obstacle for further progress as their borders get into each other. Instead of developing a new set of standard(s), this paper proposes a new approach by dividing the propulsion system into two subsystems.

This research proposes a definition of “common unit” for simplification of the hybrid-electric propulsion architectures for large civil aircraft. The common unit consists of both battery and electrical components and their reliability shall be considered for hybrid-electric propulsion.

This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.