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• Comparative tests of LPSs under the same electro-geometric conditions. • Experimental validation of the lightning-repelling performance of CTSs. • Experimental validation of the improved lightning protection performance of ESE terminals. There are still several uncertainties and conflicts regarding the lightning protection performances of conventional and nonconventional lightning protection systems. Discharge physics and experimental studies reveal the lightning protection systems' performances. However, there are also some studies suffering from commercial concerns. Theoretical efforts and test results are generally valid for specific configurations and are insufficient in generalizations. Another shortcoming of the experimental studies is that the experiments and experimental parameters are limited due to the limited laboratory facilities. This paper presents compact and comparative test results for Franklin Rods (FRs), charge transfer systems (CTSs), and early streamer emission (ESE) rods. For this purpose, the flashover voltage tests and lightning attachment rates of the mentioned protection systems are determined using switching impulses under the same electro-geometric conditions. Experimental results showed that FR and ESE terminals had similar flashover voltage and lightning attachment characteristics. CTSs could have performed better in diverting the lightning strikes when used alone. Therefore, there is a need for additional tests and field data to validate the claimed better lightning prevention performance of CTS systems and the lightning attachment performance of ESE terminals. [ABSTRACT FROM AUTHOR]

Electric vehicles, EVs, provide temporary distributed energy storage capacity for the evolving distribution grid. An aggregated storage capacity of multiple EVs is more meaningful for a distribution grid. Commercial car parks at metropolitan cities and parking lots (PLs) of hospitals, shopping centres, universities etc. are the potential areas that can be organised for these purposes. This study presents a distributed storage capacity formulation of an EV PL that can be used in distribution system reliability improvement. The storage capacity was determined by sequential Monte-Carlo simulations. Real car arrival-departure data provided by the PL authority of Istanbul, technical parameters (SoC of the batteries, battery capacity of EVs, charging and discharging rates), and some side-specific probabilistic parameters (driver behaviour, mean transportation distance etc.) were used for realistic model development. Special attention was given to some critical time periods when the storage support was more crucial for the utility grid. Moreover, future expectations for city urban planning and for travel behaviour of the community were also included in the model. Finally, impacts of several important parameters on the storage capacity of the PL were determined through sensitivity analysis. [ABSTRACT FROM AUTHOR]

Electric vehicles, EVs, provide temporary distributed energy storage capacity for the evolving distribution grid. An aggregated storage capacity of multiple EVs is more meaningful for a distribution grid. Commercial car parks at metropolitan cities and parking lots (PLs) of hospitals, shopping centres, universities etc. are the potential areas that can be organised for these purposes. This study presents a distributed storage capacity formulation of an EV PL that can be used in distribution system reliability improvement. The storage capacity was determined by sequential Monte‐Carlo simulations. Real car arrival‐departure data provided by the PL authority of Istanbul, technical parameters (SoC of the batteries, battery capacity of EVs, charging and discharging rates), and some side‐specific probabilistic parameters (driver behaviour, mean transportation distance etc.) were used for realistic model development. Special attention was given to some critical time periods when the storage support was more crucial for the utility grid. Moreover, future expectations for city urban planning and for travel behaviour of the community were also included in the model. Finally, impacts of several important parameters on the storage capacity of the PL were determined through sensitivity analysis. [ABSTRACT FROM AUTHOR]

In a competitive electric market, high maintenance costs brought the necessity of process recruitment to increase the maintenance process efficiency and to extend the asset life time. Reliability centred maintenance has been continually improved together with proper asset management techniques to meet this necessity. This study presents an improved power transformer maintenance plan for reliability centred asset management of Turkish National Power Transmission System. At first, Markov model of existing maintenance procedure is constructed. Model solutions are used to compute the reliability indices and the total costs of the transformer. Then, sensitivity analysis was performed to determine effective transition rates of the model. Finally, optimal maintenance rates and corresponding reliability indices, total cost and life time were determined using actual service data and estimated transition rates. The results are compared with the existing maintenance plan. [ABSTRACT FROM AUTHOR]

Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand, double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model and formulation of the phenomena as a constrained optimization problem. Optimization problem is then solved by using differential evolution method and particle swarm optimization algorithm. The proposed algorithm is applied to IEEE test systems. Computational accuracies of differential evolution based solutions and particle swarm optimization based solutions are discussed for IEEE 30 Bus Test System and IEEE 118 Bus Test System applications. IEEE 14 Bus Test System, IEEE 30 Bus Test System, IEEE 57 Bus Test System, IEEE 118 Bus Test System and IEEE 300 Bus Test System simulation results are compared to AC load flows in terms of computational speed. Finally the performance of the proposed method is analyzed for different outage configurations. [ABSTRACT FROM AUTHOR]

Generation and transmission unit outages cause MW line overloading and bus voltage magnitude violation problems in the remaining part of the system. This paper presents a new method for bus voltage magnitude screening for transmission line and transformer outages. Classification and ranking errors of sensitivity-based methods are overcome by imposing a non-linear constrained optimization module on the well-known linear feed-forward computational algorithms. Genetic algorithms are preferred as computation tool because of its implementation simplicity as well as for its parallel processing advantage in future computers. Accuracy of the method is tested on IEEE 57-bus test system and IEEE 118-bus test system. The results are compared with those of the full load flow to verify the strength of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Almost all of the methods developed thus far to calculate the post outage quantities in an electric power system use linear models for the sake of computational efficiency. Linear models, however, may cause relatively large computational errors especially for reactive power flows. This paper presents a new line outage simulation method for reactive power flows that minimizes the errors resulting from the use of linear system models. The proposed method formulates a line outage as a local nonlinear constrained optimization problem. Optimization is used as a feedback to correct the errors resulting from linearized equations. The method provides more accurate calculation of post-outage load bus voltage magnitudes with less computational effort. The method is applied to IEEE test systems and the results are compared with those obtained using the full power flow. [ABSTRACT FROM AUTHOR]

Excessive penetration of renewable energy resources into the distribution grid without additional preventive measures has led to several operational problems. However, most strategies developed to accommodate more renewable energy units suffered from other operational problems. Therefore, further efforts are needed to address the other key vulnerabilities of the grid in addition to maximizing the hosting capacity. In this regard, this study is devoted to a new multi-objective formulation to maximize the hosting capacity and minimize the total energy losses while satisfying the operational constraints and maximizing the energy transferred to off-peak hours. The Multi-Objective Advanced Gray Wolf Optimization (MOAGWO) algorithm is used as a solution tool. The proposed formulation and solution algorithm are tested on IEEE-33-bus and 69-bus medium voltage test systems. The impacts of energy storage systems, voltage regulators, and static var compensators on the hosting capacity and the objective functions are identified using several scenarios. The results showed that the optimal device type and locations depend on the level of DG penetration. Finally, a comparison according to two popular multi-objective performance indices showed that the quality of the Pareto front distribution obtained by MOAGWO was better than the ones obtained with the two other popular heuristic methods. • Maximization of the hosting capacity while minimizing the energy losses. • Maximization of the energy transferred to the off-peak hours through intelligent control. • Multi-Objective Advanced GWO algorithm to determine the Pareto front solutions. • Singular and collaborative impacts of the reinforcement units on the objectives. • High-quality Pareto solutions and high computational efficiency of MOAGWO. [ABSTRACT FROM AUTHOR]

The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors. [ABSTRACT FROM AUTHOR]

This study presents the influence of corona rings on impulse withstand performance of 380 kV V-insulator strings. Those strings comprising cap-and-pin type glass insulator units are one of the most common configurations in Turkish National Power Transmission System. Laboratory test setup was constructed for the centre phase of a full scaled tower. Laboratory tests are performed to investigate the lightning and switching impulse performance of the strings equipped with the existing racket type corona rings and the circular type corona rings. Withstand voltages, lower flashover probability levels, flashover routes and times to flashovers are determined under dry and wet test conditions. In addition, some 3D field simulations regarding potential and electric field distributions are also performed for the interpretation of insulator surface flashovers. [ABSTRACT FROM AUTHOR]

This paper presents corona ring optimization for 380 kV V-insulator string composed of glass insulator units. The influence of the corona ring design parameters are examined not only with regard to 3D simulations but also laboratory tests. The purpose of the simulations is to find reasonable corona ring design parameters such as corona ring diameter, corona tube diameter and installation height on the point of effective field regulation around the critical region of the string. Indoor laboratory tests including radio interference voltage levels, corona inception voltages and ac flashover voltages are conducted on the centre phase of a full scaled tower both for experimental based corona ring design as well as verifications of the simulations. After both simulations and experimental based investigations, appropriate corona ring design parameters for the string are determined for effective field regulation, minimum interference level, ac flashover performance and economic point of view. [ABSTRACT FROM AUTHOR]

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD. • Optimal DER planning and ESS management in EDNs using a three-dimensional multiobjective optimization framework • DN Reliability improvement together with power losses and annual cost minimization. • 72 h optimization period to account the seasonal characteristics of the load and the DGs. • Real load curve and wind and solar radiation data of Turkish territory. • Multi objective sine–cosine algorithm providing better Pareto fronts according to two performance indices. [ABSTRACT FROM AUTHOR]

Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. [ABSTRACT FROM AUTHOR]

• A new formulation comprising energy exchange between off-peak and peak load hours. • MOGOA comprising an additional stopping criteria. • Reduced Pareto solution space due to cost analysis at peak load hours. • Better performance than the other heuristic optimization algorithms. • Real load and DG data with site-specific constraints and incentives of Turkey. The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices. [ABSTRACT FROM AUTHOR]

• CNN-based defect classification in cable terminations. • Elimination of complex feature extraction stages for defect discrimination using automated feature extractor. • Optimization of CNN architecture and training hyperparameters. • Higher accuracy performance of the optimized CNN architecture. This paper presents a method based on convolutional neural network (CNN) for classifying workmanship defects located in 36 kV cross-linked polyethylene (XLPE) cable terminations. The main contributions of the study are to differentiate the poor workmanship defects without a hand-crafted feature extraction process, and to propose a new input type for the partial discharge (PD) recognition algorithm. Experiments are carried out on two different datasets, each of which has five typical cable termination defects. A database comprised of 1200 phase-resolved partial discharge (PRPD) defect patterns are generated, and each PRPD fingerprint recorded for 30 s is converted into an RGB image for inputting them to the CNN. Three case studies are created to increase the robustness of the algorithm by using the two datasets. The algorithm hyperparameters are optimized to improve the performance of CNN. Finally, the proposed method is compared with the state-of-art CNN algorithms used in the literature. The results show that the proposed method is viable for determining the types of potential defects in the cable terminations. [ABSTRACT FROM AUTHOR]

• Improvement of wall-type bushing thermal performance • Construction of electro-thermal model • Heat distribution and hotspot points • Copper coating of aluminum conductors • Non-magnetic flanges This paper presents the results of simulation and experimental studies which were performed to improve the temperature distribution of 36 kV wall type bushings. An electro-thermal model was developed and used in simulations together with 2D symmetrical model of the bushing. Relative impacts of conductor material, flange material, thickness of the air gap along the conductor of the bushing, thermal conductivity of the dielectric, and Cu coating thickness of Al conductor on the temperature distribution of the bushing were analysed. Several laboratory tests were also performed to determine the critical hotspot points as well as to determine the cost effective alternatives for improving the thermal performance of the bushing. [ABSTRACT FROM AUTHOR]

This study presents experimental and simulation studies on DC corona characteristics to investigate the behavior of high voltage direct current transmission (HVDC) lines. Experimental studies are performed using an indoor corona cage and various conductor configurations, which are the scaled models of the prospective HVDC line in Turkey. Following the preliminary tests with single line models of aluminum rods and stranded conductors, DC corona inception voltages (CIVs) of prospective bundled conductor configurations are measured by using a daylight UV corona camera and a high-sensitive micro ammeter. Measurements include three different sub-conductor spacing both for the positive and for the negative DC voltages. Corona currents are measured for the voltages above the CIV levels. Experimental studies are varied by complementary simulations using a FEM based software, COMSOL Multyphysics. Following the development of 3D models of the corona cage and the prospective conductor configurations, electric field distribution along the conductor surfaces are determined. CIV measurements and simulation results are used to compute the corona inception electric field strengths of the aforementioned configurations. Radio interference voltages and audible noise levels of the configurations are finally calculated at a constant test voltage using the empirical formulations and the field simulation results. The results are discussed at the end to guide the design of intended HVDC line in Turkey. [ABSTRACT FROM AUTHOR]

High penetration of distributed generation (DG) in secondary (low-voltage) distribution networks (SDN) is one of the primary challenges of network operators. Particularly in radial LV networks, renewable intermittency and bidirectional power flow may cause critical problems such as under/over voltages, line overloads and high energy losses. System operators usually consider the worst-case scenarios for the assessment of the impacts of new integrations and usually they prefer passive methods to mitigate the negative effects. However, analysis of daily operation with high resolution, stochastic DG supply and residential demand profiles can reveal both the occurrence rates and the severities of the critical events, while paving the way for the development of active mitigation methods. This study is focused on detailed daily operation analysis of SDNs with renewable-based microgrids. Seasonal DG output and residential demand profiles throughout a day were simulated using a virtual test bed to investigate the critical cases. [ABSTRACT FROM AUTHOR]

• A gamification platform to foster energy efficient behaviors in office buildings. • The gamification platform is a mobile application with interactive dashboards. • The mobile gamification platform was demonstrated in an office building. • The demonstration results suggest electricity savings of 20%. Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%. [ABSTRACT FROM AUTHOR]

In this work, a method to compute the radio interference (RI) lateral profiles generated by corona discharge in high voltage direct current (HVDC) transmission lines is presented. The method is based on a transmission line model that considers the skin effect, through the concept of complex penetration depth, in the conductors and in the ground plane. The attenuation constants are determined from the line parameters and the bipolar system is decoupled by using modal decomposition theory. As application cases, ±500 and ±600 kV bipolar transmission lines were analyzed. Afterwards, parametric sweeps of five variables that affect the RI levels are presented. Both the RI and the maximum electric field were calculated as a function of sub-conductor radius, bundle spacing, and the number of sub-conductors in the bundle. Additionally, the RI levels were also calculated as a function of the soil resistivity, and the RIV (radio interference voltage) frequency. Following this, vector optimization was applied to minimize the RI levels produced by the HVDC lines and differences between the designs with nominal and optimal values are discussed. [ABSTRACT FROM AUTHOR]

Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey. [ABSTRACT FROM AUTHOR]